Datasets:

sulovexin

/

mysummary

like 0

journal.pcbi.1005079

The Contribution of High-Order Metabolic Interactions to the Global Activity of a Four-Species Microbial Community

We are surrounded by complex communities of microbes , many that play a fundamental role in our everyday lives ., Microbial ecosystems in nature are typically composed of hundreds or thousands of microbial species , heterogeneously distributed in space and time ., Working together , these networks of microorganisms are critical in environmental remediation , food production , wastewater treatment , and human health and disease and there is great interest designing synthetic microbial ecosystems for new biotechnologies ., Given the diversity of microbial ecosystems 1 , there are many potential types of interactions between species , including all combination of positive , negative , and neutral interactions 2 ., Understanding the properties of the community interaction network will help identify strategies to study and manipulate microbial networks ., Developing a quantitative understanding of how ecosystems of microbes interact will be essential to predicting how microbial networks respond to environmental or biological changes and aid in designing synthetic communities with tailored functionality 3 ., To quantify how interactions between species impact overall community function , previous experimental work has systematically measured pairwise interactions between species , such as crossfeeding between E . coli auxotrophs 4 , 5 or natural isolates 6 , 7 ., Other work has inferred interactions between species from measurements of the population dynamics within a complex community 8–11 ., These previous studies focused on pairwise interactions and the ability of pairwise interaction models to predict network function 2 , 12 ., Given the increase in data for biological interactions , there is great emphasis on the development of predictive models of biological networks , including constraint based , Boolean , and directed network models 13–18 ., It is currently unclear how to accurately account for cell-cell interactions within models of microbial ecosystems and whether pairwise interactions are generally sufficient ., Foster and Bell measured total productivity of subsets of microbial microcosms with up to 72 species , and no evidence for positive high-order interactions was observed 19 ., In other biological contexts , measurements of interactions in neural or cytokine networks revealed pairwise interactions were dominant , although a few high-order combinations significantly altered patterns of cytokine activity 20 , 21 ., The impact of high-order interactions , between 3 or more components , have not been quantified in microbial ecosystems ., Here we examine the interaction network of a four-species microbial community through the systematic experimental measurement of multispecies interactions and a theoretical model ., Using a fluorogenic indicator , we quantify the metabolic rate of different subsets of this community at a variety of species ratios ., From these measurement , interactions coefficients are inferred that account for two to four-species interactions using a modified general Lotka-Volterra model 8 ., We found that interactions between species do influence overall community activity ., We examine the contribution of pairwise and higher interactions within the model community , and predict the impact of such interactions on community-level activity using a theoretical model ., Although in the model community , overall activity is well described by pairwise model , theoretical results highlight the importance of the interaction network and specifically high-order interactions in spatially heterogeneous populations of cells ., The four strains that comprise the community were all isolated from freshwater environments , including three isolates from the Los Angeles area and the previously isolated Shewanella oneidensis MR-1 22 ., Strains were collected near the water surface and isolated on low strength LB plates ., 16S rRNA sequencing has identified the strains as being closely related to Escherichia coli K-12 , Aeromonas veronii , and Aeromonas hydrophila , see S8 Text for details ., Throughout the paper these strains will be referred as So , Ec , Av , and Ah respectively , as shown in Fig 1A ., We aim to elucidate the general properties of the interaction network within a microbial community by exhaustively measuring the output of all subsets of the community under a specific set of conditions ., These four species were chosen based on viability under the same culturing conditions , no particular metabolic capabilities or potential for interactions were assumed ., To quantify the contribution of interactions between the species on metabolic rate , we first measured the metabolic rate of the four strains in isolation ., Strains were grown in 5 mL scale cultures of low strength LB ., After growth to an OD600 around 0 . 2 , strains were transferred to a 96 wellplate ., The metabolic rate was quantified using a fluorogenic assay for the presence of metabolic intermediates , the AlamarBlue assay containing the redox activity indicator compound resazurin 23–25 ., Resazurin based assays have been used to quantify metabolism in a variety of bacterial and eukaryotic cells 26–32 , making it a useful , universal indicator for metabolism in multispecies bacterial communities ., Resazurin-based metabolic assays are based on the reduction of non-fluorescent resazurin to fluorescent resofurin by redox active compounds inside the cell ., Although components of respiratory chain are known to reduce resazurin 33 , multiple redox active compounds likely contribute to the fluorescent signal ., Resofurin can also undergo a second , reversible redox reaction , forming a non-fluorescent compound 28 , so care must be taken to ensure that the fluorescent signal is proportional to cell number and activity , as shown in S2 Text ., Resazurin-based assays are amenable to high throughput measurements 29 , 34 , an advantage we leverage here for a comprehensive characterization of the four-species metabolic network ., In initial measurements , the relative metabolic rates of the four species were determined , as shown in Fig 1B ., To determine the influence of multispecies interactions on ecosystem outputs , we compared the overall metabolic rate of a 4-species microbial community to the metabolic rate of each strain in isolation ., If the species did not interact , the overall metabolic rate of 4 strains together would simply be the average metabolic rate ., However when mixing the 4 species together the overall metabolic rate was 31% larger than the prediction made assuming no interactions , as shown in Fig 1B ., This demonstrates that interactions between species significantly modulate the metabolic rate of one or more strains within the community ., To further dissect the distribution of interactions within our community , we measured interactions between all combinations of species ., Pairwise interactions models are common to predict the activity of microbial networks ., We measured the metabolic activity of two-species mixed cultures to determine if pairwise models could account for interactions within our microbial community ( Fig 2 ) ., The strains were grown separately , mixed together for 30 minutes , and then metabolic activity was measured using the fluorogenic indicator ., The ratio of the two species was varied between 1:7 and 7:1 to quantify how interactions between species depended upon the ratio of species ., The metabolic rate was found to be linearly proportional to the number of cells measured , as shown in S2 Text ., Metabolic activity of each species alone was measured to determine the baseline metabolic rate ., The six sets of measurements for each pair of species shown in Fig 2A were taken on six different days , demonstrating the reproducibility of interactions ., To analyze how interactions between species determined the overall metabolic rate , we implemented a model in which the overall metabolic rate is modulated by an interaction parameter , as shown in Eq ( 1 ) ,, R ( X , Y ) =R ( X ) ·Nx/Ntotal+R ( Y ) ·Ny/Ntotal+ixy·p ( Nx , Ny , Ntotal ) ,, ( 1 ), in which R ( X ) , R ( Y ) and R ( X , Y ) are the average metabolic rates of species X , Y , and X and Y together respectively , ixy is the pairwise interaction parameter that accounts for the increase or decrease of overall metabolic activity , Nx , Ny , and Ntotal are numbers of cells of species x , y , and the total number of cells in the single species control measurement , and p ( Nx , Ny , ) scales the interaction parameter based on the number of interacting cell ., These General Lotka-Volterra models have been used previously 2 ., The interaction coefficients can be positive or negative , such that the sum of the interactions did not result in non-physical negative metabolic rate ., The metabolic rate ( R ) is proportional to the slope of fluorescence vs . time curve in experiments ., We assume that p ( Nx , Ny , ) should be a function of Nx , Ny , and Ntotal , as shown in Eq ( 2 ) ,, p ( Nx , Ny , Ntotal ) = ( Nx/Ntotal ) · ( Ny/ ( Ntotal ) ,, ( 2 ), in which p is the product of ratios of species X and Y in the mixture ., With the existence of p in the Eq ( 1 ) , the interaction term is largest when equal numbers of species are present and will decay to zero as one of the populations dominates ., Note that because we measure the overall metabolic output of combinations of species , the experiments cannot separate the individual impact of species X and Y and the impact of species Y on X . Our interaction term accounts for the overall change in activity due to species-species interactions ., The metabolic rate data was fit to determine the value of the interaction parameter , ixy ., As shown in Fig 2A , the metabolic rate was measured in pairs of species over a range of ratios between the two species ., As shown in Fig 2A , modeling pairwise interactions using Eq 1 agreed well with metabolic measurements of two strain mixtures over a range of species compositions ., The data in Fig 2A suggested that interactions for mixtures of species could be captured in a single interaction parameter ., S3 Text shows the data for all species combinations , and most pairwise combinations are in close agreement with the prediction using a single interaction parameter ., To determine whether the interaction strength was valid for all combination of the 4 species , in Fig 2B we plotted the ratio of prediction to measurement vs . species ratio for all 6 species combinations ., We extended the species ratio to 0 . 625% , 1 . 25% , 6 . 25% , 93 . 75% , 98 . 75% and 99 . 375% for 3 combinations of species and found that the model fit data well even at these more extreme ratios of species ., We define the normalized interactions strength as the interaction term divided by the total metabolic rate when the species ratios are equal , which represents the maximum of the interaction term ., In Fig 2C , we show the range of normalized interactions strengths in our system for all 6 pairwise combinations of Ec , Av , Ah and So at species ratio 1:1 ., The interaction coefficients were fit using all the data between species ratios of 0 . 625 to 99 . 375% ., In our experiment , the first order normalized interaction strengths were all positive with values between 0 . 05 and 0 . 3 ., Next the model was expanded to incorporate higher-order interactions between 3 or more species ., The overall metabolic rate of the community is now ,, Rtotal=∑w=1MRxpx+∑w=1M∑x>wMiwxpwx+∑w=1M∑x>wM∑y>x>wMiwxypwxy+iwxyzpwxyz ,, ( 3 ), in which M is the total number of species in the community , iwx accounts for pairwise interaction between two species , iwxy accounts for interactions between three species , and iwxyz accounts for interactions between four species ., This equation could be adapted to incorporate more higher-order terms ., Building from the results of pairwise interactions , we approximate that higher-order interactions also dependent on the ratio of species ., Similar to Eq 2 , the scale factor p can be calculated from the following general form ,, p=∏w=1MNwNtotal ,, ( 4 ), where Ntotal is the total number of cells in the community ., Analogous to finding the pairwise coefficients , measurements of the metabolic activity of three species combinations and Eqs 3 and 4 together with the pairwise interaction coefficients already measured were used to calculate the 4 second order coefficients ., A single third order coefficient was calculated from experimental measurements of the 4 species community ., The strengths of second and third order interaction terms in three- and four-species communities respectively are listed in Fig 3A ., Fig 3B shows in a four-species community , the proportions of all interaction terms in predicted overall metabolic rate for five different species ratios ., The average contributions of 0th , 1st , 2nd , and 3rd order interaction terms , shown as red lines , sharply decrease ., The contribution of each term is calculated as the strength of interaction , defined as the sum of all interactions of a specific order divided by the total metabolic rate ., For example the strength of the 2nd order interactions would be ,, Strength of interaction=1/Rtotal*∑w=1M∑x>wM∑y>x>wMiwxyNwNxNyNtotal3 ., ( 5 ), After extracting all interaction coefficients within the 4 species community , we compared the predictive ability of models incorporating different levels of interactions ., Fig 3C compares measurements of the 4-species at different ratios of cells to versions of the model incorporating subsequently higher-order interaction terms ., The 0th order model gives us a metabolic rate that is under predicted , and adding first order coefficients greatly improves the accuracy of the prediction ., Incorporating 3- and 4-species coefficients gives an accurate prediction , but is not an improvement over the 1st-order model ., In the S4 Text we compare measurements to theory for a wider range of community composition ., On average the ratio of prediction to measurements is 0 . 82±0 . 06 for the 0th order model , 0 . 98±0 . 11 for the 1st order model , 1 . 07±0 . 13 for the 2nd order model , and 1 . 02±0 . 13 for the full 3rd order model ., The interaction network was built from the bottom up , fitting for low order coefficients from measurements of the minimum number of combined strains ., We also analyzed the data by fitting to only the four-species data , as shown in S5 Text , both using a pairwise only model and the model incorporating high-order interactions described in Eq 3 ., Fitting only the four-species data resulted in an interaction network that did not accurately describe the activity of pairwise combinations of species ., Given the importance of interactions in setting overall activity levels of multispecies microbial communities , simulations were used to explore the experimentally measured interaction network in the context of a spatially structured population , as depicted in Fig 4A ., Some natural microbial ecosystems have disperse , patchy distribution of cells 35 , 36 , potentially giving rise to many local neighborhoods of cells with a range of activity levels ., If specific combinations are significant contributors to the overall activity level of the community , the size of these microcolonies and the evenness of the population could have significant consequences on the overall community activity ., Agent-based models using Eqs 3 and 4 were used to explore the consequence of multispecies interactions in the limit of spatially isolated or non-interacting microcolonies ., A community incorporating 2-species , 3-species and 4-species interactions was simulated that contained the experimentally determined interactions parameters from Figs 2 and 3 ., Fig 4B shows the relative contribution of pairwise and higher-order interactions to the overall metabolic rate ., The system contains 8 , 400 cells , unevenly distributed with a variable number of Ah cells and equal numbers of cell Av , So , and Ec making up the remainder of the population ., The cells are randomly distributed into microcolonies such that each one has an equal number of cells ., After distributing the cells , the activity of each group of cells is calculated using Eq 3 and assuming that interactions are local , i . e . restricted to neighbors within the same colony ., The total activity of the community is the sum of the activity of all the cells in each micro-colony ., As shown in Fig 4C , the overall metabolic rate of the community is sensitive to how the species are distributed in space ., Community activity increases with colony size as larger colonies allow the positive pairwise and three-species interactions ., An even population distribution , not dominated by Ah , also leads to an increased metabolic rate as the positive interactions between the four strains are more likely to be sampled in each microcolony ., Group size also impacts the distribution of local activity levels ., Fig 4D shows the activity of each microcolony in populations containing 50% of species Ah for microcolony sizes of 1 , 3 , 20 , and 200 cells ., We observe that for 1-cell microcolonies activity is low , as groups that are too small omit even 1st-order interactions ., For small groups containing multiple cell types , the activity of individual cells broadens as a result of the variability of the composition of each microcolony ., As microcolony size continues to expand , the average composition becomes more predictable , sampling all possible interactions , and single-cell activity levels are uniform ., The distribution of activity levels in even this simple interaction network demonstrates how both the global species composition and the microscale distribution of cells can strongly influence local activity profiles ., Local “hotspots” may be present in such populations , but only when populations are fragmented into small groups ., Here we measured the metabolic interactions within a four species community of microbes , to quantify the influence of pairwise and higher-order interactions on the overall metabolic rate of the community ., In previous studies , pairwise interactions have been measured , including a large screen of Streptomyces species 7 ., Similar to these previous studies , we found a range of interactions between the species , ranging from no interaction to strongly positive ., Interestingly , no negative pairwise interactions were found in within the small set of species tried here , despite previous work finding that many pairwise interactions were negative 19 ., One possible explanation is the use of dilute LB media , a complex media that may contain compounds that negatively impact the growth of some strains ., Interactions with So were performed at 37°C , which is higher than its optimal growth temperature of 30°C 37 ., The temperature stress on So may be related to the positive pairwise interactions with the other community members ., Interactions between some species likely depend on cellular state and growth phase , and here we grew each species to exponential phase ., We also examined metabolic rates after only 30 minutes of coculture , a time scale over which changes in species ratios are small , see S1 Text for growth rates ., Our measurements capture changes in metabolic rate that occur on short timescales , and may not indicate the long term behavior of such systems , such as alteration of the growth environment by different types of cells ., In multispecies communities long-term adaption can also change interaction networks , leading to unexpected and sometimes uncertain outcomes 38–40 ., Quantitative measurements of higher-order interactions between these four species revealed that pairwise interactions dominated and were sufficient to predict community overall activity ., When not taking any interactions into account , the predictions of the overall metabolic rate were off by more than 18% ., Fig 3C shows that a pairwise model predicted the overall metabolic rate of the four species community within 2% , whereas adding the 3-species and 4-species coefficients , within the error of the measurement , did not improve predictions ., However , this by no means denies the possible influences of higher-order interactions in other communities ., The distribution of the magnitude of these high-order interactions within other , more complex communities would be valuable in determining how sensitive a community would be to changes in the community diversity ., It is possible that even rare combinations of species may have evolved to strongly interact in natural microbial ecosystems ., For a community with 100 species , there are in total >160 , 000 3-species combinations ., The difficulty in measuring these interactions increases as communities become more complex , and it is unclear if strong interactions would be seen in even higher-order combinations of species ., More work is needed to explore how much is gained by quantifying high order interactions in more complex settings ., Extending these findings to predict the activity within more complex multispecies microbial ecosystems will require a combination of predictive theoretical models and perhaps new experimental tools to quantify interactions ., With the rapid development of nanofabrication technology , microfluidic devices are widely used for single-cell analysis 41 , 42 , allowing us to study multispecies interactions at the microsystem level such as suggested in Fig 4 and elsewhere 43 , 44 ., We implemented a model in Fig 4 to explore how the species evenness and the interaction network set the overall ecosystem outputs ., For systems in which species are fragmented into small microcolonies , high-order interactions and microcolony size could play a dominant role in setting ecosystem outputs , especially in networks with strong high-order interactions such as for a toy network shown in S7 Text ., Small groups also displayed a broader range of local activity levels ., Given that a patchy distribution of cells has been observed in many natural communities 45–49 , such “hotspot” microcolonies may be important drivers of function of some real microbial communities containing high-order interactions between species ., Our results point for a need of new experimental methods to identify such species combinations in real systems ., In experiments , all strains were taken from frozen glycerol stocks and grown overnight in 10% LB media ( BD ) ., The next day , we pipetted 5 to 150 μL from the suspension cultures into 3mL 10% LB media and grew them again for 3 to 4 hours such that all cultures simultaneously grew to a final optical density at 600 nm near 0 . 2 ., Ec , Av , and As were grown at 37°C in 10% LB media , while So was grown at 30°C ., Cells were cultured at 5 mL scale and shaken at 200 rpm ., For measurements of metabolic activity , 100 μL of 10% LB media and 80 μL culture were pipetted into the wells of a 96-well microplate ., The 80 μL suspension cultures could be single-species or multiple-species mixed to different ratios ., The microplate was incubated at 37°C for 30 minutes to allow microbial communities to interact ., Finally , we pipetted 20 μL of the metabolic indicator AlamarBlue ( Thermo Scientific ) into each well and measured the fluorescence change using a well-plate reader ., Fluorescence was measured at excitation and emission wavelengths of 560 to 590 nm , and a media-only control was used to account for background fluorescence ., S6 Text shows that during the measurement , the OD600 of cultures increased , as expected given the doubling times reported in S1 Text ., For two-species combinations , we measured different ratios from 1:7 to 7:1 ., For three-species combinations , we measured 6:1:1 , 1:6:1 , 1:1:6 , 4:2:2 , 2:4:2 , 2:2:4 , 2:3:3 , 3:2:3 , 3:3:2 ., For four-species combinations , we measured 1:2:2:3 , 1:2:3:2 , 1:3:2:2 , 2:1:2:3 , 2:1:3:2 , 3:1:2:2 , 2:2:1:3 , 2:3:1:2 , 3:2:1:2 , 2:2:3:1 , 2:3:2:1 , 3:2:2:1 , 2:2:2:2 , 1:1:1:5 , 1:1:5:1 , 1:5:1:1 , 5:1:1:1 ., All combinations were repeated at least three times on different days ., The interaction coefficient from each day was solved separately and interaction coefficients from different days were used to calculate the mean , standard error , and confidence interval of each interaction parameter ., Interactions coefficients in Eq 3 were solved using Solver in excel ( GRG Nonlinear solving method ) to minimize the sum of differences between predictions and measurements of all ratios ., In higher-order models , we applied the same method to solve for higher-order interaction coefficients , using lower-order interaction coefficients solved previously in experiments involving fewer species .

Introduction, Results and Discussion, Discussion, Materials and Methods

The activity of a biological community is the outcome of complex processes involving interactions between community members ., It is often unclear how to accurately incorporate these interactions into predictive models ., Previous work has shown a range of positive and negative metabolic pairwise interactions between species ., Here we examine the ability of a modified general Lotka-Volterra model with cell-cell interaction coefficients to predict the overall metabolic rate of a well-mixed microbial community comprised of four heterotrophic natural isolates , experimentally quantifying the strengths of two , three , and four-species interactions ., Within this community , interactions between any pair of microbial species were positive , while higher-order interactions , between 3 or more microbial species , slightly modulated community metabolism ., For this simple community , the metabolic rate of can be well predicted only with taking into account pairwise interactions ., Simulations using the experimentally determined interaction parameters revealed that spatial heterogeneity in the distribution of cells increased the importance of multispecies interactions in dictating function at both the local and global scales .

Many wild microbial ecosystems contain hundreds to thousands of species , suggesting that interactions between species likely play an important role in regulating the behavior of such complex cellular networks ., Predicting how these interactions impact the overall activity of microbial communities remains a challenge ., Here we quantify the contribution of interactions between more than two species to the overall metabolic rate of a mixture of four freshwater bacteria ., We systematically measure interactions between these species and use theoretical models to examine the influence cell-cell interactions on spatially non-uniform microbial populations ., Our results demonstrate that although interactions between species are key regulators of system behavior , only considering interactions between pairs of species is sufficient to predict ecosystem activity ., Simulations demonstrate that activity at both the single-cell and population level would be strongly influenced by how microbes are distributed in space ., These findings improve our understanding of how best to examine groups of microbes that coexist in environments such as soil , water , and the human body .

cell physiology, ecology and environmental sciences, medicine and health sciences, aeromonas hydrophila, pathology and laboratory medicine, pathogens, microbiology, cell metabolism, mathematics, aeromonas, statistics (mathematics), forecasting, network analysis, bacteria, bacterial pathogens, research and analysis methods, computer and information sciences, ecosystems, medical microbiology, mathematical and statistical techniques, microbial pathogens, cell biology, ecology, microbial ecosystems, confidence intervals, biology and life sciences, species interactions, physical sciences, statistical methods, organisms

journal.pgen.1004483

UVB Induces a Genome-Wide Acting Negative Regulatory Mechanism That Operates at the Level of Transcription Initiation in Human Cells

Proper cell homeostasis and function requires expression of the DNA encoded information ., Maintenance of genome integrity and accurate replication is crucial for correctly regulated gene expression ., Transcription of thousands of coding and non-coding RNAs by the RNA polymerase II ( Pol II ) is a regulated multistep process that can be divided into five stages: pre-initiation , initiation , promoter clearance , elongation and termination ., Based on numerous genome-wide studies analyzing Pol II transcription in several metazoan organisms using chromatin immunoprecipitation followed by deep sequencing ( ChIP-seq ) it is now clear that on different regions of an expressed gene , distinct types of Pol II occupancy signals can be detected ., The “canonical” Pol II occupancy ChIP-seq profile on an average expressed gene displays Pol II molecules engaged in the major phases of transcription 1 , 2 , 3 , 4 , 5 , 6 , 7 and can be divided in three major regions:, i ) the sharp and usually high peak centered about +50 bp downstream of the transcription start site ( TSS ) , representing Pol II molecules that have entered the pre-initiation complex ( PIC ) during transcription initiation/clearance and stopped at promoter proximal pausing position ., Analyses of short transcribed RNA molecules showed that these arrested polymerases are predominantly in a transcriptionally engaged state 4 , 8 , 9;, ii ) the background-like low signals in the gene body ( GB ) , representing quickly elongating Pol II molecules; and, iii ) the broad signal downstream from the 3′ end of the annotated genes ( EAGs ) representing Pol IIs that have finished transcribing the pre-mRNA and are slowly transcribing and approaching the termination site often 4–6 kb away from the 3′end of the gene 10 , 11 , 12 , 13 ( see also below ) ., Damage or alterations of the DNA structure can threaten the progression of transcription ., Indeed , Pol II driven transcription has been reported to be disturbed by “roadblocks” on the DNA template , which arises from both environmental and endogenous sources , such as special DNA sequences , non-canonical DNA structures , topological constrains and DNA lesions 14 ., UV light is one of the most genotoxic environmental sources of transcription-blocking DNA damages ., Different wavelengths of the UV light can generate a wide range of lesions in the DNA template ., Based on its wavelength , UV light can be divided into UVA ( 315–400 nm ) , UVB ( 280–315 nm ) and UVC ( <280 nm ) ., UVC has higher energy level than UVA and UVB due to its shorter wavelength; however , UVC radiation is not relevant from a public health standpoint as it is absorbed by the ozone layer ., UV-related lesions in living organisms are induced mostly by UVA and UVB ., The ionizing energy of UVB and UVC radiation is directly absorbed by the DNA and can cause cyclobutane pyrimidine dimers ( CPDs ) , pyrimidine 6-4 pyrimidone photoproducts ( 6-4PPs ) , which are the most persistent and predominant lesions 15 ., Nucleotide excision repair ( NER ) is a mechanism that removes UV-induced lesions efficiently using two distinct pathways 16 , 17: The first , called transcription coupled repair ( TCR ) , is mainly linked to Pol II transcription and is highly specific and efficient ., TCR preferentially removes DNA lesions from the transcribed strand of active genes , allowing blocked transcription to resume ., In transcribed regions UV-induced DNA lesions are known to block the elongating Pol II complex causing persistent transcriptional arrest ., CPDs arrest transcription as they occupy the active site of Pol II ., Moreover , DNA crosslinked proteins and interstrand crosslinks block transcription by steric hindrance before the lesion reaches the active site of Pol II ., When the elongating polymerases arrest at a damage site , TCR is triggered and the corresponding lesions are repaired ., One of the first steps of the TCR consist of the recognition of the blocked Pol II elongation complex by CSB ( Cockayne syndrome b protein , also called ERCC6 ) , which will trigger the further recruitment of NER factors essential to carry out the repair process ., Amongst those factors is the general transcription factor TFIIH that plays a role both in repair and in Pol II transcription initiation 18 , 19 , 20 ., Global genome repair ( GGR ) is the second pathway of NER that mainly acts on intergenic or non-coding regions of the genome and recognizes DNA lesions based on their base-pairing and helix-disrupting properties ., GGR for some types of lesions ( such as UV-induced CPDs ) is less efficient than the repair carried out by the TCR machinery on the transcribed strand of active genes 21 , 22 ., In vitro the half-life of an arrested Pol II at a CPD can be ∼20 hours , and it covers 10 nt upstream and 25 nt downstream of the CPD 23 , 24 ., Therefore the arrested Pol II seems to create a roadblock for all the transcription on the given open reading frame , and may also block the access of the lesion by repair factors 25 , 26 ., Such persistent Pol II blocks and subsequent transcriptional arrest can initiate checkpoint signaling , which can lead to cellular apoptosis 27 ., Moreover , the stalled Pol II at a helix-distorting DNA damage can block the access of the nucleotide excision repair factors to the lesions 28 ., It seems that cells have evolved several solutions to deal with the persistently stopped Pol II elongation complexes:, i ) Pol II can bypass the lesions , but this process is slow and extremely inefficient 29 ,, ii ) CSB removes Pol II from the lesions through its Swi/Snf-like activity 19 ,, iii ) Pol II is backtracking allowing repair 30 ., The restart of transcription depends on the cleavage and reposition the 3′ end of the RNA to the active center of Pol II , which can be mediated by the TFIIS elongation factor 31 ., Persistent DNA damage can also result in the poly-ubiquitination and degradation of the largest subunit of Pol II 26 ., This would make the lesion accessible for other repair processes and allow a new round of Pol II transcription ., It has been suggested that the Pol II degradation pathway is activated only when the transcription activity of the blocked Pol II cannot be restored , and this pathway is an alternative to TC-NER ., Our knowledge about Pol II transcription inhibition after UV irradiation is based on studies carried out on a few model genes and in experiments often using lethal UVC doses ., Moreover , many studies have investigated the fate of Pol II elongation complexes during TCR , but very little information has been obtained on how TCR influences the whole Pol II transcription cycle during TCR ., Therefore , the mechanism by which Pol II transcription is affected genome-wide upon sublethal doses of UV irradiation is not yet well understood ., To investigate the fate of Pol II during TCR processes , we have investigated Pol II occupancy at a genome wide level following UVB treatment over time in human MCF7 cells ., Our results show that on about 93% of the promoters of expressed genes Pol II occupancy is seriously reduced 2–4 hours following UVB irradiation , and that the presence of Pol II is restored to “normal” , or even sometimes higher , levels 5–6 hours after irradiation ., We also identified a smaller set of genes , where the presence of Pol II at the promoter regions does not decrease , but rather increases at the promoters and also throughout the entire transcription units of these genes after UVB irradiation ., Thus , our study reveals a global negative regulatory mechanism that targets RNA polymerase II transcription initiation on the large majority of transcribed genes following sublethal UV irradiation and a small subset of key regulatory genes , where Pol II escapes the negative regulation ., To investigate the general effect of UVB irradiation on transcription in human cells , we have set up to find irradiation conditions that do not induce apoptosis and under which cells can repair UV-lesions ., To define a sublethal irradiation dose , we carried out a survival assay during which we tested the effect of 55 , 100 and 200 J/m2 of UVB irradiation on the DNA damage response proficient MCF7 human breast cancer cell line , containing wild type p53 ( Figure S1 , and see below ) ., Upon 55 , 100 and 200 J/m2 irradiation 95% , 50% and 30% of the plated cells survived the treatment , respectively ( Figure S1A ) ., Thus , for our further study we have chosen the 55 J/m2 dose of UVB ., Importantly , 55 J/m2 UVB induces the main UV-DNA lesions as we detected the presence of CPDs with an anti-CPD antibody up to 24 hours after 55 J/m2 UVB irradiation ( Figure S1B ) ., Moreover , by testing the induction of DNA-damage response markers , such as phospho-Chk1 and phospho-p53 , our experiments show that the 55 J/m2 UVB dose is enough to trigger the UV/DNA damage response of MCF7 cells ( Figure S1C ) ., Thus , throughout the study we used this sublethal UVB dose to study transcription in MCF7 cells ., It has been shown that UVC irradiation temporarily arrests Pol II transcription in human cells ( 32 , 33 and references therein ) ., We investigated whether the above-defined 55 J/m2 UVB dose has the same effect on the global transcription program of MCF7 cells ., To this end we assessed 5 fluorouridine ( 5FU ) incorporation in the cells by immunofluorescence as a marker of newly synthesized RNA and ongoing transcription by all three RNA polymerases ., To this end 5FU was added to each sample 20 min before harvesting the cells ( Figure 1 ) ., This assay revealed a reduction in the levels of nascent transcripts 1 hour following irradiation and from three hours to six hours a constant increase in the global transcription levels ( Fig . 1A and B ) ., Surprisingly , at six hours following UVB irradiation nascent transcript levels increased about three times over the non-irradiated levels ( Fig . 1A and B ) ., Such unexpectedly strong nascent RNA production overshoots have already been described in MCF7 cells after estradiol stimulation and in other systems , and when combined with time dependent RNA degradation analyses , was suggested to shape transient physiological responses with precise mRNA timing and amplitude 34 , 35 ., Note however , that the used method labels all transcripts at a single cell level produced by the three RNA polymerases , including many non-coding transcripts , abortive transcripts produced during promoter clearance , and also short upstream antisense transcription start site associated RNAs ( TSSa-RNAs ) and others 36 ., Nevertheless , our UVB irradiation experiment indicates that global nascent transcription is first inhibited by the used sublethal dose and then restarts again , suggesting that arrested polymerases may resume global transcription quickly as transcription-coupled repair is completed on the genome ., As TCR has been mainly linked to Pol II transcription 18 we investigated the effect of UVB irradiation on genome-wide Pol II behavior ., The great advantage of mapping Pol II occupancy across the genome is that it may directly reflect transcriptional activity , unlike the measurement of mRNA levels at steady state , which are the cumulative result of numerous co-transcriptional and post-transcriptional processes ., To map Pol II occupancy changes genome-wide following UVB irradiation , we carried out chromatin immunoprecipitation ( ChIP ) coupled to high throughput sequencing ( seq ) analyses using an antibody that recognizes the N-terminus of the largest subunit of Pol II ( Rpb1 ) ( N-20 ) ., MCF7 cells were either not irradiated , or treated with 55 J/m2 UVB and harvested 1 , 2 , 3 , 4 , 5 and 6 hours following irradiation ., Cells were crosslinked with formaldehyde , ChIP was carried out and the recovered DNA fragments were deep sequenced ., Specific Pol II bound sequence-reads were mapped to the human genome , and unique reads were considered for further analyses ., For the comparative ChIP-seq analyses , all the seven datasets were normalized based on background tag densities calculated on intergenic regions ( see Materials and Methods ) ., Note that our control non-irradiated data set was very comparable to that obtained in MCF7 cells by 13 ., Next , Pol II density profiles on the coding regions of all refseq genes were calculated for all datasets by using seqMINER tool 37 ., To this end , average Pol II tag density values on each ORF , starting −1 kb upstream and ending +4 kb downstream from every refseq gene were calculated and compared ., The non-irradiated sample resulted in the canonical Pol II occupancy profile showing a high , sharp peak centered around +50 bp relative to the TSS , a low density profile on the gene body ( GB ) and a higher broad peak profile downstream from the EAG ( Figure 2A , 11 ., Surprisingly , when we compared the six UVB treated samples to the non-treated control sample , by aligning the calculated mean Pol II profiles together on the same scale , we observed an unexpected genome-wide loss of Pol II signal around the TSS region of the refseq genes in the samples that were UVB treated and harvested 2 , 3 or 4 hours following the treatment ( Figure 2A and B ) ., This observation suggests that there is a general signaling pathway that, i ) stimulates paused Pol IIs to leave their promoter paused position , and/or, ii ) inhibits the formation of new initiation complexes and/or, iii ) removes Pol II from its promoter proximal pausing position somewhere between 2 to 4 hours after UVB irradiation ., Interestingly , in the UVB-treated samples that were left for 5 and 6 hours to recover before ChIP , Pol II occupancy at the TSSs of all refseq genes increased to the initial levels , suggesting that Pol II transcription has been restarted after TCR has been completed ( Figure 2A and B ) ., As in the above Pol II binding analyses , when analyzing all refseq genes at gene bodies , we did not observe an obvious GW increase of Pol II occupancy , we next re-analyzed global Pol II tag density changes in the GB regions of all transcribed genes ., For this first we selected 4500 expressed genes , from a recently published RNA-seq dataset for MCF-7 cell line 38 ( for complete gene list see Table S2 ) ., These analyses clearly indicated, i ) a significant quick increase of Pol II tag density at all transcribed genes 1 hour following UVB irradiation;, ii ) followed by a gradual decrease of Pol II binding in GBs that sink under control levels at 3–4 hours following UVB , and, iii ) a novel global increase of Pol II signal that rises again above control levels at 5–6 hours following IVB irradiation ( Figure 3 panels A–F ) ., Next , we calculated the total Pol II reads on the gene body of the 4500 highly expressed genes in the 6 time point samples following UVB irradiation ( Figure S2 ) ., In agreement with our genome wide analyses ( Figure 3 panels A–F ) , we have found a statistically significant increase of Pol II global signal in the gene body regions of all the 4500 expressed genes in the 1-hour sample , a decrease at 3–4 hours and an novel increase at 5–6 hours following UVB irradiation ., The increased Pol II occupancy values observed genome-wide at GBs 1 hour after UVB irradiation may represent the blocked Pol II complexes that are located at different lesions in the analyzed cells population ., The decrease of Pol II tag density below control levels at 3–4 hours seems to reflect reduced transcription initiation ( see below ) and/or removal of Pol II from the transcribed genome ., The novel increase in the GB regions at 5–6 following UVB irradiation may be responsible of the restarting of transcription ( see Discussion ) ., In order to carry out a more detailed investigation of the effect of UVB irradiation on the different phases of Pol II transcription , we calculated Pol II tag densities around TSSs ( −/+300 bp ) , along the GBs of the genes ( from TSS +100 bp to EAG ) and downstream from EAG ( from EAG to EAG +4 kb ) regions of the 4500 expressed genes from the control data set and the six UVB irradiated samples ( Figure 4 ) ., In addition , to identify genes with different Pol II behavior patterns we sorted the 4500 genes into clusters by using k-means clustering ( Figure 4 ) ., With this method , by using the calculated Pol II reads , we sorted the genes into distinct groups based on Pol II occupancy pattern and density ., During cluster and heat map generation to visualize Pol II density changes , values from all three regions ( TSS , GB and downstream from EAG ) were considered for the calculations including the 4500 expressed genes under control, ( c ) conditions and at each of the 6 time points upon UVB irradiation ( Figure 4 ) ., From the heat maps it is visible that the generated clusters represent genes with distinct , unique Pol II transcription responses following UVB treatment , as we can observe well-defined differences in the changes of Pol II distribution at the different regions of the annotated genes ., We found that the 4500 expressed genes can be sorted into two main groups , hereafter called A and B . Group A contains about 93% of the examined genes ( Table S2 ) and shows in contrast to group B dramatic Pol II signal loss from the promoters between 2 and 4 hours post UV irradiation ( Figure 4 and Figure S3 ) ., While on the gene promoters of group A an almost uniform Pol II signal loss can be observed between 2 and 4 hours after UVB irradiation , this group can be further subdivided into additional subgroups , depending on different Pol II behavior patterns observed mainly in the GB and/or the EAG+4000 regions ( see Aa-Ag in Figure 4 and Figure S3 ) ., Moreover , to analyze and find potential differential gene function categories between the detected distinct Pol II behavior patterns on genes belonging to the different groups and subgroups , we carried out Gene Ontology ( GO ) analyses on the identified categories of genes ( Table 1 , carried out with D . A . V . I . D . ; and Table S1 , carried out with MANTEIA ) ., Interestingly , in many sub-clusters Pol II occupancy increased at 1 hour and then again at 5 and 6 hours following UVB treatment at distinct regions of the transcription units ( Figure 4 and Figure S3 ) ., Compared to the other patterns , genes in subgroup Aa have a strong increase of Pol II signal at 6 h on TSS , GB and EAG+4000 regions , while they have a somewhat weaker decrease of Pol II signal at their TSS regions between 2 and 4 hours than subgroups Ac-Ag ( Figure 4 and Figure S3 ) ( for gene lists see Table S2 ) ., Genes in the Aa subgroup belong predominantly to ‘RNA splicing’ and ‘mRNA processing’ GO categories ( Table 1 , as defined by DAVID; and Table S1 as defined by MANTEIA; 39 , 40 , see Materials and Methods ) ., Genes in subgroup Ab have also a somewhat weaker decrease of Pol II signal at their TSS regions between 2 and 4 hours when compared to the Ac-Ag subgroups , but have a strong decrease of Pol II signal in the EAG+4000 region between 1 and 4 hours , suggesting that on these genes Pol II is rapidly terminating and/or removed from these 3′ regions ., Interestingly , this subgroup contains a number of genes that belong to the ‘negative regulation of macromolecule metabolic process’ and ‘negative regulation of gene expression’ GO categories ( Table 1 ) ., In genes belonging to subgroup Ac , in addition to the strong Pol II disappearance at the TSS region , Pol II signal decreases very strongly in the GB region between 2 and 4 hours , while this decrease is not apparent in the EAG+4000 region ., Importantly , this subgroup contains mainly genes involved in ‘ribonucleoprotein complex formation’ , ‘regulation of translation elongation and termination’ GO categories ( Table 1 ) ., Genes belonging to the subgroup Ad amongst other functions play a role in ‘mRNA metabolic process’ and have a very strong Pol II increase in the GB one hour after UVB irradiation suggesting that the gene products are very quickly required after UVB irradiation ., Genes in subgroup Ae have a very strong Pol II decrease at their promoters , but relatively modest changes in their GBs and EAG+4000 regions ., These genes , amongst other functions , fall in the ‘nucleotide and ATP-binding’ GO categories ., Genes belonging to the subgroup Af have a very strong increase of Pol II density following irradiation at 1 hour in the GB , and at 1–2 and 5–6 hour at the AEG+4000 region , suggesting that these genes may be stimulated during the first hour after UV irradiation and after that Pol II accumulates downstream from the genes ., Genes belonging in the ‘structural constituent of ribosome’ and ‘translation elongation’ GO categories are overrepresented in this subgroup ., The subgroup Ag consists of genes , which show Pol II signal loss from their promoters , but show a quick and almost constant increase of Pol II enrichment on GB and EAG+4000 regions ., Interestingly , amongst other transcription units , genes in the ‘response to radiation’ or ‘response to UV’ GO categories ( Table 1 and Table S1 ) are overrepresented in this subgroup suggesting that these genes are heavily transcribed , but without having a paused Pol II at their promoters ( Figure 4 and Figure S3 ) ., Note that in the above clusters we did not observe any correlation between gene length and Pol II behavior following UVB irradiation ., In contrast to group A , the relatively small set of genes in group B ( containing 322 genes , Table S2 ) is characterized by no general loss of Pol II signal from their promoter regions ( Figure 4 and Figure S3 ) ., In addition 1 and 5–6 hour after irradiation a strong increase of Pol II occupancy can be observed at the promoters of these genes ., Moreover , in general in these genes a significant increase of Pol II signal through the entire transcription unit can be observed after irradiation ., Interestingly , genes belonging to group B are overrepresented ( p-values 6E-07-4E-06 ) in the ‘DNA damage response’ and ‘DNA damage response , signal transduction by p53 class mediator’ GO categories , further validating the relevance of our Pol II ChIP assays and bioinformatics classifications ., These results together suggest that a general negative regulation of Pol II transcription exist in response to UVB irradiation ., Moreover , on distinct regions of the transcription units the presence of Pol II is differentially regulated following UVB irradiation , probably also depending on the function of the genes ., Nevertheless , the response to UVB irradiation can mainly be broken down in two categories of genes: those where Pol II presence at the promoters is down regulated after irradiation ( group A , about 93% of the genes ) and genes , out of which many regulate DNA damage response , signal transduction by p53 and apoptosis , where Pol II presence is increased in the TSS , GB and slightly at EAG+4000 regions ( group B , less than 10% of the expressed genes ) ., Note however that most of the NER factors are abundant in the nucleus 41 explaining why certain NER genes can be found in group Ag instead of group B . In the above detailed global analysis of Pol II behavior on expressed genes upon UVB treatment we have observed that many genes in group B , belonging to the GO categories ‘response to UV’ and ‘DNA damage response’ , have increased Pol II occupancy throughout their whole transcription unit ., Thus , we analyzed Pol II distribution at annotated repair and UV-responsive genes existing in the KEGG database 42 , which may have been missed in the above analyses because they are not expressed under ‘normal’ conditions ., We clustered the 164 characterized and annotated repair genes according to their Pol II occupancy and created heat maps as above ( Figure 5 ) ( for gene lists see Table S2 ) ., These analyses indicate that in about half of the annotated repair genes Pol II signals increase in all of the three regions or in only part of them ., We observe the following categories:, a ) Pol II tag density increases everywhere in the transcription unit with a either a strong increase at the TSS or with a strong increase only in the GB and EAG+4000 regions at almost all the analyzed time points ,, b ) Pol II occupancy slightly increases at all three regions of the transcription units following UVB treatment ,, c ) while Pol II occupancy decreases at the TSS regions , its increase is more restricted to the GB and EAG+4000 regions ,, d ) Pol II is increasing only at the EAG+4000 region ,, e ) Pol II occupancy does not increase , but rather decreases at all three analyzed regions ( Figure 5 ) ., Interestingly , in, a ) and, b ) categories there are several genes , such as DNA ligase IV ( ATP-dependent ) , cyclin D2 , p21 ( also called cyclin-dependent kinase inhibitor 1A ) , GADD45A and GADD45B , and TP53AIP1 , SESN2 , FAS , BBC3 , which are regulators of repair , cell growth or survival pathways further validating the biological significance of the present Pol II occupancy study ., To evaluate whether the above observed massive Pol II promoter clearance at 2–4 hour time points in Group A ( Figure 4 ) is due to degradation of Pol II or other PIC subunits , we prepared cell extracts from non-irradiated cells and cells 1–6 hours following UVB irradiation and tested the presence of the indicated PIC subunits by western blot assay ( Figure 6 ) ., The N-20 antibody recognizes Pol IIA and Pol IIO forms of Rpb1 , which have been suggested to correspond to hypo- ( IIA ) and hyperphosphorylated ( IIO ) carboxy-terminal repeat domains ( CTDs ) , respectively 43 , 44 ., Note , however , that these two very discrete forms of Pol II may be due to other more complex modifications as well ., Importantly , our immunoblot assays indicated no detectable degradation of Rpb1 in several independent experiments ( Figure 6A and B , and data not shown ) ., Moreover , examination of the distributions of the differentially migrating forms of Pol II revealed that between 1 and 3 hours following UVB irradiation the normal balance between Pol IIA and Pol IIO forms ( 70/30% , respectively ) is shifted towards the IIO form ( 50/50% ) , and that by 6 hours after UVB irradiation the Pol IIA and Pol IIO balance is again close to the normal non-irradiated ratio ( 65/35% ) ., Thus , our results seem to be in good agreement with previous studies showing that upon strong doses of UVC caused DNA damage Pol II is hyperphosphorylated and/or the Pol IIO forms becomes dominant 45 , 46 ., This could also explain the observed promoter clearance , as Pol II needs to be hypophosphorylated to form the PIC ., Additionally we tested the level of the phosphorylation of the CTD of Rpb1 using antibodies that recognize different and specifically phosphorylated forms of the CTD heptapeptide repeats ( Figure 6C ) ., In this assay Ser2-P of Pol II CTD does not show any significant alterations upon UVB irradiation ., In contrast , Ser5-P signal of Pol II CTD decreased immediately 1 h after UVB irradiation and this lower level seemed to be maintained during 6 hours following irradiation with a hint of recovery at the last time point ., In addition , we detected a decrease in the level of Ser7-P signal upon UVB treatment between 2–4 hours; and a progressive reappearance of the Ser7-P from the 5 h time point following irradiation ., This signal seems to follow the behavior of Pol II occupancy on the majority of the genes in Group A and is in good agreement with the finding that Ser7-P CTD may be a marker of transcription from expressed genes 44 , 47 ., Importantly , none of the tested Pol II signals indicate the degradation a Pol II Rpb1 and/or its CTD following the 55 J/m2 dose of UVB irradiation ., To test whether the degradation of additional PIC subunits would be responsible for the important Pol II clearance from the promoters upon UVB irradiation , we tested the global protein levels of TBP , TFIIB and the kinase subunit of TFIIH , CDK7 , which is known to phosphorylate the CTD of Pol II 48 44 ., Our analyses do not show any significant changes in the levels of the tested proteins ( Figure 6D ) ., These results suggest that the loss of Pol II signal from promoters after UVB irradiation is not due to a general degradation of PIC components is the nucleus ., As the general disappearance of Pol II signal from the promoters of Group A genes following UVB irradiation does not seem to be due to Pol II or other PIC component degradation , next we set out to validate the bioinformatically detected different Pol II behavior categories at the promoters ( Pol II clearance at group A and stable or increasing Pol II signal at group B ) ., To this end we used anti-Pol II ChIP coupled qPCR detection on non-treated samples and on samples incubated for 3 h and 6 h after 55 J/m2 UVB irradiation ., Pol II signals were quantified on the promoters of two randomly selected genes from group A ( rplp1 and ubc ) and B ( p21 and wdr24 ) ., Negative/mock control ChIP was carried out with Sepharose G beads alone ( NoAb ) , and for an additional control , oligonucleotides were designed to target an intergenic region , where no Pol II binding is expected ( Figure 7A ) ., The ChIP-qPCR experiment confirmed the different Pol II behavior patterns at the promoters of the selected genes ., At the promoters of genes from group A we observed a decreased Pol II occupancy in the sample that was harvested 3 hours following UVB irradiation when compared to the control ( Figure 7A ) ., As expected from the ChIP-Seq and bioinformatics results ( Figure 4 ) , both genes from group A show an increased Pol II occupancy on their promoter region in the sample that was harvested 6 hours following UVB irradiation ( Figure 7A ) ., Genes from group B show either no decrease ( p21 ) or increased ( wdr24 ) Pol II enrichment at the promoters at 3 hours after UVB treatment compared to the control ., In the case of wdr24 gene Pol II enrichment increases up to 6 hours post UVB treatment ., These ChIP-qPCR validations are in good agreement with our ChIP-seq , bioinformatics and IF results ., Next , to better understand the mechanisms that may regulate the opposite Pol II behavior on gene promoters belonging to either group A or B , we investigated whether certain PIC subunit enrichments would also be affected upon UVB irradiation ., To this end we carried out ChIP-qPCR detection using antibodies against subunits of two GTFs , the TATA-box binding protein ( TBP ) a subunit of TFIID , and p62 , a subunit of the TFIIH ( Figure 7B and C ) ., Surprisingly , TBP showed relatively stable or even increasing occupancy patterns at every tested gene from group A and B and its binding seemed to be resistant to the events that cause Pol II dissociation from the promoter ( Figure 7B ) ., In contrast , the p62 subunit of TFIIH followed the same behavior as Pol II ., The detectability of p62 by ChIP decreased on the promoters of genes belonging to group A , but it was stable on genes from group B 3 hours following UVB irradiation ( Figure 7C ) ., At 6 hours following UVB irradiation , p62 presence at promoters recovered to the non-irradiated control levels or even higher ., Thus , it seems that while TBP-containing partial PICs , or reinitiation complexes stay at the group A promoters at 3 hours following UVB irradiation , TFIIH disappears from promoters together with Pol II ., These results suggest that on group A genes upon UVB irradiation transcription might be blocked to prevent PIC formation that in return would provide “free” TFIIH and time for TCR ( see Discussion ) ., CSB is known to trigger the recruitment of NER factors , including TFIIH , to UV-induced DNA lesions to carry out the repair process ( see Introduction ) ., Thus , to test our above hypothesis concerning the sequestration of TFIIH by the TC-NER pathway away from PIC formation , we have knocked down CSB expression by using siRNA transfection in MCF7 cells ( Figure S4 ) , and have tested whether under the CSB knock-down condition Pol II and TFIIH recruitment would still be inhibited to group A promoters by UVB ( Figure 8A and B ) ., In good agreement with our hypothesis , when cells were treated with siRNA aga

Introduction, Results, Discussion, Materials and Methods

Faithful transcription of DNA is constantly threatened by different endogenous and environmental genotoxic effects ., Transcription coupled repair ( TCR ) has been described to stop transcription and quickly remove DNA lesions from the transcribed strand of active genes , permitting rapid resumption of blocked transcription ., This repair mechanism has been well characterized in the past using individual target genes ., Moreover , numerous efforts investigated the fate of blocked RNA polymerase II ( Pol II ) during DNA repair mechanisms and suggested that stopped Pol II complexes can either backtrack , be removed and degraded or bypass the lesions to allow TCR ., We investigated the effect of a non-lethal dose of UVB on global DNA-bound Pol II distribution in human cells ., We found that the used UVB dose did not induce Pol II degradation however surprisingly at about 93% of the promoters of all expressed genes Pol II occupancy was seriously reduced 2–4 hours following UVB irradiation ., The presence of Pol II at these cleared promoters was restored 5–6 hours after irradiation , indicating that the negative regulation is very dynamic ., We also identified a small set of genes ( including several p53 regulated genes ) , where the UVB-induced Pol II clearing did not operate ., Interestingly , at promoters , where Pol II promoter clearance occurs , TFIIH , but not TBP , follows the behavior of Pol II , suggesting that at these genes upon UVB treatment TFIIH is sequestered for DNA repair by the TCR machinery ., In agreement , in cells where the TCR factor , the Cockayne Syndrome B protein , was depleted UVB did not induce Pol II and TFIIH clearance at promoters ., Thus , our study reveals a UVB induced negative regulatory mechanism that targets Pol II transcription initiation on the large majority of transcribed gene promoters , and a small subset of genes , where Pol II escapes this negative regulation .

Our genome is continuously exposed to genotoxic attacks that generate aberrant DNA structures ., These can block the transcribing DNA-dependent RNA polymerase II ( Pol II ) enzyme and can lead to deleterious cellular processes ., Cells have developed several mechanisms to stop Pol II , repair the roadblocks and to restore normal polymerase traffic ., Numerous efforts investigated the fate of blocked Pol II during DNA repair mechanisms and suggested that stopped Pol II complexes can either backtrack , be removed or bypass the lesions to allow repair ., We carried out a genome-wide analysis of Pol II behavior upon a DNA damaging stress , UVB , which is relevant from the public health standpoint ., Thus , we could follow UVB-induced Pol II behavior changes on every human gene over time ., We uncovered a novel UV induced negative regulatory mechanism , which inhibits the recruitment of Pol II to the promoters of about 93% of all transcribed genes , and a small subset of gene ( including regulators of repair , cell growth and survival ) that escapes this negative regulation , probably because their gene products are required during/after UVB irradiation ., Thus , we uncover how a cell induces a global negative regulation at the level of transcription initiation in response to a genotoxic stress .

biochemistry, cellular stress responses, genomics, cell biology, gene expression, genetics, biology and life sciences, dna repair, dna, cell processes, molecular cell biology, dna transcription

journal.pcbi.1002511

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

In the past decade , several studies have used microarray gene expression data from tumors to predict the clinical outcome of patients ( see Table S1 ) ., The tumors included breast cancer 1–5 , lung cancer 6–8 , lymphomas 9–13 , leukemia 14 , 15 , and others 16–18 ., Outcome is usually measured by categorical , often binary variables such as survival up to a certain time , recurrence of tumor or metastasis before a certain time , or success of treatment ., Predicting such variables from gene expression levels can be viewed as a classification problem , and the set of genes used for prediction is commonly referred to as a signature ., Accurate outcome prediction can be used clinically to select the best of several available therapies for a cancer patient ., For instance , a low risk patient can be advised to select a less radical therapy ., Whereas differences in gene expression between tumor and healthy tissue or between different tumor tissues are often strong , gene expression differences between patients with the same type of tumor but different outcome are more subtle ., For example , distinguishing acute myeloid from acute lymphoblastic leukemia has been demonstrated to be up to 100% accurate using only a few genes 19–21 ., In contrast , outcome prediction is a much harder problem , with classification accuracies commonly in the range of 50–70% ., It is therefore not surprising that many studies suffer from one or several of the following three problems:, ( i ) limited or overoptimistic prediction accuracy ,, ( ii ) limited reproducibility , and, ( iii ) unclear biological relevance of the genes used for prediction ., For example , an early study predicting breast cancer metastasis using 70 genes 3 was subsequently found, ( i ) to have lower than initially published predictive accuracy on the same or independent data sets 22 , 23 ,, ( ii ) to be difficult to reproduce 24 , and, ( iii ) to have used 70 genes that can be easily replaced by 70 different but equally predictive genes derived from the same data , questioning the biological relevance of the particular 70 genes of the original study 25 ., Notably , predictive gene sets derived from different studies for the same disease show almost zero overlap , questioning their biological relevance ., To address and overcome these problems we have developed a computational network-based strategy for outcome prediction ., Our algorithm , NetRank , couples gene expression measurements with a network of known relationships between the genes products ., NetRank is based on Googles PageRank algorithm 26 ., PageRank uses the hyperlink information between web documents to better decide which documents are the most relevant ones ., Similarly , NetRank uses biological interaction information between genes products to better decide which genes are the most relevant for outcome prediction ., Such interaction information is available in protein–protein , transcription factor–target , or gene co-expression networks ., The inclusion of network information serves two purposes ., First , gene products with many interactions should have a higher biological relevance since they can exert a bigger influence on a biological system ., Second , considering network neighbors can help the algorithm to ignore correlations between expression and outcome that have no underlying biological causality ., Such correlations can arise simply by chance , often due to the fact that microarray measurements are noisy and that the number of samples is typically several orders of magnitudes smaller than the number of genes investigated ., We wanted to test the NetRank idea on outcome prediction for pancreatic cancer , for which no microarray-derived signature was yet published ., Pancreatic ductal adenocarcinoma accounts for approximately 130 , 000 deaths each year in Europe and the United States 27 , 28 ., It has an extremely poor prognosis with a 5-year survival rate below 2% 29 , 30 ., Currently , only a few prognostic factors for pancreatic cancer survival are used in the clinical setting , among them CA 19-9 , alkaline phosphatase , LDH , levels of white blood cells , aspartate transaminase , and blood urea nitrogen 31 ., A considerable number of protein markers for pancreatic cancer prognosis have been investigated using immunohistochemistry 32 ., However , the clinical value of most of these markers remains to be determined , and also most of these markers were found by chance or educated guesses rather than a systematic , genome-wide approach ., The aim of our study was therefore, ( i ) to carry out a genome-wide screen for genes whose expression in pancreatic cancer tissue samples reliably correlates with the patient survival time , and, ( ii ) to use these genes as a molecular signature for reliable survival prediction ., To this end , we collected and analyzed tissue samples from patients with pancreatic ductal adenocarcinoma from Germany in a multi-center study ., Applying NetRank to gene expression profiles of these samples identified seven candidate marker genes prognostic for outcome ., To assess the clinical value of our identified marker genes , we validated them on an independent patient cohort ., We found that signatures based on these markers were more accurate than traditional clinical parameters and more accurate than signatures identified with other computational approaches ., To identify signature genes , various state of the art methods exist ., To evaluate these methods in comparison to our own NetRank method on the screening dataset , the following workflow was employed ( see Figure 1 ) ., After filtering out low expression and low variance genes , 8 , 000 genes remained as potential signature genes ., Five different methods for ranking genes according to their power to discriminate between the two prognosis groups were tested:, ( i ) fold change , as defined by the ratio of a genes mean expression in one group over the other group ,, ( ii ) the t-statistic ,, ( iii ) Pearson and Spearman rank correlation coefficients of a genes expression with the survival time of the patient ,, ( iv ) the SAM ( Significance Analysis of Microarrays ) method 34 , and, ( v ) our NetRank algorithm ( see Materials and Methods for details ) ., In addition , selecting genes randomly was included as a control method ., For each method , a support vector machine classifier was trained using the 5–10 top ranked genes as features ., Prediction accuracy as defined as the percentage of correctly classified samples was evaluated with different training and test set sizes ., All feature selection and machine learning steps were subjected to Monte Carlo cross-validation , which is a recommended and relatively un-biased evaluation strategy 22 , 35 ( see Figure 1 and Materials and Methods for details ) ., We introduce NetRank , a modified version of the PageRank algorithm 36 ., As employed by the Google Internet search engine , the PageRank algorithm uses network information ( hyperlinks ) between documents in the world wide web to assess the relevance of a document ., A document is important if it is highly cited by other documents ., Moreover , citations from important documents have more weight than citations from unimportant documents ., Thus , in order to measure the relative importance of a document within the set of all web documents , PageRank ranks a document according to the number of highly ranked documents that point to it ., Similarly , NetRank assigns a score to a gene which is influenced by the scores of genes linked to it ., This linkage can be defined in several ways ., Morrison et al . 37 described an adaptation of PageRank which uses networks where genes are connected if they share a Gene Ontology annotation ., Here , we employ known transcription factor–target relationships ( from TRANSFAC 38 ) , protein–protein interaction ( from HPRD 39 ) , and gene co-expression ( from COXPRESdb 40 ) to define three different gene–gene networks , which were used with NetRank ., NetRank first assigns as a score for each gene the absolute correlation of its mRNA expression level with the patient survival time in the dataset ., The network is then used to spread this correlation to its neighbors and beyond ., The genes with the highest NetRank score are then selected as signature genes ( see Materials and Methods for details ) ., We first compared the three above mentioned networks for NetRank and found that signatures obtained using the TRANSFAC transcription factor network consistently had higher predictive accuracies than those using the protein interaction or the co-expression network ., We therefore decided to only use the TRANSFAC network for NetRank in the following experiments ., For all training set sizes , signatures selected with NetRank using the TRANSFAC transcription factor–target network showed higher predictive accuracies than those selected by any of the other four methods ( Figure 2A ) ., NetRank showed a maximum accuracy of 72% ( standard error of the mean , s . e . m . ) with a training set of 28 samples and a signature size of 7 genes ., This compares favorably to studies in other cancers , which show accuracies in the range of 50–70% ., We found that NetRank is especially beneficial for small training set sizes , where there is a 7% ( s . e . m . ) improvement in accuracy compared to the Pearson correlation method ., Since many single prognostic markers for pancreatic cancer have been described in the literature , we next asked whether markers found with NetRank were superior to these literature markers ., To this end , 51 markers identified via a literature search were used to train a support vector machine with different training set sizes ( see Materials and Methods and Table S2 ) ., Surprisingly , we found that the NetRank markers showed on average a 12% higher accuracy than the literature markers ( Figure 2B ) ., Using NetRank , we identified seven genes ( STAT3 , FOS , JUN , SP1 , CDX2 , CEBPA , and BRCA1 ) as most relevant for predicting survival in patients with pancreatic ductal adenocarcinoma ., These seven marker candidates were validated in two ways: first , by quantitative RT-PCR of the screening dataset to confirm the microarray gene expression measurements , and second , by immunohistochemical analysis of protein levels in an independent dataset of 412 patients ( the validation dataset , see Table 1 ) ., Of our seven markers , we found high expression to be associated with shorter survival for STAT3 , FOS , and JUN , and high expression associated with longer survival for SP1 , CDX2 , CEBPA , and BRCA1 ., This is in line with most previous studies ., STAT3 is a well-known oncogene and persistently activated in many human cancers , including all major carcinomas 41 ., FOS and JUN , which constitute the AP-1 transcription factor , have been linked to both tumor progression and suppression 42 ., SP1 was reported to be associated with poor prognosis in gastric cancer and recently also in pancreatic ductal adenocarcinoma 43 , 44 ., BRCA1 is a DNA damage repair protein where loss-of-function mutations typically lead to early onset of breast cancer and ovarian cancer 45 ., Figure 3A shows the direct network neighbors of the seven candidates ., The network is shown in power graph representation , which reduces the number of edges drawn without information loss 46 ., The underlying network of regulatory relationships was obtained from the TRANSFAC database 38 ., The network shows that the seven markers ( yellow ) regulate a total of 323 targets ., The correlation of the expression of a gene with the survival of the patient in the screening dataset is shown in red ., Genes with larger circles were previously described in the literature as being associated with survival in pancreatic cancer ., The marker protein with the most regulatory interactions is the transcription factor SP1 ., Some of its targets are additionally regulated by other markers such as CEBPA , STAT3 , FOS , and JUN ., One interesting module defined by the genes that are regulated by SP1 and FOS is shown in Figure 3B ., It contains many genes already known to be associated with survival in pancreatic cancer as well as some genes highly correlated with survival in our data , such as HBA1 , F3 , CCL2 , IL2 , and GJA1 ., A subset of this module is defined by genes that are also regulated by JUN ., This subset contains the genes IL2 , TGFB1 , MT2A , and GJA1 , which correlate well with survival ., Among the interaction partners of the markers , more than one-third has been previously reported as associated with survival or prognosis in pancreatic cancer , including PPARG , MUC4 , and SMAD3 47 , 48 ., A pathway analysis using KEGG 49 showed that 91 of the interacting genes are involved in signaling pathways , most prominently the MAPK and JAK-STAT signaling pathways ., Furthermore , 53 of the interacting genes are involved in known KEGG cancer pathways ( see Table S3 ) ., To validate our findings , we analyzed protein levels of our markers in an independent set of 412 patients ( the validation dataset , see Table 1 ) ., We wanted to test how well the proteins encoded by the marker genes are indicative for the survival of a patient when assessed by immunohistochemical staining of the patients tumor ., Using tissue microarrays , immunohistochemistry stainings were obtained for each of the seven marker proteins STAT3 , FOS , JUN , SP1 , CDX2 , CEBPA , and BRCA1 for each patient in the validation dataset ( see Figure S2 ) ., The predictive accuracies of the marker staining intensities ( encoded in two levels , low or high , see Materials and Methods ) were evaluated after training a support vector machine classifier in a leave-one-out cross-validation procedure ., The classifier predicted patients to belong either to a low risk ( good prognosis ) group , or to a high risk ( poor prognosis ) group ., Using backward elimination , starting from the full set of markers , markers were removed one at a time until the accuracy of the trained classifier failed to improve ., The clinical parameters tumor size ( T ) , regional lymph nodes ( N ) , distant metastasis ( M ) , histological grade ( G ) and residual tumor ( R ) were tested in the same manner for comparison ., Since some patients in the validation dataset received adjuvant therapy ( mostly chemotherapy with gemcitabine ) , and the adjuvant therapy had an influence on survival time ( although not quite as expected , see Figure S3 ) , we split the validation dataset into a group of patients with and without adjuvant therapy ., Note that the decision to treat a patient with adjuvant chemotherapy is so far not based on any molecular markers ( which was one motivation for our study ) ., Chemotherapy is part of the standard treatment for pancreatic cancer in Germany since many years and is recommended for every patient ., However , patients in a reduced state of health and patients who refuse it will not receive chemotherapy ., The accuracies of our signatures are comparable to those found in other cancer studies ., Stratford et al . 18 found six genes differentially expressed in tumors from pancreatic cancer patients with localized disease compared to metastatic disease using the significance analysis of microarrays ( SAM ) method 34 ., Based on these six genes , they classified patients into high- and low-risk groups with 1-year survival rates of 55% and 91% , respectively ., Our signatures classify patients into high- and low-risk groups with 1-year survival rates of 54% and 76% , respectively ( adjuvant six-gene signature ) and 55% and 69% , respectively ( non-adjuvant five-gene signature ) ., Unfortunately , Stratford et al . 18 did not report a classification accuracy percentage ., Most surprisingly , although patients and methods were different , the six genes identified in their study and our seven genes share one gene of the Fos family ., As mentioned before , there has hardly been any overlap among the signatures published so far for one tumor type ., The discovery of FOS in both methods thus highlights its importance for tumor progression and outcome in pancreatic cancer , and further underlines the ability of our method to find reproducible and biologically significant markers ., NetRank depends on a number of parameters ( see Materials and Methods for a full description ) : the choice of the genes initial values that spread through the network , the damping factor which influences the amount of spread , the choice of the network , and the role of noisy and uninformative genes , which are filtered out ., Next , we investigate NetRanks dependence on these parameters ., Here , we present a novel method for identifying prognostic markers from genome-wide gene expression data ., A key feature of the method is that it judges the relevance of a gene as marker not only by its expression ( or rather the correlation of its expression with survival ) , but also by the expression of its neighbors ., Thus , it can detect and therefore avoid markers that correlate with survival simply by chance or noisy measurements , but not due to an underlying biological causality ., We applied this method to microarray data from 30 freshly frozen samples of pancreatic ductal adenocarcinoma and obtained a prognostic marker set of seven genes ., This set showed an accuracy of 72% in predicting the prognosis of a patient ., To ensure validity of this result , we employed a rigorous Monte Carlo cross-validation procedure ., We then validated these genes using high-throughput immunohistochemistry of samples from surgically resected tumors from an independent cohort of 412 patients; roughly half of these received adjuvant therapy ., From the marker set we derived a six-gene signature for patients with adjuvant therapy and a five-gene signature for patients without adjuvant therapy ., Both signatures improve prediction of patient prognosis compared to the use of clinical parameters when used for immunohistochemical staining of the tumor tissue ., The additional predictive value of the signature markers compared to clinical parameters was 9% for patients with and 6% for patients without adjuvant therapy ( as the best combination of clinical parameters only showed a predictive accuracy of 61% and 59% , respectively ) ., Whereas the use of microarrays in clinical practice is limited by the large number of genes , complicated analytical methods , and the need for fresh-frozen tissue , RT-PCR or immunohistochemistry of a small number of proteins can be done routinely in a clinical setting ., Note that the samples were obtained during initial surgery , before any of the patients received adjuvant therapy ., The expression signatures we identified predicted clinical outcomes specific for patients with and without adjuvant therapy ., These signatures could be used to stratify patients for adjuvant treatment of the disease: A patient that is classified as low risk ( good prognosis ) by the adjuvant therapy signature should receive adjuvant chemotherapy treatment , whereas a patient that is classified as low risk by the no adjuvant therapy signature might have a longer survival without chemotherapy ., Our signature genes can also help to stratify pancreatic cancer patients for new therapies ., STAT3 was found to be the best single prognostic marker , with a high expression of STAT3 indicating a high risk ., STAT3 inhibitors might therefore be promising therapeutic agents ., It is known that a large percentage of pancreatic cancers feature aberrantly activated STAT3 59 ., Very recently , novel STAT3 phosphorylation inhibitors were demonstrated to suppress growth in pancreatic cancer cell lines 60 ., For breast cancer , an FDA approved microarray-based test that uses the 70-gene signature by vant Veer et al . 3 to assess the metastatic risk in patients with node negative breast cancer is commercially available and can be utilized clinically ., In a validation study on an independent data set of 307 node-negative breast cancer patients 23 , the 70-gene test was shown to have a sensitivity of 90% and a specificity of 42% ., The accuracy , however , resulted in 50% , which is equivalent to guessing ., The reported ROC curve for predicting time to distant metastases shows the same area under curve of 68% as our signature for predicting prognosis in patients with adjuvant therapy ( Figure 4A ) ., Using an appropriate cut-off , our signature also shows a high sensitivity of 83% with a specificity of 45% ( upper right corner of the ROC curve , Figure S4A ) ., It therefore can be used reliably to identify a group of patients who seem to benefit from the adjuvant therapy ., Our study emphasizes the benefit of systematic network-based approaches that incorporate background knowledge for identifying biologically relevant marker genes ., Correlations between gene expression levels and a clinical variable of interest can arise simply by chance , without any underlying biological cause , especially with few patient samples ., One example for such a spurious correlation in our screening dataset is the HBA1 gene , which encodes for hemoglobin alpha , and which showed a strong negative correlation with survival ., Although HBA1 would have been a candidate marker when ranked merely by correlation , it was not ranked among the top ten markers by NetRank ., Since we found the idea of a cancer tissue expressing hemoglobin interesting and worth exploring further , we decided to include HBA1 in the immunohistochemistry validation ., However , immunohistochemical staining for hemoglobin in the validation dataset was incapable of defining significantly different risk groups ., In addition , adding HBA1 to our signatures did not improve , but impaired their predictive accuracies ., We conclude that the strong negative correlation of HBA1 expression levels with survival time in the screening dataset might have been caused by chance and not by any underlying biologically relevant causality ., Network-based methods such as NetRank can add such causality for example in the form of known gene regulatory networks , resulting in the identification of markers that are more likely to be truly relevant ., Recent work by the Ideker lab also emphasized the benefit of network-based approaches 61 ., Their study demonstrated that markers based on protein interaction subnetworks are first more reproducible than individual marker genes and second can improve classification accuracy for the vant Veer breast cancer dataset by 8% compared to the original 70 genes 3 ., A further approach documenting the usefulness of networks employed PageRank to identify genes cross-talking between already published cancer genes 62 ., During the progress of our study , another study 63 was published which used PageRank on protein interaction networks to improve recursive feature elimination for support vector machine learning ., They found that this improved the prediction of ERBB2 status and relapse in breast cancer ., However , neither of these two studies validated their genes on an external patient cohort to demonstrate the validity of markers found with PageRanking based on biological networks ., Moreover , we found that the use of transcription factor–target networks yields more accurate signatures than the use of protein interactions networks in cancer outcome studies ., The use of background knowledge in order to get more robust and more biologically meaningful signatures comes at a price ., It is in the nature of the NetRank algorithm to favor genes with many connections , since they can increase their ranking , whereas uncharacterized genes with no connections cannot ., Hence , marker genes found with NetRank are more likely to be well known and well-described in the literature and less likely to be previously uncharacterized ., We also found that the predictive accuracy of the immunohistochemistry -based markers was lower than that of the microarray-based markers ., One potential bias stems from the different design of tissue microarrays , which vary in the number of cores per case , core size , and density ., In addition , the semi-quantitative evaluation of the immunohistochemical staining tends to be less accurate and less objective than microarray-based gene expression profiling ., In conclusion , the expression signatures we identified predicted clinical outcomes in patients with surgically resected pancreatic ductal adenocarcinoma specific for patients with and without adjuvant therapy ., Since these signatures could be used to stratify patients for adjuvant treatment of the disease , they are a potential additional piece of information in clinical decision making and can help to reduce costs , improve patient survival , and quality of life ., Two hundred forty-four freshly frozen tissue samples of pancreatic adenocarcinoma were obtained from surgical specimens from patients who underwent operations between 1996 and 2007 at German university hospitals in Berlin , Dresden , Heidelberg , Mannheim , Munich , and Regensburg ., Informed consent was obtained from all patients included in this study ., From each of the frozen tissue samples , 4 m slides were obtained , stained with hematoxylin and eosin , and re-evaluated by a pathologist ( G . K . ) experienced in pancreato-biliary pathology ., Of these , 56 tissue samples contained tumorous tissue without any contamination from normal acini or islets and had suitable RNA quality ., Of these , 30 were obtained from patients without any adjuvant therapy , and were used as the screening dataset ., The clinical characteristics of this dataset are given in Table 1 ., The validation dataset consisted of surgically resected PDAC samples from 517 patients who underwent operation between 1991 and 2008 at university hospitals in Berlin , Dresden , Jena , and Regensburg , Germany ., Informed consent was obtained from all patients included in this study ., Patients were followed up to 15 years by telephone inquiries , registry at cancer centers , and residents registration offices ., Out of the 517 patients , 105 were excluded because of missing data ., The clinical characteristics of this dataset are given in Table 1 ., After the completion of this study we became aware of the fact that two patients ( without adjuvant treatment ) were present in both our screening and validation data set ., To ensure that this caused no bias in our results , these two patients were excluded from all test sets in the validation analysis presented here ., Support vector machines are powerful supervised machine learning algorithms for classification problems 65–67 ., We used a support vector machine to classify pancreatic tumors samples into poor or good prognosis groups based on the expression levels of selected genes ., Here , we used the LIBSVM implementation as provided in the R package e1071 ( version 1 . 5-18 , obtained July 2008 from http://cran . r-project . org/web/packages/e1071/ ) ., The expression level of each gene was used as an independent feature to train the classifier ., No kind of aggregation was used ., All feature selection and machine learning steps were subjected to Monte Carlo cross-validation , which is a recommended and relatively un-biased evaluation strategy 22 , 35 described in the following ., For ranking of genes , NetRank combines the correlation of a genes expression level with the survival time of the patient with a network of known gene–gene relationships ., The ranking can be computed iteratively ., Here , we follow the notation and implementation in 37: ( 1 ) Here denotes the ranking of page after iterations , is a symmetric adjacency matrix for the gene network , so if genes and are connected , and otherwise ., is a vector of absolute Pearson correlation coefficients of gene expression values with the patient survival time , and is a fixed parameter describing the influence of the network on the rank of a page ., Setting corresponds to no influence of the network and full influence of the gene expression data , whereas setting corresponds to full influence of the network and no influence of the gene expression data ., The value appears to be used by Google 26 ., The rank of a gene depends on the rank of all genes that link to it ., Scaling by in the summation ensures that each gene has equal influence in the voting procedure ., Each gene gets a rank of automatically and also gets times the votes given by other genes ., The iteration to convergence in ( 1 ) corresponds to solving the equation ( 2 ) where is the identity matrix , is the transpose of , and ., With the choice of ( no influence of the network , full influence of the gene expression data ) , equation ( 2 ) has the solution ., That is , the rank of a gene solely depends on the correlation of its expression with survival time ., For ( full influence of the network , no influence of the gene expression data ) , equation ( 2 ) becomes ( 3 ) To identify genes mentioned in the literature as prognostic immunohistochemistry markers for pancreatic cancer , we used GoGene 68 and combined the results of queries “pancrea* prognos* immunohisto* paraffin” and “pancrea* survival immunohisto* paraffin” ., GoGene performs a PubMed query with the search term and then identifies gene names in the abstracts reported by PubMed ., Table S2 shows the literature genes with the PubMed IDs of the abstracts in which they were found .

Introduction, Results, Discussion, Materials and Methods

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade ., Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy ., However , state of the art methods used so far often found marker genes with limited prediction accuracy , limited reproducibility , and unclear biological relevance ., To address this problem , we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes ., Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Googles PageRank ., We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer , and identified seven candidate marker genes prognostic for outcome ., Compared to genes found with state of the art methods , such as Pearson correlation of gene expression with survival time , we improve the prediction accuracy by up to 7% ., Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation ., We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples ., Notably , signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade , tumor size , and nodal status ., As the amount of genomic data of individual tumors grows rapidly , our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice .

Why do some people with the same type of cancer die early and some live long ?, Apart from influences from the environment and personal lifestyle , we believe that differences in the individual tumor genome account for different survival times ., Recently , powerful methods have become available to systematically read genomic information of patient samples ., The major remaining challenge is how to spot , among the thousands of changes , those few that are relevant for tumor aggressiveness and thereby affecting patient survival ., Here , we make use of the fact that genes and proteins in a cell never act alone , but form a network of interactions ., Finding the relevant information in big networks of web documents and hyperlinks has been mastered by Google with their PageRank algorithm ., Similar to PageRank , we have developed an algorithm that can identify genes that are better indicators for survival than genes found by traditional algorithms ., Our method can aid the clinician in deciding if a patient should receive chemotherapy or not ., Reliable prediction of survival and response to therapy based on molecular markers bears a great potential to improve and personalize patient therapies in the future .

medicine, genome expression analysis, pathology, immunology, cancer risk factors, cancers and neoplasms, biomarkers, gastrointestinal tumors, algorithms, oncology, general pathology, genetics and genomics, immunologic techniques, immunohistochemical analysis, personalized medicine, biology, clinical genetics, microarrays, genetic causes of cancer, computer science, diagnostic medicine, genomics, computational biology, pancreatic cancer

journal.pntd.0000935

From Re-Emergence to Hyperendemicity: The Natural History of the Dengue Epidemic in Brazil

Dengue infection constitutes a major threat for urban populations of Latin America and Asia 1 , 2 ., Important differences in the clinical and epidemiological profile of dengue between the countries of Latin America and Southeast ( SE ) Asian have been observed ., While in SE Asian countries dengue hemorrhagic fever ( DHF ) is common and morbidity and mortality has traditionally concentrated in children under 15 years of age , in American countries the disease affects mostly adult populations and manifests primarily as dengue fever ( DF ) 3 , 4 ., Several hypotheses have been proposed to explain these differences ., It has been shown that children from Central America , Venezuela , and Colombia may not develop vascular permeability as readily as children from SE Asia after secondary dengue infection , 5–6 and that there may be a high prevalence of dengue resistance genes among black populations of Brazil and the Caribbean 7 , 8 ., An additional explanation for the low numbers of DHF in American countries may be underreporting of cases that do occur , due to technical difficulties or a limited capacity to perform diagnosis that meet the criteria of the WHO case definition 3 ., None of these explanations are fully satisfactory in explaining the differences between the two regions ., Dengue was reintroduced in Brazil in 1986 , after an absence of at least 20 years ( except for an epidemic in Roraima in 1981 and sporadic cases ) ., Since then , Brazil has become the country that reports the largest number of cases to the WHO , accounting for over 70% of cases reported in the Americas 9 , 10 ., Three serotypes currently circulate throughout the country; DENV 1 was reintroduced to Rio de Janeiro in 1986 , DENV 2 in 1990 , and DENV 3 in 2002 , and from Rio they spread to the rest of the country11 ., While prior to 2007 the majority of DHF cases in Brazil occurred among adults aged 20–40 years of age , in 2007 the annual number of DHF cases more than doubled over previous years and a shift in the age distribution was reported 12 ., In 2007 , 53% of cases occurred in children under 15 years old ., The shift was most noticeable in the Northeast region , where children accounted for 65% of the total number of DHF cases , while other regions such as the Central-West and North did not experience a significant shift and most of the DHF cases continued to occur among adults 12 ., Although the cause of this shift is likely to be multifactorial , we propose that the conditions for it were being set gradually since the re-emergence of DENV in 1986 and that the current epidemiological profile represents the transition from re-emergence to hyperendemicity ., In a setting where transmission is constant , people who are exposed for a longer time have a greater cumulative probability of infection ., In Brazil , circulation of DENV virus for over 20 years has resulted in the accumulation of immunity in older individuals , driving the average age of primary and secondary infection towards younger age groups ., Using data from a serological study performed in Recife , in Northeast Brazil , we estimate the force of infection and basic reproductive number of dengue in three areas of distinct socio-economic status for the period 1986–2006 , in order to better understand transmission intensity over this period ., We then use these estimates to simulate the accumulation of monotypic and multitypic immunity in a population previously susceptible to dengue virus , and to predict the expected age distribution of DHF cases in the future ., This study was reviewed and approved by the ethics committee of the CPqAM-Fiocruz/Brazilian Ministry of Health ( No . 49/04 ) ., Written consent to participate in the study was obtained from each person ( or their guardian ) after a full explanation of the study was provided ., All personal identifiers were removed prior to secondary data analysis at Johns Hopkins University ., This study was based on a serological sample of households in Recife , Pernambuco , Brazil , conducted between August and September 2006 ., The first dengue outbreak in the state of Pernambuco occurred in 1987 ( DENV 1 ) ., No additional autochthonous cases were reported until 1995 , when DENV 2 was introduced causing a new epidemic ., Since 1995 cases have been reported every year ., DENV 3 was first isolated in Pernambuco in 2002 13 ., The study population and methods have been described in detail by Braga et al . 14 Briefly , Recife has 1 . 5 million inhabitants ., The climate is humid , with an average temperature of 25°C and rainfall of approximately 2000 mm per year ., Three neighborhoods were selected to represent low , medium and high socio-economic areas ., A systematic age stratified sample was obtained , using the Census 2000 data that provides the total population size , number of households and age distribution in the three areas 15 ., Residents aged between 5 and 64 years were eligible for the survey ., Serum samples were screened for IgG antibodies against DENV with an enzyme-linked immunoassay commercial kit ( Dengue IgG-ELISA , PanBio , Ltd . , Brisbane , Australia ) ., Tests were performed in duplicate according to the manufacturers instructions ., This test does not determine the presence of immunity to specific dengue serotypes , but the presence of immunity to any dengue serotype ., The force of infection ( λ ) is a measure used to characterize the intensity of transmission in a given setting and estimates the per capita rate of acquisition of infection by susceptible individuals ., Age stratified serological surveys can provide information about the force of infection over a period of time , λ ( t ) , as described elsewhere 16 ., Assuming that the risk of infection does not vary with age , the difference in seroprevalence between subjects a and a+1 years of age can be attributed to the transmission intensity between a and a+1 years ago ., To estimate λ ( t ) , for the period 1986–2006 , we used a model based upon one described by Ferguson et al . 17 We estimated constant and time-varying forces of infection ., Detailed information regarding the methods used can be found in Text S1 in Supporting Information S1 ., R0 is the number of secondary infections generated by a primary case in a completely susceptible population ., R0 gives insight into the level of control that is required to reduce incidence and eventually block transmission ., Detailed information regarding the methods used to estimate R0 can be found in Text S1 in Supporting Information S1 ., To estimate the accumulation of monotypic and multitypic immunity in a population previously susceptible to dengue , we performed a discrete-time simulation by applying the forces of infection estimated from the seroprevalence data onto a simulated immunologically naive population structured by-age like the one of Recife ., We used independent data on the years in which the different serotypes were introduced into Brazil/Pernambuco to apply the estimated hazards only in those years when particular serotypes were known to have circulated 11 , 13 ., The age profile of the population was obtained from the 2000 census data ., We conducted simulations until age distributions of immunity reached equilibrium and used both constant and time-varying hazards ., Since we did not have seroprevalence data to estimate the force of infection beyond 2006 , we assumed that λ ( t ) after 2006 has been constant and equal to the average hazard over the period 1986–2006 ., All statistical analyses were performed using R statistical package ( version 2 . 10 . 1 ) ., The estimated average time-constant force of infection for the period 1986–2006 was 0 . 052 ( 95% CI 0 . 041–0 . 063 ) ., On average , each serotype infected 5 . 2% of susceptible individuals each year ., Time constant s for the three areas were 0 . 068 ( 95%CI 0 . 045 , 0 . 091 ) , 0 . 056 ( 95%CI 0 . 035 , 0 . 077 ) and 0 . 035 ( 95%CI 0 . 019 , 0 . 051 ) ., Though the difference between these forces of infection is not statistically significant , a trend is seen towards higher hazards of infection in settings of lower socioeconomic status ., As can be expected , the fit of the model improved significantly when we allowed for time-varying forces of infection ( likelihood ratio test , p\u200a=\u200a0 . 006 ) ., According to this model ( Figure 2 ) , the average yearly force of infection ranged between 0 and 0 . 057 between 1986 and 1998 , and then peaked at 0 . 26 in 1999 ., As has been reported elsewhere the correlation between incidence and estimated force of infection is poor ( r\u200a=\u200a0 . 21 ) 18 ., Given that it has been reported that between 1987 and 1995 there were no autochthonous dengue cases in the state of Pernambuco , we also fit a model constraining the force of infection for these years to be 0 13 ., The fit of this 8-parameter model was not significantly different from the fit of the model that did not constrain these hazards to be zero ( LR test , p\u200a=\u200a0 . 99 ) or from the saturated model ( LR test , p\u200a=\u200a0 . 34 ) ., Figure 1 shows the fit of, 1 ) constant ( red lines ) and, 2 ) time-varying models ( green lines ) to the age-specific seroprevalence data in the three areas and overall areas ., The correlation between the annual hazards estimated in areas 1 and 3 ( r\u200a=\u200a0 . 79 ) is high , while the correlation between 1 and 2 and between 2 and 3 is poor ( r\u200a=\u200a0 . 06 and 0 . 18 , respectively ) ., Using the time constant and time varying λs we estimated an overall R0 of dengue in Recife of 2 . 7 ( 95%CI 2 . 45 , 3 . 11 ) ., For the three areas the R0 estimates were 3 . 3 ( 95%CI 2 . 45 , 4 . 18 ) , 2 . 8 ( 95%CI 2 . 09 , 3 . 64 ) and 2 . 1 ( 95%CI 1 . 56 , 2 . 66 ) , respectively ., Figure 3 shows the age distribution of susceptible , monotypically immune and multitypically immune at different time-points after the introduction of DENV 1 , 2 and 3 into a previously susceptible population , assuming a constant risk of infection of 0 . 052/year/serotype ., As the number of years of DENV circulation increases , multitypic immunity accumulates among adults , and susceptibles and monotypically immune become increasingly concentrated in younger age groups ., Assuming that cases of DHF occur primarily among people who experience secondary infection , the age distribution of people who are at risk of secondary infection ( i . e . of people who have been exposed to a single dengue serotype ) should approximate the age distribution of DHF cases 19 ., Hence , our results suggest that as years after re-emergence go by , the mean , median and modal ages of cases will decrease ., For =\u200a0 . 052 , the model estimates that while 10 years after re-emergence the median , mean and modal age of cases ( monotypically immune ) would be 24 , 29 . 0 and 14 years respectively , these numbers would decrease to 13 , 15 . 2 and 11 years 50 years after re-emergence ., Similarly , while it is expected that only up to 27% of DHF cases would occur in children under 15 years of age 10 years after the re-emergence , 50 years after re-emergence this proportion would increase to 58% ., Figure 4 shows the age distribution of hospitalized dengue cases in Pernambuco in 2007 , based on official notification records , and the estimated distribution according to our model ( 20 years after re-emergence ) 20 ., The strength and the speed of the shift in the age distribution of immunity depend on the underlying force of infection ( Figure 5 and Table S1 in Supporting Information S1 ) ., The estimated median and modal ages of monotypically immune for =\u200a0 . 03 are 19 and 15 years respectively , 50 years after re-emergence , while these ages drop to 11 and 6 years for =\u200a0 . 07 ., Results were similar if time-varying , instead of constant forces of infection were applied , or if for each serotype was weighted taking into account the serotype predominance reported for the different years in the state of Pernambuco 13 ., A dramatic increase in the number of DHF cases and a shift in age group predominance of DHF were observed during the 2007 dengue epidemic in Brazil , the first re-emergence of the DENV-2 serotype predominance since 1990 ., Our results suggest that this shift can be partly explained by the accumulation of multitypic immunity in the adult population over time after the re-emergence of DENV-1 in 1986 , DENV-2 in 1990 and DENV-3 in 2002 ., As the length of time of co-circulation of multiple serotypes of dengue in Brazil increases , adults have a lower probability of remaining susceptible to infection ., As a result , cases become on average younger as completely susceptible individuals and monotypically immune individuals are more likely to be from younger age groups ., If the accumulation of multitypic immunity in adult population is in part responsible for the observed shift in age group predominance of severe dengue cases , we would expect similar shifts to have occurred in central and northern South America , where several DENV serotypes have been known to circulate since the 1970s ., In Mexico , Venezuela , Nicaragua and Colombia most of the severe cases occur among children <15 years of age and a similar trend is being observed in Honduras ., 21 , 22 , 23 ., In contrast , such a trend has not been observed in countries where multiple serotypes only started circulating in the 90s ., If the central/west regions of Brazil continue to experience high DENV forces of infection and multiple circulating serotypes we expect a similar shift in age group predominance to occur in the coming years ., Since DHF is more likely to occur in children , a decrease in the mean age of secondary infection might also be expected to lead to an increase in the proportion of dengue infections that lead to severe symptoms or DHF cases 24 , 25 ., In the 1990s , after DENV-2 was introduced , 0 . 06% of reported dengue cases in Brazil resulted in DHF/DSS ., This percentage increased to 0 . 21% in 2007 9 ., This observed increase in DHF may also have been a result of changes in virulence of particular dengue viruses that were circulating or due to the fact that the overall force of infection has increased as has been proposed ., According to our model , the speed of the shift is proportional to the magnitude of the average force of infection ., Higher average forces of infection lead to a more rapid shift of the age distributions of immunity and to a younger median and modal age of monotypically immune ., Thus , the shift can be expected to be slower in regions that have been exposed to weaker forces of infection or where the re-emergence of multiple serotypes was delayed ., This may explain why the shift has only been observed in major cities and certain regions of Brazil ., The Northeast region of Brazil , where Recife is located , has the highest proportion of children among DHF cases , and it has also been traditionally the region with the highest incidence rates of dengue fever since 1986 9 , 12 ., The Central-West region , where the shift is not yet apparent has shown high incidence rates of dengue fever only during the last 7 years 9 ., Our estimate of the average λ and R0 in Recife is lower than those estimated for Thailand for the period 1980–2005 ( λ\u200a=\u200a0 . 1 , R0\u200a=\u200a5 . 2 ) 26 ., Our model predicts that average forces of infection of 0 . 1 would be associated with a mean age of severe or DHF cases of 8 years , and this is consistent with what has been traditionally observed in SE Asian countries ., Both Thailand and Singapore have experienced significant decreases in transmission intensity over the last few years that have been accompanied by an increase in the average age of cases 26 , 27 , 28 ., If the force of infection in Recife continues to be as high as it has been over the last 20 years , or higher , it is likely that within the next decade the age distribution of DHF in Recife ( and other American regions with high forces of infection ) will resemble the age distribution observed in SE Asia , with most cases concentrated in the adolescent population ., However , our projections are meant to be qualitative rather than quantitative ., The actual seroprevalences observed in the future in Recife may differ from our projections depending on secular trends in the transmission intensity of dengue and population demographics ., There are several limitations to this study ., Even though our results present an explanation for why DHF may have shifted towards children over the years since introduction , the mechanism that we propose is gradual and does not explain the sudden change observed in 2007–2008 ., The recirculation of DENV-2 into certain cities in 2007 , after almost 7 years of DENV 3 predominance and the resultant increase in secondary cases may have determined the observation of an age shift in 2007 and not before , even though it had been gradually taking place 9 ., As reported by the Ministry of Health , during 1998–2006 the percentage of severe dengue cases in children increased from 9 . 5% ( in 1998 ) to 22 . 6% ( in 2001 ) ., Although our results suggest that the major driver of the shift is the accumulation of immunity in older age groups , fluctuations in serotype specific transmission intensity , serotype predominance , characteristics of the virus or serotype predominance may have also played a role in determining the visibility of the shift ., Our model predicts that after 20 years of exposure to a constant force of infection of 0 . 05 per year , children 15 years old or younger should only account for 31% of DHF cases while the data shows that in 2007 , 70% of cases in Recife occurred among children of this age group ., This discrepancy may arise due to the fact that the model does not take into account age-dependence of infection or clinical presentation ., If children are more likely to develop severe disease , then the observed distribution of cases is likely to be skewed towards lower age groups ., The fact that the available serological study does not contain serotype specific information limits our ability to estimate serotype specific forces of infection , interactions ( enhancement/inhibition ) and basic reproductive numbers ., Similarly , the cross-sectional nature of this dataset does not allow us to control for potential confounding by age dependent transmission intensity ., Longitudinal data and data from seroprevalence studies using serotype specific methods such as the PRNT are essential in order to properly reconstruct the transmission intensity over the last 20 years ., This analysis has important public health implications on planning public health responses to dengue for the next decade ., Dengue is the most rapidly spreading vector borne viral disease ., If the age shift in fact represents the transition from re-emergence to hyperendemicity , similar shifts in age are likely to be observed in the rest of Brazil , the American continent and other regions where dengue has emerged more recently .

Introduction, Materials and Methods, Results, Discussion

Dengue virus ( DENV ) was reintroduced into Brazil in 1986 and by 1995 it had spread throughout the country ., In 2007 the number of dengue hemorrhagic fever ( DHF ) cases more than doubled and a shift in the age distribution was reported ., While previously the majority of DHF cases occurred among adults , in 2007 53% of cases occurred in children under 15 years old ., The reasons for this shift have not been determined ., Age stratified cross-sectional seroepidemiologic survey conducted in Recife , Brazil in 2006 ., Serostatus was determined by ELISA based detection of Dengue IgG ., We estimated time-constant and time-varying forces of infection of DENV between 1986 and 2006 ., We used discrete-time simulation to estimate the accumulation of monotypic and multitypic immunity over time in a population previously completely susceptible to DENV ., We projected the age distribution of population immunity to dengue assuming similar hazards of infection in future years ., The overall prevalence of DENV IgG was 0 . 80 ( n\u200a=\u200a1427 ) ., The time-constant force of infection for the period was estimated to be 0 . 052 ( 95% CI 0 . 041 , 0 . 063 ) , corresponding to 5 . 2% of susceptible individuals becoming infected each year by each serotype ., Simulations show that as time since re-emergence of dengue goes by , multitypic immunity accumulates in adults while an increasing proportion of susceptible individuals and those with monotypic immunity are among young age groups ., The median age of those monotypically immune can be expected to shift from 24 years , 10 years after introduction , to 13 years , 50 years after introduction ., Of those monotypically immune , the proportion under 15 years old shifts from 27% to 58% ., These results are consistent with the dengue notification records from the same region since 1995 ., Assuming that persons who have been monotypically exposed are at highest risk for severe dengue , the shift towards younger patient ages observed in Brazil can be partially explained by the accumulation of multitypic immunity against DENV-1 , 2 , and 3 in older age groups , 22 years after the re-introduction of these viruses ., Serotype specific seroepidemiologic studies are necessary to accurately estimate the serotype specific forces of infection .

The spread of dengue virus is a major public health problem ., Though the burden of dengue has historically been concentrated in Southeast Asian countries , Brazil has become the country that reports the largest number of cases in the world ., While prior to 2007 the disease affected mostly adults , during the 2007 epidemic the number of dengue hemorrhagic fever cases more than doubled , and over 53% of cases were in children under 15 years of age ., In this paper , we propose that the conditions for the shift were being set gradually since the re-introduction of dengue in 1986 and that they represent the transition from re-emergence to hyperendemicity ., Using data from an age stratified seroprevalence study conducted in Recife , we estimated the force of infection ( a measure of transmission intensity ) between 1986–2006 and used these estimates to simulate the accumulation of immunity since the re-emergence ., As the length of time that dengue has circulated increases , adults have a lower probability of remaining susceptible to primary or secondary infection and thus , cases become on average younger ., If in fact the shift represents the transition from re-emergence to hyperendemicity , similar shifts are likely to be observed in the rest of Brazil , the American continent and other regions where transmission emerges .

infectious diseases/viral infections, infectious diseases/epidemiology and control of infectious diseases

journal.pcbi.1003974

Thermodynamic Costs of Information Processing in Sensory Adaptation

In order to perform a variety of tasks , living organisms continually respond and adapt to their changing surroundings through diverse electrical , chemical and mechanical signaling pathways , called sensory systems 1 ., In mammals , prominent examples are the neurons involved in the visual , olfactory , and somatic systems 2–5 ., But also unicellular organisms lacking a neuronal system sense their environment: Yeast can sense osmotic pressure 6 , and E . coli can monitor chemical gradients 7 , temperatures 8 and pH 9 ., Despite the diversity in biochemical details , sensory adaptation systems ( SAS ) exhibit a common behavior: long-term storage of the state of the environment and rapid response to its changes 10 ., Intuitively , one expects that for these SAS to function , an energy source – such as ATP or SAM – is required; but is there a fundamental minimum energy needed ?, To tackle this question , we first relate a generic SAS to a binary information processing device , which is tasked to perform fast information acquisition on the environment ( response ) and to record subsequently the information into its longer term memory ( adaptation ) ., Since the foundational works of Maxwell , Szilard and Landauer , the intimate relationship between thermodynamic costs and information processing tasks has been intensely studied 11–17 ., As a result , the natural mapping between a generic SAS and an information processing device allows us to quantify the minimal energetic costs of sensory adaptation ., The idea of viewing biological processes as information processing tasks is not new 7 , 12 , 18 ., However , rationalizing sensory adaptation is complicated by recent studies that have revealed that motifs in the underlying biochemical networks play a fundamental role in the thermodynamic costs ., For instance , the steady state of feedback adaptive systems must be dissipative , with more dissipation leading to better adaptation 19 , an observation echoed in the analysis of a minimal model of adaptive particle transport 20 ., Other studies have suggested that some feedforward adaptive systems may require dissipation to sustain their steady state 21 , while some may not 22 , 23 ., Furthermore , past studies 18 , 24 have approached the notion of information by considering noisy inputs due to stochastic binding , a realm in which adaptation may not be relevant due to the separation of time-scales 25 ., Here , we develop a different approach that avoids these caveats by considering a thermodynamically consistent notion of information that naturally incorporates the costs of sensing in sensory adaptation ., Specifically , we derive a collection of universal bounds that relate the thermodynamic costs of sensing to the information processed ., These bounds reveal for the first time that for a generic SAS , measuring an environmental change is energetically costly ( 6 ) below , while to erase the memory of the past is energetically free , but necessarily irreversible ( 5 ) below ., By formalizing and linking the information processing and thermodynamics of sensory systems , our work shows that there is an intrinsic cost of sensing due to the necessity to process information ., To illustrate our generic approach , we study first a minimal four-state feedforward model and then a detailed ten-state feedback model of E . coli chemotaxis ., Owing to the symmetry of its motifs topology the four-state feedforward model does not require energy to sustain its adapted state ., Instead , all the dissipation arises from information processing: acquiring new information consumes energy , while erasing old information produces entropy ., By contrast , the E . coli model sustains its nonequilibrium steady state ( NESS ) by constantly dissipating energy , a requirement for adaptation with a feedback topology 19 ., In this nonequilibrium setting , we generalize our thermodynamic bounds in order to pinpoint the additional energy for sensing over that required to maintain the steady state ., We find with this formalism that in E . coli chemotaxis the theoretical minimum demanded by our bounds accounts for a sizable portion of the energy spent by the bacterium on its SAS ., To respond and adapt to changes in an environmental signal , a SAS requires a fast variable , the activity ; and a slow variable , the memory ., For example , in E . coli the activity is the conformational state of the receptor , the memory the number of methyl groups attached to it , and the signal is the ligand concentration 7 ., Without loss of generality , we consider in the following all three variables normalized such that they only lie between 0 and 1 , and that the signal can only alternate between two values: a low value 0 and a high value 1 ., As a result of thermal fluctuations , the time-dependent activity and memory are stochastic variables ., Yet , the defining characteristics of sensory adaptation are captured by their ensemble averages and , both at the steady state and in response to changes in the signal ., At a constant environmental signal , the system relaxes to an adapted -dependent steady state , which may be far from equilibrium 19 ., In this state , the memory is correlated with the signal , with an average value close to the signal , where is a small error ., The average activity however is adapted , taking a value roughly independent of the signal , , with adaption error ., Besides the ability to adapt , SAS are also defined by their multiscale response to abrupt signal changes , which is illustrated in Fig . 1 ., For example , given a sharp increase in the signal from to 1 the average activity quickly grows from its adapted value to a peak characterized by the gain error ., This occurs in a time , before the memory responds ., After a longer time , the memory starts to track the signal , and the activity gradually recovers to its adapted value ( see Fig . 1A ) ., For a sharp decrease in the signal , the behavior is analogous ( see Fig . 1B ) ., We identify a SAS as any device that exhibits the described adapted states for low and high signals ( 0 or 1 ) and that reproduces the desired behavior to abrupt increases and decreases in the signal ( see Fig . 1C for a cartoon biochemical example ) ., While SAS typically exhibit additional features ( such as wide range sensitivity 26 , 27 ) , they all exhibit the universal features illustrated in Fig . 1 ., To facilitate the development of our formalism , we first present a minimal stochastic model of a SAS , where the activity and memory are binary variables ( 0 or 1 ) ., This model is minimal , since it has the least number of degrees of freedom ( or states ) possible and still exhibits the required response and adaptive behavior ., Treating the environmental signal as an external field that drives the SAS , the system can be viewed as evolving by jumping stochastically between its four states depicted in Fig . 2A ., The rates for activity transitions from given at fixed are denoted , and those for memory transitions from given are ., As an equilibrium model , it is completely characterized by a free energy function , which we have constructed in the Methods by requiring the equilibrium steady state to have the required signal correlations of a SAS , ( 1 ) is the energy penalty for the memory to mistrack the signal , ensuring adaptation ( with the temperature and Boltzmanns constant ) ., In fact , one can show that ., is the penalty for the activity to mistrack the signal when ; it thus becomes relevant after a signal change , but before the memory adapts to the new signal , ensuring response ., In Figs ., 2C and D the energy landscape is represented for low and high signals ( smaller radius corresponds to less probability and larger energy ) ., Note that for fixed , the adaptation error is zero when the energy penalty to misstrack the signal becomes large , the systems configuration is then and takes on the values 0 and 1 with equal probability ., Finally , the dynamics are set by fixing the kinetic rates using detailed balance , e . g . , , and then choosing well-separated bare rates to set the timescale of jumps: for activity transitions and for memory transitions , with , thereby enforcing the well-separated time-scales of adaptation ., When there is a change in the signal , this model exhibits response and adaptation as characterized in Figs ., 1A and B ( verified in S1 and S2 Figures ) , and relaxes towards a dissipationless equilibrium steady state in which detailed balance is respected ., This is in contrast to previous studies on adaptive systems , which demonstrated that maintaining the steady state for a generic feedback system breaks detailed balance 19 , 20 ., Our model , however , differs by its network topology ., As depicted in Fig . 2B , it is a mutually repressive feedforward ( all rates depend explicitly on , and the actions of and on each other are symmetric ) ., Similar topologies also underly recent suggestions for biochemical networks that allow for adaptation with dissipationless steady states 22 , 23 ., Any sensory system that responds and adapts can naturally be viewed as an information processing device ., In the steady state , information about the signal is stored in the memory , since knowledge of allows one to accurately infer the value of ., The activity , on the other hand , possesses very little information about the signal , since it is adapted and almost independent of the signal ., When confronted by an abrupt signal change , the activity rapidly responds by gathering information about the new signal value ., As the activity decays back to its adapted value , information is stored in the memory ., However , to make room for this new information , the memory must decorrelate itself with the initial signal , thereby erasing the old information ., Thus sensory adaptation involves measurement as well as erasure of information ., To make this intuitive picture of information processing precise , let us focus on a concrete experimental situation where the signal is manipulated by an outside observer ., This is the setup common in experiments on E . coli chemotaxis where the signal ( the ligand concentration ) is varied in a prescribed , deterministic way 28 ., To be specific , the initial random signal is fixed to an arbitrary value , either 0 or 1 , with probability , and the system is prepared in the corresponding -dependent steady state , characterized by the probability density ., Then , at time , the signal is randomly switched to with final value ( which may be the same as ) according to the probability ., The signal is held there while the systems time-dependent probability density , which conditionally depends on both the initial and final signals , irreversibly relaxes to the final steady state ., During this relaxation correlations between the system and the final signal value develop while the correlations with the past value are lost ., As we will see , the measure of information that captures this evolution of correlations and naturally enters the thermodynamics of sensory adaptation is the mutual information between the system and the signal ., The mutual information is an information-theoretic quantification of how much a random variable ( such as the system ) knows about another variable ( such as the signal ) , ( 2 ) measured in nats 29 ., Here , is the Shannon entropy , which is a measure of uncertainty ., Thus , the mutual information measures the reduction in uncertainty of one variable given knowledge of the other ., Of note , with equality only when and are independent ., There are two key appearances of mutual information in sensory adaptation capturing how information about the present is acquired , while knowledge of the past is lost , which we now describe ., At the beginning of our experiment at , the SAS is correlated with , simply because the SAS is in a -dependent steady state ., Thus there is an initial information that the SAS has about the initial value of the signal ., The signal is then switched; yet immediately after , the SAS has no information about the new signal value , so ., Then for the SAS evolves , becoming correlated with , thereby gathering ( or measuring ) information , which grows with time ., Concurrently it decorrelates from , thus erasing information about the old signal , which also grows with time ., This conditioning only takes into account direct correlations between and , excluding indirect ones through ., To illustrate this , we calculate the flow of information in the non-disspative feedforward model for , which is a 1-bit operation ( because ) ., Fig . 3A displays the evolution of the measured information ( in black ) , which we decomposed as ( 3 ) where ( red ) is the information stored in the memory and ( blue ) in the activity ., We see the growth of proceeds first by a rapid ( ) increase as information is stored in the activity ( grows ) while the system responds , followed by a slower growth as adaptation sets in ( ) , and the memory begins to track the signal ., At the end , the system is adapted , and there is almost no information in the activity , ., With the small errors we have , the information acquired reaches nearly the maximum value of 1 bit , which is stored in the memory ., Fig . 3B shows the erasure of information , visible by the decrease of from an initial value of nearly one bit to zero when the system has decorrelated from the initial signal ., We have seen that through an irreversible relaxation , an SAS first acquires and then erases information in the registry of the activity , followed by the memory ., The irreversibility of these information operations is quantified by the entropy production , which we now analyze in order to pinpoint the thermodynamic costs of sensing ., Specifically , we demonstrate in Methods that for a system performing sensory adaptation in response to an abrupt change in the environment , the total entropy production can be partitioned in two positive parts: one caused by measurement ( ) and the other by erasure ( ) ., The second law thus becomes ( 4 ) with the reference set to an initial state at ., The erasure piece ( 5 ) is purely entropic in the sense that it contains no energetic terms ., It solely results from the loss of information ( or correlation ) about the initial signal ., By contrast , the energetics are contained in the measurement portion , ( 6 ) where is the change in Shannon entropy of the system and is the average heat flow into the system from the thermal reservoir ., A useful alternative formulation can be obtained once we identify the internal energy ., For example , in the equilibrium feedforward model , a sensible choice is the average energy ( 1 ) ., ( Recall , that there is no unique division into internal energy and work , though any choice once made is thermodynamically consistent 30 , 31 . ), By substituting in the first law of thermodynamics , with the work , we arrive at ( 7 ) This equation shows how the measured information bounds the minimum energy required for sensing , which must be supplied as either work or free energy ., Thus , to measure is energetically costly; whereas , erasure is energetically free , but necessarily irreversible ., In particular , for sensing to occur , the old information must be erased ( ) , implying that the process is inherently irreversible , ( 8 ) Together ( 5 ) and ( 7 ) quantify the thermodynamic cost of sensing an abrupt change in the environment by an arbitrary sensory system ., We have demonstrated from fundamental principles that sensing generically requires energy ., However , ( 7 ) does not dictate the source of that energy: It can be supplied by the environment itself or by the SAS ., The distinction originates because the definition of internal energy is not unique , a point to which we come back in our analysis of E . coli chemotaxis ., Using again our equilibrium feedforward model as an example , we apply our formalism to investigate the costs of sensory adaptation ., Since this model sustains its steady state at no energy cost , the ultimate limit lies in the sensing process itself ., We see this immediately in Fig . 4 where we verify the inequalities in ( 4 ) and ( 7 ) ., Since in ( 1 ) is explicitly a function of the environmental signal , the sudden change in at does work on the system , which is captured in Fig . 4A by the initial jump in ., This work is instantaneously converted into free energy and is then consumed as the system responds and adapts in order to measure ., Thus , in this example the work to sense is supplied by the signal ( the environment ) itself and not the SAS , which is consistent with other equilibrium models of SAS 23 ., Furthermore , Fig . 4B confirms that the erasure of information leads to an irreversible process with net entropy production ., The bounds of ( 4 ) and ( 7 ) are not tightly met in our model , since we are sensing a sudden change in the signal that necessitates a dissipative response ., Nonetheless , the total entropy production and energetic cost are on the order of the information erased and acquired ., This indicates that these information theoretic bounds can be a limiting factor for the operation of adaptive systems ., We now show that this is the case for E . coli chemotaxis , a fundamentally different system as it operates far from equilibrium ., We have quantified the thermodynamic costs in any sensory adaptation system; however , for systems that break detailed balance and maintain their steady state far from equilibrium , ( 5 ) – ( 8 ) are uninformative , because of the constant entropy production ., A case in point is E . colis SAS , which enables it to perform chemotaxis by constantly consuming energy and producing entropy through the continuous hydrolysis of SAM ., Nevertheless , there is a refinement of the second law for genuine NESS in terms of the nonadiabatic and adiabatic entropy productions , 32 ., Crudely speaking , is the entropy required to sustain a nonequilibrium steady state and is never null for a genuine NESS; whereas is the entropy produced by the transient time evolution ., When the system satisfies detailed balance always , be it at its equilibrium steady state or not; when its surroundings change , the entropy production is entirely captured by ., We can refine our predictions for a NESS by recognizing that captures the irreversibility due to a transient relaxation , just as does for systems satisfying detailed balance ., Analogously to Eqs ., ( 6 ) and ( 8 ) , we derive ( see Methods ) : ( 9 ) ( 10 ) Here , is the excess heat flow into the system , roughly the extra heat flow during a driven , nonautonomous process over that required to maintain the steady state 33 ., As a result , it remains finite during an irreversible relaxation to a NESS , even though the NESS may break detailed balance ., E . coli is a bacterium that can detect changes in the concentration of nearby ligands in order to perform chemotaxis: the act of swimming up a ligand attractor gradient ., It is arguably the best studied example of a SAS ., At a constant ligand concentration , chemoreceptors in E . coli – such as the one in Fig . 1C – have a fixed average activity , which through a phosphorylation cascade translates into a fixed switching rate of the bacterial flagellar motor ., When changes , the activity of the receptor ( which is a binary variable labeling two different receptor conformations ) increases on a time-scale ., On a longer time-scale , the methylesterase CheR and methyltransferase CheB alter the methylation level of the receptor in order to recover the adapted activity value ., In this way , the methylation level ( which ranges from none to four methyl groups for a single receptor ) is a representation of the environment , acting as the long-term memory ( see diagram in Fig . 5A ) ., One important difference with the previous equilibrium model is that the chemotaxis pathway operates via a feedback ., The memory is not regulated by the receptors signal , but rather by the receptors activity ( see motif in Fig . 5B ) ., The implication is that energy must constantly be dissipated to sustain the steady state 19 , thus ( 9 ) and ( 10 ) are the appropriate tools for a thermodynamic analysis ., There is a consensus kinetic model of E . coli chemoreceptors 7 , 27 , 34–36 whose biochemical network is in Fig . 5A ., The free energy landscape of the receptor coupled to its environment is ( 11 ) ( 12 ) with the receptors characteristic energy , the reference methylation level , and the active/inactive dissociation constants ( values in Methods ) ., In ( 11 ) the first term corresponds to the energy of the receptor , and the second comes from the interaction with the environment ( de facto a ligand reservoir ) ., The dynamics of this receptor consist of thermal transitions between the states with different activity , while transitions between the different methylation levels are powered by a chemical potential gradient due to hydrolisis of the methyl donor SAM ( see Methods ) ., Continuous hydrolysis of SAM at the steady state sustains the feedback at the expense of energy , allowing accurate adaptation in the ligand concentration range , see Fig . 5B ., To begin our study , we develop an equation analogous to ( 7 ) , which requires identifying the internal energy of our system ., As stated above , we consider the binding and unbinding of ligands as external stimuli , and thus define the internal energy as ., Using the excess heat , we consistently define the excess work through , analogous to the first law ., Upon substitution into ( 9 ) gives ( 13 ) showing just as in ( 7 ) that measuring requires excess work and free energy ., Because here the internal energy is not a function of the ligand concentration , is not due to signal variation: It represents the energy expended by the cell to respond and adapt to the external chemical force ., In Fig . 5C , we compare and to during a ligand change of ., The sudden change in produces a smooth , fast ( ) increase in the free energy as the activity transiently equilibrates with the new environment ., The excess work driving this response comes mainly from the interaction with environment ., As adaptation sets in ( ) , the receptor utilizes that stored free energy , but in addition burns energy by the consumption of SAM ., Thus , in order to adapt the cell consumes the free energy stored from the environment , as well as additional excess work coming now mostly from the hydrolysis of SAM molecules ., The inequality in ( 7 ) with the measured information is satisfied at all times ., The energetic cost of responding and adapting to the ligand change is roughly , of which much has already been used by ., In comparison , the cost to sustain the chemotaxis pathway during this time is roughly ( see Methods ) ., This means that the cost to sensing a step change is about 10% of the cost to sustain the sensing apparatus at steady-state ., During this process the cell measures ( and erases ) roughly bits , less than the maximum of 1 bit despite its very high adaptation accuracy ., This limitation comes from the finite number of discrete methylation levels , so that the probability distributions in m-space for large and low ligand concentrations have large overlaps ( S3 Figure ) ., In other words , it is difficult to discriminate these distributions , even though the averages are very distinct , which results in lower correlation between the methylation level and signal ., The minimal energetic cost associated to measuring these bits ( nats ) is ., E . coli dissipates roughly during this process , thus the energetic cost of sensory adaptation is slightly larger than twice its thermodynamic lower bound ( ) ., We further explored the cost of sensing in E . coli by examining the net entropy production for ligand changes of different intensity ., In Fig . 6A , we plot the amount of information erased/measured for different step changes of the signal up to taking as lower base ., The green shading highlights the region where adaptation is accurate ( ) ., The information erased is always below 1 bit and saturates for high ligand concentrations , for which the system is not sensitive ., The total entropic cost ( that is , ) and its relation with the information erased appears in Fig . 6B ., The dependence is monotonic , and thus reveals a trade-off between information processing and dissipation in sensory adaptation ., Notably , for small acquisition of information ( small ligand steps ) it grows linearly with the information , an effect observed in ideal measurement systems 17 ., We have derived generic information-theoretic bounds to sensory adaptation ., We have focused on response-adaptive sensory systems subject to an abrupt environmental switch ., This was merely a first step , but the procedure we have outlined here only relies on the validity of the second law of thermodynamics , and therefore can be extend to any small system affected by a random external perturbation to which we can apply stochastic thermodynamics , which is reviewed in 37 ., Our predictions are distinct from ( although reminiscent of ) Landauers principle 11 , 12 , which bounds the minimum energy required to reset an isolated memory ., By contrast , the information erased in our system is its correlations with the signal ., There is another important distinction from the setup of Landauer , and more broadly the traditional setup in the thermodynamics of computation 11 as well as the more recent advancements on the thermodynamics of information processing in the context of measurement and feedback 15 , 38–45 ., There the memory is reset by changing or manipulating it by varying its energy landscape ., In our situation , the erasure comes about because the signal is switched ., The loss of correlations is stimulated by a change in the measured system – that is the environmental signal; erasure does not occur because the memory itself is altered ., Also relevant is 46 , which addresses the minimum dissipated work for a system to make predictions about the future fluctuations of the environmental signal , in contrast to the measured information about the current signal , which we have considered ., Our results predict that energy is required to sense changes in the environment , but do not dictate that source of energy ., Our equilibrium feedforward model is able to sense and adapt by consuming energy provided by the environment ., E . colis feedback , however , uses mostly external energy to respond , but must consume energy of its own to adapt ., The generic bounds here established apply to these two distinct basic topologies , irrespective of their fundamentally different energetics ., For E . coli , to quantify to what extent is affected by SAM consumption and ligand binding , a more detailed chemical model is required in conduction with a partitioning of the excess work into distinct terms ., An interesting open question in this regard , is why nature would choose the dissipative steady state of E . coli , when theoretically the cost of sensing could be paid by the environment ., For a ligand change of , in the region of high adaptation , the information measured/erased is bits ., We observed that the corresponding average change in the methylation level for a chemoreceptor is , suggesting that a methylation level can store bits for such 1-bit step response operations ., Despite the small adaptation error , information storage is limited by fluctuations arising from the finite number of discrete methylation levels ., Receptors cooperativity , which is known to reduce fluctuations of the collective methylation level , may prevent this allowing them to store more information ., On the energetic side , we have shown that the cost of sensing these ligand changes per receptor is around 10% of the cost of sustaining the corresponding adaptive machinery ., We also showed that the energetic cost of binary operations is roughly twice beyond its minimum for large ligand changes , in stark contrast with everyday computers for which the difference is orders of magnitude ., Taken together these numbers suggest that 5% of the energy a cell uses in sensing is determined by information-thermodynamic bounds , and is thus unavoidable ., Future work should include addressing sensory adaptation in more complex scenarios ., One which has recently aroused attention is fluctuating environments , which so far has been addressed using trajectory information 44 , 45 , 47 ., However , under physiological conditions this is unlikely to play a significant role given the large separation of time-scales between binding , response , and adaptation 25 ., Another scenario is a many bits step operation , in which instead of high and low signals a large discrete set of ligand concentrations is considered ., Frequency response and gradient sensing are also appealing 27 , since in them the system is in a dynamic steady state in which the memory is continuously erased and rewritten ., Analysis of such scenarios is far from obvious , but the tools developed in this work constitute the first step in developing their theoretical framework ., We determine a collection of rates that exhibit response and adaptation as in Fig . 1 by first decomposing the steady state distribution as ., As a requirement to show adaptation , the memory must correlate with the signal , which we impose by fixing ., Next , in the steady state the activity is , or since is binary the probability is about ., Recognizing that is small , the average is dominated by adapted configurations with ., Thus , adaption will occur by demanding that and , with a model parameter ., Finally , to fix the activity distribution for non-adapted configurations , , we exploit the time-scale separation ., In this limit , after an abrupt change in the signal , the activity rapidly relaxes ., To guarantee the proper response , we set and ., Using the symmetry condition we complete knowledge of ., The energy levels are obtained using the equilibrium condition , where we choose as reference ., Equation ( 1 ) is an approximation of this energy to lowest order in the small errors ., Finally , the kinetic rates are obtained using either the approximate or exact energy function , imposing detailed balance , and keeping two bare rates , and , for activity and memory transitions: for activity transitions and for memory transitions ., The bounds in ( 5 ) and ( 6 ) follow from a rearrangement of the second law of thermodynamics 48 ., Consider a system with states for SAS with signal-dependent ( free ) energy function in contact with a thermal reservoir at temperature ., The system is subjected to a random abrupt change in the signal ., Specifically , the initial signal is a random variable with values ( which are in the main text ) , which we randomly change at to a new random signal with values ., For times , we model the evolution of the systems stochastic time-dependent state as a continuous-time Markov chain ., We begin our analysis by imagining for the moment that the signal trajectory is fixed to a particular sequence ., Then our thermodynamic process begins prior to by initializing the system in its -dependent steady state ., At , the signal changes to and remains fixed while the systems probability density , which conditionally depends on the entire signal trajectory , evolves according to the master equation 49 ( 14 ) where is the signal-dependent transition rate for an transition ., The transition rates are assumed to satisfy a local detailed balance condition , , which allows us to identify the energy exchanged as heat with the thermal reservoir in each jump ., Eventually , the system relaxes to the steady state corresponding to the final signal value ., Since the signal trajectory is fixed , this process is equivalent to a deterministic drive by an external field , and therefore the total entropy production rate will satisfy the second law 48 ( 15 ) where is the rate of change

Introduction, Results, Discussion, Methods

Biological sensory systems react to changes in their surroundings ., They are characterized by fast response and slow adaptation to varying environmental cues ., Insofar as sensory adaptive systems map environmental changes to changes of their internal degrees of freedom , they can be regarded as computational devices manipulating information ., Landauer established that information is ultimately physical , and its manipulation subject to the entropic and energetic bounds of thermodynamics ., Thus the fundamental costs of biological sensory adaptation can be elucidated by tracking how the information the system has about its environment is altered ., These bounds are particularly relevant for small organisms , which unlike everyday computers , operate at very low energies ., In this paper , we establish a general framework for the thermodynamics of information processing in sensing ., With it , we quantify how during sensory adaptation information about the past is erased , while information about the present is gathered ., This process produces entropy larger than the amount of old information erased and has an energetic cost bounded by the amount of new information written to memory ., We apply these principles to the E . colis chemotaxis pathway during binary ligand concentration changes ., In this regime , we quantify the amount of information stored by each methyl group and show that receptors consume energy in the range of the information-theoretic minimum ., Our work provides a basis for further inquiries into more complex phenomena , such as gradient sensing and frequency response .

The ability to process information is a ubiquitous feature of living organisms ., Indeed , in order to survive , every living being , from the smallest bacterium to the biggest mammal , has to gather and process information about its surrounding environment ., In the same way as our everyday computers need power to function , biological sensors need energy in order to gather and process this sensory information ., How much energy do living organisms have to spend in order to get information about their environment ?, In this paper , we show that the minimum energy required for a biological sensor to detect a change in some environmental signal is proportional to the amount of information processed during that event ., In order to know how far a real biological sensor operates from this minimum , we apply our predictions to chemo-sensing in the bacterium Escherichia Coli and find that the theoretical minimum corresponds to a sizable portion of the energy spent by the bacterium .

physics, thermodynamics, biophysics theory, biology and life sciences, physical sciences, biophysics

journal.ppat.0030023

The Role of Myelin in Theilers Virus Persistence in the Central Nervous System

The mechanisms by which viruses escape immune detection and establish persistent infections are extremely diverse ., Over the past years , much has been learned about various ways in which viruses manipulate the innate and the adaptive immune system to their advantage ., However , establishing a persistent infection may require more than dealing directly with the immune system ., In this article , we describe how the infection of myelin and oligodendrocytes by virions transported in the axons of infected neurons is a critical step in the establishment of a persistent infection of the central nervous system ( CNS ) by Theilers murine encephalomyelitis virus ( TMEV ) ., TMEV is a picornavirus of mouse , transmitted by the oral/fecal route , which causes a chronic neurological disease when it reaches the CNS 1 ., Although CNS disease is rare in the wild , it can be obtained routinely in the laboratory after intracerebral inoculation ., Once in the CNS , the virus causes an acute encephalomyelitis with infection of neurons , and to a lesser extent , of macrophages and astrocytes in gray matter 2 ., This “early disease” lasts approximately 2 wk , after which the virus is either cleared by the immune response , or persists in the CNS if the animals are genetically susceptible ., Persistence of the infection causes gait disorders and incontinence , also referred to as “late disease” 3 ., Susceptibility to persistent infection is multigenic with a major effect of the H2 locus 1 ., The virus does not persist in neurons but in glial cells of the white matter of spinal cord , mainly macrophage/microglial cells and oligodendrocytes , the myelin-making cells , and to a lesser extent astrocytes 4 , 5 ., This persistent infection of white matter is focal and is accompanied by chronic inflammation made of CD4+ and CD8+ T cells , of B cells , and of activated macrophages ., Primary demyelination , with conservation of axons , is ubiquitous in these foci ., However , some axonal damage , including in noninflamed white matter , has been documented 6 ., Taken together , these clinical and pathological findings are very reminiscent of those of multiple sclerosis ( MS ) in human ., As a result , the infection by TMEV of genetically susceptible mouse strains , such as the SJL/J and C3H strains , is a classical MS model 1 ., CNS myelin , the main target in MS , is an extension of the cytoplasmic membrane of the oligodendrocyte that wraps itself many times around axons ., Most of the cytoplasm is extruded from myelin; however , cytoplasmic channels remain that connect the myelin sheath to the oligodendrocyte cell body ., These channels contact the axon at the level of nodes of Ranvier , forming the so-called paranodal loops , as well as along the internode ( see Figure S3 ) 7 ., Several structural proteins , in particular myelin basic protein ( MBP ) and proteolipid protein ( PLP ) are important for the development and maintenance of myelin ., MBP binds to the cytoplasmic surface of the myelin leaflets and is thought to play a role in myelin compaction ., Due to the presence of more than one promoter and of alternate splicing , the Mbp gene codes for several isoforms of the protein 8 ., Some of them are expressed in the immune system , in particular in T cells and macrophages ., The BG21 isoform seems to downregulate T-cell activation 9 ., PLP is an abundant integral membrane protein of myelin that may play a role in membrane apposition 10 ., The shiverer mutation is a large deletion of the Mbp gene 11 that causes an extremely severe reduction of the amount of myelin in the mutant 12–14 ., Several years ago we observed that C3H mice homozygous for the shiverer mutation were completely resistant to persistent infection by TMEV , whereas wild-type C3H mice were susceptible ., In shiverer mice inoculated intracranially , TMEV infects neurons in the gray matter for about 10 d and causes an “early disease” very similar to that of wild-type mice ., However , the virus then disappears from the CNS instead of persisting ., Another myelin mutant , the rumpshaker mouse , which bears a point mutation in the Plp gene , is also resistant to persistent infection ., For both mutants , resistance cannot be overcome by increasing viral dose 15 ., The extreme resistance of shiverer and rumpshaker mice indicated that the Mbp and Plp mutations interacted with essential steps in the pathway leading to viral persistence ., The experiments described in this article focused on the shiverer mutant and were designed to identify this step , thereby gaining new insights into the complex mechanisms that lead to the persistence of a picornavirus in CNS white matter and to a disease very similar to MS . Our results show that the infection of myelin cytoplasmic channels and of oligodendrocytes by virus coming from axons of infected neurons is critical for the establishment of persistent infection ., We present several hypotheses concerning the role of myelin in persistence ., The golli isoforms of MBP ( BG21 and J37 ) are expressed in the immune system , in particular in T-lymphocytes ., BG21 seems to be a negative regulator of T-cell activation 9 , 16 ., The function of J37 is less clear ., Although BG21 is still expressed in shiverer mice , J37 is not ., Therefore , the shiverer mutation may affect the adaptive immune responses against TMEV and allow the virus to persist in the CNS ., To test for this possibility , we constructed immunological chimeras between wild-type ( C3H+/+ ) and shiverer ( C3Hshi/shi ) mice ., Mice were lethally irradiated and their immune systems were reconstituted with autologous or heterologous bone marrow cells ., The degree of chimerism of the PBLs was examined by PCR 7 wk after grafting ., Two pairs of PCR primers were used ., One , located within the shiverer deletion , amplified wild-type DNA only ., The other pair spanned the large deletion and amplified mutant DNA only ( Figure 1A ) ., Figure 1B shows representative results obtained with four different mice ., At least 85% of PBLs of all mice used were from the bone marrow donor ., Age-matched , bone marrow–grafted , as well as control wild-type and shiverer mice , were inoculated intracerebrally with 106 plaque-forming units ( PFU ) of TMEV ., Viral loads in spinal cord were measured 45 d postinoculation ( p . i . ) ., The results for control mice showed the expected susceptibility and resistance of , respectively , wild-type and shiverer mice ( Figure 1C , Table S1 ) ., Figure 1C also shows that susceptibility to persistent infection was not determined by the origin of bone marrow cells in chimeric mice ., The shiverer mice that received wild-type bone marrow remained resistant , whereas the wild-type mice that received shiverer bone marrow remained susceptible ., Therefore , the radiosensitive immune cells of shiverer mice are not responsible for resistance to persistent infection ., This is in sharp contrast to the resistance of other resistant strains , such as the C57BL/6 , which is due to viral clearance by the adaptive immune responses ., Microglial cells , the resident CNS macrophages , secrete cytokines and chemokines and play an important role in the recruitment of inflammatory cells to the site of infections 17 ., Because they turn over very slowly , if at all , they are not exchanged by bone marrow grafting 18 ., Since they express some MBP isoforms , including J37 19 , they could be involved in the resistance of the shiverer mouse ., To test for this possibility , wild-type and shiverer mice were injected intracranially with 10 μg of polyinosinic:polycytidilic acid poly ( I:C ) , a double-stranded RNA mimic that activates microglial cells by binding to toll-like receptor 3 ( TLR3 ) 20 ., Inflammatory cells were extracted from the CNS 18 h later and analyzed by flow cytometry for the expression of CD11b , a marker of cells of monocytic origin , and CD45 , an activation marker for these cells 21 ., The results showed that the percentage of activated cells of monocytic origin was the same for wild-type and shiverer mice ( Figure S1A ) ., Neurons and astrocytes can express TLR3 and can be a source of interferon during CNS viral infections 22–24 ., However , since they do not express MBP , it is unlikely that the resistance of shiverer mice was due to an increase of interferon secretion by these cells ., Lastly , we compared the inflammatory cells present in the brain of wild-type and shiverer mice , 5 d p . i . with TMEV ., Inflammatory cells were stained for CD3 , CD4 , and CD8 to study T-lymphocytes , and for CD19 and CD11c to study B cells and dendritic cells , respectively ., The results of flow cytometry measurements are shown in Figure S1B ., The frequency of each cell type was the same for wild-type and shiverer mice ., Therefore , the shiverer mutation does not affect the activation of microglial cells , the recruitment of inflammatory cells to the CNS following infection , and more generally , the adaptive , bone marrow–mediated immune response to the virus ., Since the immune response was not responsible for the clearance of the virus by shiverer mice , we considered the possibility that the mutation impaired an important step of the viral life cycle in the CNS ., A time course experiment showed that the viral load in brain and spinal cord was the same for wild-type and shiverer mice up to day 7 p . i . and that the virus disappeared from the CNS of the mutant between day 7 and day 11 p . i . ( Figure S2 and Table S2 ) ., To look for differences in the pattern of CNS cell infection , coronal frozen sections of the temporal region of brain were reacted with fluorescent antibodies against the viral capsid , and serial sections were examined with a fluorescent microscope ( Figure 2 ) ., The virus was present mainly in cortex ( Figure 2A and 2E ) , hippocampus ( Figure 2B and 2F ) , and hypothalamus ( Figure 2C and 2G ) ., The pattern was the same for wild-type and mutant mice ., The nature of infected cells was examined using two-color immunofluorescence ., Colocalization of fluorescent markers was assessed on optical sections obtained with the Zeiss Apotome fluorescent microscope ., A systematic survey of infected cells showed that the majority of them , both in wild-type and in shiverer mice , expressed the neuron-specific NeuN marker ., Figure 2D and 2H show examples of infected neurons in both cases ., Macrophages , derived from infiltrating monocytes or from activated microglia , are a major viral reservoir during persistent infection ., Because they are also infected during early disease 2 , it was possible to compare the permissiveness to TMEV of CNS macrophages in vivo in wild-type and mutant mice , 5 d after intracranial inoculation ., This was fortunate , since permissiveness of primary macrophages and of macrophage cell lines is notoriously difficult to control and depends largely on culture conditions 25 ., Inflammatory cells were extracted from the brain , labeled with anti-CD11b and anti-viral capsid antibodies , and analyzed by two-color flow cytometry ., CD11b is expressed by infiltrating monocytes/macrophages and by resting as well as activated microglial cells 21 ., Mice injected intracranially with 25 μg of poly ( I:C ) and sacrificed 18 h later served as controls ., The poly ( I:C ) -induced inflammatory cells were used to set the threshold for detection of viral capsid antigens ., Figure 3A and 3B shows an example of dot plot obtained in this experiment ., The intensity of fluorescence for viral antigens was highly variable from cell to cell , probably reflecting various stages of the viral cycle ., As can be seen in Figure 3C , the percentage of CD11b+ cells that were TMEV positive was not statistically different between wild-type and shiverer mice ., This experiment did not differentiate between infected macrophages , in which virus replicated , and macrophages ingesting infected cells and cellular debris ., To distinguish between the two , we measured the production of infectious virus by CNS macrophages using an infectious center assay ., Inflammatory cells were obtained as described above for flow cytometry ., Serial 10-fold dilutions of cells were plated on monolayers of indicator BHK21 cells ., The number of plaques obtained per macrophage for eight wild-type and eight shiverer mice is shown in Figure 3D ., Although there is a trend for shiverer macrophages to produce fewer infectious centers , the difference was not statistically significant ., Taken together , the results showed that the permissiveness of macrophages was the same in wild-type and in mutant mice ., MBP , a major myelin protein , is expressed by oligodendrocytes ., Since these cells are infected during late disease , we considered the possibility that the shiverer mutation could alter their permissiveness to the virus ., Oligodendrocytes are not infected during early disease 2; therefore , comparing the permissiveness of wild-type and shiverer oligodendrocytes could not be done in vivo , as was done for macrophages ., Instead , we prepared primary cultures of oligodendrocytes from both types of mice and infected them in vitro at a multiplicity of infection of 500 PFU/cell ., Because it is impossible to obtain oligodendrocyte cultures that are entirely devoid of astrocytes , and because astrocytes in culture are permissive to TMEV , it was not possible to compare viral titers ., Instead we used two-color immunofluorescence to characterize the infection ., Cells were fixed at various times p . i . and reacted with an anti-2′ , 3′-cyclic nucleotide 3′ phosphohydrolase ( CNPase ) antibody ( an oligodendrocyte and myelin-specific marker ) and an anti-viral capsid hyper immune serum ., Infected oligodendrocytes were identified by colocalization of both markers ( Figure 4A ) , and the percentage of CNPase-positive cells that expressed viral capsid antigens was measured ( Figure 4B ) ., The figure shows that the variation of this percentage with time was the same for both types of oligodendrocytes ., The shape of the curve indicates that a single cycle of viral infection was achieved in approximately half the oligodendrocytes in the culture ., Although the difference was not statistically significant , a slightly larger proportion of shiverer oligodendrocytes were infected , indicating that , if anything , shiverer oligodendrocytes might be slightly more permissive than wild-type oligodendrocytes ., Although most of the myelin sheath consists of juxtaposed plasma membranes , myelin also contains cytoplasmic channels , some of which , the paranodal loops and the inner loop , are in intimate contact with the axon ( Figure S3 ) ., Interestingly , there is evidence that these channels may contain TMEV capsid antigens during persistent infection 5 ., We re-assessed this notion using immunofluorescence and Apotome microscopy ., Longitudinal and transverse frozen sections of the spinal cord of persistently infected C3H mice were reacted with an anti-CNPase antibody , an anti-viral capsid serum , and di-aminido phenylindol ( DAPI ) , a nuclear stain ., As shown in Figure 5A , capsid antigens often formed linear patterns with the same longitudinal orientation as axons ., This pattern has been observed previously for both viral RNA and viral antigens ( unpublished data ) ., In double-labeled sections , viral antigens and CNPase colocalized in these patterns ., Colocalization was more easily appreciated in transverse sections in which viral antigens and CNPase sometimes formed ring structures surrounding axons ( Figure 5B ) ., Such rings may correspond to paranodal loops or to the inner or the outer loop ( Figure S3 ) , which spiral around the axon ., Capsid antigens were also observed in the CNPase-positive cell bodies of oligodendrocytes ( Figure 5C ) ., These results confirmed that myelin sheaths may contain viral capsid antigens during persistent infection ., They indicate that the linear pattern of viral RNA and antigens observed during persistent infection of white matter is due the infection of myelin , more than to the presence of viral particles in axons ., Since the virus infects neurons during early disease and is transported axonally 26 , 27 , myelin might be exposed to axonally transported virus , and the infection could spread secondarily to the cell body of the oligodendrocyte ., Alternatively , oligodendrocyte cell bodies could be infected first , by virions diffusing from infected neurons , or more indirectly via the blood stream , and the infection could spread outwardly to myelin sheaths ., To differentiate between these possibilities , we took advantage of the anatomy of the retina and of the optic nerve , both integral parts of the CNS ., Ganglion cells of the retina , which are adjacent to the vitreous chamber , send their axons caudally through the optic nerve where they are myelinated by oligodendrocytes ., The optic nerve does not contain neuron cell body; it contains only axons , oligodendrocytes , astrocytes , and microglial cells ., We reasoned that if ganglion cells could be infected by virus injected in the vitreous chamber of the eye , and if the virus were transported in optic nerve axons , it should be possible to follow the spread of the virus from axons to glial cells in vivo in a fully compartmented system ., Axonal transport could be assessed by looking for the infection of neurons in the lateral geniculate nucleus ( LGN ) to which ganglion cells project ( see Figure 6A ) ., By infecting one eye only , the contralateral optic nerve could serve as a control for axonal versus hematogenous source of virus ., Ganglion cells from one side project to the LGN of the contra- or the ispsilateral side because of axon decussation at the chiasma ., Therefore , only the pre-chiasmatic section of the optic nerve was examined ., Wild-type mice were inoculated in the vitreous chamber of the right eye with 106 PFU of TMEV , as described in Materials and Methods ., Mice were sacrificed every day until day 5 p . i . , and the eyes , the optic nerves , and the brain were prepared for immunohistological examination ., Serial sections of the retina were stained for viral capsid antigens and nuclei were stained with DAPI ., As shown in Figure 6B , infected cells were conspicuous in the retina ., Cells in the intermediate layer were the first to be infected ., Later , the virus spread to the layer of ganglion cells ., The insert in Figure 6B shows that infected cells were neurons , as demonstrated by double staining with the NeuN antibody ., To assess axonal transport in the optic nerve , serial-frozen coronal sections of brain were prepared at the level of the thalamus and reacted with the anti-capsid serum and DAPI ., The LGN was located and scanned systematically to record the presence of infected neurons ., Viral antigens appeared in the LGN and nowhere else in thalamus , between days 3 and 5 p . i . , demonstrating axonal transport ., Figure 6D shows an example of infected cells in LGN , 5 d p . i . Finally , serial-frozen sections of the pre-chiasmatic segment of the ipsi- and contralateral optic nerves of the same mice were stained for viral capsid antigens and for CNPase ( oligodendrocytes ) or glial fibrillary acidic protein ( GFAP ) ( astrocytes ) ., Viral antigens were first detected at day 4 p . i . Figure 6E–6G shows an example of a section of the ipsilateral optic nerve stained for the CNPase and viral markers ., Viral antigens were observed in the cell bodies and cellular extensions of oligodendrocytes as well as in myelin ., For each infected cell encountered , colocalization was assessed by scanning optical sections , 0 . 7 μm thick , obtained with the Apotome microscope ., An example of a series of optical sections of an infected oligodendrocyte is shown in Video S5 ., The distribution of infected cells along the optic nerve appeared random ( Figure 6C ) ., There were no more infected cells toward the retina than toward chiasma making it very unlikely that the virus had diffused from the infected retina along the optic nerve ., Figure 6H–6J and Video S6 show that some infected cells were astrocytes ., In sharp contrast , no viral antigen was detected in the contralateral pre-chiasmatic optic nerve ., Therefore , the source of infection of myelin and oligodendrocytes and astrocytes in optic nerve is virus-transported in axons ., The experiments described so far suggest that the virus traffics from the axon to myelin cytoplasmic channels and from there to the oligodendrocyte cell body ., Alternatively , oligodendrocyte cell bodies could be infected by virus diffusing from degenerating axons , and the infection could spread outwardly from the cell body to the myelin ., We took advantage of the shiverer mouse mutant to distinguish between the two possibilities ., Oligodendrocyte cell bodies are present in normal numbers in this mutant , but the amount of myelin is considerably reduced ., We reasoned that if myelin is required for the infection of oligodendrocytes , the number of infected oligodendrocyte cell bodies should be reduced in shiverer mice ., In a first step , we compared the level of virus replication in retina , in wild-type and shiverer mice , 2- and 5-d post-intravitreous inoculation ., Total RNA was extracted from the eye , and the level of minus-strand viral RNA was measured by real-time PCR ., Minus-strand viral RNA is found only in infected cells ., Therefore , this assay was not biased by viral particles from the inoculum ., Figure 7A shows that virus replication was very similar for the two kinds of mice ., In a second step , we compared the time of arrival of the infection in the LGN for wild-type and mutant mice using immunofluorescence as shown above and found no difference ( unpublished data ) ., Therefore , we concluded from the results of these controls that the amount of virus transported in the optic nerve must be very similar in wild-type and shiverer mice ., We then systematically scanned frozen sections of optic nerves doubly stained for viral capsid antigens and CNPase , or for viral capsid antigens and GFAP , and the phenotype of each infected cell was recorded ., A total of 12 wild-type and nine shiverer mice were examined ., As shown in Figure 7B , 50%–100% of infected cells were oligodendrocytes in C3H wild-type mice ., In contrast , no infected oligodendrocytes were found in six out of nine C3H shiverer mice ., In the other three , only 30% of infected cells were oligodendrocytes ., Therefore , the shiverer mutation considerably hampered the infection of oligodendrocytes by axonally transported virions ., The axons in the optic nerve of the shiverer mouse are not entirely naked , but may have a single , or a few , turn ( s ) of myelin around them 28 ., This may explain the small number of oligodendrocytes that were infected in shiverer optic nerves ., In summary , our results show that myelin is infected by axonally transported virus and that the infection spreads secondarily from the myelin to the oligodendrocyte cell body ., This traffic is interrupted by the shiverer mutation ., Since this is the only difference in the viral life cycle observed in this mouse mutant that is resistant to persistent infection , we infer that infecting myelin cytoplasm is essential for the persistence of Theilers virus in the CNS ., Pathogenesis consists of an extremely large number of molecular interactions between the pathogen and its host ., Genetic approaches , using host as well as pathogen mutants , are among the most powerful tools to identify essential steps in such a complex situation ., Our previous , unexpected observation that shiverer and rumpshaker myelin-mutant mice were totally resistant to the persistent infection of the CNS by TMEV suggested to us that myelin may play a critical role in the establishment or the maintenance of the persistent infection ., In the present work , we investigated the mechanism of resistance of the shiverer mutant with the hope of uncovering a hitherto undescribed step of pathogenesis , and we showed that the infection of myelin and oligodendrocytes from the axons of infected neurons is essential for viral persistence ., Resistance to viral infections is often mediated by immune responses ., Using immune chimeras and other tools , we found that this was not the case for TMEV persistence in the CNS ., Therefore , we decided to follow the virus in the CNS , step by step , after intracranial inoculation ., We observed that neither the early infection of neurons nor the permissiveness of oligodendrocytes and macrophages , the target cells during persistence , was affected by the shiverer mutation ., We confirmed an observation made by electron microscopy decades ago , namely that the cytoplasmic channels of myelin are a site of viral expression during persistence 5 ., We took advantage of the anatomy of the optic tract , from retina to LGN , to examine viral traffic between neurons , the main target during early disease , and glial cells ., We showed that TMEV traffics from axons to myelin and oligodendrocyte cell bodies and that the shiverer mutation , which renders mice resistant to persistent infection , interrupts this traffic ( Figure 8 ) ., Interestingly , Tsunoda and Fujinami proposed a similar “inside-out” model of viral spread from myelin to oligodendrocyte , based on entirely different considerations 6 , 29 ., Why should the infection of myelin be important for the persistence of TMEV ?, TMEV replicates and spreads continuously through the CNS , although at a slow pace , during persistent infection 30 , 31 ., Therefore , it must stay constantly ahead of the various arms of the immune response ., For picornaviruses , the first to come into play are mediated by TLR3 , TLR7 , and the cytoplasmic helicase MDA-5 ., These receptors are located in endosomes/lysosomes , or in the cytoplasm in the case of MDA-5 ., They sense viral RNA and induce the nuclear translocation of transcription factors IRF-3 , IRF-5 , IRF-7 , and NFkb and the expression of interferons and other cytokines such as TNF-alpha 32 ., Clearly , the activation of this signaling cascade will depend on the organization of the cytoplasm of the infected cell ., Nothing is known about the presence of the various components of these pathways in the cytoplasmic channels of myelin ., However , one wonders about signalization cascades from the paranodal and internal loops to the very distant cell nucleus in cells with such unusual cytoplasmic organization ., Is it possible that the interferon response to a virus that enters myelin from the axon is retarded compared to that in a more compact type of cell ?, The main effector of the clearance of TMEV in genetically resistant mice is class I-restricted CD8+ cytotoxic T-lymphocytes recruited to the site of infection by the secretion of chemokines 33 ., Could the myelin be a haven protected from cytotoxic T-lymphocytes ?, In the classical pathway the loading of class I molecules with peptides takes place in the endoplasmic reticulum ., Since myelin cytoplasmic channels are far removed from the perinuclear endoplasmic reticulum of the oligodendrocyte cell body , viruses entering myelin from the axon may enjoy an environment where viral epitopes cannot be loaded on class I molecules efficiently ., Furthermore , inefficient interferon secretion might delay the recruitment of cytotoxic T-lymphocytes ., Also , by using axons to travel to distant sites within the CNS , the virus may escape from a local hostile environment , with cytokine secretion and inflammation , to new , virgin territories ., Finally , neutralizing antibodies participate in viral clearance in the CNS of resistant mouse strains 34 ., Interestingly , the structure of the node of Ranvier , which has been extensively characterized at the molecular level 7 , makes it impossible for immunoglobulins to diffuse through the septate-like junctions into the internodal space ( between axon and myelin ) ., Therefore , the virus might be protected from antibodies while it traffics from the axon to the myelin cytoplasm ., Clearly , the shiverer mutation , which causes a severe neurological deficit , was not selected for the advantage it gives mice by protecting them from TMEV persistent infection ., However , it is possible that more innocuous polymorphisms of structural proteins have been selected because they interrupt the traffic of aggressive pathogens through a key organ such as the CNS ., Viruses have evolved an endless number of strategies to adapt to specific , highly specialized environments including the CNS ., This paper points to the previously unrecognized use of axon/myelin interactions to foster viral persistence ., It warrants looking for a similar role of myelin in the persistence in white matter of other viruses , including in humans ., TMEV strain DA , grown on BHK-21 cells , had a titer of 2 ×108 PFU/ml ., Concentrated virus was used in some experiments ., In this case , the clarified medium of infected BHK21 cells was layered on top of a 30% sucrose cushion and centrifuged at 17 , 000 rpm at 4 °C for 17 h in a Kontron TST 28 . 38 rotor ., The virus pellet was re-suspended in 10 mM Tris HCl ( pH 7 . 4 ) ., The titer of concentrated virus was 109 PFU/ml ., C3HeB/FeJ and C3H shi/shi mice , referred to as “wild-type” and “shiverer” mice , respectively , in this article , were obtained from The Jackson Laboratory ( http://www . jax . org ) ., 4- to 6-wk-old , sex- and age-matched mice were used in all experiments ., Anesthetized mice were inoculated intracranially with 106 PFU of TMEV in 5 μl ., Intra-vitreous injections were performed as follows: The conjunctiva was carefully cut with microsurgical scissors and forceps ( Corneal ) over approximately 1 mm , on the external side of the eye , to give access to the sclera ., The sclera was punched with a 31-gauge needle ., 2 ×106 PFU of concentrated TMEV in 2 μl was injected between the crystalline lens and the retina with a microsyringe and a 33-gauge blunt-ended needle ( Hamilton , http://www . hamiltoncompany . com ) ., The virus had been mixed with a 1:100 dilution of a neutralizing anti-interferon type I serum ( a gift from Ion Gresser , Curie Institute ) in order to enhance viral replication in the retina ., Anesthetized mice were perfused with PBS through the left ventricle ., Brains and spinal cords were dissected out and homogenized in Tri Reagent ( Molecular Research Center , http://www . mrcgene . com ) by passing the tissue through needles of decreasing diameter ( 18- , 21- , and 23-gauge ) ., RNA isolation was performed according to the manufacturers protocol ., RNA was precipitated with ethanol , re-suspended in water , and its concentration was determined by spectrophotometry ., 10 μg of extracted RNA was mixed with 3 . 5 μg of random hexamer primers p ( dN ) 6 ( Boehringer , http://www . boehringer-ingelheim . com ) , denatured for 20 min at 65 °C , then re-natured at room temperature to allow hybridization of the hexamer primers to the template ., Reverse transcription was performed with AMV reverse transcriptase ( Promega , http://www . promega . com ) at 42 °C for 90 min ., To follow viral replication in retina , the eyes were dissected out and homogenized with a motor pellet pestle ( Kimble/Kontes , http://www . kimble-kontes . com ) in the Qiagen RNeasy RLT buffer ( Qiagen , http://www1 . qiagen . com ) ., RNA extraction was performed according to Qiagens protocol ., The RNA extracted from one eye was mixed with 8 pmol of TM346 primer , in order to reverse transcribe viral minus-strand viral RNA only , and 8 pmol HPRTb primer ( Table S3 ) ., The mixture was heated at 65 °C to denature RNA and DNA , and cooled at room temperature to allow hybridization of the primers to the RNA ., Reverse transcription was performed with AMV reverse transcriptase at 42 °C for 90 min ., Quantitative PCR was performed on the reverse transcription products using sequence specific Taqman probes , 2 × qPCR mastermix ( Eurogentec , http://www . eurogentec . com ) and an ABI Pr

Introduction, Results, Discussion, Materials and Methods

Theilers virus , a picornavirus , persists for life in the central nervous system of mouse and causes a demyelinating disease that is a model for multiple sclerosis ., The virus infects neurons first but persists in white matter glial cells , mainly oligodendrocytes and macrophages ., The mechanism , by which the virus traffics from neurons to glial cells , and the respective roles of oligodendrocytes and macrophages in persistence are poorly understood ., We took advantage of our previous finding that the shiverer mouse , a mutant with a deletion in the myelin basic protein gene ( Mbp ) , is resistant to persistent infection to examine the role of myelin in persistence ., Using immune chimeras , we show that resistance is not mediated by immune responses or by an efficient recruitment of inflammatory cells into the central nervous system ., With both in vivo and in vitro experiments , we show that the mutation does not impair the permissiveness of neurons , oligodendrocytes , and macrophages to the virus ., We demonstrate that viral antigens are present in cytoplasmic channels of myelin during persistent infection of wild-type mice ., Using the optic nerve as a model , we show that the virus traffics from the axons of retinal ganglion cells to the cytoplasmic channels of myelin , and that this traffic is impaired by the shiverer mutation ., These results uncover an unsuspected axon to myelin traffic of Theilers virus and the essential role played by the infection of myelin/oligodendrocyte in persistence .

Theilers virus persists in the central nervous system of mice and causes a chronic disease that resembles multiple sclerosis , a common demyelinating disease of humans ., The virus infects neurons for one to two weeks , but later on it persists in the white matter , in oligodendrocytes and also in macrophages ., Oligodendrocytes are the myelin-making cells of the central nervous system ., Strikingly , in mice with a genetic defect of myelin , the virus infects neurons normally but is unable to persist ., Understanding the reason for the lack of persistence in this mutant mouse should pinpoint an essential step in the complex process resulting in persistence ., In this article , we show that resistance to persistent infection is not mediated by the immune system and is not due to inefficient viral replication in oligodendrocytes or macrophages ., Instead , we show that virus transported in axons traffics into the myelin , and that this traffic is interrupted by the myelin mutation ., This unsuspected axon to myelin traffic of Theilers virus is necessary for viral persistence ., Our results warrant looking for a similar phenomenon in other persistent infections of the nervous system , including in humans .

viruses, neurological disorders, virology, mus (mouse), neuroscience, animals

journal.pntd.0004440

Identification and Genomic Analysis of a Novel Group C Orthobunyavirus Isolated from a Mosquito Captured near Iquitos, Peru

The Orthobunyavirus genus comprises a diverse set of viral species , represented by multiple serogroups , including: Bunyamwera , California , Group C , and Simbu 1 ., Their RNA genome includes three segments ( Small S , Medium M , and Large L ) ., The L segment encodes a RNA polymerase ( RdRP ) ; the M segment encodes two glycoproteins ( Gc and Gn ) in addition to a non-structural protein ( NS ) ; and the S segment encodes both a nucleocapsid protein ( NP or N protein ) and a non-structural protein ( NSs ) 2 , 3 ., Group C viruses were first identified in Brazil around 1950 ., Members of the California serogroup , including La Crosse , California encephalitis , Inkoo , and Tahyna viruses , are known to cause disease in humans 4–8 ., Similarly , members of the Bunyamwera serogroup , including Cache Valley and Bunyamwera viruses 9 , 10 , Simbu serogroup , including Akabane , Iquitos , and Schmallenberg viruses 11–13 , and Group C , including Caraparu , Itaya , Marituba , and Oriboca viruses 14–16 , are known to cause disease in humans or domestic animals ., Because infection with Group C viruses results in a non-differentiated febrile ( dengue-like ) illness and the lack of available diagnostic assays for these viruses , it has been difficult to associate these viruses with human disease ., However , a study by Forshey et al . 17 identified 30 cases of human illness associated with Group C orthobunyaviruses , many of them Caraparu-like , and estimated that about 2 . 5% of febrile illnesses in the region were due to infection with an orthobunyavirus ., The goal of our study was to sample , sequence and assemble a novel member of the genus Orthobunyavirus that had been isolated from a pool of Culex portesi mosquitoes captured in Peru in order to provide further genomic insights of this potentially disease-causing virus ., The animal work was approved by the USAMRIID Institutional Animal Care and Use Committee ., Research was conducted under an IACUC approved protocol in compliance with the Animal Welfare Act , PHS Policy , and other Federal statutes and regulations relating to animals and experiments involving animals ., The facility where this research was conducted is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , International and adheres to principles stated in the Guide for the Care and Use of Laboratory Animals , National Research Council , 2011 ., Mosquitoes were captured at Aotus monkey-baited traps as part of an enzootic dengue study conducted in the vicinity of Iquitos , Peru 18 ., Mosquitoes were identified to species , pooled ( up to 25 specimens/pool ) , frozen on dry ice , and kept at -70°C until tested for infectious virus ., Mosquito pools were triturated in 2 ml of diluent 10% heat-inactivated fetal bovine serum in Medium 199 with Earles salts , NaHCO3 and penicillin ( 100 U/ml ) , streptomycin ( 100 μg/ml ) , and nystatin ( 100 U/ml ) ., The suspensions were clarified by centrifugation ( 3 , 000 rpm for 10 min ) and tested for virus by plaque assay on Vero ( African green monkey kidney , ATCC CCL81 ) cell monolayers ., A 0 . l-ml aliquot of each original mosquito suspension and a 1:100 dilution of these suspensions were inoculated into duplicate wells of Vero cell monolayers ., A second overlay , containing neutral red stain , was added 2 or 6 d later ., If plaques were observed , the agar was removed , and the cells washed with fresh diluent and the resulting viral suspensions aliquoted into cryovials and frozen at –70°C ., An aliquot of each suspension was inoculated onto confluent monolayers of Vero cells grown in a T-25 culture flask with 5 ml of liquid cell culture medium and observed daily for evidence of cytopathology ., Cell cultures showing cytopathic effects were frozen at –70°C ., Later , they were thawed , the suspension clarified by centrifugation at 3 , 000 rpm for 5 min , and then stored as 0 . 5-ml aliquots at –70°C for virus identification studies ., The Vero passage 2 stock of one of these viruses , PE-M-0139 ( isolated from a pool of 25 Cx . portesi mosquitoes captured in June 2002 ) , was used in these studies ., Total RNA from the Vero passage 2-cell culture supernatant was reverse transcribed using random hexamers , and the resulting cDNA was amplified using multiple displacement amplification ., A sequencing library was prepared using the Nextera XT protocol , and sequenced on an Illumina HiSeq 2500 instrument ., An initial HiSeq run of 47 , 871 , 860 reads was supplemented with a second HiSeq run of 204 , 323 , 558 reads , yielding 252 , 195 , 418 total 100bp paired-end reads ( NCBI BioProject PRJNA290192 ) ., Initial analysis of the metagenomic sample involved a de novo assembly and taxonomic classification approach via MetAMOS 19 , IDBA_UD 20 , Kraken 21 and Krona 22 ., However , initial inspection of the classified contigs and unassembled reads provided a convoluted picture of sample constituents , with only two reads classified as a member of the genus Orthobunyavirus ( S1 Fig ) ., LMAT 23 ( v1 . 2 . 3 ) was run on the dataset , only 5 reads were assigned to the genus Orthobunyavirus ., The reads were adapter clipped and quality trimmed using ea-utils , part of MetAMOS 19 ( fastq-mcf command , default parameters ) using the Nextera XT adapter sequence CTGTCTCTTATACACATCT ., To complement the de novo approach , putative orthobunyavirus reads were recruited to a diverse set of orthobunyavirus genomes via blastn 24 ( e-value 0 . 1 , word size 7 ) using a custom orthobunyavirus database ( Caraparu , Zungarococha , Oropouche viruses , containing L , M and S segments ) downloaded from RefSeq 25 ., The reference-based strategy filtered the nearly 50 million reads down to 234 , 280 paired-end reads ( 0 . 5% of the sample ) ; blast did not report any read alignments to existing S segment sequences ., Assembly of the recruited subset was performed with IDBA-UD ( —pre_correction—num_threads 8—step 10 ) ; assembly was also attempted with SOAPdenovo 26 and Velvet-SC 27 , but these produced fragmented assemblies ., The assembly was inspected for misassemblies by mapping all recruited reads back to the assembled contigs using Bowtie 2 28; a total of 121 , 901 reads mapped to the L segment ( 1762X avg . coverage ) and 29 , 599 reads mapped to the M segment ( 617X avg . coverage ) ., Coverage plots of the read mappings were visualized in IGV 29 ., One junction in the assembled M segment was found to lack read support and was not consistent with related M segments ( S4 Fig red arrow ) ., A second round of recruitment was performed , including reads from the full assembly covering the region containing the erroneous deletion ., The misassembled region was corrected after including these additional reads and rerunning IDBA_UD , resulting in consistent read support across both L and M segments ., In addition , a full de novo assembly of the 50 million reads was performed ( IDBA-UD 20 , default parameters ) , resulting in 340 , 327 total contigs ., Contigs assembled with the full HiSeq dataset were screened against the Human genome ( hg19 ) and Green Monkey ( BioProject PRJNA215854 ) draft sequence to identify host sequence and misassembled contigs ., The recruited assembly was compared to the IDBA-UD 20 assembly on the full dataset using NUCmer 30 ., Orthobunyavirus ( L and M ) contigs were identified using both blastx and HMMER ., An exhaustive search for the 900-1000bp S segment was performed , without success , using HMMER 31 ( HMM profile http://pfam . xfam . org/family/PF00952 , against all contigs using hmmpress and hmmscan ) ., Based on known conserved terminal hairpin sequences found in the UTR regions in Orthobunyavirus genomes 33 , 34 , we searched for terminal hairpin sequences ( AGTAGTGTGCT ) near both 5’and 3’ends in the L , M , and S segments ( within the first 300 nt ) using BLAST 24 ( e-value = 10 , word size = 7 ) , to determine if the assembly was complete on both ends ., Amino acid ( aa ) sequences were aligned using MUSCLE 35 ( default parameters ) , back translated to the original nucleotide sequences , edited to trim sequences from both ends that could not be reliably aligned , and then realigned with MUSCLE ., Phylogenetic trees were subsequently reconstructed for both a global set of 101 orthobunyavirus genomes ( L segment ) and also on six Group C orthobunyavirus genomes ( L and M segments ) , with FastTree2 36 ., Default parameters were used , and bootstrap support was determined by resampling the site likelihoods 1000 times and applying Shimodaira-Hasegawa test 37 ., To determine the potential for El Huayo virus to replicate in a vertebrate host , we inoculated three Syrian hamsters intraperitoneally with 0 . 2 ml of a suspension containing 106 . 5 PFU/ml ( 105 . 8 PFU/hamster ) of the Vero passage 2 stock of El Huayo virus ., The hamsters were anesthetized daily and three mosquitoes were allowed to take a blood meal from each of the hamsters ., These engorged mosquitoes were then triturated individually in 1 ml of diluent and tested for infectious virus by plaque assay on Vero cells as described above ., Hamsters were observed for 21 days for signs of illness ., The El Huayo assembly yielded three contigs ( Table 1 ) with alignments to orthobunyavirus sequences , with best hits for all three segments to Peruvian Caraparu strains 38 ., We were unable to identify the known terminal hairpin sequences in the UTR regions , suggesting incomplete assembly of the segments and/or increased divergence in the known conserved region ., The de novo assembly of the L and M segments with the first HiSeq dataset was more fragmented than the recruitment approach ( 95 contigs vs . 2 contigs ) with >95% of aligned de novo contigs identical to the recruited assembly ., However , the recruitment approach significantly reduced depth of coverage ( 50-fold average reduction in coverage for both segments ) , with a more dramatic effect on the M segment ( 100-fold ) compared to L segment ( 5-fold ) , due to the high level of divergence from the reference strain ., Differences between the two assemblies were investigated further with dnadiff 39; the full de novo assembly had multiple small insertions with respect to the reference-recruited assembly ., These insertions were found to have high identity hits to Rhesus macaque and Green monkey genomes , yet were lacking from both Caraparu genomes and the reference-recruited assembly ., Closer inspection of these insertions identified them as retroviral sequences and contained within likely misassembled contigs ( S3 Fig ) ., Phylogenetic analysis of the L segment suggests that this virus is closely related to Caraparu viruses comprising Group C orthobunyaviruses ( Fig 1 ) ., We placed it within the Group C phylogeny , consistent with previously published phylogenetic relationships of orthobunyaviruses isolated from Peru 1 ., El Huayo virus therefore appears to represent a novel , previously uncharacterized subclade of Group C viruses ., Orthobunyaviruses are known to have high rates of reassortment 40 , and although both L and M segments were most closely related to Caraparu virus ( Fig 2 , Table 2 ) , there is increased polymorphism observed in M relative to L compared to other orthobunyavirus genomes ., In addition , Caraparu virus strain FMD0783 was found to be the most similar ( nt/aa ) to both the M and S segments , while strain IQD5973 ( from Iquitos , Peru ) was the most similar ( nt/aa ) to segment L . El Huayo virus replicated to moderate titers in Syrian hamsters , with peak viremias of about 107 . 2 PFU/ml occurring on day 3 after infection ( Fig 3 , Table 3 ) ., None of the hamsters displayed signs of illness , and all were well 21 days after infection ., This is the first report of El Huayo virus , a novel member of the Group C orthobunyaviruses ., Although rarely associated with human disease in nature , Group C viruses are known to cause febrile illness 13 , 41 ., The lack of reported cases is almost certainly due to a lack of diagnostic assays available for this group , and members of this group may be responsible for much of the dengue-like illnesses reported in areas of South and Central America where Aedes aegypti are not common 42 ., In fact , Forshey et al . 17 estimated that about 2 . 5% of febrile illnesses in the region were due to infection with an orthobunyavirus , but were misdiagnosed as dengue ., Culex portesi , the species from which El Huayo virus was isolated , is a common species known to preferentially feed on rodents and marsupials 43 , 44 and numerous viruses , including Caraparu-like viruses have been isolated from this species 45–47 ., The ability of El Huayo virus to replicate to fairly high titer in hamsters indicates that like many other Group C virus , rodents may be involved in the natural maintenance cycle for this virus 48 ., Thus , the natural cycle for El Huayo virus appears to be between Cx ., portesi and rodents in the Amazon Basin region ., Because these viruses have a segmented genome , and because genetic reassortment has been demonstrated in this family/genus 49 , the orthobunyaviruses are an ideal model for studying the evolution of novel viruses by genetic reassortment ., How reassortment affects disease in humans and the ability of these viruses to replicate in vector species are key open questions ., In our initial comparative analysis , the best matches in our reference database shared ~60–80% nucleotide identity and 70–90% identity at the amino acid level with the ( translated ) novel S , M and L segment sequences , respectively ., Given the low sequence identity of segment M relative to segment L , segment M might represent a novel reassortment; the region from 1500-2500bp contains a dramatic reduction in similarity to all known segment M strains available in RefSeq ., High divergence relative to existing genomes is a challenge for homology detection methods; sensitivity must be increased to detect divergent matches , but the increase in sensitivity also leads to potential misclassifications ., Sensitive profile alignment methods based on hidden Markov models can detect protein domain signatures in cases where extreme divergence makes other methods infeasible 18 , such as in the case of the highly divergent S segment recently reported for Brazoran virus 26 which was double the size of previously published orthobunyavirus S segments ., Its S segment contained no known homology to existing segment S proteins; however , similar to what we report here , it did have conserved orthobunyavirus domains that were detected via InterProScan 27 ., While insufficient sequencing depth in our initial HiSeq run prohibited detection of the S segment , adding another HiSeq run allowed for the detection of this small viral segment ., This result highlights that lower abundance sequences in environment samples may often be missed , and sequencing depth is still an important tool for uncovering low abundance novel viruses from metagenomic samples ., Based on amino acid sequence similarity , the orthobunyavirus genome of El Huayo virus reported in this study is most closely related to Caraparu virus Peruvian strains IQD5973 and FMD0783 38 , both recently deposited in GenBank ., This recent growth in publicly available Group C orthobunyavirus genomes enabled the reliable placement of our novel strain within the Group C serogroup ., Prior to Huang et al . 2014 , there were no complete genomes ( including all three segments ) from within Group C . Lack of complete genomic sequences of serogroups of interest can lead to misclassification or misidentification , evidenced by a recent study that reported that a collection of Group C genomes likely require further validation 38 ., This highlights the importance of efforts to populate reference databases ., There exists a vast underrepresentation of viral diversity for various clades , and of particular interest to this study , there are only a small number of South American orthobunyavirus sequences ., Continuing efforts are required to fill out viral reference databases to ensure reliable identification and characterization of novel Bunyaviridae genomes ., An additional confounding factor for novel virus identification and assembly is host endogenous retroviral elements 50 ( S2 Fig and S3 Fig ) ., Aggressive assembly strategies can result in chimeric host-plus-virus assemblies in which sequence shared by both virus and host results in false joins between the two genomes; specifically , retroviral elements integrated into host genomes ., We have shown that a recruitment-based strategy , even at relatively high levels of amino acid divergence , can prove useful for avoiding co-assembly of host and target virus ., However , this approach requires the presence of reference strains in the database and is prone to under-recruitment of reads in highly polymorphic regions ., In summary , while advances in sequencing technology allow for the discovery of novel viruses present at low abundances in a sample , care must be taken to properly address confounding factors .

Introduction, Materials and Methods, Results, Discussion

Group C orthobunyaviruses are single-stranded RNA viruses found in both South and North America ., Until very recently , and despite their status as important vector-borne human pathogens , no Group C whole genome sequences containing all three segments were available in public databases ., Here we report a Group C orthobunyavirus , named El Huayo virus , isolated from a pool of Culex portesi mosquitoes captured near Iquitos , Peru ., Although initial metagenomic analysis yielded only a handful of reads belonging to the genus Orthobunyavirus , single contig assemblies were generated for L , M , and S segments totaling over 200 , 000 reads ( ~0 . 5% of sample ) ., Given the moderately high viremia in hamsters ( >107 plaque-forming units/ml ) and the propensity for Cx ., portesi to feed on rodents , it is possible that El Huayo virus is maintained in nature in a Culex portesi/rodent cycle ., El Huayo virus was found to be most similar to Peruvian Caraparu virus isolates and constitutes a novel subclade within Group C .

Arthropod-borne viruses remain a significant cause of human and domestic animal disease and new viruses are constantly being discovered ., RNA virus discovery and assembly remains a challenge due to highly polymorphic genomes , current lack of breadth and depth of publicly available viral genomes , and confounding factors due to host sequence and sequencing biases ., We describe the discovery and genome assembly of El Huayo virus , a group C orthobunyavirus isolated from a pool of Culex portesi mosquitoes captured near Iquitos , Peru , and named for the Jardin Botanicao Arboretum El Huayo near where the Cx ., portesi from which the virus was isolated were captured ., Although orthobunyaviruses are not commonly associated with human disease , Group C members are widespread in Central and South America and known to cause febrile illness ., The discovery , and genome assembly , of El Huayo virus may help to explain numerous dengue-like illnesses where Aedes aegypti are not commonly found .

sequencing techniques, invertebrates, dengue virus, medicine and health sciences, pathology and laboratory medicine, pathogens, microbiology, vertebrates, animals, sequence assembly tools, mammals, viruses, genomic databases, rna viruses, genome analysis, molecular biology techniques, mammalian genomics, insect vectors, microbial genomics, research and analysis methods, sequence analysis, hamsters, viral genomics, sequence alignment, medical microbiology, epidemiology, microbial pathogens, biological databases, molecular biology, disease vectors, insects, animal genomics, arthropoda, mosquitoes, rodents, flaviviruses, virology, database and informatics methods, viral pathogens, genetics, biology and life sciences, genomics, amniotes, computational biology, organisms

journal.pcbi.1004148

Preferred Supramolecular Organization and Dimer Interfaces of Opioid Receptors from Simulated Self-Association

Experimental evidence accumulated over almost two decades supports the ability of all three major opioid receptor ( OR ) subtypes ( μ-OR , δ-OR , and κ-OR ) to form homomeric and heteromeric complexes , a feature that is common to several other G Protein-Coupled Receptors ( GPCRs ) ( see 1 for a recent review ) ., In particular , heteromerization between μ-OR and δ-OR and between δ-OR and κ-OR has been suggested to expand the repertoire of OR signaling by modulating ligand binding , receptor signaling , and/or trafficking properties 2 ., This modulation has a direct translational relevance in view of the putative role that OR heteromers play in opioid-induced adverse effects ( e . g . , the development of analgesic tolerance 3 , 4 ) , and in the observed potentiation of morphine-induced analgesia by δ-OR-selective antagonists 5 ., Thus , targeting OR oligomers directly may lead to novel drugs with potentially greater selectivity and reduced side effects compared to molecules targeting individual receptors ., While a few ligands have already been proposed to target OR heteromers ( e . g . , see 6–11 ) , it remains unclear how they bind and activate these receptor complexes ., To begin addressing these questions and eventually use these and other molecules as tools to elucidate the physiological relevance of OR oligomers , ligand-bound models of these receptor complexes are highly desirable ., However , building such models is not feasible in the absence of reliable information about their interface of dimerization/oligomerization in a physiologically relevant environment ., The recent X-ray crystal structures of the μ-OR 12 and κ-OR 13 have suggested specific receptor-receptor interactions involving transmembrane ( TM ) helices TM5 and TM6 or TM1 , TM2 , and helix 8 ( H8 ) ., Although these interfaces are thermodynamically stable according to our recent free-energy calculations of μ-OR and κ-OR homo-dimers in an explicit lipid-water environment 14 , the possibility cannot be ruled out that other receptor-receptor interactions are also feasible in the cell membrane , and perhaps more kinetically favorable than those inferred from crystallography ., Here , we carried out extensive , unbiased coarse-grained ( CG ) molecular dynamics ( MD ) simulations of freely diffusing ORs in an explicit lipid-water environment to evaluate differences and similarities in the supramolecular organization and preferred dimeric interfaces of all three major receptor subtypes ., All the twenty-five different simulations we carried out showed multiple association events between ORs initially located far apart and moving freely in the lipid bilayer ., As an example , Fig . 1 ., shows the location of each of the 16 receptor molecules in one of the five simulations executed on the κ-OR system ( run #1 ) at different simulation times ( specifically 0 , 2 , 6 , and 10 μs ) ., While the formation of several receptor-receptor complexes could be observed during simulation of the μ-OR/μ-OR , δ-OR/ δ-OR , κ-OR/κ-OR , δ-OR/μ-OR , and δ-OR/κ-OR systems , each individual protomer in the complex seldom shared more than two interfaces , thus favoring filiform rather than branched or compact high-order arrangements ( see simulation snapshots at 6 and 10 μs in Fig . 1 , as well as S1–S5 Movies ) ., Notably , linear arrays of receptors were observed in early atomic force microscopy images of the prototypic GPCR rhodopsin in native membranes 19 , and were further supported by early CG simulations 20 ., Below , we speculate that the formation of chains of receptors may be influenced by lipid dynamics ( see the Dynamic Behavior of Lipid Molecules section for a rationale ) ., Importantly , once formed , no receptor complex dissociated over the maximal simulated time of 10 μs , but rather only minor interface rearrangements were observed ., This observation is in line with experimental estimates of dimer lifetime ( a few seconds ) obtained from recent single-molecule imaging studies of different , individually labeled , GPCRs in living cells 21 ., Since dissociation is not observed in the simulations reported here , a direct calculation of free-energies is precluded ., However , analysis of the executed twenty-five simulations instills confidence in that they might have captured the majority of fastest-forming dimer interfaces of ORs ., The five different simulation trajectories obtained for each OR system were pooled together to derive statistically meaningful information about the various dimer interfaces that formed during simulation ., Since each protomer structure was kept fairly rigid by elastic network forces during the simulations ( see Materials and Methods for details ) , no allosteric communication between protomers or inter-dependence between dimer interfaces were expected ., Interfaces were defined based on the minimal number of residues on each receptor TM helix within a certain distance cutoff from each other , and were clustered based on the similarity between inter-protomer contact maps ( see details of the analysis under Materials and Methods ) ., Tables 1 and 2 report the preferred OR homo-dimer and hetero-dimer interfaces , as derived from Bayesian analysis of the pooled trajectories ., The following interesting observations can be made on the basis of this analysis ., First of all , not all possible combinations of TMs were found to be involved in dimer interfaces during the simulated timescale of 10 μs ., The only interfaces that formed in all studied homo- and heteromeric systems are: TM1 , 2 , H8/TM1 , 2 , H8 , TM1 , 2/TM4 , 5 ( also TM4 , 5/TM1 , 2 for hetero-dimers ) , and TM1 , 2/TM5 , 6 ( also TM5 , 6/TM1 , 2 for hetero-dimers ) ., Other interfaces , such as TM4 , 5/TM4 , 5 , TM4 , 5/TM5 , 6 , TM5/TM5 , and TM5/TM1 , 2 did not form in at least one of the five studied μ-OR/μ-OR , δ-OR/ δ-OR , κ-OR/κ-OR , δ-OR/μ-OR , and δ-OR/κ-OR systems ., In particular , the TM4 , 5/TM4 , 5 interface , which has been suggested to constitute a possible GPCR dimer interface by various experimental assays ( reviewed in 22 ) , only formed during simulation of the δ-OR/δ-OR system , and with a lower frequency with respect to other interfaces formed by δ-OR homomers ., Notably , neither TM3 nor TM7 were ever found to be involved in a dimer interface , whether formed by the same or different receptor subtypes ., Another important observation from our study is that TM helices appear with different frequencies at a dimer interface depending on the OR system ., Specifically , the TM1 , 2 helices appear most frequently at the observed dimer interfaces of δ-OR and κ-OR , followed by TM4 , 5 and TM5 , 6 , with the latter helix pair being more involved in a dimer interface in μ-OR compared to δ-OR and κ-OR ., However , it is noted that the calculated confidence intervals for frequencies of the specific interfaces are quite broad and overlapping , and therefore the estimated differences between the three OR subtypes may not be as relevant as they appear to be ., Several interfaces observed in the simulations reported here are structurally similar to some of the putative dimer interfaces inferred from recent GPCR crystal structures ( see S2 Table for a list of currently available GPCR crystal structures showing parallel receptor arrangements ) ., To allow a quantitative comparison , we calculated the minimum Cα root mean square deviation ( RMSD ) distance between members of the cluster of dimeric complexes that formed during the simulations and each crystal structure listed in S2 Table ., The cases where this distance resulted to be less than 10 Å are reported in S3 Table for homo-dimers and in S4 Table for hetero-dimers ., The calculated RMSD values of S3 and S4 Tables suggest that the dimer interface from simulations that is closest to one inferred from crystal structures is the TM1 , 2 , H8/TM1 , 2 , H8 interface ., In particular , δ-OR and κ-OR form TM1 , 2 , H8/TM1 , 2 , H8 interfaces in both homo- and hetero-dimers that are very close ( RMSDs below 4 . 3 Å ) to that seen in the crystal structure of κ-OR ( 4DJH 13 ) ., The closest crystal structure to the TM1 , 2 , H8/TM1 , 2 , H8 interface that forms during μ-OR simulations is not the one inferred by the μ-OR crystal structure ( 4DKL 12 ) , but rather the one suggested by a β1-adrenergic receptor ( B1AR ) crystal structure ( 4GPO 23 ) ., Figs ., 2A , 3A , and 4A show , as an example , the TM1 , 2 , H8/TM1 , 2 , H8 homo-dimer interfaces formed during simulation of δ-OR , κ-OR , and μ-OR , respectively , overlapped onto the closest crystal structures , i . e . , 4DJH or 4GPO ., The relatively small RMSD values listed in S3 Table , indicate that the simulations of the δ-OR system also reproduced both symmetric and asymmetric dimer interfaces inferred from CXCR4 crystal structures 24 ( see S2 Table for details ) with reasonable accuracy ., Specifically , the interface herein termed TM1 , 2/TM5 , 6 deviated only 6 . 48 Å from the asymmetric interface revealed by 3OE8 ( after overlapping the dimer from simulation with chains A and B of 3OE8 ) whereas the interface herein called TM4 , 5/TM5 , 6 deviated only 6 . 66 Å from an interface inferred from 3ODU ( after overlapping the dimer from simulation with chains A and B of 3ODU ) and 6 . 62 Å from an interface inferred from 3OE8 ( after overlapping the dimer from simulation with chains B and C of 3OE8 ) ., Fig . 2B and 2C show structural overlaps of the TM1 , 2/TM5 , 6 and TM4 , 5/TM5 , 6 interfaces of δ-OR dimers with 3OE8 ., Larger RMSD values were obtained for the identified TM5/TM5 interface in both κ-OR and μ-OR simulations ( see S3 and S4 Tables ) after comparison with the available GPCR crystal structures of interacting parallel receptors that are listed in S2 Table ., Specifically , in κ-OR , this interface is 8 . 56 Å apart from the putative TM5 , 6-TM5 , 6 dimer interface inferred from the μ-OR crystal structure ( after overlapping the dimer from simulation with chain A of 4DKL and its periodic image ) ., Notably , μ-OR simulations did not produce dimeric arrangements that were close enough to the crystallographic TM5 , 6-TM5 , 6 interface of μ-OR , in spite of it being thermodynamically stable as we recently demonstrated through free-energy calculations 14 ., The closest structure to the identified TM5/TM5 in μ-OR simulations was the interface termed TM4 , 5/TM4 , 5 in the B1AR crystal structure corresponding to PDB code 4GPO ( RMSD of 8 . 82 Å , after overlapping the formed dimer with chain A of 4GPO and its periodic image ) ., While the calculated RMSD of the identified TM5/TM5 interfaces of κ-OR and μ-OR homo- and hetero-dimers with respect to available crystal structures appear to be quite large , visual inspection of these overlaps ( Figs . 3B and 4B for κ-OR and μ-OR , respectively ) shows that the slight rotation of one protomer needed to match the two configurations would not dramatically change the nature of those interfaces ., Thus , we speculate that the reason why the simulations reported herein are unable to reproduce the crystallographic TM5 , 6-TM5 , 6 interface of μ-OR is that this interface may need longer times to form than its slight modifications ( more on this below ) ., To understand whether there are interfaces that are kinetically favored over others , i . e are fast forming in an explicit membrane environment , we calculated interface-specific dimerization rates ( kon ) for all simulated OR systems by fitting a Poisson model to the association instances observed during simulation ., The results are reported in Tables 3 and 4 for the simulated homo- and hetero-dimers of ORs , respectively ., The fastest forming homo-dimer interfaces are: TM1 , 2/TM4 , 5 and TM1 , 2/TM5 , 6 for δ-OR/δ-OR , TM1 , 2 , H8/TM1 , 2 , H8 and TM1 , 2/TM4 , 5 for κ-OR/κ-OR , and TM5/TM5 for μ-OR/μ-OR ., Notably , TM1 , 2 , H8/TM1 , 2 , H8 , TM4 , 5/TM1 , 2 , and TM4 , 5/TM5 , 6 are the fastest interfaces for the δ-OR/κ-OR hetero-dimer , whereas the TM5/TM5 interface is the fastest forming for the δ-OR/μ-OR hetero-dimer ., This observation further stresses the higher propensity for κ-OR to have the TM1 , 2 helices involved in a fast-forming dimer interface ., In contrast , TM5 is more likely to be involved in a fast-forming dimer interface when one of the receptor partners is μ-OR ., Analysis of the dynamic behavior of the POPC ( herein referred to as ‘lipid’ ) and cholesterol molecules during the simulations reported here reveals considerable dynamical heterogeneity , in that regions of high mobility appear to be surrounded by slower molecules ., Such a behavior , which is typical of glass-forming fluids and supercooled liquids 25 , and was also suggested for membrane proteins ( e . g . , see 26–28 ) , has also been recently reported for water at the interface of globular proteins 29 ., We speculate that this dynamical heterogeneity also controls membrane diffusion and viscosity near OR dimer interfaces , and plays an important role in modulating the rate of receptor association and the structure of the complex ., Visualization of the twenty-five simulation trajectories reported herein showed a clear variation in the dynamic behavior of lipid molecules during receptor dimerization ., As seen in S1–S5 Movies , which are provided as representative simulation trajectories of the δ-OR/δ-OR , κ-OR/κ-OR , μ-OR/μ-OR , δ-OR/κ-OR , and δ-OR/μ-OR systems , respectively , the density of slow lipids becomes higher ( dark blue in S1–S5 Movies ) as protomers approach each other , suggesting that slow lipids may interfere with dimer formation at specific interfaces ., To quantitatively investigate the role of lipid molecules in regulating OR dimerization , we calculated exchange and persistence time ( tX and tP , respectively ) distributions of the lipids at different positions relative to isolated receptors ., These two quantities characterize the diffusion properties ( D ) of the lipids and the effective viscosity ( η ) of the membrane around the protein , respectively ( see details in the Methods section ) ., Typical observed average exchange times 〈tX〉 of lipids in the bulk membrane , i . e . away from the receptors , are ~10 ns ( see S1 Fig . ) , corresponding to a lipid diffusion coefficient D≈d2/〈tX〉 = 10−6 cm2/s , ( or D≈2 . 5×10−7 cm2/s , when accounting for the effective time scaling for the CG force field we used ) ., In the simulations reported here , the average exchange time of lipids increases up to 〈tX〉≈40–50 ns at specific regions near the OR surface , giving D≈2 . 5×10−7 cm2/s ( or , effectively , D≈6 . 2×10−8 cm2/s ) ., Notably , similar values of lipid diffusion constants have recently been reported in the literature 26 , 27 for comparable CG force fields , and a similar behavior was implied ., In the bulk membrane , the equivalence between the exchange and persistence times implies an inverse relationship between viscosity and diffusion coefficient in homogeneous systems , known as the Stokes-Einstein relation ., The presence of dynamical heterogeneity , with slow lipids close to the protein surface , corresponds to a breakdown of the Stokes-Einstein relation for lipid dynamics in this region ., In other words , correlated lipid motion leads to an increased membrane effective viscosity , decoupled from the diffusional motion of the lipids , so that the inverse relationship between the diffusion coefficient and viscosity ( ηD ∝ constant ) , is no longer homogeneously valid ., Analysis of the lipid dynamics around isolated ORs shows longer average persistence times 〈tP〉 ( see Fig . 5 , panels A , B , and C for δ-OR , κ-OR , and μ-OR , respectively ) at specific locations of the protein surface , up to 100 ns ., In general , persistence times increase more than exchange times 〈tX〉 , so that the ratio 〈tP〉/〈tX〉 is usually larger than 1 ( see Fig . 5 , panels D , E , and F for δ-OR , κ-OR , and μ-OR , respectively ) ., Usually , lipid molecules in the region of helices TM1 , 2 display the shortest persistence times during simulation compared to the region of helices TM4 , 5 and TM5 , 6 ., This is interesting in view of the observed prevalence of interfaces that involve helices TM1 , 2 and the corresponding fastest on-rates , compared to the generally less frequent participation of TM4 , 5 and TM5 , 6 in dimeric interfaces of ORs ., Based on this observation , it is tempting to speculate that this local effect on the membrane viscosity ( i . e . , 〈tP〉/〈tX〉>1 ) and the presence of long-lived lipid microstates with long persistence times near the surface may delay the formation of specific interfaces , making them kinetically disfavored with respect to others ., The viscosity of the environment has a well-established , direct effect on the kinetics of biological processes as indicated by the expression of the rate constant in the widely applied Kramers’ framework 30:, k≃mω†ω2πηexp ( −G†kBT ), where m is the effective mass associated with the order parameter used to describe the biological process , G† is the free-energy of the transition state , ω and ω† are the curvatures of the free-energy profile at the bottom and top of the barrier , and η is the viscosity ., According to this expression , the higher viscosity observed in regions of the environment with long persistence times results in slower rates , and therefore slower kinetics of the process ., To quantitatively assess the relationship between regions of slow lipid dynamics around OR protomers and reduced kinetic rates , we calculated the position-dependent translational diffusion coefficients ( DP ) of μ-OR protomers at the identified homo-dimeric interfaces using a Bayesian inference approach described in the literature 31 ., According to these calculations , the diffusion coefficients of μ-OR at homo-dimeric interfaces displaying slower on-rates ( i . e . , TM1 , 2 , H8/TM1 , 2 , H8 , TM1 , 2/TM4 , 5 , and especially TM4 , 5/TM5 , 6 ) are significantly reduced when the two protomers are within distances of a few nanometers ., As reported in S5 Table , average diffusion rates of ~5×10−7 cm2/s estimated when the protomers are far apart ( d>50 Å ) are reduced to ~1–2×10−7 cm2/s as the inter-protomer distance reaches values between 40 and 50 Å ., At shorter distances ( d<40 Å ) , the diffusion rates decreased to less than 10−7 cm2/s for the slow-forming interfaces , but did not change much for the fast-forming interfaces ( i . e , TM1 , 2/TM5 , 6 and TM5/TM5 ) ., This observation provides a possible explanation why the TM5 , 6/TM5 , 6 interface seen in the μ-OR crystal structure did not form during the 10 μs simulations , notwithstanding its thermodynamical stability 14 ., As mentioned before , the two protomers of the TM5/TM5 dimer identified by simulations need to rotate to obtain the crystallographic TM5 , 6/TM5 , 6 configuration ., We estimated the free-energy associated with such a rotation using the results of steered MD simulations , and assuming no conformational changes occurring within the individual protomers based on the CG model we employed ., These results , which are reported in S4 Fig ., , are consistent with a high free-energy barrier ( ~10 kcal/mol ) between the identified TM5/TM5 and the crystallographic TM5 , 6/TM5 , 6 dimers , further supporting the hypothesis that longer time scales are needed for the latter to form ., As also evident when viewing the S1–S5 Movies , lipid regions of length scales of a few nanometers between approaching receptors prior to dimer formation can also become locally trapped/restricted in motion ( so-called “jammed” regions ) ., While a complete kinetic analysis of the dimerization process that includes lipid dynamics cannot be achieved using the data reported herein , the simulations suggest a distinctive role for long-lived lipid microstructures as they appear to decrease the dimerization on-rate at specific interfaces , and kinetically select specific dimeric arrangements among all different possibilities ., We also investigated the dynamics of cholesterol molecules by calculating their persistence and exchange times around isolated δ-OR , κ-OR , and μ-OR , using the same strategy employed for the study of the lipid molecules ., Average values of cholesterol exchange times are reported in S2 Fig ., , panels A-C , for δ-OR , κ-OR , and μ-OR , respectively , whereas average persistence times and persistence-to-exchange ratios of cholesterol molecules surrounding isolated δ-OR , κ-OR , and μ-OR are reported in Fig . 6A-C and 6D-F , respectively ., Notably , regions with long cholesterol persistence times appear to be generally co-localized with regions with strong lipid molecule persistence , suggesting that both cholesterol-protein interactions and cholesterol-lipid interactions contribute to the kinetic selection of specific dimer interfaces ., Preferred cholesterol interacting sites at the surface of GPCR molecules have been reported in some of the published crystal structures ., For instance , a cholesterol binding pocket was identified in a groove characterized by highly conserved residues ( so-called “consensus-motif” residues ) between the intracellular ends of helices TM2 and TM4 in two B2AR crystal structures , i . e . , the carazolol-bound 2RH1 32 and the timolol-bound 3D4S 33 ., Cholesterol molecules were also observed in the ultra-high resolution crystal structure of the A2A adenosine receptor corresponding to PDB code 4EIY 34 ., While the “consensus motif” residues identified in B2AR are conserved in A2A , no cholesterol was observed at the intracellular end of helices TM2 and TM4 ., In contrast , three cholesterol molecules were found at the extracellular sides of TM2 , 3 , as well as TM5 , 6 and TM6 , 7 ., While no cholesterol molecules were resolved in the κ-OR or δ-OR crystal structures , electron density was attributed to a cholesterol molecule in the μ-OR crystal structure ( 4DKL ) , at the same location between TM6 and TM7 as seen in the A2A crystal structure 4EIY ., Notably , the aforementioned “consensus motif” residues on TM2 ( Y2 . 41 , S2 . 45 ) are not conserved in members of the opioid receptor family ., Moreover , the calculated high persistence time of cholesterol close to the extracellular ends of helices TM6 and TM7 from simulations of all three OR subtypes ( see Fig . 6 . ) is consistent with the location of cholesterol molecules found in the ultra-high-resolution adenosine A2A crystal structure 4EIY and in the μ-OR crystal structure 4DKL ., Although the palmitoylation site C3 . 55 at the intracellular end of TM3 was proposed to constitute part of a cholesterol preferred binding site in the groove lining the intracellular region between TM4 and TM5 35 , this region does not show increased persistence times of cholesterol molecules in the simulations reported here ., It must be noted that in the simulations reported here , the calculation of persistence and exchange times of both POPC and cholesterol near dimer interfaces is limited by the relative motion of one protomer with respect to one another ., Thus , for these specific calculations , we used the results of previous simulations 14 of μ-OR crystallographic dimers , i . e . , TM1 , 2 , H8/TM1 , 2 , H8 and TM5 , 6/TM5 , 6 , where the relative orientation and distance of the protomers in the dimer were maintained constant ., As illustrated in Fig . 7 , the protein surface adjacent to the TM1 , 2 , H8/TM1 , 2 , H8 and TM5 , 6/TM5 , 6 dimer interfaces is in contact with regions of the membrane with slower dynamics ., These regions of long persistence lipids right at the dimer interface may provide a mechanistic explanation for the preferential arrangement of receptors into extended linear arrays rather than compact or branched aggregates ., In summary , the simulations reported here suggest that both the formation of specific dimer interfaces and the overall topology of oligomeric aggregates depend on the kinetic kon rates ., Through calculation of both persistence and exchange times of lipid and cholesterol molecules during simulation , we show the presence of ‘jammed’ lipid regions that exhibit long persistence times and non-Poissonian dynamics , and we speculate that these regions play an important role in modulating the kinetics of GPCR di-/oligomerization ., Slower diffusion at the interface between integral membrane proteins and the membrane has been extensively investigated , leading to the distinction between annular and “non-annular” lipid behavior ( e . g , see 26 , 27 ) ., Averaged measures of lipid diffusion have been reported ( e . g . , see 27 ) as a function of the radial distance from the receptor , and have showed a generally slower lipid motion at small radial distances from the protein surface ., Although informative , these measures fail to discriminate between the different behavior of lipids adjacent to different helices of the receptor , and are therefore of limited use ., While reported measurements of the extent of local time-averaged lipid displacement ( proportional to the local average velocity of lipid molecules ) 26 have allowed to identify regions of the protein surface likely to be in contact with slower lipids , the average velocity is not a direct measure of the membrane viscosity , thus complicating the interpretation of the results ., The present analysis provides rigorous information about the local behavior of the viscosity , thus allowing to analyze its relation to OR dimer formation at specific interfaces ., The results reported here complement those of previous studies , and suggest the use of persistence and exchange times to assess the important role of metastable lipid structures in GPCR dimer formation ., Our assessment of the rheological properties of the lipid bilayer also complements the analysis of membrane elasticity and mechanical properties via macroscopic empirical models 36–38 ., In particular , the results reported here provide direct evidence for the effect of the microscopic dynamics of lipids and cholesterol at dimeric interfaces on the mesoscopic length- and time-scales of receptor interactions , further supporting an essential role of the lipid membrane in determining the identity of homomeric or heteromeric complexes of GPCRs ., The atomic coordinates of non-protein molecules were removed from the PDB files of the crystallographic structures of the mouse δ-OR ( PDB ID: 4EJ4 15 ) , mouse μ-OR ( PDB ID: 4DKL 12 ) , and chain A of the human κ-OR ( PDB ID: 4DJH 13 ) receptors ., Missing or unresolved residues ( specifically , residues 263–270 in μ-OR and residues 262 and 301–307 for κ-OR ) were built using ROSETTA 39 ., The conformation for the intracellular loop 3 ( IL3 ) was selected as the minimum energy structure reported by ROSETTA ., For μ-OR , the lowest energy conformation that also did not interfere ( inter-lock ) with the IL3 of the adjacent receptor at the TM5/TM6 interface was selected ., For δ-OR , the crystallographic IL3 was rebuilt using ROSETTA between residues 241 and 258 ., Notably , the root mean square deviation ( RMSD ) of this loop conformation from the resolved loop of the newest high-resolution crystal structure of δ-OR 40 is 0 . 46 Å overall ., Throughout this article we use the Ballesteros-Weinstein numbering scheme to facilitate comparison between the receptor subtype TM regions 41 ., Accordingly , the first number of this scheme indicates the TM helix in which the residue in question resides , and the second number indicates the position of the residue relative to the most conserved residue in the helix , which is always numbered 50 ., The receptors were converted to a CG representation under the MARTINI force field ( version 2 . 1 ) 16–18 and a modified elastic network was applied , as reported previously in the literature 42 ., According to recent experimental findings 35 , the receptors were also palmitoylated at the C3 . 55 position , with a palmitoylate chain consisting of 4 C1 beads , with a bond length of 0 . 47 nm , a force constant of 1250 kJ mol-1 nm-2 and angles of 180° , with a force constant 25 kJ mol−1 ., Eighteen orientations ( each rotated 20 degrees with respect to each other , thus covering 360 degrees around the z-axis of the receptor ) of the CG receptors were each embedded in explicit CG POPC/10% cholesterol membranes ( with a protein/lipid ratio of approximately 1:100 ) solvated with MARTINI CG water and neutralizing counterions ., Temperature coupling at 300 K was achieved with the V-rescale algorithm , and pressure coupling at 1 bar was achieved with the Berendsen algorithm ., Simulation were performed with GROMACS version 4 . 5 . 3 43 ., The systems were minimized and equilibrated with harmonic restraints of decreasing strength over 10 ns , on protein backbone beads ., Water and counter ions were removed and sixteen of these membrane/protomer systems were randomly selected ( repeat selections were permitted ) and combined to create five 16-receptor setups for each of the studied homomeric ( μ-OR/μ-OR , δ-OR/δ-OR , and κ-OR/κ-OR ) and heteromeric ( δ-OR/κ-OR and μ-OR/δ-OR ) systems , which were re-solvated , neutralized , and subsequently minimized ., The heteromeric systems contained eight receptors of each subtype ., Thus , twenty-five independently constructed 16-receptor systems , each with approximately 57 , 000 beads , were generated ., Each of these systems was simulated for 10 μs with a timestep of 20 fs , to give 50 μs of pooled simulation time for each homo- and heteromeric receptor system , and 250 microseconds in total , across all systems ., Periodic boundary conditions were employed , and neighbor lists were updated every 10 steps ., The Shift algorithm was used for electrostatic interactions ., A single cut-off of 1 . 2 nm was used for Van der Waals interactions ., For each receptor , we calculated the Cα contact map δ with all possible dimerization partners , and defined as “dimeric” the pairs for which at least 10 residues on each receptor were at a distance below an assigned cutoff ( 8 Å ) ., Dimeric interfaces were clustered by a k-means algorithm using the distance induced by the Frobenius norm on the contact map matrices , and the clusters automatically labeled according to the TM regions involved in receptor-receptor interactions ., Specifically , the dissimilarity of two interfaces k1 and k2 was defined as, dk1k2=‖δijk1−δijk2‖, for hetero-dimers , while for homo-dimers the symmetrized form, dk1k2=min ( ‖δijk1−δijk2‖ , ‖δijk1−δjik2‖ ), was used to account for the equivalence of the dimer with swapped protomers ., The marginal contact map averages over the NC interfaces k in cluster C were calculated for one receptor as, δj ( C ) =1NC∑k∈C∑iδij ( k ), while for the interacting one the analogous expression with i and j swapped inside the sum was used ., Helices for which at least 3 residues were involved in the interface , were included in the label of the cluster ., We used a Bayesian inference framework to pool the information from the different trajectories and calculate estimates of the interface prevalence for each dimer and on-rates ., The number of dimerization instances in a given trajectory i that yielded an interface belonging to cluster C can be described by a stochastic variable Ni , C with a multinomial distribution, p ( NiC ) =∑CNiC∏CNiC !, ∏CpiCNiC, where the probability of each cluster is pi , C = wC/ΣCwC , wC being the overall weight of the cluster ., We defined wC = exp ( aC ) , used non-informative normal priors ( with zero mean and large standard deviation ) for the parameters aC , and employed Gibbs sampling to obtain the posterior distributions of pC , for which we report the average as well as ( 2 . 5% , 97 . 5% ) confidence intervals ., While best estimates of the probabilities pC are well approximated by the pooled fractions , the larger confidence intervals obtained by Bayesian inference reflect the variations in the different trajectories ., Estimates of the on-rate for dimerization kon ( C ) at each interface C were obtained assuming that associations are independent of each other , and that the number of interface-specific dimerization events n follows a Poisson distribution:, p ( n ) = ( t\u200akon ( C ) ci ) nn !, e−t\u200akon ( C )

Introduction, Results and Discussion, Materials and Methods

Substantial evidence in support of the formation of opioid receptor ( OR ) di-/oligomers suggests previously unknown mechanisms used by these proteins to exert their biological functions ., In an attempt to guide experimental assessment of the identity of the minimal signaling unit for ORs , we conducted extensive coarse-grained ( CG ) molecular dynamics ( MD ) simulations of different combinations of the three major OR subtypes , i . e . , μ-OR , δ-OR , and κ-OR , in an explicit lipid bilayer ., Specifically , we ran multiple , independent MD simulations of each homomeric μ-OR/μ-OR , δ-OR/δ-OR , and κ-OR/κ-OR complex , as well as two of the most studied heteromeric complexes , i . e . , δ-OR/μ-OR and δ-OR/κ-OR , to derive the preferred supramolecular organization and dimer interfaces of ORs in a cell membrane model ., These simulations yielded over 250 microseconds of accumulated data , which correspond to approximately 1 millisecond of effective simulated dynamics according to established scaling factors of the CG model we employed ., Analysis of these data indicates similar preferred supramolecular organization and dimer interfaces of ORs across the different receptor subtypes , but also important differences in the kinetics of receptor association at specific dimer interfaces ., We also investigated the kinetic properties of interfacial lipids , and explored their possible role in modulating the rate of receptor association and in promoting the formation of filiform aggregates , thus supporting a distinctive role of the membrane in OR oligomerization and , possibly , signaling .

Opioid receptors associate with each other in the plasma membrane , following a mechanism that has been implicated in either beneficial or adverse effects , depending on the environment and the interacting partners ., Whether or not the different opioid receptor subtypes share a similar propensity to form di-/oligomers and specific receptor-receptor interactions is still unclear on the basis of the published data ., This information , however , is necessary to predict stabilizing or de-stabilizing mutations and to design experiments to clarify the role of oligomerization in opioid receptor function ., The inferences provided by the extensive molecular dynamics simulations reported herein constitute a first step in this direction .

journal.pcbi.1005045

The Computational Properties of a Simplified Cortical Column Model

For more than a century , neuroscientists have worked to refine descriptions of cortical anatomy , either differentiating or consolidating models of cortical circuits 1 ., The notion that a fundamental neuronal circuit performs a canonical computation in neocortex , that can be generalized across species and areas , is of fundamental value to both experimental and theoretical neuroscientists ., Douglas and Martin provided evidence for such a canonical microcircuit in the cat striate cortex , as well as a descriptive model of its structure 2 , 3 ., The fundamental building block of circuits on this scale is the cell type specific population ., For example , the Douglas and Martin microcircuit model implicated distinct cell types and cortical laminae in its function ., Each individual population in the circuit might perform linear or nonlinear transformations on its inputs , depending on the parameterization of the model 4 ., The individual cells that make up the population might be spatially segregated ( i . e . distinguished by layer ) or might be intermingled , and distinguished by genetically defined cell type or projection pattern ., The microcircuit can then be conceptualized as a modular collection of populations , with scale and composition dependent on function ., Whole brain regions are assembled from ensembles of microcircuits that together perform its overall function , the clearest example being orientation columns in V1 ., Over time , the microcircuit model can be refined , constrained by including cell type specific parameterizations , synaptic properties , detailed microcircuit anatomy , and other relevant experimentally measured data of a particular cortical area ., When taken together , the cumulative result of multiple recurrently connected canonical circuits might perform the complex nonlinear computations necessary to implement models of higher-order cognitive function ., Furthermore , many theoretical models of cortical processing involve hierarchical arrangements of processing stages , and the evidence for such a hierarchical organization , particularly in the visual system , is generally accepted ( for example , 5 ) ., Informed by the seminal work of Hubel and Wiesel 6 in the perception of orientation , the catalogue of algorithms for which there exists models relying on a staged , hierarchical implementation has grown significantly ., Beyond perception , hierarchical theories include invariant object recognition ( for example 7; see 8 for a review ) , selective visual attention ( see 9 for a review ) and models of Bayesian inference via predictive coding ( for example 10 ) ., In order to perform any of these hierarchical computations , individual elements within the hierarchy must perform an intermediate stage of processing ., It is hypothesized that these intermediate stages implement a local canonical computation , and their hierarchical arrangement subserves ( or even defines ) a global information processing stream ., In this study , we examine the computational properties of the Potjans and Diesmann 11 cortical column ., The main focus of that study was the construction of a realistic computational model of cortex ., Here we ask what type of computation this model might subserve as a candidate canonical model of cortical processing ., Based on its properties , we then speculate about the role of such a processing unit in an abstract hierarchical computational scheme ., We find that simultaneous excitation to L2/3 and L4 offset in their effects on L5 , in essence performing a subtractive computation between two step inputs ., Additionally , we find that the model possesses a linear computational regime under the condition that incoming inputs do not preferentially target inhibitory or excitatory populations within a layer ., We then examine the response of the model to sinusoidal inputs , again finding evidence of linear computation ., In the discussion , we relate these findings to the role of such a processing element in light of theories of hierarchical computation ., The Potjans and Diesmann 11 cortical column model is composed of 8 recurrently connected homogeneous populations of neurons , totaling approximately 80 , 000 neurons and . 3 billion synapses ., Each neuron receives background Poisson input , and is recurrently connected to neurons in other populations via a population-specific connection probability matrix derived by combining data and methods from several studies ., In our study both network and single neuron parameters from 11 are used ., The population statistic approach used in Iyer et al . 12 assumes synapses that instantaneously perturb the voltage distribution of the postsynaptic population ., Therefore , we assume that the fast kinetics of synapses in the Potjans and Diesmann cortical column ( τs = . 5 ms ) can be well-approximated by the DiPDE formalism ( For a discussion regarding the effect of non-instantaneous synapses , see 12 , “Methods: Non-instantaneous synapses” ) ., As a consequence , the coupling of these shot-noise synapses is instantaneous , perturbing the voltage distribution directly by a constant . 175 mV for excitatory synapses , and - . 7 mV for inhibitory synapses ., These values are computed from the total charge resulting from a single synapse of weight w in 11 using their notation ( See 12 for additional details ) :, Δ v = Q C m = 1 C m ∫ 0 ∞ I ( t ) d t = w C m ∫ 0 ∞ exp ( - t / τ s ) d t ., ( 1 ) Connection probabilities , synaptic weight distributions , and delay distributions are taken directly from 11 ( with the exception of the L4e → L2/3e connection probability , which was doubled to . 088 , following 13 ) ., This was done to define equal synaptic strength for all excitatory connections ( The original strength for this one connection was doubled relative to other projections ) , while maintaining roughly the same overall projection strength ., The connection probability was multiplied by the size of the presynaptic population to parameterize an effective multiplier ( in-degree ) on the incoming firing rate from a presynaptic population ., Because they minimally impact the firing rate dynamics of the leaky integrate-and-fire model , refractory periods were simplified from 2 ms to zero ., The only significant deviation from the Potjans and Diesmann model was a decrease in the mean background firing rate across all populations by a factor of 8 . 54 , and subsequent increase in the synapse strength of these connections by an equal amount ., This modification leaves the mean synaptic input from background unchanged from the original model , but increases the variance of this stochastic input ., After this change , the intrinsic oscillations of the original NEST model are significantly damped ( but not completely eliminated; see Fig 1 ) ., The matched DiPDE model does not exhibit intrinsic oscillations , although in general population density models are capable of exhibiting this phenomenon 14 ., Each population is initialized to a normal distribution of membrane voltages , with a mean at the reset potential and standard deviation of 5 mV ., Before application of any additional input ( i . e . , step or sinusoidal drive ) , background excitation is applied to each population as specified in 11 , and simulated for 100 ms to reach a pre-stimulus steady state ., When driving the model , additional layer-specific excitatory stimulus is input into the target layers, ( s ) , and simulated for an additional 100 ms . For step inputs , the difference of the final steady-state less the pre-stimulus steady-state ( i . e . after discarding the initial start-up transient dynamics ) define the layer-specific firing rate output perturbation ., In this study , all simulations of the model were performed using a numerical simulation of the displacement partial integro-differential equation ( DiPDE ) modeling scheme proposed in 12 with a time-step of . 1 ms . At this temporal resolution , a 200 ms DiPDE simulation requires 31 seconds running on a 2 . 80 GHz Intel Xeon CPU ., The corresponding NEST simulations 15 , 16 included in Fig 1, ( c ) and 1, ( d ) require 402 seconds each ( single processor ) , and results from 100 of these simulations are averaged to obtain the mean firing rate pictured ., In each of these 100 averaged NEST simulations , connectivity matrices and initial values for voltages were randomized ., The population density approach in computational neuroscience seeks to understand the statistical evolution of a large population of homogeneous neurons ., Beginning with the work of Knight and Sirovich 17 ( See also 18 , 19 ) , the approach typically formulates a partial integro-differential equation for the evolution of the voltage probability distribution receiving synaptic activity , and under the influence of neural dynamics ., Neuronal dynamics typically follow from the assumption of a leaky integrate-and-fire model ., We implement a numerical scheme for computing the time evolution of the master equation for populations of leaky integrate-and-fire neurons with shot-noise current-based synapses ( For a similar approach , see 20 ) ., τ m d v d t = - v + Δ v ∑ i δ ( t - t i ) v > v t h ⇒ v → v r ( 2 ) Here τm is the membrane time constant , v is the membrane voltage , Δv is the synaptic weight , vth is the threshold potential , and vr is the reset potential ( here taken to be zero for simplicity ) ., Extending 12 , each population receives input from both background Poisson input and recurrent connections from each cortical subpopulation ., We emphasize that this is not a stochastic simulation; for example , the background Poisson drive is not a realization of a Poisson process , but rather the effect of a Poisson-like jump process on the evolution master equation ., At each time step , a density distribution representing the probability distribution of membrane voltages for each population is updated according the differential form of the continuity equation for probability mass flux J ( t , v ) ( Here p ( t , v ) is the probability distribution across v at time t on ( −∞ , vth ) ; see 21 for more information ) :, ∂ p ∂ t = - ∂ J ∂ v ( 3 ) The voltage distribution is modeled as a discrete set of finite domains ( See Fig 2 ) ., Synaptic activation of input connections drive the flux of probability mass between nodes , while obeying the principle of conservation of probability mass ., As a result , a numerical finite volume method is an ideal candidate for computing the time evolution of the voltage density distribution , and we numerically solve Eq 3 with a finite volume method ., The spatial ( voltage ) domain, D = v m i n , v θ ⊂ R ( 4 ), is subdivided into a set of non-overlapping subdomains, V = { v i ⊂ D } ., ( 5 ) Each subdomain contains a control node pi that tracks the inflow and outflow of probability mass due to synaptic activation and passive leak ., At each time step , pi is updated by considering probability mass flow resulting from synaptic activation from all presynaptic inputs as well as leak; for simplicity we will describe the update rule assuming a single presynaptic input ., Additionally , we will assume a single synaptic weight , although in general this approach works equally well for a distribution of synaptic weights ., Under these assumptions , the discretized version of Eq 3 can be formulated as:, d p i d t = - Δ J i Δ v i ( 6 ) Δ J i = f i + 1 2 - f i - 1 2 ( 7 ) = ( j ( s , i ) - - j ( l , i ) + ) - ( j ( s , i ) + - j ( l , i ) - ) ., ( 8 ) Here f i ± 1 2 denotes flux across the right or left subdomain boundary , js denotes flux resulting from the input population ( via synaptic activation ) , and jl denotes flux from the leak; the superscript is a convenience that denotes the overall sign ( i . e . inflow or outflow ) of the contribution of the term to pi ., Synaptic activation contributes j, ( s ) to the overall flux by displacing probability mass ( pΔv ) with a transition rate λin , the presynaptic firing rate ., By directly computing the probability mass flux as Δt → 0 over the subdomain boundary ( while enforcing probability mass conservation ) , the contribution of passive leak j ( l ) to the overall flux can be formulated as a transition rate that increases exponentially with time constant τm as the voltage of the subdomain boundary being crossed increases ., To summarize , the flux contributions to the ith subdomain are:, j ( s , i ) + = p k Δ v k λ i n ( 9 ) j ( s , i ) - = p i Δ v i λ i n ( 10 ) j ( l , i ) + = p i + 1 v i + 1 2 τ m ( 11 ) j ( l , i ) - = p i v i - 1 2 τ m ( 12 ) Here the synaptic influx j ( s , i ) + depends on pk , the probability mass in subdomain vk located a distance w = vi − vk ( the synaptic weight ) from vi ., In the special case of i = 0 and w > 0 , the node that acts as the reset value for probability mass that exceeds the spiking threshold vθ ( i . e . the boundary condition ) receives probability mass from all nodes less than w from vθ ., Because these updates result from a linear update from probabilities , the entire time evolution can be formally represented as:, d p d t = ( L + S ) p ( 13 ), where leak and synaptic input contributions have been separated into two separate discrete flux operator matrices ., At this step , it is trivial to include additional synaptic inputs S0 , S1 , …Sm , yielding a formal solution over a single time step Δt:, p ( t + Δ t ) = exp Δ t L + ∑ s = 0 m S s p ( t ) ( 14 ), for some initial probability distribution p ( t ) ., At each time step , the synaptic input matrices Sk are updated to reflect the changes in firing rate of the presynaptic populations ( if necessary ) ., Probability mass that is absorbed at threshold and inserted at the reset potential defines the fraction of the population that spiked; after normalization by the discrete time step Δt , this defines the output firing rate ., The output firing rate provides the rate of a Poisson process that drives any recurrently-connected postsynaptic populations ., Probability mass flux through the boundary vθ into the subdomain at i = 0 defines the instantaneous firing rate of the population , computed as:, λ o u t ( t ) = ∑ s = 0 m j ( s , 0 ) + Δ t ( 15 ) Recurrent coupling between simulated populations is accomplished by assigning λout of the presynaptic population to λin of the postsynaptic population ., The source code for DiPDE is released as an open source python package under the GNU General Public License , Version 3 ( GPLv3 ) , and is available for download at http://alleninstitute . github . io/dipde/ ., The package includes an example implementation of the cortical column model analyzed in the main text , absent any inputs in excess of background excitation ., In this section we describe the repertoire of computations caused by step inputs over and beyond background excitation ( See Fig 1, ( c ) and 1, ( d ) for an example simulation , compared to 100 averaged leaky integrate-and-fire ( LIF ) simulations ) into a coarse-grained population-statistical version of the Potjans and Diesmann cortical column model ( See Fig 1, ( a ) for a visual summary of projections in the column model; for a complete model description see 11 , Tables 4 and 5 ) ., The targeting of cell types ( i . e . target specificity ) has important consequences for the responses caused by incoming inputs ., We examine the consequences of three types of target specificity , summarized in Fig 1, ( b ) for incoming excitatory projections into a given layer within the column ., The excitatory and inhibitory target specificity regimes excite their respective cell types , while the balanced regime does not preferentially target either subpopulation ., Unless otherwise specified , the step input has a firing rate of 20 Hz , and models a convergent connection with 100 independent presynaptic sources per target neuron ., Fig 3 provides an overview of output perturbations evoked by step input into a given layer , under each target specificity condition ., In effect , this provides an at-a-glance summary of the catalogue of computations that the cortical column can perform , given a 20 Hz step pulse excitatory input into a single layer ., We find that the effect of driving L2/3 under any specificity condition has a depressing effect on the activity in L5 ., In contrast , when driving L4 or L5 , activity across almost every population in the network increases or decreases when driving the excitatory or inhibitory subpopulation , respectively ., Fig 3 also demonstrates that under balanced target specificity ( yellow ) , the effects of inputs into L2/3 and L4 are nearly equal-and-opposite with respect to the output of L5 ., We summarize this comparison across all output layers in Fig 4 which additionally plots the combined effect of inputs simultaneously into L4 and L2/3 ., This plot demonstrates that these two inputs approximately offset; we explore this observation further in the next section ., We note that the input layers involved in this subtraction are implicated in bottom-up vs . top-down comparisons in the theory of hierarchical predictive coding 22 ., Also conspicuous is the output population reporting this subtraction; L5 pyramidal neurons provide the dominant cortical output , including the pons , striatum , superior colliculus , and to value encoding dopaminergic neurons in the VTA or SNc 23 where subtraction errors might skew reward expectations ( see Discussion for further details ) ., Linear computations are characterized by simultaneously exhibiting homogeneity ( i . e . multiplicative scaling in the sense of a linear map ) and additivity with respect to inputs ., The previous section examined output perturbations across layers and target specificity profiles of a single strength ( 20 Hz firing rate ) ., In this section , we first examine the effect of linearly increasing the strength of the input , testing the homogeneity of the system ., Fig 5 extends Fig 3 by providing a summary across an increasing range of input strengths ., Under balanced target specificity ( middle column of panels ) , the magnitude of each population response exhibits a scaling behavior , linear in the input magnitude ., In contrast , when neurons are targeted with a cell type specific bias , the response of certain subpopulations can be nonlinear ., The clearest example of the nonlinear influence of the inhibitory subpopulation occurs when driving layer 5 ., Through both an increase in direct self-inhibition , and indirect reduction of self-excitation via the L5e subpopulation , excitatory drive into L5i can paradoxically decrease activity , an effect described previously in inhibition-stabilized recurrent networks 24 ., Eventually this effect reverses when the L5e activity is completely inhibited ., Fig 6 demonstrates that , likewise , additivity is violated ( somewhat , as the points deviate from the identity line ) when preferentially targeting inhibitory neurons ., Each point in the figure depicts the result of driving two separate layers with a 20 Hz firing rate input , and considering the perturbation in firing rate of each subpopulation ( specified in the legend ) ., For a given target specificity condition , two independent simulations are run , for each of the two input layers; the sum of the perturbation they evoke is plotted on the vertical axis ., The output resulting from a single simulation with two equal inputs into each input layer , is plotted on the horizontal axis ., When a point lies along the identity line , this implies additivity ., This figure implies a conclusion similar to the homogeneity study above: as the target specificity moves from excitatory to inhibitory , the firing rate computation performed on laminar inputs by the cortical column changes from linear to weakly nonlinear ., In the previous section , we demonstrated that balanced 20 Hz firing rate inputs to L2/3 and L4 approximately offset each other in the output evoked in L5 ., The homogeneity and additivity demonstrated above indicate that L5 will actually reflect a subtraction operation on these two inputs ., We return to this point in the discussion ., Given the amount of recurrent connectivity in the model , its linear response to step inputs under balanced target specificity might seem surprising ., However , it is known that balanced networks can exhibit linear responses to external inputs ( See , for example , 25 ) ., Although the model parameterization is taken from the literature , we also investigated the sensitivity of this linear response to perturbations in model parameters ., By perturbing the connection probability matrix ( Table 5 “Connectivity” in 11 ) , we defined 1000 alternative models ., Specifically , each entry in the matrix was multiplied by a normally distributed random number with unit mean , and standard deviation taken as 5% of the entry ( negative values were thresholded to zero ) ., The homogeneity of response to each new model was assessed by linearly extrapolating the perturbation resulting from a 10 Hz firing rate step input from the results obtained from a 5 Hz step input ., The absolute value of the prediction error:, Δ F = ( F 10 - F 0 ) - 2 · ( F 5 - F 0 ) ( 16 ), quantifies the difference between the extrapolated value , and the true value obtained by direct simulation of a 10 Hz firing rate input ., Here F indicates the firing rate after reaching steady-state , and the subscript indicates the strength of the step input ., Intuitively , this quantity will be zero when a linear extrapolation can predict the data ( i . e . a linear relationship between inputs and outputs ) ., Nonzero values indicate the failure of a linear extrapolation , and thus a nonlinear dependence of the output firing rate on the input over the regime of 0–10 Hz perturbations ., S1 Fig shows a stacked histogram of this prediction error for the 1000 perturbed models , across all combinations of target specificity , laminar drive , and output population ., Under balanced target specificity ( middle column ) , the prediction error is reliably smaller , particularly when layers 4 and 5 are targeted ( middle two rows ) ., This implies that the linear relationship between inputs and outputs under balanced input of the original cortical column model is insensitive to small perturbations in the connection probability matrix ., A similar result holds for additivity predictions , shown in S2 Fig . For the same perturbed models , the additive prediction error is defined as the sum of output responses in two layers from two different simulations , minus the output resulting from driving the two layers in the same simulation ., Again , the model under balanced target specificity is less sensitive to perturbations than when cell types are selectively driven ., Therefore , we conclude that the observation of linear responses in model output in the previous section is not a result of fine tuned parameters ., In the previous section , we demonstrated that target specificity can determine the linearity of the model response under step inputs ., To further investigate the linearity of the transformation that the column applies on its inputs , we next consider sinusoidal drive above and beyond background drive ( See Fig 1, ( d ) for an example simulation , compared to 100 averaged LIF simulations ) ., S3 Fig summarizes the nonlinear distortion in each populations response under a 5 Hz peak amplitude sinusoidal drive ., Only responses with a peak amplitude greater than . 05 Hz are plotted ., Total harmonic distortion ( THD ) compares the power present in the harmonics of the driving frequency in the sinusoidal input signal that perturbs a subpopulation above and beyond the background firing rate:, T H D ( f ) = ∑ i = 2 ∞ V i 2 V 1 ( 17 ) Here Vi is the power spectral density ( PSD , 26 ) of the ith harmonic of the principal ( driving ) frequency ., This figure reinforces the conclusion from the previous section , that the target specificity of the sinusoidal drive can affect the nonlinearity of transformations resulting from population-level processing ., In particular , balanced drive minimizes the harmonic distortion imposed by the dynamics within the model ., In contrast , inhibitory drive into layer 5 produces nonlinear responses throughout the column , in agreement with observations about the homogeneity of responses to step inputs ( cf . Fig 7 ) The low THD of the output signals from balanced drive indicate that the firing rate y ( t ) of a population in the column model can be approximately modeled as a linear filter on the input signal x ( t ) plus a baseline x0:, y ( t ) = x 0 + ∫ 0 ∞ x ( t - τ ) h ( τ ) d τ ( 18 ) Fig 8 provides a numerically computed description of three examples of this linear filtering , resulting from balanced drive from L2/3→L5e , L4→L5e , and L4→L23e ., Of all possible input/output pairs , these examples show the least signal attenuation from the amplitude Ain of the input signal x ( t ) to the amplitude Aout of the output signal y ( t ) ( i . e . the largest impact on changes to subpopulation firing rate ) ., Clearly evident in the first two figures are first-order lowpass filters , similar to feedforward systems found in 4 with significantly higher synaptic weights ( relative to threshold ) ., These filters both have a cutoff frequency near 15 Hz , implying a corresponding RC time constant near the membrane time constant ( 10 ms ) of neurons in the system ., Interestingly , this observation is in agreement with the very general prediction of predictive coding theories , that high frequencies should be attenuated when passing from superficial to deep layers 22 ( See Discussion ) ., Transmission from L4 to L2/3 is band-passed near the 10–30 Hz range ., In this study , we examine what input/output transformations a popular model of a cortical column performs on layer-specific excitatory inputs ., Transformations are defined as perturbations to the steady-state mean firing rate activity of subpopulations of cells in response to step and sinusoidal inputs in excess of background drive ., Because the mean firing rate is a population-level quantity , we use a population statistic modeling approach , by numerically computing the population voltage density using DiPDE ( http://alleninstitute . github . io/dipde/ ) , a coupled population density equation simulator ( See Numerical Methods ) ., This approach enables a fast , deterministic exploration of the stimulus space and model parameterization ., Our approach begins with a data-driven model as a starting point , and then examines the computations its dynamics subserve , as opposed to fitting a model to a preselected set of dynamical interactions resulting from assumptions about cortical computation ., Our goal is to discover robust evidence for theories of cortical function using knowledge about structure ( synthesized by Potjans and Diesmann 11 into their cortical column model ) , while limiting biases and a priori functional assumptions ., We consider three discrete regimes of input specificity: excitatory preference , no preference ( i . e . balanced , in which both excitatory and inhibitory cells in any one layer receive the same external input ) , or inhibitory preference ., We find that balanced target specificity results in output perturbations that scale linearly with input strength and combine linearly across input layers ., In contrast , selective targeting of a particular cell type ( especially the inhibitory subpopulation ) leads to nonlinear interactions ., Additionally , we find that equal , simultaneous , and balanced inputs into L2/3 and L4 are offset in their effect on the L5 firing rate; combining this with the observation of linearity implies that perturbations in L5 activity represent a subtraction from L4 activity of L2/3 activity ., The inhibitory effect of L2/3 input on L5e output appears to be largely mediated by L2/3 interneurons inhibiting L5 pyramids ( c . f . 27 , their Fig 4 ) while the excitatory effect of L4 on L5 is a network effect resulting from multiple projection pathways ., We conclude that the cortical column model implements a subtractive mechanism that compares two input streams and expresses any differences in the mean activity of L5 ., While this computation can be implemented via other inputs , this combination is interesting because no target cell type specificity is required ., How does this observation of a mechanism for subtraction relate to existing theories of cortical processing ?, Predictive coding 10 , 28 postulates a computation that compares an internal model of the external environment to incoming sensory signals , in order to infer their probable causes 29 , 30 ., The subtractive dynamics supported by the cortical column model could accomplish this ., However , this would imply that sensory signals are represented dynamically in one layer , an environmental model in the supragranular layer , and that their functionally relevant difference is relayed by the infragranular layer ( layer 5 ) ., The internal granular layer ( layer 4 ) is the obvious candidate for incoming environmental evidence , given its specialized role in receiving input from the primary sensory thalamus ., Similarly , the role of the infragranular layer in driving subcortical structures involved in action ( basal ganglia , colliculus , ventral spinal cord ) seem compatible with the proposition of layer 5 representing the output of a comparison operation ., Although more speculative , this leaves the supragranular layer responsible for generating the internal environmental model , which seems reasonable given its abundance of intracortical projections and increased development in higher mammals ., These speculative roles of the various cortical layers conform to abstract models of canonical microcircuits ( See , for example , 31 ) ., This is especially true when placed in a hierarchy of processing stages , for example in hierarchical predictive coding ( hPC ) 22 ., In this framework , sequential processing stages generate top-down predictions , and pass bottom-up prediction errors , at each level in the hierarchy ., In primates , the laminar segregation of these streams is easily aligned with the anatomical characterization from Felleman and Van Essen 5 , with feedforward connections targeting L4 , and feedback connections avoiding L4 ., In rodents , the relation between lamination and hierarchy is less clear 32 ., Although the central theme of distinct populations of forward-projecting neurons targeting L4 vs . backward projecting neurons avoiding L4 in the visual system seems conserved 33 , these distinct populations are not segregated by layer , but instead intermingled 34 ., Therefore , future experimental attempts to establish connections between hierarchically defined visual processing regions and theoretical models may require projection-target-segregated ( or perhaps genetically-segregated , if projection markers can be established ) , as opposed to laminae-segregated , cellular subpopulations ., An additional connection between the hPC model and the results of the simulations in this study is presented in Fig 8 ., Here the response of the deep layer of the model to stimulation in either of the two superficial layers is well characterized by a linear low-pass filter ., Interestingly , this filtering is a prediction of hPC , where high frequencies should be attenuated when passing from superficial to deep pyramidal cells 22 ., The low-pass filtering prediction arises from the hypothesis that cortex is performing a form of Bayesian filtering , by attempting to update an estimated quantity using noisy measurements ., These noisy estimates by their nature have higher-frequency content than the uncorrupted “true” quantity being estimated , and so the appearance of a smoothing transform is not surprising ., However , it is surprising that our model , formulated without assuming any underlying computation ( especially not Bayesian filtering ) , performs this smoothing at a dynamical stage precisely where the anatomically-informed hPC model requires it ., Taken together , the convergence of expe

Introduction, Materials and Methods, Results, Discussion

The mammalian neocortex has a repetitious , laminar structure and performs functions integral to higher cognitive processes , including sensory perception , memory , and coordinated motor output ., What computations does this circuitry subserve that link these unique structural elements to their function ?, Potjans and Diesmann ( 2014 ) parameterized a four-layer , two cell type ( i . e . excitatory and inhibitory ) model of a cortical column with homogeneous populations and cell type dependent connection probabilities ., We implement a version of their model using a displacement integro-partial differential equation ( DiPDE ) population density model ., This approach , exact in the limit of large homogeneous populations , provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials ., It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics ., We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties ., When inputs are constrained to jointly and equally target excitatory and inhibitory neurons , we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals ., One of these , a simple subtractive operation , can act as an error signal passed between hierarchical processing stages .

What computations do existing biophysically-plausible models of cortex perform on their inputs , and how do these computations relate to theories of cortical processing ?, We begin with a computational model of cortical tissue and seek to understand its input/output transformations ., Our approach limits confirmation bias , and differs from a more constructionist approach of starting with a computational theory and then creating a model that can implement its necessary features ., We here choose a population-level modeling technique that does not sacrifice accuracy , as it well-approximates the mean firing-rate of a population of leaky integrate-and-fire neurons ., We extend this approach to simulate recurrently coupled neural populations , and characterize the computational properties of the Potjans and Diesmann cortical column model ., We find that this model is capable of computing linear operations and naturally generates a subtraction operation implicated in theories of predictive coding ., Although our quantitative findings are restricted to this particular model , we demonstrate that these conclusions are not highly sensitive to the model parameterization .

medicine and health sciences, engineering and technology, nervous system, signal processing, electrical circuits, membrane potential, electrophysiology, neuroscience, signal filtering, simulation and modeling, probability distribution, mathematics, population biology, microcircuits, research and analysis methods, animal cells, probability theory, population metrics, cellular neuroscience, cell biology, anatomy, synapses, physiology, neurons, biology and life sciences, cellular types, physical sciences, population density, electrical engineering, neurophysiology

journal.pbio.0060114

Distinct DNA Exit and Packaging Portals in the Virus Acanthamoeba polyphaga mimivirus

The prevailing model for genome translocations in icosahedral viruses entails a molecular motor that is localized at a single vertex and comprises a packaging ATPase and a portal complex 1–5 ., The particular structural features revealed by the vertex-portal assembly have been argued to facilitate both genome delivery 6 and genome encapsidation 3 , 6 , 7 ., Although the functional implications of these features have been recently challenged 8 , 9 , their apparent conservation led to the paradigm that a single vertex-portal system plays a crucial and general role in both genome injection and packaging in icosahedral viruses 6 ., Vertex-portal assemblies were , however , characterized only in herpesviruses that contain an external lipid membrane 3 , 4 , 10 , and in tailed double-stranded DNA ( dsDNA ) bacteriophages in which membranes are absent 1 , 5–7 , 11 ., This point is noteworthy in light of recent studies , which implied that DNA packaging machinery in viruses containing an inner membrane layer is fundamentally different from the vertex-portal apparatus of herpesviruses and bacteriophages 12–14 ., Specifically , inner membrane–containing viruses were shown to contain putative DNA-packaging ATPases that , in addition to the regular Walker A and B motifs , carry a conserved motif that might act as a membrane anchor 12–14 ., The structural aspects that underlie genome translocation mechanisms deployed by these viruses remain , however , largely unknown 15 ., The amoeba-infecting virus Acanthamoeba polyphaga mimivirus is a member of the nucleocytoplasmic large DNA viruses ( NCLDV ) clade that comprises several eukaryote-infecting viral families such as the Phycodnaviridae , Iridoviridae , and Asfarviridae 16 ., As in all members of NCLDVs , the Mimivirus is composed of a core containing a dsDNA genome , which is surrounded by a lipid membrane that underlies an icosahedral capsid 17–19 ., The capsid is , in turn , covered by closely packed 120-nm-long fibers that form a dense matrix at their attachment site 17–19 ., The closely packed fibers and the dense layer at the base of these fibers represent a unique feature of the Mimivirus ., In addition , a single modified vertex has been detected in mature particles 18 ., With a 1 . 2–mega base pair ( Mbp ) dsDNA genome and a particle size of ∼750 nm , the Mimivirus represents the largest virus documented so far , blurring the established division between viruses and single-cell organisms 17 , 18 , 20 ., Prompted by these unique features , we conducted high-resolution studies of the Mimivirus life cycle within its amoeba host , focusing on genome delivery and packaging stages that remain poorly understood in all members of the NCLDV clade ., By performing cryo-scanning electron microscopy and electron tomography on cryo-preserved host cells at different post-infection time points , we demonstrate that DNA exit occurs in phagosome-enclosed viral particles through a massive opening of five icosahedral faces of the capsid ., This large-scale capsid reorganization , which occurs at a unique , structurally modified icosahedral vertex , allows for the fusion of the internal viral membrane with the membrane of the host phagosome ., The fusion leads , in turn , to the formation of a massive membrane conduit through which DNA delivery occurs ., In conjunction with single-particle reconstruction studies that indicated the presence of two successive membrane layers underlying the Mimivirus protein shell 18 , these observations raise the possibility that the Mimivirus genome is released into the host cytoplasm and is translocated toward the host nucleus enclosed within a vesicle that is derived from the viral inner membrane ., We further show that DNA packaging into preformed Mimivirus procapsids proceeds through a non-vertex portal , transiently formed at an icosahedral face distal to the DNA delivery site ., Along with comparative genomic studies 12 , 13 , these results imply a viral packaging pathway reminiscent of DNA segregation in bacteria , a pathway that might be common to internal-membrane–containing viruses ., Taken together , the observations reported here may indicate that Mimivirus and potentially other large dsDNA viruses have evolved mechanisms that allow them to effectively cope with the exit and entry of particularly large genomes ., Extracellular Mimivirus particles were sectioned following cryo-fixation and examined by transmission electron microscopy ( TEM ) ., Notably , all TEM specimens in the current study were preserved through the high-pressure freezing technique that , in sharp contrast to conventional chemical fixation protocols , allows for instantaneous immobilization of all structures in their native morphology ., As such , this preservation method is generally considered to be highly reliable and hence optimal for electron tomography studies 21 ., The extracellular particles reveal an unprecedented 5-fold star-shaped structure that is localized at a single icosahedral vertex and extends along the whole length of the five icosahedral edges that are centered around this unique vertex ( Figure 1A ) ., Geometric considerations of an icosahedron structure modified along five icosahedral edges that is randomly sliced indicate that if all viral particles include such a massive assembly , parts of this structure should be discerned in 75%–80% of the sections used for TEM analysis , depending on the thickness ( 70–80 nm ) of the sections ., In ∼500 extracellular viruses examined , the 5-fold assembly or parts thereof were detected in ∼400 particles ( 80% ) , thus demonstrating that all viral particles contain this structure ., None of the examined extracellular viral particles or of the intracellular particles ( see below ) revealed more than one star-shaped structure per particle , a finding fully consistent with single-particle cryo-TEM studies in which a single modified vertex was detected 18 ., The presence of the star-shaped assembly was further confirmed by cryo-TEM studies conducted on whole extracellular Mimivirus particles that were vitrified in their hydrated state ., Due to the interference of the extremely dense fiber layer that surrounds the viral capsids 18 , the 5-fold structure could not be detected in mature particles , but was clearly and consistently discerned in immature , fiber-less viruses that constitute a small yet significant ( ∼10% ) population of the viruses that are released upon lysis of the amoeba cells at the completion of the infection cycle ( Figure 1B ) ., To ascertain that the 5-fold assembly represents a general and genuine feature , >500 extracellular Mimivirus particles were analyzed by cryo-scanning electron microscopy ( cryo-SEM ) ., These studies corroborate the presence of a massive 5-fold structure at a unique vertex of the particle ., The assembly is detected in fiber-covered Mimivirus where it appears as crevices , but is particularly conspicuous and consistently revealed in immature fiber-less particles , where it takes the form of prominent ridges ( Figure 1C and 1D , respectively ) ., The crevices that characterize the 5-fold structure in mature particles ( Figure 1C ) imply that this particular structure is depleted of fibers , in contrast to all other regions of the capsid ., Electron tomography ( Figure 1E and Video S1 ) and volume-reconstruction analyses ( Figure 1F–1H ) were performed on viral particles within infected amoeba cells at final infection stages ( 12 hours post-infection ) , where cells are crammed with mature viruses ., The analyses indicate that the Mimivirus capsid is composed of two superimposed shells characterized by conspicuously different densities ., This observation , obtained from three tomography analyses conducted on different intracellular viral particles , is consistent with single-particle reconstruction studies , which indicated the presence of a protein shell surrounded by a distinct layer that corresponds to a dense base of fibers 18 ., In addition to the two shells , a prominent star-shaped structure is discerned in the intracellular Mimivirus particles ( Figure 1E ) ., Volume-reconstruction analysis of the star-shaped structure indicates that the outer shell adopts a partially open configuration ( corresponding to the dark star-shaped region in Figure 1F ) ., This open region is , however , completely sealed by the underlying inner shell ( Figure 1G ) , an observation compatible with the ridges that delineate the 5-fold star-shaped structure in immature fiber-less particles ( Figure 1D ) ., Figure 1H , which represents a superposition of the two shells , demonstrates the perfect match between the ridges at the inner shell and the regions in which the outer shell is missing ., A recent study implied that the initial stages of Mimivirus infection occurs by phagocytosis 19 ., Our observations support this notion by demonstrating the presence of phagosomes containing one or several viral particles within infected amoeba cells at early ( 2–3 h ) post-infection time points ( Figures 2 and 3 ) ., The different morphological aspects revealed by the phagosome-enclosed viral particles are straightforwardly interpreted as a result of sectioning the viruses along different planes , as clarified in the inset in Figure 2A and demonstrated in Video S2 ., Specifically , in a randomly sliced section that contains the star-shape assembly and is parallel to field of view depicted in the inset , a star-shape structure is detected , as demonstrated in Figure 1A , 1E–1H , and in Figure 2A ., If , on the other hand , the TEM section is sliced along a plane parallel to that illustrated in the inset yet located below the star-shape assembly , only unmodified vertices will be detected , as indeed is the case for the viral particle shown in Figure 2B ., Sections perpendicular to a single star-like assembly should reveal either one or two modified vertices , which correspond to slices along the blue and red lines in the inset , respectively ., These two morphological aspects are indeed manifested by the viral particles shown in Figure 2C and in Figure 3A ( particle 2 ) , which reveal a single modified vertex , and by particle 1 in Figure 3A , in which two modified vertices ( marked by red arrowheads ) are visible ., Such two modified icosahedral edges that belong to the same star-shaped assembly are particularly evident in thick sections that are sliced along the red line in the inset , as indeed shown in Figure 3B and in Video S2 ., In conjunction with the geometric considerations described above , a statistical analysis conducted on more than 100 phagosome-enclosed viral particles ( which basically represent mature virions ) indicate that all intracellular Mimivirus particles contain a modified , star-shaped vertex , and that this vertex is unique , as is the case for the extracellular Mimivirus particles ., Figure 3A shows a tomographic slice of a phagosome in which three viral particles were captured at three successive uncoating stages ., Volume reconstruction of the particle 1 ( early uncoating ) reveals that in this virus , both the outer ( red ) and inner ( orange ) capsid layers are opened at the star-shaped assembly ( Figure 3B ) ., The opening of both shells is in contrast with the morphology revealed by extracellular viruses ( Figure 1 ) , as well as by intracellular particles during early phagocytic stages ( Figure 2C ) , in which the inner shell appears to be completely sealed ., This opening allows for the lipid layer underlying the capsid shell ( blue layer in Figure 3B ) to protrude and extend towards the phagocytic membrane ., This stage is represented by the viral particle 2 in Figure 3A ., The final uncoating stage is demonstrated by the particle 3 ( Figure 3A ) ., Surface rendering analysis of this virus demonstrates a massive opening of five triangular icosahedral faces that occurs at the star-shaped vertex and results in a fusion of the viral ( light blue ) and phagocytic ( dark blue ) membranes ( Figure 3C ) ., The three uncoating stages are visible in the tomogram shown in Video S2 ., We interpret these observations as indicating that the star-shaped structure , which we coin “stargate” , represents a device that mediates a large-scale capsid opening , thus allowing for the protrusion of the inner viral membrane and a subsequent viral-phagosome membrane fusion ., This fusion results in the formation of a massive membrane tube through which the genome core is released into the host cytoplasm ., The notion that DNA delivery occurs following the formation of a membrane conduit is supported by the presence of empty capsids within phagosomes ( our observations and 19 ) ., The large-scale capsid opening at the stargate site , and the membrane tube are depicted in a schematic model ( Figure 4 ) , which is based on the tomography ( Figure 3A and Video S2 ) and surface rendering ( Figure 3C ) of particle 3 in Figure 3A ., To identify the factors that promote the stargate opening within the host phagosome , and in light of extensive fusion of lysosomes with phagosomes in which viral uncoating occurs ( Figure 3A and Video S2 ) , isolated Mimivirus particles were exposed to acidic conditions ( pH 6 . 5 , 5 . 5 , and 4 . 5 ) in the absence or presence of lysozyme ., None of these treatments triggered stargate opening , implying that other or additional factors are involved in effecting this structural reorganization ., Exposure of particles to elevated temperature ( 83 °C ) for 30 min resulted in a release of membranal structures that specifically occurred at the stargate site in ∼10% of the particles ( Figure 5A ) ., While physiologically irrelevant , this finding implies that the stargate represents a structurally susceptible site , a conjecture further supported by the observation that a small population ( <1% ) of extracellular particles reveals a conspicuous 5-fold opening ( Figure 5B ) ., These capsids might represent faulty viral particles , or particles that have ejected their genome and then released to the medium upon viral-induced lysis of the host cells at the completion of the infection cycle ., Following release , the Mimivirus genome is imported into the host nucleus and then translocated to a cytoplasmic viral factory where viral assembly occurs 19 ., TEM studies of infected and cryo-fixed amoeba cells reveal that already at 8 h post-infection , viral factories are studded with empty , fiber-less procapsids that are only partially assembled , as well as with icosahedral procapsids undergoing DNA packaging ( Figures 6 and 7 ) 19 ., The occurrence of DNA packaging into procapsids at the periphery of the factories ( green arrowheads in Figures 6 and 7 ) was supported by specific DNA staining and Br-dU experiments ( unpublished data ) ., Intriguingly , in some particles , the genome appeared to be translocated at a vertex ( Figure 6A ) 19 , whereas in others , DNA translocation proceeds through an aperture located at an icosahedral face ( Figure 6B and 6C ) ., A statistical survey of a large number ( >50 ) of intracellular viral factories indicated that at any thin section of the factory analyzed in TEM , 20–25 viral particles are present at various stages of assembly ., Out of these assembling virions , 4–5 particles were captured at the stage of DNA packaging , and within this population , packaging through a face-located aperture , as shown in Figure 6B and 6C , was consistently detected in 2–3 virions ., Thus , in more than 200 analyzed particles that undergo DNA packaging , a face-centered rather than a vertex-centered packaging is visible in more than 120 ( ∼60% ) particles ., Projection images derived from TEM studies of thin sections cannot provide , however , unequivocal data on the precise site of the packaging process , as such data can be masked or incorrectly interpreted due to the angle of the site within the TEM section relative to the electron beam ., To obtain deeper insights into the DNA packaging process in Mimivirus , we performed electron tomography and volume reconstruction analyses on three randomly chosen procapsids during their assembly on the periphery of the viral factories ., A slice of a tomogram obtained from one of these assembling procapsids ( Figure 7A; the whole tomogram is shown in Video S3 ) demonstrates that DNA packaging proceeds through an aperture that spans the outer and inner capsid shells , as well as the internal membrane , and is located at the center of an icosahedral face ., The aperture , which is sealed following completion of DNA packaging as implied by the structure of mature particles , adopts a cone shape with diameters of 35 nm and 20 nm at the outer and inner shells , respectively ., These features , clearly discernible in the reconstructed volume of the particle ( Figure 7B ) , are detected in all three tomograms of assembling procapsids ., Notably , whenever stargates are discerned in electron microscopy sections of assembling viral particles , they are invariably detected at the distal site of the factory , pointing away from the replication center ( Figures 6 and 7 ) ., This finding , which is consistent with earlier observations 19 , is particularly evident in tomograms obtained from relatively thick sections ( Figure 7 and Video S3 ) ., To substantiate our TEM results , we have isolated viral factories by gentle lysis of infected amoeba cells at 8–10 h post-infection , thus capturing successive assembly stages ., SEM studies of factories isolated at 8 h post-infection show immature viral particles that abut on the periphery of the factories and reveal conspicuous stargates ( Figure 8A and 8B ) ., Due to the dense fiber layer , stargates are hardly discernible in SEM analysis of mature particles , which are located further away from the periphery ., Notably , in viral factories isolated at a 10 h post-infection ( Figure 8C ) , only mature particles , which presumably cover and mask the immature particles , can be detected ., Thus , the SEM results , obtained from >50 isolated viral factories , corroborate the TEM studies conducted on intracellular factories , and strongly imply that the stargate structures represent an early stage of the viral assembly ., Mimivirus infection is initiated by phagocytosis 19 , and genome delivery occurs upon exposure of the virus to cues within the host phagosome ., While the nature of these cues remains unknown , detection of multiple lysosomes undergoing fusion with the phagosomes ( Figure 3A and Video S2 ) may imply that lysosomal activity promotes the opening of the viral capsid ., The observations reported here indicate that this opening entails a unique portal , the stargate , which is located at a single icosahedral vertex ( Figures 1–5 ) , in keeping with previous single-particle studies 18 , in which a single modified vertex has been identified ., These studies , as well as our electron tomography observations ( Figure 1E–1H ) revealed that the Mimivirus is composed of a protein shell surrounded by an outer layer corresponding to a dense base of fibers ., Our cryo-TEM ( Figure 1B ) , cryo-SEM ( Figure 1C and 1D ) , and electron tomography of cryo-fixed specimens ( Figure 1E–1H ) indicate that the stargate is located within the protein shell , extending along the whole length of five icosahedral edges that are centered around a single icosahedral vertex , thus forming an assembly of unprecedented morphology and dimensions ., The icosahedral edges appear as prominent ridges in the protein shell , which are clearly discerned in immature , extracellular viral particles that lack the dense fiber layer ( Figure 1D ) ., Our observations further indicate that while the outer shell surrounds most of the inner protein shell , it is absent along the icosahedral edges that constitute the stargate ( Figure 1F ) ., This fiber-less region is likely to enable the cues that trigger the opening of the stargate to reach their specific target in the inner protein shell ., Notably , the stargate is detected in extracellular Mimivirus particles , in phagosome-enclosed virions , in mature intracellular viral particles present within the amoeba host cells at the final infection stage ( Figures 1 and 2 and Video S1 ) , as well as in assembling virions ( Figures 6–8 ) , thus indicating that this prominent assembly is present in the Mimivirus capsid throughout the virus life cycle ., The large-scale conformational change of the capsid whereby the five icosahedral faces centered on the unique stargate vertex open up , allows the extrusion of the viral membrane that underlies the viral protein shell ., This extrusion is followed by the fusion of the viral membrane with the phagosome membrane , thus resulting in the formation of a large membrane conduit ( Figures 3–5 and Video S2 ) through which the Mimivirus genome is presumably released into the host cytoplasm ., The actual mode of DNA release remains unclear , as in all virus-containing phagosomes inspected in this study ( >100 ) , only mature viruses , viruses at various uncoating stages , or empty viral particles could be discerned ( Figures 2 and 3 ) ., In light of the size of the Mimivirus genome , the failure to capture genome release is intriguing ., On the basis of cryo-TEM studies of Mimivirus particles 18 that implied the presence of two successive membrane layers underlying the protein shell ( as is the case for at least one additional member of the NCLDV clade , the African Swine Fever Virus 22 , 23 ) , it can be hypothesized that the Mimivirus genome is released into the host cytoplasm enclosed within a vesicle ., Such a vesicle might be derived from the inner membrane layer , whereas the outer membrane forms a conduit for this DNA-containing vesicle by fusing with the phagosome ., This conjecture provides a rationale to the need for the massive opening of the capsid that is reported here , a possible reason for the failure to capture DNA release ( as a vesicle-mediated release would likely be a fast process ) , as well as a plausible answer to the question how is the viral genome protected against host nucleases during its transport to the host nucleus ., Moreover , the notion of a vesicle-mediated exit and transport of the Mimivirus genome provides a potential and highly attractive solution to the question of how is a 1 . 2-Mbp DNA molecule translocated through the extremely crowded cytoplasm of the host , which has been shown to present a supreme barrier for translocation of long DNA molecules 24 ., The notion of genome release and transportation within a vesicle that is derived from internal viral membranes is , to the best of our knowledge , unprecedented and is being currently investigated ., Notably , while DNA injection and packaging in the internal-membrane–containing tail-less bacteriophage PRD1 appear to occur through a unique vertex 25 , in vitro studies implied that PRD1 delivers its genome through a membrane tube 26 ., For this to occur , the PRD1 capsid must open up in a yet uncharacterized process that might be similar to the genome release process occurring in Mimivirus ., High-resolution structural studies of PRD1 life cycle will be required to address this intriguing possibility ., Mimivirus assembly occurs in cytoplasmic viral factories 19 ., DNA is packaged into preformed procapsids located at the periphery of factories ( Figure 6 ) 19 ., Studies of intracellular factories ( Figures 6 and, 7 ) as well as of viral factories isolated at various post infection time points ( Figure, 8 ) indicate that the formation of the stargate structure occurs at a very early stage of the viral assembly ., These observations imply that in addition to acting as a DNA release portal , the stargate might be involved in the initiation of Mimivirus particles assembly ., Such an initiation role is in keeping with the fact that capsids incorporate only one portal that is located at a unique vertex , and this symmetry-breaking step can only be rationalized in terms of a singular event , as is the initiation stage ., Indeed , previous studies indicated that portals are involved in the initiation of capsid assembly in herpesviruses and several bacteriophages such as T4 and SPP1 27 , 28 Our electron tomography and volume reconstruction analyses , supported by TEM and SEM studies , demonstrate that DNA packaging in Mimivirus proceeds through a transient aperture located at a distal site of the stargate site ., These studies further indicate that in contrast to all heretofore-characterized viruses , Mimivirus genome packaging occurs at an icosahedral face rather than at a vertex ( Figures 4–6 and Video S3 ) ., Notably , 3-nm-wide pores detected on the 3-fold axes in “open” procapsids of the α3 bacteriophage of the Microviridae family were proposed as possible DNA entry sites 29 , 30 ., In the current study , such a face-centered , non-vertex , DNA packaging site is directly demonstrated ., The functional significance of this finding becomes apparent in light of recent biochemical and comparative genomic studies , which indicated that inner-membrane–containing viruses such as bacteriophage PRD1 and members of the NCLDV clade ( including Mimivirus ) code for proteins that are closely homologous to ATPases of the FtsK/SpoIIIE/HerA superfamily 12–14 ., These ATPases were proposed to act as membrane-anchored motors that pump DNA through a closing membranal septum during bacterial and archaean division 31 , 32 ., On the basis of these findings and considerations , it has been suggested that viruses containing inner membranes package their genomes through a pumping mechanism akin to the DNA segregation pathway deployed in bacteria and archaea 12–14 ., Our observations complement this conjecture ., Since FtsK/SpoIIIE/HerA ATPases mediate a strictly unidirectional mode of DNA translocation 31 , 32 , this system is unlikely to be responsible for both exit and packaging of viral genomes ., In keeping with this notion , we identify distinct exit and entry portals in Mimivirus ., Moreover , whereas a vertex-centered motor for DNA packaging in bacteriophages and herpesviruses represents a thermodynamically sensible solution , because it minimizes vertex-portal interactions , such a setting would be incompatible with a pumping system that must rely on robust motor–membrane interactions ., Such interactions can , however , be maximized when the packaging motor is located within an icosahedral face ( rather than on an icosahedral vertex ) ., In addition , our conjecture that the DNA entry portal is sealed once packaging is concluded is consistent with recent studies that indicated that the DNA translocating ATPase SpoIIIE promotes membrane fusion following completion of bacterial DNA segregation 33 ., Interestingly , the cone-shaped aperture through which DNA is packaged is characterized by a diameter of 20 nm at the inner shell , thus capable of accommodating more than a single DNA duplex , as indeed is implied by TEM studies ( Figures 6 and 7 ) ., Of note in this context is the conjecture that several SpoIIIE rings might fuse to form a larger ATPase ring 31 ., The size and genome complexity of the Mimivirus call into question the conventional division between viruses and single-cell organisms ., Our findings , which support the conjecture that the DNA packaging mechanism deployed by internal-membrane–containing viruses might share structural and functional patterns with bacterial DNA segregation 12–14 , further substantiate the notion that the conventional division between viruses and single-cell organisms should be re-examined ., Moreover , the observations concerning the stargate and its massive opening , the DNA packaging machinery , as well as the possibility raised here that the exit and transportation of the genome occur within a vesicle derived from a viral internal membrane , may indicate that Mimivirus and potentially other large dsDNA viruses have evolved mechanisms that allow them to effectively cope with the exit and entry of particularly large genomes ., Because structure , rather than genomic sequence , represents the most reliable determinant for viral lineage 34 , the structural features underlying the Mimivirus replication cycle raise intriguing questions ., The presence of distinct portals for genome exit and entry , as well as the shape of the stargate and the unprecedented face-centered location of the packaging portal , may indicate that Mimivirus represents a unique specimen ., It is , however , enticing to suggest that these features , along with their functional and evolutionary implications , are shared by diverse viruses containing internal membranes ., This conjecture , which is consistent with comparative genomic studies 12 , 13 , 16 , 34 , as well as with the notion that an inner membrane represents a key factor for viral evolution and classification 35 , 36 , is being currently tested by high-resolution studies of the replication cycles of various inner membrane-containing viruses ., Finally , for only a small fraction of the open reading frames in Mimivirus , genome function has been attributed 17 ., The observations reported here may stimulate further studies on the Mimivirus that will focus on heretofore uncharacterized structural features , including the stargate , its putative role in Mimivirus assembly , and its massive opening , as well as the face-centered DNA packaging apparatus ., Such studies are likely to provide deeper insights into the unusually complex genome of this virus and into the factors that directed and dictated its evolution ., Acanthamoeba polyphaga were cultivated and infected by Mimivirus as previously described 20 ., Infected cells at various post infection time points were cryo-immobilized by the high-pressure freezing technique 21 , using an HPM high-pressure freezer ( BAL-TEC ) ., Samples were then freeze-substituted ( Leica EM AFS ) in dry acetone containing 2% glutaraldehyde and 0 . 1% tannic acid for 60 h at −90 °C , and warmed up to room temperature over 24 h ., Following acetone rinses , samples were incubated in 0 . 1% uranyl acetate ( UA ) and 1% OsO4 for 1 h , infiltrated with increasing concentrations of Epon over 6 d , and polymerized at 60 °C ., Thin sections ( 50–70 nm ) , obtained with an Ultracut UCT microtome ( Leica ) were post-stained with 1%–2% uranyl acetate and Reynolds lead citrate and examined using FEI Tecnai T12 TEM operating at 120 kV ., Images were recorded on a MegaView III CCD ( SIS ) ., Preparation of vitrified Mimivirus and cryo-TEM studies were as described in 18 ., For electron tomography , semi-thick sections ( 170–200 nm ) decorated on both sides with 12-nm colloidal gold markers were prepared as described above , and post-stained with 2% UA ., Double-tilted image series were acquired in FEI Tecnai F-20 TEM operating at 200 kV ., Images were recorded on a 4kx4k TemCam CCD camera ., Acquisition was performed at 1° intervals over a range of ±68° , using SerialEM program 37 ., Alignment and 3D reconstruction were performed with IMOD image-processing package 38 ., IMOD and Amira 4 . 1 packages were used for modeling ., Viruses purified by filtration were fixed with 2% gluteraldehyde in Cacodylate buffer for 1 h ., Viruses were deposited on poly-L-lysine–treated formvar-coated 200-mesh Ni grids , post-fixed with 1% OsO4 , 1% tannic acid , and 1% uranyl acetate ., Dehydration in increasing ethanol concentrations was followed by critical point drying using CPD30 ( BAL-TEC ) ., Samples were sputter-coated with 2-nm Cr and visualized in the high-resolution SEM FEG Ultra 55 ( Zeiss ) ., For cryo-SEM experiments , samples were fixed in 2% gluteraldehyde for 1 h , washed with DDW and deposited on Aclar disk ( EMS ) ., Samples were frozen by plunging in liquid ethane , freeze-dried for 1 h at −100 °C in a BAF60 freeze-fracture device ( BAL-TEC ) and rotary shadowed at 45° with 2-nm platinum-carbon and 5-nm carbon at −120 °C ., Samples were transferred to Ultra 55 SEM using a VCT100 vacuum-cryo-transfer , and observed at −120 oC ., Replication factories were isolated using the spheroplast methodology 39 , 40 ., Specifically , Acanthamoeba polyphaga were cultivated and infect

Introduction, Results, Discussion, Material and Methods

Icosahedral double-stranded DNA viruses use a single portal for genome delivery and packaging ., The extensive structural similarity revealed by such portals in diverse viruses , as well as their invariable positioning at a unique icosahedral vertex , led to the consensus that a particular , highly conserved vertex-portal architecture is essential for viral DNA translocations ., Here we present an exception to this paradigm by demonstrating that genome delivery and packaging in the virus Acanthamoeba polyphaga mimivirus occur through two distinct portals ., By using high-resolution techniques , including electron tomography and cryo-scanning electron microscopy , we show that Mimivirus genome delivery entails a large-scale conformational change of the capsid , whereby five icosahedral faces open up ., This opening , which occurs at a unique vertex of the capsid that we coined the “stargate” , allows for the formation of a massive membrane conduit through which the viral DNA is released ., A transient aperture centered at an icosahedral face distal to the DNA delivery site acts as a non-vertex DNA packaging portal ., In conjunction with comparative genomic studies , our observations imply a viral packaging pathway akin to bacterial DNA segregation , which might be shared by diverse internal membrane–containing viruses .

Two fundamental events in viral life cycles are the delivery of viral genomes into host cells and the packaging of these genomes into viral protein capsids ., In bacteriophages and herpesviruses , these processes occur linearly along the genome , base pair after base pair , through a single portal located at a unique site in the viral capsid ., We have addressed the question of whether such a linear translocation through a single portal also takes place for viruses harboring very large genomes , by studying genome delivery and packaging in the amoeba-infecting virus Acanthamoeba polyphaga mimivirus ., With 1 . 2 million base pairs , this double-stranded DNA genome is the largest documented viral genome ., By using electron tomography and cryo-scanning electron microscopy , we identified a large tunnel in the Mimivirus capsid that is formed shortly after infection , following a large-scale opening of the capsid ., The tunnel allows the whole viral genome to exit in a rapid , one-step process ., DNA encapsidation is mediated by a transient aperture in the capsid that , we suggest , may promote concomitant entry of multiple segments of the viral DNA molecule ., These unprecedented modes of viral genome translocation imply that Mimivirus—and potentially other large viruses—evolved mechanisms that allow them to cope effectively with the exit and entry of particularly large genomes .

virology

The mechanisms that promote genome delivery and packaging in the giant virus Mimivirus call into question the conventional distinction between viruses and single-celled organisms.

journal.pgen.1004378

Genome-Wide Nucleosome Positioning Is Orchestrated by Genomic Regions Associated with DNase I Hypersensitivity in Rice

The fundamental unit of chromatin is the nucleosome , which consists of 147 bp of DNA wrapped around a histone octamer containing four core histones ( H3 , H4 , H2A , and H2B ) 1 ., Since the DNA has to bend sharply around the surface of the histone octamer , flexible or intrinsically curved sequences are favorable for nucleosome formation 2 ., In contrast , poly ( dA:dT ) stretches , which are intrinsically stiff , have been shown to be unfavorable for nucleosome formation and are more enriched in linker sequences 3–5 ., The intrinsic properties of poly ( dA:dT ) are also important for nucleosome depeltion , promoter accessibility and transcriptional activity 6 ., In vitro nucleosome assembly studies in yeast ( Saccharomyces cerevisiae ) and Caenorhabditis elegans have confirmed the DNA sequence preferences in nucleosome formation 7 , 8 ., However , nucleosome organization in vivo is determined by several factors that can override the sequence preferences , including gene transcription , action of nucleosome remodeling complexes , and presence of histone variants and histone modifications 2 , 6 ., In fact , a sequence preference-based model could only explain ∼50% of the in vivo nucleosome positions in S . cerevisiae 9 ., Similarly , only 20% of the human genome is occupied by preferentially positioned nucleosomes 5 ., It is important to take such numbers with caution , however , as the calculations are affected by the sequencing methodology and the cell/tissue types used in analysis 10 ., Relationships between nucleosome organization and gene expression have been well demonstrated in several model eukaryotes ., Phased nucleosome arrays have been observed on both sides of the promoters of active genes 5 , 8 , 11–15 ., The promoter itself was traditionally considered to be nucleosome free or depleted , producing what is often called a “nucleosome-free region” ( NFR ) ., The first nucleosome downstream and upstream of the promoter are named +1 and −1 nucleosomes , respectively ., Nucleosomes after the +1 or before the −1 nucleosome become progressively less phased ., Nucleosome positioning in the human genome appears to correlate with the levels of Pol II in the promoter region: better phasing is observed with higher levels of Pol II and less phasing with lower levels of Pol II 13 ., So far , the majority of the nucleosome organization studies have been focused on genomic regions associated with transcription ., It is unclear , however , what factors determine nucleosome positioning in intergenic regions ., Rice ( Oryza sativa ) has been used as model species for plant genome research ., The rice genome is relatively small ( ∼400 Mb ) and is one of the best sequenced genomes in higher eukaryotes 16 ., Various genome-wide genomic and epigenomic datasets have been developed in rice 17–22 ., Thus , rice provides an excellent model system for nucleosome positioning studies ., We generated genome-wide nucleosome positioning data in rice ., We mapped both nucleosome positioning and DNase I hypersensitive site ( DHS ) datasets in the rice genome ., We discovered that DHSs associated with different genomic regions , including promoters , genes , and intergenic regions , were all flanked by strongly phased nucleosome arrays ., Our results support the barrier model for nucleosome organization ., The DHSs , which are likely bound to regulatory proteins , can serve as the barriers to organize phased nucleosome arrays on both sides ., Thus , genome-wide nucleosome positioning appears to be orchestrated by genomic regions associated with regulatory proteins ., DHSs are markers of regulatory DNA and span all classes of cis-regulatory elements , including promoters , enhancers , insulators , silencers and locus control regions 23 ., We applied a strategy of mapping both nucleosome positioning and DHS datasets to examine whether nucleosome positioning is associated with all cis-regulatory elements across the rice genome ., All datasets used in the analysis were developed using rice leaf tissue at the same developmental stage ( see Materials and Methods ) ., Rice chromatin was digested by micrococcal nuclease ( MNase ) into mono-nucleosome size ., Mono-nucleosomal DNA was isolated and sequenced ( MNase-seq ) using Illumina sequencing platforms ., We obtained a total of 38 million ( M ) single-end reads from our first MNase-seq experiment and mapped ∼26 M to unique positions in the rice genome ., We also conducted pair-end sequencing of an independent MNase-seq library , obtained 274 M paired-end reads , and mapped ∼231 M read pairs to unique positions in the rice genome ., We previously identified a total of 97 , 975 DHSs ( leaf tissue ) in the rice genome 24 ., We grouped these DHSs into five categories based on their locations in the genome: 13 , 272 in proximal promoters ( within 200 bp upstream of a TSS ) , 13 , 607 in distal promoters ( 200–1000 bp upstream of a TSS ) , 25 , 922 within genes , 4 , 249 in downstream regions of genes ( within 200 bp downstream of the end of transcription ) , and the remaining 41 , 602 in intergenic regions ., We then aligned both DNase-seq and MNase-seq reads to the rice genome ., Strikingly , we observed peaks of read alignments oscillating from both sides of DHSs , indicating the presence of regularly spaced , phased nucleosomes ., This phenomenon was evident both in forward and reverse oriented reads ( represented by positions of their 5 ends ) and in both single-end reads ( Figure 1 ) and paired-end reads ( Figure S1 ) ., The highest amplitudes of the oscillations were immediately adjacent to boundaries of the DHSs , suggesting that the nucleosomes close to the DHSs were more phased than those far from the DHSs ., Phased nucleosomes were not observed in regions flanking randomly selected genomic regions ( Figure 1F ) ., The pattern of phased nucleosome arrays surrounding the DHSs is highly similar to the phased nucleosomes surrounding the promoters of active genes reported in model animal species 5 , 11 , 13 ., We also examined nucleosome phasing surrounding TSSs in the rice genome independently of DHSs ., Clearly-phased nucleosomes were detected downstream of TSSs of expressed genes ( Figure 2A ) , but not downstream of TSSs of non-expressed genes ( Figure 2B ) , similar to the patterns observed in human and yeast genomes 5 , 11 , 13 ., However , phased nucleosomes were not detected upstream of TSSs of expressed genes ( Figure 2A ) , although phased nucleosomes were detected on both sides of the promoter DHSs ( Figures 1A , 1B ) ., In contrast , phased nucleosomes were observed on both sides of TSSs in human and yeast genomes 5 , 11 , 13 ., We noticed that the average lengths of most DHSs in different genomic regions , except for those located in proximal promoters , were similar in the rice genome , with ∼50% DHSs in the size of 35–150 bp ., In contrast , the lengths of DHSs in proximal promoters were more variable , including ∼79% DHSs >150 bp ( Figure 2C ) ., We suspected that the variable lengths of the DHSs in proximal promoters may mask the detection of nucleosome phasing in front of TSSs ., We sorted the DHSs in proximal promoters based on lengths and examined the nucleosome positioning of all active genes associated with these DHSs ., Phased nucleosomes were observed on both upstream and downstream of the TSSs of these genes ( Figure 2D ) , which confirmed our prediction ., We wanted to examine if phased nucleosomes are associated with the binding sites of specific rice transcription factors ., IDEAL PLANT ARCHITECTURE1 ( IPA1 ) , a member of the SPL transcription factor family , is a key regulator in determining plant architecture and enhancing grain yield in rice 25 ., A genome-wide IPA1-binding site map has recently been developed using ChIP-seq method and shoot apices tissue from 4-week-old rice seedling 26 ., We found that 87 . 8% of the IPA1-binding sites ( 5 , 298 of 6 , 032 ) are associated with DHSs , despite of the fact that the DHS data was developed from 2-week-old seedling tissue 24 ., An IPA1-binding site was considered to be flanked by phased nucleosome if the ±50 bp regions of the site overlap with a phased nucleosome ., Under this criteria , 33 . 2% ( 1 , 757 of 5 , 298 ) of the IPA1-binding sites were flanked by phased nucleosomes ( see an example in Figure 3 ) , which is significantly higher than the frequency observed from 5 , 298 randomly selected regions ( 24 . 3% , binomial test , p<0 . 001 ) ., In addition , 5 , 197 and 2 , 898 of the IPA1-binding sites contain the IPA1-binding motif , GTAC , and another over-represented motif , TGGGCC/T , respectively 26 ., We found that 33 . 1% of the GTAC-containing sites and 36 . 2% of the TGGGCC/T-containing sites were flanked by phased nucleosomes under the same criteria ., Mapping of both DNase-seq and MNase-seq datasets revealed peaked MNase-seq reads from both forward and reverse strands on both sides of DHSs ( Figures 1A–1D ) ., These results suggest that the DHS regions , although highly sensitive to DNase I cleavage , may span a structure that is more inhibitory to MNase digestion than the DHS-flanking regions ., The most likely candidate for this predicted structure is a phased nucleosome within each DHS ., This predicted nucleosome partially overlapped with the TSSs in proximal promoters ( Figure 1A ) ., We named this predicted nucleosome as “-1 nucleosome” because of its location in front of the TSS ., The mapping results and our prediction are in agreement with a recent report that active promoters and other regulatory regions in the human genome are not nucleosome free , but are enriched with special nucleosomes containing both of the widely conserved histone variants H3 . 3 and H2A . Z 27 ., These regions were previously considered as “nucleosome free” because nucleosomes carrying both H3 . 3 and H2A . Z are unusually unstable under the conditions that were commonly used for nucleosome preparation 27 , 28 ., This instability is believed to facilitate the access of transcription factors and regulatory proteins 27 ., Nucleosome formation in promoters was detected during the activation of the zygotic genome of zebrafish 29 ., The DHSs in intergenic regions were associated with a unique nucleosomal positioning pattern ., The intergenic DHSs lacked the forward MNase-seq peak and the reverse MNase-seq peak , respectively , on the two sides of the DHSs ( Figure 1E ) , suggesting that either these DHSs lack nucleosomes or the nucleosomes are poorly phased ., Thus , intergenic DHSs are likely more dynamic with nucleosome occupation , which could mask the identification of a positioned nucleosome ., Intergenic DHSs are highly enriched with enhancers in mammalian species 23 , 30 ., Thus , many of these regions may be associated with regulatory proteins in a cell type-specific manner , which would also mask the identification of positioned nucleosomes in datasets generated from tissues with mixed cell types , such as leaf ., We previously demonstrated that rice DHSs generally lack histone modification marks associated with histone H3 ., However , intergenic DHSs were uniquely enriched with H3K27me3 , suggesting a dynamic nucleosome occupation in these regions 24 ., Since the DHSs in proximal promoters were more variable in lengths ( Figure 2C ) , we further investigated the positions of the -1 nucleosomes relative to the DHSs with different lengths ., We divided the DHSs into five different groups based on their lengths ( 320–480 bp , 200–320 bp , 140–200 bp , 80–140 bp , and 20–80 bp , respectively ) ., DHSs within the same group were aligned by their 5 ends ., All DHSs with a length >140 bp showed a similar nucleosomal positioning pattern ( Figures 4A , 4B , 4C ) ., These DHSs appeared to span a single , phased nucleosome , although the DNA length of the DHSs in 320–480 bp is close to two nucleosomes , which may reflect nucleosomes with longer linkers , or nucleosomes tightly associated with other regulatory proteins ., These results indicate that the -1 nucleosome in these promoters can accommodate variable amounts of DNA , perhaps reflecting the existence of diverse proteins that interact tightly with the -1 nucleosome or with promoter DNA ., The sizes of 2 , 495 DHSs ( out of 11 , 718 ) in proximal promoters were <140 bp , which is shorter than the sequences required to wrap a single nucleosome ., These DHSs did not appear to span a nucleosome , but appeared to be enriched in the 3′ portion of the -1 nucleosome ( Figure 4D ) or were located between the -1 and +1 nucleosome ( Figure 4E ) ., Thus , the small DHSs tend to be located in the linker regions ., The levels of DNase I sensitivity within these small DHSs were clearly lower than those of the DHSs >140 bp ( Figure 4 ) ., We observed a superposition between the forward and reverse MNase-seq reads in genic and promoter regions , which indicates very little or no space between 5 ends of forward and reverse oriented reads ( Figures 1A–1C ) ., However , a clear shift between the forward and reverse reads was observed in intergenic regions ( Figure 1E ) ., We wondered if this shift was caused by longer linkers that connect the phased intergenic nucleosomes ( Figure S2 ) ., We investigated the lengths of linkers between phased nucleosomes associated with different genomic regions ., We used paired MNase-seq reads and employed 1-bp resolution to calculate the distribution of forward and reverse MNase-seq reads rather than using the 20-bp windows that we used for the other analyses ., We measured the distance between maxima of adjacent peaks from reverse to forward strand , respectively , to estimate the length of the linkers between two adjacent nucleosomes ., Assuming a constant nucleosome core DNA length of 147 bp , the average length of linkers between two phased nucleosomes in intergenic regions was 35 . 3 bp , which was significantly longer than the average lengths of linkers between two adjacent nucleosomes within genes ( 8 . 1 bp ) and in proximal promoters ( 8 . 5 bp ) ( Figure 5A , p<0 . 005 , Kolmogorov–Smirnov test ) ., We also calculated linker lengths in the human genome using human MNase-seq data 13 , and found a similar pattern as in rice: the linker length in intergenic regions in the human genome was 38 . 7 bp , compared to only ∼11 . 5 bp and 10 . 1 bp , respectively , for the linkers in proximal promoters and genic regions ( Figure 5A ) ., A weakness of the above method of calculating linker length is that it is influenced by the severity of MNase digestion as MNase can either digest into the nucleosome core DNA or fail to completely digest the linker DNA ., Thus , we used an alternative method to estimate the linker lengths in different genomic regions in rice ., Since the position of the nucleosome center ( dyad ) , which can be identified as the middle position of each paired-end read , is not affected by different levels of MNase digestion , we can calculate the spacing of between two adjacent nucleosomes using the midway point between paired MNase-seq reads rather than 5 ends ., We found that the average spacing between two nucleosomes adjacent to intergenic DHSs was ∼191 bp ( Figure 5B ) , which is significantly longer than the spacing between nucleosomes adjacent to DHSs in proximal promoters ( 175 bp ) and genes ( 176 bp ) ., The average spacing of nucleosomes associated with various histone modification marks was recently reported in human CD4+ T cells 5 ., The average spacing of nucleosomes associated with H3K4me1 and H3K27ac , both euchromatin marks , are 178 bp and 179 bp , respectively ., In contrast , the average spacing of nucleosomes associated with H3K9me3 and H3K27me3 , both heterochromatin marks , are 205 bp 5 ., Thus , linkers of nucleosomes in heterochromatin are significantly longer than the linkers of nucleosomes in euchromatin ., These results are in agreement with the linker length difference in genic and intergenic regions observed in both rice and human genomes ( Figure 5 ) ., We exploited the genomic datasets from the human genome to examine a similar association of DHSs with nucleosome positioning ., Human CD4+ T cell line has been extensively used in epigenomics profiling , including histone modifications 31 , nucleosome positioning 13 , and DHS mapping 32 ., We found that the relationship between DHSs and nucleosome positioning using datasets from the CD4+ T cell line was highly similar to the patterns observed in rice ., The DHSs in proximal promoters ( Figure 6A ) , genes ( Figure 6B ) , and intergenic regions ( Figure 6C ) were flanked by phased nucleosomes ., Interestingly , a similar shift between the forward and reverse MNase-seq reads was also observed in intergenic regions ( Figure 6C ) ., Since H2A . Z-associated nucleosomes were found in regions that were previously thought to be nucleosome free , we investigated if DHSs in the human genome span H2A . Z-associated nucleosomes ., Mapping of H2A . Z ChIP-seq dataset 31 together with DHS data 32 revealed a phased H2A . Z-associated nucleosome within DHSs in proximal promoters and genic regions in the human genome ( Figures 6A , 6B ) ., The intergenic DHSs tended to locate between two phased H2A . Z nucleosomes ( Figure 6C ) ., These results suggest that human DHSs span a phased H2A . Z nucleosome , which is also supported by previous data that a single H2A . Z nucleosome can be mapped within CTCF-binding sites in low-salt condition in the human genome 27 ., The positions of the H2A . Z nucleosomes within human DHSs are highly similar to the implicated nucleosome within rice DHSs ., Thus , we predict that the implicated nucleosome associated with rice DHSs likely contains H2A . Z , which serve as ‘place holders’ to facilitate binding of tanscription factors ., The instability and dynamic replacement by regulatory proteins of these nucleosomes result in the DHSs in these genomic regions ., Genome-wide nucleosome positioning maps have been generated in several eukaryotes , including yeast 9 , 11 , 33–35 , Drosophila melanogaster 12 , C . elegans 36 , humans 5 , 10 , 13 , and Arabidopsis thaliana 37 ., It has been well documented that only a subset of nucleosomes are phased in any genome ., Most consistently , active genes form highly phased nucleosomes flanking the TSSs , which led to the suggestion that transcription may promote nucleosome organization 8 , 38 ., Proper function of the adenosine triphosphate ( ATP ) -dependent chromatin remodeling enzymes was recently found to be key for nucleosome positioning in yeast 39–41 and mammalian species 42 ., It also suggests that transcription or the transcription initiation complexes do not play a direct role in nucleosome phasing surrounding TSSs 40 , which is also supported by the fact that genes with poised Pol II in the human genome exhibited a similar pattern of nucleosome phasing to the expressed genes 13 ., A barrier model was proposed to explain genome-wide nucleosome positioning 3 , 43 ., Nucleosomes can be organized passively at regular intervals surrounding a barrier ., The barrier model can be used to explain the phased nucleosome arrays surrounding TSSs in that each TSS indirectly dictates a phased position for the next adjacent nucleosome ., Whatever factors that determine spacing of nucleosomes in that context would then force the subsequent nucleosome to also be phased , and so on until an array of phased nucleosomes is formed ., A barrier can only enforce its effect within a limited distance , resulting in the decay of nucleosome phasing away from the barrier ., The effect of the barriers appear to be bidirectional since phased nucleosome arrays are formed on both sides of the TSSs ., Gaffney et al . ( 2012 ) recently mapped nucleosomes surrounding the binding sites of 35 different transcription factors in human lymphoblastoid cell lines ., Strongly positioned nucleosome arrays were found to flank the binding sites , including those at least 1 kb away from a known TSS 10 ., Phased nucleosome arrays were observed around the binding sites of other regulatory proteins , such as the mammalian insulator protein CTCF 5 , 44 and repressor protein NRSF/REST 5 ., Hughes et al . ( 2012 ) recently studied nucleosome positioning of S . cerevisiae strains containing large genomic regions from other yeast species 15 ., Nucleosome-depleted regions ( NDRs ) fortuitously arose in coding regions of the foreign genomic sequences ., Interestingly , these NDRs are associated with binding of TFIIB , an essential component of the RNA polymerase II core transcriptional machinery , and were flanked by phased nucleosomes 15 ., These results are all in favor of the barrier model because the binding of a regulatory protein to both promoters and non-promoter regions can create a barrier for nucleosome organization ., The regulatory proteins reported to be involved in nucleosome positioning include nucleosome remodelers and transcription factors , including activators , components of the preinitiation complex and elongating Pol II 6 ., We demonstrate that DHSs in the rice genome are flanked by phased nucleosome arrays on both sides ( Figure 1 ) , which is highly similar to the nucleosome arrays flanking TSSs ., Phased nucleosome arrays were associated with DHSs located in different genomic regions , including those inside of genes and intergenic regions ., A similar association of DHSs with phased nucleosomes was also observed in the human genome ( Figure 6 ) ., It has been well documented in different eukaryotes that DHSs represent regions associated with various regulatory proteins ., For example , the binding patterns of 21 developmental regulators in Drosophila were quantitatively correlated with DNA accessibility in chromatin that can be measured by the DNase I sensitivity 45 ., More strikingly , 94 . 4% of a combined 1 , 108 , 081 binding sites from all human ENCODE transcription factors fall within DHSs 23 ., Similarly , we previously found that ∼90% of the binding sites of two of the best characterized transcription factors in A . thaliana , APETALA1 and SEPALLATA3 , were covered by DHSs 46 ., Thus , the association of DHSs with phased nucleosome arrays shows that the barrier model can be extended to an entire genome: any genomic region associated with regulatory proteins can serve as a barrier for nucleosome organization , and these regions can be either directly associated with transcription , such as promoters , or indirectly associated with transcription , such as the insulators ., This model would also predict different nucleosome positioning profiles in different organs/tissues and in different developmental stages due to differential binding of regulatory proteins ., A DHS-based barrier can be permanent , such as the promoters associated with constitutively expressed genes , or be temporarily , such as binding sites of transcription factors associated with tissue- or organ-specific gene expression ., Regulatory proteins can bind DNA tightly or loosely ( or dynamically , with transient nucleosome formation in the same region ) , which may result in “hard” barriers or “soft” barriers ., Hard barriers will result in well positioned and well phased nucleosome arrays; whereas soft barriers may result in “fuzzy” and less phased nucleosome arrays ., In Drosophila , the binding sites of transcription factors that are flanked with strongly positioned nucleosome arrays were more sensitive to DNase I digestion and have more pronounced DNase I footprints 10 ., These results support that the levels of transcription factor occupancy at the binding site determine the levels of positioning of the flanking nucleosome arrays , thus , the level of “hardness” of the barrier ., In summary , we demonstrate that DHSs located across the rice genome are flanked by strongly phased nucleosome arrays ., We confirmed the same phenomenon in the human genome by analyzing publically available datasets ., Our results support the barrier model for nucleosome organization as a general feature of eukaryote genomes ., We propose that genome-wide nucleosome positioning in the eukaryotic genomes is orchestrated by genomic regions associated with regulatory proteins ., Rice cultivar “Nipponbare” seeds were germinated at room temperature for three days ., Germinated seeds were then sowed in soil to continue to grow in the greenhouse ., The seedlings continued to grow for two weeks under 12 hrs day/night cycles and 32°C/27°C corresponding to day and night , respectively ., The seedlings were then harvested for nuclei isolation , the same growing stage/condition used for developing DNase-seq and RNA-seq datasets previously 24 ., The nuclei were then digested with a series of concentrations of micrococcal nuclease ( MNase ) ., The MNase-digested DNA was separated using 2% agarose gel containing ethidium bromide and visualized under UV light ., Nuclei were digested into ∼80% nucleosome monomers and ∼20% dimers ., The mono-nucleosomal DNA was then excised from the gel and purified using a gel purification kit ( Qiagen , 28006 ) ., The purified DNA was used for MNase-seq library development , including end blunting , adding “A” base to the blunt DNA fragments , ligating “A” tailed DNA fragments with either single-end adapter or pair-end adapter , and enriching ligated DNA fragments by PCR ., The final , amplified DNA was purified and sequenced with 36 bp SR ( single reads ) or PE ( paired end ) using Illumina sequencing platforms ., We mapped the MNase-seq reads to the rice genome ( TIGR release 5 ) using MAQ software 47 with default parameters ( except 1-bp mismatch allowed ) ., Only the reads aligning to a unique position in the rice genome were used for further analysis ., DNase-seq and RNA-seq dataset were generated from our previous work 24 ., Methods for mapping DNase-seq and RNA-seq reads were described previously 24 ., We used the same methods to analyze datasets from human CD4+ T cell line , including DNase-seq dataset 32 , MNase-seq dataset 13 , and H2A . Z ChIP-seq 31 ., All sequence reads from human CD4+ T cell line were aligned to human genome build 37 of NCBI using MAQ software using default parameters ( except 1-bp mismatch allowed ) ., We used F-seq 48 with 200-bp bandwidth parameter to identify rice DHSs ., To control the FDR of the identified DHSs , we generated 10 random datasets each containing the same number of sequence reads as our DNase-seq dataset ., The FDR was calculated as ratio of DHSs identified from random datasets to DHSs identified from the DNase-seq dataset ., We controlled the FDR<0 . 05 ., We used the same method and parameters as Boyle et al . 32 to identify the DHSs in human CD4+ T cell line ., We employed nucleR 49 to predict phased nucleosomes based on pair-end MNase-seq data using nonparametric method ., We removed all fragments >200 bp ( distance between the paired reads ) and trimmed the fragments to the middle 40 bp to remark the position of dyad ., The dyad positions were transformed by Fast Fourier Transform to show distribution of nucleosomes in Figure 3 and to identify the phased nucleosomes ., The programs for data processing and statistical test were written in Perl or R ( http://www . r-project . org/ ) ., MNase-seq data has been deposited to NCBI under accession number GSE53027 .

Introduction, Results, Discussion, Materials and Methods

Nucleosome positioning dictates the DNA accessibility for regulatory proteins , and thus is critical for gene expression and regulation ., It has been well documented that only a subset of nucleosomes are reproducibly positioned in eukaryotic genomes ., The most prominent example of phased nucleosomes is the context of genes , where phased nucleosomes flank the transcriptional starts sites ( TSSs ) ., It is unclear , however , what factors determine nucleosome positioning in regions that are not close to genes ., We mapped both nucleosome positioning and DNase I hypersensitive site ( DHS ) datasets across the rice genome ., We discovered that DHSs located in a variety of contexts , both genic and intergenic , were flanked by strongly phased nucleosome arrays ., Phased nucleosomes were also found to flank DHSs in the human genome ., Our results suggest the barrier model may represent a general feature of nucleosome organization in eukaryote genomes ., Specifically , regions bound with regulatory proteins , including intergenic regions , can serve as barriers that organize phased nucleosome arrays on both sides ., Our results also suggest that rice DHSs often span a single , phased nucleosome , similar to the H2A . Z-containing nucleosomes observed in DHSs in the human genome .

The fundamental unit of chromatin is the nucleosome , which consists of 147 bp of DNA wrapped around a histone octamer containing four core histones ( H3 , H4 , H2A , and H2B ) ., Nucleosome positioning in the genome affects the DNA accessibility for regulatory proteins , and thus is critical for gene expression and regulation ., Genomic regions associated with regulatory proteins are associated with a pronounced sensitivity to DNase I digestion , and are thus called DNase I hypersensitive sites ( DHSs ) ., It is well known that only a subset of nucleosomes are reproducibly positioned in eukaryotic genomes ., However , it is less clear what factors determine genome-wide nucleosome positioning , especially in intergenic regions ., We mapped both nucleosome positioning and DHS datasets across the rice genome ., We discovered that DHSs located in a variety of contexts , both genic and intergenic , were flanked by strongly phased nucleosome arrays ., We confirmed the same association of DHSs with phased nucleosomes in the human genome ., We conclude that genomic loci associated with a diverse set of regulatory proteins are major determinants of nucleosome phasing , and this is true in both genic and intergenic regions .

biotechnology, plant science, plant genomics, biology and life sciences, plant biotechnology

journal.ppat.1006199

A mouse model of paralytic myelitis caused by enterovirus D68

Enterovirus D68 ( EV-D68 ) was first identified in 1962 after it was isolated from four children in California with acute respiratory illnesses 1 ., Enteroviruses are typically spread through fecal-oral transmission , and are associated with diarrheal illnesses , undifferentiated fever with rash , hand-foot-and-mouse disease , meningitis , and encephalitis 2 ., EV-D68 , however , possesses several properties more similar to rhinoviruses than conventional enteroviruses , including optimal replication in the cooler temperatures of the upper respiratory tract ( 33°C ) , acid sensitivity , and spread primarily via respiratory , rather than fecal-oral , transmission 1 , 3 , 4 ., Passive surveillance data from the National Enterovirus Surveillance System ( NESS ) indicated that EV-D68 has been a rare cause of respiratory illness , with only 26 documented cases of EV-D68 in the United States ( US ) from 1970–2005 5 ., However , within the last decade , EV-D68 outbreaks have become more common worldwide 6 , 7 , and in the second half of 2014 , the US experienced an unprecedented EV-D68 respiratory disease outbreak with over 1150 cases reported nationwide 8–10 ., This number is almost certainly an underestimate , as only the most severe cases underwent pathogen identification , and rapid tools for laboratory confirmation of EV-D68 in the clinical setting were not widely available until mid-2015 11 ., Coincident with the US EV-D68 respiratory outbreak , physicians began reporting an increased number of cases of acute flaccid paralysis with a striking resemblance to poliomyelitis 12–16 ., The paralysis occurred primarily in young children ( median age ~7 ) 17–20 ., Many children experienced prodromal symptoms of fever and upper respiratory illness before the onset of limb weakness 14 , 16–21 ., Magnetic resonance imaging ( MRI ) showed signal abnormalities in the anterior horns of the spinal cord , the location of motor neurons innervating upper and lower limbs 14 , 16 , 19–21 ., The CDC established guidelines for reporting acute flaccid myelitis ( AFM ) , defined as acute limb weakness with characteristic spinal cord imaging abnormalities on MRI occurring in children on or after August 2014 ., Under these guidelines , 120 confirmed cases of AFM from 34 states were documented in 2014 17 ., Sporadic cases meeting the CDC definition , from 0 to 7 per month , continued to be reported in 2015 , and in 2016 the CDC reported 132 confirmed cases of AFM from 37 states ., 17 ., Case-control study data from the 2014 cluster of AFM cases in Colorado showed that the odds of a child with AFM having had a concomitant EV-D68 respiratory infection were over 10 times greater than for controls with acute respiratory disease 22 ., EV-D68 RNA was detected in respiratory secretions as the predominant pathogen in about half of the affected children tested in 2014 , although EV-D68 RNA was amplified from cerebrospinal fluid from only one 2014 AFM patient to date 18 , 19 ., There was no statistical correlation of AFM with infection by any other pathogen in respiratory samples 22 , and in-depth metagenomic sequencing of CSF failed to reveal an alternative infectious etiology 18 ., Given the lack of consistent CSF isolation , a potential causal role of EV-D68 in AFM has not been formally established ., Several additional pieces of evidence , however , suggest that EV-D68 has the capacity to produce central nervous system ( CNS ) disease ., Other viruses in the Enterovirus genus , such as polioviruses , enterovirus 70 , and enterovirus 71 , are established causes of acute flaccid paralysis ., EV-D68 has also been described as a cause of neurological disease in two previous case reports 5 , 23 ., The first involved a young adult who developed acute flaccid paralysis with EV-D68 detected in CSF in 2005 5 ., The second involved a 5-year-old boy who developed fatal EV-D68 meningomyeloencephalitis with neuron loss in motor nuclei of the brain and cervical spinal cord in 2008 23 ., Changes in the viral genome can also potentially contribute to virulence and spread ., EV-D68 strains have undergone significant evolutionary shifts since their original isolation 7 , 18 ., The Fermon and Rhyne strains , obtained from respiratory swabs of children in California in 1962 , are considered the prototype EV-D68 strains 1 ., During the mid-1990’s , the prototype lineage separated into two clades , designated A and C 7 ., Clade B further separated from clade C in the mid-2000s 7 ., A clade named B1 then separated from clade B around 2010 18 ., Most of the EV-D68 respiratory cases that occurred in the 2014 epidemic were caused by a single lineage of clade B1 viruses related to strains previously seen circulating in the US , Asia , and southern Europe from 2011–2013 9 ., A minority of cases were caused by clade A and C members 9 ., Of the AFM patients analyzed in the United States , up to half had respiratory sputum samples positive by reverse transcription polymerase chain reaction ( RT-PCR ) for EV-D68 , with increasing likelihood of EV-D68 detection if the sample was collected closer to the onset of prodromal febrile/respiratory symptoms 18 , 19 ., Metagenomic sequencing of available EV-D68 positive respiratory samples in one study from California and Colorado mapped all AFM-associated EV-D68 strains to clade B1 18 ., Because non-clade B1 strains were less prevalent , however , it is possible this study was unable to detect an association between AFM and other clades ., In order to develop an animal model of EV-D68-associated neuropathogenesis , we screened both 2014 outbreak and prototype 1962 EV-D68 strains for the ability produce paralysis in mice ., The 2014 strains tested were isolated from clinical respiratory samples from the 2014 outbreak and represented clades A , B , and B1 ., Five EV-D68 strains from the 2014 outbreak ( clade A strain KY/14-18953; clade B strains IL/14-18952 and CA/14-4231; B1 strains MO/14-18947 and CA/14-4232 ) and two prototype strains ( Fermon and Rhyne ) were screened for the ability to cause neurological disease in two day-old outbred Swiss Webster mice ( Fig 1A and 1B ) ., Intracerebral injection of viral strains was chosen to investigate neurovirulence and neurotropism by bypassing potential barriers to viral central nervous system ( CNS ) entry ., Strains were injected at the highest available viral titer to maximize the possibility of eliciting disease ( see Methods ) ., Mice were monitored daily for up to 4 weeks or until death occurred ., Following injection , four out of the five contemporary strains tested ( KY/14-18953 , IL/14-18952 , MO/14-18947 , and CA/14-4232 ) induced paralytic disease in 33–100% of mice ( Fig 1B ) ., Paralysis occurred in these mice between dpi 3 –dpi 9 for all strains ., Mortality ( 5–70% ) varied by strain , and most of the mice that died had paralysis ( 31 out of 38 deaths , 80% ) ., Example images of mice injected with one of these strains , MO/14-18947 , can be seen in Fig 1C and S1–S3 Movies ., One contemporary strain , CA/14-4231 , failed to induce paralysis or death despite its close phylogenetic relationship to IL/14-18952 ( Fig 1A and 1B ) ., The degree of limb involvement ranged from monoparesis to quadriparesis ., Paralysis in very young mice ( postnatal days 5–7 ) could be identified as a reduction of movement in one or more limbs , leading to a reduced ability to crawl and turn when evaluated on a flat surface ( Fig 1C , left panel; S1 Movie ) ., In older , more mobile mice ( ~8 days old or more ) , paralysis could be assessed along a continuum from mild loss of motor function , as exemplified by toe or knuckle walking , to the complete inability to use the limb for ambulation ( Fig 1C , center and right panels; S2 and S3 Movies ) ., Affected limbs appeared to hang in unnatural positions and developed atrophy over time ., Sensation in affected limbs remained grossly intact as determined by response of vocalization and attempt to move away from the noxious stimuli ( toe pinch test ) 24 ., Compared to the contemporary strains , mice injected with the prototype strains , Rhyne and Fermon , had fewer signs of morbidity or mortality ., One mouse in the Rhyne group ( n = 1 out of 18 , 6% ) developed a transient right hindlimb weakness that was obvious only during ambulation ( Fig 1B ) ., Signs of weakness began on dpi 5 but disappeared by dpi 9 ., The mouse was able to sit with this limb in proper position while at rest , and the limb did not show signs of atrophy ., Rhyne has been reported to cause a myositis 1 , and this may have accounted for the weakness noted ., Mice injected with either Fermon ( n = 24 ) or rhabdomyosarcoma ( RD ) cell culture media ( n = 36 ) showed no evidence of paralysis ( Fig 2B ) ., Two mice , one in the Fermon group and one in the Rhyne group , died early ( dpi 2–4 ) , although they did not show signs of neurological disease or paralysis prior to death ., The cause of death in these two mice was unclear ., One strain from clade B1 , MO/14-18947 , was chosen for further in-depth analysis of the paralysis phenotype ., MO/14-18947 was chosen because it belongs to clade B1 , the predominant circulating EV-D68 clade in 2014 ., In mice infected with MO/14-18947 , the onset of paralysis occurred most frequently between dpi 3–5 , although onset occasionally occurred as late as dpi 9 ( Fig 1D ) ., Mice that developed earlier paralysis were more likely to die than mice that developed paralysis at a later age , with an overall death rate in paralyzed mice of 33% ., Mice dying of infection had more severe paralysis , as defined by the average number of limbs affected , compared to mice surviving infection ( Fig 1E ) ., Following intracerebral injection of the virus , forelimbs were most commonly affected at paralysis onset ( 52% ) , although some mice presented with hindlimb paralysis ( 30% ) or both forelimbs and hindlimbs affected ( 18% ) ., Up to 48% of mice experienced disease progression to other limbs after onset of initial paralysis ( Fig 1E ) ., The majority ( 72% ) of mice surviving to dpi 28 showed no motor recovery as quantified by the number of limbs affected; the others ( 28% ) recovered between dpi 9 to 15 ., Recovery occurred most often in mice with milder disease ( e . g . only one limb affected ) ( Fig 1E ) ., Mice injected with MO/14-18947 generated neutralizing antibody titers against EV-D68 , regardless of the presence or absence of paralysis ., Sera of mice tested at dpi 12 ( n = 12 ) had antibody titers ranging from 1:40 to 1:1 , 280 ., Neutralizing antibody titer increased in MO/14-18947-infected mice tested at dpi 28 ( n = 11 ) , with a titer range of 1:640 to 1:>10 , 240 ( Fig 1F ) ., Neutralizing titers in MO/14-18947 mice were compared to those in non-paralytogenic strains in order to determine whether these strains were able to evoke a serological response in the host ( Fig 1F ) ., Fermon frequently failed to generate a detectable antibody response in infected mice ., Only two mice out of 10 mice tested at dpi 12 developed neutralizing antibodies with titers ranging from 1:20–1:80 , whereas none of the Fermon-infected mice tested at dpi 28 ( n = 18 ) had detectable neutralizing titers ., In contrast , mice infected with Rhyne ( n = 7–10 ) and CA/14-4231 ( n = 8–10 ) both generated a sustained neutralizing antibody response against their respective strains , similar to those seen in mice infected with MO/14-18947 ( Fig 1F ) ., Mice injected with media control did not have detectable anti-EV-D68 antibody titers at either dpi 12 ( n = 8 ) or dpi 28 ( n = 8 ) ., To confirm EV-D68 infection of these strains in mice , viral titers were examined in the skeletal muscle after intramuscular inoculation ., All strains examined , except Fermon , replicated to equivalent viral titers in the muscle regardless of the ability to induce paralysis ., Fermon did not replicate and did not cause paralysis ., The ability to infect skeletal muscle corresponded to the ability to produce neutralizing antibodies ( S1 Table ) ., Viral growth in spinal cords from groups of paralyzed mice injected intracerebrally with EV-D68 MO/14-18947 , as well as from litters of mice tested before the typical onset of paralysis ( dpi 0 and dpi 2 ) , was quantified by TCID50 assay ( Fig 2A ) ., Infectious virus was not detected in the spinal cords of any mice on dpi 0 , but mean viral titer increased progressively in the spinal cords of mice tested on dpi 2 and dpi 4 ., The mean spinal cord titer then remained >1000 TCID50 in mice tested through dpi 8 , after which it dropped steadily and became undetectable by dpi 12 ., Two-step quantitative RT-PCR ( qRT-PCR ) for EV-D68 utilizing primers targeting the VP1 capsid gene was used to detect EV-D68 RNA extracted from whole spinal tissue from mice from each time point post-infection ( 20 ) ., Results of the qRT-PCR analysis paralleled the results of the TCID50 assay , although titer by qRT-PCR remained detectable at dpi 12 , indicating a longer time of detectability of viral genome as compared to infectious particles in tissue ( Fig 2B ) ., Infectious virus was detected in the brains of dpi 0 mice by TCID50 assay approximately an hour after injection ( average 102+/-1 . 05 TCID50/mL ) ., However , by dpi 2 the virus could only be detected in brains from 2 out of 11 mice by TCID50 assay , and from dpi 4 to 12 no virus was detected in brain of any paralyzed animals ., Infectious virus remained below the limits of quantification by TCID50 assay in sera from all paralyzed mice ., Pathological examination of mice infected intracerebrally with MO/14-18947 revealed marked injury and loss of motor neuron populations in the anterior horn of the spinal cord corresponding to the ipsilateral affected limb ( Fig 3 ) ., Fig 3 illustrates a typical case with marked injury and loss of the motor neuron population in the anterior horn ipsilateral to the affected right limb as indicated by loss of choline acetyltransferase ( ChAT ) staining , a specific marker of spinal cord anterior horn motor neurons , and NeuN staining , a general marker of neurons ( Fig 3A ) ., In contrast , the motor neuron population corresponding to the unaffected left limb appeared intact ( Fig 3A ) ., Examination of a consecutive spinal cord section stained for EV-D68 VP2 capsid protein revealed viral antigen within the few remaining motor neurons on the affected side ( Fig 3B–3D ) ., No staining was seen on the unaffected side ( Fig 3B ) or in media injected control mice ( S1 Fig ) ., To demonstrate the presence of virus at an earlier time point following infection ( during which the motor neuron population was relatively intact ) , spinal cords from dpi 3 mice from a litter injected intracerebrally with MO/14-18947 that had not yet begun to show signs of paralysis were examined ., EV-D68 VP2 antigen was consistently detected within motor neurons in these mice by immunostaining ( Fig 3E ) ., Unfortunately , direct co-localization of EV-D68 antigen and ChAT was not obtainable due to inability to find compatible primary antibodies from different host species for co-labeling , so consecutive sections ( 10 um apart ) were used for these staining experiments ., Transmission electron microscopy ( TEM ) images of the cervical spinal cord anterior horn from a dpi 4 MO/14-18947 injected mouse with forelimb paralysis confirmed the presence of dying cells , consistent in location and morphology with motor neurons , filled with cytoplasmic clusters of ~30 nm particles morphologically consistent with enteroviruses ( Fig 4A and 4B ) 25 ., In order to determine whether routes of EV-D68 infection other than intracerebral injection could produce paralytic disease , mice were infected intramuscularly , intranasally , or intraperitoneally with the MO/14-18947 strain ., Intramuscular injection of EV-D68 into the left hindlimb produced paralysis in 100% ( n = 18 out of 18 , 100% ) of injected animals ., Paralysis onset occurred from dpi 2 to 3 in the injected hindlimb before often progressing to the contralateral hindlimb and then forelimb ( s ) ( Fig 5A , S4 Movie ) ., EV-D68 RNA in the spinal cord was detected by RT-PCR in additional mice ( n = 5 out of 5 , 100% ) examined on dpi 3 following left hindlimb injection , with an average estimated copy number per spinal cord of 105 . 3±0 . 7 ., Examination of the spinal cords from dpi 3 mice injected into the left hindlimb revealed infection of motor neurons and viral antigen consistent with signs of paralysis ( Fig 5B ) ., An additional group of mice ( n = 8 out of 8 ) injected with MO/14-18947 in the right forelimb also developed paralysis starting in the injected limb between dpi 2–4 , consistent with the onset of paralysis seen in the hindlimb injections ., In contrast , intramuscular injection with RD control media failed to produce paralysis ( n = 0 out of 14 , 0% ) ., Although rare , paralysis following intranasal infection was observed in 2 of 73 mice ( 2 . 7% ) of mice showing signs of paralytic disease ., Onset of paralysis occurred in these mice between dpi 8 and 10 ., One mouse developed paralysis in the right forelimb only ( Fig 5C; S5 Movie ) ; the other had mild left forelimb paralysis and appeared generally weak in all limbs ., The mouse with paralysis onset at dpi 10 was sacrificed on dpi 12 ( Fig 5C ) , and examined for the presence of EV-D68 RNA in its spinal cord tissue by RT-PCR ., It was found to be positive for EV-D68 in its spinal cord tissue with an estimated EV-D68 genome copy number of 104 . 0 , comparable to viral titers found in mice after intracerebral injection ., The other mouse that developed signs of paralysis after intranasal infection was sacrificed for histological examination on dpi 8 and showed viral antigen in the motor neurons of the cervical spinal cord ( Fig 5D ) ., Intraperitoneal injection of MO/14-18947 produced disease in only 1 out of 22 ( n = 4 . 5% ) mice ., Paralysis in this mouse occurred in the right rear leg on dpi 5 ., To determine whether EV-D68 was the direct cause of the paralytic disease in mice by fulfilling Koch’s postulates 26 , spinal cord lysate from a dpi 4 mouse with signs of paralysis following intracerebral injection of MO/14-18947 was cultured in rhabdomyosarcoma ( RD ) cells ., Significant cytopathic effect ( CPE ) was noted within 3 days in the inoculated RD cell culture ., The spinal cord-passaged cell culture lysate was passed through a 0 . 22 μM filter syringe and injected intracerebrally into the brains of naïve mice ( Fig 6A ) ., 38% ( n = 9 out of 24 ) injected with the cell culture lysate developed paralytic disease between dpi 3 and 8 ., This rate of paralysis is consistent with that seen in previous experiments ( e . g . , Fig 1B ) , especially when considering that the inoculum was nearly 100-fold lower than that used in other experiments ( TCID50 104/mL compared with TCID50 106/mL ) ., Analysis of 3 mice that developed early severe paralysis revealed high spinal cord viral titers by TCID50 ( Fig 6B ) ., Metagenomic deep sequencing followed by SURPI ( sequence-based ultra-rapid pathogen identification ) analysis confirmed the presence of EV-D68 strain MO/14-18947 RNA in the original spinal cord lysate , the RD cell culture lysate , and the spinal cord tissue of the mice injected with the spinal cord-passaged cell culture lysate ( Fig 6B; Tables A-D in S1 Text ) 27 ., No paralysis or other signs of disease were seen in mice ( n = 12 ) injected with RD cell culture lysates that had been inoculated with normal mouse spinal cord ., To evaluate the role of immune sera in protection against disease , 1 day-old mice were injected intraperitoneally with either pooled immune sera against MO/14-18947 ( neutralizing antibody titers: 1:320–640 ) or pooled control normal mouse sera ( neutralizing antibody titer undetectable ) and then challenged 24 hrs later with intracerebral injection of MO/14-18947 ( 2 . 3 x 106 TCID50/mL ) ., 57% of mice ( n = 12 out of, 21 ) receiving normal mouse sera developed paralysis compared to only 4 . 5% of mice ( n = 1 out of, 22 ) treated with EV-D68 immune sera ( p < 0 . 0002 by Fisher’s Exact Test ) ., There were no deaths in the immune sera treated group and 18% mortality in mice receiving normal mouse sera ( p = 0 . 05 by Fisher’s Exact Test ) ., We have described a mouse model of spinal cord infection and paralysis caused by clinical isolates of EV-D68 ., Of five 2014 EV-D68 strains tested , four strains induced a paralytic disease in mice following intracerebral injection ., An in-depth characterization of one of these strains , clade B1 MO/14-18947 , revealed that this paralysis replicated key features of human AFM including a lower motor neuron pattern of paralysis with a predilection for the upper limbs , limited motor recovery over time , and no sensory or cerebral involvement 13 , 16 , 19 , 20 ., MRI studies and electromyography of AFM patients suggest loss of motor neurons without damage to sensory pathways ( 12 , 17 , 24 ) ., Consistent with these findings , paralyzed mice exhibited no gross loss of sensory function , and viral antigen was detected almost exclusively in cells consistent with motor neurons as determined by ChAT staining , morphology , and anatomical location within the spinal cord ., EV-D68 appears to have a specific tropism for spinal cord motor neurons as demonstrated by the absence of significant growth in brain and the restricted pattern of antigen distribution and neuronal injury in the spinal cord ., The kinetics of viral growth closely paralleled the observed development of paralysis , suggesting that direct viral injury , rather than a post-infectious immune-mediated process is the most likely mechanism of neuronal cell loss and subsequent paralysis ., Fulfillment of Koch’s postulates supports a causal role of EV-D68 infection in the development of paralytic disease in this model ., We also established that EV-D68 neuroinvasion could occur by several alternative routes in addition to producing disease after intracerebral injection ., Although intranasal infection rarely produced paralytic disease , mice that developed signs of paralysis exhibited spinal cord infection similar to that seen following other routes of infection ., The rarity of paralytic disease following intranasal infection ( ~3% ) of EV-D68 is consistent with the low incidence of AFM after EV-D68 respiratory infection in humans ( likely <1% ) ., Surprisingly , intramuscular infection produced paralysis even more consistently than intracerebral infection ., As EV-D68 appears to have specific tropism for motor neurons , we hypothesize that intramuscular infection may be a more efficient and direct pathway to motor neuron infection than intracerebral injection ., The exact pathways and mechanism of spread for EV-D68 after virus inoculation at specific sites ( intramuscular , intracerebral , or intranasal ) remain to be fully elucidated ., In the current study , mice injected intramuscularly in either the forelimb or the hindlimb initially developed paralysis in the inoculated limb ., Similarly , the intranasally inoculated mice and the majority of the intracerebrally inoculated mice initially developed forelimb paralysis ., This pattern of paralysis onset after different routes of inoculation is most consistent with viruses that spread along neural pathways ., Neural spread typically involves initial infection of and injury to the segments of the spinal cord containing neurons innervating the site of inoculation 28 ., In contrast , viremic spread characteristically results in uniform involvement of motor neurons innervating forelimbs and hindlimbs , regardless of the site of inoculation 28 ., The rarity of paralysis following intraperitoneal inoculation ( a proxy for intravenous inoculation ) and the lack of infectious virus in sera of paralyzed mice in this model argue against a critical role for viremic spread in neuropathogenesis ., Notably , EV-D68 viremia has only been rarely identified in human AFM patients ( 1 in 25 , or 4% , as reported in one study ) 18 ., Ultimately , several mechanisms may facilitate spread ( neural or viremic ) depending on the route of infection , as have been described for polioviruses 29 ., To date , only EV-D68 strains from clade B1 have been isolated from AFM patients 18 ., A previous study identified six polymorphisms confined to AFM-associated clade B1 strains that could represent genetic changes associated with neurovirulence 18 ., However , in the current study , strains from multiple clades ( A , B , and B1 ) produced paralysis in neonatal mice ( a 2014 clade C strain was not available for testing ) ( S2 Fig ) ., These data indicate that the clade B1-specific polymorphisms and sequence homology alone do not fully explain paralysis in this mouse model ., In addition , the observed molecular epidemiologic association of clade B1 with AFM may be due to the high overall prevalence of this clade circulating in the population in 2014 18 ., Unexpectedly , one contemporary clade B strain , CA/14-4231 , failed to induce paralysis , despite its close phylogenetic relationship to a neurovirulent clade B strain , IL/14-18952 ( S2 Fig ) ., Failure to induce paralysis was not a result of the inability to infect mice ( S1 Table ) , suggesting that CA/14-4231 may lack genetic sequences critical for neurovirulence ., Further comparative analyses using infectious clones will likely to be needed to establish the determinants of neurovirulence and host range of EV-D68 in mice ., It would also be informative to test EV-D68 strains from Europe , North America , and Africa associated with smaller outbreaks in 2009/2010 ( clades A , B , and C ) , but these were not available for the current study 7 , 30 ., Furthermore , population factors , such as spread in a large immunologically naïve population and host-specific genetic susceptibility , also cannot be fully ruled out as contributors to AFM ., The previous finding of a sibling pair , both infected by identical strains of a clade B1 EV-D68 strain , yet only one developing AFM 18 , 19 , points to the potential importance of such host-related factors in viral neuropathogenesis ., Finally , a mouse model is an important first step in the in vivo screening of potential drug and vaccine therapies against EV-D68 , for which there are currently no established treatments ., In the current study , treatment of naïve neonatal mice with EV-D68 immune sera containing anti-EV-D68 antibodies , but not normal mouse sera , prevented paralysis and death after viral challenge ., Additional mouse studies testing specific antiviral agents or delaying passive antibody administration until later in the disease course are currently in progress ., The results presented here suggest that immunomodulatory strategies such as vaccination or the use of EV-D68 hyperimmune sera may be potentially effective strategies for treatment or prevention of EV-D68 associated neurological disease ., Our findings are of particular relevance given the recent surge in AFM cases in 2016 17 ., All studies were done in accordance with the University of Colorado IACUC and Animal Use Committee ( B-34716 ( 03 ) 1E ) ., Mice were cared for in adherence to the NIH Guide to the Care and Use of Laboratory Mice ., Mouse pups exhibiting paralysis were euthanized if unable to nurse ., Mice were anaesthetized with inhaled isoflurane before tissue collection or perfusion ., All EV-D68 viral strains were obtained from the American Type Culture Collection ( ATCC ) or from the California Department of Public Health ( courtesy of Shigeo Yagi ) ., Viral stocks were grown in RD cells ( ATCC ) at 33°C and 5% CO2 until most cells were dead or dying ., Cells debris was removed from RD grown stocks by ultracentrifugation ., Titers of viral stocks were determined by TCID50 assay as calculated by the Kärber method ., The TCID50/mL titers for each strain used in this paper are as follows: Fermon– 8x106 , Rhyne– 1x107 , MO/14-18947 ( clade B1 ) – 5x106 , CA/14-4232 ( clade B1 ) – 1x105 , IL/14-18952 ( clade B ) – 5x107 , CA/14-4231 ( clade B ) – 3x107 , KY/14-18953 ( clade A ) – 2x106 ., The pure culture stock used in the Koch’s postulate experiment was grown from whole spinal cord lysate on RD cells ., The stock was passed through a 0 . 22-micron sterile syringe filter before injection ., All experiments were performed on NIH Swiss Webster mouse pups of both sexes from Envigo ( Indianapolis , IN ) ., Mouse litters were randomly assigned to experimental groups ., Unless otherwise specified , virus infections were performed on two day-old mice ., For intracerebral infection , mice were injected with ~20 μL via insulin syringe ( 29 G needle ) with undiluted virus or RD control media stock into the right hemisphere just anterior to lamboid suture ., For intramuscular infection , mice were injected with ~20 μL via insulin syringe ( 29 G needle ) with undiluted virus into the medial aspect of the left hindlimb ., For intraperitoneal infection , mice were injected with ~20 μL via insulin syringe ( 29 G needle ) with undiluted virus into the peritoneal cavity ., For intranasal infection , a total of 40 μL of undiluted virus was micro-pipetted onto the noses of a post-natal day 2 pups in two boluses of 20 μL with a 30-minute interval between exposures ., Mice were examined daily for signs of paralysis ., For passive transfer experiments , 100 μL of pooled sera was given by intraperitoneal injection via insulin syringe on post-natal day 1 to mice randomized between two litters ., Immune sera was pooled from dpi 28 mice ( n = 12 ) previously injected intracerebrally with US/MO/14-18947 ., Control sera were pooled from dpi 28 mice ( n = 12 ) previously injected intracerebrally with RD control media ., The passive antibody transfer experiment data is displayed as a combination of two replicate experiments ., Mice were randomized between control and experimental conditions for each replicate ., In all studies , no mice were excluded from analyses ., Serum was collected for TCID50 analysis and neutralizing antibody titer analysis ., After collection , whole blood was placed immediately on ice , spun at 14 , 000 rpm for 10 minutes at 4°C , and then the serum was removed and placed in a fresh tube ., Serum was stored at -20°C if used for antibody neutralization or at -80°C if used for TCID50 analysis ., Brains were removed intact and placed in a BeadBug tissue homogenizer tubes ( Benchmark Scientific , Edison , NJ ) with 3 . 0 mm beads and brought to a standard volume of 1 mL with PBS ., Spinal cords were removed intact as previously described and placed in a BeadBug tissue homogenizer tubes with 3 . 0 mm beads and brought to a standard volume of 0 . 3 mL with PBS 32 ., Muscle tissue ( anterior and posterior lower leg muscles ) was removed and weighed , and it was then brought to a standard volume of 0 . 3 mL of PBS ., Tissues were then mechanically lysed in the BeadBug microtube homogenizer ( Benchmark Scientific , Edison , NJ ) ., 60 μL of each tissue was removed for TCID50 analysis ., Titer of virus in each tissue was determined by TCID50 assay and final viral titer was calculated using the Kärber method ., Lack of growth below the detectable limit was graphed as a titer of zero ., For RT-PCR , total RNA from the remaining spinal cord sample was extracted using a Qiagen RNeasy Plus Micro Kit with RNA carrier ( Hilden , GER ) to facilitate RNA pull-down from this small tissue ., Total RNA from each spinal cord sample was used to make cDNA ., Each spinal cord sample was converted to cDNA using a Bio-Rad iScript RT supermix ( Berkley , CA ) ., Equivalent volumes of cDNA were used for PCR with degenerate primers targeting the VP1 gene ( 20 ) ( PCR protocol: 95°C 3 min , 40x cycles of: 95°C 10 sec , 53°C 30 sec , 72°C 30 sec , melt curves: 65–95°C in 0 . 5°C steps ) ., Samples were compared to a plasmid standard curve 11 ., Temperature melt curves temperature and slope were used to assess the quality of each sample ., The starting genome copy number for each dilution of the standard curve was estimated using

Introduction, Results, Discussion, Materials and methods

In 2014 , the United States experienced an epidemic of acute flaccid myelitis ( AFM ) cases in children coincident with a nationwide outbreak of enterovirus D68 ( EV-D68 ) respiratory disease ., Up to half of the 2014 AFM patients had EV-D68 RNA detected by RT-PCR in their respiratory secretions , although EV-D68 was only detected in cerebrospinal fluid ( CSF ) from one 2014 AFM patient ., Given previously described molecular and epidemiologic associations between EV-D68 and AFM , we sought to develop an animal model by screening seven EV-D68 strains for the ability to induce neurological disease in neonatal mice ., We found that four EV-D68 strains from the 2014 outbreak ( out of five tested ) produced a paralytic disease in mice resembling human AFM ., The remaining 2014 strain , as well as 1962 prototype EV-D68 strains Fermon and Rhyne , did not produce , or rarely produced , paralysis in mice ., In-depth examination of the paralysis caused by a representative 2014 strain , MO/14-18947 , revealed infectious virus , virion particles , and viral genome in the spinal cords of paralyzed mice ., Paralysis was elicited in mice following intramuscular , intracerebral , intraperitoneal , and intranasal infection , in descending frequency , and was associated with infection and loss of motor neurons in the anterior horns of spinal cord segments corresponding to paralyzed limbs ., Virus isolated from spinal cords of infected mice transmitted disease when injected into naïve mice , fulfilling Koch’s postulates in this model ., Finally , we found that EV-D68 immune sera , but not normal mouse sera , protected mice from development of paralysis and death when administered prior to viral challenge ., These studies establish an experimental model to study EV-D68-induced myelitis and to better understand disease pathogenesis and develop potential therapies .

Reports of polio-like paralysis , referred to as acute flaccid myelitis ( AFM ) , have recently emerged in association with infections caused by enterovirus D68 ( EV-D68 ) ., In the second half of 2014 , 120 cases of AFM , mostly in young children , were reported during a nationwide outbreak of EV-D68 respiratory disease ., The number of AFM cases has risen again in 2016 ., Although epidemiological evidence between EV-D68 infection and AFM is accumulating , a causal link has not been definitely established ., Here we demonstrate that strains of EV-D68 recovered during the 2014 epidemic can cause a paralytic illness in mice that resembles human AFM ., Evidence that EV-D68 causes paralysis in this mouse model include: ( 1 ) loss of spinal cord motor neurons innervating paralyzed limbs , ( 2 ) detection of virus in the spinal cord and , specifically , motor neurons , ( 3 ) transmission of neurological disease when injecting virus isolated from spinal cords of paralyzed mice into naïve mice , thus fulfilling Koch’s postulates , and ( 4 ) the ability to prevent AFM by pre-administering serum containing EV-D68 antibodies from previously infected mice ., This experimental mouse model can be used to better understand the pathogenesis of EV-D68-induced CNS disease and to facilitate the development of potential therapies .

medicine and health sciences, respiratory infections, nervous system, neuroscience, pulmonology, motor neurons, animal models, model organisms, experimental organism systems, mammalian genomics, research and analysis methods, sequence analysis, spinal cord, infectious diseases, sequence alignment, animal cells, bioinformatics, enterovirus infection, mouse models, animal genomics, cellular neuroscience, dna sequence analysis, neuroanatomy, anatomy, cell biology, database and informatics methods, neurons, genetics, biology and life sciences, cellular types, viral diseases, genomics

journal.pbio.1001412

The Molecular Basis for Recognition of CD1d/α-Galactosylceramide by a Human Non-Vα24 T Cell Receptor

Natural killer T ( NKT ) cells are a highly conserved lineage of T lymphocytes found in both human and mice that are involved in the modulation of the immune response in autoimmunity , infection , and tumor development 1 ., Unlike conventional CD4+ and CD8+ αβ T cells that recognize peptides presented by MHC molecules , NKT cells are reactive to a broad range of self and foreign lipids displayed by the MHC class I–like molecule CD1d 2 , 3 ., This reactivity is initiated by the recognition of the CD1d-lipid complex via the NKT T cell receptor ( NKT-TCR ) followed by Th1 and/or Th2 biased cytokine secretion that can regulate the activity of other immune cells such as conventional αβ T cells , B cells , and Natural Killer ( NK ) cells 4 ., The most extensively studied NKT cells in humans and mice are invariant ( iNKT ) or type I NKT cells that express TCRs composed of a highly conserved α chain encoded by a Vα24-Jα18 rearranged gene segment in humans and Vα14-Jα18 in mice ., This invariant α chain is covalently paired with a β chain in which the variable region is encoded in humans by the Vβ11 gene and can be Vβ8 , Vβ7 , or Vβ2 in mice 1 ., NKT cells expressing these TCRs have a pre-activated phenotype that is due to the expression of the transcription factor pro-myelocytic leukemia zinc finger ( PLZF ) 5 , 6 and are also characterized by high reactivity towards the potent stimulatory lipid antigen α-galactosylceramide ( αGalCer ) 7 ., In both humans and mice there are additional classes of T cells that respond to CD1d , one that expresses diverse TCRs but do not respond to αGalCer; these are generally called Type II or non-invariant NKT cells 8 ., These NKT cells are typically reactive to lipid antigens such as sulfatide and use an entirely different molecular strategy for recognizing the CD1d/lipid complex 9 , 10 ., A third group of T cells exist that do respond to CD1d presenting αGalCer and also express TCRs different from that of the iNKT-TCR ., In mice these NKT cells express a TCR comprised of a Vα10-Jα50/Vβ8 pair 11 ., These cells are called Vα10 NKT cells and show a preference for α-glucosylceramide ( αGlcCer ) over αGalCer; indeed , Vα10 NKT cells can produce a several magnitudes greater cytokine response relative to iNKT cells when stimulated by the related α-glucuronosyldiacylglycerl ( α-GlcA-DAG ) 11 ., In humans this third group of CD1d reactive T cells express TCRs with many different Vα domains joined with Jα18 , paired with the Vβ11 domain 12 , 13 ., In contrast to both Type I and Type II NKT cells , these T cells do not typically express CD161 , a Natural Killer cell marker found on NKT cells 13 ., They have been called Vα24− NKT cells or CD1d-restricted , Vα24− T cells due to their use of alternative Vα domains rearranged to Jα18 , paired with the Vβ11 domain in their TCRs ., These cells are found in all individuals sampled 13 at appreciable frequency ( ∼10−5 ) 14 and express either the CD8αβ or CD4 co-receptors , can be cytotoxic , and can secrete IL-2 , IFN-γ , and IL-13 ( and in some cases IL-4 ) 13 ., In contrast to human iNKT cells , they express low to intermediate levels of PLZF and have a naïve phenotype 14 ., Importantly , these NKT cells have shifted lipid specificities from that of iNKT cells with an inability to recognize and respond to αGlcCer 12 ., The distinctive difference in reactivity between αGalCer and αGlcCer suggests that this population of NKT cells focuses on a different repertoire of lipid antigens than those of iNKT cells ., Despite the variability that exists in NKT cell populations , most of our current knowledge of NKT cell recognition of antigen derives from structural studies that have focused on self and foreign lipid antigen recognition by Type I iNKT TCRs 15 ., iNKT-TCRs recognize , through their complementary determining regions ( CDR ) loops , a composite surface composed of the α-helices of CD1d and the solvent exposed head group of the CD1d-presented lipid antigens ., The CDR3α loop plays a prominent , conserved role in CD1d-lipid recognition , predominantly via residues encoded by the Jα18 segment , which is found in all iNKT TCRs ., There are also important contributions from the CDR1α and CDR2β loops , which explain the restricted use of specific Vα and Vβ domains ( which encode the CDR1 and CDR2 loops ) 16 , 17 ., For each Vβ chain used in mouse , the docking of iNKT-TCRs on the CD1d/lipid antigen surface is remarkably conserved 18 , 19 , indeed variation of the lipid antigen is accommodated mainly through structural modifications of the lipid antigen as opposed to changes in the iNKT TCR footprint 20–24 ., The number of human iNKT TCR complex structures are fewer yet reflect some flexibility in docking of the iNKT TCR depending on the lipid antigen 16 , 19 , 23 , 25 , yet appear to be similarly anchored via conserved positioning of the CDR3α loop ., The crystal structure of a murine Vα10 NKT TCR in complex with murine CD1d-αGlcCer 11 has shed light onto the molecular mechanisms that murine non-canonical NKT TCRs use to recognize CD1d ., Despite significant sequence divergence in the α chain amino acid sequence ( 40% sequence identity ) , the Vα10 NKT TCR assumes a very similar docking mode to that of the iNKT TCR on CD1d ., However , unlike the iNKT TCR , all CDR loops of the Vα10 NKT TCR contribute to CD1d/αGlcCer recognition , with seemingly important contacts being contributed by the CDR2β and CDR3β loops ., Thus the two Vα chains of these divergent murine NKT cell populations ( iNKT and Vα10 ) have convergently evolved a similar molecular strategy for recognizing CD1d ., Recently , crystal structures of the Type II NKT TCR recognition of CD1d presenting sulfatide 9 and lysosulfatide 10 provided an interesting contrast to the conserved recognition of CD1d by the iNKT and murine Vα10 TCRs ., The Type II TCRs use all six CDR loops in CD1d/ligand engagement and dock on a separate site on CD1d , concentrating on residues surrounding the A′ pocket ., Thus , NKT cells have a range of docking modes used in CD1d/ligand engagement ., Structural data on NKT cell recognition in humans remains limited , and information of how Vα24− T cells recognize CD1d/lipid is , to our knowledge , absent ., To better understand how this functionally distinct human T cell population recognizes CD1d/lipid , we have co-crystallized a Vα24− TCR with CD1d/αGalCer and present here the structure of this complex resolved to 2 . 5 Å resolution ., This structure provides an excellent model by which to understand how functionally distinct human T cells , via their TCR , can recognize CD1d with a shifted specificity from that found in the iNKT cell population ., In order to understand the molecular basis of Vα24− TCR recognition of CD1d , we expressed a soluble , heterodimeric version of the extracellular domains of the J24 . N22 TCR 12 , which uses the Vα3 . 1 ( TRAV17 ) gene segment rearranged with Jα18 complexed with Vβ11 , in insect cells ., The purified TCR was co-crystalized with recombinant , soluble CD1d loaded with αGalCer; X-ray data were collected to 2 . 5 Å , and the structure was solved via molecular replacement ., Data collection and refinement statistics are listed in Table 1 ., One TCR/CD1d/αGalCer ternary complex was identified in the asymmetric unit ., All components of this complex were well resolved in the electron-density , enabling unambiguous assignment of TCR-CD1d/lipid antigen contacts ., Table 2 presents a comparison between the amino acid sequences of the α and β CDR loops of the Vα24− ( Vα3 . 1+ ) TCR studied here and an iNKT Vα24+ TCR studied previously 25 ., Vα3 . 1 and Vα24 share 46% amino acid identity overall , with only 33% ( 2/6 ) identity at the CDR1α and 15% ( 1/7 ) at the CDR2α loop ., However , the shared usage between these TCRs of the Jα18 segment and the canonical DRGSTLGR motif that it encodes gives high sequence identity to the CDR3α loops of these TCRs with different residues encoded only at the Vα-Jα junction , with ATY and VVS motifs in the Vα24− and Vα24+ TCRs , respectively ., The Vβ11 domain is also shared between these TCRs; therefore , the CDR1β and CDR2β sequences are identical ., However , the rearranged CDR3β loops differ due to differences introduced during the rearrangement process ., Overall , the Vα24− TCR recognizes CD1d/αGalCer with the α and β chains oriented on CD1d in a parallel fashion unlike the typical diagonal mode of MHC-I peptide-TCR complexes and similar to that of iNKT-TCR and Vα10 NKT-TCR in complex with CD1d/αGalCer ( Figure 1A and 1B ) 11 , 16 , 19 ., However , the binding angle of the Vα24− TCR in relation to the CD1d/αGalCer surface is more acute than the almost perpendicular orientation observed with the Vα24+ iNKT TCR-CD1d/αGalCer structure ( Figure 1A ) 16 , 19 ., The CDRα loops adopt a similar yet slightly shifted footprint for the α-chain , yet the β-chain CDR loop positioning is counter-clockwise rotated compared with the Vα24+ TCR complexed with αGalCer 16 , 19 , which is even more extreme than rotations observed in structures of human NKT-TCRs complexed with CD1d presenting LPC or βGalCer ( Figure 1B ) 23 , 25 ., The TCR-CD1d-lipid contacts mostly fall in the F′ pocket area of the CD1d molecule ( Figure 1C ) , where there are slight differences in TCR contact surface between the Vα24− and Vα24+ ., The total buried surface area ( BSA ) between the Vα24− TCR and the CD1d-αGalCer complex was 747 A2 , which is slightly smaller than the previously reported interface area for the Vα24+ TCR , ∼910 A2 ., This difference is more pronounced in the β-chain loops with ∼37% less contribution in the Vα24− complex ( 205 . 7 A2 versus 325 . 3 A2 for the Vα24− and Vα24+ , respectively ) ., The conformation and positioning of αGalCer presented by CD1d is almost identical in both complexes with the Vα24+ and the Vα24− TCRs ., The sphinosine base and acyl chain of αGalCer fall in the F′ and A′ pockets , respectively ( Figure 1D ) ., The αGalCer headgroup also adopts a very similar conformation , with solvent exposed with the sugar oxygens displayed for recognition by the TCR ., The conformation of the α helical side chains of CD1d were also highly conserved between the Vα24+ and Vα24− complex structures , with only a few exceptions that are noted later in the text ., In all three human iNKT TCR-CD1d/lipid complexes that have been resolved to date , the CDR1α loop makes important contacts with the lipid headgroup 16 , 19 , 23 , 25 ., In recognition of αGalCer and βGalCer the Oγ of Ser30 and the mainchain carbonyl oxygen of Phe29 make hydrogen-bonds ( some water-mediated ) with the 3′OH of αGalCer and βGalCer , and in the case of LPC , the Oγ Ser27 and the mainchain carbonyl oxygen of Phe29 establish hydrogen bonds with the phosphate oxygens of the phosphorylcholine headgroup ., Pro28 establishes van der Waals ( VDW ) contacts with the galactose headgroup; mutagenesis of this residue has a marked effect on recognition but is likely due to global structural changes in the conformation of the TCR as this mutation also disrupted binding of a conformational-specific antibody 17 ., In our structure the Vα24− CDR1α loop is slightly shifted from the Vα24+ CDR1α loop ( Figure 1B ) ; therefore , the equivalent structural positions to the Vα24+ S27P28F29S30 motif are T26S27I28N29 in Vα24− ., Despite the chemical and structural differences of the CDR1α loops between these TCRs , specific side-chain-mediated hydrogen bonds are still formed in the Vα24− CDR1α loop , both with the galactose headgroup of αGalCer and through VDW contacts with CD1ds Val72 ( Figure 2A and Table 3 ) ., The shifted position of Ser27 in this complex enables a hydrogen bond between its Oγ with the 6′OH of αGalCer , whereas the Nδ2 of Asn29 hydrogen bonds with the 3′OH and 4′OH of αGalCer and Asn29 also forms VDW contacts with the galactose headgroup ., Therefore , alternative residues in the CDR1α loop are effectively used in recognition of αGalCer with a focus on the 4′OH of the galactose ring , with a novel contact with CD1d also noted ., We have also noted residues in the CDR2α loop that make water-mediated contacts with the αGalCer galactose headgroup: Ser50 and Asn51 both establish water-mediated hydrogen bonds with the 4′OH of αGalCer ( Figure 2B ) ., In the other human complexes , Phe51 of the Vα24+ CDR2α loop makes VDW contacts with both βGalCer and LPC , however hydrogen bonds have not been noted for the CDR2α loop of Vα24+ TCRs ., In contrast to the sequence and contact differences at the CDR1α and CDR2α loops , the residues of the CDR3α loop in the Vα24− TCRs adopt a similar conformation to that of the Vα24+ iNKT TCRs ( Figure 2C ) ., Yet despite the similarity in footprint , the Vα24− CDR3α loop establishes fewer contacts with CD1d and αGalCer than does the CDR3α loop of the iNKT TCR ( Table 3 ) ( 25 instead of 32 , respectively , for CD1d and eight instead of 19 , respectively , for αGalCer ) ., There are fewer hydrogen bonds ( two versus eight with CD1d and one versus four with αGalCer ) and , in the case of αGalCer , fewer than half ( seven versus 15 ) VDW contacts of those observed in the Vα24+ complex ., The residues of the Vα24+ CDR3α were previously shown to be energetically critical for CD1d/αGalCer recognition 17 , a finding recapitulated in our data ( discussed further below ) despite the lower contact number ., While the CDR3α loop serves to anchor human iNKT TCRs on the CD1d/lipid platforms with highly similar conformations 16 , 19 , 23 , 25 , the remaining loops have demonstrated rotational flexibility in how they are positioned over the CD1d/lipid surface , in particular at the CDR2β , which establishes energetically critical contacts with CD1d 17 ., A similar rotation is seen in the Vα24− TCR docking on the CD1d/αGalCer platform in the complex structure presented here ( Figure 1B and Figure 3A ) ., As in the Vα24+ complexes , the involvement of the CDR2β loop in CD1d binding is predominantly mediated by Tyr48 and Tyr50 ., Despite an average shift of 4 . 6 Å between the Vα24− and Vα24+ CDR2β CA backbones , the rotationally flexible tyrosine side chains maintain highly similar contacts between the two complexes ( Figure 3A ) ., Glu83 on CD1d takes a central role in contact with the CDR2β in both complexes , establishing a hydrogen-bonded network with both Tyr48 and Tyr50 hydroxyls ., Met87 also contributes VDW contacts with Tyr50 in both complexes ., However , in contrast to the Vα24+ complex , where Glu56 of the CDR2β establishes a robust salt-bridge with Lys86 of CD1d ( 3 . 7 Å distance ) , in the Vα24− complex Lys86 has shifted such that is it 4 . 6 Å from Glu56 ( Figure 3A ) ., Thus , the critical contacts of the CDR2β loop are maintained in the Vα24− complex despite large main chain shifts of the CDR2β backbone ., The highly variable CDR3β loop has been demonstrated to confer reactivity to specific lipids presented by CD1d by both human 26 and mouse 27 iNKT cells ., In the Vα24− complex , the CDR3β loop is well resolved in the electron density and establishes only one weak hydrogen bond and a VDW contact with Gln150 on CD1ds α2-helix via Ser97 ( Figure 3B ) ., Thus , unlike the murine Vα10 NKT TCRs , which have CDR3β sequence specificity and use this loop in CD1d binding , this Vα24− TCR does not appear to rely heavily on its CDR3β loop for binding ., The availability of a Vα24− TCR also expressing a Vα3 . 1 domain ( named 5B ) 28 in the unliganded state allows a direct comparison between the loop structures between the TCR examined here ( bound to CD1d ) and a Vα24− , Vα3 . 1+ , TCR in its unbound state ., Due to the use of different Jβ gene segments that results in global domain orientation shifts , the TCRs are not perfectly superimposable ( Figure 4A ) and there are two amino acid differences in the CDR3α sequences of these TCRs due to junctional diversity ( Figure 4B ) ., Alignment of the two Vα3 . 1 domains shows the CDR1 and CDR2 loops are essentially identical structurally ( Figure 4B ) , yet examination of the CDR3α loops ( Figure 4B ) shows significant structural differences ., While the unliganded structure of J24 . N22 is not known , modeling of the 5B TCR onto our complex structure suggests a large shift in loop conformation would need to occur in the CDR3α loop for it to dock onto CD1d/αGalCer in a similar fashion ., Because of the similarities between these TCRs in all other loops save the CDR3β , it is very likely that the 5B TCR would dock in a similar fashion as seen here ., Thus in contrast to the Vα24+ NKT TCRs recognition of CD1/αGalCer , where loop conformation was highly conserved in the liganded and unliganded state , we suggest that the CDR3α loop can be flexibile in Vα3 . 1+ , Vα24− TCRs , similar to what was previously seen in the iNKT TCR recognition of CD1d/LPC 25 ., To evaluate the kinetics involved in binding of our Vα24− TCR with CD1d/αGalCer , we used surface plasmon resonance to measure the association ( kon ) and dissociation rates ( koff ) of this interaction and determine the dissociation constant ( KD ) ( Figure 5A ) ., We also used this to calculate KD by equilibrium analysis ( Figure 5A , insets ) ., We included an iNKT ( Vα24+ ) TCR in our kinetic measurements such that we could compare these values to a representative of the iNKT population ., The affinity of the Vα24− TCR used in this study for CD1d/αGalCer ( 2 . 1 µM kinetic , 2 . 5 µM equilibrium ) was similar to the affinity we measured for the iNKT TCR ( 2 . 1 µM kinetic , 1 . 9 µM equilibrium ) as well as affinities from previous measurements with Vα24− TCRs ( using Vα3 . 1 and Vα10 . 3 domains ) 28 ., Stronger affinities ( 0 . 5 µM ) have been noted for other human iNKT TCRs 17 ., We sought to further evaluate the residues contributing most to Vα24− TCR binding to CD1d/αGalCer ., We chose key TCR residues identified as interacting with CD1d/αGalCer in our complex and evaluated their contribution to binding via alanine-scanning mutagenesis and SPR ., We first evaluated the CDR1α loop residues Ser27 and Asn29 , as these appeared to mediate the side-chain-specific contacts that differed most from the Vα24+ TCRs ., While mutation of Ser27 to Ala ( S27A ) did not drastically change Vα24− TCR binding kinetics , mutating Asn29 to Ala ( N29A ) resulted in a significant disruption to binding with changes in both the association and dissociation rates and an increase in the KD by an order of magnitude ( Figure 5B ) ., Thus the CDR1α loop provides a clear contribution to Vα24− TCR binding to CD1d/αGalCer ., Previous mutational analysis of the CDR1α loop of a Vα24+ TCR 17 of Pro28 to Alanine disrupted binding , however this was assumed to be due to changes in the TCR architecture as conformational-specific antibodies failed to bind this mutant ., Mutation of the CDR2α side chains Ser50 and Asn51 had subtle effects on kon and koff ( Figure 5B ) yet did not appear to have a substantial effect on the overall affinity of CD1d/αGalCer binding , similar to what we observed with mutation of Ser97 in the CDR3β loop sequence ., Because of the similarities in CDR3α loop contacts between Vα24− and Vα24+ TCRs , we included a mutation of Arg95 of the CDR3α as a positive control; this side chain has been shown to be central to iNKT TCR binding to CD1d/αGalCer 17 ., We also observed that mutation of this side chain to Ala ( R95A ) abrogated binding of the Vα24− TCR and thus supports the importance of the CDR3α loop to Vα24− TCR docking ., Our complex structure of a Vα24− TCR with CD1d/αGalCer provides a model by which to understand how this diverse population of CD1d-restricted human T cells recognize antigen ., These cells differ from iNKT cells in their specificity , effector function , and the markers expressed on their cell surface; these factors combined argue that these cells provide another arm of T-cell-mediated lipid recognition in humans ., Here we provide a structural and biophysical foundation upon which to understand the molecular basis of differential reactivity observed at the cellular level in this NKT cell population ., Despite the divergent amino acid sequences encoded by the Vα3 . 1 domain for the CDR1α and CDR2α loops , the Vα24− TCR adopts a similar footprint to that of Vα24+ iNKT TCRs ., This docking orientation is primarily dictated by the conserved docking of the CDR3α loop , containing the highly similar sequence encoded by the Jα18 segment of iNKT TCRs ., The contacts mediated by the other loops , while not identical to those of iNKT TCRs , were very similar , suggesting that despite sequence differences in the Vα loops they could establish contacts with similar regions of the CD1d/αGalCer surface ., The αGalCer headgroup position was almost identical to that observed in the iNKT complex structures 16 , 19 ., This docking mode , also shared with that of the murine Vα10 NKT TCR 11 , is strikingly different from that of the recently resolved type II NKT TCR structures 9 , 10 , where the TCRs dock on an entirely different surface of CD1d ( the A′ pocket ) and use all six of the TCRs CDR loops in recognition ( similar to what is observed in conventional αβ TCR recognition of MHC/peptide ) ., These structures demonstrate that CD1d-restricted T cells can use at least two divergent ways to recognize their antigens 29 ., Our complex structure provides a useful model to compare other Vα24− TCRs structures , notably the structure of a highly related unliganded TCR called 5B 28 ., If we assume the 5B TCR would dock similarly to the Vα24− TCR examined in our study , a significant conformational change would have to occur in 5Bs CDR3α loop ., This conformational flexibility was a feature we also observed in human iNKT TCR binding to CD1d/LPC 25 ., In contrast to what was observed with the iNKT TCR complex structure with CD1d/αGalCer 16 , 19 , this suggests that not all CD1d-TCR interactions are “lock and key” and that changes to CDR3α loop conformation may contribute to differences in binding kinetics and thermodynamics ., A similar phenomenon of loop movement was observed in the murine Vα10 NKT TCR upon binding 11 ., The CDR3α loop footprint on CD1d/αGalCer is conserved in all the iNKT-TCR/CD1d structures noted to date as it is here ., However , the number of contacts in this complex structure were less than that observed in the iNKT-TCR CD1d/αGalCer complex structure , yet the binding affinities measured for the Vα24+ and Vα24− TCRs in this study did not differ substantially ( ∼2 µM for both TCRs ) ., The alanine-scanning mutagenesis revealed important contributions from the CDR1α loop ( in particular , residue N29 ) in the Vα24− TCR binding that were not noted in Vα24+ TCR binding ( mutation of the equivalent position , S30 in the Vα24+ TCR , showed little effect 17 ) ., This shift of importance toward the CDR1α likely compensates for fewer CDR3α loop contacts and would explain the altered reactivity patterns of Vα24− TCRs for lipids that are recognized similarly by Vα24+ TCRs ( such as αGlcCer and αGalCer , discussed more below ) ., We cannot rule out that contributions from other loops , such as the CDR2α and CDR3β , contribute as well; while individual mutagenesis of these residues had small effects upon TCR binding , in combination they may have a cumulative effect in binding CD1d/lipid , evident only when they are mutated in concert ., Extensive studies in the mouse iNKT cell system have revealed how lipid ligands are structurally modified during recognition by the iNKT TCR ., Even though extensive structural variability exists in the glycolipid headgroups , each carbohydrate structure adopts a similar orientation when bound by the TCR 20–24 ., Therefore , contributions of the CDR1α in recognition of alternative lipids , both α- and β-linked glycolipids , could be an important factor in Vα24− T cell reactivity towards different lipids ., Directly relevant to this point is the clear distinction between Vα24− T cells and Vα24+ iNKT cells in their differential reactivity to the α-linked glycolipids αGlcCer and αGalCer ., Vα24+ iNKT cells respond well to both lipids , whereas Vα24− T cells do not respond to αGlcCer ., The only difference present between these two lipids is the orientation of the 4′OH group on the sugar ring ( glucose versus galactose ) ., Our structural and biophysical data provide an explanation for this difference in reactivity ., Asn29 , a residue in the Vα24− CDR1α , establishes both VDW and hydrogen bonds with the 3′OH and 4′OH ., Mutation of this residue to alanine results in an order of magnitude decrease in binding of the Vα24− TCR , presumably due to disruption of these contacts ., Furthermore , the CDR2α loop residues Ser50 and Asn51 establish water-mediated hydrogen bonds with the 4′OH that may help to stabilize the interaction despite lacking clear energetic contributions ( as assessed in our alanine-mutagenesis studies ) ., We therefore propose that modification to the 4′OH between the galactose ( αGalCer ) and glucose ( αGlcCer ) structure is the primary molecular factor mediating the differences in reactivity of the Vα24− population of CD1d-restricted T cells ., The alternative contacts with the carbohydrate headgroup in the iNKT TCR/CD1d/αGalCer structure may explain why iNKT cells can respond to both lipids; the main contacts with the 4′OH are mediated by Ser30 , which when mutated to alanine only had a minimal effect on binding 17 ., The greater number of contacts and BSA of the Vα24+ TCR CDR3α loop on CD1d/αGalCer may make these T cells relatively insensitive to variation in the glycolipid headgroup at other positions ., The difference in 4′OH recognition may translate to alternative reactivity to other glycolipid and non-glycolipid lipid structures both in development of these T cells in the thymus and their effector functions in the periphery ., Despite their shared use of Jα18 and Vβ11 , the Vα24− T cells are differentiated from iNKT cells in their development and activation state; presumably altered TCR recognition of a selecting antigen during thymic development plays a role in these differences ., Our structure provides a model by which to understand the molecular basis of this altered reactivity ., Our results , which focus much of the differences in reactivity to αGlcCer on the CDR1α loop and its interaction with the 4′OH , contrast with the murine Vα10 NKT cell preferred reactivity to αGlcCer 11 , where preference in binding appears due to many factors ., The highly convergent recognition of αGlcCer by these TCRs distributes the binding contacts over much of the CDR loop surfaces 11 ., While mutagenesis data for these residues are not available , it is clear there are differences in the nature of the contacts between the Vα10 and iNKT TCRs with CD1d ( VDW versus hydrogen bonds ) , that many new contacts are established with CD1d , and therefore modification to the sugar ring may have more of a distributed effect over the Vα10 NKT interaction than what we observe in our Vα24− TCR complex structure ., Both structures , however , provide molecular models for the observed differences in lipid reactivity and demonstrate how divergent NKT TCR structures can convergently recognize similar CD1d/lipid antigen structures ., The molecular basis of the differences in recognition we have described here are the first clues into understanding why Vα24− cells are developmentally and functionally distinct from the iNKT population ., The ectodomain region of human CD1d and human β2microglobulin ( β2m ) were co-expressed in insect cells and purified as described 25 ., The cDNAs corresponding to the α and β chains of the Vα24+ NKT TCR clone J24L . 17 and the α and β chains of Vα24− TCR clone J24N . 22 were separately cloned into different versions of the pAcGP67A vector each containing a 3C protease site followed by either acidic or basic zippers and a 6xHis tag ., Both chains were co-expressed in Hi5 cells via baculovirus transduction ., The heterodimeric TCRs was captured with Nickel NTA Agarose ( Qiagen ) and further purified by anion exchange and size-exclusion chromatography ., Mutants of the Vα24− TCR ( S27A , N29A , S50A , N51A , R95A for the alpha chain , and S97A for the beta chain ) were generated through overlapping PCR with specific primers containing the desired mutation ., Mutant heterodimeric TCR was expressed in insect cells as described above ., Purified human CD1d was used for loading with αGalCer at room temperature with a three molar excess of lipid for 16 h ., The excess of lipid was then removed with a Superdex 200 ( 10/30 ) column ( GE Healthcare ) ., A human CD1d construct bearing a 3C protease site + 6X-Histidine tag at the C-terminus was expressed in Hi5 cells and purified as described 25 ., All interaction experiments were performed in a BIAcore 3000 Instrument ( GE Healthcare ) ., Three hundred RUs of wild-type Vα24− NKT TCR or a mutant version of it were captured in a flow channel of an Ni-NTA sensor chip ( GE Healthcare ) previously treated with NiCl2 ., Insect-cell-derived recombinant IgFc was used to block unbound sensor chip surface to minimize nonspecific binding events ., Increasing concentrations ( 0 , 0 . 037 , 0 . 111 , 0 . 333 , 1 , 3 , 9 , and 27 µM ) of CD1d–αGalCer were injected at a flow rate of 30 µl/min in 10 mM Hepes pH 7 . 4 , 150 mM NaCl , and 0 . 005% Tween-20 ., Both kinetic and equilibrium parameters were calculated off of these curves using BIAevaluation software 3 . 2RC1 ( GE Healthcare ) and GraphPad Prism ., Nickel agarose-purified Vα24− TCR was digested with 3C protease for 16 h at 4°C to remove the zippers and His tags and purified by anion exchange chromatography in a MonoQ column ( GE Healthcare ) ., Endoglycosidase F3 ( EndoF3 ) was used next at a 1∶10 enzyme-to-protein ratio for 2 h at 37°C in order to minimize the sugar content present in the protein ., The digested protein was purified by a new round of anion exchange followed by size-exclusion chromatography ., Both αGalCer-loaded CD1d and EndoF3-treated Vα24− TCR protein samples were mixed in HBS at 1∶1 molar ratio and concentrated in Nanosep Centrifugal Devices ( Pall Life Sciences ) to 10 mg/ml ., Initial hits were found in 0 . 1 M sodium acetate , 20% PEG 4000 , and were optimized to birefringent crystals that grew in 0 . 1 M sodium acetate pH 5 . 0 , 17% PEG 4000 , and 0 . 1 M ammonium acetate ., Crystals were cryo-cooled in mother liquor supplemented with 20% glycerol prior to data collection ., All data sets were collected on a MarMosaic 300 CCD at the LS-CAT Beamline 21-ID-G at the Advanced Photon Source ( APS ) at Argonne National Laboratory and processed with HKL2000 30 ., The structure of the ternary complex was solved by molecular replacement with the program Phaser 31 using the human CD1d–β2m ( Protein Data Bank ( PDB ) accession number 1ZT4 ) and an iNKT Vα24+ TCR ( 2EYS ) as search models ., Refinement with Phenix software suite 32 was initiated through rigid body and followed with XYZ coordinates and individual B-factor refinement ., These first steps of refinement yielded clear unbiased and continuous density for αGalCer ., Next , extensive cycles of manual building in Coot 33 and refinement were carried out and ligands such as αGalCer or covalently bound sugars were introduced guided by Fo−Fc positive electron density ., Ligands structures and chemical parameters were defined with C . C . P . 4 . s Sketcher 34 and included in subsequent refinement and manual building steps ., Translation/libration/screw ( TLS ) partitions were calculated and incorporated at later refinement stages ., All the refinement procedures were performed taking a random 5% of reflections and excluding them for statistical validation purposes ( Rfree ) ., Intermolecular contacts and distances were calculated using the program Contacts from the CCP4 software package 34 , interface surface areas were calculated using the PISA server ( http://www . ebi . ac . uk/msd-srv/prot_int/pistart . html ) , and all structural figures were generated using the program Pymol ( Schrödinger , LLC ) ., Coordinates and structure factors

Introduction, Results, Discussion, Materials and Methods

CD1d-mediated presentation of glycolipid antigens to T cells is capable of initiating powerful immune responses that can have a beneficial impact on many diseases ., Molecular analyses have recently detailed the lipid antigen recognition strategies utilized by the invariant Vα24-Jα18 TCR rearrangements of iNKT cells , which comprise a subset of the human CD1d-restricted T cell population ., In contrast , little is known about how lipid antigens are recognized by functionally distinct CD1d-restricted T cells bearing different TCRα chain rearrangements ., Here we present crystallographic and biophysical analyses of α-galactosylceramide ( α-GalCer ) recognition by a human CD1d-restricted TCR that utilizes a Vα3 . 1-Jα18 rearrangement and displays a more restricted specificity for α-linked glycolipids than that of iNKT TCRs ., Despite having sequence divergence in the CDR1α and CDR2α loops , this TCR employs a convergent recognition strategy to engage CD1d/αGalCer , with a binding affinity ( ∼2 µM ) almost identical to that of an iNKT TCR used in this study ., The CDR3α loop , similar in sequence to iNKT-TCRs , engages CD1d/αGalCer in a similar position as that seen with iNKT-TCRs , however fewer actual contacts are made ., Instead , the CDR1α loop contributes important contacts to CD1d/αGalCer , with an emphasis on the 4′OH of the galactose headgroup ., This is consistent with the inability of Vα24− T cells to respond to α-glucosylceramide , which differs from αGalCer in the position of the 4′OH ., These data illustrate how fine specificity for a lipid containing α-linked galactose is achieved by a TCR structurally distinct from that of iNKT cells .

Certain lineages of T cells can recognize lipids as stimulatory antigens when presented in the context of CD1 molecules ., We know how most Natural Killer T ( NKT ) cells react with this unusual ligand because they use a single invariant T cell receptor ( TCR ) alpha chain to do the job ., NKT cells place particular emphasis on their CDR3α and CDR2β loops in recognition of antigen—these complementarity determining regions ( CDRs ) are the hypervariable parts of the TCR that “complement” an antigens shape ., How do these other T cells recognize closely related yet distinct lipid antigens ?, Here we show that human CD1d-restricted T cells , typically called Vα24− T cells due to their use of diverse Vα domains in their TCRs , use similar molecular strategies to respond to lipid antigens presented by CD1d ., To this end we present a 2 . 5 Å complex structure of a Vα24− TCR complexed with CD1d presenting the protypical lipid , α-galactosylceramide ( αGalCer ) ., The TCR examined in this study notably shifts its binding slightly , placing more emphasis on the interaction with the CDR1α loop as revealed through alanine scanning mutagenesis ., This shift explains the inability of these T cells to respond to lipids that vary at this site of contact ( the 4OH ) , like the related α-linked glucosylceramide ., These results provide a molecular basis for the fine-specificity of different CD1d-restricted T cell lineages .

immunology, biology

Human Vα24− CD1d-restricted T cells use variation in their CDR1α loop to respond to lipid antigens presented by CD1d, altering their specificities from that of invariant natural killer T cells.

journal.pntd.0007577

Kankanet: An artificial neural network-based object detection smartphone application and mobile microscope as a point-of-care diagnostic aid for soil-transmitted helminthiases

Soil-transmitted helminths ( STH ) such as Ascaris lumbricoides , hookworm , and Trichuris trichiura affect more than a billion people worldwide 1–3 ., However , due to lack of access to fecal processing materials , diagnostic equipment , and trained personnel for diagnosis , the mainstay of STH control remains mass administration of antihelminthic drugs 4 ., To diagnose STH in residents of rural areas , the present standard is the Kato-Katz technique ( estimated sensitivity of 0 . 970 for A . lumbricoides , 0 . 650 for hookworm , and 0 . 910 for T . trichiura; estimated specificity of 0 . 960 for A . lumbricoides , 0 . 940 for hookworm , and 0 . 940 for T . trichiura ) 5 ., However , this method is time-sensitive due to rapid degeneration of hookworm eggs 5 ., Other methods , including fecal flotation through FLOTAC and mini-FLOTAC still have higher sensitivity ( 0 . 440 ) than direct fecal examination ( 0 . 360 ) , but require centrifugation equipment , which is expensive and difficult to transport 6 ., Multiplex quantitative PCR analysis for these three species is a high sensitivity and specificity technique ( 0 . 870–1 . 00 and 0 . 830–1 . 00 , respectively ) , but can only be performed with expensive laboratory equipment 7 , 8 ., Spontaneous sedimentation technique in tube ( SSTT ) analysis has been found in preliminary studies to be not inferior to Kato-Katz in A . lumbricoides , T . trichiura , and hookworm 9 , 10 ., Since it requires no special equipment and few materials , it has the potential to be a cost-effective stool sample processing method in the field ., Mass drug administration campaigns are the prevailing strategy employed to control high rates of STH ., Such campaigns , however , are focused on treating children and do not necessarily address the high infection prevalence rates of STH in adults , which in turn may contribute to the high reinfection rates 11 , 12 ., Technology that facilitates point-of-care diagnosis could enable mass drug administration programs to screen adults for treatment , monitor program efficacy , aid research , and map STH prevalence ., In areas close to STH elimination , such a tool could facilitate a test-and-treat model for STH control ., One avenue for point-of-care diagnostic equipment is smartphone microscopy ., Numerous papers have already demonstrated the viability of using smartphones 13–15 and smartphone-compatible microscopy attachments ( USB Video Class , or UVC ) 16 as cheap point-of-care diagnostic tools ., Studies have tried direct imaging , as with classical parasitological diagnosis 17 , fluorescent labeling 14 , and digital image processing algorithms to aid diagnosis 18 ., To address the need for trained parasitologists to make the STH diagnosis , this study investigated artificial neural network-based technology ( ANN ) ., ANN , a framework from machine learning , a subfield of artificial intelligence , has seen a rapid explosion in range of applications , from object detection to speech recognition to translation ., Rather than traditional software , which relies on a set of human-written rules for image classification , a method explored in other studies 19 , ANN image processing stacks thousands of images together and uses backpropagation , a recursive algorithm to create its own rules to classify images ., A previous study has applied ANN-based systems to diagnostic microscopy of STH with moderate sensitivity , using a device of comparable price to a smartphone to image samples and applying a commercially available artificial intelligence algorithm ( Web Microscope ) to classify the samples ., However , such a device requires internet connection to function and was only validated on 13 samples 20 , 21 ., Another study has created and patented an ANN-based system to identify T . trichiura based on a small dataset of sample images ( n<100 ) 22 ., However , there is no precedent in current literature for extensive ( n>1 , 000 ) ANN-based object detection system training for multiple STH species , nor use in smartphones , nor offline use ( disconnected from the internet ) , nor field testing in specimens ., This study developed such a system , named Kankanet from the English word network and the Malagasy word for intestinal worms , kankana ., This study also uses a smartphone-compatible mobile microscope , or UVC , with a simple X-Y slide stage ., As a proof-of-concept pilot study for ANN-assisted microscopy , this project aimed to address two key obstacles to point-of-care diagnosis of STH in rural Madagascar: ( 1 ) the lack of portable and inexpensive microscopy , and ( 2 ) the limited capacity and expertise to read microscope images ., This project evaluated the efficacy for diagnosis of three species of STH of ( 1 ) a UVC and ( 2 ) Kankanet , an object-detection ANN-based system deployed through smartphone application ., This study was a part of a larger study on the Assessment of Integrated Management for Intestinal Parasites control: study of the impact of routine mass treatment of Helminthiasis and identification of risk areas of transmission in two villages in the district of Ifanadiana , Madagascar ., This study has received institutional review board approval from the Stony Brook University ( ID: 874952–13 ) and the national ethics review board of Madagascar: Comité d’Éthique de la Recherche Biomédicale Auprès du Ministère de la Santé Publique de Madagascar ( 41-MSANP/CERBM , June 8 , 2017 ) ., As a prospective study , data collection was planned before any diagnostic test was performed ., In accordance with cultural norms , consent was first required from the local leaders before engaging in any activities within their purview ., All participants received oral information about the study in Malagasy; written informed consent was obtained from adult participants or parents/legal guardians for the children ., Since this study was meant to evaluate diagnostic methods and did not produce definitive results , no diagnostic results from this study were reported to the patients ., All inhabitants of the two study villages were given their annual dose of 400 mg albendazole one year before this study , and received another 400 mg albendazole dose within a month of the conclusion of the study by the national mass drug administration effort ., A unique identifier was assigned to each participant to allow grouping of analysis data for each patient ., All data was stored on an encrypted server , to which only investigators had access ., The two villages under study , Mangevo and Ambinanindranofotaka ( geographic coordinates: 21°27S , 47°25E and 21°28S , 47°24E ) , are rural villages situated on the edge of Ranomafana National Park , about 275 km south of Antananarivo , the capital of Madagascar ., Over 95% of households in Ambinanindranofotaka ( total population , n = 327 ) and Mangevo ( total population , n = 238 ) engage in subsistence farming and animal husbandry ., The villages , accessible only by 14 hours’ worth of footpaths , are tucked between mountain ridges covered with secondary-growth rainforest ., The study was conducted between 8 Jun 2018 and 18 Jun 2018 ., All residents of each village were given a brief oral presentation about the public health importance , symptoms and prevention of STH; subjects above age 16 , the Madagascar cut-off age for adulthood , who gave voluntary consent to participate in the study were given containers and gloves to collect their own fecal samples ., Parents gave consent for their assenting children and collected their fecal samples ., One fecal sample from each participant was submitted between the hours of sunrise and sunset ., Samples were processed for analysis within 20 minutes of production by participant ., Cognitively impaired subjects were excluded ., Each fecal sample produced three slides for microscopic analysis: ( 1 ) one slide was prepared according to Kato-Katz ( KK ) technique from fresh stool; ( 2 ) one slide was prepared according to spontaneous sedimentation technique in tube ( SSTT ) from 10% formalin-preserved stool; ( 3 ) one slide was prepared according to Merthiolate-Iodine-Formaldehyde ( MIF ) technique from 10% formalin-preserved stool ., As a reference test , a modified gold standard was defined as any positive result ( at least one egg positively identified in a sample ) from standard microscopy by trained parasitologists using ( 1 ) KK , ( 2 ) SSTT , and ( 3 ) MIF techniques ., Intensity of infection ( measured by eggs/gram ) of A . lumbricoides , T . trichiura , and hookworm were obtained by standard microscopy reading of KK slides by multiplying the egg count per slide reading by the standard coefficient of 24 ., SSTT technique followed standard protocol 23 ., This measure was defined to increase the sensitivity of the reference test ., A standard Android smartphone was attached to a UVC ( Magnification Endoscope , Jiusion Tech; Digital Microscope Stand , iTez ) for microscopic analysis of KK and SSTT slides in the field ( Fig 1 ) ., Clinical information or results from any other analyses of the fecal samples was not made available to slide readers during their analysis ., TensorFlow is an open-source machine learning framework developed by Google Brain ., Using the TensorFlow repository , this study developed Kankanet , an ANN-based object detection system built upon a Single Shot Detection meta-architecture and a MobileNet feature extractor , a convolutional neural network developed for mobile vision applications 24 , 25 ., Based on a dataset of 2 , 078 images of STH eggs , Kankanet was trained to recognize three STH species: A . lumbricoides , T . trichiura , and hookworm 26 ., 597 egg pictures were taken by a standard microscope and 1 , 481 were taken by UVC ., The efficacy of Kankanet diagnosis was evaluated with a separate dataset of 186 images with a comparable distribution of species and imaging modalities ., The detailed breakdown of the composition of these image sets is shown in Table 1 , which shows percentage distributions by species and imaging modality to show concordance in image distribution between training set and evaluation set ., The following hyperparameters were used: initial learning rate = 0 . 004; decay steps = 800720; decay factor = 0 . 95 , according to the default configuration used to train open-source models released online ., To improve the robustness of the model , the dataset was augmented using the default methods of random cropping and horizontal flipping ., The loss rate was monitored until it averaged less than 0 . 01 , as shown in Fig 2 , after which the model was frozen in a format suitable for use in a mobile application ., Based on this protocol , two models were trained: It took Model 1 around 81 and Model 2 around 12 epochs , or iterations through the entire training dataset , to reach the loss rate of less than 0 . 01 ., These models were then validated by being tested from randomly selected images from the evaluation image set ( n = 185 ) , images that were not included in the training set ., Once trained , these models analyze images in real time , project a bounding-box over each detected object , and display the name of the object detected , along with a confidence rating ( Fig 3 and Fig 4 ) ., The true readings of each image in the training and test image sets were determined by a trained parasitologist ., The Kankanet models then were used to read test set images , and correctly identified eggs were considered true positives , incorrect objects identified as eggs were considered false positives , undetected eggs were considered false negatives , and images without eggs or detected objects were considered true negatives ., Evaluation of model sensitivity and specificity was performed with the following test image sets: The open-source TensorFlow library contains a demo Android application that includes an object-detection module ., Following the protocol for migrating this TensorFlow model to Android 27 , the original object detection model on the app was swapped out for the Kankanet model ., As per the original app , the threshold for reporting detected objects was set at 0 . 60 confidence ., Intended sample size was calculated based on June 2016 prevalence rates in Ifanadiana , Madagascar ( n = 574 ) : A . lumbricoides 71 . 3% ( 95% CI 67 . 7–75 . 1 ) ; T . trichiura 74 . 7% ( 95% CI 71 . 1–78 . 2 ) ; hookworm 33 . 1% ( 95% CI 29 . 2–36 . 9 ) 28 ., Following the calculations for a binary diagnostic test for the species with the lowest prevalence , hookworm , with a predicted sensitivity of the test of 90% and a 10% margin of error , the required sample size to have adequate power was determined to be 115 ., For A . lumbricoides and T . trichiura , which have higher prevalence rates , a sample size of 115 gave sufficient power to support a sensitivity of 70% with a margin of error of 10% ., This study used a sample size of 113 fecal samples ., Readings from the UVC on KK and SSTT slides were compared against the modified gold standard , which is defined as any positive result from a standard microscopy reading of KK , SSTT , and MIF techniques by a parasitologist ., In SPSS , sensitivity and specificity of the UVC reading were calculated for each species with KK , SSTT , and combined analysis ., Separate analyses were calculated for different intensities of infection as classified according to WHO guidelines 4 ., Cohen’s Kappa coefficient ( K ) was calculated for each type of fecal processing method to determine comparability to the modified gold standard reading ., Results from Kankanet interpretation were compared to visual interpretation of the same images by a trained parasitologist ., The two models were evaluated for sensitivity , specificity , positive predictive value , and negative predictive value using SPSS ., There were no samples that had missing results from any of the tests run ., The number of positive samples identified by standard microscopy through the Kato-Katz , MIF , and SSTT preparation methods are shown in Table 2 , as well as the composite reading used as the modified gold standard in this study of the three tests ., The number of samples of A . lumbricoides and T . trichiura at each intensity level is reported in Table 3 ., There were no participants heavily infected with T . trichiura ., Since it was not possible for the KK slides to be transported to the laboratory in time for quantification of hookworm eggs , we were unable to detect the intensity of infection of these cases ., The UVC performed best at imaging A . lumbricoides ( Tables 4 and 5 ) , demonstrating higher sensitivity in SSTT preparations ( 0 . 829 , 95% CI . 744- . 914 ) than in KK ( 0 . 579 , 95% CI . 468- . 690 ) , and high specificity in both SSTT and KK ( 0 . 971 , 95% CI . 915–1 . 03; 0 . 971 , 95% CI . 915–1 . 03 ) ., These sensitivity numbers increased with increasing infection intensity ( Fig 5 ) ., UVC imaging of SSTT slide preparations of samples with AL showed a substantial level of concordance with the modified gold standard reading , which was obtained through standard microscopy ( K = 0 . 728 ) , and UVC imaging of KK slide preparations demonstrated moderate concordance with the modified gold standard ( K = 0 . 439 ) ., For T . trichiura , the UVC demonstrated low overall sensitivity through SSTT and KK ( 0 . 224 , 95% CI . 141- . 307; 0 . 235 , 95% CI . 151- . 319 , respectively ) , but high specificity ( 0 . 917 , 95% CI . 761–1 . 07; 1 , 95% CI 1 . 00–1 . 00 ) ., As infection intensity of T . trichiura increased , however , sensitivity increased ( Fig 5 ) ., According to WHO categories for infection intensity , sensitivity for low-intensity infections was 0 . 164 , which increased to 0 . 435 in moderate-intensity infections ., There was little agreement with the modified gold standard ( K = 0 . 038 for SSTT , K = 0 . 063 for KK ) ., The UVC also demonstrated low sensitivity to hookworm eggs in both SSTT ( 0 . 318 , 95% CI . 123- . 513 ) and KK ( 0 . 381 , 95% CI . 173- . 589 ) preparations ., Model 1 , which was trained and evaluated on microscope images only , demonstrated high sensitivity ( 1 . 00; 95% CI 1 . 00–1 . 00 ) and specificity ( 0 . 910; 95% CI 0 . 831–0 . 989 ) for T . trichiura , low sensitivity ( 0 . 571; 95% CI 0 . 423–0 . 719 ) and specificity ( 0 . 500; 95% CI 0 . 275–0 . 725 ) for A . lumbricoides , and low sensitivity ( 0 . 00; 95% CI 0 . 00–0 . 00 ) and specificity ( 0 . 800; 95% CI 0 . 693–0 . 907 ) for hookworm ., Table 6 shows the full breakdown of sensitivity , specificity , positive predictive value , and negative predictive value of the different analyses performed by Model 1 and Model 2 ., Though Model 1 was also evaluated for its performance on UVC pictures of STH , it failed to recognize any , and thus the results are not tabulated ., Model 2 was trained on images taken both with microscopes and with UVC , and was tested with both types of images ., It outperformed Model 1 in every parameter , with high sensitivity and specificity for microscope images all across the board and for UVC images of A . lumbricoides and hookworm ., It performed poorly on UVC images of T . trichiura ( sensitivity 0 . 093 , 95% CI -0 . 138–0 . 304; specificity 0 . 969 , 95% CI 0 . 934–1 . 00 ) , but had moderate PPV and NPV values ( 0 . 667 and 0 . 800 , respectively ) ., This study found that UVC imaging of SSTT slides , though of low quality , still could be read by trained parasitologists with a high sensitivity ( 0 . 829 , 95% CI . 744- . 914 ) and specificity ( 0 . 971 , 95% CI . 915–1 . 03 ) in A . lumbricoides , which is comparable to literature estimates of KK sensitivity at 0 . 970 and specificity of 0 . 960 5 ., The UVC showed lower sensitivity for KK preparations ( 0 . 579 , 95% CI . 468- . 690 ) ., This UVC does not have sufficient image quality to be used with T . trichiura or hookworm diagnosis , which have thinner and more translucent membranes ., Despite UVC imaging having high sensitivity for A . lumbricoides , the 14% difference in sensitivity needs improvement , with a goal of reaching similar sensitivity to standard microscopy , before it can be feasibly used in large-scale STH control efforts ., UVC’s specificity of 0 . 971 ( 95% CI 0 . 915–1 . 03 ) surpasses that of standard microscopy KK’s 0 . 960 specificity ., Though currently shown to have insufficient sensitivity or specificity for use with T . trichiura or hookworm diagnosis , these are limitations believed to be related to the particular microscope peripheral used in this study ., This UVC achieved maximum magnification of approximately 215X at 600 px/mm; its resolution was 640x480 pixels ., The magnification level with this peripheral is sufficient , as other studies have shown success with T . trichiura with magnification levels as low as 60X 29 ., However , for the purposes of STH imaging , improvement of resolution and light source in this UVC may be necessary ., Another study successfully imaged T . trichiura and hookworm at a resolution of 2595x1944 pixels , which is substantially higher than the 640x480 with this peripheral 20 ., This UVC’s light source comes from the same direction as the camera , rather than shining through the sample as in most microscopy , which may have reduced image quality and imaging ability ., Development of a proprietary microscope is another solution , which many other studies have employed: a mobile phone microscope developed by Coulibaly et al . has demonstrated similarly high sensitivity for Schistosoma mansoni ( 0 . 917; 95% CI 0 . 598–0 . 996 ) , Schistosoma haematobium ( 0 . 811; 95% CI 0 . 712–0 . 883 ) and Plasmodium falciparum ( 0 . 802 , 1 . 00 ) 30 , 31; other studies that employ ball lenses or low-cost foldable chassis show slightly lower sensitivity/specificity values 29 , 32 ., Independent development of a smartphone microscope could substantially improve the sensitivity and specificity of these devices to an acceptable level for healthcare use , that is , not inferior to standard microscopy , while simultaneously decreasing the cost per microscope ., However , the advantage of using a commercially available microscope is ease of access for rapid , large-scale implementation and feasibility for low-income rural areas with a heavy burden of STH ., In the context of these villages in rural Madagascar , where STH prevalence can be as high as 93 . 0% for A . lumbricoides , 55 . 0% for T . trichiura , and 27 . 0% for hookworm as measured in 1998 33 , yet only school-aged children receive for mass drug administration , a rule-in test with high specificity , which this UVC achieves , can be useful to reliably identify adults who would also require antihelminthics ., Another context in which this tool may be especially useful is areas close to elimination of STH , to reduce the amounts of antihelminthics needed for STH control 34 ., Though Kankanet interpretation of UVC and microscope images yielded lower sensitivity than trained parasitologist readings of these images , Kankanet Model 2 still achieved high sensitivity for A . lumbricoides ( 0 . 696; 95% CI 0 . 625–0 . 767 ) and hookworm ( 0 . 714; 95% CI 0 . 401–1 . 027 ) on both microscope and UVC images ., Model 2 showed high sensitivity for T . trichiura in microscope images ( 1 . 00; 95% CI 1 . 00–1 . 00 ) , but low in UVC images ( 0 . 083; 95% CI -0 . 138–0 . 304 ) ., Model 1 achieved lower sensitivity and specificity for all species , and could not accurately interpret UVC images ., Model 2’s overall sensitivity for A . lumbricoides , T . trichiura , and hookworm ( 0 . 696 , 0 . 154 , and 0 . 714 , respectively ) may not seem very high at first ., However , these are sensitivity results given for recognizing individual eggs ., As an indication for treatment with antihelminthics would only require one egg per fecal sample slide to be positively identified , the real likelihood of this ANN-based object detection model giving an accurate reading is much higher than the per-egg sensitivity cited here ., For example , even in an infection of A . lumbricoides at the middle of the range considered low-intensity ( 2500 eggs per gram ) , a slide would contain 104 eggs , making the sensitivity of detection of infection in the slide nearly 1 . 00 ., The difference in sensitivity and specificity between the models can be explained by the differences in image sets used for training ., Model 2 was trained with an image set of over twice the size of Model 1’s image set; Model 2’s image set also contained images from both UVC and standard microscopy modalities ., It was a robust model , accurately detecting STH in images with multiple examples of multiple species , despite being trained on an image set containing mostly A . lumbricoides ., It demonstrated a very low rate of false positives , considering the amount of debris apropos to fecal samples ., The Kankanet models can be improved by developing a larger image dataset , exploring other object detection meta-architectures , and optimizing file size and computational requirements ., A greater number and more even distribution of images of parasite species would improve object detection model sensitivity ., Standard laboratory processing and diagnosis of STH is extremely time-consuming and expensive and hence , not often practical for rural low-income communities ., As smartphone penetrance will only increase in the coming years , medical technology should leverage smartphones as portable computational equipment , as use and distribution of such software requires no additional cost ., Because it is able to be attached to smartphones and requires no external power source than the smartphone itself , UVC is a suitable microscopy option for point-of-care diagnosis ., In addition , the smartphone application used in this study did not require internet access , unlike those of previous studies 20 ., UVC and Kankanet are cost-effective , with only the initial cost of $69 . 82 for the microscope and stage setup , as well as the negligible cost of fecal analysis reagents ., In the case of SSTT , only microscope slides and Lugol’s iodine would be needed for fecal processing ., These initial costs are readily defrayed by the thousands of analyses performed with just one unit , the work-hours gained by timely treatment of STH and prevention of STH re-infection , and the reduction of unnecessary drug administration and concomitant drug resistance ., A detailed cost analysis comparing the cost of standard microscopy and the Kankanet system for 2-sample Kato-Katz testing of 10 villages in rural Madagascar ( estimated 3000 people total ) is shown in Table 7 ., Whereas standard microscopy ends up costing around 1 . 33 USD per person tested , the Kankanet system costs around 0 . 56 USD per person tested ., ANN-based object detection systems such as the one introduced here can be useful for screening STH-endemic communities in the context of research , mass drug administrations and STH mapping programs ., In addition , Kankanet , rather than replacing human diagnosis , could be a useful diagnostic training aid for healthcare workers and field researchers ., With sustained use of such a tool , these workers may more quickly learn how to identify such eggs themselves ., Limitations of this study include that the UVC used was of insufficient image quality to produce accurate imaging of T . trichiura and hookworm ., The Kankanet models employed used a dataset limited to two imaging modalities: standard microscopy and UVC , and with images of only three species of STH; in addition , images for this dataset were only taken of samples prepared under KK conditions , so the efficacy of this system can only be assessed for those conditions ., We conclude that parasitologist interpretation of UVC imaging of SSTT slides can be a field test comparable to standard microscopy of KK for A . lumbricoides ., Second , we conclude that ANN interpretation is a feasible avenue for development of a point-of-care diagnostic aid ., With 85 . 7% sensitivity and 87 . 5% specificity for A . lumbricoides , 100 . 0% sensitivity and 100 . 0% specificity for T . trichiura , and 66 . 7% sensitivity , 100 . 0% specificity for hookworm , Kankanet Model 2 has demonstrated stellar results in interpreting UVC images , even though it was trained with a limited proof-of-concept dataset ., We hope that continued expansion of the Kankanet image database , improved imaging technology , and improvement of machine learning technology will soon enable Kankanet to achieve rates comparable to those of parasitologists .

Introduction, Methods, Results, Discussion

Endemic areas for soil-transmitted helminthiases often lack the tools and trained personnel necessary for point-of-care diagnosis ., This study pilots the use of smartphone microscopy and an artificial neural network-based ( ANN ) object detection application named Kankanet to address those two needs ., A smartphone was equipped with a USB Video Class ( UVC ) microscope attachment and Kankanet , which was trained to recognize eggs of Ascaris lumbricoides , Trichuris trichiura , and hookworm using a dataset of 2 , 078 images ., It was evaluated for interpretive accuracy based on 185 new images ., Fecal samples were processed using Kato-Katz ( KK ) , spontaneous sedimentation technique in tube ( SSTT ) , and Merthiolate-Iodine-Formaldehyde ( MIF ) techniques ., UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy . Relative to a gold standard defined as any positive result from parasitologist reading of KK , SSTT , and MIF preparations through standard microscopy , parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity ( 82 . 9% ) and specificity ( 97 . 1% ) in A . lumbricoides to standard KK interpretation ( 97 . 0% sensitivity , 96 . 0% specificity ) ., The UVC could not accurately image T . trichiura or hookworm ., Though Kankanet interpretation was not quite as sensitive as parasitologist interpretation , it still achieved high sensitivity for A . lumbricoides and hookworm ( 69 . 6% and 71 . 4% , respectively ) ., Kankanet showed high sensitivity for T . trichiura in microscope images ( 100 . 0% ) , but low in UVC images ( 50 . 0% ) ., The UVC achieved comparable sensitivity to standard microscopy with only A . lumbricoides ., With further improvement of image resolution and magnification , UVC shows promise as a point-of-care imaging tool ., In addition to smartphone microscopy , ANN-based object detection can be developed as a diagnostic aid ., Though trained with a limited dataset , Kankanet accurately interprets both standard microscope and low-quality UVC images ., Kankanet may achieve sensitivity comparable to parasitologists with continued expansion of the image database and improvement of machine learning technology .

For rainforest-enshrouded rural villages of Madagascar , soil-transmitted helminthiases are more the rule than the exception ., However , the microscopy equipment and lab technicians needed for diagnosis are a distance of several days’ hike away ., We piloted a solution for these communities by leveraging resources the villages already had: a traveling team of local health care workers , and their personal Android smartphones ., We demonstrated that an inexpensive , commercially available microscope attachment for smartphones could rival the sensitivity and specificity of a regular microscope using standard field fecal sample processing techniques ., We also developed an artificial neural network-based object detection Android application , called Kankanet , based on open-source programming libraries ., Kankanet was used to detect eggs of the three most common soil-transmitted helminths: Ascaris lumbricoides , Trichuris trichiura , and hookworm ., We found Kankanet to be moderately sensitive and highly specific for both standard microscope images and low-quality smartphone microscope images ., This proof-of-concept study demonstrates the diagnostic capabilities of artificial neural network-based object detection systems ., Since the programming frameworks used were all open-source and user-friendly even for computer science laymen , artificial neural network-based object detection shows strong potential for development of low-cost , high-impact diagnostic aids essential to health care and field research in resource-limited communities .

invertebrates, medicine and health sciences, engineering and technology, helminths, tropical diseases, hookworms, geographical locations, parasitic diseases, animals, cell phones, neuroscience, artificial neural networks, ascaris, ascaris lumbricoides, pharmaceutics, artificial intelligence, computational neuroscience, drug administration, neglected tropical diseases, africa, computer and information sciences, madagascar, communication equipment, people and places, helminth infections, eukaryota, equipment, nematoda, biology and life sciences, drug therapy, soil-transmitted helminthiases, computational biology, organisms

journal.pcbi.1004358

Dynamic Allostery of the Catabolite Activator Protein Revealed by Interatomic Forces

The regulation of DNA transcription is one of the key factors for the control of cell functions , a classic example of which is the lac-operon model ., In this gene regulatory mechanism , the catabolite activator protein ( CAP ) promotes DNA transcription initiation ., The homodimeric protein CAP ( Fig 1A ) binds cyclic adenosine monophosphate ( cAMP ) and DNA ., The N-terminal nucleotide binding domain ( NBD; Fig 1B blue ) acts as a dimerization domain and is globular ., The C-terminal DNA binding domain ( DBD; Fig 1B green ) is bound via a flexible linker to the cAMP binding domain and consists of a helix-turn-helix motif ., CAP activity is regulated mainly through negative cooperativity of the two cAMP binding events: the binding affinity of the second cAMP is reduced by nearly two orders of magnitude 1–3 ., Once CAP is activated by cAMP , it binds to a specific DNA region , and thereby enhances downstream transcription 4–6 ., While the binding of CAP to DNA is largely understood 7 , the allosteric mechanism of cAMP binding is still elusive ., The puzzling fact that the binding of the first cAMP leads to no recognizable structural change at the second binding pocket 8 but still reduces its binding affinity by two orders of magnitude 1–3 has led to numerous investigations ., The crystal structure of singly cAMP bound CAP shows a distance of more than 20 Å between the two binding pockets 9 , and therefore direct Coulombic interaction between the two cAMP cannot explain the negative cooperativity ., Also , CAP structures , resolved by X-ray or Nuclear Magnetic Resonance ( NMR ) , with no or two cAMP molecules bound , do not explain the observed allostery ., Instead , NMR measurements and isothermal calorimetry ( ITC ) on a truncated construct ( no DBD ) 8 revealed that the binding of cAMP gives rise to a primarily dynamics-driven allosteric mechanism ., More specifically , a change in dynamic fluctuations in the unliganded protomer upon cAMP binding to the other protomer render the binding of a second cAMP molecule entropically unfavorable ., Similarly , changes in the conformational entropy of CAP due to a global redistribution of internal dynamics was shown using NMR and ITC to also decisively impact DNA binding ., Molecular Dynamics simulations and free energy calculations have supported this view on CAP dynamic allostery 10 ., These and similar results of other allosteric proteins have led to an ensemble-based view of protein allostery 11 , 12 , according to which local or global conformational motions on various time scales are affected by ligand binding 8 , 13 , thereby triggering a change in protein affinity or activity ., Nevertheless , even in the absence of conformational changes , allostery requires the distant effector and functional sites of a protein such as CAP to be coupled ., Thus , we here propose a well-defined communication pathway , i . e . a subset of residues primarily involved in correlating effector binding with protein activity , as the basis of the dynamic allostery of FAK 14 ., We here aim at determining the distinct allosteric pathways that could explain the negative cooperativity of cAMP binding to CAP and its impact on CAP-DNA association ., To this end , we used Force Distribution Analysis ( FDA ) 15 , 16 , a Molecular Dynamics ( MD ) based analysis of inter-atomic forces propagating through the structure upon an external perturbation , which here is cAMP binding ., This method is similar to calculations of molecular stresses 17 , 18 ., FDA has previously allowed to track pathways underlying structural allostery 19 , 20 as well as dynamic allostery as observed in the methionine repressor MetJ , another gene regulatory protein 21 ., We compared inter-atomic forces within three different forms of CAP: the apo , the single and double cAMP bound forms , which we are going to refer to in the following as apo , cap1 and cap2 , respectively ., FDA allows to highlight atomic interactions involved in allosteric signal transmission even for very small atomic displacements , in contrast to other computational methods which typically rely on large-amplitude motions ., In particular , even if atomic displacements are largely absent , and allostery instead is largely based on fluctuations redistributing within the protein upon effector binding , changes in mean inter-atomic forces capture these dynamic effects due to the anharmonic nature of the underlying potential 21 ., We thoroughly validate the MD simulations of the three states of CAP by comparison to experimental X-ray and NMR data ., We detect a primary signalling pathway between the two cAMP binding site of the CAP homo-dimer similarly involved in both cAMP binding events , which can explain the anti-cooperativity of cAMP binding ., Also , we put forward a symmetric pathway between the NBD and DBD of CAP critically involved in signalling towards the DNA binding site ., Our results highlight hot spots of CAP’s dynamic allostery , which are testable by experiments , and suggest that allosteric signalling pathways and entropy driven allostery do not exclude each other but instead can represent different perspectives of the same mechanism ., In the following , we will refer to the apo state , the single and double cAMP bound states of CAP as apo , cap1 and cap2 ., Starting from the high-resolution crystal structure of cap2 ( PDB id: 1G6N 9 ) , we performed 9 independent Molecular Dynamics ( MD ) simulations of each of the three differently liganded states ( see Material and Methods section ) ., The production time of the 27 simulations was 100ns , resulting in 2 . 7μs of CAP trajectories ., We validated the accuracy of the simulations by comparing atomic fluctuations with X-ray ( B-factor ) and NMR ( Squared Generalized Order Parameters for the Methyl Group Symmetry Axis , S2axis ) data as well as biologically relevant motions with available experimental data ., The calculated residual root mean square fluctuation ( RMSF ) of cap2 correlates reasonably ( R = 0 . 84 ) with the experimental B-factors of the X-ray structure ( PDB id: 1G6N , S1 Fig ) ., Both the crystal structure and the cap2 simulations show ( S1 Fig ) high fluctuations only in the outer loops of the DNA-binding domain ( DBD ) ., Interestingly , the B-factors and the RMSF indicate higher fluctuations for one protomer than the other , an asymmetry that is not reflected in the NMR measurements , for which signals inherently represent averages over the two protomers ., To compare our simulations even more directly with available NMR data , we additionally calculated CH3 order parameters ( S2axis ) for cap2 as described by Hu et al . 22 ( see Material and Methods section ) ., We obtained an average order parameter of 0 . 511 , which is in satisfying agreement with the average value of 0 . 529 from NMR experiments 23 , and follows nicely the trend between MD and NMR based average order parameters observed previously for other proteins 24 ., Interestingly , the 2-cAMP bound CAP falls into the more flexible regime of the seven other proteins compared therein ., A residue-to-residue comparison ( S2 Fig ) yielded a correlation of 0 . 61 between MD and NMR data for cap2 ( whole protein ) , and of 0 . 71 considering only the NBD ., Correlation between S2axis derived from MD and NMR has been previously found to be similarly low for other proteins 24 ., In our case , it might be particularly challenged by starting the MD simulations from an X-ray structure ( pdb id: 1G6N ) instead of a cAMP-bound solvent NMR structure , for which no wild-type homo-dimer structure with 2 cAMP has yet been solved ., Overall , with regard to entropies and order parameters , we find our MD simulations to largely reproduce the cAMP-dependent flexibility of CAP ., We next analysed the similarity of the collective motions observed in our MD simulations with those described by the corresponding experimental structures ., We computed collective modes of motion for the three different states from each set of 9 simulations , and compared them to the ‘inactivation motion’ as described by the apo and cap2 NMR structures ( 2WC2 and 1G6N , respectively 9 , 25 ) ., Interestingly , the 4th eigenvector , obtained from the apo simulations which is expected to relax towards the apo state , indeed correlates with the experimentally suggested inactivation motion ( S1 Movie ) with a correlation coefficient of up to 0 . 54 ( or 0 . 61 for 4th eigenvector of cap1 ) ., By contrast , a lower correlation coefficient , of maximum 0 . 42 , was found between the 20 largest amplitude eigenvectors of the 2cAMP bound state and the experimental inactivation motion ., This indicates that the computed dynamics partially and yet specifically capture the functional motions described by experimental structures ., As a final sanity check of our models , we also compared the experimentally and computationally observed motions of the DBD involved in DNA binding ., To this end , we projected our simulations on the first eigenvector derived from 11 X-ray structures , all of which possessed two cAMP , but for which the DNA could be absent or present ., This eigenvector corresponds to the previously described large-scale DNA binding motion 9 , 26 , and involves an almost rigid rotation of the DBD by ~25 degrees ., X-ray structures with bound DNA clearly separated from structures not coupled to DNA along this eigenvector , with only two intermediate conformations ( which show non-canonical cAMP binding ) ., Remarkably , in spite of the limited time scale of our MD simulations , projections of conformations explored during the MD simulations and experimentally resolved structures 9 , 26–29 , on this particular eigenvector showed that the cap2 state is able to follow this motion further than the apo and cap1 states and even overlaps with DNA-bound X-ray structures ( Fig 2B ) ., This suggests that even though we started from the same active cap2 structure , the sampled conformational space partially diverged during the unbiased MD simulations along directions in agreement with experiments ., We note that even , the cap2 ensemble preferentially populates a region to the left of the experimental structures , a diversion which however , is minor , and could be due to crystal packing effects ., Because it has been shown experimentally that the binding anticooperativity in CAP is entropy driven 8 , 23 , we compared experimental and simulated entropy changes upon cAMP binding ( Fig 2A ) ., Configurational entropies were obtained from both our recently developed force covariance ( FC ) estimator 30 and the more established quasi-harmonic ( QH ) approximation 31 for the full protein ., Briefly , QH and FC entropy estimators both assume the system to be harmonic and estimate the vibration frequencies from correlated atomic coordinates ( QH ) or from correlated atomic forces ( FC ) ., Atomic forces have been shown to deviate less from harmonics than coordinates , such that the harmonic approximation becomes more accurate if based on force covariance 30 ., An entropic penalty for the overall process of −TΔScap2-apo = 20 kcal . mol-1 due to the binding of two cAMP ligands has been measured for the full protein construct with NMR , through S2axis order parameters 23 ., This result is in qualitative agreement with our simulations , which corroborate an estimated overall entropic penalty ranging from 6 to 54 kcal mol-1 , depending on the selection of atoms for the analysis as well as the estimation method used ( Fig 2A , magenta ) ., Available experimental NMR NH-bond order parameters of a truncated construct ( without DBD ) suggested a small entropic penalty for the first cAMP binding of −TΔScap1-apo = 3 . 2 kcal . mol-1 , followed by a much larger penalty of −TΔScap2-cap1 = 18 . 1 kcal . mol-1 for the second cAMP binding event 8 ., From our simulations , both methods ( QH and FC ) instead consistently estimate the entropy changes of the first binding event −TΔScap1-apo to be small but favourable ( −10 vs −4 kcal mol-1 for FC and QH , respectively ) ., QH , however , is very sensitive to the selection of atoms included in the analysis , and implausibly estimates an entropy change of 25 kcal . mol-1 for the full protein ., For the second binding event , QH and FC both corroborate the experimentally measured entropic penalty , ranging from 10 to 26 kcal mol-1 depending on the selection of atoms for the analysis ., The partial discrepancy for the first binding event may partly be attributed to the fact that the NMR experiments used a truncated construct without DBD ., We hypothesize that full length CAP in contrast to the truncated protein shows a favourable entropy change upon binding the first cAMP , which is yet to be tested experimentally ., Having validated the set of MD simulations comprising the apo , cap1 , and cap2 states , we next asked how cAMP binding is allosterically regulated such that it occurs anticooperatively ., To reveal the allosteric network of cAMP binding in CAP , we calculated the change in pairwise residue forces upon each nucleotide binding event , averaged over the nine independent 100 ns simulations ., We observed convergence of the force differences after approximately six of the nine MD simulations ( S3 Fig ) ., By convention , the first protomer will refer to the one where a cAMP molecule is present in the cap1 state ., In order to distinguish amino acids from both protomers , we added a prime to residue numbers of the second protomer , such as Arg123’ ., To explore the perturbation and potential allosteric communication caused by the first binding event , we first analysed the network of force difference between the apo and the 1cAMP bound ( cap1 ) states ., Fig 3 shows a graphical representation of the changes of residue-residue forces upon binding the first cAMP , with edges drawn between residues that exhibit force differences beyond a given cut-off ., Only the largest connected network ( in terms of number of amino-acids involved ) is shown to further reduce the noise due to the undersampling in the MD simulations ., At a force difference cut-off of 50 pN , the largest network is located around the site of perturbation , i . e . the binding site of the first cAMP ( Fig 3A ) ., This network involves mainly the first protomer , especially residues in direct contact with the nucleotide , but also residues His31 to Ala36 in contact with the P-loop ( see Fig 1B ) and residues from the loop Leu73-Gln80 ., Remarkably , the force network spans residues all the way down to the N-terminal half of the H3-helix ( see Fig 1B ) of the first protomer and five residues of the H3-helix of the second protomer ( Ala122’ , Arg123’ , Leu124’ , Gln125’ and Thr127’ ) ., We also observed signal propagation from Asp68 to the anti-parallel β4/β5-sheet ( Ser46 , Val49 , Ser62 , Tyr63 , Leu64 and Asn65 , Fig 1B ) ., A larger yet weaker force network becomes visible at a decreased cut-off of 40 pN ( Fig 3B ) , where we observed signal propagation from the first to the second cAMP binding site ., This network shows two distinct connection pathways , referred to in the following as pathways A and B , with a distinct set of molecular interactions at the protomer interface ( Fig 3C and 3D ) ., Pathway A is composed of only three specific residue pairs: Leu73-Leu124’ , Leu124’-Arg123’ and Arg123’-Glu72’ ., The presence of the ligand in the binding pocket results in a stronger hydrophobic packing interaction between Leu73 and Leu124’ ( Fig 4A ) ., The signal is then propagating through backbone interactions from Leu124’ to Arg123’ ., Finally , the Arg123’ side-chain gains flexibility upon binding of the first cAMP ( Fig 4B ) , leading to altered pairwise interactions between Arg123’ and Glu72’ ., Arg123’ is in close contact with the cAMP molecule in the second protomer , and mutation of this residue drastically decreases CAP activity 32 ., Likewise , Glu72’ interacts directly with the second cAMP in the 2 cAMP-bound X-ray structure ( the starting structure of our MD simulations ) through a hydrogen bond with the sugar moiety ., Also this residue has been shown to be crucial for nucleotide binding and CAP activity , as shown in site-directed mutagenesis studies 33 ., Pathway A as revealed by FDA now suggests these two residues to be directly involved in the allosteric communication for anticooperative binding of cAMP ., Pathway B mainly involves Arg123 and the residue pair Arg122-Glu77’ , both bridged by Val126 ( Figs 3D and 4B ) ., Nucleotide binding strongly modifies the Arg123 conformation ., Indeed , the force difference network identified a number of residues with different interaction patterns with this arginine ( including Asp68 , Phe69 , Ile70 and Glu72 ) ., This shift in Arg123 conformations triggers slight modifications in the arginine backbone that are transmitted to the Arg122 through the H3-helix ., As a result , the Arg122 side-chain is more mobile and has weaker interactions with Glu77’ ., We observed even stronger repulsion in the cap1 state , as the non-bonded energy term is more positive ( S4 Fig ) ., Glu77’ shows stronger interactions with its surrounding residues , including Gln80’ through side-chain interactions ( a hydrogen bond was observed over 13% of the apo trajectory against 34% in the cap1 state , S4 Fig ) ., The modified polar interaction network of pathway B bridges the two promoters and thereby complements the first signal transduction ( pathway A ) between the nucleotide binding pockets ., Interestingly , the observed structural adaptations upon the first cAMP binding event partially also involve dynamical changes of the same residues ( S5 Fig ) ., Most importantly , we observed stiffening , measured by residual dihedral order parameter differences , in the loop comprising residues 68–72 and the P-loop region of the first protomer as well as residues Glu72’/Glu77’ of the second protomer , all of which highlighted by FDA ., Binding of the second cAMP entails local force changes within host protomer resembling those already seen for the first binding event ( Fig 5A , also compare Fig 3A ) ., Again , large residue-residue force differences were observed in the protomer hosting the additionally bound nucleotide , including the P-loop region and residues nearby ( His31’ to Ala36’ ) , the loop from Leu73’ to Gln80’ and also again the upper β4/β5-sheet ( see Fig 1B ) ., Similar interaction changes , upon the binding of the first and second cAMP , at least in the proximity of the binding pockets , are expected , given the high similarity of ligand-protein interactions ., FDA encouragingly recovered this expected behaviour ., However , at the same cut-off of 50 pN , the network after the second binding event now extends over ~15 Å from the rather local network around the cAMP binding cleft detected upon the first binding event ., It now reaches the β-strands 2 and 7 to finally join the P-loop of both protomers , and even residues in close contact with and within the DBDs ( Fig 5A and 5B ) ., This new distant signal propagation towards the DBD is likely connected to the DBD activation motion observed in our MD simulations ( Fig, 2 ) and in available experimental structures 9 , 25 ., The long-range nature of the cap1-cap2 force network is also in agreement with the global stiffening of CAP after the binding of the second cAMP observed experimentally 8 ., An even larger , but slightly weaker ( 40 pN ) force network now reaches all the way to the C-terminal region of the H3-helix ( see Fig 1B ) ., We would like to emphasize that this network was absent before binding of the second cAMP ., This region is crucial for regulation and is known to undergo large conformational changes after CAP activation ., Experimental structures showed that cAMP binding promotes addition of two helix turns at this C-terminal region 25 , 34 ., FDA identifies a single connection pathway between the two protomers for the second nucleotide- binding event , involving almost the same residue pairs we observed for the allosteric propagation of the first binding event ., ( Fig 5C ), Indeed , the previously described pathway B is very similar to the one involved in the signal transmission upon the second cAMP binding event , again including Glu77’-Arg122 and Arg123-Asp68 , bridged by modified backbone interaction between Arg122 and Arg123 ., Interestingly , Asp68 , which played a key role in the first binding event , is also important after the binding of the second nucleotide ., Asp68 propagated the perturbation due to cAMP binding symmetrically to the anti-parallel β-sheet core and then reach the β4/β5-hairpin in close contact with the DBD This symmetric pathway , bridging Asp68 to Glu58 , involves sequentially Leu64 , Val47 , Ser46 , Lys89 , Ala88 and Arg87 ., In this network , main chains play an important role ., The β-bundle which connect the cAMP pocket with the NBD-DBD interface acts as a good signal propagator through H-bond interactions ., We observed in both protomers a significant change in the interaction between Glu58 ( β-hairpin ) and Arg87 ( β-strand 2 , Fig 5D ) side chains , the last residue pair of this network ., Structural data has suggested the distance Glu58-Arg87 to critically change as a function of the activation state 9 , 25 , namely to decrease by about 5 Å upon CAP activation ., We observed the same trend in our MD simulations: the Glu58-Arg87 minimal distance was larger in the apo and cap1 states in comparison to the cap2 state , for both protomers ( Fig 6 ) ., Interestingly , the network further reaches Gln174 located on the DBD in the second protomer ., We computed dihedral order parameter difference for each residue from the cap1 and the cap2 simulations ( S6 Fig ) , and obtained significant changes for some of the key residues highlighted by FDA ., We observed stiffening of Glu77’ , Glu78’ , Gly79’ and Gln80’ , due to their enhanced interaction with Arg122 ., We here aimed at deciphering the allosteric mechanism of CAP to explain the anticooperativity of cAMP binding and the cAMP-dependent activation of CAP for DNA binding ., We performed MD simulations of three CAP states , without cAMP ( apo ) , with a single ( cap1 ) and two bound cAMP molecules ( cap2 ) ., Experimental NMR data 8 showed that the anticooperativity in CAP is of mainly entropic nature , with changes in atomic fluctuations upon ligand binding around largely unaffected mean positions of the protein coordinates ., We were able to semi-quantitatively reproduce the entropic penalty of anticooperative cAMP binding from atomic force correlations using a recently developed force-covariance ( FC ) entropy estimator 30 ., Furthermore , to investigate the mechanism of negative cooperativity and CAP activation , we used FDA to reveal changes in the protein’s internal force network upon cAMP binding ., FDA gave insights into the allosteric mechanisms of CAP , which helped to identify minute allosteric rearrangements at the domain interfaces ., We obtained allosteric networks for the first and second binding event , which both span the two nucleotide binding pockets , but only for the second cAMP binding also reach into the DBD ., Calculated pathways involve the amino acid pairs Glu72’-Arg123’ and Leu73-Leu124’ ( pathway A ) and Arg122 , Glu77’ and Arg123 ( pathway B ) ., Using FDA and subsequent structural analysis , we identified critical residues along the signal propagation pathways , the functional role of which are partially supported by mutational studies ., For instance , it has been shown that mutating Glu72 , a residue we find within an allosteric link to Arg123 , impacts cAMP binding and allostery in CAP 32 ., Likewise , a mutation of Arg123 , which FDA suggests to be implicated in both communication pathways , modifies CAP activity 33 ., Our studies additionally revealed Glu58 , located at the β4/β5 hairpin , as being involved in force transmission towards the DBD , as this crucial site symmetrically stands out in the force networks of both protomers ( Figs 5D and 6 ) ., This suggests this residue as an interesting candidate for mutations that we predict to result in decoupling of the allosteric regulation of DNA binding from that of cAMP binding ., Similarly , residue pairs involved in the signal transmission toward the DBD ( Ser46 , Val47 , Arg87 , Ala88 and Lys89 ) could be good candidates for mutations in order to decouple cAMP binding from CAP activation ., In this network , FDA delineates especially Asp68 to be crucial for global signal transmission in CAP , suggesting it as another interesting previously untested mutagenesis candidate ., Our data complements and allows the interpretation of the enormous collection of insightful X-ray and NMR data on CAP structure , dynamics and allostery ., Previous conclusions on CAP allostery have relied on averaged NMR data ( chemical shifts or order parameters ) from the two CAP protomers justified by assuming symmetry of the CAP dimer ., By contrast , we here find this symmetry to hold only partially ., In particular , we obtained allosteric pathways between the CAP protomers , crossing the α-helix H3 , which break the symmetry ., Our results indicate that it would be desirable to address the challenge to distinguish between the protomers of CAP even in the apo or cap2 states , when collecting experimental data ., We expect the CAP force networks to differ from networks of correlated fluctuations , as the latter relies on high amplitude motions and thus occur in more flexible regions such as loops 21 ., Systematically analysing the different features revealed by correlated coordinate fluctuations as obtained from PCA or driven MD simulations 35 as opposed to those from our force network for a set of allosteric proteins would in this regard be of interest ., While our simulations recover the experimentally observed entropic penalty of the second binding event , we also identified shifts in the mean structure of the protein that can additionally give rise to the anti-cooperativity for cAMP binding ., More specifically , significant side chain adjustments right at the cAMP binding pocket and the protomer-protomer interface hamper the binding of the second cAMP ., In particular , Arg123’ ( Fig 4A ) , which we proved that it is a key residue for the allosteric signalling in CAP , in the cap1 state populated a region normally occupied by cAMP , thereby occluding the empty binding cleft more than in to apo state ., This “enthalpic” component is not in contradiction with NMR experiments , for which minor mean displacements of side-chains , in particular those lacking methyl groups , are challenging to track ., Overall , our work extends the entropy-centric view on CAP ., It suggests atomic forces and stresses , which intriguingly have been previously shown to be a signature of folded proteins 36 , as a useful measure of protein regulation—with force covariance as an entropy estimator and force distribution as a tool to reveal allosteric communication independently of the nature of the allosteric mechanism , being it structural , dynamic , or both ., The MD simulations were performed using GROMACS 4 . 0 . 5 37 with the AMBER force field 03 38 ., The parameters for the cAMP molecule were determined with the Generalized Amber Force Field 39 in conjunction with the program Antechamber 40 ., The crystal structure of the E . coli catabolite activator protein ( PDB id:1G6N 9 , Uniprot id: P0ACJ8 ) was used for the MD simulations ., The protonation states of the amino acids were calculated with the WHAT IF software package 41 ., A triclinic simulation box was filled with TIP3P water 42 and sodium/chloride ions at a physiological concentration of 120 mM with a resulting overall system charge of zero ., All simulations were run in the NpT ensemble ., The temperature was kept constant at 300 K by coupling to the Nose-Hoover thermostat with τt = 0 . 1 ps ., The pressure was kept constant at p = 1 bar using isotropic coupling to a Parrinello-Rahman barostat with τp = 1 ps and a compressibility of 4 . 5x10-5 bar−1 ., After energy minimization , the LINCS algorithm 43 was used to constrain all bonds ., Lennard-Jones interactions were calculated using a cut-off of 1 nm ., Long-range electrostatics were calculated by Particle-Mesh Ewald ( PME ) summation 44 ., Every state ( apo , one cAMP bound—cap1 , two cAMP bound—cap2 ) of the CAP system was minimized , using the steepest descent algorithm ., For each state , nine trajectories with different random starting velocities were calculated , first , in a 200 ps position restraint run ( restraint force constant = 1000 kJ•mol−1 . nm2; time step = 2 fs ) , followed by a 6 ns equilibration run and a 100 ns production run ( time step = 2 fs ) , resulting in 27 trajectories with a total length of 2862 ns ., Only the 100 ns production runs were further analysed ., System coordinates were saved every 20 ps , resulting in 45 , 000 conformations for each state ( apo , cap1 and cap2 ) ., The average size of the triclinic simulation box was 89 x 101 x 97 Å , resulting in a system volume of about 87 , 0000 Å3 with about 87 , 000 atoms ., We performed Principal Component Analyses ( PCA ) on concatenated trajectories of either apo , cap1 or cap2 states ., A PCA consists in diagonalizing the co-variance matrix of Cartesian coordinates , in order to delineate collective motions sampled during our MD simulations ., Eigenvectors describe motions and eigenvalues inform about the amplitude of the corresponding motions ., To reduce the number of degrees of freedom , only the main-chain atoms were taken into account to build the co-variance matrices ., We further compared only the first five most collective motions to experimentally known ones , such as the activation or the DNA binding motions ., The motion were compared in the 3D space using motions matrices which is described in detail elsewhere 45 , 46 ., A motion matrix is created using a difference of two Cα-Cα distance matrices ., We then computed a correlation coefficient between the two motion matrices ., The method does not need any fitting as Cα-Cα distance matrices are signatures in internal coordinates ., In order to validate our simulations , we have computed squared generalized order parameters for the Methyl Group Symmetry Axis ( S2axis ) ., S2axis were computed for all terminal C-CH3 bond vectors ( or S-CH3 in the case of methionines ) using multiple windows of 3 ns , as described by Hu et al . 22 ., A S2axis C-CH3 bond vector in the 3D space x , y and z , is defined as follow:, Saxis2=32〈x2〉2+〈y2〉2+〈z2〉2+2〈xy2〉2+〈xz2〉2+〈yz2〉2−12, ( 1 ) To have a broad view of the CAP dynamics , and not only dynamical behaviour residues which possess a methyl group , we computed dihedral order parameters for every residues of the protein , as implemented in GROMACS 4 . 5 . 3 47 and described by Van der Spoel and Berendsen 48 ., This analysis was done on concatenated trajectories of the three states ( apo , cap1 and cap2 ) ., We then computed order parameters differences residue per residue , to determine dynamical behaviour modification of amino acids after the two cAMP binding events ., In the Force Distribution Analyses ( FDA ) 15 , 16 , the forces between each atom pair i and j were analysed at each trajectory step ., All terms in the force field were considered , including both non-bonded and bonded terms , except forces including water and ions , as well as PME forces ., The more recent FDA version 16 in conjunction with GROMACS 4 . 5 . 3 47 was used here ., For a residue-wise analysis , inter-residue forces Fuv were calculated from the norm ( magnitude ) of the force vector resulting from summing up over all force vectors Fij between atom pairs i and j within the two residues u and v:, Fuv=‖∑ijF→ij‖;i∈u , j∈v, ( 2 ), We note that time averaged pairwise forces can be different from zero even at equilibrium , in contrast to atomic forces which average to zero over time ., To enhance the signal to noise ratio , the pairwise forces Fuv calculated from each frame in the trajectories were averaged over

Introduction, Results, Discussion, Material and Methods

The Catabolite Activator Protein ( CAP ) is a showcase example for entropic allostery ., For full activation and DNA binding , the homodimeric protein requires the binding of two cyclic AMP ( cAMP ) molecules in an anti-cooperative manner , the source of which appears to be largely of entropic nature according to previous experimental studies ., We here study at atomic detail the allosteric regulation of CAP with Molecular dynamics ( MD ) simulations ., We recover the experimentally observed entropic penalty for the second cAMP binding event with our recently developed force covariance entropy estimator and reveal allosteric communication pathways with Force Distribution Analyses ( FDA ) ., Our observations show that CAP binding results in characteristic changes in the interaction pathways connecting the two cAMP allosteric binding sites with each other , as well as with the DNA binding domains ., We identified crucial relays in the mostly symmetric allosteric activation network , and suggest point mutants to test this mechanism ., Our study suggests inter-residue forces , as opposed to coordinates , as a highly sensitive measure for structural adaptations that , even though minute , can very effectively propagate allosteric signals .

The Catabolite Activator Protein ( CAP ) is a well-studied example for how cellular catabolite levels are integrated into the gene regulation ., Its affinity for a specific stretch of DNA can be switched on by the binding of two nucleotide molecules termed cAMP to its two protomers ., Even though the nucleotides occupy structurally identical binding pockets , the second cAMP binding occurs at an affinity orders of magnitude lower than the first cAMP binding ., The question arises how , in the absence of structural changes , the first binding can affect the second ., An answer from experiments has been that the communication is largely of entropic nature , i . e . the second cAMP binding would lead to a pronounced reduction in atomic fluctuations of the protein without affecting the atomic mean positions ., We here revisited this question by performing Molecular Dynamics simulations ., By measuring correlations of forces , a newly derived method outperforming the more common coordinate-based approach , we could recover the previously determined entropic penalty ., In addition , however , we observed unobtrusive structural changes of side-chain interactions leading to the occlusion of the second binding pocket that add a critical ‘enthalpic’ component hitherto overlooked ., Our study provides a mechanistic view onto the intriguing anti-cooperativity of CAP .

journal.pbio.1000367

Variability in the Control of Cell Division Underlies Sepal Epidermal Patterning in Arabidopsis thaliana

During development , complex patterns of specialized cell types emerge de novo ., Pattern formation occurs in a changing environment where cells proliferate and differentiate , and we are interested in how regulation of cell division contributes to the patterning of an organ 1 ., One system for investigating this problem is the development of the Arabidopsis sepal epidermis , which forms a characteristic cell size pattern ranging from giant cells stretching one fifth the length of the sepal to small cells stretching one hundredth the length of the sepal ( Figure 1A–C; giant cells marked in red ) ., The sepal is the outermost , green , leaf-like floral organ , which acts defensively to enclose and protect the developing reproductive structures ., The sepals open at maturity when the flower blooms ., Although the function of having a wide range of pavement cell sizes is unknown 2 , it is possible that the diversity in cell sizes plays a role in defense against insect predators , helps the plant respond to water stress , or has a mechanical role ( see Discussion ) ., Within the flower , sepals are unique in containing such a pattern of diverse cell sizes and consequently giant cells have been used as a marker for sepal organ identity 3–6 ., Outside the flower , a similar cell size pattern containing giant cells is found in the Arabidopsis leaf epidermis ( Figure S2H ) 7 ., To understand the cellular basis of pattern formation , we need to investigate the development of the organ in real time with sufficient temporal and spatial resolution ., When combined , three recent advances make this possible ., First , by imaging living and developing tissues , it is possible to track individual cells and their divisions to determine the consequences of the division pattern on development 8–12 ., Second , automated image processing can be used to extract quantitative data from images 13–15 ., Third , computer modeling can be used to explore the consequences of temporally and spatially realistic biological hypotheses 13 , 16–19 and can make predictions that can be tested with further imaging ., In particular , many developmental models of multicellular plant tissues have been used to explore hypotheses about the role of transport of the plant hormone auxin in the shoot , root , and leaf primordia 20 ., Models have also been used to predict the spacing of the hair cells in epidermis of the leaf ( trichomes ) and the root ( root hairs ) ; however , these models did not take into account the effect of cell division on the pattern 21 , 22 ., Several modeling strategies have been used to create multicellular structures including L-systems , dynamical grammars , cellular potts models , weak spring models , and finite element models 23 , 24 ., The combination of live imaging , image processing , and modeling are central to the computational morphodynamic approach to understanding plant growth 23 ., In this study we use a computational morphodynamic approach to determine how the timing and position of cell division creates a specific pattern of cell sizes in Arabidopsis ., We chose the sepal epidermis , instead of the leaf epidermis , as the system for addressing this question because sepals are easily accessible for live imaging and the cells are roughly rectangular , making the size distribution readily apparent for automated image processing ., The size of a cell is controlled by its growth rate and its frequency of division ., Plant cells are confined by their cell walls 25 , which cannot slide relative to a neighboring cell 26 , 27 ., Consequently within a layer such as the epidermis , it is unlikely that one cell can grow substantially faster than its neighbor ., On the other hand , the frequency of division is regulated by the cell cycle ., In the extreme case , cells enter the specialized endoreduplication cell cycle in which they replicate their DNA but fail to divide 28–30 ., Consequently , endoreduplicating cells increase in both size and DNA content 7 ., The principle that ploidy level , or the number of copies of the chromosomes , is generally correlated with cell size was first described as the karyoplasmic ratio about one hundred years ago by Boveri , Hertwig , and their colleagues 31 , 32 ., Since then , the constancy of the ratio between DNA content and cytoplasmic volume had been demonstrated in various cell types including epidermal cells 7 and hair cells ( trichomes ) of Arabidopsis 33 , 34 as well as nearly every organism from bacteria 35 to mammals 31 , 36 ., The disruption of many Arabidopsis cell cycle regulators has been shown to affect both endoreduplication and cell size ( reviewed in 37 ) ; however , these studies have examined the average responses of a whole population of cells and have not been able to resolve the timing of the responses of individual cells or how these individuals together generate a pattern 38 ., Here we ask how the temporal regulation of cell division , endoreduplication , and growth combine to create the pattern of giant cells and small cells in the sepal epidermis ., We use live imaging to determine the timing and position of each cell division in the outer ( abaxial ) sepal epidermis and track the lineages of these cells throughout early sepal development ., This information is used to create a computational model that captures the essential aspects of the dynamics of epidermal pattern formation and reproduces them in silico ., The model is then tested against the measured in vivo cell size distribution ., We show that the distribution of cell sizes is formed through variability: probabilistic decisions to enter endoreduplication , noise in the duration of the cell cycle , and variation in the daughter cell sizes ., Finally , we show that the model is predictive in that changing model parameters generates the phenotypes of mutants ., The largest group of cells in the outer ( abaxial ) sepal epidermis of Arabidopsis appears to be a distinct class in that they bulge from the plane of the epidermis ( Figure 1A–C ) ., We designate these as giant cells ( Figure 1B–C ) ., Giant cells average 11 , 000 µm2 ( ±5 , 400 µm2 s . d . ) in area and 360 µm ( ±150 µm s . d . ) in length ( n\u200a=\u200a62 ) ., The longest giant cells can reach 800 µm ., The giant cells are interspersed between smaller cells , which are level with the plane of the epidermis ., Both giant cells and small cells have a range of sizes and constitute the pavement cells , which are the primary epidermal cell type that serve to form a protective barrier for the organ ., In addition the epidermis contains hair cells ( trichomes , which are not present on all sepals ) and guard cells , which constitute 29% ( ±3% s . d . , n\u200a=\u200a12 sepals ) of the cells in the outer sepal epidermis ., Guard cells surround the stomatal pores and regulate gas exchange 39 ., Large cells in plants are typically highly endoreduplicated; however , Galbraith et al . had previously found that floral buds do not contain endoreduplicated cells 40 ., To address this discrepancy , we used flow cytometry to measure nuclear DNA content in mature sepals ., Only non-endoreduplicated nuclei were detected in the internal cells of the sepal ( 2C and 4C ) , indicating that endoreduplication occurs in the epidermis where 1 . 0%±0 . 01% of the cells are 16C and 5 . 5%±1 . 4% of the cells are 8C ( Figure 1D–E; ±95% confidence interval , n\u200a=\u200a5 replicates totaling 31 , 744 nuclei ) ., This result parallels the leaf epidermis where the giant cells are also 16C 7 indicating that in both leaves and sepals , pavement cells can undergo as many as three endocycles ., We next verified that cell size largely correlates with DNA content in sepals as had been observed in leaves 7 ., DNA content correlates directly with cell area with an R-squared correlation value of 0 . 82 , indicating that about 80% of the variation in cell size can be explained by differences in endoreduplication ( Figure 1F ) ., Giant cells are primarily 16C , although occasionally 8C giant cells are found ., Therefore endoreduplication is a primary determinant of cell size and we looked for mechanisms through which endoreduplication can control the cell size pattern ., We first drew a conceptual model to predict the cell division pattern that gives rise to the pattern of giant cells and small cells within a small region of the sepal ( Figure 1G ) ., Growth and cell division in the epidermis are constrained to maintain a single clonal cell layer 41 ., Previously , Traas et al . had hypothesized that in such a constrained tissue , cell size is controlled through the timing of endoreduplication 2; however , this hypothesis has not been tested ., If all of the cells grow at the same rate , then the earlier a cell stops dividing and enters endoreduplication , the larger it becomes while the other cells continue to divide , thus retaining their small size ., We hypothesize that after a mitotic division period each cell has three cell cycles , which can be either mitotic or endocycles ( Figure 1G ) ., We term these three cell cycles the patterning cell cycles since they will generate the cell size distribution ., We further hypothesize that the decision of each cell to endoreduplicate is random and is governed by a probability that may change in time ( Figure 1G ) ., At the first cell cycle , each cell makes the random decision with probability p1 to endoreduplicate to 4C and double its area , or with probability 1-p1 to divide and remain 2C ., Once a cell has decided to endoreduplicate , it continues to endocycle and cannot resume mitotic divisions 42 , although exceptions in specialized circumstances have been observed 43 ., Therefore , in the next cell cycle , all of the 4C cells endoreduplicate to 8C and grow to 4 times their original area ., Simultaneously , the 2C cells decide to endoreduplicate to 4C with probability p2 and the remaining cells divide ., In the final patterning cell cycle , those cells that decided to endoreduplicate earliest become the 16C giant cells ., The remaining 2C cells decide to endoreduplicate with probability p3 or divide and become the smallest cells ., The specialized division patterns of stomatal development follow at the end of this process and 2C cells continue to divide with probability ps ( Figure 1G ) ., In this model , the timing of entry into endoreduplication generates the spatial pattern of cell size within a small region of the sepal ., To test our hypothesis that timing of endoreduplication determines the final cell size , we tracked cell divisions and endoreduplication in living sepal primordia ( Figure 2 ) ., Live imaging confirms that giant cells stop dividing and begin endoreduplicating in the young sepal primordium while smaller cells continue to divide as predicted ( n\u200a=\u200a109 lineages from 3 sepals; Figure 2; Videos S1–S3 ) ., In the initial time frame just after the sepal primordia are formed , all of the nuclei are small , falling into approximately two size categories , indicating that all cells are still 2C or 4C depending on their stage in the cell cycle ( Figure 2A and C ) ., In plant cells , nuclear size is highly linearly correlated with DNA content 42 , 44 , and the expression of the H2B-YFP fusion protein has been shown to have no effect on cell cycle dynamics 45 ., From this early time point the giant cells never divide and their nuclear size increases , indicating that they are endoreduplicating ( Figure 2B arrows ) ., Generally we find that endoreduplicating cells do not divide , consistent with our hypothesis , but on rare occasions an endoreduplicating cell has been observed to divide ( Video S3 ) ., During this same 3-d period , the smaller cells undergo primarily one to four rounds of division although a few cells undergoing five rounds are present ( Figure 2C ) ., This is consistent with the hypothesis that small cells have only three patterning cell cycles plus a couple of stomatal divisions ., The differentiation of stomata is observed near the top of the sepal at the end of the 3-d period ( Figure 2B ) ., To confirm that the cell size corresponds with the division pattern , we tracked the cell size in six lineages ( Figures 2D and S1; Video S2 ) ., Through the first 24 h the sizes of all the cells are relatively uniform ., After 24 h , the size of the giant cells steadily increases as they endoreduplicate ., In contrast , each time a cell divides the volume is split , resulting in two smaller cells in the small cell lineages ., We developed a computational model to reproduce the cell size pattern of the approximately 1 , 600 cells ( Figure S2J ) in the wild type sepal epidermis ( Figure 3G , Video S4 ) ., Although the conceptual model ( described above , Figure 1G ) reproduces a local area of the sepal , without a very large starting population of cells , it fails to produce the whole organ ., To determine how the complete pattern is formed , we used additional live imaging experiments as the basis for the creation of the Intercalary Growth Model ( IGM ) ( see Text S1 ) ., First , we imaged the emergence of the sepal primordium from the floral meristem to determine the number and arrangement of cells , which was used as the basis for the cellular template ( initial geometrical structure of cells ) for the model ., The sepal initiates from a region on the lateral side of the floral meristem that is approximately 8 cells wide ( Figure 3A , B; Video S5; n\u200a=\u200a2 sepal primordia ) ., This result is consistent with sectoring data that suggested the sepal emerges from a file of 8 cells on the floral meristem 46 ., Therefore , we initiate the model as a file of 8 cells ( Figure 3G ) ., The differentiation of stomata and the termination of cell divisions progress in a wave from the top to the bottom of the sepal as have been previously observed in the leaf 47 , 48 ., The top cells stop diving while cells in the remainder of the sepal continue to proliferate ( Figure 3C–D; Video S6 ) ., Furthermore , the cells in the whole top half of the young sepal primordium generate only the very tip of the mature sepal ( Figure 3E , F ) suggesting that they have undergone few further divisions ., Cells originating from the lower middle of a young sepal primordium form the top half of the sepal as they undergo the patterning divisions to create giant cells and small cells ., In contrast the bottom cells in the sepal primordium proliferate to give rise to the whole bottom half of the mature sepal , indicating that they continue proliferative mitotic divisions and only enter the patterning division stage later ., We incorporate this wave of progressive maturation into the IGM model by allowing the basal layer of cells , which we call the generative layer , to proliferate giving rise to apical daughters or additional generative layer cells throughout sepal development ( Figure 3G ) , similar to the concept of the intercalary meristem of grass leaves 49 ., This generative layer starts as a file of 8 cells as determined by imaging the initiation of the sepal ., Although the generative layer is an oversimplification , a population of cells within the base of the sepal generally maintains their ability to proliferate ., In the model , the upper daughter cells of the generative layer enter the patterning cell divisions as described in the conceptual model , giving these cells three cell cycles in which to divide or endoreduplicate plus entry into stomatal development ( Figures 1G , 3G; Video S4; stomatal development is not modeled ) ., To find the probabilities with which cells enter endoreduplication at each cell cycle ( p1 , p2 , and p3 ) we fit a population model similar to 50 ( see Text S1 for details ) to the final distribution of endoreduplicated cells as determined by flow cytometry ( Figure 1D ) ., The probability of endoreduplicating increases as cell cycles progress from a low value of p1 to a high value of p3 ( see Text S1 ) ., For the cells that divide , only horizontal or vertical division planes are allowed ., The plane is chosen that produces daughter cells with the length to width ratio closest to 2∶1 ., As a result cells divide in both orientations in the model ( Video S4 ) , similar to the living sepal where both planes of division are commonly observed ( Videos S1–S3 ) ., At the end of these three patterning cell cycles , the cells stop dividing , stop growing , and their size is measured ., Although these cells have stopped growing , in the visualization of the model , they continue to expand due to constraints of the geometrical model ( see Text S1 ) ., Thus the wave of termination in cell division observed arises naturally from the model as the upper cells terminate after their cell cycles while the subsequent progeny of the generative layer simultaneously start their patterning cell cycles ( Figure 3G ) ., Sepal development progresses until about 1 , 600 cells are produced , similar to wild type sepals ( Figure S2J ) ., We conclude that repeating the patterning process with each new set of cells arising from the generative layer appears similar to a sepal in that the wave of proliferation terminates from the top ., Furthermore , the model produces a sufficient number of cells and we next determined whether the cell size pattern produced matches the in vivo pattern ., We tested whether the model can predict the cell size distribution in the sepal epidermis ., If only the probability of endoreduplication is included , the IGM model produces four discrete cell sizes , one for each cell ploidy in the sepal epidermis ( Figure 3H ) ., These results correspond with our conclusion that the timing of endoreduplication and hence the number of endocycles completed is the major determinant of cell size ., To test whether the sepal cells fall into four strict cell size categories as predicted by the model , we measured the in vivo cell areas through semi-automated image processing ., This dataset was not used in the generation of the model and is therefore an independent test of the model ., In this dataset , the areas of pavement cells range from 45 µm2 to 17 , 414 µm2 with no clear distinct size classes although presumptive 2C and 4C peaks are visible ( n\u200a=\u200a3 , 295 cells from 12 sepals ) ( Figure 3J ) ., The higher level of cell size variability observed in real sepals than produced by the model when only endoreduplication is allowed to vary ( Figure 3H ) suggested that although endoreduplication is the primary determinant of cell size , other factors also contribute to the distribution of cell sizes around the mean established for each ploidy level ., To identify the factors that contribute to variability in cell size , we reexamined the wild type live imaging results ( Figure 2 ) ., First , contrary to the original assumptions of the hypothesis , live imaging shows that the lengths of the cell cycles are not equal and divisions are not synchronous ( Figure 2C–D; Figure S1 ) ., For example , the two brown daughters at 24 h have divergent cell cycle times ( Figure 2C ) ., The upper daughter divides twice by 48 h , while the lower daughter divides once by 60 h ., Throughout this longer cell cycle , the bottom daughter grows and reaches a larger size before division than the upper neighbors with faster cell cycles , indicating that cell cycle time is one of the primary factors that add variability to the cell size around the mean established by the ploidy of the cell ., We measured the distribution in cell cycle times from the live imaging data and found that it was a broad distribution ( Figure 3I ) ., We sampled cell cycle times in the IGM from this distribution to reproduce the asynchrony observed in vivo ( Video S4; see also Text S1 ) ., As a result , for each ploidy the in silico cells form a distribution around the mean cell size ( Figure 3K ) ., Second , contrary to the original assumptions of the hypothesis , normal pavement cell division does not generally produce daughter cells with precisely equal areas ., The standard deviation in daughter cell sizes is 8 . 5% ., In addition , the divisions in the stomatal lineage are highly asymmetric as previously described ( Figure S1 ) 39 ., Therefore we select the cell division plane in the IGM to create daughter cells with a 10% standard deviation in areas ( Figure 3G; Video S4 ) ., With asynchronous cell cycles and slightly unequal divisions , the IGM produces a wide distribution of cell sizes similar to that of the in vivo sepal ( Figure 3K ) ., If additional divisions to represent the stomatal lineage are included , the cell size distribution produced by the model is not significantly different from in vivo cell size distribution , indicating that the simple rules of the model are sufficient to predict the in vivo cell size pattern ( see Text S1 for statistics ) ., To further test the model , we used it to make two predictions about how changing the parameters affects the cell size pattern ., First , when we increase the probability of entering endoreduplication at the first cell cycle , p1 , more cells enter endoreduplication early and consequently the resulting simulated sepals have more giant 16C cells at the expense of small cells ( Figure 4A and Video S7 ) ., Second , at the other extreme , if we set p1 equal to 0 , all of the cells divide in the first cell cycle and the model produces sepals with no giant 16C cells but a distribution of the smaller cell sizes ( Figure 4E and Video S8 ) ., Based on these results we predict that a dramatic alteration in the cell size pattern should reflect a change in the probability of endoreduplication at a given time ., To test the first prediction biologically , we identified plants with an altered cell size pattern in the sepal epidermis ., When the cell cycle inhibitor KIP RELATED PROTEIN1 ( KRP1 ) is moderately overexpressed in the epidermis ( pATML1::KRP1 ) , numerous giant cells form in the sepals ( Figure 4B–D compare to Figure 1A–C ) 51 ., While strong expression of cell cycle inhibitors in the KRP family has been shown to block the cell cycle entirely , moderate levels of KRP overexpression result in increased endoreduplication 52 ., pATML1::KRP1 sepals contain 1 . 8% ( ±0 . 5% s . d . ) 16C cells , nearly double the percentage in wild type sepals ( 1 . 0%±0 . 1% s . d . ) ( Figure 4I ) ., Although the pATML1::KRP1 sepals appear to be covered with giant cells , the actual number of giant cells is roughly doubled , which matches the increase in ploidy observed through flow cytometry ., The large size of the giant cells means that a small increase in cell number causes giant cells to cover much of the area of the sepal ., We then asked whether the time at which cells start to endoreduplicate was altered in real pATML1::KRP1 sepals ., Live imaging of pATML1::KRP1 sepals revealed that a larger fraction of the cells stopped dividing and started endoreduplicating at early stages of sepal development corresponding to an increase in the probability of entering endoreduplication ( Figure 5A–D; Videos S9–S11 ) ., The nuclei in pATML1::KRP1 sepal primordia appear enlarged earlier than wild type giant cell nuclei do , suggesting entry into endoreduplication occurs earlier in pATML1::KRP1 than wild type ( Figure 5A , C ) ., We conclude that promoting early endoreduplication through overexpression of the cell cycle inhibitor KRP1 produces increased numbers of giant cells in the pattern as predicted by the computational model ., To isolate plants at the other extreme of the cell size distribution corresponding to the second prediction of the model , we conducted a mutagenesis screen for plants lacking giant cells in the sepals ( see Supplemental Procedures Text S1 ) ., One of the mutants isolated was named loss of giant cells from organs ( lgo ) because the giant cells in both leaves and sepals were absent ( Figure 4F–H compare to Figure 1A–C; Figure S2H–I ) ., 16C cells are absent in the lgo sepal epidermis and the proportion of 8C cells is reduced ( Figure 4I ) ., We conclude that the LGO gene is necessary for giant cell formation in the plant ., As a direct test of the model we asked whether all lgo cells continue to divide throughout early development and enter endoreduplication later than wild type as predicted ., The model passes the test since live imaging shows that all of the cells in the lgo sepal divide at least once after wild type giant cells have stopped dividing , indicating that entry into endoreduplication is delayed ( Figure 5D–G; Videos S12–S14 ) ., The division patterns of all of the cell lineages of lgo appear similar to the wild type small cell lineages ( Figure 5G compared with Figure 2C ) ., Through positional cloning we found that the LGO gene encodes a member of a plant specific cell cycle inhibitor family ( SIAMESE RELATED 1 At3g10525 ) ( Figure 4J; Figure S2A–D; see Text S1 for details ) 53–55 ., SIAMESE , the founding member of this cell cycle inhibitor gene family , promotes endoreduplication in the hair cells ( trichomes ) , which are another highly endoreduplicated epidermal cell type ( 32C–64C ) 53 , 55 ., In siamese mutants , the hair cells divide when they should endoreduplicate , creating multicellular hair cells 55 ., This phenotype parallels the loss of giant cells in lgo due to extra divisions instead of endoreduplication ., Consistent with the tradition of renaming gene family members when the function is determined 56 , 57 , we hereby rename the SIAMESE RELATED 1 gene as LGO ., LGO is broadly expressed throughout the plant 53 ., Overexpression of LGO produces a phenotype similar to pATML1::KRP1 with additional giant cells ( Figure S3 ) ., Taking the results on the loss of lgo function and the gain of KRP1 function together , we conclude that cell cycle inhibitors are important for setting the timing of entry into endoreduplication and consequently the cell size pattern ., It is unclear whether LGO promotes the early entry of pavement cells into endoreduplication only at the first cell cycle or promotes endoreduplication at all cell cycles ., A higher proportion of 4C cells are found in lgo sepals ( Figure 4I ) ; however , the class of 4C cells includes both cells that have endoreduplicated and mitotic cells in the G2 phase after DNA replication of the cell cycle ., Previous work suggests that the extra 4C cells in lgo-1 mutants are likely to be mitotic cells that are in G2 ., Cell cycle inhibitors including LGO are postulated to bind to and inhibit CYCLIN D CDKA;1 complexes that regulate the transition from G1 to S phase 53 , 54 , 58 , 59 ., In the absence of lgo , the lack of this inhibition might be expected to increase the speed of entry into S phase of the cell cycle and consequently a greater fraction of the population of cells would be in G2 , adding to the 4C population ., A similar result was seen due to the overexpression of CYCLIN D3;1 , which increased the number of 4C cells , and these cells were determined to be cells in G2 60 , 61 ., In any case , the functional effect of the lgo-1 mutation is to delay endoreduplication , which causes an absence of giant cells in sepals ., We measured DNA content in the rosette leaves of lgo-1 and wild type plants ., Highly endoreduplicated 32C cells remain in lgo leaves ( Figure S2E ) ., Examining the leaves reveals that giant cells are absent similar to sepals corresponding with the reduction in 16C cells ( Figure S2H–I ) ; however , other endoreduplicated cell types such as hair cells ( trichomes 32C ) ( Figure S2F–G ) and the enlarged cells covering the midvein develop normally , which explains the presence of 32C cells in lgo leaves ., LGO is expressed in trichomes so it is possible that the function of LGO in trichomes is obscured by redundancy with SIAMESE 62 ., These results indicate that the function of LGO is developmentally specific to regulating endoreduplication in epidermal pavement cells to produce the cell size pattern ., Similarly the SIAMESE gene , which is closely related to LGO , promotes endoreduplication specifically in trichomes 55 ., We further tested whether the IGM model could reproduce the cell size distributions of lgo-1 and pATML1::KRP1 ., We measured the in vivo cell size distributions of lgo and pATML1::KRP1 using semi-automated image processing as we had for wild type sepals ( Figures 6A and 3J ) ., As expected , the largest cells corresponding to the giant cells are absent in lgo-1 and increased in pATML1::KRP1 ( Figure 6A arrow ) ., In addition , the entire cell size distribution curves are shifted slightly relative to wild type: pATML1::KRP1 toward the right , indicating an overall increase in cell size , and lgo slightly toward the left , indicating a slight decrease in cell sizes ., Measuring the cell cycle time distributions from the live imaging data showed that the average cell cycle time for pATML1::KRP1 is longer than wild type and for lgo-1 is shorter than wild type ( Figure 6C–D; for statistics see Text S1 ) ., Sampling from these cell cycle time distributions as well as changing the probability of endoreduplication in the model produces cell size distributions reflecting both the effect on giant cells and the overall shifts in the cell size curves ( Figure 6B ) ., Again , this is an independent test of the model because the cell size data were not used to generate the model ( see Text S1 ) ., The shifts in the cell size curves confirm that the length of the cell cycle and its regulation by cell cycle inhibitors is an important determinant of the cell size pattern ., While it is clear that endoreduplication promotes the enlargement of individual cells through lack of division , it is not clear whether endoreduplication increases the overall growth of the organ or tissue as has been proposed 7 , 30 , 40 ., Contrary to this idea , the IGM model assumes that giant cells grow at the same rate per unit length as small cells , suggesting that changes in endoreduplication and the resulting cell size pattern will not affect the growth or resulting size of the sepal ., We first tested the validity of this assumption by reexamining the live images of wild type sepals ( Figure 2 ) ., The red giant cell and the entire neighboring brown cell lineage grow to the same size , supporting the assertion that growth is uniform in a local area and that cell size is controlled by the division of space created by the new cell walls ( Figure 2C , yellow line ) ., The endoreduplicated cell is not growing faster per unit wall length than the dividing cells ., Although growth of neighboring cells is uniform , growth on a global scale is not , as indicated by the smaller size of the developing giant cells in the bottom of the sepal than those at the top ( Figure 2B compare red giant cell to the blue outlined giant cell ) ., Understanding the global control of growth of the sepal is an open question for the future ., The assumption of equivalent growth of neighboring cells is true on the local level and we retain it in the IGM ., In terms of the overall growth of the whole plant or the sepal , altering the proportion of cells endoreduplicating had little effect ( Figure 7A–B ) ., Contrary to expectation , increased endoreduplication in pATML1::KRP1 plants has a slight inhibitory effect on growth ( p<0 . 001 ) , whereas decreased endoreduplication in lgo plants causes a slight increase in growth ( p<0 . 5 ) ( Figure 7B ) ., These results demonstrate that endoreduplication does not increase the overall growth of the organ ., The computational model presented here is a step toward the development of a complete cellular model of the formation of a plant lateral organ ., Combined with previous modeling of plant morphodynamics 20 , 23 , 24 , it is a step closer to achieving a complete computational model of a plant ., Our sepal model successfully reproduces the pattern of cell sizes in a developing sepal epidermis , showing that this pattern arises entirely from three parameters—the probability of a cell entering endoreduplication , variations in time of cell division , and variability in daughter cell size at division ( F

Introduction, Results, Discussion, Materials and Methods

How growth and proliferation are precisely controlled in organs during development and how the regulation of cell division contributes to the formation of complex cell type patterns are important questions in developmental biology ., Such a pattern of diverse cell sizes is characteristic of the sepals , the outermost floral organs , of the plant Arabidopsis thaliana ., To determine how the cell size pattern is formed in the sepal epidermis , we iterate between generating predictions from a computational model and testing these predictions through time-lapse imaging ., We show that the cell size diversity is due to the variability in decisions of individual cells about when to divide and when to stop dividing and enter the specialized endoreduplication cell cycle ., We further show that altering the activity of cell cycle inhibitors biases the timing and changes the cell size pattern as our model predicts ., Models and observations together demonstrate that variability in the time of cell division is a major determinant in the formation of a characteristic pattern .

How the regulation of cell division contributes to cell patterning in an organ is an important question in developmental biology ., We chose to study cell size patterning in the Arabidopsis sepal , the green leaf-like floral organ , because it contains a wide range of cell sizes—from giant cells to small cells—and because sepals , as the outermost floral organ , are accessible for live cell imaging ., In this study we image the early development of living sepals and follow each of the cell divisions to determine how cells of different sizes are created ., We observe that the times when cells divide and when they stop dividing are highly variable ., Using computational modeling , we then show that a model in which these decisions are made randomly with the probabilities we observed in vivo can recapitulate the production of the range of cell sizes seen in the living sepal ., We also show that changing these probabilities within our model robustly predicts the novel cell patterns observed in mutant plants with altered cell division timing ., We conclude that probabilistic decisions of individual cells—rather than deterministic , organ-wide mechanisms—can produce a characteristic and robust cell size pattern in development .

developmental biology/plant growth and development, developmental biology/pattern formation, plant biology/plant growth and development, developmental biology/morphogenesis and cell biology

Live cell imaging and computational modeling explains how variability in the timing of cell division generates a characteristic pattern of cell sizes during development.

journal.pcbi.1004933

Structural Determinants of Misfolding in Multidomain Proteins

Protein misfolding and aggregation are well-known for their association with amyloidosis and other diseases 1 , 2 ., Proteins with two or more domains are abundant in higher organisms , accounting for up to 70% of all eukaryotic proteins , and domain-repeat proteins in particular occupy a fraction up to 20% of the proteomes in multicellular organisms 3 , 4 , therefore their folding is of considerable relevance 5 ., Since there is often some sequence similarity between domains with the same structure , it is easily possible to imagine that multidomain proteins containing repeats of domains with the same fold might be susceptible to misfolding ., Indeed , misfolding of multidomain proteins has been observed in many protein families 6 ., Single molecule techniques have been particularly powerful for studying folding/misfolding of such proteins , in particular Förster resonance energy transfer ( FRET ) and atomic force microscopy ( AFM ) ., For instance , recent studies using single-molecule FRET , in conjunction with coarse-grained simulations , have revealed the presence of domain-swapped misfolded states in tandem repeats of the immunoglobulin-like domain I27 from the muscle protein Titin 7 ( an example is shown in Fig 1e ) ., Domain-swapping 2 involves the exchange of secondary structure elements between two protein domains with the same structure ., Remarkably , these misfolded states are stable for days , much longer than the unfolding time of a single Titin domain ., The domain-swapped misfolds identified in the Titin I27 domains are also consistent with earlier observations of misfolding in the same protein by AFM , although not given a structural interpretation at the time 8 ., In addition , AFM experiments have revealed what appears to be a similar type of misfolding in polyproteins consisting of eight tandem repeats of the same fibronectin type III domain from tenascin ( TNfn3 ) 9 , as well as in native constructs of tenascin 8 , and between the N-terminal domains of human γD-crystallin when linked in a synthetic oligomer 10 ., In addition to domain-swapped misfolding , an alternative type of misfolded state is conceivable for polyproteins in which the sequences of adjacent domains are similar , namely the formation of amyloid-like species with parallel β-sheets ., Theoretical work in fact made the prediction that such species would be formed in tandem repeats of titin domains 11 ., Recently , time-resolved single-molecule FRET experiments on tandem domains of I27 have revealed a surprising number of intermediates formed at short times , which include an unexpected species that appears to be consistent with the previously suggested amyloid-like state 12 ., However , since only the domain-swapped species persisted till long times , and therefore are the most likely to be problematic in cells , we focus on their formation in this work ., A simplified illustration of the mechanism for folding and misfolding , based on both coarse-grained simulations as well as single-molecule and ensemble kinetics 7 , 12 , is shown in Fig 1 , using the Titin I27 domain as an example ., Starting from the completely unfolded state in Fig 1a , correct folding would proceed via an intermediate in which either one of the domains is folded ( Fig 1b ) , and finally to the fully folded state , Fig 1c ., The domain-swapped misfolded state , an example of which is shown in Fig 1e , consists of two native-like folds which are in fact assembled by swapping of sequence elements from the N- and C-terminal portions of the protein ., The final structure in Fig 1e comprises what we shall refer to as a “central domain” formed by the central regions of the sequence ( on the left in Fig 1e ) and a “terminal domain” formed from the N- and C-termini ( on the right ) ., The intermediate structure in Fig 1d , suggested by coarse-grained simulations 7 , and supported by experiment 12 , has only the central domain folded ., This central domain can itself be viewed as a circular permutant 13 of the original native Titin I27 structure , as discussed further below ., While domain-swapped misfolding of tandem repeats has been identified in a number of proteins to date , there are several other proteins for which it does not occur to a detectable level ., For instance , extensive sampling of repeated unfolding and folding of a polyprotein of Protein G ( GB1 ) by AFM revealed no indication of misfolded states , in contrast to Titin 14 ., Similarly , early AFM studies on polyUbiquitin also did not suggest misfolded intermediates in constant force unfolding 15–20 , and lock-in AFM studies of refolding 21 were fully consistent with a two-state folding model , without misfolding ., More recent AFM 22 studies have suggested the formation of partially folded or misfolded species , which have been attributed to partial domain swapping in simulations 23 , but these are qualitatively different from the fully domain-swapped species considered here ., Therefore , it is interesting to ask the general questions: when included in tandem repeats , what types of protein structures are most likely to form domain-swapped misfolded states , and by what mechanism ?, In order to investigate the misfolding propensity of different types of domains , we have chosen seven domains , based on, ( i ) the superfamilies with the largest abundance of repeats in the human genome 24 ,, ( ii ) proteins for which some experimental evidence for misfolding ( or lack thereof ) is available and, ( iii ) proteins for which data on folding kinetics and stability is available for their circular permutants ( only some of the proteins meet criterion, ( iii ) ) ., The circular permutant data are relevant because the misfolding intermediates suggested by simulations and experiment 7 , 12 can be viewed as circular permutants of the original structure ( Fig 1d ) ., Each of the chosen proteins is illustrated in Fig 2 and described briefly in Materials and Methods ., We study the folding and misfolding of the seven protein domains , using the same structure-based model as that successfully employed to treat Titin I27 7 , 12 ., Molecular simulations are carried out to characterize the possible structural topologies of the misfolded intermediates and the mechanism of their formation ., Our model is consistent with available experimental information for the systems studied , in terms of which proteins misfold and what misfolded structures they tend to form ., We then investigated what factors influence the propensity of multidomain proteins to misfold ., The simplest rationalization of the propensity of a multidomain protein for domain-swapped misfolding would seem to be offered by parameterizing a kinetic model based on the scheme shown in Fig 1 , particularly for the steps Fig 1a–1b versus 1a–1d ., We hypothesized that the propensity to misfold might be characterized in terms of the folding kinetics of the isolated circular permutants representing the domain-swapped intermediates in Fig 1d ., However , contrary to this expectation , we found that the stability of such isolated domains , rather than their folding rate , is the main determinant of misfolding propensity ., Although superficially this appears to differ from previously suggested kinetic models 12 , it is completely consistent , with a specific interpretation of the rates ., Building on this understanding , we developed a very simplified model which can be used to predict which domains are likely to be susceptible to domain-swapped misfolding ., Finally , we have investigated the effect of the composition and length of the linker between the tandem repeats on the misfolding propensity ., Tandem Src homology 3 ( SH3 ) domains ( Fig 2a ) are widely found in signal transduction proteins and they share functions such as mediating protein-protein interactions and regulating ligand binding 25 ., Kinetic and thermodynamic properties of native and all the possible circular permutations of SH3 single domain have been well characterized 26 ., Two different circular permutant constructs of the sequence are known to fold to a circularly permuted native conformation ( PDB accession codes are 1TUC and 1TUD ) that is similar to the wild-tpe ( WT ) protein 26 ., With a similar function to the SH3 domains , Src homology 2 ( SH2 ) domains ( Fig 2b ) are also involved in the mediation of intra- and intermolecular interactions that are important in signal transduction 27 ., The SH2 domains are well-known from crystallographic analysis to form metastable domain-swapped dimers 28 , 29 ., Fibronectin type III ( fn3 ) domains ( Fig 2c ) are highly abundant in multidomain proteins , and often involved in cell adhesion ., We have chosen to study the third fn3 domain of human tenascin ( TNfn3 ) , which has been used as a model system to study the mechanical properties of this family ., Single-molecule AFM experiments revealed that a small fraction ( ∼ 4% ) of domains in native tenascin ( i . e . the full tenascin protein containing both TNfn3 and other fn3 domains ) 8 , with a similar signature to that observed for I27 ., Subsequently , misfolding events have been identified in a polyprotein consisting of repeats of TNfn3 only 9 ., Interestingly , a structure has been determined for a domain-swapped dimer of TNfn3 involving a small change of the loop between the second and third strand 30 ., PDZ domains ( Fig 2d ) are one of the most common modular protein-interaction domains 31 , recognizing specific-sequence motifs that occur at the C-terminus of target proteins or internal motifs that mimic the C-terminus structurally 32 ., Naturally occurring circularly permuted PDZ domains have been well studied 33–35 , and domain-swapped dimers of PDZ domains have been characterized by NMR spectroscopy 36 , 37 ., Titin ( Fig 2e ) is a giant protein spanning the entire muscle sarcomere 38 ., The majority of titin’s I-band region functions as a molecular spring which maintains the structural arrangement and extensibility of muscle filaments 39 ., The misfolding and aggregation properties of selected tandem Ig-like domains from the I-band of human Titin ( I27 , I28 and I32 ) have been extensively studied by FRET experiments 7 , 24 ., In the earlier work on tandem repeats of I27 domains , around 2% misfolding events were reported in repeated stretch-release cycles in AFM experiments 8 ., A slightly larger fraction ( ∼ 6% ) of misfolded species was identified in single-molecule FRET experiments and rationalized in terms of domain swapped intermediates , captured by coarse-grained simulations 7 , 11 ., In contrast , with the above misfolding-prone systems , there are certain polyprotein chains have been shown be resistant to misfolding , according to pulling experiments ., For instance little evidence for misfolding was identified in a polyprotein of GB1 14 ( Fig 2g ) , with more than 99 . 8% of the chains ( GB1 ) 8 folding correctly in repetitive stretching–relaxation cycles 14 ., Lastly , we consider polyUbiquitin ( Fig 2f ) , for which there is conflicting experimental evidence on misfolding ., Initial force microscopy studies showed only the formation of native folds 15 , with no misfolding ., Later work suggested the formation of collapsed intermediates 22 , however the signature change in molecular extension of these was different from that expected for fully domain-swapped misfolds ., A separate study using a lock-in AFM 21 found Ubiquitin to conform closely to expectations for a two-state folder , without evidence of misfolding ., For this protein , there is a strong imperative to avoid misfolding , since Ubiquitin is initially expressed as a tandem polyUbiquitin chain in which adjacent domains have 100% sequence identity , yet this molecule is critical for maintaining cellular homeostasis 40 ., A coarse grained structure-based ( Go-like ) model similar to the earlier work is employed for the study here 7 , 41 ., Each residue is represented by one bead , native interactions are attractive and the relative contact energies are set according to the Miyazawa–Jernigan matrix ., The model is based on that described by Karanicolas and Brooks 41 , but with native-like interactions allowed to occur between domains as well as within the same domain , as described below 7 ., All the simulations are run under a modified version of GROMACS 42 ., For the seven species we studied in this work , the native structures of single domains that were used to construct the models for SH3 , SH2 , PDZ , TNfn3 , Titin I27 , GB1 and Ubiquitin correspond to PDB entries 1SHG 43 , 1TZE 44 , 2VWR , 1TEN 45 , 1TIT 46 , 1GB1 47 and 1UBQ 48 respectively ., For the single domains of SH3 ( 1SHG ) , TNfn3 ( 1TEN ) and GB1 ( 1GB1 ) , additional linker sequences of Asp-Glu-Thr-Gly , Gly-Leu and Arg-Ser , respectively , are added between the two domains to mimic the constructs used in the corresponding experiments 9 , 14 , 26 ., Construction of the Titin I27 model was described in our previous work 7 ., In order to allow for domain-swapped misfolding , the native contact potentials within a single domain are also allowed to occur between corresponding residues in different domains , with equal strength ., Specifically , considering each single repeat of the dimeric tandem that has L amino acids , given any pair of residues ( with indices i and j ) that are the native interactions within a single domain , the interaction energy for the intradomain interaction ( Ei , j ( r ) ) is the same as the interdomain interaction between the residue ( i or j ) and the corresponding residue ( j + L or i + L ) in the adjacent domain , i . e . Ei , j ( r ) = Ei+L , j ( r ) = Ei , j+L ( r ) = Ei+L , j+L ( r ) ., To investigate the folding kinetics of the dimeric tandem , a total of 1024 independent simulations are performed on each system for a duration of 12 microseconds each ., Different misfolding propensities are observed at the end of the simulations ., With the exception of Ubiquitin and GB1 , the vast majority of the simulations reached stable native states with separately folded domains ., A small fraction of simulations form stable domain-swapped misfolded states ., All the simulations are started from a fully extended structure , and run using Langevin dynamics with a friction of 0 . 1 ps−1 and a time step of 10 fs ., We note that all the generated domain-swapped misfolding structures , containing the central and terminal domains , can be monitored by a reaction coordinate based on circular permutated native-like contact sets ., Each circularly permuted misfold can be characterized according to the loop position K in sequence where the native domain would be cut to form the circular permutant ( K = 0 corresponds to the native fold ) ., If a native contact Cnative = ( i , j ) exists between residues i and j in the native fold , the corresponding native-like contacts for the central ( Cin ( K ) ) and terminal domains ( Cout ( K ) ) of the domain swapped conformation are generated as, C i n ( K ) = ( i + Θ ( K − i ) L , j + Θ ( K − j ) L ) , C o u t ( K ) = ( i + Θ ( i − K ) L , j + Θ ( j − K ) L ) ,, where Θ ( x ) is the Heaviside step function and L is the length of each single domain ( plus interdomain linker ) ., Sin , K is the set of native-like contacts Cin of the central domain , and Sout , K is the set of all the native-like contacts Cout of the terminal domain ., Sin , K and Sout , K can be used to define a contact-based reaction coordinate to analyze the kinetics of the dimeric tandem misfolding ., The corresponding fraction of contacts for the central domain could be calculated by:, QK ( χ ) =1N∑ ( i , j ) ∈Sin , K11+eβ ( rij ( χ ) −λrij0 ) , ( 1 ), where N is the total number of domain swapped contacts , SK = Sin , K ∪ Sout , K ( equal to the total number of native contacts ) , rij ( χ ) is the distance between residue i and j in the protein configuration χ ., r i j 0 is the corresponding distance in the native structure for native-like contacts , β = 50 nm−1 and λ = 1 . 2 is used to account for fluctuations about the native contact distance ., The equilibrium properties of a single domain of each system are obtained from umbrella sampling along the native contacts Q as the reaction coordinate ., The obtained melting temperature of each system is listed in Table A in S1 Text ., A temperature at which the folding barrier ΔGf of approximately ∼ 2 . 5 kBT is chosen for the 2-domain tandem simulations for reasons described below ., The stability ΔGs is calculated as, Δ G s = - k B T ln ∫ Q ‡ 1 e - F ( Q ) / k B T d Q / ∫ 0 Q ‡ e - F ( Q ) / k B T d Q , ( 2 ), where kB and T are the Boltzmann constant and temperature respectively ., Q‡ is the position of the barrier top in F ( Q ) , separating the folded and unfolded states and F ( Q ) represents the free energy profile on Q . Barrier heights ΔGf were simply defined as ΔGf = G ( Q‡ ) − G ( Qu ) , where Qu is the position of the unfolded state free energy minimum on Q . We calculated the relative contact order 49 , RCOK of different circular permutants K via, RCO K = 1 L · N ∑ ( i , j ) ∈ S in , K | i - j | , ( 3 ), where L is the length of the single domain , and N is the total number of the native like contacts ( the same for different K ) ., Sin , K is the contacts set of the circular permutant corresponding to the “central domain” of the misfolded state ., Note that the contact order calculation here is using residue-based native contacts ( the same ones defined as attractive in the Gō model ) , instead of all atom native contacts ., An Ising-like model was built based on the native contact map , in which each residue is considered either folded or unfolded and so any individual configuration can be specified as a binary sequence , in a similar spirit to earlier work 50–52 ., Interactions between residues separated by more than two residues in the sequence are considered ., To simplify the analysis , we also consider that native structure grows only in a single stretch of contiguous native residues ( native segment ) , which means the configurations such as …UFFFUUUUU… or …UUUUUFFFU… are allowed , however , …UFFFUUUFFFU… is not allowed ( “single sequence approximation” ) 50 ., Each residue which becomes native incurs an entropy penalty ΔS , while all possible native contacts involving residues within the native segment are considered to be formed , each with a favourable energy of contact formation ϵ ., The partition function for such a model can be enumerated as:, Z = ∑ χ exp − G ( χ ) k B T = ∑ χ exp − n ( χ ) ϵ − N f ( χ ) T Δ s k B T , where kB and T are the Boltzmann constant and temperature ., G ( χ ) is the free energy determined by the number of native contacts n ( χ ) in the configuration χ , and the number of native residues , Nf ( χ ) ., The distribution of the microstates ( χ ) can be efficiently generated by the Metropolis-Hastings method with Monte Carlo simulation ., In each iteration , the state of one randomly chosen residue ( among the residues at the two ends of the native fragment and their two neighbouring residues ) is perturbed by a flip , from native to unfolded or from unfolded to native , taking the system from a microstate χ1 with energy E1 to a microstate χ2 with energy E2 ., The new microstate is subject to an accept/reject step with acceptance probability, P acc = min 1 , exp ( - E 2 - E 1 k B T ) ., ( 4 ) To mimic the folding stability difference between native and circular permutant folds , a penalty energy term Ep has been added whenever the native fragment crosses the midpoint of the sequence from either side ( the function θ ( χ ) above is 1 if this is true , otherwise zero ) ., That situation corresponds to formation of a domain-swapped structure , in which there is additional strain energy from linking the termini , represented by Ep ., We only use the Ising model here to investigate formation of the first domain ( either native or circular permutant ) , by rejecting any proposed Monte Carlo step that would make the native segment longer than the length of single domain , L ., In order to characterize the potential misfolding properties of each type of domain , we have used a Gō-type energy function based on the native structure ., Such models have successfully captured many aspects of protein folding , including ϕ-values 53 , 54 , dimerization mechanism 55 , 56 , domain-swapping 57–60 , and the response of proteins to a pulling force 61 , 62 ., More specifically , a Gō type model was used in conjunction with single-molecule and ensemble FRET data to characterize the misfolded states and misfolding mechanism of engineered tandem repeats of Titin I27 7 , 12 ., We have therefore adopted the same model ., Although it is based on native-contacts , it can describe the type of misfolding we consider here , which is also based on native-like structure ., Note that this model effectively assumes 100% sequence identity between adjacent domains , the scenario that would most likely lead to domain-swap formation ., It is nonetheless a relevant limit for this study , as there are examples in our data set of adjacent domains having identical sequences which do misfold ( e . g . titin I27 ) and those which do not ( e . g . protein G ) ., For each of the folds shown in Fig 2 , we ran a large number of simulations , starting from a fully extended , unfolded chain , for sufficiently long ( 12 μs each ) such that the vast majority of them reached either the correctly folded tandem dimer , or a domain-swapped misfolded state similar to that shown in Fig 1e for titin ., In fact , for each protein , a number of different misfolded topologies are possible , illustrated for the Src SH3 domain in Fig 3 ., Each of these domains , shown in conventional three-dimensional cartoon representation in the right column of Fig 3 and in a simplified two-dimensional topology map in the left column , consists of two native-like folded ( or misfolded ) domains ., For convenience , we call the domain formed from the central portion of the sequence the “central domain” and that from the terminal portions the “terminal domain” ., We have chosen to characterize each topology in terms of the position , K , in sequence after which the central domain begins ., Thus , the native fold has K = 0 , and all the misfolded states have K > 0 . Typically , because of the nature of domain swapping , K must fall within a loop ., Of course , there is a range of residues within the loop in question that could be identified as K and we have merely chosen a single K close to the centre of the loop ., This position , and the central domain , are indicated for the Src SH3 misfolded structures in Fig 3 ., We note that each of these central domains can also be considered as a circular permutant of the native fold , in which the ends of the protein have been joined and the chain has been cut at position K . With this nomenclature in hand , we can more easily describe the outcome of the folding simulations for the seven domain types considered in terms of the fraction of the final frames that belonged to the native fold , versus each of the possible misfolded states ., These final populations are shown in Table 1 ., We see that for five of the domains ( SH3 , SH2 , PDZ , TNfn3 , Titin I27 ) , misfolded structures are observed , with total populations ranging from 5–10% ., For the remaining two domains , Ubiquitin ( UBQ ) and protein G ( GB1 ) , no misfolded population is observed ., The ability to capture domain-swapped misfolds with simple coarse-grained simulations potentially allows us to investigate the origin of the misfolding , and its relation , if any , to the topology of the domain in question ., However , we also need to benchmark the accuracy of the results against experiment as far as possible , in order to show that they are relevant ., There are two main sources of information to validate our results ., The first is the overall degree of domain-swapped misfolding for those proteins where it has been characterized , for example by single molecule AFM or FRET experiments ., Qualitatively we do observe good agreement , where data is available: in experiment , domains which have been shown to misfold are TNfn3 ( AFM ) and Titin I27 ( AFM , FRET ) , which are both found to misfold here , while there is no detectable misfolded population for protein G ( AFM ) , again consistent with our results ., We also do not observe any misfolding for Ubiquitin , consistent with the lack of experimental evidence for fully domain-swapped species for this protein 15–23 ., Quantitatively , the fractional misfolded population is also consistent with the available experimental data ., For instance , the frequency of misfolded domains in native tenascin is ∼ 4% as shown by previous AFM experiments 8 , the misfolded population of I27 dimers is ∼5% in single-molecule FRET experiments 7 while the misfolded population of GB1 domains in polyproteins ( GB18 ) is extrememly low ( < 0 . 2% ) 14 ., Even though the observed misfolding population of the misfolded tandem dimer is low , it is potentially a problem considering that many of the multidomain proteins in nature have large number of tandem repeats , such as Titin which contains twenty-two I27 repeats 63 ., Recent FRET experiments on I27 tandem repeats have shown that the fraction of misfolded proteins increases with the number of repeats ., For the 3- and 8-domain polyproteins , the fraction of misfolded domains increases by a factor of 1 . 3 and 1 . 8 , respectively , relative to a tandem dimer 12 ., The second type of evidence comes from experimental structures of domain-swapped dimers ., For several of the proteins , bimolecular domain-swapped structures have been determined experimentally ., While no such structures have yet been determined for single-chain tandem dimers , we can compare the misfolded states with the available experimental data ., For each experimental example , we are able to find a corresponding misfolded species in our simulation with very similar structure ( related by joining the terminis of the two chains in the experimental structures ) ., The domain swapped dimers solved obtained from experiments ( Fig 4a , 4c , 4e and 4g ) are strikingly similar to the domain swapping dimeric tandem from simulations , which are the domain swapped SH3 domains when K ( sequence position after which the central domain begins ) = 37 ( Fig 4b ) , SH2 with K = 72 ( Fig 4d ) , TNfn3 with K = 28 ( Fig 4f ) and PDZ with K = 23 ( Fig 4h ) ., Most of these states have relatively high population among all the possible misfolds as observed from the simulations ( “Population” in Table 1 ) ., While the coverage of possible domain swaps is by no means exhaustive , the observed correspondence gives us confidence that the misfolded states in the simulations are physically plausible ., Having shown that the misfolding propensities we obtain are qualitatively consistent with experimental evidence ( and in the case of Titin I27 , in semi-quantitative agreement with single-molecule FRET ) , we set out to establish some general principles relating the properties of each domain to its propensity to misfold in this way ., We can start to formulate a hypothesis based on the alternative folding and misfolding pathways illustrated in Fig 1 ., Native folding has as an intermediate a state in which either the N- or the C-terminal domain is folded ., In contrast , on the misfolding pathway , the first step is formation of the central domain , followed by that of the terminal domain ., This parallel pathway scheme suggests that a descriptor of the overall misfolding propensity may be obtained from the rate of formation of a single correctly folded domain , relative to that of the central domain ( neglecting back reactions , because this are rarely seen in our simulations ) ., We can study the central domain formation in isolation , since these structures are just circular permutants of the native fold , i . e . the two proteins have the same sequence as the native , but with the position of the protein termini moved to a different point in the sequence , as is also found in nature 35 ., These structures can be thought of as originating from the native by cutting a specific loop connecting secondary structure elements ( the free energy cost of splitting such an element being too high ) , and splicing together the N- and C- termini ., In the context of the tandem dimers , the position at which the loop is cut is the same K that defines the start of the central domain in sequence ., We investigate the role of the central domain by characterizing the free energy landscape of the single domain of each system , as well as all of its possible circular permutants , using umbrella sampling along the reaction coordinate QK ., QK is exactly analogous to the conventional fraction of native contacts coordinate Q 64 , but defined using the corresponding ( frame-shifted ) contacts in the circular permutant pseudo-native structure ., The index K indicates the position along the sequence of the WT where the cut is made in order to convert to the circular permutant ., The free energy surfaces F ( QK ) of two representative systems , SH3 and Ubiquitin , are shown in Fig 5 , with the data for the remaining proteins given in the Fig A in S1 Text ., The free energy barrier height for folding ΔGf and the stability ΔGs are listed in the Table 1 ., The free energy plots indicate that the single domains of Ubiquitin and GB1 are stable only for the native sequence order , and not for any of the circular permutants ., Based on the type of misfolding mechanism sketched in Fig 1 , one would expect that unstable circular permutants would result in an unstable central domain , and consequently no stable domain-swappping misfolding would occur in the dimer folding simulations , as we indeed observe ., This is also consistent with previous studies of polyproteins of GB1 and Ubiquitin using using AFM experiments , which reveal high-fidelity folding and refolding 14 , 65 , 66 ., We note that only under very strongly stabilizing conditions is any misfolding observed for ubiquitin dimers: running simulations at a lower temperature ( 260 K ) , we observe a very small ( 1 . 3% ) population of misfolded states from 1024 trial folding simulations ., At a higher temperature of 295 K , once again no misfolding is observed ., In contrast to the situation for GB1 and Ubiquitin , all of the circular permutants of the SH3 domain in Fig 5 are in fact stable , although less so than the native fold ., The destabilization of circular permutants relative to native is in accord with the experimental results for the Src SH3 domain 26 ( rank correlation coefficient stabilities is 0 . 80 ) ., The other domains considered also have stable circular permutant structures ., This is consistent with the fact that all of these domains do in fact form some fraction of domain-swapped misfolded states ., The simplest view of the misfolding mechanism would be as a kinetic competition between the correctly folded intermediates versus the domain-swapped intermediates with a central domain folded ( i . e . a “kinetic partitioning” mechanism 67 ) ., In this case one might naively expect that the propensity to misfold would be correlated with the relative folding rates of an isolated native domain and an isolated circular permutant structure ., However , the folding barriers ΔGf projected onto Q ( for native ) or QK ( for circular permutants ) show little correlation to the relative frequency of the corresponding folded or misfolded state , when considering all proteins ( Table 1 ) ., Since this barrier height may not reflect variations in the folding rate if some of the coordinates are poor ( yielding a low barrier ) or if there are large differences in kinetic prefactors , we have also directly computed the folding rate for the circular permutants of those proteins which misfold , and confirm that the rates of formation of the native fold and circular permutants are similar ., We indeed obtain a strong correlation between the folding rate o

Introduction, Materials and Methods, Results

Recent single molecule experiments , using either atomic force microscopy ( AFM ) or Förster resonance energy transfer ( FRET ) have shown that multidomain proteins containing tandem repeats may form stable misfolded structures ., Topology-based simulation models have been used successfully to generate models for these structures with domain-swapped features , fully consistent with the available data ., However , it is also known that some multidomain protein folds exhibit no evidence for misfolding , even when adjacent domains have identical sequences ., Here we pose the question: what factors influence the propensity of a given fold to undergo domain-swapped misfolding ?, Using a coarse-grained simulation model , we can reproduce the known propensities of multidomain proteins to form domain-swapped misfolds , where data is available ., Contrary to what might be naively expected based on the previously described misfolding mechanism , we find that the extent of misfolding is not determined by the relative folding rates or barrier heights for forming the domains present in the initial intermediates leading to folded or misfolded structures ., Instead , it appears that the propensity is more closely related to the relative stability of the domains present in folded and misfolded intermediates ., We show that these findings can be rationalized if the folded and misfolded domains are part of the same folding funnel , with commitment to one structure or the other occurring only at a relatively late stage of folding ., Nonetheless , the results are still fully consistent with the kinetic models previously proposed to explain misfolding , with a specific interpretation of the observed rate coefficients ., Finally , we investigate the relation between interdomain linker length and misfolding , and propose a simple alchemical model to predict the propensity for domain-swapped misfolding of multidomain proteins .

Multidomain proteins with tandem repeats are abundant in eukaryotic proteins ., Recent studies have shown that such domains may have a propensity for forming domain-swapped misfolded species which are stable for long periods , and therefore a potential hazard in the cell ., However , for some types of tandem domains , no detectable misfolding was observed ., In this work , we use coarse-grained structure-based folding models to address two central questions regarding misfolding of multidomain proteins ., First , what are the possible structural topologies of the misfolds for a given domain , and what determines their relative abundance ?, Second , what is the effect of the topology of the domains on their propensity for misfolding ?, We show how the propensity of a given domain to misfold can be correlated with the stability of domains present in the intermediates on the folding and misfolding pathways , consistent with the energy landscape view of protein folding ., Based on these observations , we propose a simplified model that can be used to predict misfolding propensity for other multidomain proteins .

simulation and modeling, fluorophotometry, protein structure, thermodynamics, research and analysis methods, fluorescence resonance energy transfer, proteins, structural proteins, repeated sequences, molecular biology, spectrophotometry, free energy, physics, biochemistry, biochemical simulations, tandem repeats, protein domains, genetics, biology and life sciences, physical sciences, genomics, computational biology, spectrum analysis techniques, macromolecular structure analysis

journal.ppat.1006359

Genetically-barcoded SIV facilitates enumeration of rebound variants and estimation of reactivation rates in nonhuman primates following interruption of suppressive antiretroviral therapy

A major obstacle to developing a cure for HIV is the establishment in early infection of long-lived viral reservoirs , defined as sources of virus that can persist over extended periods despite seemingly effective suppressive combination antiretroviral therapy ( cART ) , that can cause recrudescent viremia if cART is interrupted ., While multiple anatomic sites and cell compartments likely act as viral reservoirs , it has been argued that latently infected resting CD4+ T cells represent the most significant long-lived viral reservoir for HIV-1 1–7 ., During latency , these reservoirs are unrecognized by host immune responses and cells containing integrated latent proviruses are unaffected by current cART , which acts only by blocking new rounds of infection ., For patients to safely stop treatment , the immune system must be able to control rebound infection ( sustained cART free remission or functional cure ) , or all reactivatable replication-competent virus must be completely eradicated ., Numerous studies are in progress to test therapies designed to decrease viral reservoir size and prolong ART-free remission ., A critical element for evaluating the effectiveness of these therapies is an accurate measurement of reservoir size before and after treatment ., These assessments have typically involved ex vivo estimates and have been based on total cell-associated viral DNA ( CA-vDNA ) measurements 8–10 , stimulation of PBMCs or enriched CD4+ T cells to measure the frequency of cells producing viral RNA ( vRNA induction assay or TILDA ) 11–14 or the frequency of cells harboring replication competent virus ( quantitative viral outgrowth assays , QVOA ) 1 , 3 , 13 , 15 , 16 ., However , each method for estimating reservoir size has shortcomings ., Ho et al . demonstrated that the QVOA tends to underestimate the amount of replication competent virus present in any sample , as not all latent proviruses will reactivate after a single stimulation event 16 ., Additionally , QVOA requires large source specimens , is time and labor intensive , and has limited precision and dynamic range ., On the other hand , PCR detection of viral DNA tends to greatly overestimate the reservoir size , as much of the viral DNA detected in these assays does not encode full length replication competent virus due to large deletions or APOBEC mediated mutations ., While the vast majority of intact , APOBEC mutation-free genomes are replication competent and could contribute to rebound viremia 16 identifying and quantifying these genomes requires near-full genome sequencing which is time consuming and expensive , necessarily precluding its use in large cohorts of patients ., While accurate assessment of the size of the viral reservoir is central to the evaluation of HIV cure strategies , none of the ex vivo assays directly assess the size of the viral reservoir that can lead to recrudescent viremia after cART interruption ., Most studies evaluating the effects of novel therapies on viral reservoir size are dependent on these ex vivo assays , however due to sample size and assay sensitivity issues , “undetectable” viral measurements do not necessarily indicate an absence of reactivatable virus , so as experimentation progresses , ultimately these treatments still require testing in HIV+ patients with the eventual discontinuation of cART to test for functional cure ., In these instances , time to rebound after treatment interruption is considered the most direct measure of cure intervention treatment efficacy 17 ., This approach might be effective for revealing large differences between treatment groups , which cause significant differences in time to detectable rebound viremia , however effects of treatments that result in small but potentially meaningful changes in reservoir size may be too subtle to be detected with this approach 17 ., This will be particularly true for individuals with large reservoirs where even large differences between treatment groups will be difficult to detect using only time to rebound , and in groups with highly divergent interpatient reservoir size which will affect time to detectable rebound viremia ., Therefore , alternative approaches for evaluating the functional reservoir size ( i . e . the cells that can contribute to systemic viremia once therapy is removed ) and the effects of new therapeutic interventions on the reservoir size are needed ., AIDS virus infected non-human primates ( NHPs ) represent useful models to study viral reservoir establishment and to evaluate changes in reservoir size with novel interventions ., Until recently , consistent and complete viral suppression was difficult to achieve in SIV-infected rhesus macaques with cART regimens developed for HIV-1 infection in humans ., However , there are now several classes of drugs , including nucleos ( t ) ide reverse-transcriptase inhibitors , protease inhibitors , and integrase inhibitors that have been evaluated and shown to be effective for suppression of SIV and SHIVs in infected macaques ., Recently , cART regimens have been developed that can effectively , durably and sustainably reduce plasma vRNA to clinically relevant levels ( below 15–50 copies per mL ) 18–22 ., These regimens result in similar viral suppression dynamics to those observed in humans ., Additionally , drugs are typically administered daily without any “drug holidays” or accidental missed doses ., Frequent blood sampling and standardized assays provide assurances of successful suppression ., Finally , NHPs may be removed from cART without the ethical implications involved in removing HIV-1 infected humans from treatment ., To more fully realize the potential of NHP models for evaluation of candidate cure approaches , we developed a novel , barcoded virus system that allows for a deep genetic assessment of the number of rebounding viruses , in conjunction with time to rebound viremia measurements ., This novel barcoded virus is fully and stably replication competent in vitro and in vivo and can be used to establish infection with a large number of otherwise sequence identical viral clonotypes bearing unique barcode sequences ., Following cART treatment and interruption , the number and relative proportion of each rebounding clonotype can be measured with next generation sequencing of the barcodes , using high template input that allows for the discrimination of individual rebounding clonotypes ., By combining viral growth rates ( the rate at which the virus grows once achieving detectable systemic infection ) and the relative proportion of each rebounding clonotype , the frequency of rebound of each clonotype can be estimated in each animal ., This approach is likely more sensitive than measuring time to detectable viremia alone because it is less affected by natural variation among individual animals , and consequently requires smaller group sizes to distinguish statistically significant differences in reservoir size ., This approach allows for detection of both small or large changes in the viral reservoir population , a distinction which may be critical for evaluating interventions resulting in real , but only modest changes in reservoir size ., Our use of this system in initial in vivo studies demonstrates that the time of initiation and duration of cART administration in NHPs can alter the size of the reservoir , allowing for tightly controlled experimental design and execution , an idea also introduced by Whitney et al ( 19 ) ., These data will help inform HIV cure research by providing a basic understanding of the biology of latency establishment , maintenance , and reactivation and will facilitate evaluation of potential therapies intended to reduce reservoir size ., Twenty-six purpose-bred Indian-origin male rhesus macaques ( Macaca mulatta ) weighing on average 7kg ( range 5-9kg ) were housed at the National Institutes of Health ( NIH ) and cared for in accordance with the Association for the Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) standards in an AAALAC-accredited facility and all procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee of the National Cancer Institute ( Assurance #A4149-01 ) ., Animals were maintained in Animal Biosafety Level 2 housing with a 12:12-hour light:dark cycle , relative humidity 30% to 70% , temperature of 23 to 26°C and all animals were observed twice daily by the veterinary staff ., Filtered drinking water was available ad libitum , and a standard commercially formulated nonhuman primate diet was provided thrice daily and supplemented 3–5 times weekly with fresh fruit and/or forage material as part of the environmental enrichment program ., Environmental enrichment: Each cage contained a perch , two portable enrichment toys , one hanging toy , and a rotation of additional items ( including stainless steel rattles , mirrors , and challenger balls ) ., Additionally , the animals were able to listen to radios during the light phase of their day and were provided with the opportunity to watch full-length movies at least three times weekly ., At the start of the study , all animals were free of cercopithecine herpesvirus 1 , simian immunodeficiency virus ( SIV ) , simian type-D retrovirus , and simian T-lymphotropic virus type 1 ., All animals were treated with enrofloxacin ( 10 mg/kg once daily for 10 days ) , paromomycin ( 25 mg/kg twice daily for 10 days ) , and fenbendazole ( 50 mg/kg once daily for 5 days ) followed by weekly fecal culture and parasite exams for 3 weeks to ensure they were free of common enteric pathogens ., At least a 4-week post-treatment period allowed time for stabilization of the microbiome prior to use in this study ., Primers were designed to introduce an MluI restriction site between the vpx and vpr accessory genes ., These primers contain regions complementary to either the vpx or vpr genes with the MluI restriction site appended to the 3’ end of each primer ., Amplicons were generated from SIVmac239 template using a generic primer upstream of SbfI and downstream of the EcoRI restriction site ., The vpx-containing fragment was digested with MluI and SbfI , and the vpr-containing fragment was digested with MluI and EcoRI ., SIVmac239 plasmid digested with SbfI and EcoRI was ligated with the two digested amplicons overnight at 16°C using T4 DNA ligase ( NEB ) ., 5μL of the ligation reaction was transformed into Stbl2 cells ( Invitrogen ) and plated on agar plates containing 100μg/mL ampicillin ., Resulting colonies were checked for correct assembly and insertion of the MluI site ., This clone was termed SIVmac239-Mlu ., The barcode insert was synthesized as single stranded forward and reverse barcoded templates ( IDT ) that were comprised of 10 random bases , flanked on either end by a stretch of bases complementary to the same region on the opposite primer to function as a molecular “clamp” with MluI sticky ends on both ends of the dimer ( Fig 1A ) ., To generate primer dimers , the forward and reverse barcode primers were mixed in equal proportion and heated to 95°C ., The temperature was slowly lowered at a rate of 1 . 5°C/min to allow primer pairs to anneal ., SIVmac239-Mlu was digested with MluI and the DNA was purified with a Qiaex II kit ., The digested SIVmac239-Mlu and primer dimers were mixed and ligated at 16°C overnight ., The primer sequence was designed such that upon ligation of the primer into the MluI site of the SIVmac239-Mlu , the MluI site would be destroyed ., Thus , the ligation product was digested again with MluI to linearize any genome not containing a primer dimer insert , and the digestion product cleaned and purified with a Qiaex II kit ., The eluted product was transformed into Stbl2 cells , and transformants were grown up in LB amp overnight ., The plasmid library was extracted from the bacterial preparations using the Qiagen MaxiPrep kit ., Virus was prepared in HEK-293T cells transfected with the prepared SIVmac239M plasmid library using Mirus Trans-IT 293 transfection reagent as described by the manufacturer ., Culture medium was changed at 24hr post-transfection , and culture supernatants were collected at 48hr ., Supernatants were passed through a 0 . 45μm filter and stored at −80°C in 0 . 5 or 1 mL aliquots ., Viral infectivity was determined using TZM-bl reporter cells ( reference no . 8129; NIH AIDS Research and Reference Reagent Program ) , which contain a Tat-inducible luciferase and β-galactosidase gene expression cassette ., Infectivity was determined by assessing the number of β-galactosidase expressing cells present after infection with serial dilutions of viral stocks ., After dilution correction , wells containing blue cell counts falling within a linear range were averaged and used to determine the titer of infectious units ( IU ) per mL in the viral stock as previously described 23 ., RNA was isolated from plasma or viral stock using QIAamp Viral RNA mini kit per manufacturer’s instructions ., RNA was eluted from the column with 65μL elution buffer ., cDNA was synthesized from the extracted DNA using Superscript III reverse transcriptase ( Invitrogen ) and a reverse primer ( Vpr . cDNA3: 5’-CAG GTT GGC CGA TTC TGG AGT GGA TGC-3’ at position 6406–6380 ) ., The reaction mixture was prepared as previously described with initial incubation at 50°C for one hour then increased to 55°C for an additional hour ., Temperature was increased to 70°C , and the reaction incubated for 15 minutes ., Each reaction was then treated with RNaseH and incubated at 37°C for 20 minutes ., qRT-PCR was used to quantify the cDNA synthesized in the previous step using the primers VpxF1 5’-CTA GGG GAA GGA CAT GGG GCA GG-3’ at 6082–6101 and VprR1 5’-CCA GAA CCT CCA CTA CCC ATT CATC-3’ at 6220–6199 ., PCR was used to amplify the cDNA and add MiSeq adaptors directly onto the amplicon ., Reactions were prepared using High Fidelity Platinum Taq per the manufacturer’s instructions , using primer VpxF1 and VprR1 combined with either the F5 or F7 Illumina adaptor sequence containing a unique 8 nucleotide index sequences ., Template input values ranged from 5x103 copies to 1x106 copies ., Reaction conditions used are as follows: 94°C , 2min; 40x 94°C , 15sec; 60°C , 1:30min; 68°C , 30sec; 68°C , 5min ., Following PCR , 10μL from each reaction was pooled and purified using the QIAquick PCR purification kit ., The resulting eluted DNA was quantified using the QuBit ., The combined DNA sample was diluted to 3 . 0nM and 5μL of this diluted sample was placed in a new tube and denatured with 5μL 0 . 2N NaOH ., This sample was vortexed and centrifuged at 280xg for 1 minute ., The sample incubated at room temperature for 5 minutes , and 990μL of chilled HT1 buffer added ., This sample was then diluted to 12 . 5pM ., The control PhiX library was treated similarly ., 2μL of the PhiX library was combined with 3μL Tris-HCl pH 8 . 5 , 0 . 1% tween-20 ., 5μL of 0 . 2N HCl was added to the library , and the sample vortexed and centrifuged at 280xg for 1 minute ., The sample was incubated at room temperature for 5 minutes , and 990μL of chilled HT1 buffer added ., Multiplexed samples and PhiX library were then loaded on the MiSeq reagent tray , and the run initiated ., For low-template samples , we used single genome amplification ( SGA ) followed by direct Sanger sequencing to assess the frequency and number of unique barcodes ., cDNA synthesis and PCR was performed as described above but using a limiting dilution of cDNA prior to PCR amplification ., This method provides representative proportionality and excludes PCR-induces errors 24 ., Samples that were multiplexed were separated into individual samples using Geneious software and the unique 8-nucleotide index ., After barcode splitting , individual barcoded clonotypes were identified by sequencing the first 50 bases of vpr ., The 34 bases immediately upstream of this alignment were extracted and presumed to encode the inserted barcode region ., Sequences obtained from infected animals were identified by comparison to all barcodes identified in the stock ( 14 , 357 ) ., Only identical matches to a defined barcode were counted as an authentic input sequence ., Given the short duration of infection prior to initiation of cART and the limited size of the insert , the vast majority of sequences were identical to a known barcode and were thus identified ., Some identified barcodes contained 1 or more deletions in the insert region ., This deletion resulted in 1 or more bases of vpx necessarily included in the extracted “barcode” ., Although these unique but shorter barcodes represent only 0 . 7% of all inserts observed , they were included in the comprehensive tally of all barcodes because they remain a unique and genetically identifiable insert ., Since all samples were quantified by real-time PCR , the theoretical limit of detection was estimated for each sample as the minimum number of sequences that would result from a single copy of an input template ., Sequences below this threshold were discarded ., Viral replication curves were prepared by culturing CD8+ T-cell-depleted Indian-origin rhesus macaque peripheral blood mononuclear cells ( PBMCs ) ( CD8+ depletion performed using Miltenyi Biotec CD8+ microbeads ) in RPMI supplemented with 10% fetal bovine serum ( FBS ) , 2mM l-glutamine , and 100U/mL penicillin and 100μg/mL streptomycin ( RPMI-complete ) , stimulated for 3 days with 5μg/mL phytohemagglutinin ( PHA ) and IL-2 ( 100U/mL ) ., Stimulated PBMC cultures were infected with SIVmac239 or SIVmac239M at an MOI of 0 . 01 or 0 . 001 ( as determined by TZM-bl ) ., 24hr post-inoculation , cell cultures were washed with phosphate buffered saline ( PBS ) twice and once with RPMI-complete to remove excess virus ., Viral replication was monitored over 14 days by detection of supernatant SIV p27 antigen in an enzyme-linked immunosorbent assay ( ABL ) according to the manufacturer’s provided protocol ., In total , 26 animals were intravenously infected with 2 . 2x105 IU ( 1mL ) of transfection produced SIVmac239M ., All 26 animals were used to enumerate the number of detectable barcodes measured during primary infection ., Of these 26 animals , two animals were followed for over 3 months to assess early viral replication kinetics ( peak and set point viral load ) of SIVmac239M ., Four of the other infected animals began antiretroviral treatment beginning at day 6 post infection and continued for 82 days ., Each animal received a combination antiretroviral therapy ( cART ) regimen comprising a co-formulated preparation containing the reverse transcriptase inhibitors tenofovir ( TFV: ( R ) -9- ( 2-phosphonylmethoxypropyl ) adenine ( PMPA ) , 20 mg/kg ) and emtricitabine ( FTC; 50 mg/kg ) administered by once-daily subcutaneous injection , plus raltegravir ( RAL; 150-200mg ) given orally twice daily ., At the time of interruption from cART , three animals were infused at the day of cART interruption with autologous CD8+ T cells transduced with an anti-SIV Gag T-cell receptor ( animals MK9 and KTM ) or with an irrelevant receptor ( animals KMB and KZ2 ) plus daily subcutaneous injections of IL-2 at 10 , 000 IU/kg for 10 days ., The total number of infused cells ranged from 4 . 6 to 6 . 4x109 cells with <1% of the cells CD4+ ., In these animals , infused cells did not traffic to lymphoid or GI tissues and persistence of the cells was poor ., Animal KTM died due to procedural complications at the time of cART interruption and was therefore excluded from subsequent analyses ., In a separate cohort of 6 animals ( study 2 ) , therapy was initiated on day 4 post-infection with the same therapeutic regimen ( TFV , FTC , RAL ) described above with the addition of the protease inhibitor indinavir ( IDV; 120mg BID ) and ritonavir ( RTV; 100mg BID ) for the first 9 months ., In study 2 , cART treatment was continued for 305 , 374 , or 482 days , with two animals discontinuing therapy at each time point ., The remaining 14 animals were used to enumerate the number of replicating clonotypes during primary infection ., Whole blood was collected from sedated animals ., Plasma for viral RNA quantification and PBMCs for proviral DNA assays were prepared from blood collected in EDTA Vacutainer tubes ( BD ) ., Following separation from whole blood by centrifugation , plasma aliquots were stored at 80°C ., PBMCs were isolated from whole blood by Ficoll-Paque Plus ( GE Healthcare ) gradient centrifugation ., Plasma viral load determinations for SIV RNA were performed over the duration of the study using quantitative real-time PCR as described previously 25 ., The limit of detection of this assay is 15 vRNA copies/mL ., Quantitative assessment of cell-associated viral DNA and RNA in PBMC pellets was determined by the hybrid real-time/digital RT-PCR and PCR assays essentially as described in Hansen et al . 26 but specifically modified to accommodate cell pellets ., 100μL of TriReagent ( Molecular Research Center , Inc ) was added to cell pellets in standard 1 . 7mL microcentrifuge tubes and the tubes sonicated in a Branson cup horn sonicator ( Emerson Electric , St . Louis ) for 15 seconds at 60% amplitude to disrupt the pellet ., Additional TriReagent was added to a final volume of 1mL and the remainder of the protocol was carried out as described previously 26 ., Limit of detection is evaluated on a sample by sample basis , dependent on the number of diploid genome equivalents of extracted DNA assayed ., SIVmac239M viral stock was randomly distributed into 168 aliquots with 5 , 000 viral cDNA templates per aliquot ., After next-generation sequencing of the barcode region , a bimodal frequency distribution of the number of copies of a given sequence in a single aliquot was observed ., Many sequences were present at very low copy number , likely representing erroneous sequences generated during the PCR amplification and/or sequencing process ., By contrast , sequences present at high copy numbers ( representing authentic ‘input’ barcode sequences ) were also observed in each aliquot ., A mixture model approach was used to model the frequency of both the erroneous and input sequences ., If X is a random variable corresponding to the number of copies of an individual sequence , then the distribution of X in an aliquot f ( x ) can be modeled as a mixture distribution of X for the erroneous sequences fE ( x ) and the authentic barcode input sequences fI ( x ) ., This can be written as:, f ( x ) =pfE ( x ) + ( 1−p ) fI ( x ), ( 1 ), where p is the proportion of erroneous sequences in an aliquot , and ( 1−p ) is the proportion of input sequences in an aliquot ., Based on observed sequences , we fitted a model where the number of copies of the input sequences follows a lognormal distribution , while the erroneous sequences follow a power law distribution ., The above distribution function is fitted to the number of copies of each unique sequence in an aliquot , using the function mle from MATLAB ( R2014b ) ., An optimal cutoff number of copies of a sequence for each aliquot was determined as the value where the theoretical distribution in the mixture model reaches a minimum ., The sequences above the cutoff were designated putative input sequences and the sequences below the cutoff putative erroneous sequences ., Moreover , the percentage of input sequences classified as erroneous and the number of erroneous sequences classified as input was estimated ., The method above identifies the number of putative input sequences in each aliquot , however we also estimate that 2–5% of these are actually erroneous sequences that are classified as input barcodes ., Since generation of PCR/sequencing error is likely a random event in a given aliquot , we might expect that most erroneous sequences will be confined to one or a few of the 168 aliquots ., However , since we expect ≈10 , 000 total input sequences in the stock , and observe around 2000 sequences per aliquot , then we should see most input sequences in many aliquots ., Based on this observation , the probability of observing a sequence in n aliquots is given by the mixture of binomial distributions:, p ( n ) =fBin ( N , p1 ) + ( 1−f ) Bin ( N , p2 ), ( 2 ), in which p1 is the probability of observing the erroneous sequences , p2 is the probability of observing an input sequence , and f is the proportion of erroneous sequences ., The above distribution function is fitted to the histogram of the number of aliquots each sequence is observed in ( using the function mle in MATLAB v . R2014b ) ., Using the fitted distribution function , we could find a cutoff value that can be used to determine the total number of input sequences across all aliquots ., We can also estimate the false positive and false negative rates around this cutoff ., Additionally , we also tested for a binomial model with non-constant proportion in the input sequences ., However , allowing for a distribution in the proportion of input sequences did not yield a better fit ( p = 0 . 78 , likelihood ratio test ) , hence we found no evidence for a distribution in clone size of the input sequences ., In order to estimate frequency of reactivations , we assumed exponential viral growth at the earliest stage of infection ., The time between ith and ( i + 1 ) th reactivations , Δi = ti+1 − ti , can be estimated from ratios Ri=ViVi+1=eg ( ti+1−ti ) , i = 1 , … , n − 1 of rebounders as shown by the following formula:, Δi=lnRig ., ( 4 ), In order to find the growth rate g of each rebounder ( assumed to be the same ) , we assume that reactivation occurs in average every Δ days ., Thus the total viral load ( i . e . : the sum of all variants ) at time t after treatment can be expressed by formula:, V ( t ) =V0eg ( t−t0 ) +V0eg ( t−t0−Δ ) +V0eg ( t−t0−2Δ ) …+V0eg ( t−t0− ( n−1 ) Δ ) , ( n−1 ) =⌊ ( t−t0 ) /Δ⌋ ., ( 5 ), Taking into account that ( e−gΔ ) m , m = 0 , … , n − 1 , is a geometric progression , we can reduce the function ( 5 ) so it will take the form:, V ( t ) =V0eg ( t−t0 ) 1−e−gΔ ( ⌊t−t0Δ⌋+1 ) 1−e−gΔ ,, ( 6 ), where ⌊x⌋ is the largest integer not greater than x ., The function ( 6 ) has discontinuity that may create some obstacle in finding the global minimum during fitting ., Thus , for the purpose of fitting we removed the discontinuities in ( 6 ) by substituting ⌊ ( t − t0 ) /Δ⌋ with ( t − t0 ) /Δ and rewrite the expression ( 6 ) for the log of viral load:, lnV ( t ) =lnV0+ln ( eg ( t−t0 ) −e−gΔ ) −ln ( 1−e−gΔ ), ( 7 ), In order to use average time between reactivations that can be obtained from the ratios of founder virus data , as it was described above , we substitute Δ in ( 7 ) by the estimate of the mean , Δ¯=L¯g , where L¯=1 ( n−1 ) ∑i=1n−1lnRi ., Thus , we obtain the formula:, lnV ( t ) =lnV0+ln ( eg ( t−t0 ) −e−L¯ ) −ln ( 1−e−L¯ ) ,, ( 8 ), where n is the number of founder viruses in the dataset ., Model was fitted ( using Prism 6 . 07 , GraphPad Software Inc . San Diego , Ca , USA ) to exponential phase of growth of virus in monkeys having V0 as a shared parameter ., We reasoned that a molecularly barcoded SIV clone would have great utility for studies of HIV/SIV latency , viral reservoir establishment and maintenance , and viral rebound upon therapeutic interruption ., To generate this barcoded virus , the MluI restriction recognition sequence ( ACGCGT ) was introduced into the SIVmac239 infectious molecular clone ( IMC ) between the stop codon of vpx and the start codon of vpr ., A genetic cassette consisting of 10 random bases with 7 complementary bases flanking each end was ligated into the SIVmac239 clone using the introduced MluI restriction site ( Fig 1A ) ., Importantly , the genetic insert is bidirectional , effectively doubling the discriminating power of the barcode ., Following ligation , a large bacterial plasmid library was generated and was then used for large-scale virus production via transfection of HEK-293T cells ., All produced virus was collected , pooled , and aliquoted , such that single aliquots contain a representative sampling of all genetic variants generated ., Thus , the generated virus stock contained variants of SIVmac239 that differed only within a 34-nucleotide insertion harboring a 10 base-stretch of random nucleotides in an otherwise genetically clonal genome ., These 34 bases comprise the viral barcode and the virus stock was designated SIVmac239M ., The goal of generating SIVmac239M was to produce a phenotypically homogeneous viral population with extensive diversity contained entirely within a small region of the genome suitable for deep sequencing and with a known distribution of the genetically distinct viral barcodes ( or viral clonotypes ) ., Therefore , it was necessary to determine the genetic diversity and abundance of each clonotype in the virus stock ., When sequencing such a large potential number of genetic variants , it can be difficult to discern between sequences arising from PCR or sequencing error and those representing true input viral clonotypes ., To distinguish these sequences , viral RNA was extracted , synthesized into cDNA , and distributed into 168 aliquots each containing 5 , 000 viral templates ., Following PCR and Illumina-based sequencing of each aliquot , the number of unique sequences was compared to the total sequence count ., Using a limited template input with massive oversampling of sequencing ( at least 100-fold over-sequencing per template ) , we found a clear bimodal distribution of both PCR-induced errors ( power-law distributed , with a high proportion of single sequences ) , and authentic clones ( log-normally distributed , with sequences present at high frequency ) ( Fig 1B ) ., We then identified the threshold number of copies separating the erroneous from the authentic barcode sequences in each aliquot ., Using this approach , we detected a total of 14 , 357 unique clonotypes across the 168 aliquots ., These clonotypes were then rank ordered by the number of replicate aliquots in which each was found ., Of the 5 , 021 sequences found in only one aliquot , we estimated that only approximately 100 of these were likely to be authentic input barcodes based on the distribution of the 168 aliquots ( Eq 2 ) ., Therefore , the vast majority of input barcodes were contained within the top 9 , 336 sequences ., Phylogenetic analysis of these 9 , 336 identified clonotypes was performed to determine the genetic relatedness of each barcoded clone ( S1 Fig ) ., Of the 9 , 336 identified barcodes , 5 , 519 were inserted in one direction , and 3 , 817 were inserted in the inverse direction ., To quantify genetic relatedness between barcodes , we performed pairwise comparisons of each barcode ( S2 Fig ) ., We found two distinct populations ( representing the two barcode orientations ) , with an average nucleotide difference of 7 bases ., Genetic analysis also revealed 105 barcodes with one or more base pair deletions generating slightly smaller barcodes ., These short inserts are likely due to errors in the molecular generation of the barcoded clone and although these barcodes are truncated , they retain their usefulness because they can still be genetically distinguished from the rest of the variant pool ., Overall , these data support the conclusion that we have generated a genetically diverse , synthetic viral population approaching 10 , 000 individual viral clonotypes ., Prior to use in nonhuman primates , viral infectivity and replication of SIVmac239M was assessed using TZM-bl reporter cells and primary rhesus lymphocytes , respectively ., This stock contained 2 . 2x105 IU/mL , which was equivalent to the infectious titer of a stock of parental SIVmac239 produced using the same approach ., To assess the replication capacity of SIVmac239M , CD8+ T-cell-depleted PBMCs were inoculated with equivalent infectious units of SIVmac239M or the parental SIVmac239 and samples were collected every 2–3 days ( Fig 2A ) ., SIVmac239M displayed peak virus replication levels on day 7 , corresponding to a detected 1 . 8ng of reverse transcriptase ( RT ) /mL of culture supernatant ., The viral growth curves were comparable betwe

Introduction, Methods, Results, Discussion

HIV and SIV infection dynamics are commonly investigated by measuring plasma viral loads ., However , this total viral load value represents the sum of many individual infection events , which are difficult to independently track using conventional sequencing approaches ., To overcome this challenge , we generated a genetically tagged virus stock ( SIVmac239M ) with a 34-base genetic barcode inserted between the vpx and vpr accessory genes of the infectious molecular clone SIVmac239 ., Next-generation sequencing of the virus stock identified at least 9 , 336 individual barcodes , or clonotypes , with an average genetic distance of 7 bases between any two barcodes ., In vitro infection of rhesus CD4+ T cells and in vivo infection of rhesus macaques revealed levels of viral replication of SIVmac239M comparable to parental SIVmac239 ., After intravenous inoculation of 2 . 2x105 infectious units of SIVmac239M , an average of 1 , 247 barcodes were identified during acute infection in 26 infected rhesus macaques ., Of the barcodes identified in the stock , at least 85 . 6% actively replicated in at least one animal , and on average each barcode was found in 5 monkeys ., Four infected animals were treated with combination antiretroviral therapy ( cART ) for 82 days starting on day 6 post-infection ( study 1 ) ., Plasma viremia was reduced from >106 to <15 vRNA copies/mL by the time treatment was interrupted ., Virus rapidly rebounded following treatment interruption and between 87 and 136 distinct clonotypes were detected in plasma at peak rebound viremia ., This study confirmed that SIVmac239M viremia could be successfully curtailed with cART , and that upon cART discontinuation , rebounding viral variants could be identified and quantified ., An additional 6 animals infected with SIVmac239M were treated with cART beginning on day 4 post-infection for 305 , 374 , or 482 days ( study 2 ) ., Upon treatment interruption , between 4 and 8 distinct viral clonotypes were detected in each animal at peak rebound viremia ., The relative proportions of the rebounding viral clonotypes , spanning a range of 5 logs , were largely preserved over time for each animal ., The viral growth rate during recrudescence and the relative abundance of each rebounding clonotype were used to estimate the average frequency of reactivation per animal ., Using these parameters , reactivation frequencies were calculated and ranged from 0 . 33–0 . 70 events per day , likely representing reactivation from long-lived latently infected cells ., The use of SIVmac239M therefore provides a powerful tool to investigate SIV latency and the frequency of viral reactivation after treatment interruption .

Elucidation of HIV dynamics is essential for a thorough understanding of viral transmission , therapeutic interventions , pathogenesis , and immune evasion ., The complex dynamics of reservoir establishment and viral recrudescence upon therapy removal present the primary obstacles to developing a functional cure ., We sought to develop a virus model system for use in nonhuman primates that allows for the genetic discrimination of nearly 10 , 000 otherwise isogenic clones ., This “synthetic swarm” adds a genetic component to viral dynamics where individual viral lineages can be tracked and monitored during infection ., Here we utilized this model to identify the dynamics of viral reservoir establishment and rebound ., We found that after 300 or more days of therapy , between 4 and 8 distinct viral lineages could be detected upon therapeutic intervention ., Using the relative proportion of each distinct genetic barcoded virus and the overall viral load curve , we could estimate the time and rate of reactivation from latency ., On average , we found 1 reactivation event every 2 days with reactivation of the first rebounding variant within days of therapeutic interruption ., This virus model will be useful for testing various approaches to reduce the latent viral reservoir and to molecularly track viral dynamics in all stages of infection .

medicine and health sciences, body fluids, viral transmission and infection, microbiology, vertebrates, animals, mammals, primates, animal models, experimental organism systems, microbial genetics, viral load, old world monkeys, research and analysis methods, rhesus monkeys, sequence analysis, infectious diseases, monkeys, bioinformatics, viral replication, blood plasma, macaque, viremia, anatomy, blood, virology, physiology, database and informatics methods, genetics, biology and life sciences, viral diseases, amniotes, organisms

journal.pgen.1002154

Integrating 5-Hydroxymethylcytosine into the Epigenomic Landscape of Human Embryonic Stem Cells

The potency and fate of a cell can be influenced strongly by the covalent modification of cytosine methylation at carbon five ., This critical epigenetic mark influences cellular potency and differentiation by modulating DNA-protein interactions , which direct epigenomic states and transcriptional processes , allowing otherwise common genomes to be expressed as distinct cell types ., DNA-methylation-mediated epigenomic processes include dosage compensation , control over aberrant retrotransposon expression , and regulation of centromeric and telomeric heterochromatin 1 ., The importance of such processes is exemplified by the essential requirement for DNA methyltransferases ( DNMT1 , DNMT3A , and DNMT3B ) in embryonic and early mammalian development 2 , 3 ., Coincident with critical roles for DNA methyltransferases in the regulation of pluripotency , Fe ( II ) /α-ketoglutarate-dependent hydroxylation of 5-mC to 5-hydroxymethylcytosine ( 5-hmC ) by Ten-eleven translocation ( Tet ) family proteins also contributes to the maintenance of pluripotency 4–6 ., Discovery of this new epigenetic modification raises the possibility that 5-hmC could alter chromatin structure and thereby contribute to gene regulation ., Recent functional studies have shown that Tet proteins , particularly Tet1 and Tet2 , are required for ES cell self-renewal and maintenance ., However , despite the emergence of these important roles for Tet family proteins , and therefore 5-hmC-associated regulation in ES cells , the genomic- and chromatin-associated contexts of 5-hmC have gone unexplored in human embryonic stem cells ., Although there are detailed chromatin state maps of histone modifications in human embryonic stem cells , much less is known about the distinction between 5-hmC and 5-mC localization , largely because of the inability of bisulfite sequencing to resolve the two marks 7 , 8 ., Recent studies indicate distinct differences in the presence of stable 5-hmC and Tet1 in mouse ES cells , where strong promoter-proximal Tet1 binding is inversely correlated with the presence of both 5-mC and 5-hmC 9–13 , providing putative support for a Tet1-associated demethylation mechanism in the maintenance of unmethylated active promoters ., Interestingly , these studies indicate that while Tet1 binding sites are highly enriched at transcription start sites ( TSSs ) in mouse ES cells , a significant fraction of detectable 5-hmC lies within gene bodies and other regulatory regions , which is also consistent with our previous study mapping 5-hmC genome-wide in mouse cerebellum 14 ., Furthermore , at regions bound by both Polycomb ( PRC2 ) and Tet1 , the presence of 5-hmC is associated with a repressive state , indicating diverse regulatory roles for 5-hmC that depend at least in part on its chromatin context ., Whether localization of 5-hmC with other distinct chromatin signatures results in diverse regulatory mechanisms remains to be explored ., To unravel the biology of 5-hmC , we recently developed a selective chemical labeling method for 5-hmC by using T4 bacteriophage ß-glucosyltransferase to transfer an engineered glucose moiety containing an azide group onto the hydroxyl group of 5-hmC , which in turn can chemically incorporate a biotin group for detection , affinity enrichment , and sequencing ., Here , to understand the role of 5-hmC in the epigenomic landscape of pluripotent cells , we profiled the genome-wide 5-hmC distribution and correlated it with the genomic profiles of 11 diverse histone modifications and six transcription factors in human ES cells ., By integrating genomic 5-hmC signals with maps of histone enrichment , we link particular pluripotency-associated chromatin contexts with 5-hmC ., Intriguingly , through additional correlations with defined chromatin signatures at promoter and enhancer subtypes , we found distinct enrichment of 5-hmC at enhancers marked with H3K4me1 and H3K27ac ., These results suggest potential role ( s ) for 5-hmC in the regulation of specific promoters and enhancers ., In addition , our results provide a detailed epigenomic map of 5-hmC from which to pursue future functional studies on the diverse regulatory roles associated with 5-hmC ., To assess the distribution and general chromatin context of 5-hmC in human embryonic stem ( ES ) cells , we first evaluated the cytogenetic localization of both 5-mC and 5-hmC by immunostaining metaphase chromosomes of human ES cells ( Figure S1 ) ., Both 5-mC and 5-hmC were clearly present along the chromosomal arms ( Figure 1A–1D ) ; however , 5-mC displayed a distinctly strong signal at centromeric heterochromatin regions on all metaphase spreads examined ( Figure 1B , n>5 ) ., Strikingly , at these same regions , 5-hmC appears completely depleted from 5-mC-enriched pericentromeric regions ( Figure 1A , 1E–1H ) ., Given both the defined epigenetic architecture and distinct sequence content of relatively stable centromeric heterochromatic regions , these results may suggest an association of 5-hmC with more epigenetically dynamic loci , such as those throughout chromosome arms , and perhaps exclusion from more epigenetically stable heterochromatin , such as that present in metaphase centromeres ., To further evaluate the epigenomic context of 5-hmC , we first established a genome-wide map of 5-hmC in human H1 ES cells by selectively enriching 5-hmC-containing fragments of DNA and subjecting them to high-throughput sequencing ., We used a previously established approach to transfer a chemically modified glucose moiety , 6-N3-glucose , onto the hydroxyl group of 5-hmC , which in turns allows cycloaddition of biotin for affinity enrichment and deep sequencing ., We prepared and sequenced libraries from 5-hmC-enriched as well as unenriched DNA from the same preparation and sequenced to a depth of >10 million unique , non-duplicate reads per condition ., Analyses of chromosome-wide 5-hmC densities showed that , while unenriched input genomic reads were distributed amongst chromosomes close to randomly , as expected by chance , 5-hmC exhibited enrichment or depletion on specific chromosomes ( Figure 2A ) ., To further localize regions of 5-hmC enrichment , we identified 5-hmC peaks genome-wide ., In total , we identified 82 , 221 regions as significantly enriched for 5-hmC ( p-value threshold of 1e-8 , Table S1 ) ., Association of 5-hmC-enriched regions with annotated genomic features indicated significant overrepresentation of 5-hmC within genes and depletion at intergenic regions ( Figure 2B ) , consistent with what has been observed previously in both mouse cerebellum and mouse ES cells 9 , 11–14 ., Within genes , 5-hmC peaks were particularly enriched in exons ( Figure 2B , 6 . 14-fold over expected based on the genomic coverage of these regions ) , whereas we saw much lower frequency within intronic regions ( Figure 2B , 1 . 33-fold over expected ) , which is likely a result of the increased GC content within exons relative to introns ., 5-hmC peaks were also significantly enriched within intragenic CpG islands ( CGIs ) ( 17 . 6-fold over expected ) and are more frequent than expected by chance at intergenic CGIs ( Figure 2B ) ., Interestingly , we find significantly more 5-hmC peaks overlapping predicted enhancers than was expected ( 8 . 6-fold over expected , Figure 2B ) ., These results indicate that in addition to gene body-associated regulatory roles , 5-hmC may also function within other genomic regions important for gene modulation ., We also assessed the general sequence content of these peaks , including GC content and dinucleotide frequencies ., We found that the frequency of CpG dinucleotides within 5-hmC-enriched regions was no greater than randomly chosen regions of the genome and significantly lower than CGIs , whereas CA , CC , and CT dinucleotides each exhibited an O/E >1 and enrichment relative to random genomic locations ( Figure 2C ) ., Furthermore , GC content as a whole was significantly reduced compared with CGIs , and slightly increased relative to random genomic loci ( Figure 2D ) ., These data suggest that 5-hmC-enriched loci occur most often in regions of the genome with moderate GC content and that it occurs less frequently within a high density of CpGs ., In order to determine the specific chromatin contexts associated with 5-hmC in human embryonic stem cells , we obtained sequence data derived from immunoprecipitation of 5-mC ( MeDIP ) ( GSM456941 ) and 11 diverse histone modifications in H1 hES cells 15 ., MeDIP , histone-ChIP , and unenriched input reads derived from the same experiments were binned genome-wide at 1 , 5 , and 10 kb ., MeDIP and histone-specific signals were normalized to input values ( ChIP-Input ) ., 5-hmC-enriched reads were binned genome-wide using identical parameters ., Input-normalized 5-hmC signals were then subsequently correlated with input-normalized histone modification and 5-mC MeDIP values within the same genomic bin for all bins genome-wide in order to generalize the relative correlation between 5-hmC , 5-mC , and diverse histone modifications ( Figure 3 ) ., We found that data binned at various sized intervals exhibited generally similar patterns on a genomic scale when comparing the relative correlations between 5-hmC and the various histone modifications tested ., We find that in general , on a genomic scale , 5-hmC and 5-mC detected by MeDIP correlate better than any histone-specific mark tested ( Figure 3A , r2\u200a=\u200a0 . 448 ) , consistent with the fact that 5-hmC is derived from 5-mC and with previous reports showing a significant amount of overlap between the two marks in mouse ES cell genomes 9 , 11 , 13 ., Although it is difficult to assess the ratio of 5-mC:5-hmC from genome-wide bisulfite sequencing data ( Methyl-Seq ) , we also determined the association between 5-hmC and 5-mC+5-hmC detected by Methyl-Seq ( Figure S2 ) ., Within the CG context , regions with higher 5-hmC also tend to have a higher percentage of 5-mC+5-hmC , as would be expected ., However , there are also a large number of regions with a high percentage of 5-mC+5-hmC that contain very low levels of 5-hmC and are therefore presumably dominated by 5-mC ( Figure S2A ) ., These results are again consistent with the notion that 5-hmC is derived from 5-mC ., We also compared 5-hmC signals to 5-mC+5-hmC within the non-CpG context , which occurs in human ES cells more frequently than in differentiated cell types 16 ., 5-hmC has been reported to occur within non-CpG contexts in mouse ES cells as well 11 ., Our analyses indicate that regions containing high levels of 5-hmC tend to harbor less non-CpG methylation ( Figure S2B and S2C ) ., However , due to the low percentage of both CHG and CHH methylation throughout the genome , it is difficult to resolve the extent to which 5-hmC may occur at non-CpG sites and analyses do not exclude the possibility that 5-hmC occurs within a non-CpG context in human ES cells ., Further resolution of single base pair 5-hmC will be required to conclusively establish the sequence contexts of hydroxymethylated cytosines ., Correlations between 5-hmC and the 11 histone modifications tested were largely , with a few notable exceptions , in agreement with the previously observed associations between histone modifications and the percentage of overall DNA methylation ( 5-mC+5-hmC ) assessed by Methyl-Seq 15 ., Consistent with the correlations between Methyl-Seq and histone modifications , we find a relatively strong association between 5-hmC and H3K4me1 ( r2\u200a=\u200a0 . 293 ) and H3K4me2 ( r2\u200a=\u200a0 . 152 ) compared with H3K4me3 ( r2\u200a=\u200a0 . 0518 ) ( Figure 3B–3D ) ., The relatively strong correlations between 5-hmC , H3K4me1 , and H3K4me2 compared to H3K4me3 are also consistent with earlier observations showing enrichment of 5-hmC within active gene bodies , but depletion at TSSs ., We also saw a relatively strong correlation between H3K18ac , a mark that directly regulated CBP/p300 enhancer complexes with transcriptional activation 17 , 18 , and 5-hmC ( Figure 3G , r2\u200a=\u200a0 . 324 ) ., A significantly smaller albeit moderate correlation was found between 5-hmC and H3K27ac , H3K27me3 ( with H3K27ac > H3K27me3 ) , and H4K5ac ( Figure 3H , 3I and 3L ) ., Both H3K9ac and H3K9me3 exhibited relatively low levels of correlation with 5-hmC ( Figure 3E and 3F ) ., Surprisingly , we see a relatively weak correlation between 5-hmC and H3K36me3 ( Figure 3J ) ., H3K36me3 is known to correlate well with gene expression levels and has been linked to transcriptional elongation in hES cells 19 , but is largely absent from TSSs ., H3K36me3 is also one of the few histone marks for which there is a strong correlation with methylated DNA , as detected by bisulfite sequencing 15 ., These results suggest the possible enrichment of H3K36me3 or 5-hmC on distinct groups of gene bodies in hES cells , which could depend on the level of gene expression ., Together , the correlations between 5-hmC , 5-mC , and the 11 specific histone modifications tested indicate that , in addition to being generally associated with more euchromatic accessible chromatin , 5-hmC may be linked to diverse gene regulatory elements and transcriptional regulatory processes in human ES cells ., Both cytogenetic localization of 5-hmC and genome-wide correlations with 11 diverse histone modifications indicate links between 5-hmC , more accessible euchromatic chromatin , and gene regulation ., To test the dependence of gene-associated 5-hmC distributions on expression levels in human ES cells , we measured 5-hmC signals at genes with varying expression as measured by RNA-Seq RPKM 16 ., Overall , 5-hmC displays a strong promoter-proximal bias in hES cells , while also being enriched within gene bodies , albeit to a lesser degree relative to the TSS ( Figure 4A–4E ) ., Interestingly , we observed a distinct forking in the 5-hmC distribution around the TSS as expression levels rose , ultimately transitioning to a bimodal distribution at more highly expressed genes compared with genes expressed at lower levels ( Figure 4A–4E ) ., However , the correlation between 5-hmC and both TSSs and gene bodies is not strictly linear ., 5-hmC tends to be higher , both within the gene body and at the TSS , at genes expressed within the 25–75% range of all genes based on RNA-Seq RPKM ( Figure 4C and 4D ) , compared to the top 25% of expressed genes ( Figure 4A ) ., Meanwhile , at genes within the bottom 25% , 5-hmC is mainly enriched directly over the TSS and only moderately enriched within the gene body ., Thus , at genes exhibiting lower expression , 5-hmC is present directly at the TSS ( Figure 4E ) , whereas genes with intermediate expression display higher gene body 5-hmC and a distinct bimodal distribution ( Figure 4A ) at the TSS ., At the most highly expressed genes , 5-hmC exhibits a similar distribution to that seen on intermediately expressed genes , but overall lower levels at both the TSS and gene body ., These results are consistent with the observed dual function of 5-hmC in mouse ES cells , where the Polycomb complex PRC2 may act in combination with Tet1 to influence the distribution of 5-hmC at repressed genes , while at more highly expressed genes the presence of Tet1 , without PRC2 , results in loss of 5-hmC at the TSS and establishment of a bimodal distribution 9 , 10 ., To further explore the enrichment of 5-hmC at gene bodies with intermediate levels of expression , we directly compared the distribution of 5-hmC to that of H3K36me3 in and around genes ranked by expression level ( Figure 4A ) ., H3K36me3 is an intragenically enriched histone modification that also correlates well with gene expression levels 19 ., We found that genes with the highest intragenic 5-hmC also had relatively low intragenic H3K36me3 ( Figure 4A ) , consistent with the relatively low genome-wide correlations between binned 5-hmC and H3K36me3 ( Figure 3J ) ., The same genes were also those expressed at intermediate levels ( 25–75% range based on RNA-Seq RPKM ) ., At the top 25% of expressed genes , H3K36me3 is highly enriched within gene bodies and transcription end sites ( TES ) , while 5-hmC tends to be lower at both TSSs and gene bodies compared to genes expressed at intermediate levels ( Figure 4A ) ., These data suggest a complex relationship between 5-hmC , H3K36me3 , and gene expression levels in human ES cells ., One possible explanation could be that 5-hmC functions to temper transcription at the genes that are not fully committed to a constitutive expression state ., At genes expressed at the lowest levels , 5-hmC may play a role at the TSS to represses full-length transcription , while still maintaining the transcriptional potential of the marked genes ., Such a role is consistent with the previously reported interaction between TSS 5-hmC and repression by Polycomb group complexes , which repress many developmentally regulated genes in ES cells 9 , 10 ., At the genes with intermediate expression levels , 5-hmC may temper expression at both TSS and gene body ., At genes with the highest expression , TSS- and gene body-associated 5-hmC may be , at least in part , replaced by H3K36me3 to allow full transcriptional potential ., We note that such distributions of 5-hmC in ES cells is distinct from that observed in mouse brain , where 5-hmC is largely depleted from TSSs , enriched within gene bodies , and correlates well with gene expression levels ( Szulwach and Jin , unpublished observations and 14 ) ., These differences may reflect stem cell-specific and brain-specific roles for 5-hmC-mediated gene regulation ., Such differences may be accounted for by the relative enrichment of Tet1 in ES cells and/or yet-to-be-identified Tet-family co-factors compared to more differentiated cell types ., In promoter-proximal regions of embryonic stem cells , 5-hmC exhibits a TSS-associated bias that is dependent on gene expression level ( Figure 4 ) ., To further understand the relevance of this bias in terms of chromatin context , we examined the distribution of 5-hmC around 18 distinct promoter subtypes defined on the basis of their chromatin signatures 15 ., Among 11 promoter subtypes with significant enrichment of the histone modifications tested in H1 hES cells , we found that 5-hmC distributions within the same regions could be classified into two groups ., The first group reflected the distribution of 5-hmC at more highly expressed genes , with 5-hmC displaying a marked depletion directly over the TSS and a bimodal distribution around the TSS ( Figure 5A , 5B ) ., This distribution corresponded to a strong H3K4me3 signal , consistent with an inverse correlation between 5-hmC and H3K4me3 ( Figure 5A–5C ) ., Flanking the region of depletion were two peaks of 5-hmC , which overlapped with regions of H4K4me1 and H3K4me2 enrichment ., A clear example of this could be seen at the well-characterized promoters of the DNMT3A locus , itself a highly expressed gene in ES cells ( Figure 5C ) ., The bimodal distribution of 5-hmC , H4K4me1 , and H3K4me2 around TSSs might reflect paused promoters , at which divergent RNAPII is known to display pausing , and could suggest an influence of 5-hmC on transcription pausing at such promoters in hES cells ., The second group of promoters displayed lower 5-hmC signal overall , but a more even distribution over the promoter regions , without a distinct region of depletion ( Figure 5D–5F ) , and reflected the distribution of 5-hmC at genes expressed at intermediate or low levels ( Figure 4 ) ., Again , the distribution of 5-hmC correlated well with the presence of H3K4me1 and H3K4me2 , while H3K4me3 was also present ( Figure 5E and 5F ) ., We also noted that this group of promoters displayed an overall weaker signal in each histone modification tested , relative to promoters exhibiting bimodal distributions of both 5-hmC and various histone modifications ( Figure 5B and 5E ) , which likely represents the expression status of this group of genes ., Assessment of 5-hmC at an additional seven promoter types , which displayed low levels of modified histone enrichment in H1 hES cells , also displayed low levels of 5-hmC ( Figure S3 ) and less distinct distribution patterns , consistent with a link between defined histone modifications and 5-hmC at TSSs ., Association of 5-hmC-enriched regions with annotated genomic features suggested that , in addition to playing important roles within gene bodies and gene proximal regions , 5-hmC might also function at distinct regulatory elements , including enhancers ( Figure 2B ) ., To address the potential role of 5-hmC at enhancers as well as the distinct chromatin contexts associated with each , we determined the distribution of 5-hmC at 12 different sets of predicted enhancers defined on the basis of chromatin signature 15 ., Strikingly , we found that 5-hmC marked each of five enhancer subtypes displaying enrichment of H3K4me1 , H3K18ac , H4K5ac , and H3K27ac in H1 hES cells , while enhancer subtypes exhibiting less enrichment of these marks also tended to be less enriched for 5-hmC ( Figure 6A and 6B ) ., A clear example of a 5-hmC-associated enhancer occurred upstream of the ES-specific gene PRDM14 , where a 5-hmC peak was identified as directly overlapping an E8 type enhancer ( Figure 6C ) ., PRDM14 has been reported as an integral factor contributing to pluripotency via interactions with the core transcriptional circuitry in ES cells 20 , 21 ., This may suggest a functional role for 5-hmC , in combination with at least H3K4me1 , at this upstream enhancer in maintaining expression of PRDM14 and contributing to the pluripotency of human ES cells ., In combination with the general enrichment of 5-hmC peaks at predicted hES cell enhancers ( Figure 2B ) , these data demonstrate distinct marking of ES cell enhancers with 5-hmC and defined chromatin signatures ., We further tested the distribution of 5-hmC around a set of 12 ChIP-rich regions that were previously identified as exhibiting enrichment of specific histone modifications , but that lay outside of defined promoters or predicted enhancer regions ( Figure S4 ) 15 ., In general , 5-hmC signals were significantly lower at such regions , and few patterns were apparent ., However , we did find that ChIP-rich regions with H3K36me3 displayed markedly lower levels of 5-hmC and that regions enriched for K3K9me3 actually exhibited depletion of 5-hmC ( Figure S2 ) , consistent with the lower genome-wide correlations we found between 5-hmC and these two histone modifications ( Figure 4E and 4I ) ., DNA methylation has been implicated in regulating transcription factor binding dynamics and has been found to differentially mark sites of core pluripotency-associated transcription factors in ES cells 16 ., We therefore asked whether or not 5-hmC marked sites bound by six transcription factors mapped genome-wide in H1 hES cells , including the pluripotency-associated transcription factors NANOG , OCT4 , and SOX2 , as well as more general factors , such as p300 and TAF1 ( Figure 7 ) ., At sites of all types we could detect a slight enrichment of 5-hmC and direct overlap between subsets of 5-hmC peaks and transcription factor binding sites , consistent with previous observations in mouse ES cells detecting 5-hmC at transcription factor binding sites 9 , 11 , 13 ., However , signals varied across factors ., Among pluripotency-related factors , we find distinct marking and enrichment of 5-hmC at of only NANOG sites ( Figure 7A ) ., An example of 5-hmC enrichment at a NANOG binding site was seen directly upstream of DNMT3B ( Figure 7B ) , a gene expressed a high levels in ES cells ., Consistent with a lack of 5-hmC at many TSSs , we also observe depletion of 5-hmC at TAF1 interaction sites ( Figure 7A ) ., Although we observed good correlation between histone modifications demarcating enhancers and enrichment of 5-hmC at specific subtypes of enhancers defined by chromatin signature , we did not observe distinct 5-hmC marking at p300 sites ( Figure 7A ) ., We further addressed this by asking what the overlap was between the 82 , 221 identified 5-hmC enriched regions ( Table S1 ) , predicted enhancers 15 and p300 sites 16 ., As expected a large proportion of p300 sites ( 1795 of 3094 , 58% ) overlap predicted enhancers ( Figure 7C ) ., However , the fraction of predicted enhancers explained by p300 binding remained quite low ( 1 , 795 of 58 , 023 , 3 . 1% ) , suggesting a significant amount of enhancer regulation by p300 independent mechanisms ., Interestingly , we find that while only a small fraction of p300 sites ( 166 of 3094 , 5 . 4% ) overlap 5-hmC enriched regions , a significant percentage of predicted enhancers ( 19 , 973 of 58 , 023 , 34 . 4% ) overlap with 5-hmC enriched regions ( Figure 7C ) ., Furthermore , sites that were enriched for 5-hmC , bound by p300 , and predicted as enhancers were quite rare , occurring only 25 times ., These data suggest that significant portion of predicted enhancers are also enriched in 5-hmC , but lack p300 binding , and may indicate a role for 5-hmC in regulating p300 independent enhancers ., Together these results indicate that 5-hmC may also influence the chromatin states at protein-DNA interaction sites , thereby modulating the function of key transcription factors and diverse enhancer subtypes ., Recent studies have shown that Tet family proteins can catalyze 5-methylcytosince ( 5-mC ) conversion to 5-hydroxymethylcytosine ( 5-hmC ) and play important roles in self-renewal and cell lineage specification in embryonic stem ( ES ) cells 4–6 , 11 , 22 ., These findings suggest a potential role for 5-hmC-mediated epigenetic regulation in modulating the pluripotency of ES cells ., To unveil this new regulatory paradigm in human ES cells , here we used a selective 5-hmC chemical labeling approach coupled with affinity purification and deep sequencing that we developed before to establish the genome-wide distribution of 5-hmC in human ES cells ., Integration of 5-hmC distributions with genome-wide histone profiles led us to identify the pluripotency-linked chromatin contexts associated with 5-hmC ., Through association with genomic features defined on the basis of chromatin signatures , we find 5-hmC-mediated marking of not only specific promoters and gene bodies , but also distinct enhancer subtypes , including those marked with H3K4me1 and H3K27Ac ., Lastly , we find 5-hmC is associated with the binding sites of specific core pluripotency transcription factors and a lack of 5-hmC at others ., Our results suggest that 5-hmC is an important epigenetic modification associated with the pluripotent state that could play role ( s ) in a subset of promoters and enhancers with defined chromatin signatures in ES cells ., By correlating genome-wide distributions of 5-hmC with those of 11 diverse histone marks , we found that 5-hmC displayed relatively strong correlations with H3K4me1 and H3K4me2 versus H3K4me3 , which , as expected , is consistent with previous correlations between DNA methylation detected by Methyl-Seq and histone modifications 15 ., 5-hmC also exhibited a strong correlation with H3K18ac , a mark regulated by CBP/p300 at enhancers that is associated with transcriptional activation ., We also found more modest correlations with H3K27ac , H3K27me3 , and H4K5ac , and very low correlations with H3K9ac and H3K9me3 ., However , our data suggested that 5-hmC was not strongly correlated with H3K36me3 , a histone modification previously linked to DNA methylation detected by Methyl-Seq ., This intriguing difference suggested differential marking of gene bodies by 5-hmC and H3K36me3 in pluripotent cells ., Direct comparisons of genic 5-hmC and H3K36me3 indeed revealed that genes with the highest levels of TSS and gene body 5-hmC tend to exhibit intermediate levels of expression and harbor less intragenic H3K36me3 , compared to genes with the highest levels of expression ., Although a number of intriguing explanations might account for these observations , one possibility is that 5-hmC may function to temper transcription at both the TSS and gene body of intermediately expressed genes , while maintaining their potential to be more fully expressed when needed ., Upon full activation , 5-hmC may be at least partially removed as the transcriptional unit acquires H3K36me3 and commits to a more fully active state ., Restriction of 5-hmC at the TSS of repressed genes and its presence at both TSSs and gene bodies of intermediately expressed genes may also indicate distinct regulation of 5-hmC at these locations ., At TSSs of genes that are repressed or expressed at low levels , Polycomb group complex , PRC2 , may interact with 5-hmC to repress but maintain the potential for expression of targeted genes , as has been previously suggested 9 , 10 ., However , such distributions are distinct from those observed in mouse cerebellum 14 , where 5-hmC is significantly enriched compared to ES cells , largely absent from TSSs , and high within gene-bodies , positively correlating gene-expression ., Thus , distinction of mechanisms differentially influencing the state and regulation of 5-hmC within genes bodies in the context of gene expression outcomes will be important towards understanding the role of 5-hmC in both brain and ES cells ., Our genome-wide analyses of 5-hmC also revealed a general promoter-proximal bias of 5-hmC around RefSeq transcripts in human ES cells , which is consistent with the recently published work on mapping 5-hmC in mouse ES cells 9 , 11–13 ., This TSS-associated bias was also dependent on gene expression levels , with 5-hmC transitioning from a position directly over the TSS at repressed genes to a bimodal distribution at more highly expressed genes , likely reflecting the observed dual function of 5-hmC in mouse ES cells 9–13 , although this correlation was not strictly linear ., Interestingly , we find that the bimodal distribution of 5-hmC is also strongly correlated with the distributions of H3K4me1 and H3K4me2 , but inversely correlated with H3K4me3 ., The bimodal distribution of 5-hmC , H4K4me1 , and H3K4me2 around TSSs might reflect the establishment of divergent paused RNAPII , which is known to play a critical regulatory role at developmentally regulated transcripts in ES cells 23 , 24 ., This could thereby point to an influence of 5-hmC on transcription pausing at such promoters in hES cells ., We also noted that such a promoter-proximal bias of 5-hmC in ES cells is distinct from that observed in mouse brain , where 5-hmC is largely depleted from TSSs and enriched within gene bodies ( Szulwach and Jin , unpublished observations and 14 ) , where it also correlates well with gene expression ., This could suggest that such a bias reflects a stem cell-specific role for 5-hmC-mediated gene regulation at and around certain TSSs ., Such differences may be accounted for by the enrichment of Tet1 , or yet-to-be-identified co-factors of Tet1 , in ES cells relative to more differentiated cell types ., Analyses of 5-hmC-enriched peaks and their correlation with enhancer-associated specific histone modifications , such as H3K4me1 , H3K18ac , and H3K27ac , suggested that , in addition to being present at promoters , 5-hmC could also mark other diverse regulatory elements in the genome , such as enhancers ., Interestingly , assessment of 5-hmC distributions at the predicted enhancers in H1 hES cells demonstrated the enrichment of the epigenetic mark at specific enhancer subtypes , including those enriched for K3K4me1 , H3K27ac , H3K18ac , and H4K5ac ., Despite a good correlation between 5-hmC and histone marks demarcating enhancers , we found that only small fraction of regions bound by p300 were also enriched for 5-hmC ., Finally , we examined the correlation of 5-hmC distributions with the genome-wide binding sites of six transcription factors that have been linked to maintaining the pluripotency of ES cells 16 ., We find that 5-hmC can also mark NANOG binding sites , while being depleted at TAF1 sites ., These results further suggest diverse roles for 5-hmC in regulating the accessibility of transcr

Introduction, Results, Discussion, Materials and Methods

Covalent modification of DNA distinguishes cellular identities and is crucial for regulating the pluripotency and differentiation of embryonic stem ( ES ) cells ., The recent demonstration that 5-methylcytosine ( 5-mC ) may be further modified to 5-hydroxymethylcytosine ( 5-hmC ) in ES cells has revealed a novel regulatory paradigm to modulate the epigenetic landscape of pluripotency ., To understand the role of 5-hmC in the epigenomic landscape of pluripotent cells , here we profile the genome-wide 5-hmC distribution and correlate it with the genomic profiles of 11 diverse histone modifications and six transcription factors in human ES cells ., By integrating genomic 5-hmC signals with maps of histone enrichment , we link particular pluripotency-associated chromatin contexts with 5-hmC ., Intriguingly , through additional correlations with defined chromatin signatures at promoter and enhancer subtypes , we show distinct enrichment of 5-hmC at enhancers marked with H3K4me1 and H3K27ac ., These results suggest potential role ( s ) for 5-hmC in the regulation of specific promoters and enhancers ., In addition , our results provide a detailed epigenomic map of 5-hmC from which to pursue future functional studies on the diverse regulatory roles associated with 5-hmC .

Recent studies revealed the oxygenase-catalyzed production of 5-hydroxymethylcytosine ( 5-hmC ) as a modification to mammalian DNA ., 5-hmC is known to play important roles in self-renewal and cell lineage specification in embryonic stem ( ES ) cells , suggesting a potential role for 5-hmC–mediated epigenetic regulation in modulating the pluripotency of ES cells ., To unveil this new regulatory paradigm in human ES cells , here we use a 5-hmC–specific chemical labeling approach to capture 5-hmC and profile its genome-wide distribution in human ES cells ., We show that 5-hmC is an important epigenetic modification associated with the pluripotent state that could play role ( s ) in a subset of promoters and enhancers with defined chromatin signatures in ES cells .

genome sequencing, genomics, chromosome biology, genetics, epigenetics, biology, human genetics, genetics and genomics

journal.ppat.1000624

EBNA1-Mediated Recruitment of a Histone H2B Deubiquitylating Complex to the Epstein-Barr Virus Latent Origin of DNA Replication

Epstein-Barr virus ( EBV ) is a gamma herpesvirus that infects over ninety percent of people worldwide ., As part of its latent life cycle , EBV efficiently immortalizes the host cell and predisposes it to a number of malignancies , including Burkitts lymphoma , nasopharyngeal carcinoma , gastric carcinoma , Hodgkins disease and a variety of lymphomas in immunosuppressed patients 1 ., In latently infected cells , replication and maintenance of the viral genome require the latent origin of replication , oriP and the EBNA1 protein ., OriP is comprised of two functional elements , the dyad symmetry ( DS ) and the family of repeats ( FR ) , which contain four and twenty copies of an 18 bp palindromic EBNA1 binding site respectively 2 , 3 ., Replication of oriP-containing plasmids requires EBNA1 binding to the DS 4 ., EBNA1 binding to the FR is required for the mitotic segregation of the oriP-containing plasmids and transactivation of several latency genes 5 , 6 ., EBNA1 binds DNA through residues 459–607 , which form the DNA binding and dimerization domain ( EBNA1-DBD ) 7–9 ., High resolution structures of the EBNA1-DBD , alone and in complex with its DNA binding site , have revealed details of the interaction of EBNA1 with DNA 10–12 ., EBNA1-DBD comprises two subdomains: residues 504–604 , referred to as the core-domain , and residues 461–503 , referred to as the flanking domain ., The core domain is a β-barrel structure that forms the dimerization interface and makes transient sequence-specific contacts with the DNA through an α-helix 10 , 13 ., The flanking domain consists of an α-helix ( residues 477–489 ) oriented perpendicular to the axis of the DNA , which contacts the major groove through Lys 477 , and an extended chain ( amino acids 461–469 ) that runs along the base of the minor groove of the DNA , making sequence-specific contacts through Lys-461 , Gly-463 and Arg-469 11 ., In addition to binding specific DNA sequences , EBNA1 is also known to interact with several host-cell proteins , which in some cases have been shown to mediate EBNA1 functions at oriP 14–18 ., EBNA1 can also affect cellular processes through sequestration of cellular proteins , as best exemplified by the EBNA1 interaction with the ubiquitin specific protease USP7 , also referred to as Herpesvirus Associated Ubiquitin Specific Protease ( HAUSP ) ., USP7 was originally identified as a binding partner of the ICP0 protein of herpes simplex virus ( HSV ) 19 and , since then , several cellular targets of USP7 have been identified including the p53 tumour suppressor protein 20–24 ., In response to genotoxic stress , USP7 binds and deubiquitylates p53 thereby protecting it from proteasome-mediated degradation ., In addition to cleaving polyubiquitin chains , USP7 has been reported to reverse monoubiquitylation in some proteins ( eg . p53 and FOXO4 ) , thereby affecting their subcellular localization 25 , 26 ., Similarly , the Drosophila homologue of USP7 was found to contribute to epigenetic silencing by reversing monoubiquitylation of histone H2B , and this activity required USP7 to be in complex with guanosine 5′ monophosphate synthetase ( GMPS ) 27 ., Our studies on the EBNA1-USP7 interaction have shown that EBNA1 binds the N-terminal domain of USP7 ( USP7-NTD ) , which is distinct from the catalytic domain , and is the the same domain that is bound by p53 28 ., EBNA1 and p53 bind the same pocket in this domain but EBNA1 does so with an affinity that is approximately 10-fold higher than that of p53 28 , 29 ., As a result , EBNA1 interferes with the binding and stabilization of p53 by USP7 and with p53-mediated apoptosis in response to DNA damage 29 , 30 ., In addition , we recently found that EBNA1 disrupts promyelocytic leukemia ( PML ) nuclear bodies ( also called ND10s ) in nasopharyngeal carcinoma cells by inducing the degradation of the PML proteins 30 ., This activity required USP7 and the EBNA1-USP7 interaction , indicating that this interaction can modulate cellular events in addition to p53 levels ., EBNA1 deletion analysis showed that the USP7 binding sequence in EBNA1 was just N-terminal to the flanking DNA binding domain and subsequent peptide binding assays identified EBNA1 residues 436–450 as sufficient for this interaction 28 , 29 ., A crystal structure of an EBNA1 peptide bound to the USP7-NTD revealed multiple interactions of EBNA1 residues 442–448 with amino acids in a shallow groove of the TRAF domain formed by the USP7-NTD 29 ., In particular interactions mediated by Ser447 in EBNA1 were shown to be critical for USP7 binding ., Given the large size of USP7 ( 135 kDa ) and the proximity of its binding site to the EBNA1-DBD residues that are inserted in the DNA minor groove ( amino acids 461–469 ) , we wondered whether the USP7 interaction interfered with EBNA1 binding to DNA ., Here we report that , contrary to our expectations , USP7 had a large stimulatory effect on the DNA-binding activity of EBNA1 in vitro and can form a ternary complex with DNA-bound EBNA1 ., Furthermore , USP7 was found to bind GMPS , forming a complex active in histone H2B deubiquitylation , and this complex was recruited to oriP in EBV-infected cells resulting in decreased H2B ubiquitylation ., We initially assessed the effect of USP7 on the DNA binding activity of EBNA1 using electrophoretic mobility shift assays ( EMSAs ) with a version of EBNA1 that has a shortened Gly-Ala repeat but has wildtype activity for all known EBNA1 functions ( referred to as EBNA1; Figure 1A ) ., Purified EBNA1 was incubated with radiolabelled DNA containing a single EBNA1 recognition site ( site 1 from the DS element ) in presence and absence of excess purified full length USP7 ., We consistently observed that USP7 stimulated the DNA binding activity of EBNA1 as shown in the representative experiment in Figure 1B ( left panel ) , while no obvious effects on EBNA1-DNA interactions were seen with nonspecific proteins such as BSA ( Figure 1B , right panel ) ., Results from multiple experiments showed a 20-fold increase in the DNA binding affinity of EBNA1 in the presence of USP7 , resulting in a shift in the dissociation constant ( Kd ) from 85±7nM for EBNA1 alone to 4 . 3±0 . 4 nM for EBNA1 in presence of USP7 ., This increase in DNA binding affinity was largely dependant on the ability of EBNA1 to bind USP7 , as the DNA binding ability of a truncation mutant of EBNA1 ( EBNA1452–641 ) containing the DNA-binding and dimerization region but lacking the USP7 binding site was much less affected by USP7 ( on average showing a 4-fold increase in DNA binding in the presence of USP7; Figure 1C ) ., EBNA1 dimers bound to DNA are known to interact with each other resulting in the crosslinking of multiple DNA fragments through large EBNA1 complexes ( referred to as looping or linking interactions ) 31–33 ., These complexes are retained in the wells of the gel in EMSAs as shown in Figure 1B , precluding analysis of the effect of USP7 on the migration of the DNA complexes ., The linking interactions of EBNA1 are mediated largely by amino acids 325–376 and to a lesser degree by EBNA1 N-terminal residues 32 , 34 ., To further evaluate the effect of USP7 on the DNA binding ability of EBNA1 without the confounding effects of DNA linking , we repeated the EMSAs with the EBNA1 truncation mutant 395–641 ( Figure 1A ) , which contains the USP7 binding site and the DNA-binding region but lacks sequences that cause DNA linking ., When the DNA binding affinity of EBNA1395–641 was measured in the presence and absence of excess USP7 , USP7 was consistently found to stimulate DNA binding by EBNA1395–641 ( Figure 2A , left panel ) , resulting in a 50-fold decrease in the calculated Kd from 233±76 nM for EBNA1395–641 alone to 4±1 . 8 nM for EBNA1395–641 in presence of USP7 ., This experiment also showed that the bound DNA migrated more slowly in the presence of EBNA1395–641 and USP7 than with EBNA1395–641 alone , suggesting that USP7 formed a ternary complex with EBNA1395–641 and DNA ., Since EBNA1 is known to bind to the N-terminal TRAF domain of USP7 ( USP7-NTD ) 28 , 29 , we examined whether this domain was sufficient to stimulate EBNA1395–641 binding to DNA ., When EBNA1395–641 titrations were performed in the presence of excess USP7-NTD , the DNA binding activity was increased 8 to 16-fold in multiple experiments , ( Figure 2A , right panel ) indicating that the USP7-NTD was partially , but not completely , responsible for the stimulatory effect of USP7 on EBNA1 DNA binding activity ., Consistent with the USP7 result , the USP-NTD was found to decrease the migration of the EBNA1-bound DNA suggesting that it can bind the EBNA1-DNA complex ., We also examined the stimulatory effect of USP7 on DNA binding by EBNA1395–641 by incubating a fixed amount of EBNA1395–641 ( sufficient to bind a small fraction of the DNA probe on its own ) with increasing amounts of USP7 prior to the addition of the DNA binding site Figure 2B , left panel ) ., EMSAs performed in this way showed that USP7 had a dose-dependent effect on the DNA binding activity of EBNA1395–641 ., The possibility that USP7 itself had some ability to bind the DNA probe was tested by titrating USP7 with the DNA in the absence of any EBNA1 , but USP7 alone did not shift the DNA probe even at very high concentrations of USP7 ( Figure 2B , right panel lanes 8–12 ) ., Similarly , the USP7-NTD on its own did not bind the DNA-probe ( Figure 2B , right panel lanes 1–7 ) ., The experiments in Figure 2A indicated that USP7 can bind the EBNA1-DNA complex resulting in a supershift while the titration performed with lesser amounts of USP7 in Figure 2B did not show a supershift ., To investigate this discrepancy , we preformed EBNA1-DNA complexes ( using EBNA1395–641 as above ) then added increasing amount of USP7 ( Figure 2C ) ., EMSAs confirmed that USP7 was able to supershift the EBNA1395–641-DNA complex but only at higher concentrations of USP7 ( compare lanes 6 and 7 to lanes 2–5 ) ., To confirm that the supershifted band contained EBNA1 , complexes formed as in lanes 2 and 7 were incubated with an EBNA1-specfic antibody prior to electrophoresis ., In both cases the antibody supershifted the bands to the gel wells , whereas no effect of the antibody was seen on the migration of the DNA probe in the absence of EBNA1 ( Figure 2C , lanes 8–10 ) ., The results indicate that USP7 can form a ternary complex with DNA-bound EBNA1 under some conditions ., During initial EBV infection , EBNA1 assembles on its recognition sites in oriP and remains stably bound to these sites in all types of latently infected cell lines ., Therefore it was not possible to determine the effects of USP7 on EBNA1 assembly on oriP using latently infected cells ., Instead , we assessed the effect of USP7 on the initial association of EBNA1 with oriP by treating EBV-negative nasopharyngeal carcinoma cells ( CNE2Z ) with siRNA against USP7 or GFP ( negative control ) and then transfecting these cells with an oriP plasmid expressing EBNA1 or an EBNA1 mutant ( Δ395–450; see Figure 1A ) that we previously showed was specifically defective in binding USP7 14 and a plasmid lacking EBNA1 binding sites ( pLacZ ) as control for nonspecific DNA binding ., Chromatin immunoprecipitation ( ChIP ) assays were then performed using EBNA1-specific antibodies to assess the degree of EBNA1 association with the the oriP FR and DS elements and lacZ ( negative control ) as compared to nonspecific rabbit IgG ., EBNA1 was readily detected on both the DS and FR elements after siGFP treatment but the association with both elements was greatly decreased by USP7 silencing ( Figure 3A , middle panels ) ., As expected , there was little association of EBNA1 with lacZ and this was unaffected by USP7 silencing ( right panel ) ., Consistent with the in vitro results , Δ395–450 bound less efficiently to both the DS and FR elements than did wildtype EBNA1 , despite being expressed at equivalent levels as EBNA1 ( see Figure 3A , left panel ) ., Moreover , unlike wildtype EBNA1 , the interaction of Δ395–450 with the FR and DS elements was not affected by USP7 silencing ., Therefore we conclude that USP7 can stimulate the assembly of EBNA1 on oriP elements in vivo ., In addition to binding the oriP elements , EBNA1 can interact in a more transient manner with a third region of the EBV genome ( referred to as region III ) , consisting of two lower affinity EBNA1 recognition sites within the BamHI-Q fragment , and this interaction can negatively regulate the Qp promoter used for EBNA1 expression in some types of EBV latency 3 , 35 , 36 ., Due to the transient nature of the EBNA1 interaction with region III , we asked whether USP7 might promote the EBNA1-region III interaction in latently infected cells ., D98/Raji cells were used for these experiments since these EBV-infected cells are more transfectable than the Raji cells from which they were derived ., D98/Raji cells were transfected with siRNA against USP7 or GFP then ChIP experiments were performed using EBNA1-specific antibody and primer sets for region III ., While we did not achieve complete silencing of USP7 in these experiments ( Figure 3B , left panel ) , its down-regulation was consistently found to decrease the association of EBNA1 with region III ( Figure 3B , right panel ) , indicating that USP7 can also modulate EBNA1-DNA interactions in the context of an EBV infection ., The above in vitro analyses raised the possibility that EBNA1 may recruit USP7 to oriP in EBV-infected cells ., To test this possibility we conducted ChIP experiments in EBV-positive B-lymphocytes ( Raji cells ) ., Antibodies against EBNA1 or USP7 were used to immunoprecipitate these proteins from sheared Raji DNA and compared to non-specific rabbit IgG as a negative control ., Immunoprecipitates were analyzed by quantitative real-time PCR using primers specific for the DS and FR regions in oriP and for the promoter region of the BZLF gene , located 40 kb away from oriP ., EBNA1 is known to be constitutively bound to the FR and DS elements 37 , 38 and , consistent with this , was readily detected on both the FR and DS DNA fragments ( with better recovery of the DS element as has been previously observed;16 , 39 , 40 ) but was not detected on the BZLF1 fragment ( Figure 4A ) ., The USP7 antibody consistently isolated more FR DNA fragment than either the DS or BZLF1 fragments ( Figure 4A ) ., Recovery of the FR region ( but not the DS region ) was significantly higher than that of the BZLF1 region with a p-value of 0 . 0004 ., The results indicate that USP7 is preferentially recruited to FR and is consistent with the higher enrichment of EBNA1 at the FR ., USP7 is known to regulate p53 levels but this would not seem to explain why it is recruited to oriP ., To gain insight into other potential functions of USP7 , we used a proteomics approach to identify cellular protein partners of USP7 ., To this end , increasing amounts of purified USP7 was coupled to resin to generate a series of USP7 affinity columns and a constant amount of human cell extract was passed through each column ., Proteins retained on the columns were eluted with 1 M NaCl , followed by 1% SDS , and the recovered proteins were analysed by SDS-PAGE and silver staining ( Figure 5A ) ., Only 1 band ( at approximately 70 Kda ) was observed to be specifically retained on the USP7 column , showing a titratable interaction with USP7 as expected for a specific protein interaction , and this was identified by MALDI-ToF mass spectrometry as GMP synthetase ( GMPS ) ., The interaction between USP7 and GMPS was further examined by glycerol gradient sedimentation analysis of the purified proteins ., For these experiments , GMPS , like USP7 , was generated using a baculovirus and extensively purified ., Analysis of the individual proteins by glycerol gradient sedimentation showed that USP7 migrates close to its calculated molecular mass of 130 Kd indicating that it is monomeric ( Figure 5B , top panel ) ., This is consistent with previous analytical centrifugation analyses 28 ., GMPS was found to migrate at a similar position as USP7 despite its smaller molecular mass of 77 Kda suggesting that it forms dimers ( Figure 5B , middle panel ) , as occurs for E . coli GMPS 41 ., When USP7 and GMPS were combined , their positions in the gradient shifted to a higher molecular weight form , confirming that the two proteins directly interact ( Figure 5B , bottom panel ) ., The size of this complex ( approximately 200 Kda ) suggested that it consisted of one USP7 and one GMPS molecule ., A previous study reported that Drosophila USP7 formed a complex with GMPS in Drosophila embryos and that this complex deubiquitylated histone H2B thereby contributing to polycomb-mediated silencing 27 ., This prompted us to investigate whether the human USP7-GMPS complex also functioned to deubiquitylate histone H2B ., To this end , we purified total histones from HeLa cells by the acid extraction method and incubated them with purified USP7 ( at a MW ratio of USP7∶histones of 1∶1000 ) for various times prior to Western blot analysis ., Histone H2B and its monoubiquitylated form ( Ub-H2B ) were initially detected using an antibody specific to histone H2B , and USP7 was found to have some ability to deubiquitylate H2B on its own ( Figure 6A , left panel ) ., Histone H2A and its monoubiquitylated form were detected in the same assay with antibody specific to H2A , however , in contrast to the H2B results , USP7 was not observed to deubiquitylate H2A ( Figure 6A , right panel ) ., To determine if GMPS affected the ability of USP7 to deubiquitylate H2B , we repeated the experiments including different amounts of GMPS ( Figure 6B ) ., The Ub-H2B was more readily detected using an anti-ubiquitin antibody , providing a more robust signal to follow and this band is shown in Figure 6B ., We found that the addition of GMPS at amounts stoichiometric to USP7 increased the cleavage of Ub-H2B by USP7 at each time point examined ( compare “1∶1” samples to “USP7” samples within each panel ) ., Increasing the amount of GMPS 10-fold had no further stimulatory effect ( compare “1∶10” samples to “1∶1” samples in the left panel ) , while decreasing the amount of GMPS 10-fold abrogated the stimulatory effect ( compare “10∶1” samples to “1∶1” samples in the right panel ) ., These results are consistent with GMPS stimulating deubiquitylation of H2B by USP7 by forming a stoichiometric complex with USP7 and are inconsistent with GMPS acting catalytically ., We also asked whether the stimulatory activity of GMPS was specific to H2B deubiquitylation or also occurred for other USP7 targets ., To this end , we incubated USP7 , with or without equal amounts of GMPS , with p53 that had been polyubiquitylated in vitro and we followed the p53 forms by Western blotting with a p53 antibody ( Figure 6C ) ., In this case , we saw no obvious difference in the kinetics of cleavage of the ubiquitylated forms by USP7 with or without GMPS , indicating that GMPS does not affect all USP7 targets equally and rather has specificity for Ub-H2B ., To assess whether USP7 regulates histones in human cells , we down-regulated USP7 in HeLa cells with siRNA treatment then prepared total histones as for the in vitro assays ., The ratio of monoubiquitylated to nonmodified forms of H2A and H2B were then determined by Western blotting using antibodies against H2A and H2B ., An example of the results obtained is shown in the gel images in Figure 6D as compared to results with the same cells treated with siGFP as a negative control ., We consistently observed an increase in the ratio of Ub-H2B to total H2B after USP7 silencing , as compared to GFP silencing ( negative control ) , but we did not see a reproducible effect on the H2A monubiquitylated form ., Results from three independent experiments are shown in histogram in Figure 6D ., Therefore the in vivo studies support the conclusions of the in vitro results , that USP7 can regulate H2B monoubiquitylation ., We next investigated the relevance of the USP7-GMPS interaction for EBNA1 , in particular whether GMPS could form part of the USP7-EBNA1-DNA complex ., We examined this in two ways: First , we tested possible interactions between DNA-bound EBNA1395–641 with GMPS with and without USP7 by EMSAs ( Figure 7 ) ., The binding of EBNA1395–641 to the DNA probe was assessed on its own or after incubation of the same amount of EBNA1 with USP7 or GMPS and the migration of the DNA complexes was assessed ., As observed above , USP7 shifted the EBNA1-DNA complex to a slower migrating form indicative of a ternary complex ( Figure 7 , compare lanes 2 and 3 ) ., On the other hand , the same amount of GMPS did not alter the mobility of the EBNA1-DNA complexes ( Figure 7 , compare lanes 2 and 4 ) ., This was expected since there is no evidence of a direct interaction between EBNA1 and GMPS ., However , when USP7 , GMPS and EBNA1 were combined ( the same amounts as when tested individually ) , and then added to the DNA , these complexes shifted to a position higher than that of the USP7-EBNA-DNA ternary complex as shown in lanes 5 and 6 of Figure 7 ( compare to lane 3 ) ., However neither GMPS , USP7 nor GMPS+USP7 interacted with the DNA in the absence of EBNA1 ( Figure 7 , lanes 8–10 ) ., The results suggest that USP7 mediates an interaction between GMPS and the EBNA1-DNA complex resulting in the formation of a quaternary complex ., We also examined the possible association between USP7-GMPS complexes and EBNA1 in vivo , by determining if GMPS localized with EBNA1 and USP7 on EBV chromatin ., ChIP experiments performed on Raji cells , showed that , like USP7 , GMPS was preferentially detected at the FR element of oriP over the DS element or the BZLF1 region ( Figure 4A , right panel ) ., This is consistent with the recruitment of the USP7-GMPS complex to the FR through EBNA1 ., We next investigated whether recruitment of GMPS to the FR was dependent on USP7 , as suggested by the EMSA experiments ., These experiments required down-regulation of USP7 by siRNA treatment and could not be performed in Raji cells due to their low transfection efficiency ., Instead , the more readily transfectable D98/Raji fusion cells were used , which retain the EBV genomes from Raji cells 42 ., USP7 was confirmed to be down-regulated in these cells following treatment with siRNA against USP7 but not siRNA against GFP ( negative control ) , while GMPS levels were not affected ( Figure 4B , left panel ) ., ChIP analysis of GMPS from these cells showed that , as in Raji cells , GMPS was preferentially localized to the FR region , and that down-regulation of USP7 resulted in decreased levels of GMPS at the FR ( P value 0 . 01 relative to FR-siGFP samples; Figure 4B , right panel ) ., If the USP7-GMPS complex functions to deubiquitylate histone H2B , then the loss of this complex from the FR would be expected to increase the level of Ub-H2B in this region ., We investigated this possibility by performing ChIP experiments with and without USP7 silencing , using an antibody that recognizes only the ubiquitylated form of H2B 43 ., To control for possible differences in the number of histones at each region we performed the same experiment with antibody against total histone H2B and expressed the Ub-H2B as a ratio of this value ., In Figure 4C ( left panel ) the change in the fraction of Ub-H2B after USP7 silencing is shown from multiple experiments ( in relation to siGFP treatment ) ., While we saw considerable variability on the level of Ub-H2B at the BZLF1 region , we consistently observed that USP7 silencing resulted in increased levels of Ub-H2B at the FR and had little effect on Ub-H2B levels at the DS ., The results support the model that USP7 is needed for recruitment of GMPS to the FR and subsequent deubiquitylation of histone H2B ., Since EBNA1 binding to the FR is known to activate transcription from the LMP1 and Cp promoters 44 , 45 , we examined the possibility that the recruitment of the USP7-GMPS complex to the FR might also affect H2B ubiquitylation at these promoters ., To this end , ChIP was performed on D98/Raji cells before and after silencing USP7 , using antibodies against Ub-H2B and total H2B ., The recovery of the LMP1 and Cp promoter regions was quantified for each treatment and the change in the fraction of Ub-H2B after USP7 silencing was determined ., Silencing of USP7 consistently resulted in increased Ub-H2B at both the LMP1 and Cp promoters , with the strongest effect on the Cp promoter , whereas H2B ubiquitylation at the oriLyt region of EBV ( negative control ) was not affected by USP7 silencing ( Figure 4C , right panel ) ., The results suggest that the USP7-GMPS complex not only affects H2B ubiquitylation at the FR but also at promoters controlled by the FR ., The above observations suggest that EBNA1-mediated recruitment of the GMPS-USP7 complex to the FR may contribute to transcriptional activation by this element through alteration of Ub-H2B at the FR and/or promoters under FR control ., To test this possibility , we treated EBV-negative CNE2Z cells with siRNA against USP7 or GFP then co-transfected them with a reporter plasmid in which expression of chloramphenical acetyl transferase ( CAT ) is under FR control and with a plasmid expressing either EBNA1 , the EBNA1 Δ395–450 mutant that is unable to bind USP7 or no EBNA1 ( oriP plasmid ) ., CAT assays were then performed on each sample to assess degree of transcriptional activation ( Figure 8 ) ., As expected strong transcriptional activation was seen after siGFP treatment in the presence of EBNA1 but not in its absence and , as previously reported 14 , Δ395–450 had slightly reduced transcriptional activity ., USP7 silencing caused a significant decrease in transcriptional activation by EBNA1 ( P value 0 . 004 ) but did not significantly affect transactivation by Δ395–450 ., These results support the model that recruitment of the USP7-GMPS complex by EBNA1 contributes to EBNA1-mediated transcriptional activation ., EBNA1 forms a stable complex with host cell USP7 and this interaction can promote cell survival , at least in part through interfering with p53 stabilization by USP7 and through disrupting PML nuclear bodies 14 , 28–30 ., Here we provide the first evidence that the EBNA1-USP7 interaction also contributes to EBNA1 functions at EBV oriP ., This study stemmed from the unexpected observation that USP7 greatly stimulated the DNA binding activity of EBNA1 in vitro and could form a ternary complex with DNA-bound EBNA1 ., EBNA1 appears to be constitutively bound to oriP elements in latent EBV infections in proliferating cells 37 , 38 and , in these cases , the functional relevance of these observations for oriP-related functions most likely lies in the ability of USP7 to form a ternary complex with DNA-bound EBNA1 , as verified at the FR element in EBV-infected cells ., In keeping with this hypothesis , we found that USP7 within this complex can mediate an interaction with GMPS which promotes deubiquitylation of histone H2B and that USP7 contributes to EBNA1-mediated transcriptional activation ., However we have also shown that USP7 can stimulate the assembly of EBNA1 on oriP elements in transfected plasmids suggesting that USP7 might play a role in the initial association of EBNA1 with these elements upon initial EBV infection , and/or during the switch from the EBV latency form in nonproliferating cells , in which EBNA1 is not expressed ( referred to as the latency program 46 ) , to latency forms in proliferating cells in which EBNA1 is expressed and bound to oriP ., In addition , we have shown that USP7 can stimulate EBNA1 binding to region III in the EBV genome which , under some circumstances , negatively regulates EBNA1 expression 35 , 36 , raising the possibility of a role for USP7 in EBNA1 autoregulation from the Qp promoter ., We have previously shown that EBNA1 residues 441–450 bind to the USP7-NTD 28 , 29 ., The ternary complex formed between USP7 and DNA-bound EBNA1 also appears to require the interaction of the USP7-NTD with the EBNA1 441–450 region for the following two reasons ., First , the USP7-NTD was sufficient to supershift the EBNA1-DNA complex ., Second , USP7 did not supershift the complex formed by DNA and EBNA1452–641 , which lacks the USP7 binding site but retains full DNA binding activity ., However , it is curious that we observed partial but not complete stimulation of EBNA1 DNA binding by the USP7-NTD ., We had previously assessed the ability of all USP7 stable domains to bind EBNA1 by examining the retention of partially proteolysed USP7 on an EBNA1 affinity column and only the USP7-NTD was found to bind EBNA1 28 ., However , this does not eliminate the possibility that other regions of USP7 might have weak affinities for EBNA1 ., Our in vitro data are consistent with a model in which the USP7-NTD binds EBNA1 residues 441–450 to bring USP7 to EBNA1 , enabling subsequent weaker or less specific interactions of other regions of USP7 with the EBNA1 DNA binding or C-terminal regions ( 452–641 ) ., This might explain why the DNA binding activity of EBNA1452–641 was weakly stimulated by USP7 ., Another possible interpretation of the in vitro data is that the interaction of the USP7-NTD with EBNA1 is stabilized by the rest of USP7 due to effects on the structure of the USP7-NTD ., However we do not think this is likely because the USP7-NTD is a TRAF domain that is stably folded in the absence of the rest of USP7 29 , 47 ., While stoichiometric amounts of USP7 were sufficient to stimulate the DNA binding activity of EBNA1 , only at higher USP7 concentrations was USP7 observed to be stably associated with the EBNA1-DNA complex in vitro ., This indicates that the affinity of USP7 for free EBNA1 is higher than for DNA-bound EBNA1 and that a higher effective concentration of EBNA1 or USP7 may be necessary to drive the interaction of these proteins on DNA ., This conclusion is also supported by the observation that USP7 is preferentially associated with EBNA1 on the FR element over EBNA1 on the DS element of oriP ., The FR element is bound by 20 EBNA1 dimers as compared to 4 EBNA1 dimers at the DS element and , in both cases , the dimers within the element interact with each other to form a larger EBNA1 complex 31 , 32 ., As a result the effective concentration of EBNA1 at the FR is higher than at the DS and this may drive recruitment of USP7 ., An increasing number of human cellular protein binding targets of USP7 have been identified including p53 , Mdm2 , FOXO , March 7 and PTEN , all of which can be deubiquitylated by USP7 20–24 , 26 ., Our proteomic profiling of USP7 protein interactions identified GMPS as another USP7 binding partner ., We expect that other USP7 binding partners were not identified by this method due to their low abundance or transient nature of the interaction in response to particular stimuli ( such as occurs with the USP7-p53 and USP7-FOXO interactions ) ., The interaction of USP7 with GMPS is unique in that it appears to affect the activity of USP7 for specific substrates , as opposed to being a substrate itself ., This is supported by the fact that GMPS levels are not altered when USP7 is silenced ( as shown in Figure 4B ) ., The finding that human USP7 forms a stable complex with GMPS fits well with the observations of van der Knaap et al 27 , where Drosophila USP7 was found to co-purify with GMPS ., Our glycerol gradient sedimentation analyses indicated that human USP7 and GMPS form a 1∶1 complex and in vitro assays show that GMPS stimulates the ability of USP7 to deubiquitylate H2B ( but not H2A ) , as observed for the Drosophila GMPS-USP7 complex ., Van der Knapp et al 27 also showed that the stimulation of Drosophila USP7 activity by GMPS did not require the catalytic activity of GMPS ., Our in vitro results are consistent with this conclusion because stimulation of USP7 deubiquitylation activity for H2B required stoichiometric amounts of GMPS ( indicative of formation of a USP7-GMPS complex ) and did not oc

Introduction, Results, Discussion, Materials and Methods

The EBNA1 protein of Epstein-Barr virus ( EBV ) plays essential roles in enabling the replication and persistence of EBV genomes in latently infected cells and activating EBV latent gene expression , in all cases by binding to specific recognition sites in the latent origin of replication , oriP ., Here we show that EBNA1 binding to its recognition sites in vitro is greatly stimulated by binding to the cellular deubiquitylating enzyme , USP7 , and that USP7 can form a ternary complex with DNA-bound EBNA1 ., Consistent with the in vitro effects , the assembly of EBNA1 on oriP elements in human cells was decreased by USP7 silencing , whereas assembly of an EBNA1 mutant defective in USP7 binding was unaffected ., USP7 affinity column profiling identified a complex between USP7 and human GMP synthetase ( GMPS ) , which was shown to stimulate the ability of USP7 to cleave monoubiquitin from histone H2B in vitro ., Accordingly , silencing of USP7 in human cells resulted in a consistent increase in the level of monoubquitylated H2B ., The USP7-GMPS complex formed a quaternary complex with DNA-bound EBNA1 in vitro and , in EBV infected cells , was preferentially detected at the oriP functional element , FR , along with EBNA1 ., Down-regulation of USP7 reduced the level of GMPS at the FR , increased the level of monoubiquitylated H2B in this region of the origin and decreased the ability of EBNA1 , but not an EBNA1 USP7-binding mutant , to activate transcription from the FR ., The results indicate that USP7 can stimulate EBNA1-DNA interactions and that EBNA1 can alter histone modification at oriP through recruitment of USP7 .

Epstein-Barr virus ( EBV ) infections persist for the lifetime of the host largely due to the actions of the EBNA1 viral protein ., EBNA1 enables the replication and stable persistence of EBV genomes and activates the expression of other EBV genes by binding to specific DNA sequences in the EBV genome ., We have shown that the cellular protein USP7 stimulates EBNA1 binding to its DNA sequences and that EBNA1 recruits USP7 to the EBV genome , which in turn recruits another cellular protein GMP synthetase ., The complex of USP7 and GMP synthetase then functions to alter the chromatin structure at a region of the EBV genome that controls EBV persistence ., These changes to the EBV genome are likely important for enabling the persistence of EBV genomes in infected cells .

molecular biology/dna replication, molecular biology/histone modification, virology/persistence and latency, virology/viral replication and gene regulation, molecular biology/transcription initiation and activation, virology/viruses and cancer, molecular biology/chromatin structure

journal.pcbi.1003213

Mammalian Rest/Activity Patterns Explained by Physiologically Based Modeling

Sleep recordings in 127 mammalian species 1 have revealed a rich array of phenotypes with respect to the temporal organization of rest/activity and sleep/wake patterns 2 ., These phenotypic differences between species are thought to reflect evolutionary adaptations to specific temporal niches ( the times at which an animal is usually active ) 3 ., In mammals , temporal niches span a continuum from diurnal ( day-active ) to cathemeral ( no time-of-day preference ) to nocturnal ( night-active ) 4 ., In addition , the shape of the activity waveform varies; some species are active in a single daily peak , while others have two or more daily peaks , e . g . , crepuscular ( dawn- and dusk-active ) animals ., Temporal niche can also change seasonally , developmentally , or in response to other environmental stimuli 5 , 6 , 7 ., Remarkably , some rodents switch from diurnal to nocturnal in the laboratory when provided access to a running wheel 8 , 9 , 10 ., Our goal is to develop a theoretical framework to compare these diverse phenotypes and quantitatively relate them to a small number of underlying physiological mechanisms ., Recently , physiological mechanisms that control the timing of rest/activity and sleep/wake patterns have been identified ., Chief among these is the master circadian pacemaker in the suprachiasmatic nucleus ( SCN ) of the hypothalamus 11 ., Surprisingly , however , there are no systematic differences in circadian function between nocturnal and diurnal mammals 12 ., In both groups , the SCN is similar in structure 13 and has highest neuronal firing rates during circadian daytime 14 ., Furthermore , melatonin secretion , which is under SCN control in mammals , always occurs during circadian nighttime 15 ., Therefore , temporal niche must be largely determined by mechanisms outside of the SCN , including downstream modulation of SCN output 16 ., As shown in Figure 1 , the SCN projects to the subparaventricular zone ( SPZ ) , with subsequent relays to the dorsomedial hypothalamus ( DMH ) , and from there to nuclei that regulate sleep/wake and rest/activity , including the ventrolateral preoptic area ( VLPO ) and orexinergic neurons of the lateral hypothalamic area ( LHA ) 11 ., Experimental results confirm that SCN output is inverted in nocturnal animals at the SPZ 17 ., This results in inversions in neuronal activity patterns in many downstream wake-promoting regions 18 and physiologic outputs 19 , relative to diurnal animals ., The acute effect of light on behavior , i . e . , masking of circadian rhythms , also shapes the temporal organization of rest/activity patterns 20 ., Typically , light exposure increases vigilance in diurnal animals ( positive masking ) and decreases vigilance in nocturnal animals ( negative masking ) ., Masking strength depends on the measure of vigilance used , with stronger effects of light on waking behaviors such as locomotor activity and feeding than on time spent awake 21 , 22 , 23 ., The dominant pathway for masking is hypothalamic , as lesions that sever the retinohypothalamic tract abolish masking 24 ., The intergeniculate leaflet and visual cortex modify masking strength , but neither structure is required for masking 25 , 26 ., Candidate pathways for masking thus include retinal input to the VLPO 27 , pretectum 28 , and SCN 29 , 30 ., Following SCN lesions , masking persists in some reports 31 , 32 , 33 and is weakened or abolished in others 34 , 35 ., We speculate that this inconsistency could reflect incidental damage to fibers passing near the SCN , such as retinal fibers to the VLPO or pretectum ., Studies of mammals that change their temporal niche have discovered a potential interaction between masking and circadian modulation ., When Nile grass rats switch from diurnal to nocturnal behavior , they also switch from positive to negative masking 36 ., The same phenomenon is observed in the degu , a normally diurnal rodent 37 ., However , degus can also express a stable intermediate phenotype in which masking is inverted but the circadian rhythm is not 37 ., These findings , along with lesion studies 6 , show that changes in circadian modulation and masking can occur independently ., Untangling the relative contributions of masking and circadian modulation to rest/activity patterns is a challenging experimental task ., Therefore , we developed a new physiologically based mathematical model that includes both mechanisms ., Using this model , we were able to capture a wide array of experimentally-observed rest/activity patterns and relate them to underlying physiology ., As shown in Figure 1 , the model encompasses key sleep-regulatory nuclei in the brainstem and hypothalamus , including the mutually inhibitory sleep-promoting VLPO and wake-promoting monoaminergic ( MA ) nuclei , which together comprise the sleep/wake switch 38 ., The DMH relays the circadian signal via two pathways:, ( i ) to VLPO ( the DMH/VLPO relay ) , and, ( ii ) to LHA ( the DMH/LHA relay ) ., In the model , SCN output is modulated at the SPZ by a multiplicative factor , , with corresponding to diurnal ( SCN and SPZ firing in phase ) and corresponding to nocturnal ( circadian inversion , with SCN and SPZ firing out of phase ) ., Masking is modeled as a direct retinal input to the VLPO , with an excitatory input corresponding to negative masking and an inhibitory input corresponding to positive masking ., In this model , MA firing rate , , is used to distinguish between sleep ( s−1 ) and wake ( s−1 ) ., Higher MA firing rates are assumed to correspond to higher levels of activity , which is justified by experimental observations 39 ., For more details of the model , see the Methods and Text S1 ., The lack of correspondence between SCN firing patterns and diurnal/nocturnal preference indicates that other mechanisms must be involved in determining temporal niche ., We hypothesized that modulation of SCN output by the SPZ could account for the degree to which an animal is either diurnal or nocturnal ., We expected circadian modulation to be the dominant mechanism for this , rather than masking effects of light , because differences in the phasing of rest/activity patterns between diurnal and nocturnal species can persist even when they are free running in constant darkness ., To examine the effects of SPZ modulation in isolation , we omitted masking , omitted the DMH/LHA relay , and simulated a rodent entrained to a 24-h light/dark ( LD ) cycle with 12 h of 100 lux ( i . e . , bright enough to achieve entrainment of the circadian rhythm to the LD cycle ) ., With , the SPZ fires out of phase with the SCN , resulting in a sleep-promoting circadian signal during the light phase ., As shown in Figure 2A for , this results in higher MA firing rates ( ) during the dark phase and a nocturnal phenotype ., is not continuously high at any circadian phase , instead alternating frequently between high and low values across the day ., As in previous work , the model is defined to be awake whenever s−1 ., The rapidly alternating dynamics for therefore correspond to polyphasic sleep/wake patterns , as typically seen in rodents ., The time series for sleep/wake state is shown in Figure 2F , along with the average value ( in 10 min windows , over 60 days ) , displaying a nocturnal phenotype ., We note that the model includes noise to generate more realistic variability , but the polyphasic sleep/wake patterns in Figure 2 and later sections are not dependent on the inclusion of noise; they are also a feature of the deterministic dynamics for these parameter values 40 ., Increasing the value of results in a continuous variation in phenotype from strongly nocturnal for ( Figure 2A and 2F ) to slightly nocturnal for ( Figure 2B and 2G ) to cathemeral for ( Figure 2C and 2H ) to slightly diurnal for ( Figure 2D and 2I ) to strongly diurnal for ( Figure 2E and 2J ) ., With , the SPZ fires in phase with the SCN , resulting in a wake-promoting circadian signal during the light phase ., The average total wakefulness across the day also varies with , because SCN output does not promote wake and sleep symmetrically , as discussed below ., Using our model , we thus showed that modulation of the circadian signal at the SPZ is a sufficient mechanism to explain much of the variability in temporal niche observed experimentally ., However , this mechanism alone can not account for all differences in the daily activity waveform ., The activity waveforms in Figure 2 are all unimodal ( i . e . , have a single daily peak when time-averaged ) , whereas many species have two or more distinct activity peaks per day ., Currently , it is unknown which physiological factors determine the shape of the daily activity waveform ., We hypothesized that interactions between the two relay pathways , DMH/VLPO and DMH/LHA , could affect the shape of the waveform ., In principle , the two pathways could be either cooperative or competitive in terms of their actions on the sleep/wake switch ., Using our model , we simulated both types of interactions in a primate ., We again omitted masking so as to examine this effect in isolation ., When the pathways act cooperatively in relaying a diurnal signal ( i . e . , the DMH inhibits the sleep-promoting VLPO and excites the wake-promoting LHA ) , MA firing rates peak near the middle of the active phase ( Figure 3A ) ., However , when the pathways act competitively ( i . e . , the DMH inhibits both the VLPO and the LHA ) , MA firing rates have a bimodal waveform ( Figure 3B ) ., In the latter case , the dominant DMH/VLPO pathway maintains a diurnal sleep pattern , but orexinergic LHA neurons send a weak sleep-promoting signal near the middle of the wake episode ., This results in a dip in activity early in the day , the timing of which is explained below ., By varying the single model parameter corresponding to the synaptic connection strength from the DMH to LHA , we found that we are therefore able to change activity patterns from unimodal to bimodal ., This is a heretofore unidentified mechanism for generating bimodal activity patterns ., This finding justifies the apparent functional redundancy of multiple circadian relays; additional relays provide additional degrees of freedom for modulating the SCN output signal and behavior ., Bimodal waveforms are seen in many species; for reference we include the activity patterns of a spider monkey in Figure 3C ., The timing and relative spacing of the peaks of activity are different between the simulation in Figure 3B and the experimental data in Figure 3C , with respect to the LD cycle ., The data are shown only as an illustrative example of a mammalian species with a bimodal activity pattern; we do not attempt to achieve a best model fit ., We have shown in previous work that the timing of the sleep/wake and rest/activity cycle can be modified with respect to the LD cycle by changing values of some of the parameters of the circadian and sleep homeostatic processes 41 ., We do not pursue that idea further here ., The unimodal vs . bimodal dynamics can be better understood by examining the average waveforms of the circadian and sleep homeostatic processes in the cooperative and competitive cases ., When the relays are cooperative ( Figure 4A ) , both circadian drives for wakefulness – the DMH/VLPO and the DMH/LHA – peak near the middle of the light period , with the amplitude of the DMH/VLPO drive being much larger than the amplitude of the DMH/LHA drive ., The homeostatic drive to sleep increases across the first half of the light period , when activity is high ( Figure 3A ) , and decreases thereafter , when activity is low ., When the relays are competitive ( Figure 4B ) , the DMH/VLPO drive still promotes wakefulness during the light period and sleep during the dark period , but the DMH/LHA relay is now in anti-phase ., This results in a weaker drive to sleep during the dark period , and therefore a slighter earlier awakening ., Upon awakening , the net circadian drive is close to zero and the homeostatic drive for sleep is low , similar to the cooperative case ., This results in an initial period of high activity ( Figure 3B ) and a rapid rise in sleep homeostatic pressure ( Figure 4B ) ., However , because the two circadian drives are in anti-phase , the overall circadian drive for wakefulness has lower amplitude and does not rise as quickly during the light period ., Combined with the high homeostatic pressure , this results in a greater drive to sleep during the light period than in the cooperative case ., As seen in Figure 4C , the combination of high homeostatic pressure and low circadian drive for wakefulness early in the light period results in a relatively consolidated block of sleep on most days ., This nap is reflected in the flattening of the average homeostatic sleep drive early in the light period ( Figure 4B ) ; the reason the homeostatic pressure does not decrease more rapidly on average is because the timing of this nap is variable ., In the deterministic case , shown in Figure S1 , the timing of this nap is not variable , leading to a more consistent dip in average activity near the middle of the light period ., Following the early nap , homeostatic pressure is lower and the circadian drive for wakefulness is stronger ., As shown in Figure 4C , this leads to an extended period of intermittent activity and wakefulness in the afternoon ., This activity gradually transitions into nighttime sleep around a clocktime of 15 h , which corresponds to the turning point for the average homeostatic sleep drive ., In many respects , this behavior is similar to that observed in spider monkeys 12 , which show a robust peak in morning activity , followed by a period of low activity , which is then followed by a long afternoon period of intermittent activity ., The main difference between those data and this model simulation is that the data show a slight increase in activity towards the end of the wake period , whereas our model predicts a gradually tailing off of activity across the afternoon for the parameter values used here ., While many species are solely nocturnal or solely diurnal , some species switch between these temporal niches under certain conditions ., These switches are associated with inversions in circadian modulation and/or the masking response to light 37 ., Currently , it is an open question whether temporal niche switching relies on simultaneous inversions in both of these factors , or whether inversions in circadian modulation alone are sufficient ., We hypothesized that masking of activity by light must play a critical role in temporal niche switching ., To test this hypothesis , we referred to the experiment of Kas and Edgar 9 , where a diurnal degu inverted its activity patterns when provided access to a running wheel ( Figure 5A ) ., Switching was achieved almost immediately each time access to the running wheel was provided or removed ., Switching was also achieved in constant darkness ( DD ) , demonstrating that masking alone could not account for the phenomenon ., The model we developed provides the unique ability to infer the relative contributions of masking and circadian modulation from the observed behaviors ., We simulated degu rest/activity patterns using model parameter values that correspond to a diurnal rodent ., In the absence of a running wheel , we simulated a diurnal circadian signal ( ) and positive masking of activity by light ., To simulate the effects of introducing a running wheel , we modeled three possible physiological responses:, ( i ) Wheel-induced inversion of both the circadian signal ( from diurnal to nocturnal ) and masking ( from positive masking to negative masking ) , shown in Figure 5B;, ( ii ) Wheel-induced inversion of just the circadian signal , shown in Figure 5C;, ( iii ) Wheel-induced inversion of just masking , shown in Figure 5D ., These responses to the running wheel were assumed to occur instantaneously ., The two corresponding parameters ( and the masking strength parameter ) were the only parameters varied within the three simulations ., Only three other model parameters were used to fit the model to this specific data set: intrinsic circadian period was set to 23 . 0 h to match the free-running period in DD; mean offset of the circadian signal was set to match daily activity duration under LD without a wheel; and circadian sensitivity to phase resetting by light was set sufficiently high to ensure entrainment to LD ., All other model parameters had been constrained rigorously in previous work and took the values given in Table S1 ., As a model proxy for activity , we measured in sliding 10 min windows ., Windows with s−1 are plotted as bouts of high activity in Figure, 5 . Simulating both masking inversion and circadian signal inversion ( Figure 5B ) yields a simulated activity record that is strikingly similar to the experimental data shown in Figure 5A ., To our knowledge , this is the first reproduction of such a complex rest/activity phenotype by any mathematical model ., Distinct transitions between diurnal and nocturnal phenotypes are reproduced under both LD and DD conditions ., The model also reproduces multiple features to which it was not specifically fitted , including the extended duration of activity under LD without a wheel as compared to LD with a wheel , and the persistence of occasional activity bouts during the dark phase without the wheel and the light phase with the wheel ., One notable discrepancy between these model simulations and data is the lack of a crepuscular activity profile under LD in the simulations; in the experimental data , activity peaks are seen at the times of lights on and off in LD , and at activity onset in DD ., The simulation using inversion of the circadian signal but not masking ( Figure 5C ) captures many features of the experimental data in Figure 5A , including inversion of activity in DD ., A switch to primarily nocturnal activity under LD conditions in the presence of the wheel is also reproduced , but the transition is much less distinct than in the data ., This is because masking remains positive , so the circadian signal and masking provide conflicting signals under LD conditions ., The simulation using inversion of masking but not the circadian signal ( Figure 5D ) fails to reproduce the inversion of activity patterns under DD conditions , because there is no light to respond to ., Furthermore , under LD conditions , masking inversion is predicted to reduce activity during the light period , but not induce a complete inversion of rest/activity cycles ., This is consistent with the results of a previous study in degus , in which a switch to negative masking ( but no circadian inversion ) under LD conditions resulted in no change to core body temperature during the dark phase , but significantly decreased temperature during the light phase 37 ., In that instance , core body temperature during the light phase was similar to that of the fully nocturnal phenotype ( i . e . , both negative masking and circadian inversion ) ; however , the light intensity used ( 350–400 lux ) was considerably higher than in the Kas and Edgar study ( 30 lux ) ., Our results therefore support the hypothesis that changes in masking and SCN output can occur independently , but they also demonstrate that , under these experimental conditions , inversions in the circadian signal and masking must occur together to achieve a complete temporal niche switch ., These insights are gained by our novel strategy of reproducing rest/activity behavior from a mathematical model of the underlying physiology ., Bilateral SCN lesions have been shown experimentally to result in fragmented and non-circadian rest/activity patterns 42 ., Interestingly , these patterns are quasiperiodic , with a period of approximately 4 h in the squirrel monkey 43 ., The reason for this periodicity is presently unknown ., To investigate the cause of this phenotype , we simulated the experimental protocol of Edgar et al . 43 , in which both intact and SCN-lesioned animals remained in 500 lux constant light ( LL ) conditions for several weeks ., We simulated lesions by setting SCN output to zero ( for intact , for SCN lesions ) ., Positive masking by light was modeled in both cases , as this persists after SCN lesions in squirrel monkeys 31 ., No other parameters were varied to simulate lesions ., Only three other model parameters were used to fit the model to the data: the intrinsic circadian period was set to 25 . 2 h to match the LL data in the intact animal; the mean offset of the circadian signal was set to match daily wake duration in the intact animal; and the sleep homeostatic time constant was chosen to produce a primarily monophasic sleep pattern ( i . e . , one main sleep bout per day ) in the intact animal , with similar consolidation to experimental data 44 ., All other parameters took their nominal values , given in Table S1 ., Although only fitted to the intact animal , the model reproduces realistic sleep/wake patterns for both intact and lesioned animals , as shown in Figure, 6 . With intact SCN , sleep is primarily monophasic , and free runs with a 25-h period ( Figure 6A ) ., With SCN lesions , the model correctly predicts that sleep is polyphasic ( Figure 6B ) ., Total daily sleep time is increased after SCN lesions , from 36% to 52% in data , and from 36% to 59% in simulations ., Plotting percentage time awake as a function of circadian time shows a high degree of consistency between data ( Figure 6C ) and simulation ( Figure 6D ) ., Spectral analysis of activity patterns ( measured from drinking patterns for data , and for model ) shows a strong primary ( fundamental ) component at the circadian ( ∼25, h ) period for intact animals , with smaller peaks at the second harmonic ( ∼12 . 5, h ) period for data ( Figure 6E ) and simulation ( Figure 6F ) ., With SCN lesions , the circadian component is lost ., The data show a spectral peak at an ultradian ( shorter than 24, h ) period of approximately 4 h ( Figure 6G ) ., The model similarly predicts a spectral peak at 5 . 2 h ( Figure 6H ) ., This ultradian sleep/wake cycle is due to the continued action of the sleep homeostat in the model; sleep pressure increases during wakefulness and decreases during sleep ., The period of the ultradian cycle is therefore determined by the homeostatic time constant ., The fact that the models predicted 5 . 2-h period closely matches the 4-h period seen experimentally is remarkable , since the model parameters were not chosen to reproduce this feature ., In fact , the homeostatic time constant was estimated based purely on sleep/wake patterns in the intact animal ., This finding was also highly robust; increasing or decreasing the homeostatic time constant by 25% was found to produce ultradian periods of 5 . 8 h and 4 . 5 h , respectively ., The prediction of ultradian sleep/wake cycles is therefore an emergent feature of the model and , to our knowledge , provides the first strong evidence for the sleep homeostatic process being the generator of this cycle in SCN-lesioned animals ., Experimental advances over the past decade have identified core mechanisms underlying mammalian sleep/wake regulation 38 ., Consequently , mathematical modeling has become a powerful tool for relating overt behavior to physiology 40 , 45 , 46 , 47 , 48 , 49 , 50 ., Here , we used a model of circadian signal modulation and masking by light to perform a novel analysis of how these two mechanisms influence the temporal organization of both rest/activity and sleep/wake patterns in a variety of species ., Our findings demonstrate that these two mechanisms can together account for much of the observed interspecies variability in temporal niche ., Furthermore , we showed that complete switching between diurnal and nocturnal phenotypes under LD and DD conditions requires simultaneous inversions in both masking and the circadian signal ., Future studies are required to understand how these changes are coordinated and how the relevant environmental signals are transduced ., Understanding which neural mechanisms influence temporal niche has important evolutionary implications ., It is postulated that the evolution of homeothermy enabled mammals to exploit a wider range of temporal niches 51 ., However , it is not known precisely which components of sleep/wake and circadian circuitry were necessary to enable changes in temporal niche ., Saper et al . 11 suggested that the multi-step neuronal pathway from SCN to sleep/wake switch may allow the SCN output signal to be flexibly modulated and integrated with other physiological signals ., Our results support this hypothesis , showing that modulation of a single oscillator ( i . e . , SCN ) output can yield a full spectrum of switchable phenotypes , from diurnal to nocturnal , as well as bimodal activity patterns ., Integration with other signals , including food entrainment 52 and sleep homeostasis 40 , could give rise to additional and more varied phenotypes ., We modeled circadian signal inversion at the SPZ , but alternative mechanisms for temporal niche switching may have evolved independently in other species ., For example , the mole rat switches from diurnal to nocturnal behavior by changing how the SCN responds to light 53 ., Similarly , a switch from nocturnal to diurnal behavior can be induced in mice by knocking out two genes that affect the retinal response to light 54 ., Additionally , the phase relationships between arousal systems may be more complex than modeled here 55 , due to different firing patterns within different parts of the SCN 56 and autonomous clock behavior within other neuronal populations 57 ., In future , our model could be used to investigate these phenomena ., Many mammals display bimodal activity patterns ., However , the physiological basis for this activity profile remains poorly understood ., Pittendrigh and Daan proposed a two-oscillator model to account for this , in which each oscillator generates a single activity peak 58 ., Such a mechanism appears to be present in Drosophila 59 and may underlie the splitting of activity into two distinct oscillations under LL conditions in some mammals 60 ., While a two-oscillator system is one possible mechanism for generating bimodal activity patterns , our modeling shows that bimodal activity patterns can also emerge naturally from two ( or more ) competing outputs of a single oscillator in interaction with known sleep-regulatory systems ., Simulating SCN lesions , we reproduced the ∼4-h ultradian sleep/wake cycles of SCN-lesioned squirrel monkeys without making any special additions to the model or even adjusting any existing parameters , besides changing the amplitude of the SCN signal to zero in the SCN-lesioned case ., The period of this sleep/wake cycle is determined by the value of the homeostatic time constant , which was estimated from sleep patterns in the intact animal ., Our results thus provide quantitative evidence that ultradian sleep/wake cycles in SCN-lesioned animals can be explained parsimoniously by continued action of the sleep homeostatic process ., This can be related to our recent finding that the ultradian REM/NREM sleep cycle in humans can also be generated by the very same sleep homeostatic process 61 , with the intriguing implication that both polyphasic sleep/wake cycles and ultradian REM/NREM sleep cycles may have the same physiological basis ., Furthermore , our model reproduced the increase in average daily sleep duration caused by SCN lesions in the squirrel monkey ., Whether the circadian signal is wake-promoting or sleep-promoting remains a matter of debate ., While SCN lesions increase daily sleep duration in some species 43 , they reduce daily sleep duration in others 42 ., These conflicting findings have led to the notion that the SCN may actively promote both wake and sleep at different circadian phases , with the relative sleep/wake balance differing between species 62 ., This dual output can be explained by the fact that SCN efferents employ multiple neurotransmitters 63 and this has been modeled explicitly elsewhere 46 ., We allow for this in our model by incorporating a constant offset in the SCN signal to the SPZ; depending on its value , the SCN may be exclusively wake-promoting , exclusively sleep-promoting , or alternately wake-promoting and sleep-promoting at different circadian phases ., After fitting parameters to the species simulated here , our model predicts that the circadian signal is exclusively wake-promoting in humans and squirrel monkeys , and alternately wake-promoting and sleep-promoting in degus and spider monkeys ( see Tables S1 and S2 for all parameter values ) ., These predictions may be tested experimentally in future work ., Going forward , one of the greatest challenges for the field will be reconciling the rest/activity patterns observed in the laboratory – on which we primarily focused here – with those observed in more natural settings 64 , 65 ., While work in the laboratory has provided a basic understanding of the salient physiological mechanisms and their key interactions , new methods , including models , will be needed to extend this knowledge to the inevitably more complex real-world scenarios ., One advantage afforded by modeling is the ability to freely manipulate variables that would be extremely challenging to manipulate experimentally , and to then relate these variables to observable phenotypes ., Modeling can also help to predict and target experimental protocols that will be most sensitive to the quantities of interest ., In these respects , our approach is extremely powerful; our model successfully reproduces very diverse behaviors , generates new testable predictions , and yields unique insights into the underlying physiology ., The model is based on a previously developed and validated model 40 , 66 , extended here to include the circadian relay system and masking by light ., As shown in Figure 1 , the model includes sleep-regulatory neuronal populations in the brainstem and hypothalamus , the circadian pacemaker and its system of relays to the sleep-regulatory system , and the effects of light on both the circadian pacemaker and the sleep-regulatory populations ., The sleep-regulatory populations are the wake-promoting monoaminergic ( MA ) population and the sleep-promoting ventrolateral preoptic area ( VLPO ) , which are mutually inhibitory and comprise the sleep/wake switch ., Mean cell body voltages , , and mean firing rates , , are defined for the MA ( ) and VLPO ( ) populations ., The dynamics of these two populations are modeled by two coupled first-order differential equations 67 , ( 1 ) ( 2 ) where terms represent connection strength to population from , is a decay time constant , is input from cholinergic and other sources , is additive Gaussian-distributed white noise , and and are constants ., Inputs to the sleep-regulatory VLPO and MA from other sources are respectively represented by ( 3 ) ( 4 ) The term is the homeostatic sleep drive , with obeying the first-order differential equation ( 5 ) where is a time constant and is constant ., During wake , saturates approximately exponentially to ., During sleep , decays approximately exponentially to ., The term in Eq ., 3 represents the direct ( masking ) effects of light on the VLPO , with excitatory ( sleep-promoting ) input ( ) corresponding to negative masking and inhibitory ( wake-promoting ) input ( ) corresponding to positive masking ., The relationship between and environmental light is described below ., The term in Eqs ., 3 and 4 represents relayed circadian output via the DMH ., It is defined by the linear equation ( 6 ) where , , and are constants , represents modulation of SCN output by the SPZ , and represents the output of the master circadian pacemaker , the SCN ., The dynamics of are modeled by a modified van der Pol oscillator 68 , ( 7 ) ( 8 ) where is a complementary variabl

Introduction, Results, Discussion, Methods

Circadian rhythms are fundamental to life ., In mammals , these rhythms are generated by pacemaker neurons in the suprachiasmatic nucleus ( SCN ) of the hypothalamus ., The SCN is remarkably consistent in structure and function between species , yet mammalian rest/activity patterns are extremely diverse , including diurnal , nocturnal , and crepuscular behaviors ., Two mechanisms have been proposed to account for this diversity:, ( i ) modulation of SCN output by downstream nuclei , and, ( ii ) direct effects of light on activity ., These two mechanisms are difficult to disentangle experimentally and their respective roles remain unknown ., To address this , we developed a computational model to simulate the two mechanisms and their influence on temporal niche ., In our model , SCN output is relayed via the subparaventricular zone ( SPZ ) to the dorsomedial hypothalamus ( DMH ) , and thence to ventrolateral preoptic nuclei ( VLPO ) and lateral hypothalamus ( LHA ) ., Using this model , we generated rich phenotypes that closely resemble experimental data ., Modulation of SCN output at the SPZ was found to generate a full spectrum of diurnal-to-nocturnal phenotypes ., Intriguingly , we also uncovered a novel mechanism for crepuscular behavior: if DMH/VLPO and DMH/LHA projections act cooperatively , daily activity is unimodal , but if they act competitively , activity can become bimodal ., In addition , we successfully reproduced diurnal/nocturnal switching in the rodent Octodon degu using coordinated inversions in both masking and circadian modulation ., Finally , the model correctly predicted the SCN lesion phenotype in squirrel monkeys: loss of circadian rhythmicity and emergence of ∼4-h sleep/wake cycles ., In capturing these diverse phenotypes , the model provides a powerful new framework for understanding rest/activity patterns and relating them to underlying physiology ., Given the ubiquitous effects of temporal organization on all aspects of animal behavior and physiology , this study sheds light on the physiological changes required to orchestrate adaptation to various temporal niches .

Controlled timing of all daily activity patterns is a highly adaptive trait that allows an animal to exploit its particular ecological environment ., Environmental pressures such as light cycles , temperature cycles , food availability , and timing of predator activity selectively shape the activity patterns of different species ., Mammalian species are remarkably diverse and flexible in their daily activity patterns , including a spectrum of diurnal ( day-active ) to nocturnal ( night-active ) and crepuscular ( dawn/dusk-active ) patterns ., Despite the importance of activity patterns to all aspects of behavior and physiology , the mechanisms that underlie these patterns are not well understood ., Using a computational model , we demonstrated that a wide array of activity phenotypes can be captured in terms of just two hypothesized mechanisms: reshaping of the output of the brains master circadian clock , and direct responses to light exposure ., This work links together a wide range of mammalian behaviors and sheds new light on their potential evolutionary underpinnings .

journal.ppat.1005373

Induction of Cell-Cell Fusion by Ebola Virus Glycoprotein: Low pH Is Not a Trigger

Ebola virus ( EBOV ) outbreaks continually occur and up to 90% of those infected die; currently there are no approved vaccines or antiviral therapeutics against the virus 1 , 2 ., EBOV initiates infection by fusion from within endosomes ., Experimentally , endosomal interiors are difficult to control , but systems that track the entry of several other viruses into cells have been developed and employed 3 , 4 , 5 , 6 ., Historically , these methods have relied on fusion of infectious virus or pseudovirus within cells; cell-cell fusion has not been among the systems in use for EBOV ., It is surprising that a cell-cell fusion system has not been developed , as the processing of the Ebola fusion protein , GP , and other conditions necessary for fusion have been elaborated 7 ., ( Some years ago there was an isolated report of EBOV GP-mediated cell-cell fusion , but this study has not been followed up by any other laboratory , including the original 8 ) ., Cell-cell fusion has several important advantages over intracellular fusion assays , including complete control of the aqueous solution bathing the ectodomain of the fusion protein ., In the present study we describe a direct and sensitive system to measure EBOV GP-mediated cell-cell fusion with high time resolution , thereby providing fusion kinetics ., The system exhibits the well-known central properties of EBOV entry , providing strong support for the utility of the cell-cell fusion system to explore mechanisms of EBOV entry that are not possible or practical with whole infectious virus ., EBOV GP is a prototypic class I viral fusion protein 9 ., It is synthesized as a homotrimer; each monomer is cleaved into GP1-GP2 subunits by proteases within the Golgi apparatus 10 , 11 ., The GP1 subunit is responsible for binding to the intracellular receptor Niemann Pick type C1 , ( NPC1 ) and possibly to other molecules 12 , and the GP2 subunit is responsible for membrane fusion 13 , 14 , 15 , 16 , 17 , 18 , 19 ., The two subunits of each monomer remain linked through a disulfide bond and a multitude of weak interactions 9 , 20 , 21 , 22 ., After endocytosis of the virus , the GP1 subunit is cleaved by the endosomal proteases cathepsin B and/or L 7 , 23 , 24 , 25 , 26 , while remaining attached to GP2 9 , and then binds to NPC1 14 , 15 ., The low pH within endosomes is necessary for viral fusion ., But it has not been known whether low pH directly triggers fusion by causing conformational changes in GP or whether it augments fusion by increasing the activities of the cathepsins 7 , 25 ., After developing our system , we discovered that an EBOV GP-induced fusion pore that connects two plasma membranes does not readily enlarge over time , in contrast to the pores of other viral fusion proteins ., This anomalous lack of growth may be the reason cell-cell fusion has not been successfully observed in many prior attempts that used less sensitive assays to detect fusion ., On the question of low pH , we found that activation of cathepsins by acidity is the sole cause for augmentation of fusion: if EBOV GP on the cell surface is artificially cleaved by thermolysin in the presence of cathepsin inhibitors , the extent of fusion is independent of pH ., We utilize fluorescent dye spread assays to monitor cell-cell fusion ., Effector COS7 cells transfected to express EBOV GP were loaded with calcein-AM ( CaAM , green ) and pretreated with thermolysin ( Th ) and ., It has been shown that thermolysin treatment cleaves GP1 on the viral membrane into a fusion-competent 18–19 kDa subunit 9 , 23 , 24 ., Within the laboratory , thermolysin is therefore often used in lieu of membrane-bound cathepsins to cleave the GP1 subunit into a fusion-competent form ., The COS7 cells were bound to 293T target cells that were either unlabeled or , for purposes of microscopic identification , loaded with the aqueous dye CMAC ( blue ) ., We lowered the external pH for 10 min at room temperature , reneutralized , raised temperature to 37°C , and monitored dye spread at various times ., We tracked the transfer of calcein between cells to quantify the extents of fusion; CMAC was used solely to identify the target cells ., The fraction of cells that were stained by both calcein and CMAC , 2 hr after a 10-min low pH pulse , was comparable for cell-cell fusion mediated by EBOV GP , by Jaagsiekte sheep retrovirus ( JSRV ) Env , and influenza A virus ( IAV ) hemagglutinin ( HA ) —all requiring low pH for fusion to proceed ( Fig 1 ) ., Fusion did not occur for effector cells that were mock transfected , establishing that CaAM transferred only due to fusion ( top row ) ., It is often thought that EBOV fusion requires acidic pH 7 , 25 , 27 ., But we found that thermolysin-treated effector cells expressing EBOV GP also fused to target cells at neutral pH ( 7 . 2 ) ( Fig 2A , bar 2 ) , albeit to a smaller extent than occurred 2 hrs after a 10 min exposure to an acidic pH of 5 . 7 at room temperature ( bar 1 ) ., Representative images for dye transfer are shown to the right of the bar graphs ( Fig 2A ) ., In its natural cellular setting , EBOV GP is cleaved not by thermolysin but by endosomal cathepsins B and L . In measuring fusion without prior thermolysin treatment of effector cells , we found that fusion still occurred , albeit to smaller extents ( Fig 2A , bars 3 and 4 ) ., Again , a 10-min acidification ( Fig 2A , bar, 3 ) led to greater amounts of fusion than occurred at neutral pH when measured after a 2-h reneutralization ( Fig 2A , bar 4 ) ., Mock transfected effector cells , with or without thermolysin treatment , did not support any dye transfer at neutral or low pH , verifying that fusion required EBOV GP ( e . g . , see Fig 1 ) ., The observed differences in extents of fusion between cells that were treated with thermolysin and those not were eliminated by long times of incubation after reneutralization ( Fig 2B ) ., When EBOV GP was not cleaved by thermolysin , there was a 30 min latency between the fusion trigger ( acidification and raising temperature from 10°C to 37°C , Fig 2B , dark yellow circles ) and the occurrence of fusion ., There was no latency when thermolysin cleaved the protein ( dark red squares , same fusion trigger as for dark yellow circles ) , suggesting that the 30 min latency when thermolysin was not used was due to the time it takes for a sufficient number of copies of cleaved GP to accumulate at a potential fusion site ., The extent of fusion for non-treated effector cells ( dark yellow circles ) 2 hrs after reneutralization was almost equal to that observed after a 1 hr reneutralization for thermolysin-treated cells ., But 4 hrs after a pH 5 . 7 pulse , the extent of fusion was independent of whether EBOV GP was cleaved by thermolysin ., The kinetic difference is , to a large extent , likely due to the ~30 min latency until fusion occurs ., The slopes of the linear portion for rates of fusion are comparable , suggesting that , after the latency , the kinetics of fusion are the same at low and neutral pH . The latency for EBOV GP-mediated fusion is much longer than for some viral fusion proteins , such as IAV HA 28 , but comparable to others , such as HIV Env in some studies 29 ., We thus tested , at various times , whether some of the cell pairs that had not yet fully fused had hemifused: the addition of 0 . 5 mM CPZ to cell pairs ruptures hemifusion diaphragms that have formed between cell pairs , and this is a standard means to test for hemifusion 30 , 31 , 32 ., We used thermolysin-treated effector cells to maximize cleavage of EBOV GP and found that adding CPZ either 30 , 45 , or 60 min after reneutralization did not induce any dye spread above that already observed , strongly indicating that a negligible percentage of cells were hemifused , but not fused , at any given time ., NPC1 is an intracellular receptor for EBOV GP 14 , 33 ., We compared extents of fusion for target parental HEK 293T cells versus target HEK 293T cells that stably overexpressed NPC1 ., Effector cells that were not treated with thermolysin yielded fusion at pH 7 . 2 ( Fig 3A , bar 2 ) , and a greater extent of fusion after a 10-min acidic pH 5 . 7 pulse ( Fig 3A , bar 1 ) ., The extent of calcein spread was greater for target cells overexpressing NPC1 ( Fig 3A , bars 3 and, 4 ) than for parental 293T cells ( Fig 3A , bars 1 and, 2 ) for matching conditions ., Fusion was still pH-dependent for target cells overexpressing NPC1: calcein spread was greater 2 hr after a 10-min pH 5 . 7 pulse ( Fig 3A , bar, 3 ) than in the absence of the pulse ( Fig 3A , bar 4 ) ., We confirmed that fusion was dependent on the presence of NPC1 by generating and purifying a recombinant soluble protein consisting of domain C of NPC1 fused to GST ( denoted sNPC1 ) ., The purity and size of sNPC1 was confirmed ( Fig 3B , inset ) ., We added sNPC1 to the external solution and found that the extent of fusion increased monotonically with the amount of sNPC1 added ( Fig 3B ) , in accord with the prior demonstration that by binding NPC1 , EBOV GP undergoes conformational changes favorable for fusion 18 ., The augmentation of fusion by sNPC1 indicated that , although there was a sufficient amount of NPC1 on cell surfaces to stimulate fusion , this amount was relatively small and fusion was consequently limited ., NPC1 is an endosomal protein 34 , but a small fraction of NPC1 may be present on the plasma membrane of a cell ., We assessed this possibility by using flow cytometry to measure binding with an antibody against NPC1 ( from LifeSpan Biosciences ) on parental 293 cells; shRNA that targeted NPC1 was stably expressed in one set of these 293 cells , and NPC1 was overexpressed in another set ( Fig 3C and 3D ) ., The level of binding of the secondary FITC-labeled antibody against endogenous NPC1 ( as measured by mean fluorescence intensity , MFI ) was 3-fold greater than in the absence of the primary Ab ( Fig 3C , bar 1 vs . bar 4 , and Fig 3D ) ., Expression was reduced for cells in which NPC1 was knocked down by shRNA ( bar 2 ) , and was greater for cells in which NPC1 was overexpressed ( bar 3 ) ., These results demonstrate that copies of NPC1 reside in the plasma membrane of the 293 cells we used as targets in cell-cell fusion experiments ., EBOV GP is certainly cleaved within endosomes as part of viral infection 26 ., Because we observed cell-cell fusion at acidic pH without adding thermolysin , it is extremely likely that a fraction of GP on the cell surface was cleaved into a fusion-competent form ., An antibody that only recognizes cleaved GP has not been reported , so we had to devise an alternate means to quantitatively measure the extent of cleavage ., We were able to distinguish between the two forms of GP by using the property that NPC1 binds to cleaved , but not uncleaved , EBOV GP ., We used a sNPC1 to examine cleaved GP by flow cytometry; in parallel , we measured the total amount of GP on cell surfaces by using an anti-FLAG antibody that bound to the FLAG tag on our GP construct ., We also created a GP construct that was intrinsically more likely to be cleaved on the cell surface: we inserted the furin recognition site RRKR at amino acids 203–206 of GP1 ( referred to as GPfurin ) , the putative cleavage site for CatL in GP1 16 , 35 ., We reasoned that because exogenous expression of furin facilitates cleavage at this inserted site , generating the fusion-active 18–19 kDa subunit , the extent of cleavage of GP on the plasma membrane as well as the extent of cell-cell fusion would be greater for this construct than for WT ., We experimentally confirmed our expectations: We determined the amount of cleaved GP on the cell surface by adding sNPC1 ( fused with GST ) to cells expressing either EBOV GP or GPfurin , and measuring their binding to an anti-GST antibody ., This antibody was detected by a FITC-conjugated secondary antibody ( Fig 4 ) ., The fraction of WT GP cleaved on parental cells ( Fig 4A , bar, 1 ) was the same for cells that were transfected with both GP and furin ( bar 2 ) ., The specificities of sNPC1 and antibody binding were confirmed by the 4–5 fold higher fluorescence than was seen for cells that did not express GP ( bar 5 ) ., It is notable that cotransfection of cells by GPfurin and furin resulted in greater cleavage ( bar 4 vs bar 3 ) ., We found that the expression of total WT GP as measured by the anti-FLAG antibody was not significantly altered by coexpression of furin ( Fig 4B , columns 1 and 2 ) , but cells that coexpressed GPfurin and furin consistently showed a decreased total GP ( compare bar 3 and 4 ) , possibly due to non-specific degradation of GPfurin ., To determine the relative percentage of cleaved GP , we normalized cleaved GP by total GP ., ( These are relative and not absolute percentages because different antibodies were used to detect cleaved vs . total GP . ), We found that a higher percentage of GP on the plasma membrane was cleaved for cells coexpressing GPfurin and furin than for cells expressing WT GP or GPfurin alone ( Fig 4C ) ., Western blot analyses , using an anti-FLAG or an anti-GP1 antibody ( kind gift of James Cunningham ) , showed that the addition of furin increased the amount of cleaved GPfurin construct as compared to GPfurin alone ( Fig 4D , lanes 4 and 5 in left and right panels ) ., Furin did not cleave any WT GP ( lanes 1 ) ., We used these constructs to verify that an increased cleavage of EBOV GP led to a greater extent of fusion ( Fig 4E ) ., Cotransfecting cells with GP and furin ( bar, 2 ) led to the same extent of fusion as did transfection of GP alone ( bar 1 ) ., In contrast , cotransfecting with GPfurin and furin led to more fusion ( bar, 4 ) than transfecting only GPfurin ( bar 3 ) ., Control experiments of transfecting only furin showed that furin , per se , did not promote fusion ( bar 5 ) ., These experiments , taken together , establish that EBOV GP does appear on the cell surface , that some of it is cleaved , and that for the GPfurin construct , cleavage is augmented by coexpression of furin ., To further confirm that the observed fusion was indeed mediated by EBOV GP , we utilized mutations that had previously been shown to greatly reduce viral infection 36 ., We used MLV pseudovirus expressing GP , and observed that , indeed , the level of infection caused by the point mutant W597A ( Fig 5 , bar 2 ) , the double mutant G598A/G599A ( bar3 ) , and the point mutant I610A ( bar, 4 ) were all substantially less than for WT GP ( Bar 1 ) ., We then measured the extents of cell-cell fusion mediated by each of the mutant proteins ., The extent of fusion in absence of thermolysin treatment supported by all three of the mutants ( Fig 5B , bars 2 , 3 , and, 4 ) was much less than for WT GP ( bar 1 ) ., Flow cytometry measurements , using the same cells as for fusion experiments , showed that each of the mutant GPs was well expressed on the cell surface ( Fig 5C ) ., These experiments provide support that reduced infectivity by EBOV correlates with reduced GP-mediated fusion ., We next tested 3 . 47 , a small molecule inhibitor against NPC1 , which prevents EBOV entry , as well as testing a neutralizing antibody ( KZ52 ) against EBOV GP ., We found that both significantly reduced EBOV GP-mediated cell-cell fusion ( Fig 6A and 6B ) ., The inhibitor 3 . 47 greatly reduced EBOV GP-mediated fusion but did not significantly alter cell-cell fusion induced by either Semliki Forest Virus ( SFV ) E1/E2 or IAV HA ( Fig 6A , 3 . 47 at 1 μM ) ., Similarly , the neutralizing antibody KZ52 , which recognizes the interface between GP1 and GP2 37 , reduced EBOV GP-mediated fusion , but not SFV-E1/E2 or IAV HA-induced fusion ( Fig 6B , KZ52 at 5 μg/ml ) ., Higher concentrations of 3 . 47 completely inhibited fusion ( S1A Fig ) , but fusion was not further reduced by increasing the concentration of KZ52 beyond that employed in Fig 6B ( S1B Fig ) ., Another central fingerprint of GP-mediated fusion is inhibition of EBOV infectivity by Bafilomycin A1 ( BafA1 ) ., By neutralizing endosomes , BafA1 inhibits infection , at least in part , by reducing cathepsin activity which in turn results is reduced cleavage of GP1 ., We found that addition of BafA1 ( 25 or 100 nM ) reduced the amount of cleaved GP that appeared on the cell surface ( Fig 6C , bar 2 vs bar 1 ) ., This occurred despite a consistently greater amount of total GP in the plasma membrane after the addition of BafA1 ( Fig 6D ) ., ( This greater amount was unexpected . Possibly , BafA1 prevented lysosomal degradation of GP . ), Normalizing the amount of cleaved GP by the total shows that cleavage of cell surface GP was significantly reduced by BafA1 ( Fig 6E ) ., Thus , all data support the conclusion that the aqueous dye spread we observe is due to fusion induced by EBOV GP ., Many of the effects of pH on kinetics and extents of EBOV GP-induced fusion we found were unexpected and quite different than those of pH-dependent fusion for other viral proteins ., Notably , the extents of fusion did not monotonically increase as pH was progressively lowered , and the apparent pH dependence qualitatively varied with the times of reneutralization ( Fig 4 ) ., After a pH 5 . 7 pulse , the extents of fusion were always greater than those achieved after more acidic pulses; following a pH 5 . 7 pulse ( at short incubation times ( i . e . , 30 min ) after the shift to neutral pH ) , more fusion was observed than for a less acidic pulse ( Fig 7A ) ., However , for pH pulses of 5 . 7 and above , as the reneutralization time was increased , the extents of fusion became less dependent on pH; fusion was independent of pH for 5 . 7 and above after a 1 h reneutralization ( Fig 7A ) ., In contrast , effector cells expressing IAV HA showed the typical and expected response of greater extents of fusion for lower pH values at all times after reneutralization; fusion events reached their full extents after a 30 min reneutralization ( Fig 7B , using the same protocol as for EBOV GP experiments ) ., Thus , IAV HA induces fusion more rapidly than does EBOV GP ., In separate experiments , we compared extents of EBOV GP-mediated fusion after a 4 h and 1 h reneutralization that followed 10 min , room temperature , acidic pH pulses ( Fig 7C ) ., After the 4 h reneutralization , fusion was relatively independent of the acidity of the pH pulse , and a low pH pulse did not greatly augment fusion ( compare filled bars to open bars , Fig 7C ) ., Equality in final extents of fusion at pH 5 . 7 and 7 . 2 could be a consequence of all cell pairs quickly fusing at low pH , thereby eliminating the possibility of further fusion , although we consider this unlikely ., In addition to single cell measurements of aqueous dye transfer , we also monitored lipid dye continuity between effector cells ( treated with thermolysin ) and target cells ., We labeled effector cells with the lipophilic fluorescent dye DiO and labeled target cells with DiI and determined extents of fusion by flow cytometry ( FACS ) ., The double positive cells ( i . e . , the third quadrant ) are clearly products of hemifusion or cell-cell fusion ( Fig 7D ) ., For effector cells treated with thermolysin , the percentage of fusion for the representative experiment was 18 . 5% at pH 5 . 7 , the optimal pH for fusion ( Fig 7D , second panel ) and only 1 . 5% at pH 5 . 0 ( first panel ) ., Averaging six separate experiments for each condition , after a 2-h reneutralization , lipid mixing was greatest for a 10-min pH 5 . 7 pulse , and less for a pH 5 . 0 pulse than for cells maintained at neutral pH ( Fig 7E ) ., The approximately two-fold greater fusion determined by flow cytometry at pH 5 . 7 than at 7 . 2 is also in agreement with the data for spread of calcein ( Fig 2 ) ., For mock-transfected effector cells , virtually no lipid dye spread was observed between effector and target cells ( Fig 7E ) , in agreement with the aqueous dye spread measurements ., Therefore it is clear that EBOV GP mediates a considerable amount of cell-cell fusion , and does so at an optimal pH of 5 . 7 ., Once calcein movement from effector to target cell commenced , it continued for EBOV GP-mediated fusion , but at an extremely slow rate ., In general , the fluorescence due to calcein never equalized between target and effector cells for EBOV GP-induced fusion ( Fig 8 ) ., In contrast , for fusion pores created by other viral fusion proteins 33 , 38 , such as JSRV Env ( Fig 8A , upper images ) , the fluorescence did equalize ., It is possible that the EBOV GP pores eventually closed , preventing calcein from attaining the same concentration in effector and target cells ., We therefore quantified the rate of transfer of calcein by plotting calcein fluorescence of effector and target cells as a function of time ( Fig 8B ) ., For EBOV GP-induced pores ( red curve ) , the transfer occurred over a time course of tens of minutes , and over this period the increasing fluorescence of a target cell never equalized the decreasing fluorescence of an effector cell ( Fig 8B ) ., The fluorescence of the effector and target cells , on the other hand , equalized within a minute or so for JSRV Env-mediated pores ( Fig 8B , blue curve ) ., The exceedingly slow transfer of calcein is a further indicator that EBOV GP-mediated pores remained extremely small ., The fact that calcein transferred , albeit slowly over long times , shows that the fusion pores did not irreversibly close ( or if they did , new pores opened ) within tens of minutes of formation ., As a control , we added saponin to effector cells and measured release of calcein to be sure that the dye did not become compartmentalized and therefore failed to transfer for reasons unrelated to the size of the fusion pore ., Release was fast from the saponin-treated cells and was almost complete within 10 s , demonstrating that the overwhelming majority of calcein was , in fact , free and mobile ., We further studied the size and rate of growth of EBOV GP-mediated fusion pores by assessing the size of dyes that can permeate these pores over time ., We loaded effector cells with CMTMR in addition to calcein ., CMTMR forms disulfide bonds with the tri-peptide glutathione , and these complexes are somewhat larger than calcein ., The complexed glutathione can also form disulfide bonds with cytosolic proteins and hence CMTMR fluorescently labels proteins that are much larger than calcein ., As a consequence , the size distribution of molecules labeled by CMTMR is expected to be quite diverse , some only somewhat larger than calcein and others very much larger ., We found that CMTMR transferred for only 2–3% of the cell pairs for which calcein exchange occurred ( Fig 8C ) ., The relative inability of CMTMR to spread indicates that fusion pores typically did not enlarge sufficiently to allow passage of a molecule of the size of the nucleocapsid of EBOV ., In actual viral infection , factors absent in our model system are probably promoting expansion of the fusion pore connecting an envelope and endosomal membrane ., We used electrical capacitance measurements to directly and quantitatively assess fusion pore size ., The slow time course for EBOV GP-mediated fusion necessitated that the tight electrical seal between the patch pipette and plasma membrane be maintained for long times ., This proved difficult in practice ., We were able , however , to electrically observe pores between cell pairs in three cases , and in these cases the pores never enlarged within 30 s of formation and generally fluctuated within small values of conductance ( Fig 9A ) ., The conductance of the fluctuating pores did not return to baseline , showing that the pores did not close , but instead remained restricted to a small size ., By way of comparison , it can be readily seen from representative traces of electrically measured fusion pores created by other viral proteins ( Fig 9B ) that fusion pores generally significantly enlarge over time ., The absence of pore enlargement for EBOV GP suggests that many of the prior attempts at monitoring cell-cell fusion mediated by this fusion protein did not succeed because the reporter molecules that needed to permeate the fusion pore for detection of fusion were too large to pass through the pore ., Although only three pores were electrophysiologically measured , the finding that each of them did not exhibit increased conductance over time implies that the slow passage of fluorescent dyes through them was not due to structures that prevent their access to the pores ., Slow pore enlargement could be due to a number of factors , including slow recruitment and incorporation of additional copies of cleaved GP into the wall of the pore , or slow accumulation of lipids into the wall ., We added NH4Cl to external media to test whether acidic intracellular compartments were essential for EBOV GP-mediated cell-cell fusion ., The addition of 10 mM NH4Cl greatly reduced fusion after a 10-min pH 5 . 7 pulse in the absence of thermolysin treatment , so as to avoid activating uncleaved EBOV GP on the cell surface ( Fig 10A ) ., In contrast , the addition of 10 mM NH4Cl did not affect fusion induced by an optimal pH pulse for either SFV E1/E2 or IAV HA ( Fig 10A ) ., Similarly , 100 μM chloroquine inhibited cell-cell fusion mediated by the fusion protein of EBOV , but not by the proteins from either SFV or IAV ( Fig 10A ) ., The elimination of fusion by the addition of 10 mM NH4Cl ( bar 2; same conditions as in Fig 10A ) was most likely caused by reducing cathepsin activity through neutralization of intracellular compartments: it was largely reversed by adding a recombinant cathepsin B to the external solution ( Fig 10B , bar 3 ) ., Because the normally acidic intracellular compartments were neutralized by NH4Cl , the pool of EBOV GP on the cell surface that was previously uncleaved must have been cleaved by the added membrane-impermeant recombinant protease ., The dose-response curves for inhibition of fusion by chloroquine ( Fig 10C ) or NH4Cl ( Fig 10D ) verified that inhibition of fusion is increased with increasing concentration of the neutralizing agent ., Therefore , even if the external solution is acidified , EBOV GP-mediated cell-cell fusion does not occur unless the acidity of intracellular organelles is maintained ., We conclude that EBOV GP present on the cell surface requires an intracellular compartment for cleavage , as is consistent with previous reports ., It is possible , however , that there are copies of cathepsins in the plasma membrane , and acidification of the external solution activates them to cleave EBOV GP ., Proteinase K ( PK ) has proved useful for assessing conformational changes that viral proteins undergo at different stages of fusion 30 , 39 ., We found that EBOV GP was PK-sensitive for all steps of the fusion process ( S1 Text and S2A Fig ) , that fusion was restored over time after removing PK ( S2B Fig ) , and that normal cellular trafficking of protein led to replacement of proteolytically digested GP with newly delivered intact GP ( S3 Fig ) ., We also used Brefeldin A ( BFA , 50 μM ) —an inhibitor of trafficking from endoplasmic reticulum to Golgi—to further characterize the consequences for fusion of altering intracellular trafficking of EBOV GP ., Here we found that treatments expected to increase the amounts of cleaved EBOV GP on the cell surface led to greater extents of fusion ( S1 Text and S4 Fig ) ., For virus internalized in endosomes , EBOV GP is believed to be cleaved by cathepsins B and L , but not by cathepsins A or D . We prevented cathepsin-induced cleavage by treating bound effector and target cells with a cathepsin B inhibitor ( CA-074 ) or a cathepsin L inhibitor ( Z-FY-CHO ) ., In the absence of thermolysin treatment , the inhibitors led to significantly reduced fusion at both neutral and low pH ( Fig 11A , compare “untreated” and “treated”: as always , changes of solutions containing membrane-impermeant buffers were used to control pH ) ., Using inhibitors against cathepsin A ( lactacystin ) or cathepsin D ( pepstatin A ) –neither of which is thought to cleave EBOV GP–did not lead to reduced fusion using the same protocol as for the cathepsin B and L inhibition experiments ( Fig 11A ) ., These results provide strong support that fusion observed in our experiments in the absence of thermolysin treatment is due to , at least in part , copies of EBOV GP on the cell surface that have their GP1 subunits cleaved by cathepsins ., These results also document that neither cathepsin A or D cleaves EBOV into a fusion-competent form ., From the results as a whole , it is clear that low pH does not induce fusion unless the GP1 subunit has been cleaved ., It is known that cathepsin activity is increased by acidity ., We suggest that low pH acts , at least in part , by augmenting cathepsin activity on the cell surface ., The same pattern of pH-dependence of fusion was observed for effector cells treated with thermolysin while cathepsin activity was continually inhibited: fusion was dependent on pH and was significantly reduced by the cathepsin B inhibitor or the cathepsin L inhibitor ( Fig 11A , thermolysin-treated , bars 2 and 3 of each set of columns ) , but was relatively unaffected by cathepsin A or D inhibitors ( bars 4 and 5 ) ., Cell-cell fusion exhibits a maximum at pH 5 . 7 ( column 4 compared to column 1–3 ) ., Several cathepsins exhibit maximal activity in the pH range of 5 . 5 to 6 . 8 40 , so the maximum extent of fusion at pH 5 . 7 would likely be due to the pH dependence of cathepsin activity on the cell surface ., Control experiments provide additional support for the conclusion that cathepsins aid EBOV GP-mediated fusion between cells ., Blocking cathepsin B ( by adding the cathepsin B inhibitor ) immediately after application of an acidic pH pulse resulted in a substantial reduction in the extent of fusion after a 2-h reneutralization ( Fig 11B , effector cells were thermolysin-treated ) ., The reduction from the control was ~2-fold; a 2-fold reduction also occurred when the cathepsin B inhibitor was constantly present ( see Fig 12B ) ., The nearly equal percentages of inhibition of fusion are expected , since in the presence of the cathepsin inhibitor , uncleaved copies of EBOV GP would not be cleaved during the period of reneutralization ., Thus , low pH appears to promote cleavage of EBOV GP by cathepsins on the cell surface ., Incubating effector cells that were not treated with thermolysin with a recombinant human cathepsin B ( rhCat B ) ( Fig 11C , bar, 2 ) increased fusion significantly over the control ( bar 1 ) ., The simplest explanation for this increase is that the recombinant protein led to a higher level of GP1 cleavage than that induced by endogenous cellular cathepsins ., To explicitly test whether increasing the activity of cathepsin increased the likelihood that GP on the cell surface was cleaved , we cotransfected cells to express GP and cathepsin B and used sNPC1 to measure the percentage of GP in the plasma membrane that was cleaved ( as described for Fig 4 ) ., This percentage was greater ( Fig 11 , column, 2 ) than the control ( column, 1 ) in the presence of cathepsin B transfection ., Using the same techniques , we also showed that adding thermolysin to solution did indeed increase cleavage of cell surface GP ( Fig 11D ) ., Does low pH directly cause conformational changes in EBOV GP to induce fusion , or does it work via increasing the activity of cathepsins , or both 7 , 20 ?, We were able to approach these questions by using the ability of BFA to effectively block delivery of EBOV GP to the cell surface and , independently , by using cathepsin inhibitors to prevent GP cleavage ., We incubated effector cells with BFA for 45 min to prevent further delivery of EBOV GP to the plasma membrane prior to a thermolysin-treatment , and maintained the presence of the drug during all solution changes ., The extent of fusion was independent of pH , and considerably less than when the trafficking inhibitor was not employed ( Fig 12A ) ., The clear conclusion is that , with BFA present , all f

Introduction, Results, Discussion, Materials and Methods

Ebola virus ( EBOV ) is a highly pathogenic filovirus that causes hemorrhagic fever in humans and animals ., Currently , how EBOV fuses its envelope membrane within an endosomal membrane to cause infection is poorly understood ., We successfully measure cell-cell fusion mediated by the EBOV fusion protein , GP , assayed by the transfer of both cytoplasmic and membrane dyes ., A small molecule fusion inhibitor , a neutralizing antibody , as well as mutations in EBOV GP known to reduce viral infection , all greatly reduce fusion ., By monitoring redistribution of small aqueous dyes between cells and by electrical capacitance measurements , we discovered that EBOV GP-mediated fusion pores do not readily enlarge—a marked difference from the behavior of other viral fusion proteins ., EBOV GP must be cleaved by late endosome-resident cathepsins B or L in order to become fusion-competent ., Cleavage of cell surface-expressed GP appears to occur in endosomes , as evidenced by the fusion block imposed by cathepsin inhibitors , agents that raise endosomal pH , or an inhibitor of anterograde trafficking ., Treating effector cells with a recombinant soluble cathepsin B or thermolysin , which cleaves GP into an active form , increases the extent of fusion , suggesting that a fraction of surface-expressed GP is not cleaved ., Whereas the rate of fusion is increased by a brief exposure to acidic pH , fusion does occur at neutral pH . Importantly , the extent of fusion is independent of external pH in experiments in which cathepsin activity is blocked and EBOV GP is cleaved by thermolysin ., These results imply that low pH promotes fusion through the well-known pH-dependent activity of cathepsins; fusion induced by cleaved EBOV GP is a process that is fundamentally independent of pH . The cell-cell fusion system has revealed some previously unappreciated features of EBOV entry , which could not be readily elucidated in the context of endosomal entry .

The devastation and transmissibility of Ebola virus ( EBOV ) are well known ., However , the manner in which EBOV enters host cells through endosomal membrane remains elusive ., Here , we have developed a convenient experimental system to mimic EBOV fusion in endosomes: cells expressing the fusion protein of EBOV , GP , on their surface are fused to target cells ., This system exhibits the known key properties of EBOV fusion ., We show that the pH-dependence of EBOV fusion is caused by the pH-dependence of cathepsins , proteases known to cleave EBOV GP into a fusion-competent form ., We demonstrate that the fusion activity of this cleaved form is independent of pH . We further show that the enlargement of the fusion pore created by EBOV GP is unusually slow in reaching sizes necessary to pass EBOV’s genome—this is atypical of virally created fusion pores ., This cell-cell fusion system should provide a useful platform for developing drugs against EBOV infection .

journal.pcbi.1005757

Dynamics and heterogeneity of brain damage in multiple sclerosis

Multiple Sclerosis ( MS ) is an autoimmune disease with a complex pathogenesis that is driven by inflammation and axon degeneration 1 ., However , the clinical phenotype of MS is very heterogeneous and the course of the disease is difficult to predict ., Neither the frequency of relapses ( disease activity ) nor the accumulated disability 2 represent an accurate predictor of disease outcome ., Relapses in MS have been modeled statistically to a negative binomial distribution 3 ., Moreover , relapses have been mathematically modeled from a mechanistic point of view that focuses on the negative feedback between pro- and anti-inflammatory responses 4 , and as a probabilistic response to self-antigen presentation 5 ., However , how damage to the central nervous system ( CNS ) advances and how clinical disability accumulates over decades , defining the clinical phenotype of the disease and its prognosis , are issues that are still poorly understood 6 ., Several hypotheses have been proposed to explain the heterogeneity and different courses of the disease ., These range from considering MS as a single disease to defining it as a disease with two distinct physiological stages ( inflammatory and neurodegenerative ) , or even as different diseases with relapse-remitting MS ( RRMS ) being defined as an autoimmune disease ( the outside-in hypothesis ) and primary-progressive MS ( PPMS ) a primary neurodegenerative disease ( the inside-out hypothesis ) 7–10 ., Alternatively , each of the four pathological patterns described in acute plaques may reflect distinct pathogenic mechanisms 11 ., CNS damage in MS is caused by acute inflammatory infiltrates ( composed of lymphocytes and macrophages ) and chronic compartmentalized inflammation ( driven by activated microglia/macrophages in the CNS parenchyma and meningeal inflammation ) , as well as by axon degeneration ( e . g . , axon degeneration due to the demyelination , neuronal pruning or cell death , or by transynaptic degeneration: Fig 1 ) 7 , 12 , 13 ., Recent pathological studies have shown that acute inflammatory damage predominates in the early stages of MS , while chronic inflammation ( smoldering plaques ) prevails at later stages , suggesting an evolution from peripheral autoimmune damage to chronic CNS inflammation 14 ., Long-term cohort studies have shown that the progressive course of MS develops after a given threshold of disability is reached , defined by a score above 4 . 0 in the Expanded Disability Status Scale ( EDSS ) ., Hence , inflammatory and neurodegenerative processes appear to be to some extent independent 15 , supporting the two-stage disease hypothesis ., Alternatively , genetic susceptibility always appears to be related with immune system dysfunction and not with disease course 16 ., Moreover , pathological inflammation is always detected in MS , in all phases of the disease , with a predominantly adaptive immune response in the early stages of the disease and a mainly innate immune response ( compartmentalized inflammation ) at later stages 13 , 14 ., Therefore , while the evolution of MS seems to be the result of the interplay between acute inflammatory relapses , chronic inflammation in the CNS , and the degeneration of axons and myelinated cells , each process could have a distinct influence on the different patient subgroups and at different stages of the disease , consistent with the single-disease hypothesis ., On top of this , we should also consider the role of functional CNS adaptation or the functional reserve at disease onset ., There is little structural damage at disease onset and thus , the breakdown of functional CNS compensation and disability increases from a certain damage threshold onward 12 ., In RRMS , the main drivers of clinical disability include demyelination , the blockage of axonal conduction and acute axon transection , whereas axon/neural loss is the main factor underpinning permanent disability during the progressive phase 17 ., While demyelination is frequently reversible during the RRMS phase , complete recovery is rarely achieved 18 ., As such , demyelination in RRMS can be expected to lead to a certain degree of permanent damage that accumulates after each relapse and that exacerbates the effects of acute axon transection ., In order to improve disease management , models of CNS damage and disease dynamics could be useful to stratify patients with similar degrees of disability , as well as to evaluate the benefits of therapies in different patient sub-types 19 ., Complex diseases can be modeled on different biological scales , from the molecular level ( e . g . , genetic networks and signaling pathways ) 20–22 , to the cellular level 4 where the cell is considered as the basic unit that integrates all the molecular information ( e . g . , lymphocyte dynamics ) , or to the tissue or whole body level 5 ., We previously developed a mathematical model of the autoimmune response at the cellular level that reproduces the dynamics of MS relapses 4 , 23 ., As a result , cell interactions were seen to particularly affect tissue dynamics at the functional level , influencing the immune response or CNS neurodegeneration ., Hence , modeling at the cellular level may bring us closer to the clinical phenotype ., Here , we have tested the hypothesis of dynamic CNS damage which states that the course and heterogeneity of MS can be generated through a specific pathogenic mechanism , namely autoimmune attack on the CNS in association with chronic inflammation , the severity and timing of each process producing the diversity in the patient subgroups ., We assessed to what extent the progression of CNS damage in MS is a dynamic process that commences at the onset of the disease , even in RRMS ., For simplicity , our model only included inflammation , demyelination and axon loss at a general level , without entering into specific mechanistic details , such as the role of the adaptive and innate immune responses , or feedback from CNS damage to the autoimmune process ., Consequently , our model does not formally exclude other alternative hypotheses ( e . g . , the two-stage disease or the inside-out hypothesis ) ., We developed a mathematical model based on ordinary differential equations ( ODEs ) to study the dynamics of CNS damage in MS , a model that contemplated the dynamics of myelinated ( healthy ) and demyelinated axons , axon loss ( acute transection or delayed degeneration ) and demyelination/remyelination as a consequence of autoimmune attack ( see Materials and methods: Fig 2A ) ., We defined the parameters of the model through a literature search ( S1 Table ) and then , by fitting it to the changes in EDSS in a retrospective , longitudinal cohort of MS patients with a long ( up to 20 years ) follow-up ., The results were validated in a second prospective cohort with a shorter follow-up ( 3 years ) , and brain volume ( BV ) quantified by MRI was used in the validation cohort to improve the fit of the cell damage in the model to the clinical phenotype ( EDSS time series ) ., In addition , to decrease the complexity of the clinical phenotype , the data were grouped using a non-supervised clustering approach ( see below ) ., The EDSS time-series from the retrospective longitudinal cohort was clustered using a modified k-means clustering method and subsequently , we analyzed each cluster as a single dataset to estimate the distinct parameters ., We identified four clusters of patients that corresponded to the different levels of disease severity ( EDSS ) and disease subtypes ( relapsing vs progressive disease: Fig 2B ) ., Thus , 86% of progressive MS cases ( SPMS or PPMS ) were included in Cluster 1 or 2 , whereas 66% of relapsing MS ( RRMS ) were grouped into Cluster 3 or 4 patients ( Table 1 ) ., We validated this clustering using a cohort that consisted of an EDSS time-series with a three-year follow-up ( see Materials and methods ) , producing a similar grouping into four clusters ., Simulations with the model were compared with the experimental EDSS time-series using the 10 parameter sets with the best data fit ( lowest objective function values , see Materials and methods: S1 Fig ) and 100 random ( t ) inputs of the cluster-specific EDSS characteristics ., The EDSS time-series closely overlapped the simulations for each cluster , confirming that the model could reproduce the diversity of MS phenotypes ( Fig 3 ) ., To evaluate the quality of the predictions for each cluster at the clinical level , we calculated the probability of reaching key clinical milestones , such as EDSS 4 . 0 or 6 . 0 15 ., The event of interest was calculated as the ratio of patients with an EDSS higher than the value of interest at each time point ., Again , the EDSS time-series were contained within the model’s simulations , confirming the ability of the dynamic model of CNS damage to reproduce the different trajectories towards a milestone of disability ( S2 Fig ) ., In order to investigate the influence of the parameters on disease dynamics , we compared their sensitivity profiles ( Fig 4A ) ., We found that the remyelination-related coefficients km and q have an important influence on the demyelination coefficient kmd , which is consistent with the fact that disrupted myelin and impaired remyelination are processes responsible for MS progression 11 ., Global sensitivity indices were calculated for the model read-outs ( EDSS and BV , the latter measured by MRI ) , show notable differences in the demyelination coefficient kd ( p = 0 . 0027 ) , as for δ ( p = 1 . 81E-09 ) and to a lesser extent q ( p = 0 . 0252 ) ., The sensitivity of the parameters with respect to EDSS or BV were almost equivalent , suggesting that there is no difference in the use of either ., Thus , there is no parameter that is non-identifiable due to the type of data , although the goodness of fit will indicate that which is best used for modeling ., We tested if the model could also reproduce the dynamic changes in BV of the validation cohort ., As expected , there was a negative correlation between the EDSS and BV time-series in the prospective cohort ( S3 Fig ) ., As indicated in the Materials and Methods , we estimated the coefficients necessary to connect the values observed to the simulated read-outs specific to the allocated cluster ( note that we did not recalculate the parameters but rather , we used the parameter estimated from the discovery cohort ) , and we then adjusted the age-dependent initial value Ami ( t = 0 ) ., For each of the clusters we compared 1000 simulated time series ( 100 simulations for the 10 selected parameter sets ) of the BV ( Vs ( t ) ) using the normalized BV values from the MS patients ., The correlation coefficient was calculated for each combination of experimental and simulated time series ., For each patient , we generated a distribution of the correlation coefficients between the experiments and simulations ( Fig 4B ) , and we observed a significant correlation between the experimental and simulated BV ( median correlation coefficients: cluster 1 , 0 . 93; cluster 2 , 0 . 94; cluster 3 , 0 . 86; and cluster 4 , 0 . 87 ) ., We first evaluated whether the model could simulate the diversity of disease subtypes ( RRMS , SPMS and PPMS ) and their severity ., Simulations were performed with the same inflammatory input ( t ) but with different sets of parameters , and these parameters were then explored manually ., Simulations reproduced the different disease subtypes ( Fig 5 ) , whereby the different disease courses -RRMS , SPMS and PPMS- were reproduced by changing the parameters of the model given that the inflammatory part of the disease remained the same ., The overlap between the inflammatory inputs and the EDSS course indicated that each significant incremental change in the EDSS is the result of a specific immune attack ( relapse ) , whereas the progressive increases in the EDSS result from changes in the parameters of chronic inflammation and neurodegeneration ( progressive phase ) ., By running simulations of our model and comparing the results with the EDSS time-series of the longitudinal cohort , we tested whether the model reproduced the phenotypes of the four patient clusters when the parameters were modified , albeit maintaining them within the biological range ( Fig 1 and S1 Table ) ., To identify the parameters that would reproduce the different MS subtypes ( RRMS , SPMS and PPMS ) , we calculated the number of patients of each subtype in each cluster and expressed this relative to the total number of patients of that subtype in the discovery cohort ., These ratios allowed us to compute linear combinations of the cluster-specific parameter sets ( the 10 combinations of parameters with the 10 lowest objective values in terms of function ) in order to define MS subtype-specific parameters sets ( S1 Fig ) ., We then compared these values between the three MS phenotypes ( using a pairwise Wilcoxon test ) , and we found the Km , Kmd , Kd and δ values to be significantly different between all three MS subtypes , whereas q only differed significantly between the RRMS and PPMS subtypes ( Fig 6; S2 and S3 Tables ) ., By definition , the biological meaning of the model parameters Km and δ are related to the rate and capacity of remyelination , respectively , while Kd and Kmd define the rates of irreversible axon degeneration for demyelinated and myelinated axons , respectively ., The subtype-specific parameters estimated above indicated that the characteristic accumulated and irreversible disability of the progressive MS subtypes ( PPMS and SPMS ) is associated with higher rates of axon degeneration ., This association was revealed by the significant monotonic increase in Kd and Kmd from RRMS to SPMS and PPMS , and the lower rate and capacity of remyelination ( a significant monotonic decrease in Km and δ from RRMS to SPMS and PPMS: Fig 6 and S2 Table ) ., However , the degeneration of chronic demyelinated axons is not a key feature distinguishing the RRMS phenotype , consistent with recent pathological studies 17 , 24 , 25 ., A statistical analysis of the model’s parameters between the disease subtypes suggested that the greater resilience to CNS damage and disability in the relapsing subtype ( RRMS ) relative to progressive MS ( SPMS and PPMS ) is related to the lower rate of axon degeneration , and more rapid remyelination ., The higher remyelination capacity in RRMS is reflected by a higher Km ( p = 0 . 0002 ) and δ ( p≤0 . 001 ) , and the lower rates of axon degeneration in RRMS are indicated by lower values for Kd ( p = 0 . 0002 ) and Kmd ( p≤0 . 0001: Fig 6 and S2 Table ) ., Another relevant question is whether the transition from relapsing to progressive MS is a dynamic process that simply involves the same biological processes or whether any additional biological events must be invoked to explain its dynamics , e . g . , the effects of neurodegeneration on axons 26 ., As such , we analyzed the transition from the relapsing to the progressive phase in terms of the changes to the variables within the model ., The model readout ( EDSS ) is defined as the maximum of Ad and D , where D refers to irreversible axon degeneration ., From the equations , one can see that D is always an increasing monotonous function ( axon transection ) , while Ad ( demyelinated axons ) is the variable derived from inflammatory relapses when the condition Ad > D is met ( otherwise , if D > Ad , the effect of Ad is masked by D ) ., Hence , disability in RRMS can be characterized by the impact of demyelination due to relapses whereas in the progressive phase of MS the impact of axon degeneration ( D , transected axons ) is stronger than the impact of demyelination ( D > Ad ) ., Thus , the nature of the model defines the transition from the relapsing to the progressive phase of MS as a dynamic evolution from Ad > D to D > Ad ., We found that the PPMS phenotype was also reproduced by the model ( Fig 5 ) , a phenotype that occurs when D > Ad at all time points ., This criterion could be met when the rate of axon degeneration is high ( large values of Kd and Kmd ) and when remyelination fails ( Km and δ are small , see Fig 2 ) , particularly given that myelinated axons are less prone to degeneration than demyelinated axons ( Kmd << Kd ) ., Thus , our model of CNS damage in MS supports the concept of MS as a single and progressive disease , with individual heterogeneity based on differences in dynamics , a notion consistent with pathological findings 27 ., A central question is whether neurodegeneration in MS is a process that is independent of inflammation , appearing at later phases in a damaged CNS , or if a single progressive process is at play that commences at disease onset and that combines both biological processes to a different extent over time ., Simulations of the model reproduced all the MS phenotypes when both inflammation and degeneration commenced at disease onset ( Fig 5 ) ., In addition , the model assumed a direct effect of adaptive immune system attacks ( the independent parameter λ ( t ) ) on demyelination-related relapses and neurodegeneration , as well as an effect of chronic immune activation ( through parameters kd and kmd ) on neurodegeneration ., Accordingly , our simulations support the concept that neurodegeneration starts from the beginning of the disease and progresses at different speeds in different patients ., However , our model did not explore other alternative hypothesis ., In this study , we tested the dynamic CNS damage hypothesis of MS , and whether all the disease phenotypes can be reproduced by the participation of the same mechanisms operating at different intensities and over different time scales: autoimmune inflammation followed by axon loss and de/remyelination ., Our simulations support the hypothesis that MS is a single disease with very heterogeneous phenotypes and they suggest a different contribution of each process to the phenotype ., The presence of irreversible axon degeneration at early disease stages would appear to be mainly due to the higher rates of degeneration ( transection ) of myelinated axons and to a lesser extent , to a weaker capacity for remyelination ., A build-up of axon degeneration is the basis of the progressive phenotype , even during early disease stages like those of RRMS ., Conversely , increased resilience in both the rates of axon degeneration and in the efficiency of remyelination at early stages of the disease are the basis of the RRMS subtype ., These results provide a theoretical framework to study the contribution of such pathogenic processes at the experimental level , as well as for the design of therapeutic strategies for MS . However , our model does not rule out alternative hypotheses , such as the inside-out hypothesis , the two-stage hypothesis or the influence of a deteriorated autoimmune response ( e . g . , epitope spreading , antigen presentation in the damaged CNS ) ., Hence , we can only state that the dynamic CNS damage hypothesis of MS is consistent with the phenotype observed , while we cannot formally rule out other explanations ., In order to use clinical data to fit the parameters to our model , we performed a clustering analysis to limit the heterogeneity of the data ., Non-supervised clustering yields four clusters that best group the data and that reproduce the main characteristics used clinically to stratify patients: namely the disease subtype ( relapsing or progressive ) and disease severity ( commonly defined as the time to reach a milestone like EDSS 4 . 0 or 6 . 0 ) ., It is striking that a simple approach such as a clustering analysis does not segregate the MS phenotypes into the three classic subgroups , supporting the current concept that RRMS-SPMS-PPMS represent a continuum with different levels of disease activity and superimposed relapses , as proposed recently 12 ., Based on this approach , our model has been optimized to match each of the clusters and such a grouping may aid patient stratification at the time of tailoring therapies based on disease course ., As such , new prospective clinical studies classifying patients into one of the four clusters and modeling the trajectory of each group based on this ODE model should provide evidence of its clinical utility for patient stratification ., Our model may have implications for the development of new therapies for MS . Pathological studies have shown that all pathogenic processes are in place from the onset of the disease 14 and they demonstrated the key role of acute axon transection due to autoimmune relapses 28 ., Based on these concepts , it has become highly desirable to obtain “no evidence of disease activity” ( NEDA ) from the early stages of the disease 29 ., Our model supports this assumption , although such predictions must be demonstrated in clinical trials or by ruling out other alternative hypotheses ., This is important because the model shows that CNS damage accumulates from disease onset and that once it reaches a given threshold , a small increase in damage has a significant impact on disability ., This can be explained by depletion of the functional CNS reserve , hindering remyelination and impairing axon conduction due to demyelination 12 ., Although there is currently much interest in remyelination therapies , our model suggests that these may only be of value within specific time windows of disease evolution ( e . g . , RRMS ) ., However , remyelination was not fully analyzed with our model and thus , our results should be considered preliminary ., Finally , our model shows a key role of axon degeneration in defining the MS phenotype , consistent with pathological evidence ., As such , developing neuroprotective or regenerative therapies should hinder the advance of disability 30 ., Our approach has some caveats ., At the formal level , this study offers support for the dynamic CNS damage hypothesis of MS but it does not formally exclude alternative hypotheses , such as the two-stage hypothesis or the inside-out hypothesis ., Considering the lack of quantitative biological data regarding these biological processes , we approached the parameter search by fitting the ODE model to the clinical phenotype ( experimental EDSS time-series ) and we then checked whether such parameters were in the range of biological processes ., Therefore , our results should be considered more a qualitative than quantitative model of CNS damage in MS . In addition , we have not modeled all the pathogenic processes that can damage the brain in detail , such as the feedback of CNS damage to autoimmune processes , chronic microglial activation , meningeal inflammation , cortical plaques , or specific neurodegenerative processes ., Future studies and more quantitative data will allow such a level of detail to be added , and enable more specific and quantitative models to be developed ., On a more positive note , we were able to model the phenomena using relatively few parameters , which means that the constitutive equations are capable of capturing MS demyelination and neuroaxonal events ., In summary , our study indicates that the pathogenic processes that drive autoimmune damage in the CNS can produce all the distinct MS subtypes and explain the clinical heterogeneity in patients ., However , while each phenotype requires specific parameters to be fulfilled , it appears that there is a distinct contribution of each biological process to the different disease stages ( perhaps reflecting different genetic susceptibility and environmental exposure ) ., Therefore , our model supports the notion that MS and its phenotypes can be explained as an autoimmune process , arguing in favor of the dynamic CNS damage hypothesis of MS . This hypothesis has implications for the development of new therapies and patient monitoring ., Principally , it means that MS should be considered and treated from the onset as a progressive disease , with a focus on preventing CNS damage , and on avoiding reaching the thresholds associated with the progressive course of the disease and more severe disability ., All the patients were recruited by their neurologists after obtaining their signed informed consent ., The IRB of the Hospital Clinic , Charite University , Karolinska Institutet , University of Zurich approved the study ., The discovery cohort was a retrospective longitudinal cohort that included 66 MS patients from the Hospital Clinic of Barcelona , Spain ( iTEM database ) and from the Hopital Civil de Lyon , France ( EDMUS database , data provided by Prof . Christian Confavreux: the raw data for the EDSS time-series are provided in the S1 File ) ., The time-series of the cohort included EDSS values during a follow-up of 5 to 20 years ., The disease subtypes at the end of the follow-up were: 22% RRMS , 60% SPMS , and 16% PPMS ., The validation cohort was a prospective cohort of 120 MS patients from the Hospital Clinic of Barcelona , with annual clinical ( EDSS ) and MRI assessment over three years ( raw data for the EDSS and BV time-series are provided in the S2 File ) ., More details of this cohort can be found elsewhere 31 ., MRI’s were acquired on a 3T Magnetom Trio scanner ( Siemens , Erlangen , Germany ) , using a 32 channel phased-array head coil ., A 3-dimensional structural T1-weighted Magnetization-Prepared Rapid Gradient Echo ( T1-MPRAGE ) was used to compute all the volumes in this study and a 3-dimensional Fluid-Attenuated Inversion Recovery ( FLAIR ) was used to manually achieve lesion segmentation ., The T1-weighted MPRAGE sequence was acquired with the following parameters: TR = 1970 ms , TE = 2 . 41 ms , TI = 1050 ms , flip angle = 9 , 208 contiguous sagittal slices with voxel size = 0 . 9 x 0 . 9 x 0 . 9 mm3 , matrix size = 256 x 256 ., The FLAIR sequence was acquired with the following parameters: TR = 5000 ms , TE = 393 ms , TI = 1800 ms , 208 contiguous sagittal slices with voxel size = 0 . 9 x 0 . 9 x 0 . 9 mm3 , matrix size = 256 x 256 ., The FLAIR image registered to T1 was used to manually segment the lesions ., Subsequently , the lesion mask obtained was used to create a healthy-like T1 and improve the following steps ., Finally , the T1 was segmented and the normalized BV was calculated using SIENA ., We used a modified k-means algorithm to cluster the patient’s EDSS time-series from the discovery cohort based on their complete EDSS time-series ., First , we normalized the EDSS data at each time point to the maximum and imputed the missing values using the K-Nearest Neighbor method ., Since k mean clustering is sensitive to the choice of the initial partition , we ran it multiple times with random starting points , and using different k values between 3 and 8 ., The center of the clusters were obtained using fuzzy c-mean clustering and through this method , we identified clusters that group all patients in the dataset ., The number of clusters was quantified using the average silhouette approach , which provided the best result at k = 4 ., We obtained the clusters that better grouped the dataset of the discovery cohort , without pre-specifying any number of clusters ( e . g . , 3 clusters to group RRMS , SPMS and PPMS ) ., To test if the clustering obtained from the long-term follow-up datasets ( i . e . : the discovery cohort ) could cluster clinical datasets when only short-term follow-ups are available ( e . g . , two-three years of annual EDSS data ) , we evaluated if the four clusters identified in the discovery cohort were also extracted from the validation cohort ., As such , we repeated the clustering process on the validation cohort with the restriction that the number of clusters should be the same as that defined in the discovery cohort ( n = 4 ) ., We calculated the ratio of misclassified patients for each of these 4 clusters in the validation cohort compared to the discovery cohort as the error rate ., We found that the error rate of the clustering using only a 2-year follow-up was 16 . 51% ± 6 . 02 ( the maximum error rate belonged to cluster 3 and the minimum rate to cluster 2 ) ., Hence , based on these results we established that classification or assignment to EDSS clusters can be achieved based on short-term EDSS observations ., Incremental changes in disability ( ΔEDSS ) were calculated using the current confirmed definition of the progression of disability based on an increase ≥1 point in the EDSS three months apart 2 ., Such ΔEDSS may be due to clinical relapses or disease progression ., For the EDSS time-series , the time intervals ( ΔT ) between consecutive ΔEDSS events ( pulses ) were counted and using MATLAB ( allfitdist custom script ) , we tested different statistical models to identify that which best described the distribution of the ΔT values ., The frequency of the ΔEDSS events in 76% of patients can be approximated by the inverse Gaussian distribution and the remaining 24% by the distribution of Generalized Extreme Values ( GEV: comparable statistical models representing rare events ) ., Furthermore , we took the median values of ΔT for each of the patients and analyzed the resulting distribution of all of them ., The best approximation of the resulting distribution was the GEV model ( cumulative distributions versus data: S4 Fig ) ., The simulated GEV distribution of the ΔT frequencies belongs to a category of extreme events of underlying processes and it was further used in the ODE model with the parameters of distribution , defined separately for each patient cluster ., MS is an autoimmune disease in which the activation of auto-reactive T cells induces chronic activation of the innate immune response and focal CNS damage , the latter manifested as demyelination and axonal loss ( Fig 1 ) 1 , 7 , 32 ., Mechanisms that drive peripheral immune tolerance and brain immune privilege can shut down the immune attack in the short term , although relapses occur that exacerbate demyelination and axon loss ., We assume that remyelination fails after some time , contributing to the steady loss of axons and the chronic compartmentalized inflammation that ultimately leads to neurodegeneration 28 ., However , inflammation persists throughout the disease and it evolves from being orchestrated in the peripheral immune system to being compartmentalized in the CNS 33 , 34 ., During the early phases of the disease , immune-mediated demyelination and acute axon transection dominate , while the progressive phases are characterized by compartmentalized CNS inflammation and degeneration of demyelinated axons due to oxidative stress , energetic failure , loss of trophic support in the oligodendrocyte-axon unit and the development of a glial scar 6 , 25 , 35 ., We took these three processes ( inflammatory attack , demyelination/remyelination and axon loss ) into account at the cellular level to model MS and reproduce the clinical phenotypes observed ., We kept our model as simple as possible using the most basic processes described in MS , and avoided modeling other processes for which there is still insufficient quantitative data for them to be modeled ( e . g . , specific inflammatory processes or oligodendrocyte loss ) ., The healthy CNS is composed of neurons and their myelinated axons , and for simplicity we do not consider the volume of glial cells ( e . g . , astrocytes and microglia ) or the somas of oligodendrocytes ., Myelin is damaged during the course of autoimmune attack ( via pro-inflammatory cytokines , antibodies ) , oxidative stress or energy failure 36 , 37 ., Yet because extensive remyelination may occur in the early to mid-phase of MS 18 , we assume that demyelination/remyelination events will follow the dynamics of the autoimmune attack until remyelination mechanisms are unable to compensate for the loss of myelin , which occurs in conjunction with the clinical transition to the progressive phase 24 ., By contrast , axon damage ( acute transection or degeneration ) does not follow autoimmune dynamics but rather , it accumulates due to the poor regenerative capacity of the CNS 12 ., Activation of the adaptive immune system in MS drives the migration of lymphocytes and monocytes into th

Introduction, Results, Discussion, Materials and methods

Multiple Sclerosis ( MS ) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system ( CNS ) ., However , it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease ., We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease , the autoimmune attack on the CNS that leads to chronic inflammation , neuroaxonal degeneration and remyelination ., We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups ., To test this hypothesis , we developed a mathematical model that was constrained by experimental data ( the expanded disability status scale EDSS time series ) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up ( up to 20 years ) ., Moreover , we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up , for which EDSS data and brain volume time series were available ., The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis ., We found that by adjusting certain parameters , albeit within their biological range , the mathematical model reproduced the different disease courses , supporting the dynamic CNS damage hypothesis to explain MS heterogeneity ., Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration , coupled to the lower capacity for remyelination ., However , and in agreement with recent pathological studies , degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype ., Moreover , the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype ., Therefore , our results support the hypothesis of a common pathogenesis for the different MS subtypes , even in the presence of genetic and environmental heterogeneity ., Hence , MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups ., These results have important implications for the design of therapeutic interventions for MS at different stages of the disease .

Multiple Sclerosis ( MS ) is an autoimmune disease in which inflammatory and degenerative processes damage the brain ., We tested the hypothesis that the variability in disease progression and the clinical heterogeneity observed in MS is driven by a single mechanism , namely the autoimmune attack on the CNS that provokes this chronic inflammation and degeneration ., As such , it is the difference in the intensity of these processes at distinct times that is responsible for establishing each of the disease subtypes defined to date ., Mathematical models of brain damage and disease course were generated that were fitted to clinical data ., We found that the phenotypes of the different MS subtypes were reproduced by the model , supporting the concept of a common pathogenesis and thus , that of a single disease in which specific dynamics can produce a variety of clinical outcomes in different groups of patients ., These results are likely to be helpful when designing new therapies for this disease .

inflammatory diseases, medicine and health sciences, pathology and laboratory medicine, multiple sclerosis, nervous system, neurodegenerative diseases, brain damage, immunology, neuroscience, simulation and modeling, demyelinating disorders, clinical medicine, signs and symptoms, nerve fibers, research and analysis methods, inflammation, animal cells, axons, epidemiology, immune response, cellular neuroscience, diagnostic medicine, cell biology, clinical immunology, anatomy, central nervous system, disease dynamics, neurology, neurons, autoimmune diseases, biology and life sciences, cellular types

journal.pntd.0005465

VivaxGEN: An open access platform for comparative analysis of short tandem repeat genotyping data in Plasmodium vivax populations

In the Asia-Pacific region , Plasmodium vivax is responsible for between 20 and 280 million malaria cases per year , inflicting a significant burden of morbidity and mortality ., Over the last decade , the prevalence of P . falciparum has declined in many endemic countries as a result of intensified malaria control interventions , but outside Africa this has been associated with a rise in the proportion of P . vivax cases , reflecting the limited efficacy of interventions against this species 1 ., This trend emphasizes the need for innovative new strategies to reduce P . vivax transmission ., A critical weakness of conventional malaria surveillance is the lack of information on the genetic dynamics of the parasite population—an important reflection of underlying transmission potential ., Previous studies have demonstrated the utility of genotyping parasite population samples at highly polymorphic short tandem repeat ( STR ) markers such as microsatellites to inform on P . vivax diversity , population structure and underlying transmission patterns 2–19 ., These simple molecular approaches complement the more traditional measures of transmission intensity as well as providing a surrogate marker for transmission intensity , informing on outbreak dynamics , reservoirs of infection , and the spread of infection spread within and across borders 20 , 21 ., However , individual projects have limited potential to address regional questions ., The challenges of imported and border malaria associated with highly mobile human populations emphasizes the need for a framework to support integrated , multinational comparative analyses ., Effective comparison between studies and sites has been confounded by heterogeneity of methodologies such as the number and location of markers used , size standards , allele calling/binning , and specifications for calling minor alleles reflecting minor clones in polyclonal infections 22 ., To address some of these challenges , the Vivax Working Group ( VxWG ) of the Asia Pacific Malaria Elimination Network ( APMEN ) has worked with research partners in 15 Asia Pacific countries to develop a consensus panel of STR markers ( MS1 , MS5 , MS8 , MS10 , MS12 , MS16 , MS20 , pv3 . 27 and msp1F3 ) and genotyping methods 23 ., The web-based VivaxGEN platform was developed to facilitate standardized allele calling , data analysis and sharing across P . vivax studies using consensus STR marker sets such as the APMEN panel ., The VivaxGEN platform provides a repository for FSA files ( the primary data files containing the raw fragment analysis data generated during capillary electrophoresis runs ) ., To date , no such repository exists for P . vivax STR data ., The capacity to derive allelic data directly from the FSA files ensures high accuracy and standardization in allele-calling between different sample batches produced at different time points and/or on different machines from possibly different studies ., This feature also supports flexibility in defining allele-calling thresholds , enabling user-defined settings that may be applied to one or more sample batches ., The VivaxGEN platform also provides tools for standard population genetic analyses that can be applied to multiple sample batches to evaluate local and regional trends in the prevalence of polyclonal infections , population diversity , structure and differentiation both spatially and temporally ., Data export tools are available to allow users to conduct more bespoke analyses not provided within the platform framework ., All genotyping data described in the manuscript has been published 4 , 9 , 12 , 14 , 34 ., As described in the original publications , all samples were collected with written informed consent from the patient , parent or legal guardian ( individuals < 18 years of age ) ., Approval was provided by the Institutional Review Board of Jiangsu Institute of Parasitic Diseases ( IRB00004221 ) , Wuxi , China; the Research Ethics Board of Health , Ministry of Health Bhutan ( REBH 2012/031 ) ; the Korea Centers for Disease Control and Prevention Institutional Review Board , Republic of Korea ( Protocol No . 2011-02CON-14-P ) ; the Eijkman Institute Research Ethics Commission , Indonesia ( EIREC 45/2011 ) ; the Ethics Review Board of Addis Ababa University College of Natural Sciences , Ethiopia ( RERC/002/05/2013 ) ; the Ethics Review Board of Armauer Hansen Research Institute , Addis Ababa , Ethiopia ( AHRI-ALERT P011/10 ) ; the National Research Ethics Review Committee of Ethiopia ( Ref . no . 3 . 10/580/06 ) ; and the Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research , Darwin , Australia ( HREC 2012–1871 , HREC-2012-1895 and HREC-13-1942 ) ., The VivaxGEN platform was developed as a multi-tier web application system , utilizing PostgreSQL as its backend Relational Database Management System ( RDBMS ) and leveraging on several common external tools for genotype data analysis ., PostgreSQL was chosen as the RDBMS as it provided ACID operations and complex SQL query optimization in an open-source package ., The backend is programmed in Python , while the web interface uses JavaScript and jQuery library for interactivity ., YAML was chosen as the format for platform configuration and data exchange/interoperability ., Sample and assay data uploading process can be performed using either batch processing with tab or comma-delimited files in conjunction with a zip file containing raw FSA files , or interactively using sample and assay editing interface ., Detailed instructions on data upload , and an accompanying tutorial dataset can be found in Tutorial 1 ( Uploading your metadata and FSA files ) provided on the VivaxGEN website and in S1 File ., VivaxGEN provides a framework to store and process raw FSA files with standardized allele calling tools ., This framework reduces the heterogeneity that may be introduced from different fragment analysis methods ., A Python based library called FATOOLS , which can also be used as a stand-alone command line utility , was developed to provide the raw FSA processing capabilities in VivaxGEN ., This library utilizes numpy ( www . numpy . org ) and scipy ( https://www . scipy . org ) scientific libraries for its numerical processing ., The library provides methods for base normalization of traces , peak scanning and classification , standard size determination , peak calling and allele annotation , as well as FSA assay quality controls ., A detailed guide on the FSA fragment analysis process in VivaxGEN can be found in the Guide on Fragment Analysis manual provided on the website and in S2 File ., Briefly , base normalization is undertaken using a top-hat morphological transform algorithm implemented in scipy ., A simple peak finding algorithm and a CWT-based peak scanning algorithm implemented in scipy are also included in the library 24 ., A combination of greedy algorithm and dynamic programming is employed for standard size alignment and size determination ., Results of each step of the FSA and fragment analysis processing are stored in the system for aiding manual inspection and assay verification ., The source code for FATOOLS is available for stand-alone usage and further development ( http://github . com/trmznt/fatools ) ., To aid the manual inspection of traces , a trace viewer is included in the web interface , as shown in Fig 1 ., Detailed instructions on the manual data editing tools can be found in Tutorial 2 ( Inspecting FSA files and data cleaning ) provided on the VivaxGEN website and in S1 File ., The trace viewer is coded in JavaScript and enables users to identify and edit incorrectly annotated alleles ., The form-based web interface also provides a number of allele and sample filtering options ., Details on the allele and sample filtering tools can be found in Tutorial 3 ( Data analysis ) provided on the VivaxGEN website and in S1 File ., Alleles can be filtered according to marker name ( Marker ) , marker failure rate in the given sample set ( Marker quality threshold ) , absolute minimum relative fluorescence unit ( RFU ) ( Allele absolute threshold ) and relative RFU of minor peaks compared to the highest intensity peak ( Allele relative threshold ) ., Suspected stutter peaks can also be filtered according to a user-defined stutter range in base pairs ( Stutter range ) and ratio ( Stutter ratio ) based on the RFU relative to the highest intensity peak in the given range ., Samples can also be filtered according to genotyping success rate across the given marker set ( Sample quality threshold ) , to exclude polyclonal infections or multi-locus genotypes that are presented more than once in the given sample set ( Sample filtering ) , or by passive versus active case detection ( Detection differentiation ) ., Sample querying and grouping can be performed using a query syntax modeled on the NCBI Entrez system with some modification ., Detailed instructions on how to perform data analysis using custom queries is provided in Tutorial 4 ( Data analysis with custom query ) provided on the VivaxGEN website and in S1 File ., Boolean operations can be applied to classify sample groups based on spatial ( by country level or by 1st , 2nd , 3rd or 4th administrative division level ) or temporal ( by year or quartile of sample collection ) definitions ., The query from the form-based web interface is converted into a YAML-based query internally , which can then be run in the database ., An interface that accepts YAML-based query is also provided , enabling the user to apply bespoke sample grouping operations not supported by the form-based web interface ., Instructions on how to perform data analysis in VivaxGEN using YAML queries is provided in Tutorial 5 ( Data analysis with YAML query ) provided on the website and in S1 File ., A suite of population genetic measures and associated statistical tests that are commonly used in STR-based P . vivax studies to gauge underlying patterns of transmission intensity , stability and boundaries , including rates of polyclonality , population diversity , genetic relatedness , population structure and out-crossing/inbreeding rates , can be applied to the genotyping data from one or more sample batches ., Population genetic measures currently supported within VivaxGEN include, ( i ) expected heterozygosity ( HE ) , an index of population-level diversity ,, ( ii ) individual infection and population average measures of the Multiplicity of Infection ( MOI ) , a measure of the genetic complexity within infections ,, ( iii ) proportion of polyclonal infections , and, ( iv ) Principal Coordinate Analysis ( PCoA ) with plots illustrating the population structure and genetic relatedness between infections based on a genetic distance matrix ., External software employed by the platform include, ( i ) LIAN for measuring linkage disequilibrium ( LD ) using the index of association ( IAS ) 25 as a gauge of out-crossing/inbreeding rates ,, ( ii ) Arlequin for measures of genetic differentiation between populations using the fixation index ( FST ) 26 ,, ( iii ) the APE ( Analysis of Phylogenetics and Evolution ) package in R for building neighbor-joining trees for assessment of genetic relatedness between infections 27 ,, ( iv ) the FactoMineR package in R for generating Multiple Correspondence Analysis ( MCA ) plots to assess population structure and genetic relatedness based on the nominal categorical data 28 , and, ( v ) the DEMEtics package in R for calculating the genetic differentiation index D 29 , 30 ., A standardized measure of genetic differentiation , FST , adjusted for marker diversity to support greater comparability between studies using different marker sets is calculated internally in VivaxGEN using the output from Arlequin and following the method described by Hedrick 31 ., Further details on the population genetic tools can be found in the Guide on Data Analysis manual provided on the VivaxGEN website and in S2 File ., The VivaxGEN platform has tools for exporting genotype data in several formats supported by other commonly used population genetics softwares including LIAN 25 , Arlequin 26 , Genepop 32 and STRUCTURE 33 ., Tab-delimited formats suitable for R’s data frame or Python’s pandas data frame are also provided ., VivaxGEN users may choose to keep their data private , accessible to all or only specified researchers or they may allow their data to be open access ., The repository currently holds data obtained from published studies on P . vivax samples from China 12 , Ethiopia 4 , Indonesia 14 , South Korea 9 and Bhutan 34 ., Private accounts have been generated for users with data sets on P . vivax samples from Iran , Malaysia , Myanmar , and Vanuatu ., The platform can be accessed at http://vivaxgen . menzies . edu . au ., The source code for the platform , licensed under GNU GPL version 3 , can be obtained from https://github . com/trmznt/plasmogen ., The VivaxGEN platform was developed as a framework to support standardized allele calling and greater ease of data sharing for comparative analyses between different STR-based studies in P . vivax ., Relative to Single Nucleotide Polymorphisms ( SNPs ) , where a maximum of 4 alleles arising from the 4 different nucleotides are possible at a given position , STRs may exhibit dozens of alleles , measured as different repeat lengths ., Although STRs offer high discriminatory potential between independent infections , comparison of STR alleles ( fragment size variants ) between different sample batches produced at different time points and/or in different laboratories is considerably more challenging than comparison of the discrete allele forms generated from the analysis of SNPs ., Despite the application of a size standard , replicates of the same sample may exhibit slight variation ( usually less than 1 bp difference ) in fragment size ., In order to address this variation , alleles can be assigned to bins encompassing a range of fragment sizes usually reflecting the size of the repeat unit ., However , whilst one researcher might assign fragment sizes of 254 . 4 bp and 255 . 7 bp to two different allele bins such as “254” and “256” respectively , another researcher might assign both alleles to bin “255” , and yet another might assign these fragment sizes to allele bin “256” , creating artificial differentiation between datasets ., As illustrated in Fig 2 , the VivaxGEN platform provides a common interface for fragment size allele calling using the raw FSA files and applying a standardized binning system , which facilitates comparability between different datasets ., By virtue of this feature , using the VivaxGEN platform , it was possible to identify a distinct , population-specific allele profile at the MS20 locus in South Korea versus Bhutan , Ethiopia and Indonesia ( Fig 3 ) ., The distinct MS20 allele profile observed in South Korea is postulated to reflect a single major reservoir of P . vivax infections , most likely from North Korea 9 ., Future data entries to VivaxGEN on MS20 genotypes from other vivax-endemic regions are likely to provide further important insights on this phenomenon and other transmission patterns ., One of the greatest challenges in genotyping Plasmodium samples ( and other microorganisms ) is the identification and characterization of polyclonal infections 22 ., Owing to artefacts such as background noise , stutter peaks , and overlapping peaks ( also known as pull-up peaks or bleed ) in multiplex reactions where amplicons are labelled with different fluoresceins ., Some of these artefacts may not be automatically detected and excluded from the peak binning during the fragment scanning process ., To address this challenge , the VivaxGEN platform provides utilities enabling visual inspection of individual electropherogram traces and editing of allele annotations ., The platform also enables user-defined relative minimum RFU thresholds for calling minor alleles: an approach that is commonly applied in STR-based Plasmodium studies to reduce the prevalence of artefact peaks , and enhance comparability in the sensitivity to detect minor peaks in samples of differing quality such as DNA derived from dried blood spots versus blood tubes 35 ., Different studies may however apply different thresholds ., A benefit of the integrated database and analytical framework in VivaxGEN is that population genetic measures such as the average MOI or proportion of polyclonal infections can be compared between different sample batches at the same user-defined threshold–and indeed multiple different thresholds can be explored ., Capitalizing on the feature to incorporate samples from multiple studies ( batches ) within an analytical procedure , we used the platform to compare multi-locus genotypes ( MLGs ) between different published datasets stored in the database ., As illustrated in Fig 4A , Multiple Correspondence Analysis ( MCA ) demonstrated clear distinction of the MLGs at the 9 APMEN standard markers between Ethiopia , Indonesia and South Korea , whilst the Bhutanese isolates displayed a broad range of MLGs with overlap in both Ethiopia and Indonesia ., It is widely acknowledged that different STR markers have different strengths in their ability to detect polyclonal infections and/or to define population structure 36 ., Amongst the APMEN panel , 5 markers ( MS1 , MS5 , MS10 , MS12 and MS20 ) have been defined as “stable” , with optimal utility for analysis of population differentiation 36 ., Therefore , the effect of repeating the analysis using the 5 stable markers was assessed ( Fig 4B ) ., A similar pattern was observed to the full marker panel , adding assurance that the clustering patterns had not been affected by the high diversity markers ., The integrated data repository , allele calling and data analysis tools in VivaxGEN promote exploratory and semi-interactive analysis in a common web interface ., Compared to other popular softwares for processing microsatellite data , VivaxGEN is unique in providing both the capability to process and store raw electropherogram data ( FSA files ) and to perform statistical and population genetic analysis commonly applied in studies of Plasmodium ( Table 1 ) ., A data export utility enables population genetic analysis outputs for a given parameter set to be downloaded from VivaxGEN to facilitate data reporting ., These features greatly simplify data processing and exploration , and should enable malaria researchers who are new to the field of population genetics to conduct robust data analysis with greater autonomy ., The integrated data repository should also foster collaborations between different research institutions and allow analyses on regional trends as well as population differences between countries ., The outcomes will inform national malaria control and elimination programs on malaria transmission dynamics , may help distinguish local from imported parasite populations and facilitate malaria surveillance ., The VivaxGEN platform is well placed to facilitate regional overviews of P . vivax population genetic patterns in different endemic settings , informing on the underlying transmission dynamics of this highly adaptive parasite ., The system is amenable to being adapted for STR-based analyses in P . falciparum and other microorganisms or other forms of genetic data such as SNP-based genotypes ., The open access source code is provided to facilitate developments for such applications .

Introduction, Methods, Results and discussion, Conclusions

The control and elimination of Plasmodium vivax will require a better understanding of its transmission dynamics , through the application of genotyping and population genetics analyses ., This paper describes VivaxGEN ( http://vivaxgen . menzies . edu . au ) , a web-based platform that has been developed to support P . vivax short tandem repeat data sharing and comparative analyses ., The VivaxGEN platform provides a repository for raw data generated by capillary electrophoresis ( FSA files ) , with fragment analysis and standardized allele calling tools ., The query system of the platform enables users to filter , select and differentiate samples and alleles based on their specified criteria ., Key population genetic analyses are supported including measures of population differentiation ( FST ) , expected heterozygosity ( HE ) , linkage disequilibrium ( IAS ) , neighbor-joining analysis and Principal Coordinate Analysis ., Datasets can also be formatted and exported for application in commonly used population genetic software including GENEPOP , Arlequin and STRUCTURE ., To date , data from 10 countries , including 5 publicly available data sets have been shared with VivaxGEN ., VivaxGEN is well placed to facilitate regional overviews of P . vivax transmission dynamics in different endemic settings and capable to be adapted for similar genetic studies of P . falciparum and other organisms .

The Plasmodium vivax malaria parasite inflicts significant morbidity in endemic populations across the globe , but has been overshadowed by the more fatal P . falciparum parasite ., In malaria-endemic regions outside of Africa , the declining prevalence of P . falciparum is coupled with a proportionate rise in P . vivax , reflecting the greater refractoriness of P . vivax to transmission interventions ., This worrying trend emphasizes the need for a better understanding of the patterns of P . vivax transmission and spread within and across borders ., Genotyping parasite population samples at short tandem repeat ( STR ) markers such as microsatellites informs on diversity , population structure and underlying transmission patterns ., We have established vivaxGEN , an online platform providing a repository for P . vivax STR genotyping data , and tools for standard population genetic analyses ., The platform currently holds publicly available data from 5 vivax-endemic countries that can be browsed on the website ( http://vivaxgen . menzies . edu . au ) ., VivaxGEN will support researchers to conduct local STR-based P . vivax studies with greater autonomy and foster collaborative studies enabling regional overviews of P . vivax diversity in different endemic settings and across borders ., The system can be adapted for STR-based analyses in other microorganisms and the open access source code is provided to facilitate these developments .

computer applications, parasite groups, medicine and health sciences, plasmodium, population genetics, tropical diseases, geographical locations, parasitic diseases, parasitic protozoans, parasitology, apicomplexa, protozoans, molecular biology techniques, population biology, ethiopia, information technology, genotyping, data processing, africa, research and analysis methods, computer and information sciences, malarial parasites, molecular biology, people and places, web-based applications, genetics, biology and life sciences, malaria, evolutionary biology, organisms

journal.pntd.0005568

Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models

Epidemics of dengue , chikungunya , and Zika are sweeping through the Americas , and are part of a global public health crisis that places an estimated 3 . 9 billion people in 120 countries at risk 1 ., Dengue virus ( DENV ) distribution and intensity in the Americas has increased over the last three decades , infecting an estimated 390 million people ( 96 million clinical ) per year 2 ., Chikungunya virus ( CHIKV ) emerged in the Americas in 2013 , causing 1 . 8 million suspected cases from 44 countries and territories ( www . paho . org ) ., In the last two years , Zika virus ( ZIKV ) has spread throughout the Americas , causing 764 , 414 suspected and confirmed cases , with many more unreported ( http://ais . paho . org/phip/viz/ed_zika_cases . asp , as of April 13 , 2017 ) ., The growing burden of these diseases ( including links between Zika infection and both microcephaly and Guillain-Barré syndrome 3 ) and potential for spread into new areas creates an urgent need for predictive models that can inform risk assessment and guide interventions such as mosquito control , community outreach , and education ., Predicting transmission of DENV , CHIKV , and ZIKV requires understanding the ecology of the vector species ., For these viruses the main vector is Aedes aegypti , a mosquito that prefers and is closely affiliated with humans , while Ae ., albopictus , a peri-urban mosquito , is an important secondary vector 4 , 5 ., We expect one of the main drivers of the vector ecology to be the climate , particularly temperature ., For that reason , mathematical and geostatistical models that incorporate climate information have been valuable for predicting and responding to Aedes spp ., spread and DENV , CHIKV , and ZIKV outbreaks 5–10 ., The effects of temperature on ectotherms are largely predictable from fundamental metabolic and ecological processes ., Survival , feeding , development , and reproductive rates predictably respond to temperature across a variety of ectotherms , including mosquitoes 11 , 12 ., Because these traits help to determine transmission rates , the effects of temperature on transmission should also be broadly predictable from mechanistic models that incorporate temperature-dependent traits ., Here , we introduce a model based on this framework that overcomes several major gaps that currently limit our understanding of climate suitability for transmission ., Specifically , we develop models of temperature-dependent transmission for Ae ., aegypti and Ae ., albopictus that are, ( a ) mechanistic , facilitating extrapolation beyond the current disease distribution ,, ( b ) parameterized with biologically accurate unimodal thermal responses for all mosquito and virus traits that drive transmission , and, ( c ) validated against human dengue , chikungunya , and Zika case data across the Americas ., We synthesize available data to characterize the temperature-dependent traits of the mosquitoes and viruses that determine transmission intensity ., With these thermal responses , we develop mechanistic temperature-dependent virus transmission models for Ae ., aegypti and Ae ., albopictus ., We then ask whether the predicted effect of temperature on transmission is consistent with patterns of actual human cases over space and time ., To do this , we validate the models with DENV , CHIKV , and ZIKV human incidence data at the country scale in the Americas from 2014–2016 ., To isolate temperature dependence , we also statistically control for population size and two socioeconomic factors that may influence transmission ., If temperature fundamentally limits transmission potential , transmission should only occur at actual environmental temperatures that are predicted to be suitable , and conversely , areas with low predicted suitability should have low or zero transmission ( i . e . , false negative rates should be low ) ., By contrast , low transmission may occur even when temperature suitability is high because other factors like vector control can limit transmission ( i . e . , the false positive rate should be higher than the false negative rate ) ., Finally , if the simple mechanistic model accurately predicts climate suitability for transmission , then we can use it to map climate-based transmission risk of DENV , CHIKV , ZIKV , and other emerging pathogens transmitted by Ae ., aegypti and Ae ., albopictus seasonally and geographically ., Data gathered from the literature 9 , 13–30 revealed that all mosquito traits relevant to transmission—biting rate , egg-to-adult survival and development rate , adult lifespan , and fecundity—respond strongly to temperature and peak between 23°C and 34°C for the two mosquito species ( Ae . aegypti in Fig 1 and Ae . albopictus in Fig A in S1 Text ) ., DENV extrinsic incubation and vector competence peak at 35°C 31–37 and 31–32°C 31 , 32 , 34 , 38 , respectively , in both mosquitoes—temperatures at which mosquito survival is low , limiting transmission potential ( Fig 1 , Fig A in S1 Text ) ., Appropriate thermal response data were not available for CHIKV and ZIKV extrinsic incubation and vector competence ., We estimated the posterior distribution of R0 ( T ) and used it to calculate key temperature values that indicate suitability for transmission: the mean and 95% credible intervals ( 95% CI ) on the critical thermal minimum , maximum , and optimum temperature for transmission by the two mosquito species ., At constant temperatures , Ae ., aegypti transmission peaked at 29 . 1°C ( 95% CI: 28 . 4–29 . 8°C ) , and declined to zero below 17 . 8°C ( 95% CI: 14 . 6–21 . 2°C ) and above 34 . 6°C ( 95% CI: 34 . 1–35 . 6°C ) ( Fig 2 ) ., Ae ., albopictus transmission peaked at 26 . 4°C ( 95% CI: 25 . 2–27 . 4°C ) and declined to zero below 16 . 2°C ( 95% CI: 13 . 2–19 . 9°C ) and above 31 . 6°C ( 95% CI: 29 . 4–33 . 7°C ) ( Fig 2 ) ., Overall , the thermal response curve for Ae ., albopictus is shifted towards lower temperatures than Ae ., aegypti , so Ae ., albopictus transmission is better suited to cooler environments ., For a more realistic scenario in which daily temperature ranged over 8°C , the transmission peak , minimum , and maximum were slightly lower for both Ae ., aegypti ( 28 . 5°C , 13 . 5°C , 34 . 2°C , respectively ) and Ae ., albopictus ( 26 . 1°C , 11 . 9°C , and 28 . 3°C , respectively ) ., The lower thermal maximum under fluctuating temperatures occurs because we incorporated empirically supported irreversible lethal effects of temperatures that exceed thermal maxima for survival ( see Materials and Methods ) ., The posterior distribution of R0 ( T ) allows us to evaluate uncertainty in key temperature values that define the transmission range , including critical thermal minimum , maximum , and optimum ., Uncertainty was higher for the critical thermal minimum for transmission than for the maximum or optimum , and the two mosquito species overlapped most for this outcome ( Fig 2 , bottom panels ) ., This result occurred because several trait thermal responses increase gradually from low to mid temperatures but decline more steeply at high temperatures ( Fig 1 ) , so uncertainty is greatest at low temperatures ., Ae ., aegypti has a substantially higher optimum and maximum temperature than Ae ., albopictus ( Fig 2 ) due to its greater rates of adult survival at high temperatures ( see Supplementary Materials for sensitivity analyses ) ., We used generalized linear models ( GLM ) to ask whether the predicted relationship between temperature and transmission , R0 ( T ) , was consistent with observed human cases of DENV , CHIKV , and ZIKV ., Specifically , we assessed whether R0 ( T ) was an important predictor of the probability of autochthonous transmission occurring and of the incidence given that transmission occurred ., We also controlled for human population size , virus species , and two socioeconomic factors ., ( Note that we focused on testing the R0 ( T ) model , rather than on constructing the best possible statistical model of human case data ., ) To do this , we used the version of the Ae ., aegypti R0 ( T ) model that includes 8°C daily temperature range , along with country-scale weekly case reports of DENV , CHIKV , and ZIKV in the Americas and the Caribbean between 2014–2016 ., We first addressed the fact that countries with larger populations have greater opportunities for ( large ) epidemics by creating two predictors that incorporate scaled R0 ( T ) and population size ., In the models of the probability of autochthonous transmission occurring we used the product of the posterior probability that R0 ( T ) > 0 ( which we notate as GR0 ) and the log of population size ( p ) to give log ( p ) *GR0 ., ( Here , and throughout , log denotes the natural logarithm . ), In the models of incidence , given that transmission does occur , we used the log of the product of the posterior mean of R0 ( T ) and population size , log ( p*R0 ( T ) ) ., To control for several socioeconomic factors that might obscure the impact of temperature , we also included log of gross domestic product ( GDP ) and log of percent of GDP in tourism ( using logs because the predictors were highly skewed , to stabilize variance ) ., These are potential indicators of investment in and/or success of vector control and infrastructure improvements that prevent transmission ., By comparing models that included the R0 ( T ) metric alone , socioeconomic factors alone , or both , we tested whether R0 ( T ) was an important predictor of observed transmission occurrence and incidence ( see Table D in S2 Text ) ., Note that R0 ( T ) is out of sample for all validation analyses because it is derived and calculated strictly from laboratory data on mosquitoes , and we perform a validation analyses for R0 ( T ) using independent case incidence reports ., For this validation step we assessed model adequacy for the transmission data in two ways ., First we used the full dataset for case incidence reports to select the best model ( Table D in S2 Text ) and to determine whether or not our predicted value of relative R0 ( T ) based on laboratory data was included in the model ( “within sample” analysis ) ., Second we used a bootstrapping approach where models were fit on subsets of the case incidence data that were randomly sampled and then predictive accuracy of the competing models ( Table D in S2 Text ) was assessed on left-out data ( “out of sample” analysis ) ., For the probability of autochthonous transmission occurring , the model that included both the R0 ( T ) predictor and socioeconomic predictors had overwhelming support based on Bayesian Information Criterion ( BIC; model PA5 relative probability = 1 , Table D in S2 Text ) ., Based on deviance explained , the models that included R0 ( T ) , with or without the socioeconomic predictors out-performed the model that did not include R0 ( T ) ( Table D in S2 Text; Fig 3A , Fig B in S1 Text ) ., In analyses of out-of-sample accuracy , models that included the R0 ( T ) metric ( with or without the socioeconomic factors ) were surprisingly accurate ., They predicted the probability of transmission with 86–91% out-of-sample accuracy for DENV ( Table D in S2 Text ) ., For CHIKV and ZIKV , models that included the R0 ( T ) metric or population alone had 66–69% out-of-sample accuracy ( Table D in S2 Text ) ., There were no significant differences in out-of-sample accuracy between the top four models , but for both DENV and CHIKV/ZIKV the best model was significantly better than the worst model see supplementary code in 39 for full results ., The lower out-of-sample accuracy for CHIKV and ZIKV likely reflects the much lower frequency of positive values and the lower total sample size of this dataset ., All results were similar for a set of models that separated GR0 from population size , so for simplicity we show the model predictors that combines GR0 and population size here ( see Table D in S2 Text and 39 for results of other models ) ., Further , from a biological perspective , the combined model better describes what we know about disease systems: if either the probability of R0 ( T ) being greater than zero is small or population size is very small , transmission is unlikely to occur ., Together , these analyses suggest that R0 ( T ) is an important predictor of transmission occurrence , but that CHIKV and ZIKV need further data to better explain the probability of transmission occurrence ( Fig 3A , Fig B in S1 Text ) ., R0 ( T ) was also an important predictor of incidence , given that autochthonous transmission did occur ., Within-sample , incidence was best predicted by the model that included both R0 ( T ) and the socioeconomic predictors ( model IM5 in Table D in S2 Text ) based on BIC ( relative probability = 1 ) ., The models that included R0 ( T ) out-performed those that did not based on deviance explained ( Table D in S2 Text ) ., In out-of-sample validation , the models that included R0 ( T ) explained the magnitude of incidence based on mean absolute percentage error ( 85–86% accuracy versus 83% accuracy for models that did not include R0 ( T ) ; Table D in S2 Text ) , but this difference was not statistically significant ., For illustration , we show the simpler model that only contains the R0 ( T ) predictor in the main text ( Fig 3B; model IM1 in Table D in S2 Text ) ., Notably , the models that contained R0 ( T ) predicted incidence well for all three viruses , despite the lower incidence of CHIKV and ZIKV ., Although predicted R0 ( T ) correlated with the observed occurrence and magnitude of human incidence for all three viruses , these observed incidence metrics were higher for DENV than for CHIKV and ZIKV ., While the reason for this difference is unclear , the most likely explanation is that DENV is much more established in the Americas , so it is more likely to be detected , diagnosed , and reported ., Because ZIKV and CHIKV are newly emerging , they may not have fully saturated the region at this early stage ., The ability of the model to explain the probability and magnitude of transmission is notable given the coarse scale of the human incidence versus mean temperature data ( i . e . , country-scale means ) , the lack of CHIKV- and ZIKV-specific trait thermal response data to inform the model , the nonlinear relationship between transmission and incidence , and all the well-documented factors other than temperature that influence transmission ., Together , these analyses show simple mechanistic models parameterized with laboratory data on mosquitoes and dengue virus are consistent with observed temperature suitability for transmission ., Moreover , the similar responses of human incidence of ZIKV , CHIKV , and DENV to temperature suggest that the thermal ecology of their shared mosquito vectors is a key determinant of outbreak location , timing , and intensity ., The validated model can be used to predict where transmission is not excluded ( posterior probability that R0 ( T ) > 0 , a conservative estimate of transmission risk ) ., Considering the number of months per year at which mean temperatures do not prevent transmission , large areas of tropical and subtropical regions , including Puerto Rico and parts of Florida and Texas , are currently suitable year-round or seasonally ( Fig 4 ) ., These regions are fundamentally at risk for DENV , CHIKV , ZIKV , and other Aedes arbovirus transmission during a substantial part of the year ( Fig 4 ) ., Indeed , DENV , CHIKV , and/or ZIKV local transmission has occurred in Texas , Florida , Hawaii , and Puerto Rico ( www . cdc . gov ) ., On the other hand , many temperate regions experience temperatures suitable for transmission three months or less per year ( Fig 4 ) ., Temperature thus limits the potential for the viruses to generate extensive epidemics in temperate areas even where the vectors are present ., Moreover , many temperate regions with seasonally suitable temperatures currently lack Ae ., aegypti and Ae ., albopictus mosquitoes , making vector transmission impossible ( Fig 4 , black line ) ., The posterior distribution of R0 ( T ) also allows us to map months of risk with different degrees of uncertainty ( e . g . , 97 . 5% , 50% , and 2 . 5% posterior probability that that R0 > 0 ) , ranging from the most to least conservative ( Fig D in S1 Text ) ., Temperature is an important driver of—and limitation on—vector transmission , so accurately describing the temperature range and optimum for transmission of DENV , CHIKV , and ZIKV is critical for predicting their geographic and seasonal patterns of spread 12 , 41 ., We directly estimated the temperature–transmission relationship using mechanistic transmission models for each mosquito species ( Fig 2 ) ., These models are built using empirical estimates of the ( unimodal ) effects of temperature on mosquito and pathogen traits that drive transmission , including survival , development , reproduction , and biting rates ( Fig 1 , Fig A in S1 Text ) ., Because these trait thermal responses are unimodal across the majority of ectotherm taxa and traits , and because the traits combine nonlinearly to drive transmission , the emergent relationship between temperature and transmission is difficult to infer directly from field data or from individual trait responses ., Here , we present a model of temperature-dependent DENV , CHIKV , and ZIKV transmission that advances on previous models because it is mechanistic , fitted from experimental trait data ( Fig 1 , Fig A in S1 Text ) , and validated against independent human case data at a broad geographic scale ( Fig 3 ) ., Mechanistic understanding is valuable for extrapolating beyond the current spatial and temporal range of transmission ( Fig 4 ) , as compared to environmental niche models , for example 5 , 42 , 43 ., Of the six previous mechanistic temperature-dependent models of DENV , CHIKV , or ZIKV transmission by Ae ., aegypti and Ae ., albopictus that we were able to reproduce , three had similar thermal optima 7 , 44 , 45 while the other three had dramatically higher optima ( 3–6°C ) 9 , 46 ( Fig E in S1 Text ) ., Two of the models were very similar to ours 44 , 45; of the remaining four models , two predicted much greater suitability for transmission at low temperatures 46 and all four predicted greater suitability at high temperatures 7 , 9 , 46 ( Fig E in S1 Text ) ., Only one of these previous models was ( like ours ) statistically validated against independent data not used to estimate model parameters , and its predictions were very similar to those of our model 44 ., Other mechanistic and environmental niche models could not be directly compared with ours 5 , 10 , 41–43 , either because fully reproducible equations , parameters , and/or code were not provided or because their predicted marginal effects of temperature were not displayed ., Visually , our maps are similar to maps based on a previous model of Ae ., aegypti and Ae ., albopictus persistence suitability indices 41 ., Recent environmental niche models of Zika distribution have shown similar but more constrained predicted distributions of environmental suitability , in part because these models include not just temperature suitability but also further environmental , socioeconomic , and demographic constraints 5 , 42 , 43 , 47 ., Even though the thermal response data are imperfect—for example , CHIKV and ZIKV thermal response data are missing—and the human case data are reported at a coarse spatial scale , the validation analyses suggest that R0 ( T ) is an important predictor of both the probability of transmission occurring and the magnitude of incidence for DENV , CHIKV , and ZIKV ., This has several key implications ., First , temperature-dependent transmission is pervasive enough to be detected at a coarse spatial scale ., Second , dynamics of the mosquito predict transmission for a suite of Ae ., aegypti-transmitted viruses , without additional virus-specific information ., Third , climate and socio-economic factors combine to shape variation in incidence across countries ., Finally , these simple predictors explain a substantial proportion of the variance in both the probability and intensity of transmission ., Predicting arbovirus transmission at a higher spatial resolution and precision will require more detailed information on factors like the exposure and susceptibility of human populations , environmental variation ( e . g . , oviposition habitat availability , seasonal and daily temperature variation ) , and socioeconomic factors ., However , as a first step our mechanistic model provides valuable insight because it makes broad predictions about suitable environmental conditions for transmission , it is mechanistic and grounded in experimental trait data , it is validated against independent human case data , and its predictions are applicable across three different viruses ., Using these thermal response models as a scaffold , additional drivers could be incorporated to obtain more precise and specific predictions about transmission dynamics , which could in turn be used for public health and vector control applications ., For this purpose , all code and data used in the models are available on Figshare 39 ., The socio-ecological conditions that enabled CHIKV , ZIKV , and DENV to become the three most important emerging vector-borne diseases in the Americas make the emergence of additional Aedes-transmitted viruses likely ( potentially including Mayaro , Rift Valley fever , yellow fever , Uganda S , or Ross River viruses ) ., Efforts to extrapolate and to map temperature suitability ( Fig 4 ) will be critical for improving management of both ongoing and future emerging epidemics ., Mechanistic models like the one presented here are useful for extrapolating the potential geographic range of transmission beyond the current envelope of environmental conditions in which transmission occurs ( e . g . , under climate change and for newly invading pathogens ) ., Accurately estimating temperature-driven transmission risk in both highly suitable and marginal regions is critical for predicting and responding to future outbreaks of these and other Aedes-transmitted viruses ., We constructed temperature-dependent models of transmission using a previously developed R0 framework ., We modeled transmission rate as the basic reproduction rate , R0—the number of secondary infections that would originate from a single infected individual introduced to a fully susceptible population ., In previous work on malaria , we adapted a commonly used expression for R0 for vector transmission to include the temperature-sensitive traits that drive mosquito population density 12:, R0 ( T ) = ( a ( T ) 2b ( T ) c ( T ) e−μ ( T ) /PDR ( T ) EFD ( T ) pEA ( T ) MDR ( T ) Nrμ ( T ) 3 ) 1/2, ( 1 ), Here , ( T ) indicates that the trait is a function of temperature , T; a is the per-mosquito biting rate , b is the proportion of infectious bites that infect susceptible humans , c is the proportion of bites on infected humans that infect previously uninfected mosquitoes ( i . e . , b*c = vector competence ) , μ is the adult mosquito mortality rate ( lifespan , lf = 1/μ ) , PDR is the parasite development rate ( i . e . , the inverse of the extrinsic incubation period , the time required between a mosquito biting an infected host and becoming infectious ) , EFD is the number of eggs produced per female mosquito per day , pEA is the mosquito egg-to-adult survival probability , MDR is the mosquito immature development rate ( i . e . , the inverse of the egg-to-adult development time ) , N is the density of humans , and r is the human recovery rate ., For each temperature-sensitive trait in each mosquito species , we fit either symmetric ( Quadratic , -c ( T–T0 ) ( T–Tm ) ) or asymmetric ( Brière , cT ( T–T0 ) ( Tm−T ) 1/2 ) unimodal thermal response models to the available empirical data 48 ., In both functions , T0 and Tm are respectively the minimum and maximum temperature for transmission , and c is a positive rate constant ., We consider a normalized version of the R0 equation such that it is rescaled to range from zero to one with the value of one occurring at the unimodal peak ., Although absolute values of R0 that are used to determine when transmission is stable depend on additional factors not captured in our model , the minimum and maximum temperatures for which R0 > 0 map exactly onto our normalized equations , allowing us to accurately calculate whether or not transmission should be possible at all ., Empirical estimates of absolute values of R0 are difficult to obtain in any case , but it is much easier to determine whether transmission is occurring and for how long ., While different model formulations for predicting R0 versus temperature can produce results with different magnitudes and potentially different overall shapes 49 , the temperatures for which R0 is above or below zero ( or one ) are mostly model independent ., For instance , two competing models differ only by whether or not the formula in Eq ( 1 ) is squared , but the square of a number ( e . g . , an absolute R0 value ) greater than one is always greater than one , and the square of a number less than one is always less than one ., Therefore , the threshold temperatures at which absolute R0 > 0 or absolute R0 > 1 will be exactly the same for either choice of formula ( Fig F in S1 Text ) ., Similarly , because different expressions for R0 , including the square of Eq ( 1 ) , map monotonically onto our function , they will produce identical estimates for the temperatures at which transmission declines to zero and peaks ( Fig F in S1 Text ) ., Consequently , our use of relative R0 adequately describes the nonlinear relationship between mosquito and virus traits and transmission ., We fit the trait thermal responses in Eq ( 1 ) based on an exhaustive search of published laboratory studies that fulfilled the criterion of measuring a trait at three or more constant temperatures , ideally capturing both the rise and the fall of each unimodal curve ( Tables S1-S2 ) ., Constant-temperature laboratory conditions are required to isolate the direct effect of temperature from confounding factors in the field and to provide a baseline for estimating the effects of temperature variation through rate summation 50 ., We attempted to obtain raw data from each study , but if they were not available we collected data by hand from tables or digitized data from figures using WebPlotDigitizer 51 ., We obtained raw data from Delatte 19 and Alto 21 for the Ae ., albopictus egg-to-adult survival probability ( pEA ) , mosquito development rate ( MDR ) , gonotrophic cycle duration ( GCD , which we assumed was equal to the inverse of the biting rate ) and total fecundity ( TFD ) ( Table D in S2 Text ) ., Data did not meet the inclusion criterion for CHIKV or ZIKV vector competence ( b , c ) or extrinsic incubation period ( EIP ) in either Ae ., albopictus or Ae ., aegypti ., Instead , we used DENV EIP and vector competence data , combined with sensitivity analyses ., Following Johnson et al . 52 , we fit a thermal response for each trait using Bayesian models ., We first fit Bayesian models for each trait thermal response using uninformative priors ( T0 ~ Uniform ( 0 , 24 ) , Tm ~ Uniform ( 25 , 45 ) , c ~ Gamma ( 1 , 10 ) for Brière and c ~ Gamma ( 1 , 1 ) for Quadratic fits ) chosen to restrict each parameter to its biologically realistic range ( i . e . , T0 < Tm and we assumed that temperatures below 0°C and above 45°C were lethal ) ., Any negative values for all thermal response functions were truncated at zero , and thermal responses for probabilities ( pEA , b , and c ) were also truncated at one ., We modeled the observed data as arising from a normal distribution with the mean predicted by the thermal response function calculated at the observed temperature , and the precision τ , ( τ = 1/σ ) , distributed as τ ~ Gamma ( 0 . 0001 , 00001 ) ., We fit the models using Markov Chain Monte Carlo ( MCMC ) sampling in JAGS , using the R 53 package rjags 54 ., For each thermal response , we ran five MCMC chains with a 5000-step burn-in and saved the subsequent 5000 steps ., We thinned the posterior samples by saving every fifth sample and used the samples to calculate R0 from 15–40°C , producing a posterior distribution of R0 versus temperature ., We summarized the relationship between temperature and each trait or overall R0 by calculating the mean and 95% highest posterior density interval ( HPD interval; a type of credible interval that includes the smallest continuous range containing 95% of the probability , as implemented in the coda package 55 ) for each curve across temperatures ., We fit a second set of models for each mosquito species that used informative priors to reduce uncertainty in R0 versus temperature and in the trait thermal responses ., In these models , we used Gamma-distributed priors for each parameter T0 , Tm , c , and τ fit from an additional ‘prior’ dataset of Aedes spp ., trait data that did not meet the inclusion criteria for the primary dataset ( Table C in S2 Text ) ., We found that these initial informative priors could have an overly strong influence on the posteriors , in some cases drawing the posterior distributions well away from the primary dataset , which was better controlled and met the inclusion criteria ., We accounted for our lower confidence in this data set by increasing the variance in the informative priors , by multiplying all hyperparameters ( i . e . , the parameters of the Gamma distributions of priors for T0 , Tm , and c ) by a constant k to produce a distribution with the same mean but 1/k times larger variance ., We chose the value of k based on our relative confidence in the prior versus main data ., Thus we chose k = 0 . 5 for b , c , and PDR and k = 0 . 01 for lf ., This is the main model presented in the text ( Fig 2 ) ., It is comparable to some but not all previous mechanistic models for Ae ., aegypti and Ae ., albopictus transmission ( Fig E in S1 Text ) ., Results of our main model , fit with informative priors , did not vary substantially from the model fit with uninformative priors ( Figs G-H in S1 Text ) ., Because organisms do not typically experience constant temperature environments in nature , we incorporated the effects of temperature variation on transmission by calculating a daily average R0 assuming a daily temperature range of 8°C , across the range of mean temperatures ., This range is consistent with daily temperature variation in tropical and subtropical environments but lower than in most temperate environments ., At each mean temperature , we used a Parton-Logan model to generate hourly temperatures and calculate each temperature-sensitive trait on an hourly basis 56 ., We assumed an irreversible high-temperature threshold above which mosquitoes die and transmission is impossible 57 , 58 ., We set this threshold based on hourly temperatures exceeding the critical thermal maximum ( Tm in Tables A-B in S1 Text ) for egg-to-adult survival or adult longevity by any amount for five hours or by 3°C for one hour ., We averaged each trait over 24 hours to obtain a daily average trait value , which we used to calculate relative R0 across a range of mean temperatures ., We used this model in the validation against human cases ( Fig 3 ) and the risk map ( Fig 4 ) ., To validate the model , we used data on human cases of DENV , CHIKV , and ZIKV at the country scale and mean temperature during the transmission window ., Using statistical models ( as described below ) , we estimated the effects of predicted R0 ( T ) on the probability of local transmission and the magnitude of incidence , controlling for population size and several socioeconomic factors ., We downloaded and manually entered Pan American Health Organization ( PAHO ) weekly case reports for DENV and CHIKV for all countries in the Americas ( North , Central , and South America and the Caribbean Islands ) from week 1 of 2014 to week 8 of 2015 for CHIKV and from week 52 of 2013 to week 47 of 2015 for DENV ( www . pa

Introduction, Results, Discussion, Materials and methods

Recent epidemics of Zika , dengue , and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae ., albopictus mosquitoes ., We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika , chikungunya , and dengue change with mean temperature , and we show that these predictions are well matched by human case data ., Across all three viruses , models and human case data both show that transmission occurs between 18–34°C with maximal transmission occurring in a range from 26–29°C ., Controlling for population size and two socioeconomic factors , temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence ., Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission , but transmission in temperate areas is limited to at most three months per year even if vectors are present ., Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones .

Understanding the drivers of recent Zika , dengue , and chikungunya epidemics is a major public health priority ., Temperature may play an important role because it affects virus transmission by mosquitoes , through its effects on mosquito development , survival , reproduction , and biting rates as well as the rate at which mosquitoes acquire and transmit viruses ., Here , we measure the impact of temperature on transmission by two of the most common mosquito vector species for these viruses , Aedes aegypti and Ae ., albopictus ., We integrate data from several laboratory experiments into a mathematical model of temperature-dependent transmission , and find that transmission peaks at 26–29°C and can occur between 18–34°C ., Statistically comparing model predictions with recent observed human cases of dengue , chikungunya , and Zika across the Americas suggests an important role for temperature , and supports model predictions ., Using the model , we predict that most of the tropics and subtropics are suitable for transmission in many or all months of the year , but that temperate areas like most of the United States are only suitable for transmission for a few months during the summer ( even if the mosquito vector is present ) .

invertebrates, dengue virus, medicine and health sciences, pathology and laboratory medicine, togaviruses, chikungunya infection, pathogens, tropical diseases, microbiology, animals, alphaviruses, viruses, chikungunya virus, rna viruses, forecasting, mathematics, statistics (mathematics), neglected tropical diseases, infectious disease control, insect vectors, research and analysis methods, infectious diseases, aedes aegypti, medical microbiology, mathematical and statistical techniques, microbial pathogens, disease vectors, insects, arthropoda, mosquitoes, flaviviruses, viral pathogens, biology and life sciences, species interactions, viral diseases, physical sciences, statistical methods, organisms, zika virus

journal.pbio.1000456

Quantitative Analysis of the Drosophila Segmentation Regulatory Network Using Pattern Generating Potentials

A central challenge in understanding metazoan genome sequences is to identify and annotate genomic regions that regulate the complex spatial and temporal patterns of gene transcription ., Analysis of the regulatory regions for many individual genes has typically identified discrete enhancers or “cis-regulatory modules” ( CRMs ) that are approximately 1 Kbp long and located at distances ranging from immediately adjacent to the start of transcription to 100 Kbp away ., These CRMs are composed of transcription factor binding sites that integrate information about the concentration of relevant factors to determine the quantitative contribution of each CRM to the expression of its target gene 1 ., A variety of experimental approaches has been utilized to identify and characterize CRMs in single gene or genome-wide studies ., For example , approximately 50 CRMs involved in the anterior-posterior ( A/P ) segmentation of the blastoderm stage Drosophila embryo 2 have been identified by reporter gene assays ., A combination of genetic studies , CRM mutagenesis , and DNA binding assays has identified individual transcription factors ( TFs ) that influence the activity of these modules ., Genome-wide identification of TF binding loci has been carried out using chromatin immunoprecipitation ( ChIP ) in a variety of systems , including yeast and cultured cells 3 , 4 ., ChIP of TFs that act to regulate dorsal-ventral or anterior-posterior patterning in Drosophila embryos identifies a set of bound genomic regions that is highly enriched in functional targets but also includes many regions whose contribution to patterned gene expression is currently unclear 5–8 ., Furthermore , while ChIP can identify targets in specific stages or cell types , a clear technical challenge for ChIP-based methods is how to systematically characterize the genome-wide occupancy of the large number of TFs in metazoans across the vast number of distinct expression states that occur during developmental and physiological processes ., Computational analysis provides a complementary means to discover functional TF–CRM interactions in the genome ., Past attempts to identify CRMs often searched for clusters of putative binding sites for combinations of TFs that act in common biological processes 9 and have been particularly successful in the identification of Drosophila segmentation modules 10 ., The statistical power of these approaches is increased by filtering for evolutionary conservation of either individual sites or regions with clusters of sites 11–13 ., In parallel , new methods to systematically determine TF-DNA binding specificities 14 , 15 have the potential to generate a relatively large number of binding specificities ( “motifs” ) in a short time ., Spurred by these advances and the increasing availability of new genomic sequences , computational approaches could , in principle , be applied more globally to determine the transcriptional regulatory function of genomic sequences ., However , several problems complicate the global computational annotation of CRMs and TF–CRM interactions ., First , there is the problem of overlapping specificities; many TFs , particularly those in common structural families such as homeodomains , have highly similar DNA binding specificities 16 , making it difficult to assign conserved binding sites to an individual TF ., Second , there is the problem of selecting the optimal combinations of TFs that should be tested together for clusters of sites; this becomes increasingly difficult as more expression states are considered ., Third , there is the problem of TF pleiotropy; for example , a subset of TFs expressed during segmentation of the Drosophila blastoderm act again during cell fate specification in the nervous system ., Genomic segments with overrepresentation of binding sites for these TFs might act during either developmental stage ., A related problem is the identification of CRMs for genes with multiple expression domains; cluster-based analysis does not automatically attribute a specific expression domain to each CRM ., Finally , there is the challenge of evaluating the relevance of individual TF–CRM interactions; while combining binding site scores for multiple TFs increases the sensitivity of CRM detection , the contribution of any individual TF to CRM function is typically smaller and more difficult to associate with a significance value ., We describe a new approach for CRM identification and annotation that begins to address these issues ., It employs a new method to estimate the potential of any genomic segment to drive a spatial expression pattern matching that of its nearby gene ., This “pattern generating potential” is computed by combining information from experimentally determined TF binding motifs , TF expression patterns , and a comprehensive database of in situ gene expression images of the Drosophila embryo ., For this approach , we developed an efficiently computable , regression-based model of expression patterns as a function of evolutionarily conserved binding sites , with parameters learned from a collection of experimentally characterized CRMs ., By incorporating TF expression patterns into the model , the contribution of potential binding sites for a factor are only considered in the subset of cells that express that factor ., Genomic regions are annotated as potential CRMs based on functional combinations of TF binding sites , while rejecting clusters of overrepresented binding sites that are inconsistent with the relevant gene expression pattern ., Whether an individual CRM contributes to all or part of the expression pattern is an automatic result of the method ., The contribution of any individual TF to this pattern can be quantitatively evaluated by examining the effect of disrupting the TFs expression pattern on the predicted activity of the CRM ., We use this method to annotate genomic sequences with the potential to regulate the initial stages of segmentation in the Drosophila embryo ., We exploit this approach to produce an associated transcriptional regulatory network model in which each TF–CRM interaction is associated with a confidence value ., We demonstrate that this approach provides additional insights into how multiple CRMs contribute to expression patterns and how individual TFs can directly or indirectly regulate the expression of multiple target genes ., This study represents a generalizable approach to produce predictive models of genome function and regulatory networks ., The availability of genome sequences for multiple Drosophila species provides an opportunity to optimize quantitative modeling of functional TF occupancy along the genome ., The basic assumption of this approach is that CRMs with conserved activity across these species will maintain some binding activity for each requisite TF while binding sites in non-functional regions will be less conserved ., We used genome-wide profiles of binding motif scores for 10 TFs ( BCD , CAD , HB , KNI , KR , GT , HKB , TLL , FKH , and CIC ) involved in the initial stages of anterior-posterior patterning or segmentation in the embryo ., These profiles were generated using the Hidden Markov Model–based Stubb program 17 that captures both weak and strong motif matches in a probabilistic framework ., We combined the motif profiles from D . melanogaster and 10 other Drosophila genomes 13 , by averaging scores from orthologous ∼500 bp regions , to create a multi-species motif profile that incorporates evolutionary conservation ., Because species more closely related to D . melanogaster are better represented in the currently sequenced set of genomes , this phylogenetic comparison is weighted more heavily towards D . melanogaster than more distant species ., In an alternative approach designed to reflect the evolutionary distances among the sequenced species , we modeled the motif score of a region as a random variable evolving through Brownian Motion dynamics along the branches of the evolutionary tree 18 , and computed the expected tree-wide average of this variable given its observed values in the extant species ( Methods ) ., This computation is performed using a new “upward-downward” algorithm that scales linearly with the number of species ., These single and multi-species motif profiles are made available through the “Genome Surveyor” interface 14 at http://veda . cs . uiuc . edu/lmcrm/ ., We used published ChIP-on-chip data for eight TFs ( BCD , CAD , GT , HB , KNI , KR , HKB , and TLL ) 6 , 19 to compare the ability of different motif profiles to distinguish the top 100 bound regions from a random set of non-coding regions ( Methods ) ., As Table 1 reveals , single species motif scores show significant discrimination between bound and random sequences ( p value <0 . 01 ) for each TF , with especially strong discrimination in the cases of BCD and HKB ( p value\u200a=\u200a2 . 0E-25 and 5 . 7E-23 , respectively ) ., We find a dramatic improvement in this discriminative ability when using multi-species motif profiles ( e . g . , the p value improves from 1 . 5E-5 to 1 . 9E-27 for CAD , from 1 . 8E-3 to 7 . 0E-15 for HB , and from 2 . 0E-4 to 3 . 1E-20 for TLL ) ., The two schemes for combining multi-species profiles produce comparable results by this measure , which are significantly better than results produced by corresponding two-species ( D . mel . and D . pse . ) motif profiles ., Both multi-species methods were also tested in CRM predictions below ., We next used these binding site profiles to predict the potential transcriptional regulatory activity of any given genomic region ., We reasoned that determining the potential of a region to generate patterned gene expression could help distinguish functional TF binding sites from regions that happened to have motif matches but were evolutionarily conserved for other reasons ., A previous study 20 described a thermodynamic model that can recapitulate the expression activity of characterized CRMs ., We developed a simpler , logistic regression model that could be readily adapted to multi-species analysis and genome-wide scanning and trained this model on a set of Drosophila CRMs ., In any regression model , the parameters of the model are adjusted such that the output of the model ( e . g . , the predicted CRM activity at each A/P position in the embryo for the entire set of training CRMs ) shows the greatest agreement with the training data ( the experimentally determined expression profiles ) ., Logistic regression models are a generalized version of linear regression where a sigmoidal ( “logistic” ) function is used to constrain the minimum and maximum output ( e . g . , CRM activity ) to 0 and 1 , respectively ( see Figure S11 ) ., The logistic regression model used here combines weighted contributions from all TFs ( using their expression and binding sites ) ., The contribution of each TF is calculated heuristically as the product of its concentration and its binding affinity to the CRM ( Figure 1A , middle panel and bottom right panel ) ., The weight assigned to each TF indicates its regulating role—positive weights are used for activators and negative weights for repressors ., We used this model to predict the anterior-posterior ( A/P ) expression profiles of 46 experimentally characterized CRMs in the segmentation network 2 , using multi-species motif profiles and expression patterns 2 , 21 of the 10 TFs mentioned above ., A binary representation of a CRMs activity profile along the A/P axis was modeled as a function of, ( i ) each TFs motif score in the CRMs sequence and, ( ii ) each TFs concentration value at that position ( “bin” ) along the axis , the bins being labeled from 1 ( most anterior ) to 100 ( most posterior ) ( Figure 1A ) ., The parameters of the model include a coefficient representing each TFs regulatory effect and a baseline expression value for each CRM ( which is constant across all bins ) ., These parameters were trained on the known expression profiles from the 46 CRMs ., Visual inspection of the results ( Figure 1B ) indicates that the expression patterns predicted by the model are in good or fair agreement with the observed expression patterns for most of the 46 CRMs ., By this qualitative assessment ( which is consistent with the more quantitative assessment using “PGP scores” defined below ) , our method compares well with the results of the thermodynamic model , although a direct quantitative comparison is not feasible ( Table S1 ) ., We tested for the possibility of the model “over-fitting” the data by comparing cross-validation results from the real data and randomized data and found a clear separation ( p value\u200a=\u200a1 . 2e-34 ) between the two ( Figure S1 ) , ruling out any significant over-fitting ., The above model provides “systems level” insights into the A/P network ., We observed that coefficients for BCD , CAD , and FKH were fit to positive values while KNI , KR , GT , HB , TLL , HKB , and CIC were fit to negative values ( Table S2A ) , broadly consistent with the activator/repressor roles known for these factors ., ( Although dual roles for some of these factors have been noted in the literature 22 , our model learns a single dominant role consistent with the dataset . ), We explored the effect of producing more complex relationships between TF expression and activity ( by adding “second order covariates , ” the squares of the term corresponding to each TF; see Methods ) and found that a second order term for BCD improved the model ( p value <E-16 ) by creating an anterior “dip” in the contribution of BCD to CRM activity ( Figure S2 ) ., This broad anterior dip is not present in the BCD concentration gradient we used as input to the model ., It may reflect previous observations that BCD levels appear higher than necessary for target gene activation by a simple BCD gradient model 23 , 24 ., Our model may not completely account for some aspect of down regulation of BCD target genes by the terminal patterning system , either by converting BCD into a repressor 25 or through regulation of other repressors 23 , 24 ., At the same time , the observation that second order covariates for the nine other TFs do not significantly improve the models predictions suggests that the linear approximation provides a reasonable description of the CRMs activities in terms of TF inputs ., We assessed the effect of using single or multi-species motif profiles in our CRM activity pattern prediction model and found that the multi-species Brownian Motion averaging-based profiles provided the best fit ( Table 2 ) ., Improved performance with multi-species scores is broadly consistent with previous studies demonstrating that A/P CRMs with conserved activity patterns and similar binding site composition can be identified in related species 11 , 26 ., Interestingly , three individual modules , eve_stripe4_6 , gt_−1 , and kni_+1 , have better predictions from the model trained with single species motif profiles ( Figure S3 ) ., In at least one case , this discordance between the single species and multi-species predictions is mirrored in evolutionary changes within the CRM: there is experimental evidence that the D . pseudoobscura ortholog of the gt_−1 module does not drive the posterior domain of gt expression that is observed for the D . melanogaster module ( S . Sinha et al . , manuscript in preparation ) ., Thus , while the overall improvement in CRM activity predictions using multi-species profiles suggests that the majority of TF–CRM interactions in the A/P patterning network examined here are conserved , there are also examples of CRMs that have functionally diverged ., One of the strengths of the A/P network as a model system is that many relevant TFs have been identified in previous molecular and genetic studies ., A potential unidentified factor was suggested by the characterization of a sequence motif “TorRE” ( Torso response element ) that is overrepresented in CRMs active at the anterior or posterior termini 27 ., This motif and a hypothetical concentration profile ( high at the two terminal regions ) was previously used in a thermodynamic model of CRM function 20 ., We considered the hypothesis that the TF Capicua ( CIC ) acts through the TorRE motif , suggested previously by 28 based on genetic data , and further examined in a later study 23 ., CIC is a transcriptional repressor that is post-transcriptionally regulated in the embryo via degradation at the anterior and posterior termini in response to Torso signaling 28 ., We determined the DNA binding specificity of CIC ( Note 1 in Text S1 ) and found it to be similar to the TorRE ( p value\u200a=\u200a0 . 0012 , Figure 2A ) , indicating that CIC can bind to most of the sites that contributed to identifying the TorRE ., We found that the motif scores of TorRE and CIC are highly correlated across the 46 modules ( correlation coefficient 0 . 62; p value\u200a=\u200a5 . 4E-6; Figure 2E ) and that CRMs with high motif scores ( i . e . , many potential binding sites ) for either factor are mostly found at the terminal regions ( Figure 2F ) ., When the regression model is trained with either the TorRE motif or the CIC motif ( and their respective concentration profiles , Figure 2B ) , the quality of fit is comparable ( Figure 2C , 2D ) ., Consistent with the complementary expression patterns for TorRE and CIC , CIC has a negative rather than positive coefficient , confirming that it generally acts as a transcriptional repressor ., Adding TorRE to a model that already includes CIC leads to no significant improvement ( unpublished data ) ., These results indicate that CIC is the TorRE binding factor and that this factor acts by repressing target CRMs in the center of the embryo rather than activating targets at the termini ., Individual direct and indirect targets of CIC are discussed below ., The ability to predict the spatial expression pattern driven by a module ( CRM activity ) suggests a method for discovery of novel CRMs: to scan the flanking genomic sequences of a gene for segments whose predicted activity pattern agrees with the genes endogenous pattern ., For this purpose , we developed a newly defined measure of similarity between expression profiles and its statistical significance; this measure is named the “Pattern Generating Potential” ( PGP ) ( Figure 3A , Methods , Note 2 in Text S1 ) ., The scoring method was designed to: ( 1 ) be sensitive to both the shape and magnitude of the predicted expression profile , ( 2 ) avoid biases towards or against overly broad or overly narrow domains of expression , and ( 3 ) automatically allow sub-domains of a genes expression pattern to be directed by the CRM ( Figure 3B ) ., To compute this score , we first calculate the average predicted CRM activity in domains of gene expression ( the “reward” term ) and domains of non-expression ( the “penalty” term ) and subtract the penalty from the reward , followed by a linear transformation generating PGP values between −1 and 1 ( Figure 3C ) ., An important feature of this score is that it can identify CRMs that contribute to only part of a genes expression pattern ( see below ) ., When applied to the 46 CRMs used in the regression model above , the PGP score was highly correlated with our visual assessments of prediction success ( Figure S4 ) ., We tested this measure on the 22 genes ( henceforth called “A/P-22” ) regulated by the 46 CRMs described above ., Expression data were obtained from whole embryo in situ hybridization images from BDGP ( http://www . fruitfly . org/cgi-bin/ex/insitu . pl ) and FlyExpress 29 ( data available at http://veda . cs . uiuc . edu/lmcrm/ ) ., We scanned the control region of each gene ( Note 3 in Text S1 ) with a sliding window of size 1 Kbp , predicted the A/P expression profile based on the motif scores in that window , and calculated the PGP ( Figure 3A ) ., An empirical p value representing the statistical significance of a putative module was estimated based on how frequently we observed a window with equivalent or greater PGP score in a genome-wide scan ., Of the 62 modules predicted at a p value threshold of 0 . 015 , 34 had significant overlap ( >50% ) with known modules , indicating 55% specificity at 74% sensitivity ( Figure S5 ) ., Seventeen of the remaining 28 predicted modules overlapped the bound regions of at least one TF ( ChIP data at 1% FDR from 6 , 19 ) , indicating that the majority of predicted CRMs are functional and/or biochemical targets of A/P factors ., Overall , we did not observe any systematic biases in the score , and modules with broad ( “gap” ) as well as sharp ( “pair-rule” stripes ) patterns were correctly predicted ., The genomic location and predicted expression activity for each of these CRMs are available at http://veda . cs . uiuc . edu/lmcrm ., The 12 known modules not recovered included 10 that had either “bad” or “fair” predictions by the regression model ( Figure 1 ) , pointing out that CRMs whose expression is poorly predicted by the model are difficult to detect in the CRM search ., For another CRM ( gt_−6 ) , the experimentally characterized activity pattern does not agree with the endogenous gene expression pattern we used ( Note 4 in Text S1 ) ., In this case , the CRM activity pattern we used 2 may reflect either an experimental artifact or expression at a different embryonic stage ., In only one case ( h_stripe1 ) , the PGP approach was unable to recover a module despite the training stage prediction being of high quality ., Thus , most of the false negatives are likely to be due to the current limitations in the ability to predict CRM expression activity ., The results of this search were compared to two previously described CRM prediction programs , Cluster Buster 9 and Stubb 17 , that search for clusters of binding sites for multiple TFs ., To ensure that the performance of the PGP method was not influenced by including the same CRMs to train parameters that were then part of the predicted set , we used a cross-validation strategy where all known modules of a single gene were left out of the training phase before predicting CRMs within the control region of that gene ., The PGP method performed better than both single and multi-species versions of Stubb and Cluster Buster ( Figure 3D ) ., Unlike the other CRM prediction approaches , the PGP method predicts which aspect of the genes pattern is regulated by an individual CRM , allowing the range of regulatory architectures for the A/P-22 genes to be examined: solitary CRMs , multiple CRMs contributing to distinct aspects of the pattern , or multiple “sibling” CRMs with a similar predicted activity ., ( We use the term “sibling” to indicate CRMs that may have effectively redundant activity within the context of our model , but possibly distinct contributions to the magnitude , temporal regulation , or robustness of patterned gene expression in vivo . ), In our predictions , there was only one gene ( btd ) with a single predicted CRM; this prediction overlaps a known CRM ( btd_head ) driving the genes expression ., In all other cases , two or more modules were predicted in a single genes control region ., These included cases where distinct aspects of a genes blastoderm expression pattern are captured by distinct predicted CRMs ( e . g . , five CRMs near the gene eve , including four known CRMs ) , a well-established phenomenon reported for primary pair-rule genes ., We also found many cases of “sibling” CRMs , where multiple modules near a maternal/gap gene were predicted to drive highly similar expression patterns ( Figure 4A ) ., We considered whether possible false positive predictions could account for this observation; if the occurrence of a second , functionally similar CRM prediction in a genes control region is an artifact of false positives , they should also be found near other randomly selected genes ., However , we find that enrichment of functionally similar CRMs near the target gene is highly significant ( p value\u200a=\u200a4E-4 , Table S3 ) ., Given the previous identification of “shadow” CRMs in the dorsal-ventral patterning network 30 , the utilization of functionally similar CRMs may be a more common theme of cis-regulatory organization than currently recognized ., We applied the PGP method to a larger collection of 144 genes with patterned expression along the anterior-posterior axis 12 ., ( A/P-22 genes were not included . ), We automatically extracted the A/P expression profiles of these genes from the FlyExpress database 29 , transformed the intensity values into binary expression domains ( Methods ) , and identified flanking sequences with PGP at the same empirical p value threshold used above ., ( Predicted sequences that did not have above-average binding site presence for one of the activators in the model or for the broadly expressed activator Stat92E were discarded . ), We identified 123 putative CRMs from 68 genes , henceforth called the “FlyExpress” gene set ( data at http://veda . cs . uiuc . edu/lmcrm; the 60 most significant predictions are shown in Figure 4B ) ., The distribution of PGP scores and their empirical p values is similar to that of A/P-22 and very different from that of bona fide non-modules that were identified as false positives in a cluster-based method to identify CRMs ( Figure 3E and Figure S6 ) ., 44% of the predicted CRMs overlapped a ChIP-chip peak ( at 1% FDR; 65% when considering peaks at 25% FDR; Table S4 ) ., The predictions included CRMs for genes with a single expression domain and genes with multiple expression domains ( e . g . , slp1 and ara , respectively; Figure 4B ) ., Among CRMs corresponding to genes with multi-domain patterns , 53% capture only one of the domains of the endogenous pattern ( e . g . , drm; Figure 4B ) while 47% capture more than one domain ( e . g . , emc ) ., Sixteen of the above CRM predictions overlapped previously verified modules , of which 12 have blastoderm stage expression that agrees with the predicted expression profile from our model ( Table S9 ) ., These provide an independent experimental validation for our CRM and activity prediction pipeline ., In addition , we tested seven CRM predictions using new reporter transgenes ., These lines were created as part of an ongoing project to systematically examine regulatory regions surrounding a subset of Drosophila genes with patterned expression in the nervous system 31 ., Only predictions in intergenic or intronic regions of at least 10 Kbp were chosen for analysis ., Selections included regions flanking genes manually annotated as “strong” or “weak” A/P patterned expression ., 4 of 7 tested regions exhibited reporter gene expression patterns resembling the predicted pattern ( Figure 5 ) ., For one of these , Ubx , the anterior boundary of reporter expression is in the correct region of the embryo , but initiation of the pattern is delayed relative to the endogenous gene and more strongly resembles the endogenous gene expression pattern at this slightly later stage; it has more posterior expression and a striped pattern likely reflecting the activity of later acting repressors not included in our model ., Two of the remaining tested reporters ( pdm2 and emc ) exhibit expression in the developing CNS , where many of the same TFs that regulate A/P patterning are re-expressed ( unpublished data ) ., It is possible that the same combinations of TFs that predict an A/P pattern in our model can act to direct patterned expression in the developing CNS ., We note that the specificity we observed here ( 57% ) is about the same as that recorded in our cross-validation tests on the A/P gene set ., We also examined the genome-wide locations of all segments with high PGP scores ( and not just those located near the genes whose expression was modeled ) ., We found these segments to be preferentially located near A/P patterned genes ., However , we also observed a large number of segments ( with high PGP ) that are apparently not associated with patterned genes ( Table S12 ) ., This suggests that the genome harbors a relatively large number of segments with PGP , and only a small subset of these actually realize this potential ., This finding further supports our rationale of searching only in the neighborhood of a gene for segments with the potential to drive the genes expression pattern ., Unlike binding site clustering methods , the PGP method uses both the binding specificities of TFs and their expression pattern to predict the activity pattern of a CRM ., Using the PGP method , it is possible to computationally assess the contribution of each TF to the CRM by asking if altering the expression of the TF affects the quality of the prediction ., We used this strategy to infer direct regulatory interactions between TFs and CRMs , depicted as edges in the transcriptional regulatory network ., To visualize the effect of removing an individual TF from the model , we simulated a “knock down” of the TF ( by setting its expression to 0 ) and compared the predicted CRM expression in this “in silico mutant” background and in “wild type” ( Figure 6A , knock down patterns shown in green ) ., Unlike traditional in vivo genetic assays , where observed changes may be the indirect effect of mis-regulation of other genes , this approach examines the direct contribution of a TF to a specific CRM ., In order to assign a statistical significance to this contribution , we developed an alternative procedure ( Methods and Figure 6A ) : CRM activity predictions were generated using random permutations of the TFs concentration profile and compared to the “true” activity , thus creating a null distribution of similarity scores ( depicted in blue ) ., The score obtained with the actual profile ( black dot ) was compared to this distribution , generating an empirical p value ., When there are few binding sites in the CRM , the TF pattern has little influence on CRM predictions and the null distribution of scores is very narrow ( unpublished data ) ., When there are more binding sites in the CRM , there is a broader distribution of similarity scores from the random profiles , and the position of the actual profile within this distribution reflects the combined contribution of the binding sites and the normal TF expression pattern on CRM activity ., Using this procedure to infer a p value for every TF–CRM combination , we constructed a transcriptional regulatory network ( Figure 6B , Figure 7 ) involving the 35 CRMs where the models quality of fit had been “good” or “fair” ( Figure 1B ) ., A total of 102 regulatory edges were predicted ( at p value <0 . 05 ) between the 10 TFs and 35 CRMs , revealing a very dense network ., ( See http://veda . cs . uiuc . edu/lmcrm . ) 82 edges were supported by ChIP-based evidence of occupancy at the strongest level ( 1% FDR ) ., 63 of the 102 edges have been previously reported in the literature , mostly by examination of CRM activity in mutant embryos lacking the TF ( Table S5 ) ., In some cases , confidence in experimentally determined TF–CRM edges is further increased by in vitro confirmation of TF binding sites by DNaseI footprinting ., For 12 of the 35 CRMs analyzed above , the FlyReg database 32 catalogs at least one such interaction with either BCD , CAD , KR , KNI , HB , GT , or TLL ., These validated TF–CRM edges were significantly enriched in our network ( Hypergeometric test , p value\u200a=\u200a0 . 0026 ) ( Figure S7 ) ., This network model can address specific questions about the role of individual TFs in A/P patterning ., For example , the concentration of the repressor CIC is a direct output of the terminal patterning system 33 , but it is not known whether this mechanism acts solely by determining the terminal expression patterns of TLL and HKB ., Terminal gene expression could be either entirely regulated by these factors or the terminal system might also directly regulate additional targets via CIC ., The regulatory network model predicts that CIC directly targets at least six CRMs corresponding to five distinct genes—tll and hkb as well as cnc , fkh , and kni ( Figure 2G ) .,

Introduction, Results, Discussion, Methods

Cis-regulatory modules that drive precise spatial-temporal patterns of gene expression are central to the process of metazoan development ., We describe a new computational strategy to annotate genomic sequences based on their “pattern generating potential” and to produce quantitative descriptions of transcriptional regulatory networks at the level of individual protein-module interactions ., We use this approach to convert the qualitative understanding of interactions that regulate Drosophila segmentation into a network model in which a confidence value is associated with each transcription factor-module interaction ., Sequence information from multiple Drosophila species is integrated with transcription factor binding specificities to determine conserved binding site frequencies across the genome ., These binding site profiles are combined with transcription factor expression information to create a model to predict module activity patterns ., This model is used to scan genomic sequences for the potential to generate all or part of the expression pattern of a nearby gene , obtained from available gene expression databases ., Interactions between individual transcription factors and modules are inferred by a statistical method to quantify a factors contribution to the modules pattern generating potential ., We use these pattern generating potentials to systematically describe the location and function of known and novel cis-regulatory modules in the segmentation network , identifying many examples of modules predicted to have overlapping expression activities ., Surprisingly , conserved transcription factor binding site frequencies were as effective as experimental measurements of occupancy in predicting module expression patterns or factor-module interactions ., Thus , unlike previous module prediction methods , this method predicts not only the location of modules but also their spatial activity pattern and the factors that directly determine this pattern ., As databases of transcription factor specificities and in vivo gene expression patterns grow , analysis of pattern generating potentials provides a general method to decode transcriptional regulatory sequences and networks .

The developmental program specifying segmentation along the anterior-posterior axis of the Drosophila embryo is one of the best studied examples of transcriptional regulatory networks ., Previous work has identified the location and function of dozens of DNA segments called cis-regulatory “modules” that regulate several genes in precise spatial patterns in the early embryo ., In many cases , transcription factors that interact with such modules have also been identified ., We present a novel computational framework that turns a qualitative and fragmented understanding of modules and factor-module interactions into a quantitative , systems-level view ., The formalism utilizes experimentally characterized binding specificities of transcription factors and gene expression patterns to describe how multiple transcription factors ( working as activators or repressors ) act together in a module to determine its regulatory activity ., This formalism can explain the expression patterns of known modules , infer factor-module interactions and quantify the potential of an arbitrary DNA segment to drive a genes expression ., We have also employed databases of gene expression patterns to find novel modules of the regulatory network ., As databases of binding motifs and gene expression patterns grow , this new approach provides a general method to decode transcriptional regulatory sequences and networks .

developmental biology, computational biology/transcriptional regulation

A new computational method uses gene expression databases and transcription factor binding specificities to describe regulatory elements in the Drosophila A/P patterning network in unprecedented detail.

journal.pcbi.1007317

Executable pathway analysis using ensemble discrete-state modeling for large-scale data

Gene set and pathway analysis have become one of the first choices for gaining mechanistic insights from high-throughput sequencing and gene/protein profiling techniques 1 ., Typically , gene set analysis uses a set of pathway genes to estimate its modulation and discounts pathway topology ., This approach ignores synergy among genes , resulting in enrichment of convergent pathways when downstream genes are modulated ., Though none of the existing methods explicitly investigate synergy among genes , current topology-based methods use graph theoretical metrics to weigh pathway nodes based on connectivity before estimating pathway modulation 1–3 ., However , it is critical to go beyond this simple characterization in order to identify key regulators from large-scale datasets for systematic prioritization of follow-up experiments ., Discrete state network modeling facilitates prioritization of experiments by using simple logic rules such as ‘AND’ or ‘OR’ to explicitly define signal integration , enabling investigation of cross-talk and downstream events as shown in our previous studies ., 4–6 ., Discrete state network modeling has been used to study high throughput gene and protein profiling data collected across multiple time-points by utilizing two different underlying models of variation 7 , 8 in addition to conventional Boolean modeling ., Fuzzy models explain variation in the gene expression levels using multiple states , unlike Boolean models that allow only binary ( on/off ) states ., Recently , fuzzy models have been used to study literature-derived prior knowledge networks using a genetic programming algorithm to derive logic rules from time course data by Liu et al . 9 ., Probabilistic Boolean Network models assume that variability arises from ambiguity in logic-rules employed rather than in amount of activation 10 , making the counterintuitive assumption that cells randomly employ one of multiple different wirings ., Many biological insights have resulted from fuzzy network 11 , 12 and Probabilistic Boolean Network 13 , 14 models , but there remains great potential for improvement in describing variation and improving applicability to cross-sectional datasets ., Unlike time course data , cross-sectional data is collected from multiple samples ( and possibly conditions ) at a single time point providing minimal information about interactions between genes ., Indeed , cross-sectional sampling is more feasible in translational studies and algorithms that derive discrete state network models from this data type would have greater applicability in translational research ., Here , we describe BONITA- Boolean Omics Network Invariant-Time Analysis , to capture cellular heterogeneity , a critical source of variability in transcriptomic data ., A portion of variance in gene expression stems from heterogeneity in the activation state of cells in addition to variation in expression levels within each cell ., This is demonstrated by gene expression in multiple stem cell types 15 16 and stimulated bone marrow-derived dendritic cells 17 ., BONITA is designed specifically to leverage this bimodality in cell-specific gene expression to perform continuous-valued simulations of molecular networks under assumptions of switch-like behavior in each cell ., Hence , BONITA network propagation ( NP ) assumes that the activity of each biomolecule is directly dependent upon the proportion of cells in which that molecule is active or , equivalently , the probability a node is active in an arbitrary cell ., The propagation of signals across multiple cells facilitates the application of NP to the cross-sectional data ., Since this NP approach should recapitulate steady states in cross-sectional data , BONITA rule determination ( RD ) finds rules that minimally change activities after NP ., These logic rules representing synergy between genes from cross-sectional data are utilized in BONITA pathway analysis ( PA ) ., Thus , by capturing integration of signals coming from multiple genes , BONITA uncovers differentially regulated pathways ., BONITA is currently implemented and tested for application to transcriptomics data , but work is under way to apply it to other types of data including proteomics , metabolomics , and phosphoproteomics ., BONITA is rigorously tested using simulated data and is applied to publicly available experimental datasets ., In addition , a comparison of BONITA-RD to an existing algorithm for time-course data 9 shows comparable performance for cross sectional data , improving applicability to translational studies ., Moreover , comparison of BONITA-PA with state-of-the-art pathway analysis methods CAMERA 18 and CLIPPER 3 shows exceptional Receiver Operating Characteristic ( ROC ) and higher specificity in detecting signaling modulations in validated experimental studies ., Finally , when applied to disease specific data from patients vs healthy humans , BONITA impact scores identify known drug targets as key regulators ., This suggests that BONITA can be used for drug discovery from large-scale high-throughput datasets ., BONITA network propagation ( NP ) runs on prior knowledge networks obtained from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) using the KEGG API ., Activating/inhibiting relationships are inherited from KEGG edge attributes 19 ., Edges in KEGG pathways contain edge type annotations; these are exploited to determine activating or inhibitory edges ., Hence , all the Boolean functions inferred by BONITA are sign-compatible functions , i . e . , they satisfy positive or negative unateness based on the interaction annotation , as described in Zhou et al 20 ., We demonstrate in S1 Text and S1 Fig that BONITA-NP infers these sign-compatible functions in an unbiased manner ., BONITA-NP assumes that the mRNA-producing cells are proportional to counts obtained from mRNA-sequencing ., To obtain the proportion of cells expressing mRNAs , the RNA-seq data is transformed to 0 , 1 domain using division by the maximum element ., This transformed data is used as a starting point to compute a series of Boolean Network simulations using synchronous or asynchronous update algorithms as described in 21 ., The ensemble averages of 1000 such repeated runs are used to define activities which are compared with the transformed data to determine fitness value in BONITA-RD below ., Comparison of methods for data transformation to 0 , 1 demonstrated that division by maximum was the best method for transforming data and that BONITA-RD , as expected , has better fits than purely Boolean simulation ( S2 Text , S2 Fig ) ., In this report , all BONITA-NP simulations were carried out for 100 steps using the synchronous update algorithm ., The maximum number of steps necessary to reach the steady state or terminal cycle of the Boolean network is the longest path between any two nodes in the network ., The longest shortest path between all pairs of nodes across KEGG networks was 17 ( S3 Text , S3 Fig ) indicating that 100 simulation steps were adequate ., The results were reported as average over the last ten steps of the simulation ., BONITA-RD implements a combination of a genetic algorithm and a node-wise local search to infer logic-rules ., BONITA assumes cross-sectional samples represent steady states and minimizes change after simulation of a network as given by:, ∑ i = 1 d 1 n ∑ j = 1 n ( D i , j - O i , j ) 2 ( 1 ), In Eq 1 , d is the number of available samples , n is the number of nodes in the network , Di , j is the value of node j in sample i , and Oi , j is the value of node j in sample i calculated by BONITA-NP ., The overall design of the rule determination algorithm is graphically represented in Fig 1 ., The genetic algorithm generates new rule sets ( individuals ) either by selecting rules for randomly chosen nodes from their parent rule sets , or by mutating ( altering ) a particular rule and incoming nodes ., At later generations , crossover events tend to produce rule sets that have already been tried in earlier generations , leading to a greater probability of mutations ., The space of potential rules is extremely large and scales quickly with in-degree ., Hence , to reduce the space of potential rules to a region that can be sampled , a maximum of three upstream regulators are selected ., This is a compromise between decreasing resolution and increasing search time ., The three upstream regulators ( U ) are sampled for nodes with >3 upstream regulators in the genetic algorithm using a probability function P ( U ) = C U , N ∑ U C U , N where CU , N is the Spearman correlation of upstream regulators with the node ( N ) for which the rule is being determined ., For all simulations shown in this report , the genetic algorithm was run for 120 generations from a starting population and constant population size of 24 ., Thus , 24 new rule sets were generated and tested at each generation ., Decreasing errors ( Fig 2a ) with a plateau before 40 generations for networks with varying complexities indicated that 120 generations are appropriate for the genetic algorithm ., The genetic algorithm searches the product of the number of possible rules at each node in the network ., In order to transform this multiplicative problem into an additive one , a node-level local search strategy was implemented ., The local search only considers the error at the node under consideration as given by, ∑ i = 1 d ( D i , j - O i , j ) 2 ( 2 ) This exhaustive search only evaluates the possible rules at each node while holding constant all other rules as well as the incoming edges to that node as determined by the genetic algorithm ., The node-level local search was initiated with the minimal error rule set from the genetic algorithm and was found to be effective in inferring the rules as shown in the results ( Fig 2b ) ., During the local search , rules within a tolerance threshold of this minimal rule are kept as equivalent rules i . e . the equivalent rule set ( ERS ) ., This set was constructed to overcome the inability to distinguish between equivalent rules with cross-sectional data ., Thus , while local search improves accuracy , it is dependent on the global search performed by the genetic algorithm to resolve the complexity of the networks ( S2 Text , S2 Fig ) ., To test BONITA-RD , simulated data representing 5 samples was generated by BONITA-NP with a rule set and initial states determined by a uniform random distribution ., Rules determined by BONITA-RD were then compared with the rule-set used to generate the data ., BONITA-PA seeks to prioritize nodes that have a large influence over signal flow through the network by assigning node-level impact scores ., The impact score , Ig , captures the change induced in the network when the node is perturbed ., Ig is given by the difference in network state after knockout and knock-in of g:, I g = ∑ i = 1 d ∑ j = 1 n ( ( O i , j - Z i , j ) 2 ) ( 3 ), In Eq 3 , j ranging from 1 to n indicates nodes in the network , i ranging from 1 to d indicates samples , Oi , j and Zi , j are BONITA-NP outputs when g = 0 and g = 1 across all iterations , respectively ., The comparison of BONITA-PA’s node impact score with graph theoretical measures of node centrality such as degree centrality , eccentricity , shortest-path betweenness , eigenvector centrality and the hubscore and authority scores obtained from the hyperlink-induced topic search algorithm showed no correlation ( S4 Text , S4 Fig ) ., The pathway modulation is measured by taking into account impact score and fold difference in the expression across conditions of interest ., Specifically , Mp is calculated as follows:, M p = ∑ 1 n l o g ( I g ) * | l o g ( q g ) | * s t d ( g ) ( 4 ), In Eq 4 , qg is the fold difference of g and std ( g ) is the standard deviation of g across all samples ., To calculate the p-value , a distribution of nodes with different impact scores having a range of fold differences is generated ., Specifically , the distribution of Mp values is generated by weighting impact scores for a specific pathway’s topology with random fold differences that are re-sampled from the gene expression data ., Pathways with at least four genes in the transcriptomic data are considered ., To compare BONITA-PA with existing pathway analysis approaches , simulated datasets that resembled biological data were constructed ., The data was generated using a negative binomial distribution with gene-wise means and dispersions from existing RNA-seq data 22 ., To simulate the modulation of pathways , the expression levels of source nodes were multiplied by log2 ( -attenuation ) where attenuation values were 0 . 0 , 0 . 5 , 1 . 0 , 1 . 5 , and 2 . 0 as described in Ihnatova et al 2 ., This attenuated signal was propagated by BONITA-NP with random rules to simulate inhibition of the entire network mediated by source node inhibition ., To test the performance of BONITA- RD , a subset of networks were obtained by searching the KEGG database for Interferon Gamma ( IFN-γ ) ., These 12 networks were used as test networks since they provide an unbiased set of networks with varying complexity , ranging in size from 13 to 346 nodes ., Signal attenuation and propagation was performed 10 times each on the 6 test networks with nodes ( genes ) in the RNA-seq data , and analysis performed using CLIPPER , CAMERA , and BONITA ., CLIPPER determines modulated pathways based on mean and concentration ( the inverse of the covariance ) matrices ., However , for simulation studies , only p-values from CLIPPER comparison of means and not concentration matrices were considered since this improved performance of CLIPPER substantially ., BONITA was rigorously assessed using RNA-seq data ., First , BONITA was compared with state-of-the-art pathway analysis approaches using data from the public domain ., Second , BONITA’s specificity in detecting disease specific pathway from patient data was investigated ., Finally , BONITA’s ability to infer rules from a de novo directed network constructed was evaluated ., Comparison of BONITA pathway analysis with CLIPPER and CAMERA was performed using previously published RNA-sequencing data measuring IFN-γ signaling modulation in human choriocarcinoma cells 23 and a study representing translational design where peripheral blood mononuclear cells from infants with mild or severe respiratory syncitial virus were assessed by RNA-sequencing 22 ., Data was processed using voom 24 for CAMERA or CLIPPER ., A set of 37 immunologically relevant KEGG pathways identified in previous studies were utilized because RSV infection and IFN-γ stimulation are expected to modulate these pathways 34 ., Furthermore , a priori selection of biologically relevant pathways reduces the requirement for correction for multiple comparisons ., Data from all studies were processed in R . To test whether BONITA identifies disease specific pathways , microarray gene expression data from a set of 36 experiments comparing patients to healthy controls in 15 unique diseases was analyzed 2 , 25 , 26 ., Previously RMA normalized and log2 transformed microarray data was downloaded and was processed to keep probe ID with highest mean expression for each gene symbol ., The data was exponentiated with base 2 before running BONITA ., The data was retrieved from Gene Expression Omnibus ( GEO ) using R packages KEGGandMetacoreDzPathwaysGEO and KEGGdzPathwaysGEO 25 , 26 ., BONITA was applied to KEGG networks associated with each disease in the data set ., Finally , a de novo directed network was generated by application of miic 27 to RSV data ., Miic constructs directed networks by inferring a coexpression network using mutual information ., Edges with higher cumulative mutual information than alternative paths are retained and directed based on topological characteristics ., For edges that remained bidirectional , two edges , one going in each direction , were inserting before running BONITA-RD ., BONITA is written entirely in Python and C using genetic algorithms from deap 28 ., It has been tested for use with Intel Distribution for Python 2 . 7 ., Generation of network representations of rules was performed by modification of previously published code from the Albert Lab 29 ., BONITA is designed to be run from the command line by a non-expert user ., Code and documentation are available on Github at https://github . com/thakar-Lab/BONITA ., BONITA Network Propagation ( BONITA-NP ) propagates continuous-valued signals across molecular networks with the assumption that bulk transcriptomic measurements are proportional to the number of cells expressing specific genes ., The signal propagation depends on the inference of logic rules performed by BONITA rule determination ( BONITA-RD ) , which is optimized to preserve steady states assumed to be represented by the cross-sectional data ., The logic rules define integration of signals coming from different genes ., To test the performance of BONITA-RD , a subset of networks were obtained by searching the KEGG database for Interferon Gamma ( IFN-γ ) ., These 12 networks were used as test networks since they provide an unbiased set of networks with varying complexity , ranging in size from 13 to 346 nodes ., Simulated data representing cross-sectional measurements were generated for each test network using BONITA-NP and were used as inputs for BONITA-RD ., Rules recovered from BONITA-RD were compared to the rules used to generate simulated data ., BONITA recovered exact rules used to generate the simulated data with 50% accuracy across test networks ., However , multiple logic rules can result in similar cross-sectional outcomes ., Hence , the multiple logic rules that produce equivalent cross-sectional outcomes were treated as ‘equivalent’ rule sets ( ERS ) ( see Methods ) ., ERS facilitated evaluation of accuracy of BONITA-RD within the limits of cross-sectional data ., BONITA-RD accuracy reached 87—99% when considering ERS among test networks ( Fig 2b ) ., The size of the ERS depicting number of rules in the set varied from just 1 rule to all possible rules ( 1 , 15 and 127 for in-degree 1 , 2 and 3 , respectively ) ., The size of the ERS was expected to be dependent on signal flow from the shared upstream nodes ., Since signal could flow directly or indirectly from such nodes to the node of interest , it is theoretically impossible to distinguish them with cross-sectional data ., Consider a network with 3 nodes , A , B , and C , and with edges from A to B , A to C , and from B to C . Under no circumstances will cross-sectional data reveal whether changes in A are propagated to C directly , via B , or both ., We wanted to understand whether the size of the ERS was driven by such unsolvable equivalences ., To identify these situations , cases where a single node ( like A in the network described above ) could influence two incoming edges were enumerated ., To this end , the sum of intersection between nodes influencing the signal along each pair of incoming edges was calculated as the sum of the shared ancestors between pairs of upstream nodes U of the node under investigation ., This total ancestor overlap is given by ∑ U 1 ≠ U 2 | A ( U 1 ) ∩ A ( U 2 ) | , for all such pairs of upstream nodes U where A is the set of all ancestors of U . The total ancestor overlap and the size of the ERS were highly correlated ( Fig 2d , Spearman r = 0 . 947 ) , demonstrating that alternative paths that were indistinguishable in cross-sectional data lead to unsolvable rules and consequently larger ERS sizes ., The strikingly high accuracy across diverse networks when considering ERS demonstrates that BONITA rule inference can correctly infer rules to the extent they are distinguishable by cross-sectional data ., Next , we investigated the impact of network complexity on BONITA-RD ., To assess the impact of network size BONITA-RD accuracy was compared with the number of nodes in each test network ., Though the test networks have a wide range of sizes , node numbers did not explain differences in accuracy across networks ( Fig 2b ) ., To further understand the differences in accuracy , we hypothesized that the accuracy of ERS would be associated with in-degree ., Though BONITA-RD restricts the in-degree to 3 , decreasing accuracy with increasing original in-degree ( Fig 2c , Spearman r = -0 . 851 ) was observed ., The in-edges are optimized by BONITA-RD , which could compound the inaccuracies introduced by size of the rule space for the nodes with 3 incoming edges ., These findings indicate that even when BONITA achieves >80% accuracy , the nodes that are incorrectly inferred have high in-degree in original network ., Having established the ability of BONITA-RD to recover rules from large-scale data , we wanted to establish BONITA’s robustness to other important factors in transcriptomic data: sample number and technical noise ., Susceptibility of BONITA to technical noise was investigated by adding random noise in the range 1-200% for each node in the network ., The BONITA-RD accuracy remains >80% with up to 10% noise in the data ( Fig 3a ) , however accuracy was 65-91% when noise to signal ratio was 50% ., Overall , accuracy dropped to 65-88% with addition of 200% noise for larger networks ., For sample size analysis , the number of samples were varied from 2 to 15 in the simulated data ., Fig 3b shows that accuracy improves from 83-95% with three samples to 91-99% with 15 samples across test networks , but accuracy was preserved to be >80% with just 2 samples ., Altogether , these simulations show that BONITA is robust to sample number and technical noise ., Typically , pathway topologies available in databases are generalized cases that can lead to false positive edges not relevant to the context of a specific study ., Hence , the robustness of BONITA to false positive edges in the prior knowledge network was assessed and compared to the existing algorithm that utilized discrete state modeling 9 ., A toy network from 9 was used to generate a dataset and false positive edges were added as multiples of that network’s edge number ., The ability of BONITA-RD to retrieve the original network was measured by structural distance ., Specifically , structural distance is the number of edges that must be added or removed to obtain the original correct network ., BONITA-RD performed substantially better with <0 . 5 times the number of edges added to the prior knowledge network than Liu’s model , but worse when false-positive edges were greater than the number of edges in the prior knowledge network ( Fig 4 ) ., Critically , BONITA application to cross-sectional data performs comparably to Liu’s model applied to time course data ., Generally , time course data is expected to make rule inference , especially of directional edges , easier ., Thus , BONITA-RD performance is robust given the limitations of cross-sectional data in rule inference and relies on reasonably accurate prior knowledge networks ., Pathway analysis is the most useful functionality of BONITA-RD ., Briefly , nodes of network representing pathway are perturbed in silico to measure network-wide changes and calculate a node-level impact score ., This impact score is then used to measure pathway-level modulation in the dataset under study ., The performance of BONITA-PA was assessed by comparing its output to previously developed topology based pathway analysis method ( CLIPPER ) and a popular gene-set enrichment method ( CAMERA ) ., CLIPPER was chosen since it was the best performing algorithm in a recent comparative analysis of network based pathway analysis techniques 2 ., The comparison was performed on the simulated data representing attenuation of pathway source node and downstream events ( details in Methods ) ., BONITA was more sensitive than previous methods especially at low levels of source node attenuation ( Fig 5 , refer to box and star ) with the area under the curves ( AUCs ) 0 . 842 , 0 . 832 and 0 . 830 for BONITA , CLIPPER and CAMERA respectively at log2 attenuation of 0 . 5 ., All the methods performed well in detecting the number of pathways for induced attenuation >1 in source nodes ( Fig 5b ) ., The performance of all the three was excellent ( . 99-1 . 00 AUC ) at log2 attenuation of 2 . 0 ., The same results hold when attenuation is not propagated through the downstream nodes of the pathways ( S5 Text , S5 Fig ) ., Thus , BONITA-PA outperforms previous state-of-the-art methods at low levels of pathway perturbation ., Moreover , even though BONITA-PA performs as well as other methods at high level of signal perturbation , it offers rules for synergy among genes unlike any other methods ., BONITA’s excellent performance on simulated data and in modeling pathway modulation calls for verifying its performance in similar experimental setting ., RNA-seq data from our previous study investigating Interferon-regulated genes ( IRG ) following stimulation of human choriocarcinoma ( Jar ) cells with IFN-γ with or without pervanadate , a protein tyrosine phosphatase inhibitor , or valproic acid , a histone deacetylase ( HDAC ) inhibitor 23 was used ., Human choriocarcinoma cells are hypo-responsive to IFN-γ stimulation due to impaired activation of the JAK-STAT pathway 30 , 31 ., This impaired activation could be released by pervanadate and/or valproic acid ., We previously showed modulation of certain IRGs by inhibitor alone ., Nonetheless , stimulation with both IFN-γ and inhibitors did not reveal elicitation of higher number of pathways using existing tools such as CAMERA in 23 ., In this study , BONITA , CAMERA , and CLIPPER were used to assess significance of 37 immune pathways in IFN-γ treatment with or without inhibitors compared to untreated cells ( Fig 6 , S1 Table ) ., Both BONITA and CAMERA identified 6 significant pathways ( p <0 . 05 ) when cells are treated with IFN-γ alone ., However , BONITA performed better in reproducing IFN-γ induced pathways in joint stimulation with inhibitors than CAMERA ., Specifically , BONITA revealed activation of two pathways only upon joint treatment of IFN-γ and either one of the inhibitors as expected from previous studies 30 , 31 ., CLIPPER performed poorly in detecting responsiveness to IFN-γ treatment and mostly detected pathways when cells were treated with the inhibitors ., Thus BONITA’s ability to detect pathways specifically upon joint stimulation is due to the inference of modulation of downstream events by upstream nodes , rather than only detecting downstream modulations ., Detecting specific pathway signals is a major challenge in genome-wide sequencing studies of human samples due to variation across individuals ., Previously , we have measured changes in isolated CD4+ T cells from infants with mild and severe respiratory syncytial virus ( RSV ) infection by genome-wide mRNA sequencing 22 ., It is well understood that the convalescent time point is critical in understanding antigen-specific long term responses required for resolving infections 32–34 ., However , our previous work indicates that the changes at the convalescent time point are attenuated ., Interestingly , BONITA-PA and CAMERA , but not CLIPPER , identified several pathways as differentially regulated across mild and severe comparison even at the convalescent visit ( Table 1 ) ., Further , BONITA-PA produces helpful network synthesis , including rules , which can be visualized easily in a network viewer such as Cytoscape , as in Fig 7 ., This network synthesis was used to investigate the Apoptosis pathway which was detected to be differentially regulated between mild and severe disease by BONITA but not CAMERA or CLIPPER at the convalescent visit ., Interestingly , 20 out of 138 nodes obtained high impact score , 5 of which also had >0 . 5 fold difference between mild and severe ., These nodes include many well-known upstream regulators such as PDGFB and PIK3CA 35–37 ., Thus , BONITA effectively prioritizes pathway modulation by emphasizing upstream regulators in translational studies ., To further test BONITA’s specificity , data from Ihnatova et al . was used 2 , 25 , 26 , which consists of 36 microarray experiments comparing patients with 15 unique diseases to healthy controls ., Each of the disease conditions represented in our datasets corresponds to one disease pathway in KEGG ., BONITA correctly found corresponding disease pathways to be significant in 22/36 datasets ( S2 Table ) ., Ihnatova et al describe that CLIPPER found a comparable number ( 24/36 ) to be significant in exactly same comparisons ., Further , BONITA has a unique capability to identify nodes with high impact scores , which we hypothesized would be potential drug targets ., Drug targets were identified using DrugBank and the targets with indications including the name of the disease pathway ( e . g . ‘acute myeloid leukemia’ ) were retained 38 ., Four datasets ( 3 acute myeloid leukemia and 1 chronic myeloid leukemia ) were identified with >1 drug target among high impact nodes designated by BONITA ., The enrichment of drug targets among high impact scores was statistically significant ( p<0 . 01 , t-test ) ., For example , FLT3 , a critical receptor tyrosine kinase mutated in up to 35% of acute myeloid leukemia ( AML ) cases was found to be one of the three highest impact genes in all three AML datasets ., FLT3 is commonly targeted for treatment of AML ., Similarly , ABL1 , part of the BCR-ABL target of imatinib , an early immunotherapy , had the top impact score in both datasets with chronic myeloid leukemia ., Since DrugBank annotations might not be complete , the top 2 impact score nodes in each disease network with p<0 . 05 in BONITA-PA were manually queried as targets of drugs either under development or approved ., This revealed that high impact nodes in each network , except those in Alzheimer disease network , were either targeted by or were the ligand of a receptor targeted by an approved or under development drug ( S2 Table ) ., In Alzheimer disease , there are no mechanistic drugs ., However , 1/4 datasets revealed TNFRSF1A , the TNF − α receptor , candidacy of which is supported by previous studies 39–41 ( S2 Table ) ., Interestingly , ADRB1 , the β − 1-adrenergic receptor was the second highest impact gene for dilated cardiomyopathy , which is often treated with beta-blockers targeting the adrenergic receptor ( S2 Table ) ., Thus , not only is BONITA-PA able to detect differences in relevant disease networks between patients and healthy control subjects , but it is also highly effective in identifying promising drug target genes ., BONITA connects upstream differences with downstream effects , identifying true cascades depicted by the pathway topology that are highly modulated in comparison of interest ., Taken together , these results show the effectiveness of BONITA-PA in prioritizing pathways for further experimental studies following genome-wide transcriptional profiling ., One of the applications of BONITA is to define co-operativity in networks inferred from the data ., Mutual information-based inductive causation ( miic ) 27 was used to generate a directed network using RSV infection dataset described in the previous section ., BONITA was run to obtain logic rules and impact scores ., BONITA predicts that absence of TAXBP1 , a gene known to participate in restricting antiviral signaling and YPEL5 , a gene involved in cell cycle progression leads to activation of TRAF3IP3 , which is supported by previous studies 42 , 43 ., Finally , MYC and SP100 are hypothesized to activate MX1 together ., This is particularly interesting since MX1 is a nuclear factor known to recruit SP100 and involved in antiviral response 44 ., Thus , in addition to application of BONITA for pathway analysis , it can have high utility in de novo hypothesis generation ., BONITA is , to our knowledge , the first ever attempt to use discrete-state modeling for pathway analysis and builds upon decades of work to calculate node impacts in Boolean networks , Probabilistic Boolean Networks a

Introduction, Materials and methods, Results, Discussion

Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data ., However , most existing methods do not consider signal integration represented by pathway topology , resulting in enrichment of convergent pathways when downstream genes are modulated ., Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes ., This implementation can be facilitated for large-scale data by discrete state network modeling due to simplicity in parameterization ., Here , we model cellular heterogeneity using discrete state dynamics and measure pathway activities in cross-sectional data ., We introduce a new algorithm , Boolean Omics Network Invariant-Time Analysis ( BONITA ) , for signal propagation , signal integration , and pathway analysis ., Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity , and propagates binary signals repeatedly across networks ., Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search ., The rules determine the impact of each node in a pathway , which is used to score the probability of the pathway’s modulation by chance ., We have comprehensively tested BONITA for application to transcriptomics data from translational studies ., Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivity at lower levels of source node modulation and similar sensitivity at higher levels of source node modulation ., Application of BONITA pathway analysis to previously validated RNA-sequencing studies identifies additional relevant pathways in in-vitro human cell line experiments and in-vivo infant studies ., Additionally , BONITA successfully detected modulation of disease specific pathways when comparing relevant RNA-sequencing data with healthy controls ., Most interestingly , the two highest impact score nodes identified by BONITA included known drug targets ., Thus , BONITA is a powerful approach to prioritize not only pathways but also specific mechanistic role of genes compared to existing methods ., BONITA is available at: https://github . com/thakar-lab/BONITA .

21st-century biotechnology has enabled measurements of genes and proteins at large scale by RNA sequencing and proteomics technologies ., In particular , RNA-sequencing has become a first step of unbiased interrogation ., These studies frequently produce a long list of differentially abundant genes , which become interpretable by widely used pathway analysis methods ., The pathway topologies frequently include information on how genes interact and influence each other’s expression , but current methods do not utilize this information to estimate signal flow through each pathway ., We have developed a model of binary ( on/off ) behavior that accounts for varying expression across samples as different proportions of cells expressing genes ., We model signal flow by averaging repeated simulations of individual cells passing binary signals through molecular networks ., We use this model to infer regulatory rules explaining gene expression ., These rules of signal integration for all nodes in the network are used to identify the most important genes , and to determine if a pathway’s activity is different between two groups ., BONITA compares favorably to previous approaches using simulated and real data ., Furthermore , application to 36 datasets from 15 different diseases demonstrates BONITA’s exceptional ability to detect drug targets .

medicine and health sciences, genetic networks, applied mathematics, signaling networks, genetic algorithms, simulation and modeling, algorithms, mathematics, network analysis, genome analysis, pharmacology, research and analysis methods, computer and information sciences, gene expression, drug discovery, drug research and development, gene identification and analysis, genetics, transcriptome analysis, biology and life sciences, physical sciences, genomics, computational biology

journal.pcbi.1000909

Nonlinear Gap Junctions Enable Long-Distance Propagation of Pulsating Calcium Waves in Astrocyte Networks

The 20th century witnessed crystallization of the neuronal doctrine , viewing neuron as the fundamental building block responsible for higher brain functions ., Yet , neurons are not the only cells in the brain ., In fact , almost 50% of the cells in the human brain are glial cells 1 , 2 ., Due to their apparent lack of fast electrical excitability , the potential importance of glial cells in neural computation was downgraded in favor of the critical role played by these cells in neural metabolism ., Recent experimental evidence however suggests that glial cells provide a role much more than support , including control of synapse function and formation , adult neurogenesis and regulation of cerebral blood flow ( see e . g . 3 for a review ) ., As a consequence , a new paradigm is emerging in brain science , according to which glial cells should be considered on a par with neurons ., In particular , astrocytes , the main type of glial cells in the cortex , have attracted much attention because they have been shown to communicate with neurons and with each other ., Indeed , astrocytes can integrate neuronal inputs and modulate the synaptic activity between two neurons 4 ., Neurotransmitters released from pre-synaptic neurons can bind to specific receptors on the astrocyte membrane and evoke Ca2+ elevations in the astrocyte cytoplasm 5 ., In turn , these activated astrocytes may release gliotransmitters , including glutamate and ATP , which feed back onto the synaptic terminals and modulate neuron responses 6 ., Two main types of neuronal activity-dependent Ca2+ responses are observed in astrocytes 7 , 8: ( 1 ) transient Ca2+ increases that are restricted to the very extremity of their distal processes 9 , 10 and ( 2 ) Ca2+ elevations propagating along these processes as regenerative Ca2+ waves , eventually reaching the cell soma ., The latter kind of event can even propagate to neighboring astrocytes , thus forming intercellular Ca2+ waves 11 , 12 ., Although intercellular Ca2+ waves have been extensively observed in astrocyte cultures 13 , 14 , recent experimental evidence supports the possibility that they could also occur under physiological conditions 6 , with propagation distances ranging from four 15 to up to 30 astrocytes 16 ., These results therefore indicate that waves in astrocytes may represent an effective form of intercellular signaling in the central nervous system 17 , 18 ., But further , they almost irresistibly bring about the hypothesis that this persistent astrocyte wave-based signaling could extend the repertoire of neural network communications , adding non-local interactions , both in space and in time 3 ., In order to assess this hypothesis though , several aspects of Ca2+ signaling in astrocytes remain to be elucidated ., Experimental data suggest that a stimulus impinging on an astrocyte is preferentially encoded in the modulation of the frequency ( FM ) of astrocytic Ca2+ oscillations 10 ., This type of oscillations is often characterized by pulsating waves , i . e . the propagation of peak waveforms , with width smaller than period ., However , the possibility of amplitude modulation ( AM ) or even coexisting AM and FM ( AFM ) encoding have also been inferred 19 , 20 ., Actually , the frequency and amplitude of astrocytic Ca2+ oscillations can be highly variable , depending on cell-specific properties such as Ca2+ content of the intracellular stores , or the spatial distribution , density and activity of ( sarco- ) endoplasmic reticulum Ca2+-ATPase ( SERCA ) pumps 21 , 22 ., Yet , the propagation of wave-like signalling in the context of such great variability is yet not fully understood 14 ., Much effort has also been devoted to understand the mechanisms responsible for initiation and propagation of intercellular Ca2+ waves ., From a single-cell point of view , intracellular Ca2+ dynamics in astrocytes is mainly due to Ca2+-induced Ca2+ release ( CICR ) from the endoplasmic reticulum ( ER ) stores and its regulation by inositol trisphosphate ( IP3 ) 6 ., But for the transmission of these internal signals from one astrocyte to the other , two possible routes have been uncovered ., The first one involves the transfer of IP3 molecules directly from the cytosol of an astrocyte to that of an adjacent one through gap junction intercellular hemichannels 23 ., In the second route instead , propagation is mediated by extracellular diffusion of ATP which binds to plasma membrane receptors on neighboring astrocytes and regulates IP3 levels therein 18 , 24 ., Although these two routes need not be mutually exclusive , experiments indicated that intracellular propagation through gap junctions is likely the predominant signaling route in many astrocyte types 19 , 25–27 ., Albeit experimental protocols monitor wave propagation as variations of intracellular Ca2+ , the molecule that is transmitted through gap junctions to neighboring astrocytes is not Ca2+ , but IP3 27 ., Indeed , when the IP3 in a given cell increases , some of it can be transported through a gap junction to a neighbor astrocyte ., This IP3 surge in the neighbor cell can in turn trigger CICR , thus regenerating the original Ca2+ signal ., Yet , the transported IP3 is required to reach a minimal threshold concentration to trigger CICR in the neighboring cell ., If this threshold is not reached , propagation ceases 28 ., In this regard , previous theoretical studies stressed the importance of a mechanism for at least partial regeneration of IP3 levels 29 , 30 ., Such a mechanism , coupled with IP3 transport , could induce local IP3 concentrations large enough to trigger CICR 30 , thus enabling Ca2+ wave propagation ., Production of IP3 by Ca2+-dependent PLCδ has been suggested as a plausible candidate regeneration mechanism 29 , 31 , 32 ., However , the intercellular latencies of the Ca2+ waves simulated with this mechanism are hardly reconcilable with experimental observations , hinting a critical role for gap junction IP3 permeability 29 , 30 ., In the present study , we investigated the intercellular propagation of Ca2+ waves through the gap-junctional route by a computer model of one-dimensional astrocyte network ., To account for intracellular Ca2+ dynamics , we adopted the concise realistic description of IP3-coupled Ca2+ dynamics in astrocytes previously introduced in Ref ., 33 ., We specifically focused on the influence of gap junction linearity and internal Ca2+ dynamics on the wave propagation distance ., By means of bifurcation analysis and numerical solutions , we show that nonlinear coupling between astrocytes can indeed favor IP3 partial regeneration thus promoting large-distance intercellular Ca2+ wave propagation ., Our study also shows that long-distance wave propagation critically depends on the nature of intracellular Ca2+ encoding ( i . e . whether Ca2+ signals are FM or AM ) and the spatial arrangement of the cells ., Furthermore , our results suggest that , in the presence of weak coupling , nonlinear gap junctions could also explain the complex intracellular oscillation dynamics observed during intercellular Ca2+ wave propagation in astrocyte networks 12 ., We describe calcium dynamics in astrocytes by an extended version of the Li-Rinzel model 34 , called the ChI model that we developed and studied in 33 ., A detailed presentation of this model is also given in the Supplementary Information ., Briefly , the ChI model accounts for the complex signaling pathway illustrated in Figure 1 that includes Ca2+ regulation by IP3-dependent CICR as well as IP3 dynamics resulting from PLCδ-mediated synthesis and degradation by IP3 3-kinase ( 3K ) and inositol polyphosphate ( IP ) 5-phosphatase ( 5P ) ., The temporal evolution of astrocytic intracellular calcium in our model is described by three coupled nonlinear equations: ( 1 ) ( 2 ) ( 3 ) in which the variables C , h , IP3 represent the cell-averaged calcium concentration , the fraction of open IP3R channels on the ER membrane , and the cell-averaged concentration of IP3 second messenger , respectively ., Each one of these variables is coupled to others via the set of equations that describe contributions of different biochemical pathways , as described in details in Supplementary Information ( equations S1–S4 ) alongside the complete mathematical analysis of the model features ., In a single-cell context , this model reproduces most of the available experimental data related to calcium oscillations in astrocytes ., In particular , it faithfully reproduces the experimentally reported changes of oscillation frequency and wave shape caused by SERCA pump activity modulations 22 ., Experimental evidence shows that chemical signaling between astrocytes usually takes the form of propagating Ca2+ pulses that are elicited following the gap-junctional transfer of IP3 second messenger molecules 13 ., Intracellular IP3 activates the CICR pathway , giving rise to the observed rapid transient elevations in cytosolic free calcium ., We considered three scenarios to describe the exchange of IP3 between a pair of adjacent astrocytes: ( 1 ) linear , ( 2 ) threshold-linear ( composed of a linear term operating after a threshold ) and ( 3 ) non-linear ( here described as sigmoid ) coupling ( see Figure 2 ) ., The linear model is a simple diffusive coupling; however , threshold-linear and non-linear models both transfer IP3 only when the IP3 gradient between the two adjacent cells overcomes a threshold value ., Our investigation of nonlinear coupling case was motivated by the experimental observations suggesting that gap junction permeability in itself can be actively modulated by various factors , among them different second messengers ., Indeed , there is growing evidence that gap junctions may have greater selectivity and more active gating properties than previously recognized 35 ., Several signaling pathways are able to modulate junctional permeability ., In particular , the conductance state of Cx43 , the main type of connexin in astrocyte gap junctions 36 is regulated by phosphorylation by PKC , which is also involved in IP3 degradation 37 , 38 , as well as by intracellular Ca2+ 39 ., These data suggest that astrocyte gap junction gating could be coupled to intra- and inter-cellular IP3 and Ca2+ dynamics 40 , 41 in a nontrivial fashion ., Accordingly , several previous simulation studies have explored the influence of complex ( e . g . regulated by second messengers ) gap junctions 42–44 ., We explore here their effects on intracellular Ca2+ wave propagation in astrocytes ., We consider chains of N astrocytes where each astrocyte is coupled to its two nearest neighbors via gap junctions ., Each i-th astrocyte ( i\u200a=\u200a1 , … , N ) is associated with three variables Ci , hi and IP3i , that are respectively the cytosolic Ca2+ concentration , the ratio of open IP3Rs and the intracellular IP3 concentration in this astrocyte ., The dynamics of these internal variables is given by the ChI model ( equations 1–3 and Supplementary Information for a detailed explanation ) : ( 7 ) ( 8 ) For all cells that are not at the boundaries of the astrocyte chain ( i . e . ) : ( 9 ) where the internal reaction term for IP3 , i . e . , is given by equation ( 3 ) ., By contrast , the equations for the first and last cells , namely cell 1 and N , depend on the boundary conditions ., We considered three types of boundary conditions: ( 1 ) reflective , ( 2 ) absorbing and ( 3 ) periodic ., Reflective ( zero-flux ) boundaries assume that IP3 exiting cell 1 or N can only flow to cell 2 or N−1 , respectively ., They are given by: ( 10 ) We also considered the case where cells 1 and N are absorbing , namely they entrap incoming IP3 fluxes ., This is the case of absorbing boundary conditions in which IP3 can flow from cell 2 to cell 1 , but the reverse flux ( from cell 1 to, 2 ) is always null ( and similarly for cells N and N−1 ) ., Accordingly , equations read: ( 11 ) Finally , with periodic boundary conditions , the 1D astrocyte chain actually takes the shape of a ring and the equations read: ( 12 ) To induce wave propagation in the astrocyte chain , one cell ( referred to as the “driving” cell ) is stimulated by a supplementary exogenous IP3 input ., This external stimulus is supplied through a ( virtual ) “dummy” cell , coupled to the driving cell by one of the coupling functions described above ., In this sense , the dummy cell acts as an IP3 reservoir in which the level of IP3 is kept fixed to a constant value IP3bias ., Let k be the coordinate of the stimulated cell ( driving cell ) within the 1D chain ., In this study , we usually stimulate the first cell or the central one , that is k\u200a=\u200a1 or k\u200a= ( N+1 ) /2 ( N odd ) ., Hence , IP3 dynamics in the k-th cell is given by ( 13 ) where is calculated using in equation ( 4 ) ., Most simulations done in this work were driven by a constant value of IP3bias ., In the last section though , a square positive wave stimulus was applied to the model ., Initial conditions for all cells were set in agreement with experimental values reported in astrocytes for Ca2+ and IP3 at basal conditions 45 ., The chain model consists of 3N non-linear ODEs , where the number of astrocytes in the chain , N , ranged from 1 to 100 ., Time solutions were obtained via numerical integration by a standard 4th-order Runge-Kutta scheme with a time step of 10 ms as this value showed to be the best compromise between integration time and robustness of the results ., The computational model was implemented in Matlab ( 2009a , The MathWorks , Natick , MA ) and C . Bifurcation analysis was done using XPPAUT ( http://www . math . pitt . edu/~bard/xpp/xpp . html ) ., Nonlinear time series analysis was performed using the TISEAN software package 46 ., Table S1 in the Supplementary Information lists the values of the parameters used in the model ., The Ca2+ and IP3 dynamics observed so far were all obtained using a rather high value of the coupling strength ( F\u200a=\u200a2 . 0 µM·s−1 ) ., In these conditions , the properties of the propagated waves are rather simple: a pulse-like ( or not ) wave front travels across astrocytes , with conserved shape and either stops after a few cells or invades the whole cell chain ., However , our system is a spatially extended dynamical system with large numbers of degrees of freedom ., Such systems ( e . g . coupled map lattices ) are known to manifest complex spatiotemporal behaviors when the coupling strength changes ., To get an insight on the possible propagation behavior exhibited by our model with weaker coupling , we considered the dynamics with reduced levels of gap junction permeability ( setting F\u200a=\u200a0 . 23 µM·s−1 ) ., Figure 7 shows the Ca2+ dynamics of 41 coupled FM cells and a square wave periodic stimulation applied to the central cell #21 ( see figure caption for details ) ., Visual inspection of the Ca2+ traces in each cell ( Figure 7a ) indicates that such periodic ( oscillatory ) stimulation can trigger Ca2+ waves that can propagate along the whole chain ., Importantly , this figure also evidences the occurrence of occasional propagation failures that do not seem to result from a simple spatiotemporal pattern ., Actually , observation of the temporal traces of each individual cell reveals the occurrence of pulse-like events showing up with no apparent regularity ., Accordingly , the distribution of the time-intervals between two such pulses can be very broad for some cells , with large intervals often almost as probable as small ones ( see Figure S6 ) ., Albeit consistently pulse-like , the shape of the propagated Ca2+ waves is also quite variable ., Closer inspection of the time series for the driving cell ( i . e . cell 21 ) for instance shows that the generated Ca2+ pulses vary from a single-peak waveform to multiple peaks per single pulse ( Figure 7c ) ., Furthermore , Figure 7d shows that the variability and complexity of the IP3 signals is also very large ., The lack of obvious regular behavior is particularly striking on movies showing the parallel temporal evolution of the Ca2+ and IP3 level in each cell , as in Video S1 in the Supplementary Information ., To further illustrate the complexity of the obtained dynamics , we plot in Figure 7b the trajectory of the system in the phase space of the driving cell ., It is very tempting to compare the resulting trajectories to those observed with classical low-dimensional strange attractors ., In this regard , preliminary analysis of the three time series of the driving cell using nonlinear time series analysis tools 46 suggested that the dynamics indeed corresponds to deterministic chaos , with sensitivity to initial conditions testified by a positive maximal Lyapunov exponent that we estimated between 0 . 020 and 0 . 050 s−1 ( depending on the time series under consideration ) ., The apparent complexity of the dynamics is most likely due to some form of spatiotemporal chaos , the nature of which is beyond the scope of the current article and is left to future work ., But whatever the response , these simulations evidence that complex Ca2+ wave propagation patterns can manifest at low couplings , even with spatially homogeneous cell properties and in the absence of any stochasticity source ., Calcium-mediated signalling is a predominant mode of communication between astrocytes 50 ., Consequently , it is important to understand how different biophysical mechanisms determine the ability of these brain cells to communicate over long distances ., Here , we used the computational modeling approach to study the properties of gap junction-mediated signaling in simple networks of realistically modeled astrocytes ., Using numerical simulations and tools of bifurcation theory , we showed that long-distance regenerative Ca2+ wave propagation is possible when the gap junctions are rendered by nonlinear permeability but only when most of the model astrocytes are tuned to encode the strength of incoming IP3 signal into frequency modulated Ca2+ oscillations ., There has been a long-standing debate over the nature and characteristics of intercellular Ca2+ waves observed in astrocyte networks ., The present article concerns about the purely intracellular route , which involves the transfer of IP3 molecules directly from cytosol to cytosol through gap junctions 23 ., In the extracellular route instead , propagation is mediated by extracellular diffusion of ATP and purinergic receptor activation 18 , 24 ., Although these two routes need not be mutually exclusive , experiments indicated that their relative influences vary across the brain ., Indeed , experimental evidence suggests that the purely intracellular route predominates in astrocytes of the neocortex 14 , 51 and the striatum 28 while the extracellular ( purinergic-dependent ) route seems predominant in the CA1 hippocampus area as well as in the corpus callosum 51 ., Hence , the results obtained in the present paper are expected to be relevant to the former structures ., Their relevance to the case where the two routes coexist could however be tested by simple extensions of our current model , in the spirit of the recent Ref ., 52 ., A critical issue for the modeling studies of intercellular Ca2+ waves is to explain the observed variability of Ca2+ wave travelling distance 14 ., Indeed , experimental measurements show travelling distances varying from 30 cells 16 ( that is , often outside the imaging microscope field ) down to 3–4 cells only 15 ., Models featuring purely regenerative waves ( e . g . traveling waves in the usual mathematical sense ) easily account for long distance propagations but hardly account for the observed short ones ., Conversely , nonregenerative models ( e . g . purely diffusive ones ) cannot explain long-range propagation ., A possible solution was suggested by Höfer et al . 32 ., In the model proposed by these investigators , long-range propagating Ca2+ waves are obtained via IP3 regeneration in each cell by Ca2+-activated PLCδ ., However , whenever PLCδ maximal activity is lower , regeneration becomes partial and the Ca2+ wave propagation distance decreases ., Yet this model does not include Ca2+-dependent IP3 degradation , which could be critical for the occurrence of IP3-mediated Ca2+ oscillations 33 ., This latter process in particular , can compete with PLCδ-mediated IP3 production , thus hindering IP3 regeneration and Ca2+ wave propagation ., This calls for additional factors to be taken into account to explain intercellular Ca2+ wave propagation ., A first prediction of our model is that , regenerative waves are possible in a network composed in its majority of astrocytes that encode information about incoming IP3 signals in the frequency of their Ca2+ oscillations ( FM ) ., Interestingly , the response of astrocytes in vivo to IP3 stimulation is known to exhibit high variability , both in frequency and amplitude 53–55 ., This variability could be due to cell-to-cell heterogeneity ( extrinsic noise ) in some of the CICR parameters ., In particular , this could include variability of the expression of PLCδ or of the affinity for Ca2+ of the SERCA pumps ., To our knowledge , the kinetic properties of SERCA2b have never been measured in astrocytes ., However the hypothesis that SERCA2b affinity for Ca2+ shows variability in vivo seems realistic , given the experimental literature ., First , reports of experimental measurements of the SERCA2b affinity showed somewhat variable results , ranging from 170 56 to 270 nM 57 , albeit both studies used cDNA transfection in COS1 cells ., Secondly , SERCA2b functionality can be directly modulated by quality-control chaperones of the ER , e . g . calreticulin and calnexin 58 ., In particular , there exists strong indication that calreticulin may dynamically switch SERCA affinity for Ca2+ from 170 to ∼400 nM 59 ., In this case , cell-to-cell variability in the concentration of calreticulin could result in the mixed AFM-FM cell networks studied here ., Our observations then lead to the experimental prediction that such variability or heterogeneity of the astrocyte response would have a strong impact on the propagation of intercellular calcium waves between these cells ., Notably , this scenario is also supported by several experimental studies 21 , 60 ., In particular , calreticulin has been shown to regulate Ca2+ wave propagation via direct interaction with SERCA2b thus modulation of Ca2+ uptake by this pump 61 ., In our model , the strength and the transfer properties of the gap junction coupling are critical permissive factors that allow long-range intercellular signaling between the astrocytes ., In particular , nonlinear gap junctions were found to significantly enhance the range of Ca2+ wave propagation ( as opposed to the classic linear gap junctions that caused fast dissipation ) ., Gap junctions with dynamic resistance are known to exist in cardiac networks 62 , 63 and in several other cells 35 ., Yet there is currently no direct evidence for nonlinear transfer of second messenger molecules through gap junctions between astrocytes ., Nonetheless , the activation of PKC , which is intimately related to IP3 metabolism 33 , 64 , is known to block astroglial gap junction communication and inhibit the spread of Ca2+ waves therein 65 ., Hence , in light of the existing knowledge regarding the control of gap junctional permeability by various signaling molecules 66 , it is plausible to assume that some nonlinearity should exist in astrocytes too ., The exact form of the nonlinearity of course will be dictated by the properties of the solute and the nature of its interaction with the membrane channels in the proximity of the gap junction complex ., Meanwhile , the generic form of nonlinear coupling that we considered here allowed us to get a qualitative insight into the putative effect of nonlinear coupling on signal propagation in model astrocyte networks ., In the present study , we considered a simplified setup of 1D network implemented as a regular chain of coupled cells ., Such 1D chains display attractive aspects ., In particular , we could proceed to a numerical bifurcation study of these 1D coupled-cell systems ( see Figure S2 ) , which has proven invaluable for the interpretation of the simulation results ., Such bifurcation analysis would hardly be possible in higher dimensions ( e . g . 2D ) , because the number of cells one needs to account for in 2D is much larger than in 1D at constant propagation distance ., Furthermore , a serious study of a 2D system must include the exploration of the influence of the coupling network topology 67 , which adds further parameters to the study of the robustness of the model dynamical features ., However , real astrocytes in tissues are believed to organize in quasi 2D networks with significantly more complex structure ., Our model is thus a simplification of this quasi 2D reality ., For instance , obstruction of wave propagation could dependent on the spatial dimension ., Indeed in 2D or 3D reaction-diffusion systems or on random graphs , where the strength of the coupling or the local number of neighbors can vary across the network , the wave propagation distance can critically depend on the number of stimulated cells or the distribution of the number of coupled neighbors 68 ., It is not yet clear whether our observation that linear gap junctions support only local wave propagation is restricted to regular 1D networks such as those used in the present work ., Future works will be designed to tackle this issue ., Nevertheless , in spite of its simplicity , this 1D model yields important predictions about the influence of the spatial arrangement of astrocytes ., In particular , it shows that the distribution in space of heterogeneous gap junction permeabilities can result in rich dynamics 14 , 30 ., Reducing the maximal strength of coupling between the model astrocytes imparted the individual cells with rich dynamics , possibly associated with spatiotemporal chaos ., Keeping in mind that in reality the changes in gap junction permeability are mediated by the dynamic action of different effectors , we anticipate that a network of biological astrocytes could have the capacity to self-regulate the complexity of its dynamics ., Whether or not this is the case , can be determined by experiments that selectively target the pathways of gap junction regulation ., Recent studies suggest that the astrocytes within the cortex form heterogeneous populations 69 , 70 ., Therefore , we considered the case of intercellular Ca2+ wave propagation in composite 1D networks , consisting of both FM- and AFM-encoding cells ., Our simulations predict that the propagation dynamics and distance of intercellular Ca2+ waves critically depends both on the encoding property of the cells and on their spatial arrangement ., Interestingly , the cell bodies of neighboring astrocytes within the brain are believed to distribute in space in a nonrandom orderly fashion called “contact spacing” 71 , 72 ., Our study thus suggests a possible link between contact spacing and intercellular Ca2+ wave propagation in astrocyte networks ., If , as suggested by our model , the spatial arrangement of the astrocytes , coupled to the heterogeneity of their response , conditions Ca2+ wave propagation , then contact spacing may play a critical role in intercellular wave propagations in the brain and the related computational properties of astrocyte networks ., It is now widely accepted that astrocytes and neurons are interwoven into complex networks and are engaged in an intricate dialogue , exchanging information on molecular level 3 ., By releasing different gliotransmitters ( such as glutamate and ATP ) astrocytes dynamically modulate the excitability of neurons and control the flow of information at synaptic terminals 4 ., Diffusion of glutamate and/or ATP is limited due to the action of glutamate transporters and degradation of ATP , thus defining spatiotemporal range for the local effect of astrocyte on neurons and synapses 73 ., On the other hand , long-range and temporally delayed regulation of neuronal and synaptic activity by astrocytes is believed to be mediated by intercellular Ca2+ waves spreading through the astrocyte network ., The connectivity of this astrocyte network is in turn defined by the patterns of electrical activity in neuronal network 74 ., Thus , it appears that astrocytes and neurons are organized in networks that operate on distinct time scales and utilize the principles of feedback regulation to modulate the activities of each other ., How such mutual regulation of neuronal and astrocytic networks affects the complexity of neuronal network dynamics in health and disease is a question that should be addressed by future combined experimental and modeling studies .

Introduction, Methods, Results, Discussion

A new paradigm has recently emerged in brain science whereby communications between glial cells and neuron-glia interactions should be considered together with neurons and their networks to understand higher brain functions ., In particular , astrocytes , the main type of glial cells in the cortex , have been shown to communicate with neurons and with each other ., They are thought to form a gap-junction-coupled syncytium supporting cell-cell communication via propagating Ca2+ waves ., An identified mode of propagation is based on cytoplasm-to-cytoplasm transport of inositol trisphosphate ( IP3 ) through gap junctions that locally trigger Ca2+ pulses via IP3-dependent Ca2+-induced Ca2+ release ., It is , however , currently unknown whether this intracellular route is able to support the propagation of long-distance regenerative Ca2+ waves or is restricted to short-distance signaling ., Furthermore , the influence of the intracellular signaling dynamics on intercellular propagation remains to be understood ., In this work , we propose a model of the gap-junctional route for intercellular Ca2+ wave propagation in astrocytes ., Our model yields two major predictions ., First , we show that long-distance regenerative signaling requires nonlinear coupling in the gap junctions ., Second , we show that even with nonlinear gap junctions , long-distance regenerative signaling is favored when the internal Ca2+ dynamics implements frequency modulation-encoding oscillations with pulsating dynamics , while amplitude modulation-encoding dynamics tends to restrict the propagation range ., As a result , spatially heterogeneous molecular properties and/or weak couplings are shown to give rise to rich spatiotemporal dynamics that support complex propagation behaviors ., These results shed new light on the mechanisms implicated in the propagation of Ca2+ waves across astrocytes and the precise conditions under which glial cells may participate in information processing in the brain .

In recent years , the focus of Cellular Neuroscience has progressively stopped only being on neurons but started to include glial cells as well ., Indeed , astrocytes , the main type of glial cells in the cortex , dynamically modulate neuron excitability and control the flow of information across synapses ., Moreover , astrocytes have been shown to communicate with each other over long distances using calcium waves ., These waves spread from cell to cell via molecular gates called gap junctions , which connect neighboring astrocytes ., In this work , we used a computer model to question what biophysical mechanisms could support long-distance propagation of Ca2+ wave signaling ., The model shows that the coupling function of the gap junction must be non-linear and include a threshold ., This prediction is largely unexpected , as gap junctions are classically considered to implement linear functions ., Recent experimental observations , however , suggest their operation could actually be more complex , in agreement with our prediction ., The model also shows that the distance traveled by waves depends on characteristics of the internal astrocyte dynamics ., In particular , long-distance propagation is facilitated when internal calcium oscillations are in their frequency-modulation encoding mode and are pulsating ., Hence , this work provides testable experimental predictions to decipher long-distance communication between astrocytes .

cell biology/neuronal and glial cell biology, neuroscience/theoretical neuroscience, biophysics/theory and simulation, computational biology/computational neuroscience, biophysics/cell signaling and trafficking structures, computational biology/systems biology

journal.pntd.0003419

African Adders: Partial Characterization of Snake Venoms from Three Bitis Species of Medical Importance and Their Neutralization by Experimental Equine Antivenoms

In the Sub-Saharan Africa is annually registered approximately 300 , 000 cases of accidents by snakes which results in 32 , 000 deaths and a large number of victims with permanent local tissue damage and chronic disabilities 1 ., Snakes belonging to the genus Bitis , Viperidae family , are implicated in many accidents with humans 2 ., The genus consist of 16 species , distributed in Africa and Saudi Arabia territories , and presents high intrageneric genetic distance and low monophyly 3 ., These snakes differ in size , phenotype and venom composition 4 , 5 ., Molecular data separated the genus Bitis in four monophyletic groups ., The three West African taxa of the gabonica clade ( Bitis gabonica , Bitis rhinoceros , and Bitis nasicornis ) were grouped in the subgenera Macrocerastes , besides their ambiguous relationship , and Bitis arietans was isolated in the subgenera Bitis since the bootstrap value does not support any affinity between this species and the others belonging to the genus Bitis 3 ., Variations were also observed within the same species from different geographic areas complicating the development of effective therapies 5 ., The envenomation by Bitis often results in severe local damage , hypotension , coagulopathy , thrombocytopenia and spontaneous local bleeding and , in the absence of antivenom therapy , the accident can be fatal 6–8 ., Bitis arietans is one of the three species of snakes of medical importance in Africa and its venom is considered the most toxic venom of the viper group , based on LD50 studies carried on mice 7 , 9 , 10 ., Besides the severity and high prevalence of the accidents , the biochemical properties of Bitis venoms and the mechanism involved in the pathology remain poorly understood ., Proteomic and genomic analyses showed that Bitis venoms are constituted of proteins belonging to few major families: metalloproteinases , serineproteinases , phospholipases , disintegrins and C-type lectins 4 , 5 , 11 ., Heretofore , functional studies demonstrated that Bitis venom contains metalloproteinases that degrade collagen and fibrinogen 5 , 12; a serineproteinase that cleaves kininogen releasing kallidin 13; lectins that induce calcium release 14; adenosine that induces mast cell degranulation and hypotension 15; phospholipases A2 ( bitanarin ) that reversibly blocks muscle-type nicotinic acetylcholine receptors 16; Arg-Gly-Asp-containing peptides that interfere with platelet aggregation , arietin and gabonin , 17 , 18; C-type lectin that binds to the von Willebrand factor interfering with the coagulation cascade , bistiscetin 19 , among others ., Therapeutic strategies for treating accidents by snakes belonging to the genus Bitis , not always reliable , include antivenom , vasopressors , infusion of platelets and catecholamines 6 , 8 , 10 ., An in-depth characterization of the venoms belonging to the genus Bitis will contribute to a better understanding of the mechanisms by which these venoms cause pathology and shed light on specific therapies targeting the different pathways involved in the envenomation ., Thus , the aim of this study was to characterize some toxic properties of the venoms from three species of Bitis , i . e . , Bitis arietans , Bitis gabonica rhinoceros and Bitis nasicornis , involved in the majority of the human accidents in Africa , and analyzed the in vitro neutralizing ability of two experimental antivenoms ., Bovine serum albumin ( BSA ) , gelatin type A , 1 , 10-phenanthroline ( PHE ) , ethylene diamine tetracetic acid ( EDTA ) , phenylmethylsulfonyl fluoride ( PMSF ) , cetyltrimethylammonium bromide ( CTAB ) , Coomassie Brilliant Blue R-250 , Triton X-100 , Tween 20 , hyaluronic acid , Concanavalin A ( Con A ) from Canavalia ensiformis , Wheat germ agglutinin from Triticum vulgaris ( WGA ) , 3 , 3”-diaminobenzidine tetrahydrochloride ( DAB ) and ortho-phenylenediamine ( OPD ) were purchased from Sigma ( Missouri , USA ) ., Goat anti-horse ( GAH ) IgG labeled with alkaline phosphatase ( IgG-AP ) or with horseradish peroxidase ( IgG-HRPO ) , 5-bromo-4-chloro-3-indolyl-phosphate ( BCIP ) , nitroblue tetrazolium ( NBT ) and BCA assay kit were purchased from Promega ( Wisconsin , USA ) ., Brij-35 P was purchased from Fluka—BioChemika ( Werdenberg , Switzerland ) ., EnzChek Phospholipase A2 Assay Kit was purchased from Invitrogen ( California , USA ) ., Fluorescent Resonance Energy Transfer ( FRET ) substrate , Abz-RPPGFSPFRQ-EDDnp , was synthesized and purified as described 20 ., Venoms from Bitis arietans ( Ba ) , Bitis gabonica rhinoceros ( Br; also known as Bitis rhinoceros ) and Bitis nasicornis ( Bn ) were purchased from Venom Supplies , Tanunda , Australia ., These venoms were obtained from males and females snakes , with different ages , captured in Guinea , S . Tome , Angola and Mozambique , and maintained in captivity ., Stock solutions were prepared in sterile PBS ( 10 mM sodium phosphate containing 150 mM NaCl , pH 7 . 2 ) at 5 mg/mL based on their protein concentration assessed by BCA assay kit ( Promega ) ., Venoms from Crotalus durissus terrificus and Bothrops jaracaca snakes , supplied by Herpetology Laboratory from Butantan Institute , SP , Brazil , were used as positive controls in the assays for determination of PLA2 and hyaluronidase activities , respectively ., F ( ab’ ) 2 fragments generated from antivenoms against B . arietans ( α-Ba ) or B . gabonica rhinoceros plus B . nasicornis ( α-Br+Bn ) venoms , as described by Guidolin and collaborators 21 , were kindly donated by the Antivenom Production Section from Butantan Institute , São Paulo , Brazil ., The neutralization potencies of α-Br+Bn and α-Ba antivenoms were determined as 3 . 34 mg/mL and 4 . 62 mg/mL ( mg of venom neutralized per mL of antivenom ) , respectively 21 ., F ( ab’ ) 2 fragments from antiserum against botulinic toxin , used as negative control , was kindly donated by Butantan Institute , São Paulo , Brazil ., Venom samples were separated on 8–16% gradient SDS-PAGE under reducing and non-reducing conditions ., The gels were silver stained or transferred onto nitrocellulose membranes ., The membranes were blocked with 5% BSA in PBS ., Sugar residues were detected with peroxidase labeled Con A or WGA ., Reactive proteins were detected using a solution containing 0 . 1% hydrogen peroxide , 0 . 5 mg/mL DAB in phosphate buffer ( PBS ) ., To determine the specificity and cross-reactivity of Ba and Br + Bn antivenoms against all the three Bitis venoms , the nitrocellulose membranes were incubated for 1 h at room temperature ( RT ) with the antisera diluted 1:1000 in PBS—0 . 1% BSA ., Then , the membranes were incubated with GAH/IgG-AP diluted 1:7500 in PBS , 0 . 1% BSA , 0 . 05% Tween for 1 h at RT ., Immunoreactive proteins were detected using NBT/BCIP according to the manufacturer’s instructions ( Promega ) ., Hyaluronidase activity was measured as described 22 ., Briefly , 30 μg of Bitis venoms pre-treated or not with 10 μL of neat Ba or Br + Bn antivenoms were incubated with 25 μL of the hyaluronic acid ( 0 . 5 mg/mL ) and acetate buffer ( 0 . 2 M sodium acetate-acetic acid , pH 6 . 0 , containing 0 . 15 M NaCl ) , in a final volume of 100 μL , and incubated for 30 min at 37°C ., After incubation , 200 μL of CTAB 2 . 5% in NaOH 2% was added to the samples ., The absorbances were measured at λ 405 nm in a spectrophotometer ( Multiskan EX , Labsystems , Finland ) against a blank containing hyaluronic acid , acetate buffer and CTAB ., All assays were performed in quadruplicate ., Results were expressed in units of turbidity reduction ( UTR ) per mg of extract ., Bothrops jararaca snake venom ( 30 μg ) was used as positive control ., The phospholipase A2 activity of B . arietans , B . g ., rhinoceros and B . nasicornis venoms were determined using the EnzChek Phospholipase A2 Assay Kit ( Invitrogen ) according to the manufacturer instructions ., Briefly , samples of 2 . 5 μg of the venoms , in a volume of 50 μL of PBS , were mixed with samples of 50 μL of the kit phospholipid FRET substrate , using 96-well microtitre plates , and immediately analysed in a filter-based multi-mode microplate reader ( FLUOstar Omega , BMG Labtech , Ortenberg , Germany ) at λem = 515 nm and λex = 460 nm and at 37°C ., All enzymatic assays were performed in quadruplicate , being the specific activity expressed in UF per minute per microgram of venom ., Venom from Crotalus durissus terrificus snake ( 1 μg ) and PBS were used as positive and negative controls , respectively ., The venoms of the three Bitis ssp were also submitted to a serum neutralization assay , consisting in a pre-incubation of the venoms with 10 μL of the antivenoms for 30 min at RT , and followed by the addition of the phospholipid FRET substrate and fluorimetic analysis ., Gelatinase activity in the venom samples was analyzed by zymography 23 ., Venom samples were pre-incubated ( 30’ at RT ) with 5 mM of the protease inhibitors: ethylenediamine tetraacetic acid ( EDTA ) , 1 , 10-phenanthroline ( PHE ) or phenylmethylsulfonyl fluoride ( PMSF ) ; or with the antivenoms: α-Ba ( 10 μL neat ) or α-Br+Bn ( 10 μL neat ) ; or with PBS ( positive control ) , and ran under non-reducing conditions on a 10% polyacrylamide gel containing 1% gelatin type A . The gels were washed twice for 1 h at room temperature in 2 . 5% Triton X-100 , and incubated overnight at 37°C in zymography buffer ( 50 mM Tris-HCl , 200 mM NaCl , 10 mM CaCl2 , 0 . 05% Brij-35; pH 8 . 3 ) ., Gels were stained in Coomassie Brilliant Blue solution ( 40% methanol , 10% acetic acid , and 0 . 1% Coomassie Brilliant Blue ) ., Thirty micrograms of fibrinogen were incubated with 1 μg of each Bitis spp venoms for 1 h at 37°C with gentle agitation ., Venom samples ( 1 μg ) were also pre-incubated ( 30’ at RT ) with EDTA ( 5 mM ) , PHE ( 5 mM ) , PMSF ( 5 mM ) , α-Ba ( 10 μL neat ) or α-Br+ Bn ( 10 μL neat ) ., After incubation , the samples were separated on 10% SDS-PAGE under reducing condition ., The gels were stained with Coomassie Brilliant Blue and the fibrinogenolytic activity determined by the cleavage of alpha , beta and/or gamma chains of the fibrinogen ., Bitis venom’s proteolytic activity was determined using a fluorescence resonance energy transfer ( FRET ) substrate , the peptide Abz-RPPGFSPFRQ-EDDnp ., B . arietans ( 1 μg ) , B . g ., rhinoceros ( 2 . 5 μg ) and B . nasicornis ( 0 . 25 μg ) venoms were mixed with 5 μM of FRET substrate , in cold phosphate-buffered saline ( PBS ) ., The reaction was also performed in the presence of venom pre-treated with PMSF ( 5 mM ) , PHE ( 5 mM ) , EDTA ( 100 mM ) , α-Ba ( 10 μL ) or α-Br+Bn ( 10 μL ) antivenoms ., The reactions were monitored by measuring the hydrolysis of FRET substrate in a fluorescence spectrophotometer ( Perkin-Elmer , Massachusetts , USA ) using 96-well microtitre plates ( λem = 420 nm and λex = 320 nm ) at 37°C ., Control samples were prepared in the presence of an equal volume of buffer or ethanol , used in inhibitors stock solutions ., All assays were performed in quadruplicate and the specific proteolytic activity expressed as units of free fluorescence from cleaved substrate per μg per min ( UF/min/μg ) ., Angiotensin I ( 65 μM ) was incubated for 1 h at 37°C with 1 μg of B . arietans or for 2 h at 37°C with 5 μg of B . nasicornis or B . g ., rhinoceros venoms in phosphate buffer ( 50 mM sodium phosphate , 20 mM NaCl , pH 7 . 4 ) ., The different time course and venom concentration were used in order to achieve a maximum substrate consumption of 20% ., In parallel the venoms were pre-incubated with PHE ( 5 mM ) , PMSF ( 5 mM ) , EDTA ( 100 mM ) , α-Ba ( 10 μL ) or α-Br + Bn ( 10 μL ) antivenoms for 30 min , prior the addition of angiotensin I . Hydrolysis products were analyzed by reverse-phase chromatography ( Prominence , Shimadzu ) using a Shim-Pack C-18 column ( 4 . 6 x 150 mm ) ., The HPLC conditions used for the analytical procedure were 0 . 1% trifluoroacetic acid ( TFA ) in water ( solvent A ) , and acetonitrile and solvent A ( 9:1 ) as solvent B . The separations were performed at a flow rate of 1 mL/min and a 20–60% gradient of solvent B over 20 min ., In all cases , elution was followed by ultraviolet absorption ( 214 nm ) as describe 20 ., To determine the scissile bonds in angiotensin I , the fractions were collected manually and submitted to mass spectrometry analysis ., The peptide fragments were detected by scanning from m/z 100 to m/z 1300 using an Esquire 3000 Plus Ion Trap Mass Spectrometer with ESU and esquire CONTROL software ( Bruker Daltonics , Massachusetts , USA ) ., Purified 18O-labeled or unlabelled oxidized W derivatives were dissolved in a mixture of 0 . 01% formic acid: acetonitrile ( 1:1 ) and infused in the mass ( direct infusion pump ) spectrometer at a flow rate of 240 μL/h ., The skimmer voltage of the capillary was 40 kV , the dry gas was kept at 5 . 0 L/min , and the source temperature was maintained at 300°C ., ELISA plates were coated with 1 μg/well of B . arietans , B . g ., rhinoceros or B . nasicornis venoms in PBS ( overnight at 4°C ) ., Plates were blocked with PBS-5% BSA and incubated with increasing concentrations of the F ( ab’ ) 2 antivenoms α-Ba or α-Br + Bn or with the F ( ab’ ) 2 horse serum against botulin toxin ( negative control ) for 1 h at 37°C ., After incubation , the plates were washed with PBS/0 . 05% Tween 20 and incubated with a goat anti-horse/IgG-HRPO-conjugate for 1 h at 37°C ., Then , plates were washed and the reactions developed with OPD substrate , according to the manufacturers conditions ( Sigma ) ., The absorbances were recorded in a spectrophotometer ( Labsystems , ) at λ 492 nm ., Data were analysed statistically by one way ANOVA followed by Bonferroni multiple comparison test or by two away ANOVA ., A P-value <0 . 05 was considered significant ., All the three Bitis venoms presented different electrophoretic profiles containing bands with molecular weight varying from 10 to 200 kDa ( Fig . 1A ) ., Some of these bands are probably in complex or present disulfide bonds inter- or intra-chains as observed by the presence of extra bands of lower molecular weight after reduction ( Fig . 1A ) ., All venoms presented some proteins with sugar residues , as determined by the interaction with WGA , which selectively binds to N-acetylneuraminic acid and N-acetylglucosamil residues 24 or Con A , which selectively binds to α-mannopyranosyl and α-glucopyranosyl residues 25 ( Fig . 1B ) ., The specificity of the lectin binding was confirmed by the absence of bands on the membranes incubated with lectins in the presence of the specific sugars ., Fig . 2A shows that the venoms from B . arietans , B . g ., rhinoceros and B . nasicornis presented high , but not statistically different levels of hyaluronidase activity ., The hyaluronidase activity determined for Bitis spp venoms was similar to the one detected in B . jararaca venom ( 12 UTR/mg ) ., All Bitis venoms , tested in this study , presented similar phospholipase A2 activity ., The activity of B . arietans was ~100 UF/min/μg , and of B . g ., rhinoceros and B . nasicornis were ~80 UF/min/μg ( Fig . 2B ) ., The phospholipase activity of C . d terrificus venom , used as positive control of the assay , was 4–5 times higher than the Bitis venoms , i . e . , 430 UF/min/μg ., Zymography analysis showed that B . arietans and B . nasicornis venoms have gelatinolytic activity but not B . g ., rhinoceros ( Fig . 3A ) ., The venom from B . arietans has a band of ~ 90 kDa with gelatinolytic activity , which was inhibited by EDTA , PHE and PMSF ., Moreover , B . nasicornis venom presented two bands with gelatinolytic activity , ~30 and 80 kDa ., The gelatinolytic activity of the band with ~30 kDa was inhibited neither by EDTA nor by PHE and totally inhibited by PMSF ., All venoms cleaved efficiently the fibrinogen’s α chain ., In addition , B . arietans venom also cleaved β and ɣ chains of the fibrinogen while B . nasicornis also cleaved β chain ( Fig . 3B ) ., The fibrinogenolytic activity was strongly inhibited by PHE , demonstrating the involvement of metalloproteinases in this process ( Fig . 3B ) ., All Bitis venoms showed ability to cleave the FRET substrate , the peptide Abz-RPPGFSPFRQ-EDDnp ( Fig . 4 ) ., B . arietans venom presented a specific activity of ~6000 UF/min/μg , which was completely inhibited by both EDTA and phenanthroline ., B . g rhinoceros venom showed a specific proteolytic activity of ~7000 UF/min/μg , which was only inhibited by phenanthroline ., B . nasicornis venom presented a specific proteolytic activity of ~7500 UF/min μg/ , which was abolished by PMSF , but not for EDTA or phenanthroline ., All the venoms also directly cleaved the angiotensin I generating angiotensin 1–7 ( Fig . 5B ) ., Besides angiotensin 1–7 , the cleavage of angiotensin I generated several fragments with molecular weight varying from 770 to 1170 Da ( Fig . 5B ) ., B . arietans was more active in the cleavage of angiotensin I than B . g ., rhinoceros and B . nasicornis ., For all venoms , this proteolytic activity was completely abolished by PHE and EDTA , but not by PMSF ( Fig . 5A ) ., In this study , it was also analyzed the potential of experimental horse antivenoms raised against Bitis venoms in neutralizing the different enzymatic activities described above ., Two experimental horse antivenoms were tested , i . e . , one against the venom of B . arietans ( α-Ba antivenom ) and other against B . nasicornis plus B . rhinoceros venoms ( α-Br+ Bn antivenom ) ., These two different antivenoms were produced considering the high number of accidents of B . arietans in in Africa and its venom strong immunogenecity , as compared with the B . nasicornis and B . g ., rhinoceros snake venoms 21 ., α-Ba and α-Br + Bn antivenoms presented high specific antibody titers and cross-reactivity with others Bitis ssp venoms ( Fig . 6A ) ., Western Blot analysis showed that α-Ba and α-Br + Bn antivenoms efficiently immune reacted to a vast number of proteins in the crude venoms ( Fig . 6B ) ., Surprisingly , the western blot analysis showed that α-Ba antivenom better recognized the bands with low molecular weight in the venoms of B . g ., rhinoceros and B . nasicornis than the α-Br + Bn antivenom ( Fig . 6B ) ., The neutralizing effect of α-Ba and α-Br + Bn antivenoms on the enzymatic activity was tested by pre-incubating , 30 minutes at RT , the F ( ab’ ) 2 fragments , with each venom , prior the experiments ., Neat F ( ab’ ) 2 fragments of α-Ba or α-Bn + Br sera incubated with the crude venoms were able to significantly reduced , both , the hyaluronidase and the PLA2 activities of the three Bitis spp venoms ( Fig . 2 ) ., α-Ba and α-Br + Bn antivenoms could also abolish the gelatinolytic activity of B . arietans and B . nasicornis venoms; the low molecular weight band of B . nasicornis was also strongly reduced , when incubated v:v for 30 min prior the zymography ( Fig . 7A ) ., α-Ba antivenom ( 10 μL neat ) prevented the cleavage of the fibrinogen by all the Bitis venoms ( 1 μg per reaction ) ; however , the α-Br +Bn antivenom ( 10 μL neat ) only prevented the cleavage of fibrinogen by B . g ., rhinoceros and B . nasicornis venoms , but not by B . arietans ( Fig . 7B ) ., Both antisera ( 10 μL neat ) were also able to inhibit the cleavage of the FRET substrate , Abz-RPPGFSPFRQ-EDDnp ( Fig . 8A ) ., The cleavage of angiotensin I by B . g ., rhinoceros and B . nasicornis venoms was strongly abolished when both antivenoms were used ., Nevertheless , the angiotensin I hydrolysis by B . arietans was weakly blocked by α-Br+Bn antivenom and partially inhibited by α-Ba ( Fig . 8B ) ., Venoms from Bitis snakes remain poorly studied in spite of their involvement in an alarming number of life-threatening accidents in Africa ., In the Sub-Saharan Africa most of the victims are rural workers engaged in traditional agricultural-pastoral labor , which live far from appropriate medical care , critically dependent on the availability of effective antivenom 26 ., Furthermore , the antivenom therapy is not always reliable and effective to prevent the morbidity and mortality following the envenomation for all Bitis species ., Even the most efficient and safe polyspecific antivenom , currently used for treating Bitis bite , the South African Institute of Medical Research ( SAIMR ) antivenom , is ineffective and should not be used in the treatment of bites caused by B . atropos , B . cornuta and B . caudalis while FavAfrique is mostly recommended for treatment of accidents by B . arietans , B . gabonica and B . nasicornis 26–28 ., Specific therapies tackling the different pathways activated during the envenomation and not focused only on controlling the symptoms should be taken into account during the development of new treatments 2 ., Nevertheless , the development of new treatments depends on in-depth understanding of the mechanisms by which the venom toxins cause pathology ., The low efficacy of the current treatments for envenomation by all the species of Bitis is a result of the paucity of information available and the high diversity among the Bitis species ., These facts lead us to investigate the biochemical and enzymatic characteristics of the three Bitis venom of medical importance , Bitis arietans , Bitis gabonica rhinoceros and Bitis nasicornis and focus on the toxic activities of these venoms that could be associated with the symptoms observed following the accidents with humans ., The electrophoretic profile showed that the protein components vary among the three Bitis species analyzed in this study , corroborating with data from previous biochemical 29 , proteomic 5 and genomic 4 studies ., In many animal venoms , Phospholipase A2 ( PLA2 ) is important for immobilization an digestion of the prey , as well as responsible for a variety of toxic and pharmacological actions , some of which are associated with the pathophysiology of snakebite envenoming ( reviewed in 30 ) ., All Bitis venoms here analyzed presented phospholipase activity , as also described by Calvete and colleagues in their proteomic study 4 ., Hyaluronidase activity , which is involved in a large number of biological functions including the diffusion of venom toxins from the bite site into the tissues and circulation ( reviewed in 31 ) , was also detected in the three Bitis venoms here studied ., The venoms from B . arietans and B . nasicornis also contain enzymes with gelatinolytic activity , which are absent from B . g ., rhinoceros venom ., Similarly , it was reported that the venom of B . parviocula is also devoid of gelatinolytic activity 32 ., It is interesting that the high molecular weight band with gelatinolytic activity , in both venoms , was inhibited by PHE and PMSF suggesting that these components are serineproteinases with some metal dependence to maintain their structural integrity or catalytic site’s function or , that these venoms present serine- and metalloproteinases with similar molecular mass ., However , the band with lower molecular weight and gelatinolytic activity , detected in Bitis nasicornis venom , was inhibited by PMSF but neither affected by EDTA nor PHE , indicating that a serineproteinase is responsible for this activity ., Some toxins isolated from venoms can have this dual effect upon certain substrates and be inhibited by both PMSF and PHE 33 while others , as the calcium-and zinc-independent gelatinases can be unaffected by EDTA , PHE nor PMSF 34 ., The metalloproteases present in B . arietans and B . g ., rhinoceros venoms also displayed hydrolytic activity on peptide Abz-RPPGFSPFRQ-EDDnp whilst , in the venom of B . nasicornis , this cleavage was mediated by serineproteinases ., Interestingly , the results obtained with B . nasicornis are comparable with previous report using the venom of Bothrops jararaca 35 ., Some studies showed that venoms from Bitis ssp interfere with the coagulation cascade by direct cleavage of its proteins or inducing its over-activation 36–38 ., Our data shows that metalloproteinases present in the venoms of B . arietans and B . nasicornis cleave the α- and β- chains of the fibrinogen whilst , the metalloproteinases of B . g ., rhinoceros venom cleave only the fibrinogen α-chain , as thrombin ., Metalloproteinases with fibrinogenolytic activity are present in a large number of snake venoms 39 , but unlike the thrombin , these enzymes are not inhibited by serpins , the natural inhibitor of fibrinogenolytic serineproteinases 40 ., In normal conditions , the cleavage of fibrinogen by thrombin results in clot formation that facilitate platelet aggregation and/or the formation of monomers of fibrin 41 ., Cleavage of fibrinogen’s α- and β- chains was also observed by Sanchez and colleagues 32 who demonstrated that B . arietans and B . parviocula venoms are very hemorrhagic and interfere with the coagulation cascade by delaying the activated clotting time and clotting rate ( time in which fibrin formation begins ) ., In addition , Bitis nasicornis venom also has an anticoagulant effect in vitro as demonstrated by abnormalities in the pro-thrombin time and prothrombin consumption 36 ., Low blood pressure is also commonly observed in patients envenomed by Bitis ssp venoms 42 , 43 , therefore , our interest in investigating the presence of vasopeptidases , enzymes responsible for the generation or inactivation of vasoactive peptides ., Metalloproteinases from Bitis arietans venom cleaved angiotensin I ( DRVYIHPFHL ) with high specificity generating , preferentially , angiotensin 1–7 ( DRVYIHP ) whilst , B . g ., rhinoceros and B . nasicornis venoms generated angiotensin 1–7 with lower specificity ., Angiotensin 1–7 is a counter-regulator of cardiovascular effects of angiotensin II that contributes to vasodilatation 44 , increase of nitric oxide and acts upon the platelets causing inhibition of adhesion and aggregation 45 ., In addition , all Bitis venoms also generated other fragments such as RVYIHPFHL , VYIHPFHL and YIHPFHL , most of them bioactive 46 , suggesting the presence of a metal-dependent protease with similarity to aminopeptidases ., These data corroborate with prior findings of Vaiyapuri and colleagues 47 that purified and characterized an aminopeptidase from Bitis gabonica rhinoceros venom , named rhiminopeptidase , that removes basic and neutral aminoacids from the N-terminus of the peptides ., Moreover , B . g ., rhinoceros and B . nasicornis generated another fragment RVYIHP ( see Fig . 6B ) ., The proteolytic activity of Bitis venoms described in this study was efficiently inhibited by the experimental antivenoms raised against B . arietans and B . g ., rhinoceros plus B . nasicornis venoms ., The only exception was the gelatinolytic activity of the serineproteinase with ~30kDa present in B . nasicornis venom ., In all the experiments , α-Ba and α-Br + Bn sera inhibited the proteases activity of the venom against it was produced but also the activity of the venom from others species of Bitis , except for the fibrinogenolytic activity of B . arietans venom that was not inhibited by α-Br + Bn ., The inhibition by the antivenoms was specific , since horse F ( ab’ ) 2 fragments produced against a non-related toxin , the botulinic toxin , did not abolish any of the enzymatic activities reported here for the Bitis venoms ., Both antivenoms were also able to strongly inhibit the PLA2 activity from B . arietans and B . g ., rhinoceros venoms ., However , the phospholipase activity of B . nasicornis venom was only weakly blocked by the two antivenoms ., In 2010 , Calvete and colleagues 48 , using immunodepletion approach , showed that the majority of the proteins in the crude venom from B . arietans , B . gabonica , B . rhinoceros including metalloproteinase , serinoproteinase , C-type lectin among others immunoreacted with the immunoglobulins of the Echi-Tab-Plus-ICP antivenom ., Interestingly , they also demonstrated that this antivenom could only partially react with some phospholipases A2 and disintegrins ., Envenomed patients seldom recognize the species of snake involved in the accident and this identification is normally based on the symptoms that are common among the species of the same genus ., Overall , our data suggest that an efficient venom neutralization of the three species of medical importance in Africa could be achieved by pooling the two horse experimental antivenoms , i . e . , the one against B . arietans and the other against B . gabonica rhinoceros plus B . nasicornis venoms ., In conclusion , in this report we have functionally characterized the venom from three species of Bitis involved in accidents with humans in the Sub-Saharan Africa ., These venoms possess a combination of proteases that direct affect the coagulation system via cleavage of fibrinogen , which can , consequently , prevent platelets aggregation and the systems involved in the modulation of blood pressure regulation and salt balance via generation of active vasopeptides ., We also demonstrated that some of the deleterious activities present in Bitis venoms can be efficiently blocked by antivenoms produced against B . arietans or B . g ., rhinoceros plus B . nasicornis venoms .

Introduction, Material and Methods, Results, Discussion

An alarming number of fatal accidents involving snakes are annually reported in Africa and most of the victims suffer from permanent local tissue damage and chronic disabilities ., Envenomation by snakes belonging to the genus Bitis , Viperidae family , are common in Sub-Saharan Africa ., The accidents are severe and the victims often have a poor prognosis due to the lack of effective specific therapies ., In this study we have biochemically characterized venoms from three different species of Bitis , i . e . , Bitis arietans , Bitis gabonica rhinoceros and Bitis nasicornis , involved in the majority of the human accidents in Africa , and analyzed the in vitro neutralizing ability of two experimental antivenoms ., The data indicate that all venoms presented phospholipase , hyaluronidase and fibrinogenolytic activities and cleaved efficiently the FRET substrate Abz-RPPGFSPFRQ-EDDnp and angiotensin I , generating angiotensin 1–7 ., Gelatinolytic activity was only observed in the venoms of B . arietans and B . nasicornis ., The treatment of the venoms with protease inhibitors indicated that Bitis venoms possess metallo and serinoproteases enzymes , which may be involved in the different biological activities here evaluated ., Experimental antivenoms produced against B . arietans venom or Bitis g ., rhinoceros plus B . nasicornis venoms cross-reacted with the venoms from the three species and blocked , in different degrees , all the enzymatic activities in which they were tested ., These results suggest that the venoms of the three Bitis species , involved in accidents with humans in the Sub-Saharan Africa , contain a mixture of various enzymes that may act in the generation and development of some of the clinical manifestations of the envenomations ., We also demonstrated that horse antivenoms produced against B . arietans or B . g ., rhinoceros plus B . nasicornis venoms can blocked some of the toxic activities of these venoms .

In this report we have characterized the venoms from three species of Bitis snakes involved in accidents with humans in the Sub-Saharan Africa , i . e . , Bitis arietans , Bitis gabonica rhinoceros and Bitis nasicornis ., These venoms possess a combination of proteases that can directly affect the coagulation system and the systems involved in the modulation of blood pressure regulation and salt balance via generation of vasoactive peptides ., We also demonstrated in vitro that the deleterious effects of these venoms can be efficiently blocked by experimental horse antivenoms produced against B . arietans or B . g ., rhinoceros plus B . nasicornis venoms .

journal.pntd.0007434

Preexisting chronic conditions for fatal outcome among SFTS patients: An observational Cohort Study

SFTS is an emerging infectious disease that was first reported in 2009 in rural areas in central China ., SFTSV is principally transmitted to human by tick bites in natural foci , with a possible human-to-human transmission through contacts with infected blood or body fluid 1 ., After infection , patients experienced an extensively wide clinical spectrum , with some experiencing self-limiting clinical course , while approximately 16 . 2% ( 95% CI: 14 . 6%-17 . 8% ) developing fatal outcome 2 ., Until now , the risk factors for the fatal outcome and pathogenesis mechanisms underlying the fast proceeding to fatal SFTS remained sparsely investigated 2–4 ., As has been displayed widely , numerous factors can influence the outcome of viral infections , including but not restricted to pathogen factors , host genetic susceptibility , host immunity response , host comorbidity conditions , and the effects of therapy 5–7 ., All of these factors play in a complex way to determine the final outcome after viral infection ., For SFTSV infection , our knowledge of the host-related factors that influence the pathogenesis of disease is far from adequate to allow prediction of fatal outcome ., Only older age has been associated with higher risk of fatal outcome with consistent conclusion 8–10 ., Preexisting chronic conditions , on the other hand , although long been considered to increase risk of death in a range of viral diseases , were rarely studied in SFTS 11 , 12 ., Moreover , the specific classification of the diseases was not assigned , therefore making the identification of high-risk population unlikely to attain ., In the current study , we are designed to characterize the prevalence of the common preexisting comorbidities related to the metabolic syndromes-associated diseases , including hyperlipidemia , hypertension , chronic viral hepatitis ( CVH ) , diabetes mellitus ( DM ) , cerebral ischemic stroke , heart diseases , chronic obstructive pulmonary diseases ( COPD ) , pulmonary tuberculosis and cancer in SFTSV infections and to evaluate their effect on diseases outcome ., Considering the potential interaction between SFTS and comorbidities such as DM , hyperlipidemia , hypertension on resulting in endothelial dysfunction 13–17 , we further measured the level of adhesion factors in patients with or without comorbidities as an indicator of endothelial activation/dysfunction ., The study was performed on a prospectively observed cohort of SFTSV infected patients who were recruited in Peoples Liberation Army ( PLA ) 154 hospital ( now named as The 990 Hospital of Chinese Peoples Liberation Army Joint Logistic Support Force ) ., The basic information on the cohort has been described in our previous study 2 ., Briefly , the hospital is located in Xinyang city , which is located at the southern part of Henan Province , bordering the provinces of Anhui and Hubei to the east and south respectively , representing one of the most severely inflicted cities in central China ., Since the beginning of SFTS surveillance in 2011 till 2017 , the hospital had diagnosed and treated the largest number ( over 30% ) of total SFTS cases in China 18 ., All the 2096 laboratory-confirmed SFTSV patients were used for the analysis , who met one or more of the following criteria: ( 1 ) a positive SFTSV culture ( 2 ) a positive result for SFTSV RNA by real-time RT-PCR assay ( 3 ) seroconversion or ≥4 fold increase of antibody titers between 2 serum samples collected at least 2 weeks apart ., The medical record of all the hospitalized patients was maintained in an electronic system with logic error correction function , ensuring the credibility of the data ., For the current research , a medical record review was performed to collect the information on demographic characteristics , preexisting comorbidities , clinical information , laboratory test results and treatment regimens during the entire hospitalization ., The clinical information mainly included symptoms and signs that were recorded from the daily physical examination ., The extracted laboratory results mainly included hematology , clinical chemistry , urinalysis and live function examination , which were prescribed on hospital admission and during the hospitalization ., Other laboratory indicator included blood cultures , HIV , HBV , HCV-specific antigen and antibody testing , electrocardiogram as well as chest radiograph test , which were prescribed on hospital admission ., These data were drawn from the database by a group of trained physicians using a standardized format and entered into an EpiData database ., The data were further reviewed for accuracy and consistency by a second group of epidemiologists ., For the patients who had missing information , a trained study staff interviewed the patients or their family using a standardized supplemental questionnaire ., The comorbidities that were used for the current analysis included diabetes mellitus ( both type I and type II , ICD-10 E14 . 8 ) , hypertension ( ICD-10 I10 . X02 ) , hyperlipidemia ( ICD-10 E78 . 500 ) , chronic virus hepatitis ( both HBV and HCV , ICD-10 B18 . 951 ) , cerebral ischemic stroke ( ICD-10 I64 . X04 ) , chronic obstructive pulmonary diseases ( ICD-10 J44 . 900 ) , pulmonary tuberculosis ( ICD-10 B90 . 901 ) , cancer ( ICD-10 C00-C97 ) , heart diseases ( ICD-10 I51 . 900 , due to the small sample size of individual conditions , cardiac heart failure , coronary atherosclerotic heart disease , arrhythmia and other heart diseases were combined into this category ) ., The virus load was determined using real-time reverse transcriptase polymerase-chain-reaction ( RT-PCR ) targeting the same gene segment ., Serum levels of ten adhesion factors were determined by the ProcartaPlex multiplex immunoassays panels ( Affymetrix , USA ) according to the manufacturer instructions ., The measurement of 25 cytokines levels was performed for the serum samples of the survived patient by using Cytokine Human 25-Plex Panel ( Life Technologies , USA ) ., The serum samples tested adhesion factors and cytokines were collected on admission and all were within seven days after the onset of disease ., Continuous variables were summarized as means and standard deviations ( SD ) or as medians and interquartile range ( IQR ) ., Categorical variables were summarized as frequencies and proportions ., An independent t test , a χ2 test , a Fisher exact test , or a nonparametric test was used where appropriate to calculate the differences between groups ., Logistic regression model was applied to explore the association between comorbidities and clinical information or fatal outcome ., The generalized estimating equation ( GEE ) was constructed to compare the laboratory parameters that were evaluated over time ., Cytokine and adhesion factors were compared between groups after performing 10 logarithmic transformations using generalized linear model ( GLM ) ., Age , sex , time from disease onset to admission and treatment regimens ( ribavirin , corticosteroid and immunoglobulin ) were adjusted in the above models ., Odds ratios ( ORs ) and their 95% confidence intervals ( CIs ) were estimated ., A two-sided P < 0 . 05 was considered statistically significant ., All analyses were performed using Stata 14 . 0 ( Stata Corp LP , College Station , TX , USA ) ., The study protocol was approved by the human ethics committee of the hospital PLA 154 ., Written or verbal informed consent had been obtained from all the patients or from parents/guardians on behalf of all pediatric participants ., A total of 2096 laboratory-confirmed SFTS patients who were hospitalized from 2011 to 2017 were used for analysis 2 ., The mean ( SD ) of the age was 61 . 4 ( 12 . 2 ) years old , and 1239 ( 59 . 1% ) were female ., Overall , the presence of preexisting comorbidities was reported in 779 ( 37 . 2% ) of the patients ., Hyperlipidemia was the most prevalent comorbidity ( n = 256; 12 . 2%; 95% CI: 10 . 8%-13 . 6% ) , followed by hypertension ( n = 230; 11 . 0%; 95% CI: 9 . 6%-12 . 3% ) , CVH ( n = 195; 9 . 3%; 95% CI: 8 . 1%-10 . 5% ) , and DM ( n = 142; 6 . 8%; 95% CI: 5 . 7%-7 . 9% ) and other diseases ( Fig 1 ) ., The SFTS patients with the comorbidities had older age and longer time from disease onset to admission compared those without ( both P<0 . 001 ) ( Table 1 ) ., For the 195 patients with CVH , 179 ( 91 . 8% ) were infected with HBV , 15 ( 7 . 7% ) with HCV and 1 ( 0 . 5% ) with both infection ., Among the patients , 179 had two kinds of comorbidities and 41 had three or more , mostly observed between hyperlipidemia and others ( Table 2 ) ., The case fatality rate ( CFR ) among the patients with any kind of comorbidity was 22 . 5% ( 175/779 ) , significantly higher than those without ( 12 . 5%; 165/1317; adjusted OR = 1 . 628; 95% CI: 1 . 265–2 . 096; P<0 . 001 ) ( S1 Table ) ., When the comorbidities were assessed individually , hypertension , DM , CVH and COPD were significantly associated with the development of fatal outcome ( all P<0 . 05 ) ., However , after adjusting the effect from age , sex , delay from disease onset to admission and treatment regimens ( ribavirin , corticosteroid and immunoglobulin ) , the significance only remained for DM ( adjusted OR = 2 . 304; 95% CI: 1 . 520–3 . 492; P<0 . 001 ) , CVH ( adjusted OR = 1 . 551; 95% CI: 1 . 053–2 . 285; P = 0 . 026 ) and COPD ( adjusted OR = 2 . 170; 95% CI: 1 . 215–3 . 872; P = 0 . 009 ) ( Fig 2 and S1 Table ) ., The presence of over one kind of comorbidity was associated with enhanced risk of death , with the coexistence of DM & CVH having significantly higher odds ratio of developing fatal outcome ( adjusted OR = 4 . 792; 95% CI: 1 . 345–17 . 077; P = 0 . 016 ) , in comparison with those without any comorbidity , thus indicating an interaction between them ( Table 2 ) ., We applied a logistic regression model with stepwise method to adjust the potential interaction effects that might be derived from inter-comorbidities ., The analysis showed three significant comorbidities in the model that attained higher risk of death: the presence of DM ( adjusted OR = 2 . 328; 95% CI: 1 . 534–3 . 532; P<0 . 001 ) , CVH ( adjusted OR = 1 . 557; 95% CI: 1 . 056–2 . 296; P = 0 . 025 ) , and COPD ( adjusted OR = 2 . 138; 95% CI: 1 . 195–3 . 825; P = 0 . 010 ) ., Clinical manifestations and laboratory assessments were compared between two groups ., At presentation , three of the commonly seen signs or symptoms , including dizziness , headache , chills and gastrointestinal symptoms were more frequently observed in SFTS patients with comorbidities than those without ., Severe complications , including respiratory symptoms , neurological symptoms and haemorrhagic symptoms developed with higher frequency in SFTS patients with any kind of comorbidities than those without ( Table 1 ) ., The GEE analysis displayed levels of alanine transaminase ( ALT ) , aspartate aminotransferase ( AST ) , white blood cell ( WBC ) , creatine kinase ( CK ) , globulin ( GLB ) and lactate dehydrogenase ( LDH ) were significantly elevated , while the level of albumin ( ALB ) significantly decreased among the SFTS patients with comorbidities ., No differences in SFTSV viral load or platelet counts were observed between the two groups ( Fig 3 ) ., Altogether 142 patients with DM and 1954 without were compared for their clinical manifestations and laboratory indicators ., Most of the initial symptoms and signs were reported from two groups with similar frequencies ( S2 Table ) ., On the other hand , respiratory and neurological complications were more likely to develop in the patients with DM than in patients without ., The dynamic profiles of laboratory parameters during the whole course were similar between two groups , except three higher levels of laboratory indicators ( GLB , LDH and SFTSV viral loads ) and lower levels of ALB in the patients with DM than those without ( Fig 4 ) ., When the patients were further grouped according to the maximum glucose level during the whole hospitalization ( S3 Table ) , a dose dependent effect was displayed as the decrease in ALB , together with elevation in AST , ALT , WBC , CK , GLB , LDH and SFTSV viral loads were negatively correlated with the glucose level ( Fig 5 ) ., Moreover , the glucose level significantly affected the risk of death ( Fig 6A ) ., Compared to the group with glucose < 7 . 0 mmol/L , patients with glucose between 7 . 0–11 . 1 mmol/L and glucose ≥11 . 1 mmol/L had higher death risk , with the adjusted OR estimated to be 1 . 467 ( 95% CI: 1 . 081–1 . 989; P = 0 . 014 ) and 3 . 443 ( 95% CI: 2 . 427–4 . 884; P<0 . 001 ) , respectively ( Table 3 ) ., The DM patients who received insulin therapy over four times had significant lower glucose level ( S4 Table and Fig 6B ) , which conferred a significantly reduced risk of fatal outcome ( adjusted OR = 0 . 146; 95% CI: 0 . 058–0 . 365; P<0 . 001 ) ( Table 3 ) ., Totally 139 patients had ten adhesion factors evaluated , including 50 patients with glucose level ≥ 7 . 0 mmol/L on admission ( S5 Table ) ., Only serum amyloid antigen 1 ( SAA-1 ) was found to be elevated in the patients with glucose ≥ 7 . 0 mmol/L than those with glucose <7 . 0 mmol/L ( Fig 7 ) ., Altogether 64 SFTS patients had their serum cytokines measured on admission , including 17 patients with glucose exceeding 7 . 0 mmol/L ( S6 Table ) ., Six of the 25 tested cytokine , including Interleukin-1RA ( IL-1RA ) , Interleukin-2 ( IL-2 ) , IL-4 , IL-6 , Granulocyte macrophage-stimulating factor ( GM-CSF ) , Interferon-γ ( IFN-γ ) were significantly higher in SFTS patients with high glucose level ( Fig 8 ) ., Altogether 195 patients with CVH and 1901 without were compared for their clinical manifestations and laboratory indicators ., Most of the non-specific signs or symptoms were comparable between the CVH and non-CVH groups ( S7 Table ) ., Higher frequency of neurological symptoms ( 33 . 3% vs . 25 . 6%; adjusted P = 0 . 045 ) and haemorrhagic symptoms ( 45 . 1% vs . 34 . 0%; adjusted P = 0 . 017 ) were disclosed in the CVH group ., Higher ALT , AST , CK , GLB concentration , lower platelet counts and ALB level were also related to the presence of CVH in SFTS patients ( Fig 9 ) ., Totally 98 CVH and 867 non-CVH patients with SFTSV infection had blood coagulation function test on admission available for analysis ( S8 Table ) ., The patients with CVH developed more prolongation of the prothrombin time ( PT ) and activated partial thromboplastin time ( APTT ) and thrombin time ( TT ) , all indicating occurrence of disseminated intravascular coagulation ( DIC ) ., The pattern of these parameters corresponded with decreased platelet and high prevalence of bleeding phenotype ( Fig 10 ) ., Over the past few years , several lines of evidence have supported the notion that cardiovascular disease , stroke , diabetes , respiratory diseases and renal disorders may contribute , together with old age , to severe dengue disease 19–21 ., Studies on West Nile virus 22 , Japanese encephalitis virus 23 infections , and responses to Yellow fever virus vaccination 24 , have also supported the pathogenic role of chronic comorbidities in the prognosis of infections ., Since the discovery of SFTS , although clinical phenotypes have been developed to differentiate the patients with high risk of death , host factors remained sparsely investigated ., In this study , we demonstrated the frequency of underlying conditions in SFTSV infected patients and determined their role in developing fatal outcome ., Hyperlipidemia and hypertension are the most prevalent comorbidity , while DM , CVH and COPD were more prominent in their association with fatal outcome , with 1 . 551–2 . 304 fold increase in their risk of death than the SFTS patients without comorbidities ., From the perspective of clinical features , neurological manifestation and hemorrhagic signs were more frequently seen in patients with underlying diseases , both contributing to the final fatal outcome ., A common pathogenic feature of SFTS infection is their ability to inhibit the host immune response , characterized by significantly reduced CD3-positive and CD4-positive T lymphocytes than normal 25 ., This is consistent with the clinical phenomenon that most infection occurred in the elderly , who are considered to possess compromised T-cell function 26 ., The association between DM , CVH and severe diseases might also be related to immune dysfunction ., Abnormal innate and adaptive immunity used to be disclosed in DM patients , reflected by alterations in proliferation of T cells and macrophages , and impairment in function of NK cells and B cells in DM patients 27 ., CVH , either hepatitis B or hepatitis C , was capable of inhibiting the adaptive or innate immune response 28 ., This supported the hypothesis that DM and CVH , in combination with SFTSV infection , might impair the immune system and attenuate anti-inflammatory responses , subsequently resulting in increased level and prolonged duration of viremia , which predispose patients to higher risk of death ., In addition , the preexisting DM is often linked to vascular complications featured by an activation of the inflammation cascade and endothelial dysfunction 13 , which are also identified in SFTSV infection 17 ., In line with this mechanism , we observed a remarkably enhanced expression of SAA-1 , a biomarker of endothelial dysfunction 17 , in DM-SFTS than SFTS alone ., Cytokine storm had been extensively found to play roles in the pathogenesis of SFTS 29 ., Based on the current findings , IL-1RA , IL-2 , IL-4 , IL-6 , GM-CSF and IFN-γ were elevated to remarkably high levels in DM-SFTS patients , likely contributing to the fatal outcome , together with the development of depressed immunity and endothelial dysfunction ., All these indicators showed potential to predict adverse outcome ., The insulin therapy , on the other hand , had obviously reduced the disease severity ., Therefore , it’s justified to actively identify and treat high glucose in SFTSV infection , in order to attain extra benefit of reducing viremia and enhancing disease outcomes ., Differing from DM-SFTS , the CVH-SFTS patients were prone to have higher incidence of bleeding manifestation than those without ., In line with these findings , abnormal coagulation factors , including platelet and others , were more frequently seen in CVH-SFTS ., An interactive effect on liver damage from CVH and SFTS was observed , as liver function related enzymes , especially AST , ALT and ALB , demonstrated remarkable deviation from normal level , which was indicative of progressive hepatic involvement in those patients ., As liver is the primary source for producing coagulation factors 30 , 31 , it is reasonable to deduce that the interactive effect from SFTSV and hepatitis virus can predispose the patient to more frequent bleeding ., It’s noteworthy that ALB was constantly reduced in patients with DM , or CVH or any kind of comorbidity ., ALB is the most abundant protein in plasma , representing the main determinant of plasma oncotic pressure and the main modulator of fluid distribution between body compartments ., ALB plays an import role of in plasma leakage that could be a parameter to predict the severity of diseases 32 , 33 ., Recently , the endothelial dysfunction and plasma leakage had been identified in SFTSV infection and most viral hemorrhagic fever , manifested by fluid loss from the vascular compartment and by decreased level of ALB 20 , 34 , 35 ., It is hypothesized that hypoalbuminemia could be induced from a synergetic effect of comorbidity and SFTSV infection , eventually contributing to the high morbidity and mortality ., As such , albumin administration in SFTS patients might be effective in improving the disease outcome ., The study is subject to major limitation that when assessing comorbidities , we did not allow for differentiating between those diagnosed before , after or during the infectious episodes ., Therefore , the causality between the conditions and adverse outcome cannot be inferred ., However , even in the absence of causal inference between the non-communicable and infectious diseases , these findings may guide clinicians to predict complications , at least partially , based on the presence of comorbidity ., In addition , we made no efforts to distinguish type 1 from type 2 diabetes for separate analysis , despite of their differential clinical features and etiological factors ., Instead we used the glucose level as major variable to explore the dose effect of glucose on the disease severity ., Moreover , the clinical status that were acquired from these patients were only partially used , and due to the high cost of testing adhesion factors and interleukins , we did not evaluate the entire population for these indicators , which might have caused selection bias for the inter-group comparison ., In conclusion , we provided evidence for a higher prevalence of DM , CVH and COPD in fatal SFTS patients , elucidating the possible mechanism that underlies their interactive effect in resulting in adverse outcome ., This knowledge might allow clinical physicians to identify the patients with preexisting comorbidities who may progress to a severe course , thereby to adopt aggressive interventions at early infection .

Introduction, Methods, Results, Discussion

Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging infectious disease that is caused by a novel bunyavirus SFTSV ., Currently our knowledge of the host-related factors that influence the pathogenesis of disease is inadequate to allow prediction of fatal outcome ., Here we conducted a prospective study of the largest database on the SFTS patients , to identify the presence of comorbidities in SFTS , and estimate their effect on the fatal outcome ., Among 2096 patients eligible for inclusion , we identified nine kinds of comorbidities , from which hyperlipidemia ( 12 . 2%; 95% CI: 10 . 8%–13 . 6% ) , hypertension ( 11 . 0%; 95% CI: 9 . 6%–12 . 3% ) , chronic viral hepatitis ( CVH ) ( 9 . 3%; 95% CI: 8 . 1%–10 . 5% ) , and diabetes mellitus ( DM ) ( 6 . 8%; 95% CI: 5 . 7%–7 . 9% ) were prevalent ., Higher risk of death was found in patients with DM ( adjusted OR = 2 . 304; 95% CI: 1 . 520–3 . 492; P<0 . 001 ) , CVH ( adjusted OR = 1 . 551; 95% CI: 1 . 053–2 . 285; P = 0 . 026 ) and chronic obstructive pulmonary diseases ( COPD ) ( adjusted OR = 2 . 170; 95% CI: 1 . 215–3 . 872; P = 0 . 009 ) after adjusting for age , sex , delay from disease onset to admission and treatment regimens ., When analyzing the comorbidities separately , we found that the high serum glucose could augment diseases severity ., Compared to the group with max glucose < 7 . 0 mmol/L , patients with glucose between 7 . 0–11 . 1 mmol/L and glucose ≥11 . 1 mmol/L conferred higher death risk , with the adjusted OR to be 1 . 467 ( 95% CI: 1 . 081–1 . 989; P = 0 . 014 ) and 3 . 443 ( 95% CI: 2 . 427–4 . 884; P<0 . 001 ) ., Insulin therapy could effectively reduce the risk of severe outcome in DM patients with the adjusted OR 0 . 146 ( 95% CI: 0 . 058–0 . 365; P<0 . 001 ) ., For CVH patients , severe damage of liver and prolongation of blood coagulation time , as well as high prevalence of bleeding phenotype were observed ., These data supported the provocative hypothesis that treating SFTS related complications can attain potentially beneficial effects on SFTS .

SFTS now brings about a substantial global public health concern ., Preexisting chronic conditions were thought to increase risk of severe SFTSV infections , however with sparse data mining efforts ., In this study , we quantified the frequency of chronic comorbidities in SFTS , estimated their contribution to disease severity , and separately evaluated the effect from diabetes mellitus and chronic viral hepatitis on resulting in fatal outcome .

innate immune system, medicine and health sciences, immune physiology, cytokines, chemical compounds, pathology and laboratory medicine, viral transmission and infection, immunology, microbiology, carbohydrates, organic compounds, glucose, pulmonology, diabetes mellitus, chronic obstructive pulmonary disease, developmental biology, endocrine disorders, signs and symptoms, molecular development, antibodies, viral load, immune system proteins, proteins, endocrinology, chemistry, hypertension, blood pressure, hyperlipidemia, metabolic disorders, immune system, biochemistry, diagnostic medicine, organic chemistry, virology, physiology, monosaccharides, biology and life sciences, physical sciences, vascular medicine

journal.pntd.0004851

Extended Evaluation of Virological, Immunological and Pharmacokinetic Endpoints of CELADEN: A Randomized, Placebo-Controlled Trial of Celgosivir in Dengue Fever Patients

Dengue fever is a mosquito-borne viral illness that is endemic in tropical regions around the world , with an estimated 96 million cases of dengue illness annually 1 ., Dengue is one of 17 neglected tropical diseases that the World Health Organization ( WHO ) has identified for priority attention due to its disproportionate impact on global health , with cases reported from over 100 countries 2 ., Singapore maintains an aggressive mosquito control program 3 , with spending by the National Environment Agency approaching US$50 million annually ., These efforts have successfully driven the proportion of households harboring the Aedes mosquito , the vector for dengue , to historic lows of less than 1% ., Yet , in the last decade , the incidence rates have continued to climb , with the highest rate recorded in 2013 of 404 . 9 cases per 100 , 000 with 8 deaths 4 ., Currently , there are no approved drugs for dengue ., Vaccine development has been underway since the 1970s 5 , an extraordinarily challenging effort because immunity to one serotype does not confer protection against the others ., Furthermore , a phenomenon known as antibody-dependent enhancement ( ADE ) posits that antibodies to one serotype from a previous dengue infection increases the risk of more serious forms of the illness , dengue hemorrhagic fever ( DHF ) or dengue shock syndrome ( DSS ) 6 , 7 ., Indeed , the proportion of DHF among patients with secondary dengue is much higher than those with primary dengue 8 , 9 ., Therefore , vaccine development has proceeded under the premise that a vaccine must protect against all four dengue serotypes; otherwise , potentially more serious outcomes may ensue if the subject achieves only partial immunity ., Sanofi’s tetravalent dengue vaccine CYD-TDV achieved 56% and 65% efficacy in Phase 3 field studies in Southeast Asia and Latin America , respectively ., Protection was serotype dependent with protective efficacy for DENV 2 of 35% and 42% in the two trials 10 , 11 ., In December 2015 , a number of countries , namely Mexico , Brazil , and the Philippines , approved CYD-TDV for use as a dengue vaccine ., However , the modest vaccine efficacy , particularly in patients with no previous history of dengue infection , the higher hospitalization rate among vaccinated children younger than 9 years in the third year of follow-up 12 , 13 , and the extended timeframe required to implement large-scale vaccination programs underscores a continuing need to discover and develop dengue drugs that can be used alongside vaccines ., Since 2008 , five randomized , controlled trials of dengue drugs have been completed 14–18 All adopted a strategy of repositioning , or using drugs for which human safety data were already available from approved drugs or from trials of other clinical indications ., Celgosivir , an inhibitor of alpha-glucosidase , a host enzyme required for proper processing of viral surface glycoproteins , has been given to hundreds of patients in HIV and HCV trials , but further pharma-driven development of celgosivir was discontinued because approved drugs for those indications had better or equivalent efficacy in Phase 2 trials 19 , 20 ., When tested in cell-based assays and an animal model of dengue infection , celgosivir demonstrated submicromolar activity and prevented death in mice infected with an otherwise lethal dose of virus 21 , 22 ., The strong preclinical pharmacology results motivated the conduct of a Phase 1b randomized , double-blind , placebo-controlled trial in 50 adult dengue patients ( CELADEN , NCT01619969 ) ., Although the trial did not meet the primary endpoints of lowering viremia or fever 17 , examination of data for secondary endpoints , presented here , as well as additional studies using a mouse model of infection 23 have provided insights for a new Phase 2a clinical trial with an altered regimen of celgosivir ( NCT02569827 ) ., The analysis presented here may also be informative for the design of other drug trials for dengue fever ., Celgosivir was synthesized according to US patent 5 , 017 , 563 24 by selective C-acylation of castanospermine ( MedChem 101 LLC ) ., A suspension of castanospermine and bis ( tributyl tin ) oxide ( 1:2 molar ratio ) in toluene ( 30 vol ) was refluxed under argon for 3 h ., The solution was cooled down to -17°C , then butyryl chloride ( 1 . 8 molar excess ) was added dropwise over a 10 min period ., The mixture was stirred at room temperature for 2 h ., Absolute EtOH was added , and the mixture was stirred for 30 min , followed by addition of 1 . 5M HCl/EtOH solution ( 2-fold molar ratio to butyryl chloride ) ., The mixture was stirred over night ( 18 h ) at room temperature then for 1 h at +3°C ., The precipitate was filtered , washed with hexane , and dried in vacuo ., The compound was recrystallized to obtain a product with > 99% purity by HPLC ., Celgosivir was synthesized , purified , capsuled in 100 mg doses , and packaged into blister packs at a GMP facility , Dalton Pharma Services ( Toronto , Ontario , Canada ) ., USP pregelatinized maize starch was prepared in identical capsules and blister packs for placebo ., Patient samples were obtained from a randomized , double-blind and placebo-controlled proof-of-concept trial ( CELADEN ) in Singapore to assess the efficacy and safety of celgosivir in patients with dengue fever 17 ., The Trial Protocol is included here again as S1 Text ., The Consort flowchart showing the CELADEN Trial Profile was published previously 17 and is included in this manuscript as Fig 1 ., The inclusion and exclusion criteria for CELADEN Trial was described previously and is included here as S1 Fig . The Dengue Duo ( SD Diagnostics ) point-of-care diagnostic kit was used to screen for dengue infection ., It consists of two tests , one for serum NS1 and the other for dengue immunoglobulins ( IgM and IgG ) ., Fifty dengue patients identified by Dengue Duo Diagnostics and with fever >38°C for less than 48 hr were randomly assigned ( 24 to celgosivir , 26 to placebo ) , of which 14 had DENV-1 , 32 had DENV-2 and 4 had DENV-3 infection ., The patients were housed in the clinical trial facility at the Singhealth Investigational Medicine Unit for five days during the acute illness period and returned to the study center for follow-up examinations on study days 7 , 10 and 15 ., Immunoglobulin M antibody capture or dengue IgG indirect ELISA ( Panbio Diagnostics , Providence RI ) were performed on baseline samples to identify primary and secondary infection status as described previously 17 ., The study was approved by the Singapore Health Services’s Centralized Institutional Review Board ( CIRB Ref:2012/025/E ) —and monitored independently by the Singapore Clinical Research Institute ( SCRI ) , a publicly funded clinical research organization ( CRO ) 17 ., All subjects between the ages of 21–65 years provided written consent to participate in the inpatient trial as described in detail in our previous publication 16 ., It should be noted that all deviations to the study progress during the trial was provided to the Health Sciences Authority of Singapore and also to an independent data safety monitoring board ., The CELADEN trial was registered with www . ClinicalTrials . gov , number NCT01619969 ., The method for determination of viral RNA by quantitative polymerase chain reaction ( qPCR ) is described elsewhere 17 , 22 ., Viral load reduction ( VLR ) was defined as the difference between viremia ( determined by qPCR ) at enrollment and at each study day ., The primary endpoint was the mean VLR between study days 2 and 4 inclusive ( VLR2-4 ) , which is mathematically equivalent to the area under the curve ( AUC ) of the VLR curve between those days divided by the number of days ., A plaque assay was also performed to measure viremia , as described in 22 , 25 ., The plaque assay measures the number of virus particles capable of productive infection , unlike the qPCR assay that measures RNA of all viral particles , regardless of replication competency ., Hematology and clinical chemistry assays were standard assays performed in the clinical diagnostic laboratories of Singapore General Hospital ., A sandwich-based ELISA assay using DENV-2 protein for capture and goat anti-human antibody conjugated with horseradish peroxidase as the detecting antibody ., Patient sera and positive and negative human control sera were diluted 1:100 in serum diluent prepared using PBS with 0 . 5% nonfat dry milk ., The plates were developed with tetramethylbenzidine as the substrate 26 ., PK samples were collected prior to the first dose and at 23 , 25 , 47 , 49 . 5 , 71 , 74 and 95 hr after the first dose , representing 4 trough and 3 peak levels ., Urine was collected in 12-hr periods , the volume recorded , and a sample reserved for drug assays ., Celgosivir and castanospermine concentrations were determined on a LC/MS/MS system ( Hewlett Packard 1100 with Applied Biosystems API 3200 MSMS ) ., N-dodecyl-deoxynojirimycin was the internal standard for celgosivir , and 6 , 7 dihydroxyswainsonine was the internal standard for castanospermine ., The LC column was a Waters Atlantis HILIC Silica column; the mobile phase consisted of 23% 20 mM Ammonium acetate pH 5 . 0 in pump A and 77% acetonitrile in pump B for 7 min then changed to 60% in pump A for 3 . 9 min ., The LC eluent was connected directly to a Sciex API 3200 triple-quadrupole MS equipped with electrospray ionizing ion source without splitting ., The quadrupoles were operated with unit resolution in the positive ion multiple reaction monitoring mode ., The assay had a lower limit of quantitation ( LLoQ ) of 10 ng/mL and a linear response over the range of 10 to 2000 ng/mL ., Concentration-time profiles were analyzed with Phoenix WinNonlin v6 . 3 using a one-compartment model with first-order absorption and elimination ( Model 3 ) and weighting by the inverse of predicted concentration ., Two samples were excluded from PK analysis: one was a trough sample that had a concentration nearly 5 times higher than the other trough samples from the same patient ., The other was a peak sample that had a concentration more than 10 times lower than the other peak samples from the same patient ., Both concentrations were more than 3 standard deviations from the mean of the other patients’ samples at the same time point ., Body weight , age , sex , and renal clearance were evaluated as covariates ., Renal clearance was estimated from serum creatinine levels , patient demographics , and the Crockcroft-Gault formula ., We performed simulations of several dosing regimens being considered for a follow-on trial of celgosivir in adult dengue subjects ., These were performed with Model 3 using fitted parameters for the population of patients in the CELADEN trial ., The primary virological endpoint of the trial was the mean virological log reduction between study days 2 and 4 ( VLR2-4 ) ., To explore the relationship between VLR2-4 and PK , patients’ exposure ( Cmin , Cmax or AUC ) was subdivided into 3 quantiles: zero exposure ( placebo ) , low drug exposure ( lower quantile ) and high drug exposure ( upper quantile ) , and the distribution of VLR2-4 in each group was graphed ., Patient plasma samples , drawn at 24 , 48 , 72 and 120 hr after the first dose , were analyzed for 41 cytokines and chemokines using the Human cytokine panel 1 ( Merck Millipore cat no . MPXHCYTO-60K-14 ) as per manufacturer’s protocol ., Briefly , samples were diluted 1:2 . 5 with RPMI + 10% fetal calf serum and loaded onto a Millipore Multiscreen BV 96-well filter plate ., Serial dilutions of cytokine standards were prepared in parallel and added to the plate ., Milliplex Cytokine beads were vortexed for 30 sec . and 25 μl was added to each well with culture supernatants ., Samples were then incubated on a plate shaker at 600 rpm in the dark at room temperature for 2 hr ., The plate was applied to a Millipore Multiscreen Vacuum Manifold , washed twice with 50μl of assay buffer ( PBS , pH7 . 4 , 1% BSA , 0 . 05% Tween20 , 0 . 05% sodium azide ) , and each well resuspended with 75μl assay buffer ., Twenty-five μl of biotinylated Anti-Human Multi-Cytokine Reporter was added to each well ., The plate was incubated on a plate shaker at 600rpm in the dark at room temperature for 1 . 5 hr ., Streptavidin-Phycoerythrin was diluted 1:12 . 5 in assay buffer , and then 25μl was added directly to each well ., The plate was again incubated on a plate shaker at 600rpm in the dark at room temperature for 30 minutes ., Twenty-five μl of stop solution ( 0 . 2% ( v/v ) formaldehyde in PBS , pH 7 . 4 ) was added to each well and incubated at room temperature for 5 minutes ., The plate was then applied to the vacuum manifold and each well resuspended in 125μl assay buffer and shaken for 1 minute ., Assay plates were read with Flexmap 3D systems ( Luminex Corp , Austin , TX , USA ) ., Cytokine concentrations were calculated using Bio-Plex Manager 6 . 0 software with a 5 parameter curve fitting algorithm applied for standard curve calculations ., Correlations between log10 plaque forming units ( pfu ) and log10 viral RNA copy numbers measured using qPCR at each day were evaluated by the Pearson correlation coefficient ., Fisher’s exact test was used to compare the number of negative plaque assays ( zero pfu ) between celgosivir and placebo groups by day and between primary and secondary dengue ., Student’s t-test for two independent samples was used to compare VLR2-4 between primary and secondary dengue ., The t-test was also used to compare celgosivir and placebo-treated secondary dengue patients’ platelet nadirs and differences between the maximum minus the minimum hematocrit ., Covariate dependencies on PK parameters were evaluated by linear regression and deemed statistically significant if the 95% confidence interval of the slope excluded zero ., The Kruskal-Wallis test was performed to evaluate the relationship between exposure ( quantiles of Cmin , Cmax , and AUC ) and VLR2-4 ., Two-way repeated ANOVAs were used to analyze the effects of time and treatment on the Luminex analyte concentrations ., Graphing and statistical evaluations were performed with R version 2 . 15 . 2 , Graphpad Prism v 5 . 0d or SAS statistical software ., Next-generation whole-genome sequencing of DENV samples isolated from blood at study days 1 , 2 , 3 and 4 was performed as described previously 27 , 28 ., Viral RNA was extracted from human sera using the QIAamp Viral RNA Mini Kit ( Qiagen ) , and cDNA synthesis for each serotype was carried out with the Maxima H Minus First Strand cDNA Synthesis Kit ( ThermoFisher Scientific ) using serotype-specific primers designed to bind to the 3’ end of the viral genome ., The entire DENV genome was PCR-amplified in 6 overlapping fragments , each approximately 2 kb in length with the PfuUltra II Fusion HS DNA Polymerase ( Agilent Technologies ) ., Table 1 lists the number of patient samples that were extracted and Table 2 lists the primer sequences used for the dengue serotypes ., PCR products were gel-extracted and purified using the Qiagen Gel Extraction Kit ( Qiagen ) ., For each sample , equal amounts of all PCR-amplified fragments were combined and sheared on the Covaris S2 sonicator ( Covaris ) to achieve a peak size range of 100–300 bp ( shearing conditions: duty cycle—20%; intensity—5; cycles per burst—200; time—110 seconds ) ., Samples were purified with the Qiagen PCR Purification Kit ( Qiagen ) and their quality assessed on the Agilent 2100 Bioanalyzer with a DNA 1000 Chip ( Agilent Technologies ) ., Library preparation was performed with the KAPA Library Preparation Kit ( KAPA Biosciences ) ., After end-repair , A-tailing , and adapter ligation , ligated products in the 200–400 bp range were gel-extracted with the Qiagen Gel Extraction Kit ( Qiagen ) ., Samples were subjected to 14 PCR cycles to incorporate multiplexing indices and quantified using the Agilent Bioanalyzer ., Samples were then diluted to 10 nM and pooled ., Paired-end multiplexed sequencing ( 2 x 76 bp reads ) 29 of libraries was performed on the Illumina HiSeq ( Illumina ) at the Genome Institute of Singapore ., FastQC FastQC: A quality control tool for high throughput sequence data ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) was used to check the quality of the reads from Illumina-generated FASTQ files ., Trim Galore ! was used to trim and filter the reads with minimum quality cutoff of 20 and minimum read length of 35 bp ., The consensus genome for the sample at time point one was generated using the bam2cons_iter . sh script from the Viral Pipeline Runner ( ViPR , available at https://github . com/CSB5/vipr ) , which uses the Burrows-Wheeler Aligner to perform iterative mapping of paired-end reads to the reference 29 ., DENV genome of samples isolated from later time points was then mapped against the consensus , generated based on the maximum frequency of the nucleotide at a given position , using BWA-MEM v0 . 7 . 5 aligner ., Picard Tools v1 . 95 30 were used to remove PCR duplicates and base calibration , and indel realignment was done by GATK v3 . 3 ., SNVs for each sample were detected using LoFreq2 software 31 , which incorporates base-call quality scores as error probabilities into its model to distinguish SNVs from the average sequencing error rate , and assigns a p-value to each position ( Bonferroni-corrected p-value > 0 . 05 ) ., As LoFreq has previously been applied to DENV datasets , and its SNV predictions on these datasets have been experimentally validated down to 0 . 5% allele frequency 27 , 32 , we filtered the SNPs with a threshold of coverage of >1000 and allele frequency of >0 . 5% ., SNVs that were located within primer sequences were discarded ., An in-house R script was used to group the samples and count the number of SNVs occurring at each genomic position ., The dN/dS analysis , mutation density ( SNVs per 100 bp ) and all statistical tests were also performed in R . For identification of mutational cold spot , SNVs from groups of samples were pooled and then scanned for windows ( minimum size of 40 ) with a depletion of SNVs ( binomial test; Bonferroni corrected p-value < 0 . 05 ) ., Full genomes of DENV strains isolated from treatment and placebo patients were analyzed for each DENV type independently , along with DENV genomes originating from Asia that were retrieved from NCBI GenBank ., Multiple sequence alignments were performed using MAFFT 33 ., Maximum likelihood phylogenetic trees were constructed using RAxML applying the General time reversible ( GTR ) model with gamma distributed rates across sites ( GTR+γ ) 34 ., Trees were visualized and annotated using FigTree v1 . 5 ( http://tree . bio . ed . ac . uk/software/figtree/ ) ., All the 50 patients recruited to the study who tested positive on the Dengue Duo NS1 screening kit and recruited to the study had virologically confirmed dengue 17 ( Fig 1 ) ., On days 1 , 2 and 3 , there was strong and significantly positive correlation between viremia measured by qPCR and the plaque assay ( Fig 2A–2C ) although by day 3 , only 66% of the samples had positive viremia by the plaque assay ., By day 4 , the correlation was weaker , but still significant ( Fig 2D ) , with only 34% of samples having positive viremia by the plaque assay compared to 98% by qPCR ., By day 5 , only two samples were positive for dengue virus by plaque assay ( Fig 2E ) ., There was no treatment effect when comparing the frequency of negative plaque assay results for celgosivir and placebo groups ., The kinetics of viral clearance ( qPCR ) was faster in secondary dengue than in primary dengue ( Fig 3 ) ., VLR2-4 for patients with secondary dengue ( -2 . 25 ± 1 . 04 ) was significantly lower ( p-value 0 . 002 ) than for those with primary dengue ( -1 . 46 ± 0 . 70 ) ; the difference ( 95% CI ) was 0 . 79 ( 0 . 30 , 1 . 29 ) ., On days 3 and 4 , there were a significantly higher number of negative plaque assays in secondary dengue patients compared to primary dengue patients ( Day 3: p-value<0 . 001 , OR = 10 . 8 , 95% CI 2 . 75 to 42 . 4; Day 4: p-value = 0 . 029 , OR = 5 . 79 , 95% CI 1 . 13 to 29 . 6 ) ., Celgosivir was rapidly converted to castanospermine in vivo , presumably by endogenous esterases , as expected from previous animal and human studies 19; 83% of the samples had no quantifiable levels of parent drug above the lower limit of quantification ., Observed mean castanospermine Cmin and Cmax were 430 ng/mL ( 2 . 23 μM ) and 5730 ng/mL ( 30 . 2 μM ) , respectively ( Table 2 ) ., Mean ( ± sd ) oral clearance ( CL/F ) was 132 ( ± 28 ) mL/min ., The mean volume of distribution ( V/F ) was 28 . 2 ( ± 9 . 1 ) L , and half-life was 2 . 5 ( ± 0 . 6 ) hr ., Using PK parameters from compartmental modeling , the predicted mean plasma concentration profile during the entire dosing period is shown in Fig 4 ., The data showed that the actual drug concentrations remained above 400 ng/mL during the dosing period when mean viremia levels started from greater than 6 logs and declined by more than 4 logs ( Fig 3 ) ., The target concentration of 400 ng/mL was the trough concentration in mice treated with celgosivir using a dosing regimen ( 50 mg/kg bid for 5 days ) that protected all animals from otherwise lethal dengue infection 22 ., Body weight , age and sex were not significant covariates on clearance or volume of distribution ( Fig 5A–5D and 5F ) ., On the other hand , castanospermine clearance and renal ( creatinine ) clearance were significantly correlated ( Fig 5E ) ., Drug in the urine was >99% castanospermine , and urinary recovery was 80% ± 17% ., Although only one dose was evaluated , a 3-fold range of exposure was obtained in observed Cmin and a two-fold range in Cmax and predicted AUC ( Table 3 ) ., There was a subtle trend for lowered viremia ( VLR2-4 ) with increasing Cmin ( Fig 6 ) that was not evident with AUC ., This is in line with the concept of maintaining a serum concentration above a minimum inhibitory concentration for optimizing antimicrobial/antiviral drug therapy ., Although there was a trend between VLR2-4 and quantiles of exposure of Cmin , and Cmax , neither one was significantly correlated ( Fig 6 ) ., The PK parameters obtained from model fitting were used to predict Cmin , Cmax and AUC for other dosing regimens ( Fig 7 ) ., Simulations predicted that 150 mg given 8 hourly ( total daily dose of 450 mg ) would increase steady-state Cmin by 2 . 4-fold , decrease steady-state Cmax by 20% and increase daily AUC by only 13% compared to the regimen used in CELADEN ., Doses of 200 mg every 8 hr or 150 mg every 6 hr ( total daily dose of 600 mg ) would achieve increases in steady-state Cmin by 3 . 2- and 4 . 5-fold , respectively with only a modest 33% increase in daily AUC ., The profiles for platelet count and hematocrit in the celgosivir and placebo groups are illustrated in Fig 8A and 8B ., These show that the curves for the treatment and control groups are almost exactly superimposed ., However , because the celgosivir group had a much higher proportion of secondary dengue ( 13/24 = 54% ) than the placebo group ( 5/26 = 19% ) , and secondary dengue patients typically have a greater decrease in platelets and higher increase in hematocrit ( 7 , 8 , 21 and 22 ) , the comparable profiles are suggestive that celgosivir had some benefit in secondary dengue ., When only secondary dengue cases were compared , a trend toward better outcomes in the celgosivir treated group is discernible ( Fig 8C and 8D ) ., Platelet nadir and the difference between the maximum and minimum hematocrit for secondary dengue patients are shown in Fig 8E and 8F ., The differences were not significant but are in the direction of benefit for celgosivir ., Due to the small numbers of patients in this subgroup , caution is warranted not to over-interpret these trends ., Numerous studies have demonstrated that the magnitude and quality of systemic immune response during febrile dengue illness is linked to pathological disease progression ., It has been hypothesized that the expression of proinflammatory cytokines from innate and adaptive immune effector cells plays a critical role in the cell activation , apoptosis , and vascular permeability characteristics of dengue hemorrhagic fever 35 , 36 ., A comprehensive analysis of circulating cytokines and chemokines was undertaken to assess the systemic impact of celgosivir treatment on the immune status of acutely infected patients ., Fig 9A shows the concentration of circulating analytes at all time points for all patients in the trial ., Longitudinal analysis of plasma cytokine concentrations ( Fig 9B ) demonstrated that drug treatment led to a qualitative shift in circulating cytokine and growth factor concentrations during the course of infection ., Significant increases in IL-13 and PDGF-AA concentrations were observed in celgosivir-treated patents relative to placebo-treated patients indicating an increase in Th2 polarizing cytokines with time in this group ., In support of this interpretation , drug treatment led to a corresponding decrease in circulating levels of IFNγ and TGFα ., This qualitative shift from a Th1 to Th2 profile in patients receiving treatment may be reflective of a larger shift in T-cell polarization during the course of treatment as observed in other antiviral treatments 37 ., Due to the limited number of samples for DENV3 , NGS analysis was performed only on DENV1 and DENV2 isolates ., Overall , our deep sequencing data shows positional variance throughout the DENV genome for placebo and celgosivir-treated patients ( S2 and S3 Figs ) ., Single nucleotide variants ( SNVs ) in each DENV population were called with the LoFreq variant calling algorithm ., As in other studies 28 , 31 , the majority ( 80% average across all samples ) of SNVs identified in our data set were transitions ( S4 Fig ) ., For both DENV1 and DENV2 , the bulk of the SNVs detected were present evenly in both treated and untreated populations , indicating some level of non-specific selective pressure ., However , significant difference in selection pressure or genetic drift was observed between the different treatments for any of the viral genes ( S1 Table ) ., Interestingly , the average SNV density for each dengue gene was lower for celgosivir-treated patients compared to placebo-treated control ( S2 Table ) ., No mutational hotspots were detected in any of the conditions for both DENV serotypes ., Consistent with a previous DENV1 study 32 , cold-spots were detected in NS3 of DENV1 from placebo-treated samples ( P-tp3 ) ., More cold-spots were also detected for celgosivir-treated samples ( C-tp1 and C-tp2 ) than placebo samples ( P-tp1 and P-tp2 ) for DENV1 ( S5A Fig ) ., For DENV2 , coldspots were detected mostly in NS3 and NS5 for placebo-treated samples ., For celgosivir-treated samples , coldspots show a different profile from that of DENV1 and only exists in the NS5 region ( C-tp1 and C-tp3 ) ( S5B Fig ) ., Phylogenetic analysis of the whole genome sequences of treatment and placebo samples revealed the co-circulation of multiple DENV lineages in our study ( Fig 10 ) ., In particular , for each DENV serotype , the treatment and placebo samples were derived from multiple genotypes ., Five independent lineages of DENV 2 were detected in this study , which belonged to two major DENV 2 genotypes ( Cosmopolitan and Asian ) ., While the majority of DENV 2 samples were of the Cosmopolitan genotype we observed that they were derived from lineages that have diverged many years ago , however both these lineages have been previously detected in Asia during 2004–2012 ., Two lineages of DENV 1 and DENV 3 were also detected in our samples , respectively , that belonged to different genotypes ., Samples obtained from treatment and placebo patients were inter-dispersed among all lineages–represented by blue and red labeled strains in each of the lineages , except in DENV 1 genotype IV and DENV 3 Genotype I where only one sample type was detected ( Fig 10 ) ., We were unable to assess the statistical significance of sample type for each of the lineages due to small samples numbers among the lineages ., However , these results suggest that the high genetic diversity of dengue may be a confounding factor in the endpoint analysis of this drug trial ., Currently , it is not known if the genetic changes prevalent between the different lineages would have an effect on the action of celgosivir , or related anti-dengue drugs in early phase development such as UV-4 , which also target ER alpha glucosidases 38 ., Although CELADEN did not meet its primary endpoint of lowering viremia or fever , assessment of the PK and pharmacodynamics provides valuable insights and lessons for the design of future dengue drug trials , not only of celgosivir but of other dengue antivirals as well ., An important objective of early phase clinical trials is to identify dose regimen ( s ) that are safe and tolerable and that show some evidence of pharmacological activity ., Previous clinical experience in hundreds of subjects ( healthy volunteers , HIV and HCV patients ) established that celgosivir’s maximally tolerated dose ( MTD ) is 400 mg qd ( once a day ) for 12 weeks 20 ., A small trial in HCV patients ( N = 43 ) reported asymptomatic , reversible increases in creatine kinase ( 19% for 200 mg qd , 42% for 200 mg bid and 80% for 400 mg qd ) , suggesting that a divided dose would be better tolerated than a single daily dose of the same amount of total drug 19 ., Among CELADEN patients who received celgosivir , the mean observed trough castanospermine concentration was 430 ng/mL ( 2 . 3 μM ) ., This was comparable to the corresponding Cmin in the mouse model ( 400 ng/mL ) where 50 mg/kg bid for 5 days protected all animals from dengue-related death when treated immediately after infection , and was far more effective than 100 mg/kg qd 22 ., When treatment in mice was delayed by 24 and 48 hr post-infection , survival rates were lower at 75% and 50% , respectively 21 ., In dengue patients , symptoms do not arise until several days after infection ., Therefore , it may be necessary to achieve higher trough drug concentrations to overcome the delay in treatment after becoming infected ., By decreasing the dosing interval from 12 to 8 or 6 hr , PK simulations demonstrate that 2 . 4- to 4 . 5-fold increase in Cmin are achievable with only a 13% to 33% increase in total dose ., Indeed , AG129 mice treated at peak viremia with a four-times-daily regimen had a significantly reduced viremia compared to untreated animals in a mouse viremia model using clinical isolates of DENV2 23 ., Drug clearance was significantly correlated with creatinine clearance , and 80% of the drug was recovered in the urine , indicating that renal clearance is the dominant elimination pathway , as was previously reported from animal studies 21 ., While all patients in the trial had serum creatinine levels well below the exclusion criterion of 165 μmol/l , retrospective creatinine clearance calculations indicated that one patient who had a serum creatinine of 114 μmol/ fell in the moderate renal impairment category 39 ., Although this issue did not specifically result in any AE or SAE in CELADEN , our current PK analysis suggests a greater awareness of renal function for future trials ., DENV cleared significantly more rapidly in patients with secondary dengue compared to primary dengue , confirming a previous finding 40 ., Future trials of antiviral drugs for dengue drugs that use viremia as an endpoint may need to take this into account when calculating trial size in order to achieve adequate power ., Although an ideal case Target Product Profile 41 for a dengue drug should be efficacy in both primary and secondary dengue , it is possible that for some early phase proof-of-concept studies , previous infection status could either be an inclusion criteria or a stratification variable to achieve balanced groups with respect to this parameter ., In the clinical management of

Introduction, Methods, Results, Discussion

CELADEN was a randomized placebo-controlled trial of 50 patients with confirmed dengue fever to evaluate the efficacy and safety of celgosivir ( A study registered at ClinicalTrials . gov , number NCT01619969 ) ., Celgosivir was given as a 400 mg loading dose and 200 mg bid ( twice a day ) over 5 days ., Replication competent virus was measured by plaque assay and compared to reverse transcription quantitative PCR ( qPCR ) of viral RNA ., Pharmacokinetics ( PK ) correlations with viremia , immunological profiling , next generation sequence ( NGS ) analysis and hematological data were evaluated as exploratory endpoints here to identify possible signals of pharmacological activity ., Viremia by plaque assay strongly correlated with qPCR during the first four days ., Immunological profiling demonstrated a qualitative shift in T helper cell profile during the course of infection ., NGS analysis did not reveal any prominent signature that could be associated with drug treatment; however the phylogenetic spread of patients’ isolates underlines the importance of strain variability that may potentially confound interpretation of dengue drug trials conducted during different outbreaks and in different countries ., Celgosivir rapidly converted to castanospermine ( Cast ) with mean peak and trough concentrations of 5727 ng/mL ( 30 . 2 μM ) and 430 ng/mL ( 2 . 3 μM ) , respectively and cleared with a half-life of 2 . 5 ( ± 0 . 6 ) hr ., Mean viral log reduction between day 2 and 4 ( VLR2-4 ) was significantly greater in secondary dengue than primary dengue ( p = 0 . 002 ) ., VLR2-4 did not correlate with drug AUC but showed a trend of greater response with increasing Cmin ., PK modeling identified dosing regimens predicted to achieve 2 . 4 to 4 . 5 times higher Cmin ., than in the CELADEN trial for only 13% to 33% increase in overall dose ., A small , non-statistical trend towards better outcome on platelet nadir and difference between maximum and minimum hematocrit was observed in celgosivir-treated patients with secondary dengue infection ., Optimization of the dosing regimen and patient stratification may enhance the ability of a clinical trial to demonstrate celgosivir activity in treating dengue fever based on hematological endpoints ., A new clinical trial with a revised dosing regimen is slated to start in 2016 ( NCT02569827 ) ., Furthermore celgosivir’s potential value for treatment of other flaviruses such as Zika virus should be investigated urgently ., Trial Registration: ClinicalTrials . gov NCT01619969

Dengue virus is currently threatening 40% of the world’s population ., An approximately 60% efficacious vaccine has been registered for use in Mexico , Brazil , the Philippines , Paraguay and El Salvador , but there are no approved antiviral treatments available ., We have shown that celgosivir , an endoplasmic reticulum alpha glucosidase inhibitor , has submicromolar activity against all 4 serotypes of dengue virus ( DENV ) and also efficacious in mouse model of infection ., The strong preclinical pharmacology motivated the conduct of an investigator-initiated , Phase 1b randomized , double-blind , placebo-controlled trial of celgosivir in 50 adult dengue patients ., Although the trial did not meet the primary endpoints of lowering viremia or fever , the safety profile of the drug prompted extended hematological , pharmacokinetic , immunological and viral sequence profiling ., Here we report several non-significant trends of pharmacological effect of celgosivir on platelet count , hematocrit , and NS1 clearance in secondary dengue patients ., In addition , pharmacokinetic modeling identified an alternate dosing regimen that is predicted to achieve a 4 . 5-fold increase in minimum drug concentrations during treatment ( Cmin ) with only a modest increase in overall dose ., A new Phase 2a clinical trial with an optimized dosing regimen of celgosivir ( ClinicalTrials . gov number NCT02569827 ) is scheduled to start in the latter part of 2016 .

blood cells, innate immune system, medicine and health sciences, immune physiology, cytokines, body fluids, dose prediction methods, animal models of disease, blood counts, immunology, tropical diseases, microbiology, developmental biology, pharmaceutics, molecular development, platelets, neglected tropical diseases, research and analysis methods, animal models of infection, infectious diseases, animal cells, animal studies, dengue fever, hematology, immune system, hematocrit, viremia, anatomy, blood, cell biology, physiology, biology and life sciences, cellular types, viral diseases, drug therapy

journal.pgen.1006789

PCNA ubiquitylation ensures timely completion of unperturbed DNA replication in fission yeast

It is well established that the replication machinery encounters a variety of obstacles and is thus designed with a degree of flexibility ., This plasticity of DNA replication depends on both alternative components and regulation by post-translational modification ., For example , while genetic and physical studies indicate that the leading and lagging strands are primarily replicated by DNA polymerase ε ( Polε ) and DNA polymerase δ ( Polδ ) , respectively 1–4 , this assignment is flexible: Polδ synthesises the leading strands on rare occasions 5–7 , synthesises both strands during viral replication 8 and can sustain cell viability in the absence of Polε 9 ., Key to orchestrating enzymes for DNA replication is PCNA , which serves as a scaffold for recruiting many of the numerous enzymes involved , including the replicative DNA polymerases ., In addition , PCNA ubiquitylation on lysine164 regulates DNA damage tolerance ( DTT ) ., When replication is blocked by damaged DNA bases the Rad6-Rad18 E2-E3 ligase complex binds to single stranded DNA coated with RPA and mono-ubiquitylates PCNA to promote translesion DNA synthesises by non-canonical polymerases 10 , 11 ., Subsequent to mono-ubiquitylation , PCNA can be poly-ubiquitylated by the Ubc13-Mms2-Rad5 complex 12 , 13 to initiate damage bypass by HR-dependent template switching 14 ., The level and duration of PCNA ubiquitylation is additionally regulated by constitutive deubiquitylation 15–17 ., The prevailing view is that PCNA ubiquitylation is a DNA damage-induced phenomena ., This is consistent with the budding yeast situation , where PCNA ubiquitylation is barely detectable in unperturbed S phase but robustly induced in response to replication-blocking DNA lesions 10 , 12 ., However , PCNA is robustly ubiquitylated during unperturbed replication in fission yeast 18 and significant levels of PCNA ubiquitylation are evident during unperturbed replication in frog extracts and metazoan cells 19 , 20 ., Several observations suggest that PCNA ubiquitylation is linked to DNA replication: PCNA ubiquitylation is upregulated in response to an increase in canonical replication intermediates 21–23 and a recent synthetic genetic array analysis in budding yeast showed that the PCNA ubiquitylation pathway is genetically correlated with the mechanism of lagging strand DNA synthesis 24 ., Moreover , in vitro reconstitution of PCNA ubiquitylation demonstrates that efficient mono-ubiquitylation is coupled to DNA synthesis by Polδ 25 ., Despite the accumulating evidence that PCNA ubiquitylation is linked to the processes of DNA replication , there have been no reports that examine if the process of unperturbed DNA replication is influenced by the ubiquitylation of PCNA and the role of this modification during unperturbed S phase remain unclear ., To address this question experimentally , we investigated how replication dynamics are influenced by PCNA ubiquitylation in fission yeast ., We find that , in the absence of PCNA ubiquitylation DNA replication is slower and that there is an increase in single stranded DNA gaps in S phase cells ., We also observe that PCNA ubiquitylation increases the amount of chromatin associated PCNA and influences the recruitment of Polymerase δ ., We propose that PCNA ubiquitylation facilitates the completion of Okazaki fragment synthesis ., If incomplete lagging strand synthesis activates PCNA ubiquitylation , it is possible that PCNA-Ub participates in the completion of Okazaki fragment synthesis ., To examine this possibility , we first determined the contribution of PCNA ubiquitylation to the progression of unperturbed S phase by assessing replication dynamics in synchronised populations ( Fig 1B–1F ) ., Since S . pombe Pcn1 can be modified on lysine 164 by either ubiquitin or SUMO , we first examined cells defective for the Rhp18 E3-ligase ( Rhp18 is the S . pombe homolog of S . cerevisiae Rad18 . For clarity , we refer to this E3 ligase as Rad18 through the text ) ., While S phase entry was slightly delayed in rad18Δ cells ( Fig 1C ) , bulk replication progression proceeded with similar kinetics when assessed by total bromodeoxyuridine ( BrdU ) accumulation ( Fig 1C ) ., In contrast , while cells carrying the mutation of the ubiquitylated PCNA residue , pcn1-K164R , also slightly delayed S phase entry , their progress through S phase was also defective ( S2A and S2B Fig ) ., Importantly , rad18Δ was epistatic with pcn1-K164R for the slight delay to S phase entry ( S2A Fig ) , confirming that the delay seen in rad18Δ cells is PCNA ubiquitylation dependent ., It is unclear why the pcn1-K164R mutation also conferred a ubiquitylation-independent defect in S phase progression ( S2A Fig ) ., We observed that replication timing was also perturbed and that Polε DNA association during S phase was reduced ( see below ) in a manner that was independent of the Pli1 SUMO ligase ., As pcn1-K164R is thus clearly acting as a hypomorphic allele , we concentrated our analysis on the rad18 deletion mutant cells ., To establish if PCNA ubiquitylation affected the DNA replication kinetics of specific loci we examined enrichment of BrdU across the genome during mid to late S phase by BrdU-IP in rad18+ and rad18Δ cells ( Fig 1D ) ., This showed changes to the replication dynamics , with advanced replication close to origins and delayed replication for the inter-origin regions ., Because relative BrdU enrichment between two samples does not directly reflect relative replication kinetics ( the two samples will not be at exactly the same point in S phase ) , we performed independent replication time courses for rad18+ and rad18Δ cells and normalised for replication progression in order to directly compare DNA replication timing across the genome ( Fig 1E , see Materials and methods for details ) ., Replication progression was calculated at each local region of the genome when the global genome replication level was either 25 , 50 or 75% ., I . e . we used the global extent of replication to standardise comparisons between rad18+ and rad18Δ strains such that the extent of local replication was compared between strains with equivalent global levels ., rad18Δ cells showed delayed replication at regions distal to replication origins which are , relative to origins , late replicating ( light blue , Fig 1E ) ., This was compensated for by higher local replication at many origin-associated regions that are relatively early replicating ( light red , Fig 1E ) ., Some additional peaks were also observed , for example regions 1770-kb region in Chr ., II and 3320-kb in Chr ., III , suggesting reduced fork progression rates are partially compensated for by firing cryptic origins 26 ., The distribution of BrdU at genomic regions surrounding origins would be expected to become wider as S phase progressed ( ultimately it would be flat at the end of S phase ) ., Consistent with our hypothesis that replication fork progression is subtly delayed in rad18Δ cells ( Fig 1E ) , we observed that deletion of rad18 resulted in a narrower distribution of BrdU later in S phase when compared to rad18+ control cells ( S3A–S3E Fig ) ., Control experiments where we allowed cells to progress into S phase in the presence of hydroxyurea confirmed that the two strains initiated S phase at the same origins and confirm that our sequencing methodology is reproducible ( S3B Fig ) ., To examine further whether rad18Δ caused delayed replication in regions that replicate late , a meta-analysis was performed by computationally identifying replication origins and analysing the relatively late replicating inter-origin regions ., As shown in Fig 1F the local replication extent of the early replicating origins was not perturbed in rad18Δ ., In contrast , later replicating regions show a significant decrease in their extent of replication , even when adjusted for the global replication amounts ., This effect was particularly striking in the regions that were amongst the last to be replicated ( Fig 1G ) ., Analysis of the specific loci that were most under-replicated in rad18Δ cells ( S4A and S4B Fig ) showed they correspond to those loci that we previously demonstrated to be the last to be replicated in wild type cells 5 ., These data demonstrate that the lack of PCNA ubiquitylation delays replication fork progression , with the cumulative effect manifesting most obviously at late replicating regions ., PCNA is loaded during DNA replication , functions as the replicative clamp and remains chromatin associated until the polymerase has finished replication and ligation is complete ., We speculated that PCNA ubiquitylation may contribute to PCNA retention on the chromatin ., However , in native cell extracts PCNA is progressively deubiquitylated , compromising the ability to measure PCNA ubiquitylation during chromatin association assays ., To overcome this limitation , we increased the level of PCNA ubiquitylation by engineering a strain , Purg1-rad18 , where rad18+ is under the control of an inducible promoter ( Fig 2A ) ., Fractionation of cell extracts following rad18 induction revealed that ubiquitylated PCNA was preferentially associated with chromatin ( Fig 2B ) in a manner dependent on K164 ubiquitylation ( Fig 2C and 2D ) ., This suggests the modification contributes to the stability of PCNA chromatin association ., Consistent with this , shut-off of rad18+ transcription from Purg1 when combined with induced Rad18 degradation resulted in rapid PCNA disassociation from chromatin , concomitant with deubiquitylation ( S5 Fig ) ., Because it is not practical to assay native fission yeast extracts for endogenous levels of ubiquitylated PCNA on chromatin due to its deubiquitylation by isopeptidase in native extracts we compared the total chromatin-associated PCNA in rad18+ and rad18Δ cells during S phase ., In rad18+ cells , PCNA accumulated in S phase and gradually diminished towards the completion of replication ., Comparatively , in rad18Δ cells , the amount of chromatin associated PCNA decreased during the late stages of replication ( Fig 2E ) ., This is reminiscent of the predominant effect of loss of PCNA ubiquitylation manifesting at late replicating regions ( Fig 1F and 1G ) ., We verified the observed effect of Rad18 loss on PCNA chromatin association using a photo-activated localization microscopy ( PALM ) -based technique that directly visualises DNA-associated PCNA 27 ., Briefly , this method exploits motion blurring to selectively eliminate signals arising from rapidly diffusing molecules , allowing visualisation of low mobility signals derived from DNA-associated molecules ( Fig 2F ) ., Previously we reported that low mobility PCNA ( mEos3 . 1-Pcn1 ) is notably enriched during S phase 27 ., Deletion of rad18 significantly reduced the fraction of these molecules ( Fig 2F , right ) , thus confirming that PCNA-K164 ubiquitylation results in increased amounts of loaded PCNA during unperturbed S phase ., One possible explanation for the increased amount of chromatin-associated PCNA accompanying K164 ubiquitylation is that this contributes to the function of DNA polymerases during DNA replication ., In unchallenged cells we could detect the association of Polδ , but not Polε , with PCNA by immunoprecipitation ( S6A and S6B Fig ) ., This would be consistent with the higher PCNA-dependency of Polδ function 28–30 , but may equally reflect the lower levels of DNA-associated Polε during S phase when compared to Polδ ., Increased PCNA ubiquitylation ( by Rad18 overexpression via Purg1-rad18 ) increased Polδ co-immunoprecipitation with anti-PCNA without influencing cell cycle profiles ( Fig 3A and 3B ) ., PCNA ubiquitylation and co-immunoprecipitation were also both enhanced by hydroxyurea treatment of rad18+ and Purg1-rad18 cells ., Thus , Polδ: PCNA co-immunoprecipitation intensity scaled with PCNA ubiquitylation ( Fig 3A and 3B ) ., We also noted that the PCNA which co-immunoprecipitated with Polδ was biased toward ubiquitylated forms ( Fig 3C ) and that the loss of poly-ubiquitylation ( ubc13 deletion ) showed an intermediate decrease in co-immunoprecipitation of Polδ when compared to loss of all ubiquitylation ( pcn1-K164R ) ( Fig 3D ) ., Using the PALM motion blurring assay ( see Fig 2F ) we did not detect a decrease in the Polδ immobile fraction in untreated S phase rad18Δ cells ( Fig 3E ) , possibly because our assay is insufficiently sensitive ., However , when rad18Δ cells were arrested within S phase by hydroxyurea treatment , the fraction of low mobility Polδ molecules decreased when compared to rad18+ controls , providing support for the contention that PCNA ubiquitylation contributes to Polδ function ., PCNA recruits DNA polymerases after it is loaded 31 and the affinity of PCNA: Polδ binding is not influenced by K164 ubiquitylation 32 ., Thus , the increased Polδ-PCNA association could be accounted for purely by the increased amount of PCNA on DNA due to ubiquitylation inhibiting clamp unloading ., This predicts that increasing PCNA chromatin association independently of its ubiquitylation status would lead to increased Polδ: PCNA co-immunoprecipitation ., To address this , we examined Polδ-PCNA association in cells deleted for elg1 , where PCNA chromatin association is enhanced due to inactivation of the Elg1 unloader ( Fig 3F ) 33 ., Loss of Elg1 resulted in an increase in Polδ co-immunoprecipitation with PCNA in both rad18+ and rad18Δ backgrounds ( Fig 3G ) ., This result demonstrated that the amount of loaded PCNA relates to the level of PCNA-polymerase association , although we cannot rule out the possibility that additional factors that directly respond to PCNA ubiquitylation can also influence the association ., PCNA ubiquitylation is proposed to help ‘replace’ replicative polymerases with non-canonical polymerases ., We therefore examined co-immunoprecipitation of several DNA damage tolerant polymerases , Polη , Polκ and Polζ , with PCNA ( S6C Fig ) ., Marginal Polη: PCNA co-immunoprecipitation was observed in Purg1-rad18 cells , where PCNA ubiquitylation levels were high , consistent with the ubiquitin-binding zinc-finger domain of Polη directing PCNA association ., Co-immunoprecipitation of Polκ or Polζ with PCNA was not detectable , presumably due to sparse protein levels ( S6C Fig ) ., Taken together , these data indicated that the non-canonical polymerases do not appreciably outcompete Polδ for association with ubiquitylated PCNA ., Consistent with this , neither of Polη , Polκ nor Polζ were responsible for the altered BrdU incorporation observed in rad18Δ cells during unperturbed S phase ( S6D Fig ) ., To establish if PCNA modification influences Polδ and Polε function we examined synthetic genetic interactions between rad18Δ and temperature sensitive ( ts ) polymerase mutations ( Fig 4A ) ., For cdc6-23 , ( Polδ-ts ) , concomitant rad18Δ reduced the restrictive temperature , consistent with PCNA ubiquitylation enhancing Polδ activity ., Importantly , this synthetic genetic interaction was also observed for pcn1-K164R and combining both rad18Δ and pcn1-K164R showed no additive effect ( Fig 4B ) ., For cdc20-m10 ( Polε-ts ) rad18Δ did not affect the restrictive temperature , suggesting Polε activity is not influenced by PCNA ubiquitylation ., Consistent with this , when we examined the fraction of low mobility Polε in S phase cells using PALM motion blurring , we did not detect a significant change in when rad18 was deleted ( S6E Fig ) ., Interestingly , when we examined Polε mobility in a pcn1-K164R background , a significantly lower fraction of Polε displayed low mobility in S phase cells ( S6E Fig ) ., This phenomenon was not observed in a pli1 deletion mutant ( S6F Fig ) ., Thus , the K164R mutation has effects beyond that of PCNA ubiquitylation ( c . f . S2 Fig ) which are unlikely to be related to modification by small Ub-like molecules ., During DDT ubiquitylation of PCNA promotes ssDNA gap filling opposite DNA lesions 22 , 34 , 35 ., We have confirmed ( S1E Fig ) that PCNA ubiquitylation is induced following dysfunction of Okazaki fragment synthesis and demonstrated that this increases the fraction of Polδ co-immunoprecipitating with PCNA ( Fig 3A–3C ) and can contribute to the chromatin association of this lagging strand polymerase ( Fig 3E ) ., Since Polδ repeatedly disassociates from and re-associates with the template during synthesis 32 , relatively long lived ssDNA gaps may occur stochastically between Okazaki fragments ., We reasoned that PCNA ubiquitylation could act to supress or repair such events via a DTT-like gap filling mechanism during unperturbed S-phase ( Fig 4C ) ., This predicts that the absence of PCNA ubiquitylation would result in ssDNA gap accumulation during DNA replication ., To estimate the extent of ssDNA gaps in vivo , we first utilised an S1 nuclease-based assay 36 previously developed for detecting ssDNA in replicated molecules ( Fig 5A ) ., By calculating the distribution of DNA fragment sizes from gel intensities ( S7 Fig ) we infer that rad18Δ cells displayed increased DNA fragmentation when compared to rad18+ cells , with small ( < 1 kb ) fragments accumulating in rad18Δ throughout S phase ( Fig 5B–5D ) ., As an alternative assay , we BrdUTP labelled ssDNA gaps in genomic DNA prepared in agarose plugs ., When DNA from rad18Δ cells was compared to rad18+ , increased signal was evident in mid to late S phase ( Fig 5E–5G ) ., These two experiments support a model where PCNA ubiquitylation occurs between Okazaki fragments ( Fig 4C ) and prevents the accumulation of ssDNA gap during unperturbed S phase ( see Discussion ) ., Here we have used the fission yeast model to demonstrate that , in addition to its known role in DNA damage tolerance , PCNA K164-ubiquitylation contributes to the timely completion of unperturbed DNA replication ., Our results show that PCNA association with chromatin is stabilised by PCNA-K164 ubiquitylation during S phase ., We also observed an increased co-immunoprecipitation of Polδ with PCNA when PCNA is ubiquitylated and we provide evidence that the chromatin association of Polδ is promoted by PCNA ubiquitylation ., In budding yeast , PCNA ubiquitylation is barely detectable in unperturbed S phase 22 and robustly induced in response to DNA lesions that block the canonical replicative DNA polymerases 10 , 12 ., Consequently , PCNA ubiquitylation has been studied almost exclusively in the context of its key role in DNA damage tolerance 37 ., In contrast , in fission yeast PCNA is robustly ubiquitylated during unperturbed S phase 18 and this is not significantly further induced if DNA is damaged during S phase ., Budding and fission yeast thus represent opposite ends of what appears to be a spectrum ., We note that both yeasts have approximately similar genome sizes and there is no evidence to suggest that fission yeast suffers from elevated levels of spontaneous DNA damage ., Interestingly , mammalian cells exhibit both S phase-dependent PCNA ubiquitylation and DNA damage induced PCNA ubiquitylation ( see S8A and S8B Fig ) ., It is currently not known what underlies the differences between organisms in terms of PCNA ubiquitylation in unperturbed S phase ., However , as Rad18 is activated by regions of single stranded DNA it is possible that PCNA ubiquitylation is reflecting the extent of ssDNA present when DNA replication is active ., In support of this , in budding yeast a defect in short-flap Okazaki fragment processing caused by compromising the function of the Fen1 flap endonuclease , which normally processes the 5’end of Okazaki fragments , induced detectable levels of PCNA ubiquitylation 24 ., This is explained by the accumulation of long 5’ ssDNA flaps that bind RPA and activate Rad18 ubiquitylation ., However , when Okazaki fragment-processing is proficient , the vast majority of flap structures are cleaved by Fen1 when they are 1 or 2 nucleotide in length 38 ., Thus , Okazaki fragment processing is unlikely to be a significant source of ssDNA during unperturbed S phase ., We have shown here that the lack of PCNA ubiquitylation leads the accumulation of ssDNA gaps during S phase in fission yeast ., We propose that the dynamics of Polδ disassociation from PCNA result in stochastic formation of transient gaps during lagging strand synthesis ., These gaps trigger Rad18-dependent ubiquitylation of PCNA , which stabilises PCNA on the DNA , allowing association of Polδ and rapid gap resolution ., In the absence of PCNA ubiquitylation , a proportion of these gaps persist and thus gaps are detected in our assays ., The generation of transient gaps during lagging stand synthesis likely explains the fact that PCNA is ubiquitylated during S phase in this organism ., In support of fission yeast generating increased regions of ssDNA during unperturbed DNA replication ( when compared to budding yeast ) we note that the abrogation of recombination pathways in fission yeast ( e . g . rad51Δ or rad52Δ mutants ) causes a much more severe growth defect than the equivalent loss of recombination pathways in budding yeast and that the combination of rad51 deletion with rad18 or pcnl-K164R results in synthetic lethality ( S9 Fig ) ., This suggests that homologous recombination and DTT pathways cooperatively repair ssDNA gaps , which may be abundant compared to S . cerevisiae ., In considering the origin of ssDNA during S phase that we observe in S . pombe and the differential PCNA ubiquitylation between S . pombe and S . cerevisiae in unperturbed S phase , it is interesting to consider that the kinetics of Polδ holo-enzyme dissociation from PCNA ., It has recently been reported 32 that the S . cerevisiae enzyme is more processive than its human counterpart: human Polδ dissociates more rapidly from PCNA than its budding yeast counterpart and it was estimated that ~14–31% of human Okazaki fragments are completed by two independent Polδ: PCNA association events ., Conversely , >99% of budding yeast Okazaki fragments are predicted to be completed by a single Polδ: PCNA interaction ., While the kinetics of S . pombe Polδ dissociation from PCNA has not been studied , the fact that PCNA ubiquitylation is strongly influenced by the intermediates of lagging strand DNA synthesis 21–23 ( see S1E and S1F Fig ) is consistent with the fission yeast PCNA ubiquitylation pathway , in addition to regulating translesion synthesis during DTT , functioning to maintain accurate Okazaki fragment synthesis in the face of frequent Polδ: PCNA dissociation ., Okazaki fragment synthesis is necessarily coupled , either directly or indirectly , to the movement of replication forks ., Approximately 105 and 107 Okazaki fragments are synthesised per cell cycle in fission yeast cells and human cells , respectively ., The potential for failure during this process as a consequence of premature Polδ dissociation would therefore need to be minimised by ensuring the re-association of Polδ and completion of Okazaki fragment synthesis ., We propose that this is facilitated by PCNA ubiquitylation , which ensures that PCNA is not prematurely unloaded ., The fact that we show that the loss of PCNA ubiquitylation results in the accumulation of ssDNA gaps during unperturbed S phase in S . pombe ( Fig 5 ) supports our model ., Intriguingly , preliminary analysis ( S10 Fig ) showed the positive effect of PCNA ubiquitylation on PCNA chromatin association is evident only when the Elg1 unloader complex is active , suggesting that PCNA ubiquitylation may inhibit its unloading by Elg1 , a PCNA unloading factor currently characterised only in in S . cerevisiae 33 ., In S . cerevisiae yeast , SUMOylated PCNA is the predominant modification during unperturbed S phase 12 , 39 ., Previous work showed that Elg1 preferentially interacts with SUMO-modified PCNA 40 ., However , Elg1 unloads both unmodified and SUMOylated forms of PCNA , an event which in budding yeast requires the ligation of Okazaki fragments 41 ., However , in fission yeast , and in human cells , SUMOylated PCNA is much harder to detect and the SUMO-interacting motifs identified in S . cerevisiae Elg1 are not conserved ., Thus , the influence of PCNA SUMOylation is unlikely to be prominent and we propose that the effect of PCNA ubiquitylation on stabilising PCNA is more predominant in fission yeast cells and potentially higher eukaryotes ., It has also been suggested that the unloading of PCNA in response to HU or MMS in S . cerevisiae is dependent on its ubiquitylation and concomitant activation of the DNA damage checkpoint 42 ., One interpretation of this apparent contradiction could be that , under extensive replication stress , checkpoint activation changes the response to PCNA ubiquitylation ., Alternatively , this may again reflect a difference between the two organisms in the regulation of PCNA unloading ., In fission yeast a significant proportion of PCNA is ubiquitylated during unperturbed S phase ., To avoid a global engagement of error prone DNA polymerases , we propose that the replicative polymerases remain the preferred binding partners for ubiquitylated PCNA ., However , when a replicative polymerase is stalled at a blocking lesion , the ubiquitin binding domain-containing polymerases are provided an increased opportunity to sample the damaged base ., In budding yeast the situation is distinct: PCNA is not significantly ubiquitylated in unperturbed S phase , but is robustly ubiquitylated in response to a replicative polymerase arrested at a lesion ., Thus , we would predict that the binding kinetics for the replicative and error-prone DNA polymerases will be different between the two organisms in order to maintain the same biological outcomes: an appropriate balance between unsuitable use of error prone DNA polymerases during unperturbed S phase ( to minimise constitutive mutagenesis ) and their appropriate use during DNA damage tolerance to maximise cell survival in response to DNA damage 43 ., In summary , our analysis shows that PCNA ubiquitylation , in addition to controlling DNA damage tolerance pathway usage , also participates in the timely completion of unperturbed DNA synthesis ., We propose that this function is related to the increased association of ubiquitylated PCNA with chromatin ., We suggest that , when Polδ stochastically dissociates during Okazaki fragment synthesis , the consequent ssDNA results in PCNA ubiquitylation which ensures it remains DNA-associated to facilitate the recapture of Polδ and completion of Okazaki fragment synthesis ., Standard S . pombe genetic and molecular techniques were employed as described previously 44 ., The BrdU-incorporating strains have been already reported 45 ., Polδ-GFP cells were constructed by introducing the sequence encoding GFP into the N-terminal of the cdc6 gene on S . pombe genome based on the Cre-loxP method 46 ., Polε-GFP cells were constructed by introducing GFP at the C-terminal of cdc20 gene using PCR-based integration 47 ., Purg1-rad18 strains were based on rad18Δ cells in which ORF of the rad18 gene fused with the AID degron construct 48 was used to replace the endogenous urg1 ORF 49 ., U2OS cells were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% foetal bovine serum ( DMEM-FBS10% ) in a 5% CO2 atmosphere ., The medium was exchanged with one containing 400 ng/ml of nocodazol ., Following 18 hr incubation , mitotic cells were detached by gentle shaking of the culture vessel and passaged in DMEM-FBS10% ., Cells were then either UV-irradiated ( 254 nm peak; 20J/m2 ) , or not , 2 hr prior to sampling ., At the indicated time points cells were sampled and then subjected to immunoblotting with anti-PCNA antibody ( mouse monoclonal , PC10 clone , Abcam ) ., To determine the S-phase fraction of the synchronised cells , 5μM if EdU was added into an aliquot and EdU positive cells scored 2 hr after EdU addition 50 ., 1BR3hTERT cells were cultured in DMEM-FBS10% and the medium was exchanged with DMEM without FBS ., Following 15 days , cells were passaged into DMEM-FBS10% ., Cells were UV-irradiated and scored for S-phase fraction as described ., Cell lines from GDSC collection ., Authenticated 2015 by STR profiling ., cdc25-22 cells harbouring the constructs for BrdU-incorporation were grown to exponential phase ( 0 . 2 x106 /ml ) at 25°C and synchronised at G2 phase by incubation at 36°C for 3 . 5 hr ., After adding bromodeoxyuridine ( 0 . 5 μM ) , cells were further incubated at 25°C ., At relevant time points , 1x108 cells were pelleted and subjected to genomic DNA extraction ., To detect total BrdU incorporation , dot blotting was performed as previously described 34 ., The intensity of BrdU-incorporation was established by quantifying the signal using an ImageQuant LAS 4000 imager ( GE Healthcare Life Sciences ) ., Global replication rates for each time point after release from G2 phase were estimated by dividing signal intensities at each time-points by that for 150 min , at which genome replication was completed ., Local replication rates were established from BrdU-IP-Sequencing ., Paired-end reads from high throughput sequencing were aligned to the S . pombe genome sequence ( ASM294v2 . 23: chromosomes I , II and III , downloaded from PomBase’ website ) using bowtie2–2 . 2 . 2 ., From the alignment data the position of the centre of each read was calculated and the number of reads in 300bp-bins across genome counted ., The Perl program converting alignment data to count data: ‘sam-to-count . pl’ is available on the GitHub website ( https://github . com/yasukasu/sam-to-bincount ) ., The counts at the chromosome coordinate x , CB ( t , x ) –the BrdU-IP sample derived from cells at the t-min time point , CI ( 0 , x ) –the input sample derived from cells before release from G2 ( t = 0 ) , were normalised with the total number of reads: NB ( t , x ) = CB ( t , x ) /ΣCB ( t , x ) , NI ( t , x ) = CI ( t , x ) /ΣCI ( t , x ) ., Enrichments for BrdU-incorporated fragments were calculated: E ( t , x ) = NB ( t , x ) /NI ( t , x ) ., As BrdU is an analogue of thymine and its enrichment is thus likely to be biased towards A/T rich regions , the dataset of enrichment was normalised using the A/T-ratio of each 300-bp bin AT ( x ) : E’ ( t , x ) = E ( t , x ) /AT ( x ) ., Moving average of E’ ( t , x ) with 8 bins at both side were calculated and plotted ( Fig 1D ) ., To estimate the extent of local replication , enrichments across the genome were multiplied by the global replication amount G ( t ) determined from the dot-blot assay ( Fig 1C ) : L ( t , x ) = E’ ( t , x ) ×G ( t ) ., These were then normalised with that of the last time point , at which all the cells had completed genome replication: L’ ( t , x ) = L ( t , x ) /L ( 160 , x ) ., To obtain a function of local replication extent , data of multiple time points at each 300 bp ( L’ ( 70 , x ) , L’ ( 75 , x ) , L’ ( 80 , x ) , L’ ( 85 , x ) , L’ ( 90 , x ) ) were fitted with a cumulative normal distribution function in which the global replication amount is variable , F ( G , x ) ., Using this function , Local replication extent when the global replication was 25% , 50% or 75% completed was determined: F ( 0 . 25 , x ) , F ( 0 . 5 , x ) and F ( 0 . 75 , x ) ., Fig 1E–1G and S4 Fig is derived from these datasets ., The custom R scripts used for this computational analysis are available on request ., Whole cell extracts were prepared by spheroplast lysis using Zymolyase 100T ( Seikagaku ) and lysing enzyme ( Sigma-Aldrich ) ., Extracts were fractionated into soluble and chromatin-bound fractions by centrifugation through a sucrose cushion 51 ., 5 x 108 exponentially growing cells in 50 ml YE medium were treated with 1% formaldehyde for 15 min at RT under agitation ., The crosslinking reaction was quenched by adding 2 . 5 ml of 2 . 5 M glycine ., Cells were washed with ice-cooled PBS , pelleted and re-suspended in 700 μl pf RIPA buffer ( 50mM HEPES pH7 . 5 , 1mM EDTA , 140 mM NaCl , 1% Triton X-100 , 0 . 1% ( w/v ) sodium deoxycholate ) supplemented with complete protease inhibitor ( Roche ) , 1 mM AEBSF & 1μg/ml pepstatin ( Sigma-Ald

Introduction, Results, Discussion, Materials and methods

PCNA ubiquitylation on lysine 164 is required for DNA damage tolerance ., In many organisms PCNA is also ubiquitylated in unchallenged S phase but the significance of this has not been established ., Using Schizosaccharomyces pombe , we demonstrate that lysine 164 ubiquitylation of PCNA contributes to efficient DNA replication in the absence of DNA damage ., Loss of PCNA ubiquitylation manifests most strongly at late replicating regions and increases the frequency of replication gaps ., We show that PCNA ubiquitylation increases the proportion of chromatin associated PCNA and the co-immunoprecipitation of Polymerase δ with PCNA during unperturbed replication and propose that ubiquitylation acts to prolong the chromatin association of these replication proteins to allow the efficient completion of Okazaki fragment synthesis by mediating gap filling .

PCNA is a homotrimeric complex that clamps around the DNA to provide a sliding platform for DNA polymerases and other replication and repair enzymes ., The covalent modification of PCNA by ubiquitin on lysine reside 164 has been extensively studied in the context of DNA repair: it is required to mediate the bypass of damaged template bases during DNA replication ., Previous work has shown that PCNA is modified by ubiquitin during normal S phase in the absence of DNA damage , but the significance of this modification has not been explored ., Here we show that , in addition to regulating bypass of damaged bases , lysine 164 ubiquitylation plays a role in ensuring the completion of unperturbed DNA replication .

cell cycle and cell division, cell processes, dna-binding proteins, dna damage, fungi, model organisms, polymerases, dna replication, immunoprecipitation, experimental organism systems, dna, epigenetics, synthesis phase, co-immunoprecipitation, chromatin, schizosaccharomyces, research and analysis methods, saccharomyces, chromosome biology, proteins, gene expression, schizosaccharomyces pombe, precipitation techniques, yeast, biochemistry, cell biology, nucleic acids, genetics, biology and life sciences, yeast and fungal models, saccharomyces cerevisiae, organisms

journal.ppat.1001091

Phylogenomics of Ligand-Gated Ion Channels Predicts Monepantel Effect

Nematode parasites of sheep represent one of the major constraints in the wool , meat and milk industries world wide 1 ., The gastro-intestinal parasite Haemonchus contortus , in particular , causes substantial losses ., In the mid-1950s , the existence of anthelmintic resistant worm populations came to light with the failure of phenothiazine against Haemonchus 2 ., Since then , nematode populations resistant to the three classical groups of anthelmintics , i . e . the benzimidazoles , imidazothiazoles and macrocyclic lactones , and combinations of these have been described 3–7 ., Recently , the amino-acetonitrile derivatives ( AADs ) have been reported as a new class of synthetic anthelmintics active against gastro-intestinal nematodes of sheep 8 and a first drug from this family , monepantel , was , at the time of writing licensed to market in Australasia , Europe and Latin America ( ZOLVIX; Novartis Animal Health Inc . , Switzerland; 9 ) ., Investigations to understand the mode of action of the AADs in Caenorhabditis elegans have been performed using chemical mutagenesis and gene mapping via a genetic recombination approach ., Out of 44 isolated resistant alleles , 36 were mapped to a single gene , acr-23 , designating it as a major contributor to the AAD response in C . elegans 8 ., A further study on mutant H . contortus isolates identified the gene monepantel-1 ( Hco-mptl-1 ) as a major target candidate for AADs action in this species 8 , 10 ., Both Cel-acr-23 and Hco-mptl-1 are predicted to encode a nicotinic acetylcholine receptor ( nAChR ) subunit ., These belong to the superfamily of ligand-gated ion channel ( LGIC ) subunits ., These are the modular components with the ability to form a large number of channels with different properties through heteromultimerisation ( see e . g . 11 ) , as all characterised LGIC function as penta- or tetramers ., They provide many important drug and toxin targets: levamisole and pyrantel act as agonists of nAChR 12 , paraherquamide as a competitive antagonist of these channels 13 and ivermectin modulates glutamate-gated chloride channels 12 , 14 , 15 ., ACR-23 and MPTL-1 are members of the DEG-3 subfamily of acetylcholine receptor subunit genes and distinct from those targeted by imidazothiazoles 16 , 17 ., Members of this subfamily have so far only been found in nematodes and no cross-resistance between the AADs and the imidazothiazoles have been documented 8 ., All animals appear to have about the same number of nAChR α subunits ( around 16 ) , with exception of the nematodes 18 ., Among the completely sequenced genomes from animals , those with the highest ( C . elegans ) and smallest ( Brugia malayi ) numbers of such genes are both from the nematodes 19 ., The reason for the variation in numbers of nAChR α subunits and other LGIC subunits is not clear ., However , certain LGIC subunits form heteromultimeric channels that provide prominent anthelmintic-specific drug targets ( highlighted in Figure 1 ) , in particular for ivermectin ( AVR-14 , AVR-15 , GLC-1 ) , levamisole ( UNC-38 , UNC-29 , UNC-63 , LEV-1 , LEV-8 ) and monepantel ( MPTL-1 ) ., Several new , draft nematode genomes as well as pre-publication quality assemblies are now available from ongoing or recently finished sequencing projects ( see Table 1 ) ., To learn what parts of the LGIC superfamily are unique to nematodes and in consideration of much new sequence information , we constructed a simple phylogenomic pipeline to further understand the mechanisms behind the action of monepantel ., We explored the LGIC superfamily by in silico searches , and while we found a considerable number of tentative new family members since the last such survey was made 19 , the DEG-3-subfamily remains nematode specific ., In an in vitro drug assay we further show that susceptibility to the AADs directly follows the presence of ACR-23 or MPTL-1 homologs in the genomes from the nematodes investigated ., Genome data in the form of contig or supercontig DNA sequence fasta files were downloaded from GenBank ( NCBI ) , Ensembl , WTSI , nematode . net , GSC/WUSTL , Broad and WormBase ( all attributed and referenced in Table 1 ) ., Sequences from genomes with long contiguous sequences were artificially divided into 100 kb segments ( indicated by asterisks in Table 1 ) ., Seed sequences were obtained as peptide fasta files from WormBase 20 and Uniprot 21 ., A Blast 22 screen with the seed sequences as queries against the genomic sequence databases was performed ., Only contigs with hits ( E<0 . 1 ) were searched by Genewise 23 with the PFAM 24 motifs LGIC_LBD ( PF02931 . 15 ) and LGIC_MEMB ( PF02932 . 8 ) for global scoring ( ls ) ., Splice sites were considered using the Genewise-provided worm gene model ., The seed peptide sequences were searched using the same PFAM profiles but with hmmsearch from the hmmer2 package ( by Eddy , http://hmmer . janelia . org ) ., The protein domains , conceptually translated from DNA or directly from the seed proteins , that exhibited E-values below the trusted E-value cut-off were aligned and an nj tree ( bootstrap 1000 iterations ) was constructed with clustalw 25 ., These steps were automated in bash and Perl using tools from the EMBOSS package 26 and executed on LINUX computers using less than 1 . 5Gb RAM ., Trees were visualised with Dendroscope 27 and HyperTree 28 ., For Figure 1 , furcations with bootstrap support below 50% were fused in jtreeview ( Frickey , Lupas http://www . eb . tuebingen . mpg . de/departments/1-protein-evolution/software/jtreeview/ ) ., Co-segregation with known named seed sequences in the bootstrapped tree was used for assigning putative identity to homologous genes ., Trees based on available , confirmed or predicted , full-length protein sequences ( WormBase WS195 C . brenneri , C . elegans , C . briggsae , C . japonica , C . remanei , Pristionchus pacificus and B . malayi ) were also constructed ., The same seed sequences were used to pick predicted genes with a blastp similarity ( E<0 . 01 ) for inclusion in a profile search and tree construction using the aforementioned methods ., Nematode strains C . briggsae AF16 , C . brenneri PB2801 , C . remanei PB4641 , C . japonica DF5081 and P . pacificus PS312 as well as the mutants VC1598: Cel-acr-20 ( ok1849 ) /mT1 II; +/mT1dpy-10 ( e128 ) III , NC293: Cel-acr-5 ( ok180 ) III , TU1803: Cel-deg-3 ( u662 ) Cel-des-2 ( u695 ) V and RB1226: Cel-acr-18 ( ok1285 ) V were obtained from the Caenorhabditis Genetics Center ( CGC ) , Minneapolis , USA , which is funded by the NIH National Center for Research Resources ( NCRR ) ., Caenorhabditis elegans Bristol N2 and AP134: Cel-acr-23 ( cb27 ) V 8 were kind gifts from Prof . Alessandro Puoti , University of Fribourg ., Nematodes were maintained at 20°C on Nematode Growth Medium ( NGM ) plates ( 3 g NaCl , 17 g Agar , 2 . 5 g peptone in 975 ml H2O , autoclaved , added 1 ml cholesterol ( Sigma ) prepared to 5 mg/ml in EtOH , 1 ml M CaCl2 , 1 ml M MgSO4 and 25 ml KPO4-buffer ) , and inoculated with E . coli OP50 , and transferred every 3 days ., Strongyloides ratti L3 were obtained from the feces of infected rats following standardized procedures based on the Baermann technique at the Swiss Tropical and Public Health Institute ., Freshly harvested S . ratti L3 were washed 3 times with PBS buffer and used immediately for in vitro drug testing ., The species of nematodes used in the in vitro drug test was confirmed by a PCR targeting the 18S rRNA region of C . elegans , C . briggsae , C . remanei , C . brenneri , C . japonica and P . pacificus ., Using the forward primer SSU18A and the reverse primer SSU26R 29 ( Supplementary Table S1 ) , a ∼950 bp fragment was amplified using FastStart High Fidelity PCR system ( Roche ) ., The reaction conditions were: 95°C for 10 min without Taq polymerase; 95°C for 2 min; 35 cycles of 95°C for 30 sec; 52°C for 30 sec; 72°C for 1 min 10 sec; 72°C for 10 min ., PCR products were purified using the Wizard SV PCR Clean-Up kit ( Promega ) and sequenced in both directions with SSU18A and SSU26R at Microsynth AG ( http://www . microsynth . ch ) ., Sequence quality check and assembly was done using 4Peaks ( by A . Griekspoor and T . Groothuis; http://mekentosj . com ) and a nucleotide blast was made on-line ( NCBI ) against the nucleotide collection ( nt ) ., Ivermectin and AAD-1566 were provided by Novartis Animal Health , Centre de Recherche Santé Animale , Fribourg , Switzerland ., The compounds were diluted in pure DMSO to 10 mM and 250 mM , respectively ., Appropriate dilutions of drugs were placed at the bottom of wells in 24-well plates and 1 ml NGM was added per well ., The first well in each row served as a control with 1% DMSO ., The plates were well shaken , allowed to dry at RT for several days , then inoculated with 10 µL E . coli OP50 and incubated at 37°C overnight ., Eggs were purified from adults of the different species as follows: plates were washed with 3 . 5 ml water and incubated with 1 . 5 ml 5% bleach mixed 1∶1 with 5M NaOH for 10 min at room temperature ., The eggs were washed with water and counted ., A volume corresponding to 100–200 eggs , depending on the species , was added to each well of the drug plates ., Plates were incubated at 20°C and scored microscopically for adults after 3 days and inspected again after 6 days for potential F2 generation of L1-larvae ., For the S . ratti in vitro tests 25 L3 larvae were incubated in 96-well plates containing 30 µl PBS buffer supplemented with 100 U/ml penicillin and 100 µg/ml streptomycin ( Invitrogen ) and appropriate drug dilutions ., Control wells contained the highest percentage of solvent ( 2% DMSO ) ., At each examination point ( 24 , 48 and 72 h post-incubation ) 15–20 µl of hot water ( 80°C ) was added to each well , the larval movement observed and live worms counted using a dissecting microscope ., All water stimulated wells were excluded from further reading ., Half maximal effective concentration ( EC50 ) values were calculated by non-linear regression of the sensitivity data , expressed as the percentage of surviving worms/larvae compared to the untreated control , to a sigmoidal dose-response curve of variable slope using Prism ( GraphPad Prism version 5 . 00 for Mac OS X , GraphPad Software , San Diego California USA , http://www . graphpad . com ) ., A previously described procedure was followed for RNA extraction , cDNA synthesis and rapid amplification of cDNA ends by PCR ( RACE-PCR ) for H . contortus 10 ., Briefly , total RNA was extracted from a pool of approximately 50 adult nematodes ., To generate cDNA , 1 µg of total RNA was reverse transcribed to cDNA using a d ( T ) 30 primer and a SuperScript II Reverse Transcriptase ( Invitrogen ) ., For RACE-PCR , an internal reverse primer ( Supplementary Table S1 ) was combined with splice leader sequence ( 1 or, 2 ) to obtain the 5′ untranslated region ( UTR ) , or an internal forward primer combined with a poly-dT primer for the 3′ UTR of the transcript ., We cloned and sequenced the full-length Hco-acr-5 , Hco-acr-17 and Hco-acr-24 coding sequence from H . contortus cDNA ( GenBank accessions GU109271-GU109279 ) using primer pairs NheI_acr-5_frw2 . 1 and NotI_acr-5_rev2 . 1 , NheI_acr-24_frw1 and XhoI_acr-24_rev1 , NheI_acr-17_frw1 and XhoI_acr-17_rev1 ( Supplementary Table S1 ) ., PCR products were gel purified using the Wizard SV PCR Clean-Up kit ( Promega ) and cloned into pCRII-TOPO ( Invitrogen ) ., Plasmid DNA was purified using the QIAprep Spin Miniprep kit ( Qiagen ) and three clones of each gene were sequenced using the standard primers M13 forward and reverse ., The reported sequences in Supplementary Figures S1 , S2 and S3 are each from one of the nearly identical single clones ., Hco-acr-5 , Hco-acr-17 and Hco-acr-24 coding sequence from H . contortus cDNA have been deposited with GenBank accessions GU109271-GU109279 ., MPTL-1 ACO48330 ( GenBank ) ., SwissProt entries for mentioned proteins: ACC-1 Q21005_CAEEL , ACR-16 ACH1_CAEEL , ACR-17 P91320_CAEEL , ACR-20 B1Q281_CAEEL , ACR-21 Q9N5U8_CAEEL , ACR-23 O61884_CAEEL , ACR-5 ACR5_CAEEL , ACR-8 Q23355_CAEEL , AVR-14 Q95Q96_CAEEL , AVR-15 Q95PJ6_CAEEL , CUP-4 CUP4_CAEEL , DEG-3 ACH3_CAEEL , DES-2 ACH4_CAEEL , EXP-1 Q9TZI5_CAEEL , GAB-1 GBRB_CAEEL , GGR-2 Q2WF64_CAEEL , GLC-1 O17793_CAEEL , LEV-1 ACH7_CAEEL , LEV-8 Q93329_CAEEL , MOD-1 Q58AT9_CAEEL , PBO-5 Q67X94_CAEEL , UNC-29 ACH2_CAEEL , UNC-38 ACH5_CAEEL , UNC-49 Q0PDK2_CAEEL , UNC-63 ACH6_CAEEL , ZACN ZACN_HUMAN ., Caenorhabditis elegans peptide sequences annotated with the Gene Ontology term GO:0005230 , ‘extracellular ligand-gated ion channel activity’ , were retrieved from WormBase ., These together with a similarly extracted set of human genes from uniprot and six H . contortus LGICs of the DEG-3 subfamily ( 10 and Supplementary Figure S1 , S2 and S3 ) were used as seed sequences for a Blast search against contiguous sequences from the abundant nematode , vertebrate and insect genome projects ( Table 1 ) ., Caenorhabditis elegans is arguably the only finished eukaryote genome , but the genomes published as drafts are essentially complete , and several of the ongoing projects are well underway in terms of sequencing and assembly , only so far lacking in gene annotation ., In this survey , we included data from 10 more nematode genomes , ranging from early shotgun stages to mature assemblies in annotation ., Gene finding and annotation has become a major bottleneck , after next generation sequencing techniques accelerated sequence generation ., By using Genewise to search the genome sequences directly we could also make use of unannotated genes ., To assess nematode specificity of the herein predicted LGIC genes and to obtain more phylogenetic information , we also included three platyhelminth projects , four insect and nine vertebrate genomes ( Table 1 ) ., No LGIC_LBD ( from ligand binding domain , LBD ) was found in eight plant species searched ( www . gramene . org ) , which is in agreement with previous efforts 30 ., The closest LGIC relatives in plants are highly diverged glutamate receptors 31 ., Many plant toxins act on animal LGICs ( e . g . curare , extracted from the plant Strychnos toxifera 32 ) ., Due to the lack of LGICs , the toxic compounds pose little risk to the plants themselves ., The automated approach identified 84 out of 102 annotated C . elegans LGICs using the LGIC_LBD profile alone ., Only one additional LGIC was identified when the 39 membrane binding domain hits , from the LGIC_MEMB profile , were also included ., The recall of the profile itself from full-length peptides was nearly complete ., Using hmmer2 , all 102 were found with the LBD profile and 98 with the MEMB profile ., The lower complexity of the trans-membrane domains and a presumed lower need for conserved sequence specificity , together with the often extensive and variable internal loop between trans-membrane domain 3 ( TMD3 ) and TMD4 , all complicated by a slightly larger number of introns , apparently makes the LGIC_MEMB Hidden Markov Model profile less successful for finding family members directly from genomic nucleotide data ., A domain centric approach , as used here , is highly useful to compare the whole spectrum of LGICs ., The domain approach is straightforward , can be applied directly on sequence data without prior exon prediction and gives alignments where the aligned positions are largely comparable ., It would also be much more challenging to align the protein family meaningfully over the full length ., Inclusion of more variable regions e . g . the internal loop between TMD3 and TMD4 would make the interpretation more difficult ., While the recall of C . elegans receptor subunit genes by the identification of the LBD domain directly from the genome is not complete ( 80% ) , it is reassuring that the full-length peptide results for genomes , where such are available , are similar to the ones obtained through searches on the genomic DNA level , in particular in the DEG-3 subfamily ., If genome sequence coverage is lacking altogether or if other problems keep the assembled contig size small , the number of LGICs predicted from our pipeline will be low ., If the contigs with LGIC genes are too short so that they do not encompass the introns and exons for the LBD , they will not be detected by Genewise with a global ( ls ) type PFAM LGIC_LBD motif , even if fragments were detected by the initial BLAST screening ., To help assess the reliability of the number of genes found in the face of incompleteness of the ongoing projects , we measured the average contiguous sequence length ( Table 1 ) ., Such a central measure can however be somewhat misleading for mature projects with a very high contig size variance ., Indeed , the B . malayi and L . scapularis genome sequences show low sequence unit average length ( <5 kbp ) , although the longest few contiguous sequences have considerable size ( ≫100 kbp; marked in Table 1 ) ., Gene counts for the genomes with average contig sizes below 3 kbp ( Ascaris suum , Nippostrongylus brasiliensis and Teladorsagia circumcincta ) in particular should not be taken for final ., The full length coding sequences of H . contortus genes Hc-acr-17 ( 1590 bp ) , Hc-acr-5 ( 1833 bp ) and Hc-acr-24 ( 1698 bp ) were cloned by RACE PCR ( see Materials and methods ) and sequenced ( Supplementary Figures S1 , S2 and S3 ) , helping to complete the understanding of individual DEG-3 subfamily members roles in monepantel drug action ( Figure 2 and Table 2 ) ., Both Hco-acr-5 and Hco-acr-24 carried a spliced leader 2 ( SL2 ) sequence at their 5′ end while Hco-acr-17 had a spliced leader 1 ( SL1 ) sequence ., The predicted LGIC proteins possess motifs typical for Cys-loop ligand-gated ion channels , including an N-terminal signal peptide , with the exception of Hco-acr-24 ( as determined with Phobius 33 , 34 ) , four transmembrane domains and the Cys-loop ( two cysteines separated by 13 amino acids ) ., Loops A to F , which are involved in ligand binding 35 are also present in the proteins ., These loops are not annotated for Hco-acr-17 as the alignment with other related nAChRs were the loops location are known is poor ., In loop C , there are two adjacent cysteines , defining Hco-acr-5 , Hco-acr-17 and Hco-acr-24 as nAChR α subunits ., Hco-acr-5 and Hco-acr-24 have the characteristic FxCC pattern , conserved among other ACR-5 and ACR-24 homologs , in contrast to Hco-acr-17 bearing the most common YxCC α subunit signature in loop C . We used our phylogenomic pipeline on 33 genomes of varying levels of completeness , detecting 1273 putative genes bearing the PFAM LGIC_LBD motif ( Table 1 ) ., The average number found in nematode genomes with an average sequence unit larger than 3 kbp was 56 . 1 , whereas the same number was 41 . 7 for vertebrates , 18 . 0 for insects and 0 for plants ., We also searched the nembase3 and nematode . org expressed sequence tag sets , finding a total of 27 LGICs with the LBD motif ., An average of 31 and 57 LGICs were found in parasitic and non-parasitic organisms , respectively ., The trend among the nematodes is clearly in agreement with the hypothesis that parasites have a reduced number of LGICs ., It has been suggested that this could be a consequence of the less variable environment they encounter in comparison with their free-living relatives 19 ., There is also considerable variation in LGIC number among the vertebrates ( Table 1 ) ., The teleost genomes show a larger set of LGIC , in comparison to , for example , Bos taurus and Homo sapiens ., The teleost repertoire appears to consist largely of multiple closely related variants of the terrestrial vertebrate LGIC types ., The nematodes show a larger repertoire ( Figure 1 ) ., While the platyhelminthes included in the survey showed a smaller overall number of LGICs , they did have several unique types ., LGIC subunits that are known to constitute part of drug target receptors are labelled in Figure 1 ., It is interesting to see how these drug target subunit genes form rather broad , i . e . member rich , yet nematode specific sub-branches of the superfamily tree ., Importantly , the DEG-3 family appears nematode specific ., In an optimistic outlook , several other such broad nematode specific branches exist in the tree , which could potentially be exploited as new anthelmintic targets ., Co-segregation with known named seed sequences in the bootstrapped tree was used for assigning putative identity to homologous genes ., Interestingly , we found that neither P . pacificus nor S . ratti carries an ortholog of Hco-mptl-1 ( Figure 2 ) ., Based on a single drug target model we thus predict P . pacificus and S . ratti to be insensitive to monepantel ., We proceeded to test this hypothesis in vitro ., An in vitro assay was established ., Nematodes of one species were grown on 24 well NGM plates where each four well column was treated with a different drug concentration ., An equal amount of eggs was added to each well , and the nematodes were scored microscopically after 3 and 6 days ., A final concentration of 1% DMSO was used in all wells for the drug tests , including the no-drug controls ( Figure 3 , Supplementary Figure S4 ) ., All species tested ( C . elegans , C . japonica , C . briggsae , C . brenneri , C . remanei , P . pacificus ) tolerated up to 1% DMSO ( Supplementary Figure S4 and Supplementary Table S2 ) ., For S . ratti the highest percentage of solvent ( 2% DMSO ) found in the plate was used in the control wells and well tolerated ., All Clade V species tested ( C . elegans , C . japonica , C . briggsae , C . brenneri , C . remanei , P . pacificus ) exhibited a similar sensitivity to ivermectin ( EC50≥10 nM; Supplementary Figure S7 and Supplementary Table S3 ) ., S . ratti L3 exposed to ivermectin at concentrations of 10 µM and above showed decreased survival rates 24–72 h post-incubation ( EC50 of 13 . 6 µM 72 h post-incubation ) ( Supplementary Figure S7 ) ., This served as an additional positive control for the methods ., It appears likely that drug sensitivity can be consistently determined for all strains ., In a similar experiment the assayed nematode species showed varying degrees of concentration-dependent sensitivity towards DMSO , used as a solvent for the drugs ., Even a compound with a small effect in this in vitro test could still be of therapeutic value , as levamisole clearly demonstrates ., Levamisole does not directly kill the parasitic nematodes but creates a short term reversible paralysis , sufficient to allow the host to e . g . expulse the worms 12 ., Up to mmolar concentrations of levamisole did not produce any effect detectable by our test readout ( data not shown ) ., The present study tests the hypothesis that MPTL-1 is a major target of monepantel , since a nematocide effect on P . pacificus or S . ratti , which lacks an MPTL-1 homolog , would negate this ., The P . pacificus genome has been published in a draft state , and as the assembly is nearly complete it is unlikely , but not impossible , that an eventual acr-23 ortholog could have been missed ., The S . ratti genome is still in progress , but we were able to detect a subunit that appears branched prior to the ACR-23/MPTL-1/ACR-20 and the DES-2 split ( see Figure 2 and Table 2 ) ., This species arguably helps us narrow down how deep the sensitivity to monepantel reaches in the tree ., Among the species C . elegans , C . japonica , C . briggsae , C . brenneri , C . remanei , P . pacificus as well as the mutated C . elegans strain acr-23 ( cb27 ) , C . japonica was the most sensitive to monepantel with EC50 values in the low nM range ( Figure 3 and Table 1 ) ., This is comparable to the results previously obtained for H . contortus 10 ., C . elegans was strongly affected at 1 µM , with an estimated EC50 of 0 . 19 µM ., C . remanei , C . briggsae and C . brenneri showed similar EC50 values , but we found a comparatively large number of adult C . brenneri even at higher µM concentrations , e . g . 7 . 6% at 100 µM ( Supplementary Table S4 ) ., C . brenneri has the largest assembly of LGICs in the study , and also possesses an extra DES-2 paralog and an additional ACR-23 ., Closer examination of the sequences of these copies did not present a convincing explanation of the diminished phenotype ., One explanation may possibly lie in the gene doses of the channel subunits , leading to different stochiometries of the assembled channels , as has been observed in vitro 36 ., The difference in EC50 value between C . japonica and to the other sensitive worms in the Caenorhabditis genera is already large ., While we would not venture a molecular correlate , it is interesting to observe that the both the Cjp-ACR-23 and Cjp-ACR-20 seem to have diverged somewhat from the other sensitive Caenorhabditis worms , branching prior to them , possibly retaining more of an element important for high sensitivity , common with the earlier branched Hco-MPTL-1 ., Pristionchus pacificus is rather insensitive to monepantel with an EC50 of 43 µM ( Table 2 , Figure 3 ) ., Furthermore , our in vitro test with S . ratti , bearing an early branching relative of ACR-20/ACR-23/MPTL-1 , showed that monepantel lacks activity against S . ratti ., A survival rate of 69% was observed after 72 h even with the highest concentration ( 250 µM ) tested ( Figure 3 ) ., A direct molecular mechanism is beyond the scope of the present investigation ., However , we found that the phylogenomic detection of the ortholog of Hco-mptl-1 , previously found mutated in strains insensitive to AAD-1566 10 , coincides with sensitivity to AAD-1566 ., This in agreement with our hypothesis that MPTL-1 is a major target of the drug ., acr-23 ( cb27 ) , a strain of C . elegans exhibiting a large deletion in Cel-acr-23 8 , was much less sensitive than wild type ( genome strain N2 ) , with an EC50 of 25 µM ( Table 2 , Figure 3 ) ., The difference in growth was marked and clearly visible to the naked eye ( Supplementary Figure S5 ) ., This test can naturally not rule out the involvement of other LGIC subunits or indeed other genes in the susceptibility to AAD-1566 ., However , a set of C . elegans strains mutant only in other genes of the DEG-3 family ( DEG-3/DES-2 , ACR-5 , ACR-18 , and ACR-20 ) showed no loss of sensitivity towards AAD-1566 ( Supplementary Figure S6 and Supplementary Table S5 ) ., This further strengthens the hypothesis that a subunit orthologous to MPTL-1/ACR-23 is required for the observed effect ., For species that possess an MPTL-1 ortholog ( e . g . C . elegans with Cel-ACR-23 ) , AAD-1566 is lethal in vitro at nM concentrations , and a concentration-dependent retardation of development was observed ., The strains without an Hco-MPTL-1 ortholog ( P ., pacificus and C . elegans acr-23 ( cb27 ) ) also exhibited a drug concentration-dependent developmental retardation ., However , the substance was not lethal to them at the tested concentrations , as growth could still be observed after 6 days ., Also in the case of S . ratti the survival rate of the larvae was slightly affected at high drug concentration ( 69% at 250 µM ) and less at lower concentration ., This suggests that there is at least one additional target ., One candidate is DES-2 ., In nematode strains selected for loss of sensitivity to AAD-1566 , mutations in addition to those affecting Hco-mptl-1 were found in the Hco-des-2 gene 5′ UTR , introducing two novel upstream open reading frames , possibly reducing protein expression 10 ., All tested species possess the DES-2 ortholog that bears the highest similarity to the established target outside the Cel-ACR-20/Cel-ACR-23 branch ., If MPTL-1 is a primary target , causing high nematode lethality from AAD-1566 , strains with modulations in the expression of a second target , DES-2 , would only be selected for once MPTL-1 sensitivity was lost ., It was noted in proof that in a recent study 37 Rufener et al . have expressed a functional H . contortus DES-2/DEG-3 channel in Xenopus oocytes that shows monepantel sensitivity ., Though active against a range of clade V gastrointestinal nematodes , monepantel was reported to have only limited efficacy against Trichuris ovis ( clade I ) 38 ., Genomic information to correlate this result with the absence of MPTL-1/ACR-23/DES-2 homologous subunits would be interesting ., There are a number of nematodes that , based on their complement of predicted nAChR genes , would be interesting to test for their sensitivity to AADs , but this would require other test methods ., Two Meloidogyne species bear no close MPTL-1 homologs but have an ACR-5 , homolog , which P . pacificus lacks ., Heterorhabditis bacteriophora carries a DEG-3 family complement , which is highly similar to H . contortus , and we would thus predict a similar drug effect ., Some important human parasitic nematodes of the clade I ( Trichinella spiralis ) and III ( Brugia malayi ) have more distant DES-2/DEG-3 homologs , much like Schmidtea mediteranea ., A conjecture would be that they would show sensitivity only at a higher concentration ., Tests on them could perhaps show what level of sequence identity is required , or what regions of the subunit need to be conserved , for any paralysis effect to be seen ., The family of LGIC provides many important drug and toxin targets , with nematodes bearing several unique subfamilies well diverged from those of other eukaryotes ., We have constructed a simple phylogenomic pipeline to detect LGIC subunit genes ., We survey the gene family in the many complete and ongoing sequencing projects in the nematode phylum and contrast these to genomes from some other relevant phyla to establish that the DEG-3 family indeed appears nematode specific to date ., The survey also establishes the detection of drug sensitivity groups ., Given the hypothesis that an MPTL-1 homolog is the primary target of monepantel , the phylogenomic information gathered predicts P . pacificus and S . ratti to be insensitive to the drug , while four other model nematode species were predicted to be sensitive ., These conjectures were tested experimentally ., The in vitro effect of AAD-1566 on the panel of nematodes was found consistent with the hypothesis ., All data point towards MPTL-1 as a primary target , in agreement with previous studies ., We further hypothesise an additional secondary target for AAD-1566 , possibly DES-2 ., This would explain a dose dependent growth retardation effect that is largely masked by the stronger , MPTL-1 mediated response .

Introduction, Materials and Methods, Results/Discussion

The recently launched veterinary anthelmintic drench for sheep ( Novartis Animal Health Inc . , Switzerland ) containing the nematocide monepantel represents a new class of anthelmintics: the amino-acetonitrile derivatives ( AADs ) , much needed in view of widespread resistance to the classical drugs ., Recently , it was shown that the ACR-23 protein in Caenorhabditis elegans and a homologous protein , MPTL-1 in Haemonchus contortus , are potential targets for AAD action ., Both proteins belong to the DEG-3 subfamily of acetylcholine receptors , which are thought to be nematode-specific , and different from those targeted by the imidazothiazoles ( e . g . levamisole ) ., Here we provide further evidence that Cel-ACR-23 and Hco-MPTL-1-like subunits are involved in the monepantel-sensitive phenotype ., We performed comparative genomics of ligand-gated ion channel genes from several nematodes and subsequently assessed their sensitivity to anthelmintics ., The nematode species in the Caenorhabditis genus , equipped with ACR-23/MPTL-1-like receptor subunits , are sensitive to monepantel ( EC50<1 . 25 µM ) , whereas the related nematodes Pristionchus pacificus and Strongyloides ratti , which lack an ACR-23/MPTL-1 homolog , are insensitive ( EC50>43 µM ) ., Genome sequence information has long been used to identify putative targets for therapeutic intervention ., We show how comparative genomics can be applied to predict drug sensitivity when molecular targets of a compound are known or suspected .

Increased use of anthelmintics has contributed to the emergence of drug-resistant nematodes , causing serious problems for more than one billion sheep worldwide ., The last class of compounds indicated for livestock was introduced 28 years ago ., Recently , however , Novartis AH developed a new anthelmintic active against drug-resistant nematodes of sheep , the amino-acetonitrile derivative ( AAD ) monepantel ., We have previously indirectly shown that the AADs have a novel mode of action involving acetylcholine receptor subunits: the ACR-23 protein in Caenorhabditis elegans and a homologous protein , MPTL-1 in Haemonchus contortus ., To better understand the mode of action of the AADs , we performed comparative genomics of all ligand-gated ion channel genes from a range of organisms , including members from all nematode clades ., We confirmed that MPTL-1 belongs to a unique , nematode-specific sub-family of receptor subunits ., We also found that some nematode species lack ACR-23/MPTL-1 and predicted them to be monepantel insensitive ., We challenged this hypothesis in a panel of drug tests: several species of Caenorhabditis nematodes equipped with ACR-23/MPTL-1-like receptor subunits were found susceptible to monepantel , whereas Pristionchus pacificus , closely related to these worms but lacking an ACR-23/MPTL-1 homolog , was tolerant ., The parasitic nematode Strongyloides ratti , which has only a remote homolog of DES-2 and ACR-23/MPTL-1 , was also tolerant to monepantel ., This confirms our prediction and highlights how comparative genomic data can be used to predict a drug effect .

genetics and genomics/comparative genomics, computational biology/comparative sequence analysis, infectious diseases/helminth infections, computational biology/genomics, genetics and genomics/pharmacogenomics, infectious diseases/antimicrobials and drug resistance

journal.pcbi.1002467

Deconvolution of the Cellular Force-Generating Subsystems that Govern Cytokinesis Furrow Ingression

Cytokinesis , the separation of a mother cell into two daughter cells , is a highly stereotypical cell shape change ., During most mitotic events , cytokinesis requires the careful orchestration of many cellular systems to ensure that the cell separates the genomic material into two genetically equivalent daughter cells 1 , 2 ., However , the core process can be altered to produce asymmetric cell division events in which the daughter cells differ dramatically in size and/or cell differentiation fate 3 , 4 , 5 ., For cytokinesis , myosin II is a key but non-essential mechanoenzyme that converts the energy of ATP hydrolysis into mechanical work 6 ., Myosin II works on the actin network to alter the cells mechanical properties in complex ways ., By pulling on the filaments , myosin II can slide the polymers ., This activity is the core of the traditional contractile ring model in which myosin II slides filaments , contracting the ring in a manner analogous to the contracting muscle sarcomere 7 ., However , the actin polymers are held together by various actin crosslinking proteins , each with its own unique kinetic characteristics , force-sensitivity , and concentration ., Thus , myosin II pulls on anchored actin filaments , leading to an effective tension due to the stalling of the myosin II motor in the isometric state 8 , 9 ., As a result , myosin II is not rate-limiting for furrow ingression , and previous analyses have indicated that the furrow ingresses some 30–50-fold more slowly than predicted from the myosin II unloaded actin filament sliding velocity 10 ., Ultimately , appreciating how the cell integrates three properties – biochemistry , mechanics and morphology – is the crux of understanding all cell shape changes ., Because cytokinesis proceeds through genetic strain-specific geometries and characteristic dynamics , it is particularly well suited for studying how cell shape changes arise from biochemical mechanisms ., This view has led to the concept that cytokinesis requires the function of the entire cortex and cytoplasm and is governed by two basic modules , global and equatorial actin-associated proteins 9 ., Myosin II is found throughout the cortex but in a roughly two-fold concentration gradient between the equatorial and polar cortical domains 11 ., The myosin II-mediated force generation is only one of several major mechanical systems of the cell ., Two other systems include polar protrusive forces and the viscoelasticity of the cytoskeleton 8 , 12 ., Another major mechanical component is derived from the cells surface cortical tension and surface curvature , which leads to fluid pressure differentials that make cytokinesis in particular , and cell shape change in general , hydrodynamic in character ., These pressure differentials lead to net flows of cytoplasm away from regions of high surface curvature to regions of lower curvature , allowing the furrow to ingress with dynamics that are controlled by the fluid dynamical and mechanical features of the cell 10 ., Here , we present a computational model that demonstrates how the cells major mechanical subsystems are integrated to drive and control cytokinesis ., In particular , the model considers these separate mechanical subsystems , and explains the dynamical features of wild type and mutant cytokinesis events ., Most significantly , the model demonstrates that these biomechanical systems are sufficient to explain cytokinesis ., We next sought to determine whether our model cells could undergo traction-mediated cytofission , a process whereby multinucleated cells can divide during interphase 15 ., We incorporated adhesion into the model taking advantage of recent measurements of the traction experienced by motile Dictyostelium cells ( Fig . 1C ) 16 ., Starting from a spherical cell , we applied protrusive forces in directions 180° apart ( Fig . 1D ) ., Though this assumption represents a geometrical simplification that allows us to take advantage of cylindrical symmetry , the amount of force is proportional to the cross-sectional area of the cell ( initially a circle ) and is representative of the protrusive force experienced by a cell that makes a hemispherical contact with the substrate ., This force led to relatively slow cell elongation and initially , concomitant slow furrow ingression ( Fig . 2B; Video S1 ) ., However , as the furrow narrowed , the cortical tension combined with an increase in local curvature to amplify the local stress ., This , in turn , accelerated the rate of furrow ingression , increasing the local curvature further ., This positive feedback loop caused a drastic pinching of the furrow , leading to daughter cell separation ( Fig . 2B , C ) ., It must be noted that the mean curvature depends on the 3-D nature of the local geometry which involves both axial and radial components ., The former is decreasing as the furrow ingresses , but the latter increases greatly during constriction ., Experimentally , it is documented that separate molecular mechanisms are needed to promote the scission of the bridge joining the two daughter cells 17 , 18 ., Furthermore , measurements of the furrow ingression dynamics show the existence of a bridge-dwelling step that is quantitatively separable from the mechanical stresses that drive furrow ingression 10 ., For these reasons , we did not attempt to simulate the final bridge severing and stopped the simulations at this point ., The rapid rate at which curvature-induced differences in cortical tension enabled furrow ingression in the previous simulation led us to posit whether spatial differences in the material properties of the cell could initiate ingression and eventually give rise to sufficient forces leading to cell division ., Using micropipette aspiration , we previously measured the effective cortical tension under several contrasting conditions , including interphase vs . mitotic , WT vs . myoII null , and furrow vs . polar regions and demonstrated that the furrow exhibits a 20–30% higher effective cortical tension relative to the poles 8 , 12 ., We incorporated this heterogeneity into the model and simulated cytokinesis in non-adherent ( Fig . 3A ) and adherent conditions ( Fig . 3B; Fig . S5; Video S2 ) ., In both cases , heterogeneity in effective cortical tension and the resultant difference in Laplace-like pressures cause furrow ingression ., In non-adherent cells , however , furrow ingression stops shortly after commencing and is not sufficient to cause further ingression or cell division ., By increasing the difference in effective cortical tension , we were able to achieve cell division , but this required non-physiological differences ( 3–10 fold ) in effective cortical tension between pole and equator ( not shown ) ., On the other hand , the addition of transient adhesive and protrusive forces led to successful cell division ( Fig . 3B ) ., These forces appear to be required to induce a sufficient change in morphology ( specifically , curvature ) from which cortical tension can complete furrow ingression ., It is well documented that Dictyostelium cells lacking functional myosin II cannot divide in suspension , but successfully divide when placed on an adhesive surface 19; similar observations have been made of mammalian cell culture cells 20 ( Fig . 3C ) ., Though this division is similar to those observed in WT cells , there are some significant differences ., The furrow ingression dynamics ( quantified as the time-dependent change in the relative furrow diameter ) display biphasic behavior , in which a slow phase of ingression is followed by a rapid one 10 ., We found strong agreement between the furrow-thinning dynamics predicted by our simulation and those measured experimentally in myoII null cells ( Fig . 3D; Video S3 ) ., Plotting the curvature at furrow and poles during division , it is clear that the second rapid phase of furrow ingression can be attributed to the large increase in force that comes from an increase in mean curvature at the furrow ( Fig . 3E ) as the radial component of curvature begins to dominate ., There are some noticeable differences in the shapes of the simulated cells when compared to the myoII null cells ( Fig . 3C , D ) ., In real cells , protrusions are more “stochastic” causing ruffling at the poles ., In our model , protrusive stresses are applied uniformly across the boundary and lead to a rounded shape ., The treatment of adhesions is also likely to cause some of these differences ., In our model , adhesion is modeled as a homogeneous friction , whereas in cells it is more likely to be localized , and this will affect the shape 21 ., Furthermore , in myoII null cells , cortexillin I is not as focused in the cleavage furrow as in wild-type cells 22 , 23 , which could broaden the zone of increased elasticity Having established that material heterogeneities cannot initiate division but can provide the required force to finish it , we next considered the effect of a myosin II contractile force in our simulations ., To this end , we determined the location of myosin II motors from fluorescent images of GFP-myosin II ( Fig . S1 ) and distributed a contractile force temporally and spatially based on the measured distribution of myosin II motors in the cortex ( Methods ) ., Incorporating this contractile force in simulations of non-adherent cells led to successful division ( Fig . 4A; Video S4 ) ., This demonstrates that a cell in suspension can initiate division by substituting the initial ingression provided by adhesion and protrusion on surfaces by myosin II constriction at the furrow ., We also observed division in simulations of adherent cells ( Fig . 4B; Video S5 ) ., Interestingly , cells that are adherent but do not apply protrusive forces did not divide successfully in simulation ( Fig . S2 ) ., This suggests that the primary advantage of the adherent surface is that it enables cells to apply protrusive forces ., Without these , adhesion acts to resist the myosin II forces and prevent sufficient cellular deformation that would otherwise enable cell division to proceed successfully ., Defective cytokinesis on adherent surfaces has been documented in several Dictyostelium strains that have aberrant actin polymerization ., In cells lacking coronin , an actin binding protein , attachment to the surface does not facilitate cell division 24 ., Similarly , cells lacking AbiA , a component of the SCAR complex , exhibit deficient cytokinesis in adherent conditions 25 ., Beyond the cells ability to divide in non-adherent conditions , these simulations show some further differences from those of myoII null cells ., The initial rate of furrow ingression in these simulations is faster than observed in the simulations devoid of myosin II contractile force ., This is expected as the initial deformation now includes the cooperative interaction of two force generating subsystems ., Differences are also seen in the shape of the daughter cells , as these simulations give rise to rounder cells than cells from simulations that lack myosin II contractile forces ., These observations are in agreement with experimentally measured differences between WT and myoII null cells ( Fig . 4B vs . 3B ) 10 ., Comparing the simulated furrow-thinning trajectory to that measured experimentally in WT cells did reveal some important differences ( Figs . 3 , 4 ) ., The furrows in our simulations exhibit the same sharp drop in radius that is seen in our models of myoII null cells , which can be attributed to the large rise in pressure due to the increase in curvature ., This sharp drop-off , which is not seen experimentally , leads to faster division than in real cells ., To account for this difference we considered the possible role that strain-stiffening may have on furrow ingression ., Strain-stiffening is a non-linear effect whereby materials harden when deformed sufficiently; this has been observed in several biopolymers 26 ., Hallmarks of strain-stiffening can be seen in other aspects of Dictyostelium cellular and cytokinesis mechanics in a myosin II-dependent manner ., For example , in response to pressure jumps from micropipette aspiration , cells missing myosin II show non-linear effects that are absent in WT cells , suggesting that myosin II pre-stresses the network , leading to strain-stiffening 12 ., We incorporated a phenomenological description of strain-stiffening into our model ( Methods ) and simulated the system ., As expected , the initial rate of furrow ingression was unaffected ., However , as the furrow diameter became small enough to cause strain-stiffening , the furrow ingressed more slowly , matching the rates observed experimentally ( Fig . 4C–E; Videos S6 and S7 ) ., While strain stiffening slows down the cytokinetic progression of WT strains , we have not observed this slowdown in experiments of myoII null cells ., This suggests that myosin II is a fundamental component that provides this stiffening effect , an observation that is consistent with our measured material properties of myoII null cells 8 , 12 ., Using this full model we considered the effect that changing the material properties of the cell have on the furrow ingression dynamics ., For example , we varied the parameter controlling elasticity ( K in Fig . 1B , according to Equation 11 ) and simulated furrow ingression ( Fig . S3 ) ., Increasing the elasticity constant by 40% led to a slower , more linear initial ingression ( cross-over time increased from 370 to 420 s ) , as well as slower division overall ( 415 to 495 s ) ., In contrast , decreasing the elastic constant 30% shortened the cross-over time ( 370 to 350 s ) as well as the total trajectory ( 415 to 380 s ) ., The simulated trajectories of the model with reduced elasticity are reminiscent of experiments of cells lacking globally-distributed proteins , such as RacE and dynacortin , that have a strong effect on the viscoelastic moduli and act to slow furrow ingression 10 ., Finally , the model allows us to sort out an additional point about cytokinesis furrow ingression dynamics ., In particular , it is often thought that myoII null cells divide by simply crawling apart ., However , our simulations indicate key differences in mitotic cell division for both WT ( Fig . 4B , C ) and myoII null cells ( Fig . 3B ) and interphase traction-mediated cytofission ( Fig . 2B ) ., By plotting the pole-to-pole distance as a function of time ( Fig . 4F ) , it can be seen that interphase cells drive fission solely by crawling apart ., This leads to significant pole separation as well as long and thin morphologies ( Fig . 2B ) ., In contrast , mitotic cells that have spatial heterogeneity in their mechanical properties initiate division through protrusion , but divide quite differently , with pole-to-pole distances that are similar to WT cells ., Computational modeling presents an opportunity to dissect the different subsystems that contribute to force generation and subsequent cell shape changes during cytokinesis ., Using an experimentally validated viscoelastic model of a Dictyostelium cell , and relevant measured data on adhesion , protrusion and myosin II-generated contractile forces , we successfully simulated cell division in several distinct virtual strains ., We show that cytokinesis can be divided into three distinct phases: 1 , initial furrow ingression; 2 , Laplace-like pressure dominated , and 3 , bridge-dwelling phase 10 , 27 ., Initial furrow ingression can be achieved in multiple ways using separate subsystems ., Adherent cells can pull themselves apart by applying protrusive forces in two opposite directions ., Alternatively , in the absence of adhesion , the initial ingression can come from the contractile forces provided by myosin II 28 ., We note that alone , both of these subsystems require certain special conditions to complete division; either traction to apply protrusive forces ( Fig . 2B ) or the absence of resistance from adhesion ( Fig . S2 ) ., Both our simulations and previous experimental evidence show that Dictyostelium cells can initiate cytokinesis using either of these two force producing processes ., In other cell types which are less adherent , it is possible that myosin II-driven ingression may play a more important role during this first phase of ingression ., While these subsystems are important to start cytokinesis , the major shape change occurs during phase 2 when the bulk of the force is provided by passive Laplace-like pressure differences that result from induced changes in mean curvature ( Fig . 5 ) ., Our results demonstrate that either adhesion in combination with protrusive forces or myosin II are sufficient to drive the cell to phase 2 to allow the Laplace-like pressures to take over ., Our results are also consistent with experiments of Dictyostelium cells flattened by agar overlay where full myosin II mechanochemistry is required to overcome the added mechanical stress from the compression by the sheet of agar 29 ., The combination of Laplace pressures and myosin II-generated forces are large enough to make the cell divide faster than what is observed experimentally , suggesting the presence of another component that acts to slow down cell division ., Several possibilities exist for this resistive force , including an axial compression acting on the ends of the furrow to counteract the effects of Laplace-like pressures and/or elastic relaxation 10 ., More recent observations indicate that the slowdown depends on the lever-arm length of myosin II 30 ., Wild type and a longer lever-arm mutant myosin II ( 2×ELC ) lead to furrow-thinning trajectories that are WT-like ., In contrast , a short lever-arm mutant deleted for both light chain binding sites ( ΔBLCBS ) shows myoII null-like furrow-thinning trajectory though it accumulates at the cleavage furrow , demonstrating that it is not the presence of myosin II bipolar thick filaments alone that are responsible for the slower WT furrow ingression dynamics ., Rather , the lever-arm length dependency suggests that it is the stalling of myosin II in the isometric state that is responsible for the slower ingression dynamics ., This locking of the myosin II motor on the actin filaments then leads to an increase in myosin II-mediated crosslinking and tension and consequently an increase in the furrow stiffness ( i . e . strain-stiffening ) ., While it is difficult to directly quantify the level of this increase or the time-scales over which the strain-stiffening is prominent , our simulations do suggest that non-linear strain-stiffening properties of the cortex may account for the slowdown of furrow ingression ., In actuality , all three , compressive stress , elastic relaxation and strain-stiffening , are likely to contribute to varying degrees to the slowdown ., Though most conceptions of cytokinesis contractility have focused almost exclusively on the contractile ring 7 , our simulations demonstrate that cell division is the result of multiple force-generating subsystems , acting on the cellular mechanical network ., This explanation is particularly compelling because our model , using only experimentally measured parameters , accurately reproduces WT and mutant cell division events ., While it is often considered that cytokinesis is regulated spatiotemporally by linear biochemical pathways ( such as by small GTPases and kinases ) , another level of control is equally important ., For example , myosin II not only generates contractility but also controls the cortical tension , elastic modulus , and strain-stiffening ., Thus , myosin II regulation affects both a force-generating subsystem and the mechanical network on which the force acts , highlighting the complex nature of the system ., The level set method takes an Eulerian approach , tracking a moving boundary ( denoted Γ ( t ) ) on a static Cartesian grid deformed by a continuum stress field across the simulation domain 13 ., In our simulations , we take a two-dimensional domain and assume cylindrical symmetry about the division axis ( Fig . 1A ) ., The level set formalism defines a potential function φ ( x , t ) for which the boundary is the zero-level set: Γ ( t ) =\u200a{x∈R2 | φ ( x , t ) =\u200a0} ., In our simulations , we initialize the potential function with the signed distance function , whose magnitude equals the shortest distance from a point x∈R2 to the curve Γ ( t ) and whose sign is positive if the point is outside the cell and negative otherwise ., In practice , as the potential function evolves over time , it can become quite steep or flat , leading to numerical errors ., These can be minimized by re-initializing the potential function periodically using the equation ( 1 ) where S ( φ ( x , 0 ) ) is taken as +1 inside the cell , −1 outside the cell and zero on the cell membrane ., The potential function evolves according to the Hamilton-Jacobi equation ( 2 ) The vector v ( x , t ) is the velocity of the level set moving in the outward normal direction which , in our simulations , describes the cells membrane protrusion and retraction velocities ., These are driven by a combination of active and passive stresses acting on a mechanical model of the cell , to be described next ., Previously we developed a mechanical description of a cell in the level set framework and fitted a viscoelastic model topology with parameters obtained from measurements of cells deformed using micropipette aspiration 14 ., The model assumes that the cell deformation obeys , where v is the velocity defined above , and xm is the displacement of the membrane ( Fig . 1A , B ) ., The total membrane displacement is the sum of the displacements of the cortex ( xcor ) and cytoplasm ( xcyt ) ., To describe how stresses affect these , we use a Voigt model , which consists of the parallel connection of elastic ( K ) and viscous ( D ) elements , to represent the cortex connecting the cell membrane and the cytoplasm ( Fig . 1B ) ., The viscous component describes the association and dissociation dynamics of actin cross-linkers ., The cytoplasm is modeled by a purely viscous element ( B ) placed in series with the Voigt element ., In our simulations , we use stress rather than force to drive the cellular deformations thus accounting for the extra µm2 found in the parameters in our model ., The model assumes that these displacements occur normal to the cell surface ., This neglects bending effects , which are relevant at much smaller length-scales than those we consider in modeling cytokinesis 27 , 31 ., In the simulations , the total stress ( σtot ) is applied at the cell boundary , according to: , where xcor and xcyt represent the positions of the cortex and cytoplasm , respectively ., Using the membrane displacement , xm\u200a=\u200axcor+xcyt , we can rewrite the system of equations as ( 3 ) We thus obtain the membrane velocity solving first for xcor and then for ., This value is entered into the Hamilton-Jacobi Equation ( Eqn . 2 ) ., The total net stress ( σtot ) is computed for the simulation domain as the vector sum of all stresses acting on the cell ., This includes stress contributions from active components , adhesion ( σadh ) , protrusion ( σpro ) , and myosin-based contraction ( σmyo ) , as well as passive components due to surface tension ( σten ) and volume regulation ( σvol ) ., Thus ( 4 ) These individual components are now described in detail ., Our model of adhesion uses a continuum stress field to counteract cellular deformations 32 and is based on defining an adhesion map , as previously described 33 ., Though our simulations assume that the cell has cylindrical symmetry , for the purposes of computing adhesion and protrusion , we instead consider the cross-sectional area in the ( z , r ) plane , which more closely corresponds to the contact area between cell and substrate ., We compute area densities in both r and z directions , normalized to the total cell cross-sectional area: ( 5 ) Here 1 ( z , r ) is the indicator function that equals one when the point ( z , r ) is inside the cell and zero otherwise , and the summations are done over all simulation points in either the z- or r-direction ( Fig . 1C ) ., These densities describe the fraction of the cell-substrate contact area that lie in the respective strips either in the z- or r-directions ., We multiply these two densities and scale by the maximum adhesion stress ( σadh-max ) to generate a spatial adhesion map: ( 6 ) This adhesion is applied spatially as a resistive stress element that counteracts the net effect of the other stresses ., To evaluate this new model we simulated the cellular response to a series of pulses ( Fig . S4 ) and compared this response to that of the nominal model ., Simulations that incorporate adhesion show a delayed initial response and these cells also take longer to reach steady state ., We incorporate protrusive forces based on several assumptions ( Fig . 1D ) ., First , protrusion acts at both ends of the cell to drive the cell apart ., Thus , the protrusive stress acts away from the z\u200a=\u200a0 line ., Second , the local protrusive forces depend on the contact area ( as calculated by Dr ( z ) above ) and increase as you move away from the cleavage furrow ( scaled by a linear function l ( z ) with values of zero at the center of the division axis ( z\u200a=\u200a0 ) and one at the poles ) ., Finally , the protrusive force decreases over time as the cell is dividing ., We incorporate this by including an exponential function indexed by the furrow diameter ( wf ( t ) , defined as the diameter of the cell at the midpoint along the z-axis ) ., Together , these assumptions lead to a protrusion stress whose magnitude is given by ( 7 ) where σpro-max is maximum stress applied ( Table 2 ) ., Though the stress is assumed to act along the z-axis ( Fig . 1D ) , only the component normal to the surface is used in the simulations ., The model used here is phenomenological , but captures the net movement of the membrane away from the division plane ., Other approaches , which look at finer scale effects for modeling protrusion , have been considered in the literature 34 , 35 ., An active contractile force from the work of myosin II against the cytoskeleton is present in wild type cells ., This force acts tangentially to the cortex , thereby constricting the cell and , because we assume cylindrical symmetry , this reduces the circumference ( Fig . 1E ) ., This has the net effect of reducing the furrow diameter ( with a stress reduced by a factor of 2π to account for conversion from circumference to radius ) ., Thus , to incorporate this into our model , we assume that the contractile stress acts radially inward ., The magnitude of the local force depends on two things , the maximum stress generated by myosin II and the local distribution of myosin II ., To compute the maximum stress we note that if we assume 3 . 4 µM total cellular concentration of myosin II monomers ( each monomer is composed of two heavy chains , two essential light chains and two regulatory light chains ) 11 , 36 , then a mitotic cell with a radius of 5 µm contains 1×106 myosin monomers ( 2×106 heads ) ., Given the Dictyostelium myosin II unloaded duty ratio ( 0 . 6% ) and the force generated by the power stroke of the myosin ( 3 pN ) , the maximum total force that can be generated from myosin II is ∼40 nN , assuming no load-dependent shifts in the duty ratio ., Because only ∼20% of the myosin II is found in the assembled bipolar thick filament state , most of which resides in the cortex 11 , 37 , the resulting maximal force is 10 nN ., This number is used to compute the total maximum stress by dividing by the cellular area ( 4πR2 ) ., To apportion this stress spatially , we imaged myoII::GFP-myoII cells ( mhcA ( HS1 ) :: pBIG:GFP-myosin II; pDRH:RFP-tubulin ) undergoing cytokinesis as previously described 38 ., From this movie , the GFP-myosin II fluorescent intensities were extracted to quantify myosin density ., Cell images were aligned by their centroids and along the division axis ., For each image , edge detection was performed to identify cell periphery ., Using this edge , the GFP-myosin II intensity was computed for 5 pixels ( 1 µm ) inwardly normal from the boundary , a region likely to contain cortical myosin ., An average of these intensities was assigned as the local myosin density at that boundary point ., The cell shape was averaged across both its axes of symmetry along with the GFP-myosin II distributions to construct a symmetric myosin profile along the division axis ., This profile was smoothed using a cubic smoothing spline ., For each image in the time series , a one-dimensional profile was constructed , indexed to the position along the division axis and the measured furrow diameter ( Fig . S1 ) ., The resultant map ( myo ( r , z ) ) describes the distribution of myosin as a function of radius and is used to generate a stress: ( 8 ) where n is the outward normal unit vector ., Local differences in mean curvature and surface tension give rise to spatially heterogeneous stresses on the cell ., The stress differential across the boundary , described by the Young-Laplace relationship , is given by σten\u200a=\u200aγ ( z ) κmean ( z ) n , where γ ( z ) describes the local cortical tension , κmean is the mean curvature and n is a normal unit vector ., The mean curvature , κmean , is the arithmetic mean of two principal curvatures ( κmean\u200a=\u200a½ ( κ2D+κP ) ) 39 ., The first is computed using a Lagrangian formulation based on the cellular boundary: κ2D ( x ,, y ) = ( x′y″−y′ x″ ) / ( x′2+y′2 ) 3/2 where the point ( x , y ) ∈Γ ., The primes denote spatial derivatives along the boundary and are approximated by the center weighted difference between two points 13 ., The computation of the second principal curvature takes advantage of the cells cylindrical symmetry: κP\u200a=\u200aNr ( r ) /r , where Nr ( r ) is the normal in the radial direction at a given point , and r is the radius of the cell at that location 39 ., For interphase cells , we assume that cortical tension is homogeneous around the cell with a nominal value of 1 nN/µm 10 ., For mitotic myoII null cells , we assume a spatially heterogeneous γ with values of 0 . 5 and 1 . 0 nN/µm at the pole and furrow , respectively 8 , 12 ., We interpolate these values using a Gaussian profile: ( 9 ) where R0 is the initial radius of the cell and z is the horizontal position between the pole and furrow ., In wild type cells , the cortical tension at the pole and furrow are 1 and 1 . 8 nN/µm , respectively 10 ., In these simulations , we interpolate between these two values according to the measured myosin II concentration ( described below ) ., This profile is used as a means of marking intracellular changes in the material properties of the cell during division , not necessarily implying that surface tension comes from myosin ., We considered other schemes for spatially varying the cortical tension , but all gave similar results ., For example , simulations of cells lacking myosin contractility were run varying cortical tension using a Gaussian distribution ., Additionally , we performed simulations using both the myosin density profile and a normal distribution to simulate the surface tension profile but found little difference between the two ., We assume that the cellular volume remains constant 14 ., To enforce this constraint we implement a stress ( 10 ) where n is the outward normal ., The cells volume is evaluated by assuming the cell is radially symmetric: Vactual\u200a=\u200a∫cell lengthπr ( z ) dz ., Large values of Kvol keep the cell volume relatively constant , but can lead to small oscillations as the stress overshoots the required target ., In our simulations , we set Kvol\u200a=\u200a0 . 1 nN/µm5 , which was sufficiently high to ensure that both volume changes were small but maintained the stability of the simulations , though some oscillations ( as seen in the furrow measurements in Fig . 3E ) do appear ., We assume that the elastic component of the ce

Introduction, Results, Discussion, Methods

Cytokinesis occurs through the coordinated action of several biochemically-mediated stresses acting on the cytoskeleton ., Here , we develop a computational model of cellular mechanics , and using a large number of experimentally measured biophysical parameters , we simulate cell division under a number of different scenarios ., We demonstrate that traction-mediated protrusive forces or contractile forces due to myosin II are sufficient to initiate furrow ingression ., Furthermore , we show that passive forces due to the cells cortical tension and surface curvature allow the furrow to complete ingression ., We compare quantitatively the furrow thinning trajectories obtained from simulation with those observed experimentally in both wild-type and myosin II null Dictyostelium cells ., Our simulations highlight the relative contributions of different biomechanical subsystems to cell shape progression during cell division .

Cytokinesis , the physical separation of a mother cell into two daughter cells , requires force to deform the cell ., Though there is ample evidence in many systems that myosin II provides some of this force , it is also well known that some cell types can divide in the absence of myosin II ., To elucidate the mechanisms by which cells control furrow ingression , we developed a computational model of cellular dynamics during cytokinesis in the social amoeba , Dictyostelium discoideum ., We took advantage of a large number of experimentally measured parameters and well-characterized furrow ingression dynamics for a number of different strains ., Our simulations demonstrate that there are distinct phases of cytokinesis ., Myosin II plays a role providing the stress that initiates furrow ingression ., In its absence , however , this force can be supplied by a combination of adhesion and protrusion-mediated stresses ., Thereafter , Laplace-like pressures take over and provide stresses that enable the cell to divide ., Overall , we show how various mechanical parameters quantitatively impact furrow ingression kinetics , accounting for the cytokinesis dynamics of wild type and mutant cell-lines .

physics, computer science, computer modeling, biophysic al simulations, biology, cell mechanics, computational biology, biophysics, biomechanics

journal.pcbi.1004334

Innate Immunity and the Inter-exposure Interval Determine the Dynamics of Secondary Influenza Virus Infection and Explain Observed Viral Hierarchies

Influenza is an infectious respiratory disease affecting and threatening millions of people worldwide 1 ., The invasion of the influenza virus into a host’s upper respiratory tract ( URT ) starts from a sufficient number of virions ( single viral particles ) entering the URT and infecting healthy epithelial cells ( henceforth referred to as target cells ) 2 ., The infected cells then produce progeny virions , leading to further infection of target cells and inter-host transmission ., Immune responses are activated during influenza virus infection , and contribute to the control of infection and viral clearance from the host 3 ., The innate immune response , initiated in the early stage of infection , involves production of a variety of antiviral cytokines , which provide immediate non-specific protection to the target cells against infection 4 , 5 ., Of particular importance is the cytokine interferon ( IFN , type 1 ) , whose protective functions include inducing a virus-resistant state in target cells , reducing viral replication , and activating natural killer ( NK ) cells to induce apoptosis in infected cells 6–9 ., The adaptive immune response , once stimulated by presentation of viral epitopes to lymphocytes , plays an important role in viral control ., B lymphocytes ( or B cells ) produce antibodies that neutralise free virus , and cytotoxic T lymphocytes ( or T cells ) produce cytotoxic granules that kill infected epithelial cells and other leukocytes 3 ., Following viral clearance , a portion of those B cells and T cells become long-lived memory cells which can be activated rapidly to form a defense upon re-exposure to the same or an antigenically related virus ., Due to its non-specific nature , the innate response induced by an initial exposure ( henceforth “primary infection” ) would be expected to modify the host environment and provide some protection to subsequent exposure ( henceforth “challenge” ) ., We have experimentally studied this phenomenon in detail by examining the behaviour of consecutive influenza infections as a function of the delay between exposures 10 ., By varying influenza virus types and subtypes ( three viruses , A ( H1N1 ) pdm09 , A ( H3N2 ) and influenza B , were investigated ) and the delay between the exposures ( henceforth the “inter-exposure interval” ( IEI ) ) , we found that a state of temporary immunity induced by A ( H1N1 ) pdm09 was able to block or delay infection with influenza B virus ( Fig 1 ) ., Conversely , influenza B virus showed little or no inhibitory effect on subsequent infection with A ( H1N1 ) pdm09 ( Fig 2 ) ., See 10 for all available data and a full exposition of the experimental observations ., Although such experimental studies have improved our understanding of temporary immunity and viral interference , the underlying mechanisms of how a virus is controlled and cleared by the immune system are still not fully understood ., In particular , the re-exposure experimental data revealed a number of novel properties and phenomena:, Viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure ., For example , following primary infection with A ( H1N1 ) pdm09 virus , subsequent challenge with influenza B virus was strongly inhibited ( Fig 1 ) , whereas the latter showed a very limited ability to inhibit the former ( Fig 2; only weak delays for an IEI of 1–3 days are observed ) ., These data suggest the existence of a “viral hierarchy” 10 ., What are the mechanisms accounting for the interactions between the two different viruses and the induced hierarchy for different primary–challenge virus combinations ?, By looking at the details of viral kinetic time series , four types of patterns were identified ( see Fig 3 ) , which suggest some dynamical interactions between the two viruses ., For example , an initial period of synchronised viral growth was often observed for short exposure intervals ( ≤ 3 days , see Figs 1–3 ) ., In addition to this , an initially synchronised decrease of the primary and challenge viruses was also frequently observed ( Fig 3 ) ., What are the underlying mechanisms accounting for these phenomena ?, Virus dynamics modelling has been employed to great success to gain insight into the host-pathogen interaction ., For HIV in particular , mathematical models have proven invaluable in uncovering the mechanisms of immunity , anti-retroviral drug action and developing strategies to avoid or combat drug-resistance 11–15 ., For influenza , due largely to a paucity of data and the difficulty in working with a short-lived transient infection , models have traditionally had less of an impact on our understanding of the immune response to influenza and the mechanism of viral control ., In recent years however , both qualitative and quantitative modelling studies 16–33 have begun to probe these interactions more deeply , as recently reviewed by Beauchemin et al . 34 and Dobrovolny et al . 35 ., While some studies have focused on the role of antiviral drugs in viral control ( e . g . 30 ) and others on the immune response , the majority have considered only a single viral infection ., Exceptions include the development of models of multi-strain infection that have been used to study the within-host emergence of drug-resistant 20 and pandemic influenza 33 viruses and the relative fitness of drug-resistant variants 31 , 32 ., Here , with our focus on the immune response , it is immediately obvious that the classic Target cell–Infected cell–Virus ( TIV ) model , with control solely mediated by target-cell depletion and with no allowance for cell re-growth , is unable to explain re-infection with a different challenge virus ., This provides the motivation to study experiments in which re-infection occurs as a means to explore the role of the immune response in influenza viral dynamics ., Our data affords us the opportunity to examine the relative importance and feasibility of different hypothesised mechanisms of the innate immune response , complementing the work of others who have studied how immunity may influence viral kinetics using data from single viral infections 17 , 18 , 23 , 24 , 28 ., In this paper we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees ., The proposed models differ in their hypothesised mechanisms of the non-specific innate immune response ., We evaluate the models’ capability in terms of their ability to reproduce the patterns observed in the re-exposure data , including co-infection with and suppression , delay or blocking of the challenge virus ( Figs 1–3 ) ., Our analyses demonstrate that the occurrence of those phenomena is highly dependent upon the inter-exposure interval and consistent with virus-dependent stimulation of the innate immune response ., Our paper provides the first mechanistic explanation for the recently observed influenza viral hierarchies ., Three possible antiviral mechanisms of IFN are allowed for in our model: 1 ) induction of a virus-resistant state for target cells; 2 ) a reduction in the viral production rate from infected cells; and 3 ) activation of NK cells to induce apoptosis in infected cells ( Fig 4 ) ., With the additional inclusion of a strain-specific antibody response , the following equations describe the single virus-strain system:, d V d t = p I 1 + s F - c V - μ V A - β V T , ( 1 ) d T d t = g ( T + R ) ( 1 - T + R + I C t ) - β ′ V T + ρ R - ϕ F T , ( 2 ) d I d t = β ′ V T - δ I - κ I F , ( 3 ) d R d t = ϕ F T - ρ R - ξ R , ( 4 ) d F d t = q I - d F , ( 5 ) d B d t = m 1 V ( 1 - B ) - m 2 B , ( 6 ) d A d t = m 3 B - r A - μ ′ V A ., ( 7 ) The change in viral load ( dV/dt ) includes four components , the production term ( pI/ ( 1+sF ) ) in which virions are produced by infected cells ( I ) at a rate p subject to an IFN-dependent scaling factor of ( 1/ ( 1+sF ) ) 17 , 23 , 28 , the viral natural decay/clearance ( cV ) with a decay rate of c , the neutralisation term ( μVA ) by antibody ( A ) , and a consumption term ( βVT ) due to binding to and infection of target cells ( T ) ., s indicates the sensitivity of the production rate to IFN ., The term g ( T + R ) ( 1 − ( T + R + I ) /Ct ) models target cell ( re- ) growth by both target cells and resistant cells ( those protected by the IFN ) but limited by a maximum cell number Ct ( e . g . due to the spatial capacity , 18 ) ., Target cells ( T ) are consumed by virus ( V ) due to binding ( β′ VT ) , the same process as βVT , where β ≠ β′ allows for different measurement units of assays used to detect virus ., IFN ( F ) induces the protective transition from T to R at rate ϕFT and resistant cells ( R ) lose protection , reverting to susceptible target cells at a rate ρ 28 ., Infected cells ( I ) increase due to the infection of target cells by virus ( β′ VT ) and die at a ( base ) rate δ ., The term κIF models the killing of infected cells by IFN-activated NK cells 28 ., IFN ( F ) is modelled using simple dynamics that only include production ( qI ) and natural decay ( dF ) 36 ., Antibodies ( A ) are produced by activated B cells ., We model the proportion of activated B cells by state B . The activation of B cells is induced by an increase in V . Parameter values and their justification are given in Table 1 ., For a clearer comparison between the different hypothesised mechanisms by which the innate response contributes to viral control , we consider three models of single virus , each of which includes only one of the mechanisms shown in Fig 4:, Model 1: including an IFN-induced virus-resistant state of the target cells ( by letting s = 0 and κ = 0 ) , Model 2: including an IFN-induced diminished viral production rate ( by letting ϕ = ρ = ξ = 0 and κ = 0 ) , Model 3: including killing of infected cells by IFN-activated NK cells ( by letting s = 0 and ϕ = ρ = ξ = 0 ) ., Most importantly , for these three models , the terms of antiviral action appear in different equations ., The virus-resistant terms appear directly in the equation for dT/dt and thus modulates the viral load ( V ) in an indirect way ( Model 1 ) ., Similarly , killing of infected cells by NK cells ( κIF ) exerts an indirect control on viral production by changing the infected cell kinetics ( Model 3 ) ., In contrast , Model 2 assumes direct control of viral production by IFN ( due to the term pI/ ( 1+sF ) ) ., Thus , we capture the diversity of plausible viral control mechanisms , in particular both indirect and direct pathways ., In order to capture the kinetics of primary–challenge infection experiments , we introduce a re-exposure model in which we assume that the two different viruses share the same source of target cells and IFN , but induce distinct and non-crossreactive antibody responses:, d T d t = g ( T + R ) ( 1 - T + R + I 1 + I 2 C t ) - β 1 ′ V 1 T - β 2 ′ V 2 T + ρ R - ϕ F T , ( 8 ) d R d t = ϕ F T - ρ R - ξ R , ( 9 ) d F d t = q 1 I 1 + q 2 I 2 - d F , ( 10 ) d V 1 d t = p 1 I 1 1 + s 1 F - c 1 V 1 - μ 1 V 1 A 1 - β 1 V 1 T , ( 11 ) d I 1 d t = β 1 ′ V 1 T - δ 1 I 1 - κ 1 I 1 F , ( 12 ) d B 1 d t = m 11 V 1 ( 1 - B 1 ) - m 21 B 1 , ( 13 ) d A 1 d t = m 31 B 1 - r 1 A 1 - μ 1 ′ V 1 A 1 , ( 14 ) d V 2 d t = p 2 I 2 1 + s 2 F - c 2 V 2 - μ 2 V 2 A 2 - β 2 V 2 T , ( 15 ) d I 2 d t = β 2 ′ V 2 T - δ 2 I 2 - κ 2 I 2 F , ( 16 ) d B 2 d t = m 12 V 2 ( 1 - B 2 ) - m 22 B 2 , ( 17 ) d A 2 d t = m 32 B 2 - r 2 A 2 - μ 2 ′ V 2 A 2 ., ( 18 ), An additional subscript ( 1 or 2 , following the existing ones if there is already a subscript like m1 , m2 and m3 ) has been introduced for all the relevant variables and parameters to indicate the primary and challenge viruses ., Due to a paucity of experimental data , all parameters for the two viruses are assumed to be equal unless otherwise specified ., As for the single-virus model , we also extend the re-exposure model to three models , each of which includes only one of the innate immune response mechanisms:, Model R1: including an IFN-induced virus-resistant state of the target cells ( by letting s1 = s2 = 0 and κ1 = κ2 = 0 ) , Model R2: including an IFN-induced diminished viral production rate ( by letting ϕ = ρ = ξ = 0 and κ1 = κ2 = 0 ) , Model R3: including killing of infected cells by IFN-activated NK cells ( by letting ϕ = ρ = ξ = 0 and s1 = s2 = 0 ) ., The ordinary differential equation ( ODE ) models were solved using MATLAB’s ode15s ODE solver ( The MathWorks , Natick , MA ) ., We set an absolute tolerance of 10−12 on all variables for accuracy ., For the single-exposure models ( e . g . Eqs 1—7 ) , initial conditions were V = 1 , T = Ct with all other variables set to zero at t = 0 ., For the re-exposure models ( e . g . Eqs 8—18 ) , initial conditions were V1 = 1 , T = Ct with all other variables set to zero at t = 0 ., V2 = 1 was then introduced at the time of challenge ., The resolution of the simulated time series shown in the figures was set to be one hundred points per day ., When analysing the re-exposure model results , we introduced an indicator , the moving-correlation ( MC ) coefficient , defined to be the correlation coefficient of a subset of the time series within a moving window , to indicate the periods where the rates of change of the two viral loads were either synchronised or desynchronised ., The moving-window was set to be 0 . 2 days ( corresponding to 20 points based on the time series resolution ) , which we found was sufficient to correctly capture both the relationships and the turning points ( smaller values do not further improve the determination of phase-transition points ) ., Determination of these critical phase-transition times was also confirmed by observation based on the time course of solutions ., The first peak of the secondary viral infection separating Phase 1 and 2 ( defined in the Results ) was determined by finding the points where dV2/dt = 0 ., MATLAB code is provided in the Supporting Information ., The level of IFN should significantly influence the kinetics of viral infection based on the ( three ) model formulations ., The control of the level of IFN is achieved by using different IFN production rates ( q ) ., Here we examine how the behaviour of the three models changes for different rates of IFN production and how the models compare to one another ., For Model 1 , a higher rate of IFN production ( and thus a higher attained IFN level ) is able to maintain a considerable level of healthy cells in the virus-resistant state , which in turn facilitates a relatively rapid replenishment of target cells immediately following the control of viral infection ., However Model 1 fails to prevent the occurrence of a temporary depletion of target cells ( see S1 Fig in the Supporting Information ) ., Indeed , target-cell depletion remains the underlying mechanism for control ., This is not a surprising result , as in Model 1 increasing IFN leads to a decrease of the term −ϕFT in Eq 2 , which facilitates the consumption of target cells ., A detailed study of the change in viral load for both low and higher levels of IFN production ( q ) is shown in Fig 5A and 5B , where the change of viral load ( dV/dt in Eq 1 ) is decomposed into its four components ( appearing on the right-hand side of Eq 1 ) , whose relative contributions to the change in viral load vary by the stage of infection ., Both figures show that the single virus infection may be deconstructed into three distinct stages ., In the first ( “early” ) stage of infection ( 0–2 days ) virions are primarily consumed by binding to the target cells ( due to an almost full target cell pool ) and natural decay , whereas the contribution from antibody is negligible ., The second stage ( 3–5 days ) features a significant drop in the term for binding to target cells , which confirms that high IFN levels do not prevent a temporary depletion of the target cells for Model 1 ., In the last stage , starting around day 5 , antibodies begin to dominate the removal of virions ., In contrast to Model 1 in which target-cell depletion is the primary mechanism of viral control , both Model 2 and Model 3 are able to maintain a relatively high level of target cells when a sufficiently high IFN production rate is assumed ( S2B and S3B Figs ) ., The conservation of a high level of target cells is also clearly reflected by Fig 5D and 5F wherein the curve representing binding to target cells ( βVT ) does not show the quick drop evident in Model 1’s dynamics during the second stage post infection ., This implies that the decrease in viral load in the second stage for Models 2 and 3 with a larger IFN production rate is driven by mechanisms other than effective limitation in the number of target cells ., When the IFN production rate is small ( q = 10−7 ) , as shown in Fig 5A , 5C and 5E , all three models converge ( as expected ) to generate qualitatively the same dynamical behaviours as from the simplest TIV model lacking an explicit , time-dependent innate immune response ( see S4 Fig ) ., To study how target-cell depletion varies with the IFN production rate ( q ) in greater detail , we now explore model behaviour as q increases from 10−8 to 5 × 10−5 ., With increasing q , both Model 2 and Model 3 gradually prevent a temporary depletion of target cells ( measured by the minimum of target cell number within the first 7 days post-infection ) whereas Model 1 fails to do so ( Fig 6; S5–S7 Figs show examples of full time courses for relevant model compartments ) ., Even when allowing the transition rates for the production ( ϕ ) and decay ( ρ ) of IFN to be sampled from the space { ( ϕ , ρ ) ∈ 0 , 10 × 0 , 100} , we find the minimum target cell number for Model 1 is restricted to lie within the grey region in Fig 6 ., Note that for some intermediate values of q the models may lose their ability to completely clear virus ( see S6 and S7 Figs ) , likely due to a lack of immune components or incorporation of only one innate immune mechanism for each case ( see Discussion for further comments ) ., These results confirm that Model 1 primarily utilises target cell depletion for viral control and demonstrate that Models 2 and 3 may also have different dynamical properties depending on the IFN production rate ., Having established the mechanisms Models 1–3 use to control viral infection , we now move onto an examination of the behaviours of the re-exposure models , in which two viruses ( the primary and challenge viruses ) are introduced consecutively with an inter-exposure interval ( IEI ) ., We first study Model R1 in detail , focusing on how the model recaptures the clear dependence upon the IEI shown in the experimental data ( Figs 1 and 2 ) ., We then present the results of the other two re-exposure models based on that analysis , and through a comparison evaluate the differences between the three models ., Fig 7 shows that the solutions of Model R1 , in particular the viral kinetics of the second virus ( red curves ) , change dramatically as the IEI increases from 1 day ( A ) to 14 days ( F ) ., These changes are summarised and can be explained as follows:, For a 1 day interval ( Fig 7A ) , the two viruses undergo an initially synchronised increase followed by a synchronised decrease ( i . e . co-infection ) ., The synchronised increase occurs in the very early stage of infection when target cell numbers remain sufficiently high , corresponding to the first stage of single-virus infection as examined in the previous section ., Following target cell depletion , both viruses decrease and are eventually cleared by strain-specific antibody ., The dynamics are akin to those for a single virus infection ., For a 2 day interval ( Fig 7B ) , a short period of synchronised increase is followed by a synchronised decrease ( indicated by the MC coefficient of 1 ) ., However , this is then followed by a desynchronised period ( MC coefficient of -1 ) wherein the primary virus is cleared while the challenge virus grows once more ., During the initial period of synchronised growth the challenge virus’ load is two orders of magnitude smaller than that of the primary virus and the challenge virus in Fig 7A ., This may be understood by the strong depletion of target cells by this time ., Such a low viral load does not effectively activate antibody production for that virus so that following the brief drop ( synchronised with the first virus due to temporary depletion of target cells ) the challenge virus increases again with the replenishment of the target cell population ., For a 3 day interval ( Fig 7C ) there is no period of synchronised growth ., Rather , a synchronised decrease appears immediately following challenge with the second virus due to the temporary depletion of target cells , which is the key feature of the second stage for the single-virus infection ( recall Fig 5 ) ., Challenge with the second virus during this second stage of the primary virus infection ( around 3–5 days ) leads to qualitatively the same behaviours ( shown later ) ., Similar to Fig 7B , after the initial drop , the second virus experiences a full cycle of infection ., For an IEI over 6 days ( Fig 7E and 7F ) the primary virus has essentially been cleared at the time of challenge ( stage three of the primary virus infection ) ., During this stage target cell numbers are increasing , enabling immediate infection by the second virus ., Fig 8 summarises all the observed behaviours of Model R1 and indicates the different phases including productive co-infection ( Phase 1 , grey ) , an early synchronised decrease ( Phase 2 , red ) , a desynchronised phase ( Phase 3 , green ) and final removal of the challenge virus ( Phase 4 , blue ) ., Importantly , all four phases can be easily mapped to experimental data ( Figs 1–3 ) , and always appear in the described order ., We will see later that the other two re-exposure models , although exhibiting qualitative differences from Model R1 , do not alter the order established here ., As we have analysed , the infection dynamics are closely related to the stage of the primary virus infection at the time of challenge with the second virus , explaining the strong influence of the exposure interval in both the experimental observations and model outputs ., All of our observations can be summarised in a single figure ( Fig 9A ) ., Reading horizontally , we see that the four phases ( separated by colours ) appear in order through time ., Viewed vertically , the figure shows that the choice of the IEI strongly affects the qualitative behaviours of the re-exposure model ( distinguished by dashed lines ) ., Importantly , all regions of this plane may be classified as one of the four phases , suggesting a complete picture has been obtained through this classification procedure ., Because of the concise nature of this method of showing re-exposure results , this type of figure will be used to show further results for the alternative re-exposure models ( Models R2 and R3 ) ., Continuing with the study of Model R1 , we now examine whether it can generate qualitatively different re-exposure behaviours ( i . e . generate patterns different from that in Fig 9A ) by only changing the IFN production rates of the two viruses , q1 and q2 ., As shown in the previous section on single infection , different IFN production rates drive different model behaviours ., A very small IFN production ( e . g . 10−7 ) resembles a model that lacks any IFN-induced protective effect whereas a model with a relatively large IFN production rate ( e . g . 5 × 10−6 ) shows a significant conversion of target cells to the virus-resistant state ., Results are given in Fig 9 where four different combinations of values for q1 and q2 are examined ., S10 Fig presents further combinations of values for q1 and q2 drawn from a wider range ., All sub-figures ( Fig 9; S10 Fig ) show a qualitatively similar pattern ., In particular , regardless of the level of IFN production ( and thus regardless of whether the resistant state is introduced or not ) , all show the existence of Phase 2 ( red ) which characterises the inhibitory effect of the primary virus infection on the challenge virus ., Based on our previous analyses of a single viral infection , this is a result of target cell depletion which cannot be avoided by solely introducing the virus-resistant state ., Thus , Model R1 ( with all other parameters fixed and equal for the two viruses ) fails to reproduce the hierarchy of viral infection shown in the data; e . g . primary infection with A ( H1N1 ) pdm09 strongly inhibited influenza B virus challenge dynamics ( Fig 1 ) , whereas the latter showed a very limited ability to inhibit the former ( Fig 2 ) ., The subtle quantitative differences visible in Fig 9 and S10 Fig are understood by considering that a larger q1 ( regardless of q2 ) induces a larger virus-resistant cell population ( R ) and so more rapid replenishment of the target cell pool ., Consequently , an increased q1 leads to a shorter duration of Phase 2 ( IEI of day 3–5 ) as also shown in S1 Fig . Moving on to consider Models R2 and R3 , two different patterns emerge when varying the IFN production rate , as demonstrated by comparing the top row to the bottom row in Figs 10 and 11 for Models R2 and R3 respectively ( also see S8 and S9 Figs for examples of time courses of the solutions ) ., These patterns successfully reproduce the hierarchy of viral infection observed from the experimental data ( Figs 1 and 2 ) , as we now illustrate ., Consider the case that A ( H1N1 ) pdm09 strongly stimulates the immune response ( high q ) and influenza B provides weaker stimulation ( low q ) ., Then if we take the primary virus to be A ( H1N1 ) pdm09 ( q1 = 5 × 10−6 ) and the challenge virus to be influenza B ( q2 = 10−7 ) we observe that A ( H1N1 ) pdm09 delays infection with influenza B for short IEIs ( Fig 10B and S8 Fig ) ., Conversely , if influenza B is administered first ( q1 = 10−7 ) , then challenge with A ( H1N1 ) pdm09 ( q2 = 5 × 10−6 ) results in co-infection for short IEIs ( Fig 10C ) ., Results for more combinations of values for q1 and q2 drawn from a wider range are provided in S11 Fig ( for Model R2 ) and S12 Fig ( for Model R3 ) ., In all scenarios for Models R2 and R3 , high q1 prevents depletion of the target cell pool during the primary virus infection ( see Fig 6 ) ., While exerting a weak delay on the challenge virus for an IEI of 1–3 days ( seen in both data and simulation results ) , the continued availability of target cells allows for productive replication , preventing the system from displaying Phase 2 dynamics ., Similar to Model R1 , we observe that the patterns are primarily dominated by the IFN production rate of the first virus but nearly independent of that of the second virus ( S10–S12 Figs ) ., In summary , our three models—with their alternative hypothesised mechanisms for the action of the innate response leading to viral control—are each capable of capturing the dynamics of a single virus infection and the main features of primary–challenge experiments ., However , Model R1 , with its reliance upon the virus-resistant state fails to reproduce the hierarchy of viral infection ( i . e . it always produces Phase 2 dynamics ) ., For the other two mechanisms—a decreasing viral production rate ( Model R2 ) or an induced killing of infected cells by NK cells ( Model R3 ) —we have shown that both are able to reproduce all the behaviours including the hierarchy of viral infection observed in the experimental data ., We have made these observations based on the assumption that the viruses’ underlying kinetic properties are the same and that their differing ability to induce IFN production is the mediator of observed difference ., In the Supporting Information ( S13 and S14 Figs ) , we extend our study by exploring some alternative models in which other virus-immunity parameters are allowed to vary ( in addition to the IFN production rate ) , and demonstrate that Models R2 and R3 can still reproduce the observed hierarchies , while Model R1 remains reliant upon target-cell depletion and so is less capable of capturing the observations ., We have shown that the re-exposure model can successfully reproduce the phenomena of co-infection and delay ( Fig 3 ) ., However , we also observe complete blocking of the challenge virus following primary infection in some circumstances ( Fig 1 , IEIs of 3 and 7 days , and Fig 3 ) ., Although the reason for complete inhibition remains unclear , that it only occurs in some of the experimental replicates 10 suggests that stochastic effects in terms of viral dynamics may be important ., We hypothesise that failure of the challenge virus may occur when the number of virions ( V2num ) drops to a sufficiently low value such that stochastic effects become dominant 37 ., For example , the case of an initially synchronised decrease ( see Fig 3 and Fig 7C ) makes our hypothesis possible ., Here we examine this by using a stochastic model derived from the deterministic model used to generate Fig 6C ( see the Supporting Information for details on the stochastic model , its implementation and parameterisation ) ., Fig 12 shows that , although the solution of the deterministic model shows a rebound in viral load , the stochastic model results in two possible classes of solution: delayed infection with the second virus ( “success” ) or blocked infection with the second virus ( “failure” ) ., To quantify this stochastic phenomenon , we investigate the dependence of the success/failure rate on the initial number of virions ( see Fig 12C ) ., Results show that the failure rate increases as the initial number of virions decreases in the presence of an innate immune response ., Although the control case shows a 0% chance ( 0 out of 1000 simulations ) of failure for the primary virus infection with an initial number of 40 virions , the same number of virions in the challenge inoculum is insufficient to generate any re-infection events ., However , as the initial number of virions ( for both the primary and challenge viruses ) increases from 40 to 5000 , the chance of generating successful re-infection events increases to 100% , with a half chance of success when approximately 700 virions are present in the inoculum ( as used to generate Fig 12A and 12B ) ., This implies that failure could be due to an insufficient number of successfully infecting virions in the challenge virus inoculum , even if this number is sufficient to reliably induce infection with the primary virus ., In this paper , we have investigated the role of innate immunity and its possible mechanisms of action based on both experimental data and mathematical models ., Experimental data show that infection with one virus prior to challenge with a second strain can delay/block the second viral infection ( Figs 1 and 2 and 10 ) ., We interpret these findings as evidence for a hierarchy in different viruses to induce an innate immune response , and in the role of innate immunity in controlling viral infection 10 ., To better understand the possible mechanisms accounting for the hierarchy and some interesting ( a ) synchronised infections observed experimentally ( Fig 3 ) , we constructed and analysed several mathematical models with different IFN-induced immune response mechanisms ., Our results show that, 1 ) without other ( virus specific ) mechanisms at play , a model solely with a virus-resistant state is not able to reproduce the hierarchy of viral infection; and, 2 ) the occurrence of synchronised and desynchronised phenomena is highly dependent upon both the hierarchy of viral infectious ability and the time interval between the consecutive viral inoculations ., In more detail , we have shown that the model solely with a virus-resistant state ( Model, 1 ) primarily utilises target cell depletion to control viral growth , independent of the IFN production rate ( Fig 6 , black curve ) ., The temporary depletion of target cells will strongly limit the growth of any other virus , resulting in the failure to observe a viral hierarchy

Introduction, Materials and Methods, Results, Discussion

Influenza is an infectious disease that primarily attacks the respiratory system ., Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth ., Previous modelling studies of virus–innate immune response interactions have focused on infection with a single virus and , while improving our understanding of viral and immune dynamics , have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity ., In recent experiments , we have applied consecutive exposures to different virus strains in a ferret model , and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure ., These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy ., Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees ., The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response ., We compare these alternative models in terms of their abilities to reproduce the re-exposure data ., Our results show that, 1 ) a model with viral control mediated solely by a virus-resistant state , as commonly considered in the literature , is not able to reproduce the observed viral hierarchy;, 2 ) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response ., Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections ., Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control .

Infection with the influenza virus is responsible for serious morbidity and mortality ., In otherwise-healthy individuals , infection is usually acute , lasting between 5 to 10 days ., Over this time , the virus initially replicates rapidly , before peaking and then being cleared from the body ., Despite extensive study , we do not yet fully understand the processes which lead to viral control and viral clearance from the host ., Experimental and modelling studies of single infections have previously indicated that both the innate and adaptive immune responses play an important role in combating infection ., Here we study a novel dataset on how the host responds to sequential exposure to two different strains of influenza ., We introduce a family of mathematical models of the within-host dynamics of influenza infection which allow for re-infection ., Our models allow us , for the first time , to differentiate between alternative hypothesised mechanisms by which the innate immune response acts to control viral replication ., This study improves our understanding of the innate immune response to influenza and demonstrates that re-exposure studies provide a new paradigm for further experimental research ., Our findings may contribute to the development of next-generation treatment and vaccination strategies which rely upon an understanding of the host’s immunological response to influenza infection .

journal.pcbi.1004207

Thermal Stabilization of Dihydrofolate Reductase Using Monte Carlo Unfolding Simulations and Its Functional Consequences

Protein stability is an important determinant of organismal fitness and is central to the process of enzyme design for industrial applications 1–3 ., Most proteins must be folded to carry out their functions in vitro or in vivo ., In addition , non-functional aggregation of unfolded or partially-unfolded proteins can have a deleterious effect on the fitness of an organism and can lead to protein aggregation diseases , which include Alzheimer’s and Huntington’s , in humans 4–6 ., Aggregation of poorly folded proteins can also hamper protein production for research and technological purposes 7 ., While most mutations in a natural protein are destabilizing 8 , 9 , biological proteins are not generally at their highest possible stability; some mutations will stabilize a protein , increasing the equilibrium population of the folded state 10–12 ., This stabilization can be achieved by either slowing the rate of unfolding or speeding the rate of folding , depending on the role of the mutated residue in the folding nucleation process 13 , 14 ., The unfolding temperature , Tm , at which the free energy of the folded and unfolded states coincide ( ΔG = 0 ) serves as a common measure of protein stability ., Tm is obtainable by experiment and , in theory , from simulation , although current molecular dynamics simulations are limited in their ability to capture full folding or unfolding trajectories of most proteins ( except very small fast folding domains 15 ) in a tractable amount of simulation time 16 ., Several computational methods to predict protein stability or changes in stability upon mutation have been developed and tested 17–19 ., However , the performance of these popular methods is still relatively weak 20–22 ., Other existing techniques to rationally design proteins with improved stability have involved optimization of charge-charge interactions 23 , saturation mutagenesis of residues with high crystallographic B-factors 24 , methods based on protein simulation and calculation of free energies 25–27 and comparison to homologous proteins including the ultra-stable proteins of thermophiles 28 , 29 ., We reasoned that better predictions of mutant stability might be obtained by evaluating the unfolding temperature Tm in realistic yet computationally tractable simulations of protein unfolding ., Here , we use a Monte Carlo protein unfolding approach ( MCPU ) with an all-atom simulation method and knowledge-based potential developed earlier in our lab 16 , 30 , 31 to simulate unfolding and predict melting temperatures for all possible single point mutants of E . coli Dihydrofolate Reductase ( DHFR ) ., DHFR is an essential enzyme in bacteria and higher organisms , and it is an important target of antibiotics 32 and anti-cancer drugs 33 , 34 ., Its moderate size ( 18 kDa ) makes it amenable to both simulation and experiment ., As described in the Materials and Methods section , the Monte Carlo move set consists of rotations about torsional angles ., At high temperature , the higher entropy of unfolded states overcomes the increase in energy due to loss of favorable contacts and torsional preferences , leading to unfolding ., We experimentally determine melting temperatures and catalytic activities for several predicted stabilizing mutants , and for mutants combining multiple stabilizing mutations ., Our approach allows us to identify several stabilized mutants of DHFR , and our prediction method marks an improvement over existing stability predictors such as Eris 19 , FoldX 17 , and PopMusic 18 ., Simulations of non-DHFR proteins likewise indicated that our method is useful as a general approach to simulate protein unfolding and select stabilizing mutations ., Ideally , protein stability for any sequence should be predicted in all-atom equilibrium simulations that cover multiple folding-unfolding events to determine equilibrium populations of various states of the protein ., However , despite recent progress in ab initio simulations of protein folding 15 this goal is not attainable for proteins of realistic size and biological relevance ., Currently , non-equilibrium unfolding simulations are within reach for sufficiently large proteins and the question arises whether such simulations can be used to assess mutational effects on protein stability , which is an equilibrium property ., The following analysis provides an affirmative answer to this question , under certain assumptions ., Although the idea of obtaining equilibrium free energy differences from non-equilibrium measurements is not new 35 , and protein stabilities have been calculated from molecular dynamics simulations using the Jarzynski equality , e . g . , 36–38 , such simulations require application of an external steering force; in the present paper we report the use of multi-temperature Monte-Carlo unfolding simulations in obtaining protein stabilities ., Assuming two-state unfolding kinetics 39–42 we can estimate the characteristic time required to cross the unfolding free energy barrier ( in fact it is the time spent in the native state waiting for sufficient thermal fluctuation to cross the barrier ) as:, τufp=τ0eΔG#kT, ( 1 ), where τufp is first-passage time from the folded to the unfolded state , ΔG# is the free energy barrier between the folded state and the transition state for unfolding ( see Fig . 1 ) and τ0 is the elementary time constant ., When simulation time τsim approaches τufp unfolding events are observed in simulation ., The apparent “melting temperature” , i . e . , the temperature at which unfolding events occur in simulations , therefore depends on the simulation time τsim according to Eq ., ( 1 ) :, kTmapp=ΔG#ln ( τsimτ0 ), ( 2 ), This analysis suggests that non-equilibrium first passage unfolding simulations are not suitable to predict the temperature at which a protein would unfold at equilibrium ., However the effect of mutations on stability can be predicted from unfolding simulations ., In order to see this we note that the mutational effect on protein stability ΔΔG is related to the change in the unfolding free energy barrier ΔΔG# , the difference between the WT barrier height and the mutant barrier height , shown in Fig . 1 ., ΔΔGi#= ( 1−φi ) ΔΔGieq, ( 3 ), where i denotes the mutated amino acid and φi is the φ-value for residue i which determines the fraction of interactions that this residue forms in the folding/unfolding transition state 40 , 43 , 44 ., We therefore obtain, kΔTmapp ( i ) = ( 1−φi ) ΔΔGieqln ( τsimτ0 ), ( 4 ), where ΔTmapp ( i ) =Tmapp ( i ) −Tmapp ( WT ) is the shift in apparent unfolding temperature upon a specific mutation in the i-th residue ., Introducing the relative ( to WT ) unfolding temperature ΔTmrel ( i ) =ΔTmapp ( i ) /Tmapp ( WT ) we get, ΔTmrel ( i ) = ( 1−φi ) ΔΔGieqΔG#, ( 5 ), i . e . the mutational shift in observed unfolding temperature , normalized to the observed unfolding temperature of the wild-type at the same simulation condition does not depend on the simulation length , provided that the simulation is sufficiently equilibrated in the native basin so that the rules of transition state theory apply ., The analysis of extensive kinetic and equilibrium data for multiple proteins shows that for the majority of mutations ( except for a small fraction of residues that participate in the folding nucleus ) φi ≈ 0 . 24 with remarkable accuracy and consistency 45 ., We get therefore, ΔTmrel ( i ) =0 . 76ΔΔGieqΔG#, ( 6 ), i . e . ΔTmrel ( i ) is independent of simulation time and proportional to the equilibrium free energy effect of mutations , provided that simulations have equilibrated in the native basin of attraction ., We ran MCPU on DHFR ( PDB ID: 4DFR ) at a range of temperatures , to generate simulated unfolding curves ., Unfolding steps of a sample trajectory are shown in Fig . 2 , and a flowchart of the simulation method is shown in S1 Fig . The protein was subject to a brief MD energy minimization , beginning from the WT crystallographic native state , followed by unfolding simulations at each of 32 different temperatures using all-atom Monte-Carlo ( see Materials and Methods section ) ., As shown in figures S2 Fig—S4 Fig , the RMSD and total energy increased and the number of contacts decreased as each simulation proceeded , and with increasing temperature ., ( Here , temperature is given in arbitrary simulation units . ), Plots of RMSD and contact number vs . temperature showed sigmoidal behavior , with a clearly identifiable transition temperature , and the melting temperature ( Tm ) could be determined by fitting to a sigmoidal function ( Fig . 3 ) ., Plots of energy vs . temperature ( S5 Fig ) were sigmoid-like , but with an additional rise in energy at low to intermediate temperatures , perhaps indicating pre-melting to a dry-molten globule state with loosened side chains but native-like topology 46 , 47 ., This deviation from sigmoidal behavior becomes clearer as the simulation length is increased ( S6 Fig ) ., All possible single point mutations of DHFR ( 159 * 19 = 3 , 021 ) were simulated with the Monte Carlo protein unfolding simulation protocol ., The simulated Tm values were calculated as described above ., Of the 3 , 021 mutations , 523 mutations ( 17 . 3% ) were predicted to have a stabilizing effect according to all three metrics ( energy , contacts , and RMSD ) , while 42 . 1% of mutations had a destabilizing effect according to all three metrics ., These predictions are in good agreement with statistical analysis of published experimental data and FoldX predictions 8 , 12 ., The simulated Tm values evaluated using RMSD , total energy , and number of contacts are strongly correlated , as shown in Fig . 4A ., The distribution of predicted melting temperatures ( averaged over the 3 metrics ) for all 3021 point mutants is shown in Fig . 4B ., Next , we selected a subset of predicted stabilizing mutations for subsequent in depth computational and experimental analysis ., To that end we selected the loci where multiple mutations were consistently predicted as stabilizing ., Out of this set we selected one mutation at each loci which were predicted as most stabilizing ., As a result we arrived at 23 single predicted stabilizing point mutants shown in S1 Table , which we deemed most promising for subsequent in depth computational and experimental analysis ., Furthermore , five stabilizing mutations at different sites within DHFR , shown in Fig . 5 , were combined to form the multiple mutants listed in Table 1 , with the rationale that the combination of individual stabilizing mutants often yields more stable proteins , and these mutants were likewise subjected to computational and experimental analysis ., First we test two predictions that emerge from the theoretical analysis of unfolding simulations ., The first prediction is that the apparent unfolding temperature decreases as the length of the unfolding simulation increases ( Equation 4 ) ., Secondly and most importantly the mutational change in relative ( normalized to WT ) apparent unfolding temperature is, a ) robust with respect to simulation time provided that simulations have equilibrated in the native basin and, b ) directly proportional to the effect of mutations on equilibrium protein stability ( Equation 6 ) ., We test these predictions using MCPU simulations and experiment ., We carried out two sets of MCPU simulations of different lengths: 2 , 000 , 000 and 20 , 000 , 000 steps for the 23 predicted stabilizing mutants , 15 mutants studied previously by experiment 48 ( the complete set of single mutants is listed in S1 Table ) , and the 5 stabilizing multiple mutants combining individual mutations listed in Table 1 , and compared their predicted absolute and relative simulated unfolding temperatures ( Fig . 6 ) ., Indeed both predictions of our theoretical analysis are confirmed , i . e . , the apparent unfolding temperature decreases with simulation time ( Fig . 6A ) while the relative unfolding temperature ΔTmrel is remarkably independent of simulation time ( Fig . 6B ) ., We note that due to the nature of the energy function used in our simulations , there is no obvious mapping of simulation temperature to real absolute temperature ( i . e . , in Celsius or Kelvin ) ., Conversion of simulation temperature to physical temperature would require use of experimental data ( e . g . , WT unfolding temperature and deviation of temperatures over all mutants ) and therefore would not provide a completely simulation- or theory-based prediction ., Furthermore , as noted above , the apparent absolute value of the transition temperature in the Monte-Carlo unfolding approach depends on simulation time ., Therefore , we used relative melting temperature , ΔTmrel ( i ) =ΔTmapp ( i ) /Tmapp ( WT ) , when comparing simulation results with experimental results ., As mentioned , the simulated Tm values evaluated using RMSD , total energy , and number of contacts are strongly correlated in our simulations as shown in Fig . 4A and S1 Table ., In what follows we define the computational unfolding temperature Tm as averaged over Tm values determined using these three criteria ., We cloned , expressed , and purified the 23 single point mutants of DHFR listed in S1 Table , as well as the multiple mutants listed in Table 1 ( see Materials and Methods ) ., The biophysical properties of the mutants were measured and compared with WT DHFR , as shown in S2 Table ., As many studies have shown that oligomerization can alter protein stability 23 , 48 , 49 , we first tested whether mutations induce oligomerization and/or aggregation using the gel filtration method 48 , 50 and light scattering ., The results indicated that all of the 23 mutants were monomeric at studied concentrations except for E154V , which appeared aggregation-prone ., We excluded E154V from the subsequent analysis ., As shown in S2 Table , all single mutants are catalytically active except for D27F ., D27 is known to be a key catalytic residue of E . coli DHFR 51 ., For each mutant we obtained two measures of stability: the apparent melting temperature determined by Differential Scanning Calorimetry ( DSC ) and the urea midpoint unfolding concentration ( Cm ) determined by monitoring chemical denaturation by Circular Dichroism ( CD ) with subsequent fitting to a two-state model ( see Materials and Methods ) ., Both measures of stability were highly correlated , despite the fact that thermal unfolding was irreversible ( S7 Fig ) ., Of the selected 22 single point mutations , 10 mutations were stabilizing , according to their Tm or Cm values ( S2 Table ) ., Given that statistically most random mutations are destabilizing with only a small fraction ( less than 18% ) stabilizing 8 , 12 , this statistically significant result ( p = 0 . 002 under the null hypothesis that mutations are random ) indicates that MCPU is an effective method for selecting stability-enhancing mutations ., As expected , combinations of single stabilizing mutations led to more stable multiple mutant variants , 24 , 25 , 52 as predicted by simulation ., In particular , the stability of the quintuple mutant ( T68N , Q108D , T113V , E120P , S138Y ) was found to be substantially higher than that of the wild type protein ( Table 1 ) , with Tm 7 . 2°C higher than WT , and Cm , the urea concentration at the mid-unfolding point , was 0 . 43M higher than WT ., All multiple mutants were catalytically active , and the quintuple mutant and triple mutant ( T113V , E120P , S138Y ) were found to be more catalytically active than WT ., We note that while combination of stabilizing mutations generally increases stability , the effect is less than additive ( S8 Fig ) ; for instance , the quintuple mutant is about 4°C less stable than predicted under the assumption of additive ΔTm ( a 7 . 2°C stability increase vs . predicted 9 . 6°C ) ., We computationally predicted relative unfolding temperatures of 15 DHFR mutants published earlier 48 and added these mutants to the set for analysis resulting in 42 mutants in total ., The correlation coefficient between experimental relative Tm and simulated relative Tm for the 42 mutants was about 0 . 65 , as shown in Fig . 7A ., To address the issue that both simulated Tm and DSC measurements are not strictly at equilibrium , we plotted the relation between simulated Tm and equilibrium measurement of stability in chemical denaturation by urea ., The denaturation mid-transition urea concentration Cm and computationally determined unfolding temperature exhibit even a slightly higher correlation of r = 0 . 68 ( Fig . 7B ) , demonstrating that our non-equilibrium simulation method shows good agreement with the equilibrium measurement of urea denaturation , as predicted by Equation 6 ., We also used the dataset to evaluate the effect of the number of replications and the number of MC steps on the performance of the method ., As shown in Fig . 8A , the prediction accuracy is sensitive to the number of replications ., To achieve reliable Tm predictions , at least 20 replications should be used ., However , the number of MC steps did not greatly affect prediction accuracy , provided simulations were run for at least ~ 200 , 000 steps ( see Fig . 8B ) ., In the context of the theory developed in the earlier section: “Predicting the effects of mutations on protein stability from non-equilibrium unfolding simulations , ” this initial equilibration period may allow time for equilibration within the native basin , after which simulation length does not appreciably affect the consistency of results with equilibrium stability measurements ., It has been proposed that stability imposes a constraint on protein function leading to stability-activity tradeoffs 53 , 54 ., Our data , however , paints a different picture for DHFR—of a weak positive correlation between Tm and kcat or kcat/KM ( r = 0 . 46 , p = 0 . 02 and r = 0 . 41 , p = 0 . 03 respectively ) with one notable outlier D27F , where the stabilizing mutation is made right in the active site ( Fig . 9 ) ., The D27F mutant has high thermal stability but , as noted above , is not catalytically active , indicating that there is in fact a stability-activity trade-off for this active-site residue ., Using an alignment of 290 bacterial DHFRs , we determined the DHFR consensus sequence ( S9 Fig ) ., Mutation of a non-consensus residue to the consensus residue generally resulted in protein stabilization 29 ., In 4/16 of the experimentally stabilizing mutations , a residue was changed to the consensus residue , while only 2/29 destabilizing mutations resulted from a change to consensus ., Likewise , in 18/29 destabilizing mutations , a residue was changed away from the consensus residue , while this was true for only 5/16 of stabilizing mutations ., We compared the minimum and maximum simulated Tm values obtainable by mutating a single residue to any of the 19 other amino acids ( Fig . 10A ) ., There is a weak positive correlation between minimum and maximum melting temperatures ( r = 0 . 30 , p = 10−4 ) ., Apparently , protein loci where mutations can cause significant stabilization are statistically less susceptible to destabilizing mutations and vice versa , which may be expected: once a residue is already at its most stabilizing amino acid variant , the protein cannot be stabilized further by mutation ., Distinct outliers correspond to the loci with the strongest stabilizing or destabilizing effects of mutations ., Interestingly , these outliers , which may represent structural weak spots in DHFR , tend to fall on the interface connecting the C-terminal beta hairpin and the rest of the protein ( Fig . 10B ) ., This is in fact the interface that is the first to dissociate in the Monte Carlo simulations ( see Fig . 2 ) ., We compared our computational DHFR predictions with four popular approaches to predict the effect of a mutation on protein stability: FoldX 17 , Eris 26 , PopMusic 55 , and SDM 56 ., ( S3 Table ) ., The MCPU performs better than these methods on DHFR mutants ., PopMusic shows also strong performance with highly statistically significant r = 0 . 55 between theory and experiment , however the limitation of this method is that it can consider only single point mutations ., To further evaluate MCPU performance we tested it on four additional proteins from four different SCOP structural classes: the Cro repressor protein from bacteriophage lambda ( PDB-ID 5CRO ) , the B . Subtilis major cold shock protein ( 1CSP ) , E . coli Thioredoxin ( 2TRX ) , and Gln-25 ribonuclease T1 from Aspergillus oryzae ( 1RN1 ) ., Our predictions were compared with Eris and SDM ., We did not compare MCPU results with FoldX and PopMusic as these mutations were selected in the training dataset for the two methods ., As shown in Table 2 , the correlation coefficient between MCPU predictions and the experimental Tm values , averaged over all proteins , is about 0 . 71 , which is higher than that provided by Eris ( -0 . 05 ) , for which predictions were quite poor for both DHFR and other proteins , and SDM ( 0 . 63 ) ., If we consider only the binary prediction of whether a mutation is stabilizing or destabilizing , MCPU can correctly predict 11 out of 16 mutations , while Eris and SDM correctly classify 9 and 8 mutations respectively ., The theoretical analysis of the unfolding simulations relates the effect of mutations on the equilibrium between folded and unfolded states to the effect of mutations on free energy of the folded and transition states ., It is widely believed that in the low-entropy folded state energetic factors dominate ., If so that would imply that we can get an equally good correlation between prediction and experiment by estimating the mutational effect on energy of the native state as is the case for most empirical methods ., To that end we evaluated the correlation between the energy of the minimized ( after long MC equilibration ) native state and the experimental Tm and found only a weak correlation with experimental melting temperatures ( Table 2 , last column ) , indicating that protein entropy , which is accounted for in the MCPU , in addition to enthalpy , is important in determining protein stability ., Estimates of protein stability using Molecular Dynamics are prohibitive for all but the smallest protein domains ., However using MCPU we were able to efficiently explore stabilities of all possible point mutants for an essential enzyme of a typical size ( 159 amino acids ) in a manageable amount of computational time ( approx . one hour for every 1 , 000 , 000 MC steps ) ., Although the use of rapid Monte Carlo simulations reduces simulation time and allows for a greater number of replicates , our method to predict stability effects of mutations based on non-equilibrium unfolding simulations represents a general approach that could be modified for use with conventional MD simulations , especially given the current rate of improvement in simulation speed and accuracy ., Since our method involves protein unfolding simulations and not equilibrium simulations of both folding and unfolding processes , we expect it to be especially useful for predicting mutations that mostly affect the rate of protein unfolding as highlighted in our theoretical analysis ., Low φ-value residues , which acquire contacts with other residues late in the folding process and lose contacts early in the unfolding process 14 constitute the majority of residues in proteins , with φ-value roughly constant around 0 . 24 as noted in 45 ., Combining this observation with assumptions of transition state theory , we found that for the majority of residues ( those not part of the folding nucleus 14 , 57 exhibiting anomalously high φ-values ) the observed simulation Tm relative to WT is proportional to the equilibrium stability change ΔΔG , as verified by simulation and experiment ., We establish that relative Tm is independent of simulation length , demonstrating that non-equilibrium simulations can in fact be used to quantify relative protein stability ., Many of the experimentally verified stabilizing mutations in DHFR predicted by MCPU are found in the C-terminal beta hairpin region , which is the first to unfold in simulations , prior to the main unfolding event encompassing the entire structure ( see Fig . 2 ) ., It has been shown that the source of ultra-stability in hyperthermophiles generally arises from slowing the unfolding rate , rather than increasing the folding rate 28 , so our method may be particularly suitable for discovering biologically relevant stabilizing mutations ., In addition , our results might be particularly applicable to in vivo studies , where protease digestion and/or aggregation proceed from the partially-unfolded state ., We note , however , that some stabilizing residues predicted by MCPU lie in the region of the protein that is late to unfold in simulations , including I61V , which raises the experimental melting temperature by 1 . 7°C ., These mutants , along with the destabilized outlier I155A for which relative Tm depends on simulation length ( Fig . 6 ) , are appealing candidates for further study , as they may reflect a breakdown in the simplifying assumptions of 2-state kinetic theory for proteins ., It has been hypothesized that there exists a tradeoff between enzyme activity and stability , since certain regions of an enzyme must be sufficiently flexible to promote catalysis 53 , 54 ., This conclusion was reached in 53 , 58 , based on the exploration of stability effects of mutations in the active site of beta-lactamase 53 and rubisco 58 ., Fersht and coauthors also found several stabilizing mutations in the active site of Barnase rendering the protein inactive 59 ., While we observe a similar effect with the D27F mutation in DHFR , Fig . 9 shows that exploring only mutations in the active site provides a biased view on the tradeoff between activity and stability ., Rather a vast majority of mutations throughout the protein show a qualitatively opposite trend ., The likely explanation of the distinction between an apparent tradeoff when mutations are made in the active site and the opposite trend for mutations outside of the active site is that “carving” an active site requires special selection of catalytic amino acids , which could indeed have a destabilizing effect , overall ., However our observation of a small positive correlation argues against an obligate relation between global protein dynamics and activity for DHFR , at least for the aspects of dynamics that are correlated with stability ., Warshel and colleagues reached a similar conclusion in their theoretical analysis of the role of dynamics for DHFR and other proteins in 60 ., This point has likewise been made by Bloom et al . 11 , who noted that a number of proteins have been stabilized experimentally without loss of activity , and Taverna and Goldstein argued that marginal stability is an inherent property of proteins due to the high dimensionality of sequence space and not due to a requirement of reduced stability in order to generate sufficient flexibility 61 ., A straightforward explanation for the weak yet statistically significant positive correlation between activity and stability observed in our case might be that more stable proteins have greater effective concentration of the folded ( i . e . active ) form ., It is also important to note that a weak yet statistically significant positive correlation between activity and stability for DHFR can be revealed only when stabilizing mutations are included in the analysis ., Our earlier study 48 analyzed a smaller set of primarily destabilizing mutants and did not reveal any statistically significant trend ( positive or negative ) in the stability-activity relation for DHFR ., The development of highly-stabilized DHFR mutants through our combined in silico—in vitro approach opens up promising avenues for new in vivo studies ., It has been postulated that protein stability places a fundamental constraint on the evolutionary pathways available to a protein 29 , 62 which has particular significance in the development of antibiotic resistance: higher protein stability can provide the microorganism with an increased capacity to evolve to evade antibiotic drugs 63 or , more generally , with capacity to evolve new functions 62 ., We plan to use an approach developed in our lab 48 to endogenously introduce stabilized DHFR mutants into the bacterial chromosome and we will evaluate mutant fitness relative to wild-type using growth rates and competition experiments ., These experiments will allow us to assess whether an evolutionary trade-off exists between stability and fitness in vivo , particularly in the presence of antibiotics ., We plan to apply MCPU to predict stability effects of mutations in proteins other than DHFR , in particular to develop highly stabilized mutants ., Comprehensive experimental analysis of fitness and/or stability effects of mutations 64 could be useful in assessing the predictive capabilities of this method ., In addition to predicting mutant stabilities , MCPU can provide atomic-detail molecular trajectories to rationalize the stability effects of mutations; such analysis is left to future study ., We employed an all-atom Monte Carlo simulation program incorporating a knowledge-based potential , described in previous publications 16 , 31 , 65 ., Briefly , the energy function is a sum of contact energy , hydrogen-bonding , torsional angle , and sidechain torsional terms , with an additional term describing orientation of nearby aromatic residues ., The move set consists of rotations about ϕ , ψ , and χ dihedral angles , with bonds and angles held fixed ., Moves are accepted or rejected according to the Metropolis criterion ., Mutations were introduced into the protein using the program Modeller v9 . 2 66 ., An initial minimization was carried out in NAMD 67 for 5 , 000 steps , using the default minimization algorithm and par_all27_prot_lipid . inp parameter file ( without waters ) ., An additional minimization step was carried out by running the Monte Carlo simulation program at low temperature ( 0 . 100 in simulation units ) for 2 , 000 , 000 steps ., A 2 , 000 , 000-step simulation was then run at each of 32 temperatures , averaging over all 2 , 000 , 000 steps to obtain Energy , RMSD , and number of contacts ., These results were averaged over 50 simulations , for each temperature ., Data was then plotted and fit to a sigmoid to obtain the computationally-predicted melting temperature , for each of Energy , RMSD , and number of contacts ., To assess dependence of melting temperature on simulation length , longer simulations of 20 , 000 , 000 steps were carried out with 30 replications , averaging over the final 2 , 000 , 000 steps ., For DHFR , 1 , 000 , 000 steps took approximately one hour of simulation time , on a single CPU ., We evaluated the effect of the number of MC simulation replications on the prediction results ., As shown in S9 Fig , the prediction accuracy is sensitive to the number of replications , but converges to a constant value after approximately 20 replications ., In addition , we saw that increasing the number of MC steps beyond 2 , 000 , 000 steps does not increase prediction accuracy when the protein has been simulated with at least 20 replications , despite the fact that not all simulations have converged by 2 , 000 , 000 steps ( S2 Fig—S4 Fig ) ., Sigmoidal fits were accomplished using the module “Sigmoidal , 4PL” using the software program Prism 6 ., The sigmoid function has the form: Y = Bottom + ( Top-Bottom ) / ( 1+10^ ( ( LogIC50-X ) *HillSlope ) ) The tool is accessible from Shakhnovich lab website http://faculty . chemistry . harvard . edu/shakhnovich/software The wild type dhfr gene was cloned in a pET24 expression vector under the inducible T7 promoter , then transformed into BL21 ( DE3 ) cells 69 ., Single point mutations of DHFR were constructed based on a two-step PCR-mutagenesis strategy 70 , in which the template for the PCR is the plasmid of WT DHFR ., The multiple-mutant v

Introduction, Results, Discussion, Materials and Methods

Design of proteins with desired thermal properties is important for scientific and biotechnological applications ., Here we developed a theoretical approach to predict the effect of mutations on protein stability from non-equilibrium unfolding simulations ., We establish a relative measure based on apparent simulated melting temperatures that is independent of simulation length and , under certain assumptions , proportional to equilibrium stability , and we justify this theoretical development with extensive simulations and experimental data ., Using our new method based on all-atom Monte-Carlo unfolding simulations , we carried out a saturating mutagenesis of Dihydrofolate Reductase ( DHFR ) , a key target of antibiotics and chemotherapeutic drugs ., The method predicted more than 500 stabilizing mutations , several of which were selected for detailed computational and experimental analysis ., We find a highly significant correlation of r = 0 . 65–0 . 68 between predicted and experimentally determined melting temperatures and unfolding denaturant concentrations for WT DHFR and 42 mutants ., The correlation between energy of the native state and experimental denaturation temperature was much weaker , indicating the important role of entropy in protein stability ., The most stabilizing point mutation was D27F , which is located in the active site of the protein , rendering it inactive ., However for the rest of mutations outside of the active site we observed a weak yet statistically significant positive correlation between thermal stability and catalytic activity indicating the lack of a stability-activity tradeoff for DHFR ., By combining stabilizing mutations predicted by our method , we created a highly stable catalytically active E . coli DHFR mutant with measured denaturation temperature 7 . 2°C higher than WT ., Prediction results for DHFR and several other proteins indicate that computational approaches based on unfolding simulations are useful as a general technique to discover stabilizing mutations .

All-atom molecular simulations have provided valuable insight into the workings of molecular machines and the folding and unfolding of proteins ., However , commonly employed molecular dynamics simulations suffer from a limitation in accessible time scale , making it difficult to model large-scale unfolding events in a realistic amount of simulation time without employing unrealistically high temperatures ., Here , we describe a rapid all-atom Monte Carlo simulation approach to simulate unfolding of the essential bacterial enzyme Dihydrofolate Reductase ( DHFR ) and all possible single point-mutants ., We use these simulations to predict which mutants will be more thermodynamically stable ( i . e . , reside more often in the native folded state vs . the unfolded state ) than the wild-type protein , and we confirm our predictions experimentally , creating several highly stable and catalytically active mutants ., Thermally stable active engineered proteins can be used as a starting point in directed evolution experiments to evolve new functions on the background of this additional “reservoir of stability . ”, The stabilized enzyme may be able to accumulate a greater number of destabilizing yet functionally important mutations before unfolding , protease digestion , and aggregation abolish its activity .

journal.pgen.0030075

Being Pathogenic, Plastic, and Sexual while Living with a Nearly Minimal Bacterial Genome

Organisms belonging to the Mycoplasma genus ( class Mollicutes ) are commonly described as the simplest and smallest self-replicating bacteria because of their total lack of cell wall , the paucity of their metabolic pathways , and the small size of their genome 1 , 2 ., In the 1980s , they were shown to have evolved from more classical bacteria of the firmicutes taxon by a so-called regressive evolution that resulted in massive genome reduction 3 , 4 ., One of the models attempting to improve understanding of the evolution of bacteria with small genomes proposes that erosion of bacterial genomes is more prone to occur in bacterial populations that are spatially isolated and sexually deficient 5 ., In restricted habitats , the environment is rather steady and natural selection tends to be reduced , resulting in the inactivation of many genes by genetic drift 5 , 6 ., In this scenario , DNA acquisition would be strongly limited , resulting , after losses of large genomic regions and accumulation of mutations , in genome stasis 7 ., This evolution scheme is relevant for a number of obligate intracellular bacteria , including insect endosymbionts ( e . g . , Buchnera and Wigglesworthia spp . ) , and arguably for Chlamydia , and Rickettsia spp ., The recent findings of a putative conjugative plasmid in Rickettsia felis 8 and of a substantial number of prophage , transposase and mobile-DNA genes in the insect endosymbiont Wolbachia pipientis challenged this model and it was proposed that gene inflow by horizontal gene transfer ( HGT ) may occur in some obligate intracellular species depending on their lifestyles 9 ., Mycoplasmas share with obligate intracellular bacteria a small genome size with marked AT nucleotide bias and a low number of genes involved in recombination and repair , but forces driving their evolution may not be quite the same , as they do have a very different lifestyle ., Indeed , mycoplasmas mainly occur as extracellular parasites 10 and are often restricted to a living host , with some species having the ability to invade host cells 11 ., They have a predilection for the mucosal surfaces of the respiratory and urogenital tracts , where they successfully compete for nutrients with many other organisms , establishing chronic infections ( Table S1 ) ., Therefore , mycoplasma populations are far from being isolated and inhabit niches where exchange of genetic material may take place ., The none-to-rare occurrence of HGT reported so far for mycoplasmas 12 is therefore surprising and seems to conflict with their lifestyle ., On the other hand , HGT may depend on several other factors 9 that were described as limited or lacking in most mycoplasma species and that include an efficient machinery for recombination , genetic mobile elements such as prophages or conjugative plasmids , and a means for DNA uptake ., However , this view of mycoplasma biology is changing , since homologous recombination has been demonstrated in these bacteria 13 , 14 and some new means of exchanging DNA are being discovered 15 , 16 ., Indeed , several pathogenic mycoplasma species relevant to the veterinary field and the murine pathogen M . pulmonis were recently shown to form biofilms 17 , 18 , structures that have been proposed to promote DNA exchange among bacteria ., This finding , together with previous evidence for DNA transfer under laboratory conditions in M . pulmonis via conjugation 19 , raises the exciting question of whether some mycoplasmas species are sexually competent ., Subsequently , this would suggest that mycoplasma species which co-infect the same host niches might exchange genetic material ., Remarkably , biofilm formation and the occurrence of an integrative conjugative element ( ICE ) have both been newly described in the M . agalactiae species 16 , 18 ., This pathogen is responsible for contagious agalactia in small ruminants 20 , a syndrome that includes mastitis , pneumonia , and arthritis and that is also caused by some members of the so-called mycoides cluster , such as M . capricolum subsp ., capricolum and M . mycoides subsp ., mycoides Large Colony ., Although producing similar symptoms in the same host , these species belong to two distinct and distant branches of the mollicute phylogenetic tree ( Figure 1 ) ., Their relative phylogenetic positions are irrespective of whether the tree is constructed from aligned 16S rDNA ( Figure 1A ) or from 30 aligned proteins shared by all living organisms 21 ( Figure 1B ) ., M . agalactiae belongs to the hominis phylogenetic branch , together with a closely related ruminant pathogen , M . bovis , while the six members that comprise the “mycoides cluster” belong to the spiroplasma phylogenetic branch 22 ., Whole-genome sequences are available for two members of the mycoides cluster; M . mycoides subsp ., mycoides SC 23 , which is responsible for contagious bovine pleuropneumonia 24 , and M . capricolum subsp ., capricolum 25 ., In contrast , there is a limited amount of sequence data available for M . agalactiae and M . bovis ., Mycoplasmas that have been fully sequenced in the hominis phylogenetic group are a murine pathogen M . pulmonis 26 , a swine pathogen M . hyopneumoniae ( strain 232 27; strains 7748 and J 12 ) , an avian pathogen M . synoviae 12 , and a mycoplasma isolated from fish , M . mobile 28 ( Figure 1B ) ., Mechanisms underlying ruminant mycoplasma diseases have yet to be elucidated and very little is known regarding the mycoplasma factors that are involved in virulence and host interaction ., Genes thus far identified in M . agalactiae and for which a function in relation to virulence has been predicted are, ( i ) a family of phase-variable related surface proteins , designated as Vpma , which are encoded by a locus subjected to high-frequency DNA rearrangements and could be involved in adhesion 29 , 30 ,, ( ii ) the P40 protein , which is involved in host–cell adhesion in vitro but is not expressed in all field isolates 31 , and, ( iii ) the P48 protein , which has homology to an M . fermentans product with a macrophage-stimulatory activity 32 ., Several of these gene products have homologs in M . bovis but not in mycoplasmas of the mycoides cluster ., Whole-genome comparison between phylogenetically distant mycoplasmas that colonize the same host could provide a basis from which to comprehend the factors involved in mycoplasma host adaptation ., With this initial goal , we sequenced the M . agalactiae genome of the pathogenic type strain PG2 ., Results revealed a classical mollicute genome with a coding capacity of 751 CDSs , half of which are annotated as encoding hypothetical products ., Unexpectedly , comparative analysis of the M . agalactiae genome with that of other mollicutes and bacteria suggests that a significant amount of genes ( ∼18 % ) has been horizontally transferred to or acquired from mycoplasmas of the mycoides cluster that are phylogenetically distant while sharing common ruminant hosts ., In light of these data , we re-examined mollicute genomes for HGT events with a particular focus on those that occurred after mycoplasmas branched into three phylogenetic groups ( see Figure 1 for the hominis , pneumoniae , and spiroplasma phylogenetic groups ) ., Our analyses confirm data so far reported regarding the low incidence of HGT between Mycoplasma species with the exception of that described in this study , between M . agalactiae and members of the mycoides cluster and , to a lesser extent , between M . gallisepticum and M . synoviae ., To our knowledge , this is the first description of large-scale horizontal gene transfer between mycoplasmas ., The genome of the M . agalactiae type strain PG2 consists of a single , circular chromosome; general features are summarized in Table, 1 . The genome sequence was numbered clockwise starting from the first nucleotide of the dnaA gene , which was designated as the first CDS ( MAG0010 ) ., This gene is involved in the early steps of the replication initiation process 33 and is typically located near mycoplasma origins of replication ., Indeed , dnaA boxes flanking the dnaA gene were shown in M . agalactiae to promote free replication of the ColE1-based E . coli vectors in which they were cloned 34 ., Although these experiments clearly localized the M . agalactiae oriC in the vicinity of the dnaA gene , whole-genome analysis did not indicate a significant GC-skew inversion 35 in this region ( unpublished data ) ., In contrast to other mycoplasma genomes 36 , a high level of gene-strand bias was not observed , even when restricting the analysis to the dnaA vicinity ., Overall , M . agalactiae strain PG2 possesses a typical mollicute genome , with a small size ( 877 , 438 bp ) , a low GC content ( 29 . 7 moles % ) , a high gene compaction ( 88% of coding sequence ) , and UGA preferentially used as a tryptophan codon over UGG ( Table 1 ) ., Its GC% value is slightly higher than that observed for some other mycoplasma species but is close to the average GC content ( 28% ) calculated from the 16 available mollicute genomes ., Using the CAAT-box software package , 751 CDSs were identified , 404 ( 53 . 8% ) of which had a predicted function ., The genome also contains 34 tRNA genes and two nearly identical sets of rRNA genes with two 16S–23S rRNA operons ( MAG16S1-MAG23S1 and MAG16S2-MAG;23S2 ) and the two 5S rRNA genes ( MAG5S1 and MAG5S2 ) clustered in two loci separated from each other by ∼400 kb ( Figure 2 ) ., Prediction of M . agalactiae CDS function was based on BLAST searches against SwissProt , trembl , and MolliGen databases ., For CDSs showing significant similarities with database entries , most best BLAST hits ( BBH ) were found with M . synoviae and M . pulmonis , which belong , together with M . agalactiae , to the hominis phylogenetic group ( Figure 1 ) ., Unexpectedly , a large number of BBH were also obtained with M . mycoides subsp ., mycoides SC or M . capricolum subsp ., capricolum , which both belong to the mycoides cluster ( Figure S1 ) ., Since this cluster is exclusively composed of ruminant pathogens and is relatively distant from M . agalactiae in the mollicute phylogenetic tree ( Figure 1 ) , this prompted us to closely examine the corresponding CDSs ., A total of 136 M . agalactiae CDSs were then identified as having their BBH with organisms from the mycoides cluster , with 50 having no significant similarity outside of this cluster ( Table S2 ) ., Of the remaining 86 , 73 also had a homolog in at least one in the four available genomes of the hominis group ( M . pulmonis , M . mobile , M . synoviae , and M . hyopneumoniae ) ( Table S2 ) and 13 in other mollicutes or bacteria ( Tables S2 and S3 ) ., Further phylogenetic tree reconstruction showed that 75 out of 86 CDSs display highly significant bootstrap values ( ≥ 90% ) supporting HGT with homologs of the mycoides cluster ., Among the 11 CDSs with low bootstrap values , six belong to gene clusters in which synteny is conserved in the mycoides cluster , three belong to an ICE element ( see below ) found in M . agalactiae and M . capricolum subsp ., capricolum and two others were not further considered , suggesting that ∼134 CDS have undergone horizontal gene transfer in between mycoplasma ( s ) of the mycoides cluster and M . agalactiae or its ancestor ., Of the predicted transferred CDSs , nine and 22 have a homolog either in M . mycoides subsp ., mycoides SC or in M . capricolum subsp ., capricolum , respectively , while 102 have homologs in both species ., Phylogenetic analysis and similarity comparisons of the 102 CDSs did not allow us to conclude whether they were more similar to M . mycoides subsp ., mycoides SC or to M . capricolum subsp ., capricolum ( Figure S2 ) ., Additionally , one CDS ( MAG4270 ) had a homolog only in M . mycoides subsp ., capri , for which only a limited number of sequences are available ., The occurrence of HGT was further supported by the genomic organization in M . agalactiae of 115 of the predicted transferred genes that occur as clusters containing two to 12 elements with approximately half of them displaying the same organization as in M . mycoides subsp ., mycoides SC and M . capricolum subsp ., capricolum genomes ., Eleven of these clusters , which are distributed all over the M . agalactiae genome , are shown in Figure, 2 . As previously mentioned , 73 of the predicted transferred CDSs have an ortholog in genomes of the hominis group ., In a hypothesis regarding transfer from the mycoides cluster to M . agalactiae , one might expect to detect pseudo-paralogs 37 in the M . agalactiae genome , with one inherited from an ancestor of the hominis group , while the other was acquired by HGT ., Indeed , in 17 unambiguous cases , vertically and horizontally inherited pseudo-paralogs were found ., As an example , the gene encoding the glucose-inhibited division protein is present as a single copy in the genomes of M . pulmonis , M . synoviae , M . mobile , and M . hyopneumoniae ., In M . agalactiae , two copies of this gene were found; one , MAG2970 , has a BBH in M . pulmonis , while the other , MAG1470 , has a BBH in M . mycoides subsp ., mycoides SC ., The oligopeptide ABC transporter locus ( opp genes ) is another interesting example , since opp genes occur twice in M . agalactiae , at two distinct loci ., As shown in Figure 3 , one opp locus ( designated as the type 1 ) is composed of four opp genes ( B–D and F ) , the sequences of which are highly similar to those of one of the two M . pulmonis opp loci ., The other opp locus of M . agalactiae ( type 3 , Figure 3 ) is composed of five opp genes ( A–D and F ) , the sequences and organization of which are closer to one of the two opp gene loci of M . capricolum subsp ., capricolum and M . mycoides subsp ., mycoides SC ., Phylogenetic analyses of the oppB genes of types 1 and 3 with homologous sequences of other mycoplasma species suggest different origins for the two M . agalactiae opp loci ., While the type 1 was inherited from a common ancestor of the hominis branch , the type 3 was laterally acquired from the mycoides cluster ., A third , isolated , copy of the oppB gene ( MAG4700 ) was predicted in the M . agalactiae genome , and might represent a relic of a displaced opp operon , as its best orthologs were found in mycoplasmas of the hominis group ., For CDSs found only once in the genome of M . agalactiae , the situation might be more complex , as illustrated by the glycerol kinase/glycerol uptake facilitator operon , glpK–glpF ( MAG4470–MAG4480 ) , which was unambiguously found to originate from a mycoides ancestor ( Figure S3 ) ., This operon occurs as a single copy in all mycoplasma genomes of the hominis group but is absent from M . synoviae ., Because of the relative phylogenetic closeness of M . agalactiae and M . synoviae ( Figure 1B ) , the question arises as to whether glpK–glpF was lost in their common ancestor and acquired later on by M . agalactiae from the mycoides cluster ., While examining M . agalactiae candidates for HGT , sequence alignments showed that 38 are truncated versions of their homologs in M . capricolum subsp ., capricolum and M . mycoides subsp ., mycoides SC , or were annotated as pseudogenes ( Table S2 and Figure S2 ) ., Additionally , only 14 CDSs were suspected to have undergone HGT between M . agalactiae and species of the pneumoniae phylogenetic group or non-mollicute bacteria ( Table S3 ) ., Since restriction–modification ( RM ) systems serve in bacteria as a tool against invading DNA 38 , it was of interest to specifically search for these systems in light of the high level of HGT in M . agalactiae ., One locus encoding a putative RM system is composed of six genes with homology to type I RM systems ( Figure S4 ) and was designated hsd ., It contains, ( i ) two hsdM genes ( MAG5650 and MAG5730 ) , coding for two almost identical modification ( methylase ) proteins ( 94% identity ) , which would methylate specific adenine residues;, ( ii ) three hsdS genes ( MAG5640 , MAG5680 , and MAG5720 ) , each coding for a distinct RM specificity subunit ( HsdS ) that shares homology with the others ( between 50% to 97% similarities ) ; and, ( iii ) one hsdR pseudo-gene ( MAG5700/MAG5710 ) , which is interrupted in the middle by a stop codon and would otherwise encode a site-specific endonuclease ( HsdR ) ., Finally , the hsd locus contains two hypothetical CDSs ( MAG5660 and MAG5670 ) and one gene ( MAG5690 ) , whose product displays 76 . 9 % similarity to a phage family integrase of Bifidobacterium longum 39 and motifs found in molecules involved in DNA recombination and integration ., In M . pulmonis , the hsd locus has been shown to undergo frequent DNA rearrangements but the gene encoding the putatively involved recombinase is located elsewhere on the genome 26 , 40 ., Apart from this locus , only three other unrelated M . agalactiae CDSs display similarities with the restriction–modification system , one of which was annotated as a pseudogene ., Mycoplasma lipoproteins are of particular interest because they have been proposed to play a role in the colonization of specific niches and in interaction with the host 11 , 41 ., In order to identify the putative lipoproteins encoded by the M . agalactiae genome , we combined results obtained by PS-SCAN analysis with the detection of a signature that was defined by using MEME/MAST software and a set of previously identified mycoplasma lipoproteins ( see Material and Methods ) ., This strategy resulted in the prediction of 66 lipoproteins , 85% of which were annotated as hypothetical proteins ., The remaining 15% correspond to the previously characterized Vpmas , P40 , P30 , and P48; and to two CDSs homologous to the substrate-binding protein of an oligopeptide ( OppA , MAG0380 ) and to an Alkylphosphonate ABC ( MAG5030 ) transporter , respectively ., Among the genes encoding the 66 predicted lipoproteins , our analyses indicated that the corresponding genes of 19 have undergone HGT with the mycoides cluster ( see Tables S2 , S5 , and S6 ) ., These 19 CDSs were annotated as hypothetical proteins , however , four ( MAG2430 , MAG3260 , MAG6480 , and MAG7270 ) share a high level of similarity , and constitute , with nine other polypeptides ( MAG0210 , MAG0230 , MAG1330 , MAG1340 , MAG3270 , MAG4220 , MAG4310 , MAG6460 , and MAG6490 ) , a protein family ., A MEME/MAST analysis indicated that the 13 proteins of this family shared one to ten repeats of a 25 amino-acid motif A ( KNWDNTVSNVTNDMSSMFxGAKKSFNQDNILS ) ( Figure S5 ) ., This motif is highly similar to the DUF285 domain of unknown function predicted in a large number of mycoplasma lipoproteins and found only in the mycoides cluster and in some non-mollicute bacteria ( i . e . , Listeria monocytogenes , Enterococcus faecalis , Lactobacillus plantarum , and Helicobacter hepaticus ) ., A second motif B ( FMPKNVTKVKVPKELPELEKIVTSLEKAFKGN ) was also found in most of the family proteins ., Of the 13 members of the family , whose corresponding genes are distributed all over the chromosome , five were predicted to be lipoproteins; the others may constitute a reservoir of sequence to generate surface variability ., Altogether , these data suggest that M . agalactiae has inherited a family of genes encoding potentially variable lipoproteins that are otherwise specific to the mycoides cluster ., Another remarkable lipoprotein family is found in the portion of the genome ( MAG7050–MAG7100; Figure S4 ) that encodes the phase-variable , related Vpma products ., The Vpma family has been extensively described 29 , 30 and was previously shown to present typical elements of mobile pathogenicity islands 29 ., However , comparison of the Vpmas coding sequences with other mycoplasma genomes indicate that they are specific of the M . agalactiae species , although their variation in expression and genetic organization closely resembles the Vsp system found in the close relative M . bovis 42–44 ., No similar system or coding sequences was found in the mycoides cluster ., To our knowledge , attempts to naturally transform M . agalactiae or other mycoplasma species have failed , suggesting that HGT , if it occurs , is mediated via another mechanism ., Only a limited number of viruses or natural plasmids have been described so far in mycoplasmas that could account as vehicles for HGT , apart from a new ICE that has been described in a few Mycoplasma spp ., 12 , 15 ., In a recent study , we documented the occurrence of such an element in M . agalactiae strain 5632 ( ICEA5632 ) as chromosomal multiple copies and as a free circular form 16 ., One copy , ICEA5632-I , was fully sequenced and Southern blot analyses suggested that it occurs in a minority of strains that did not include the PG2 type strain 16 , 45 ., However , detailed sequence analyses performed in this study revealed that 17 CDSs of the M . agalactiae PG2 genome display different levels of similarities to CDSs present in ICEA5632-I and in other ICEs ( Table S4 ) found in M . capricolum subsp ., capricolum ( ICEC ) , M . fermentans ( ICEF-I and –II ) 15 , and M . hyopneumoniae strain 7448 ( ICEH ) 12 ., These seventeen CDSs are clustered in the PG2 genome within a unique 20-kb locus , ICEAPG2 ( Figure 4 ) , and those with an ortholog in M . fermentans ICEF and/or M . agalactiae ICEA5632-I were designated as in previous reports 15 , 16 ., Surprisingly , best alignments for ICEA products of the PG2 strain were consistently obtained with M . capricolum subsp ., capricolum ICEC counterparts , with an average of 40% identity and 75% similarity , whereas alignments with ICEA5632-I or ICEF gave lower values ., This close relationship between ICEAPG2 and ICEC was confirmed by bootstrap values of the phylogenetic trees inferred from the amino acid sequence of TraG , TraE , ORF19 , and ORF22 ( Figure S6 ) ., Moreover , ICEAPG2 and ICEC share three homologous CDSs ( noted as x , y , and z in Figure 4 ) lacking in ICEA5632-I and other ICEs ., All these results indicate a close relationship between ICEAPG2 and ICEC , and suggest that the ICEs found in strains PG2 and 5632 have a different history ., In strain PG2 , the gene encoding TraE ( MAG3910/MAG3920 ) , a major actor in DNA transport across the conjugative pore , was found to be disrupted ., In addition , a total of 11 out of the 20 ICEAPG2-CDSs might represent pseudogenes ( hatched arrows in Figure 4 ) , due to the presence of stop codons and/or frameshifts ., Finally , regions directly flanking ICEAPG2 do not display the typical motifs found on each side of integrated ICEF and ICEA5632 ., These data strongly suggest that ICEAPG2 is unlikely to be functional ., In M . agalactiae strain 5632 , ICEA5632-I excision leads to a chromosomal site that is reorganized into an “empty” locus carrying remnant motifs that cover a 476-bp sequence 16 ., Interestingly , in the PG2 chromosome , a 476-bp sequence located ∼ 270 kb upstream from ICEAPG2 was found that is 94% identical to the sequenced “empty” ICEA5632-I locus , and includes the putative remnant motifs in the same order and spacing ( Figure S7 ) ., Unfinished sequence data from the strain 5632 reveals that this 476-bp sequence is actually part of a larger ( ∼40 kb ) synthenic region between PG2 and 5632 ., The high number of CDSs predicted to have undergone HGT between M . agalactiae and organisms of the mycoides cluster prompted us to examine possible HGT events among other mycoplasma species whose genomes have been sequenced ., For each mycoplasma genome , the CDSs with a BBH in a phylogenetic group different from that of the query were then identified ( see Materials and Methods ) ., Phylogenetic analyses , when possible , were applied to detect which , among the identified CDSs , were candidates for HGT ( Table 2 ) ., Overall , this analysis clearly pointed out two cases of significant HGT levels , between the mycoides cluster and M . agalactiae and between M . gallisepticum and M . synoviae ., Detailed examination of the data revealed a clear picture for M . synoviae , in which all identified CDSs but one designate M . gallisepticum as the HGT partner ( Tables 2 , S8 , and S9 ) ., This is confirmed by the reciprocal data in M . gallisepticum , although in several cases the phylogeny was not strong enough to support with certainty a direct association with M . synoviae ., These data are consistent with a previous study in which HGT between those two species was suspected 12 ., No significant HGT was detected among other mycoplasma species across phylogenetic groups apart from that described above between M . agalactiae and mycoplasmas of the mycoides cluster ( see also Tables S5 and S6 ) ., For the human mycoplasma M . penetrans , which has the largest genome of the dataset , a fairly large number of CDSs had BBH in a phylogenetic group other than the pneumoniae group ., However , none of these candidates for HGT were confirmed by further phylogenetic analysis ., Sixteen genome sequences from different mycoplasma species are now available in public databases and provide comprehensive data for comparative genomic studies that will , for instance , contribute to the understanding of their intriguing regressive evolution ( by loss of genetic material ) from Gram-positive bacteria with low GC content ., Indeed , mycoplasmas are thought to be fast-evolving bacteria , as supported by their positioning on some of the longest branches of the bacterial phylogenetic tree 21 ., This observation is in agreement with their small genome size , and hence with their limited DNA-repair capabilities 46 ., Consequently , mycoplasma genomes would be prone to accumulate mutations that would contribute to further downsizing ., In this scenario , acquisition of new genes by HGT was not considered to play a major role in mycoplasma evolution ., Indeed , statistical analyses predicted that the smallest proportion of HGT occurred among bacteria in symbiotic or in parasitic species , including mycoplasmas 47 ., Nonetheless , a few remarkable cases of HGT involving mycoplasmas have been described that include the independent displacements of the rpsR and ruvB genes with orthologs from ɛ–Proteobacteria 48 , 49 and the horizontal transfer of the surface-protein VlhA encoding gene among three phylogenetically distant mycoplasmas ( M . gallisepticum , M . imitans , and M . synoviae ) , which are respiratory pathogens of gallinaceous birds 50 , 51 ., More recently , sequencing of the M . synoviae genome suggested that ∼3% of the total genome length has undergone HGT in between M . gallisepticum and M . synoviae 12 ., Analyses performed in this study confirmed this trend using a different approach , which estimated that ∼3%–8 % of their CDS have been involved in HGT in between the two avian species ., However , these values are much lower than the ones found for M . agalactiae , in which 10%–18% of its coding genome was predicted to have undergone HGT with mycoplasmas belonging to the mycoides cluster ., This proportion represents , to our knowledge , the highest extent of HGT for a bacterium with a small genome size ( <1 Mb ) ., The scattering of the HGT loci all over the M . agalactiae genome suggests the occurrence of multiple HGT events and/or the shuffling via intrachromosomal recombination events of alien genes after integration ., Although HGT events could be confirmed by phylogenetic analyses , it was not possible to identify significant biases in the GC composition of the transferred genes that would distinguish them from ancestral genes ., It is likely that the HGT events in M . agalactiae did not take place recently and/or that the acquired sequences quickly adjusted to their new genome pattern ., In fact , it has been shown that the bias in GC content is not a reliable indicator for detecting HGT events 52 , 53 ., Demonstrating the acquisition of genes by HGT is not trivial , especially among mycoplasma species that share a number of genetic features and are phylogenetically clustered ., Analyses of the M . agalactiae genome with respect to HGT with mycoplasmas of the mycoides cluster revealed roughly two categories of CDSs: one composed of CDSs with several homologs and their BBH within the mycoides cluster , and one composed of CDSs that have few or no homologs but are highly similar to CDSs of the mycoides cluster ., While for the first category , phylogenetic tree reconstruction can demonstrate or refute HGT , the issue is more delicate for the second ., For instance , 50 CDSs of M . agalactiae have no homolog other than in the phylogenetically distinct mycoides cluster , raising the question of whether these genes were laterally acquired from these mycoplasmas or from a third common partner that has yet to be identified ., In addition , sharing the same host might have resulted in M . agalactiae and mycoplasmas of the mycoides cluster retaining a common ancestral set of genes that were lost in all other species that do not colonize ruminants ., Although these alternative hypotheses cannot be formally ruled out , they all imply a series of parallel , independent events ., Taking into account that M . agalactiae , when compared to other sequenced mycoplasmas species of the same phylogenetic group ( Figure 1B ) , is located on one of the most ramified branches of the phylogenetic tree , this scenario seems unlikely ., The more global analyses performed on the available genomes from mollicutes ( with the exception of phytoplasmas ) and on M . agalactiae identified four species in which HGT has taken place ., Detailed results clearly identified only two pairs of partners , each from a different phylogenetic group:, ( i ) M . agalactiae and the mycoplasmas of the mycoides cluster , and, ( ii ) all mycoplasma pathogens of ruminants and M . gallisepticum and M . synoviae , two pathogens of poultry ., This striking observation tends to indicate that mycoplasmas sharing a common host have the capacity to exchange genetic material ., These mycoplasma species are the only ones sequenced thus far that are located in different phylogenetic groups but share the same lifestyle in terms of ecological niches ( Table S1 ) ., Indeed , other sequenced species that share the same host all clustered into the same phylogenetic group ( human mycoplasmas of the pneumoniae group ) and therefore our approach will not detect HGT among these mycoplasmas ., For one human mycoplasma , M . penetrans , a number of putative HGTs were found ( see Tables 2 and S7 ) but none were supported by phylogenetic analyses ., The occurrence of HGT among human mycoplasma species cannot be dismissed by this study and remains to be investigated ., A striking feature of the HGT in this bacterium is that nearly all the events were predicted to have occurred with species of the mycoides cluster , which are , with M . agalactiae , pathogens for ruminants ., Sharing this common environment would have favored the transfer of genetic material between these mycoplasmas and the fixation of genes leading to an increased fitness as parasites of ruminants ., Interestingly , ∼30 % of the genes that have undergone HGT with a mycoides ancestor correspond to membrane-associated proteins , including several transporters or lipoproteins ( Table 3 ) ., As surface proteins such as lipoproteins are supposed to play a major role in the mycoplasma–host interaction , this finding supports the proposal that genes acquired by HGT may have significantly favored the colonization of ruminants by the mycoplasma ., Noticeably , a family of 13 CDSs of M . agalactiae has undergone HGT with the mycoides cluster ., The predicted proteins contain repeats of a domain of unknown function ( DUF285 ) ., The distribution of this domain in mycoplasmal proteins is strictly restricted to species belonging to the mycoides cluster ., Whether this family , which includes several lipoproteins , participates in the interaction between the mycoplasma and its ruminant host remains to be elucidated ., At present , it remains rather difficult to evaluate the selective advantage that could h

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

Mycoplasmas are commonly described as the simplest self-replicating organisms , whose evolution was mainly characterized by genome downsizing with a proposed evolutionary scenario similar to that of obligate intracellular bacteria such as insect endosymbionts ., Thus far , analysis of mycoplasma genomes indicates a low level of horizontal gene transfer ( HGT ) implying that DNA acquisition is strongly limited in these minimal bacteria ., In this study , the genome of the ruminant pathogen Mycoplasma agalactiae was sequenced ., Comparative genomic data and phylogenetic tree reconstruction revealed that ∼18% of its small genome ( 877 , 438 bp ) has undergone HGT with the phylogenetically distinct mycoides cluster , which is composed of significant ruminant pathogens ., HGT involves genes often found as clusters , several of which encode lipoproteins that usually play an important role in mycoplasma–host interaction ., A decayed form of a conjugative element also described in a member of the mycoides cluster was found in the M . agalactiae genome , suggesting that HGT may have occurred by mobilizing a related genetic element ., The possibility of HGT events among other mycoplasmas was evaluated with the available sequenced genomes ., Our data indicate marginal levels of HGT among Mycoplasma species except for those described above and , to a lesser extent , for those observed in between the two bird pathogens , M . gallisepticum and M . synoviae ., This first description of large-scale HGT among mycoplasmas sharing the same ecological niche challenges the generally accepted evolutionary scenario in which gene loss is the main driving force of mycoplasma evolution ., The latter clearly differs from that of other bacteria with small genomes , particularly obligate intracellular bacteria that are isolated within host cells ., Consequently , mycoplasmas are not only able to subvert complex hosts but presumably have retained sexual competence , a trait that may prevent them from genome stasis and contribute to adaptation to new hosts .

Mycoplasmas are cell wall–lacking prokaryotes that evolved from ancestors common to Gram-positive bacteria by way of massive losses of genetic material ., With their minimal genome , mycoplasmas are considered to be the simplest free-living organisms , yet several species are successful pathogens of man and animal ., In this study , we challenged the commonly accepted view in which mycoplasma evolution is driven only by genome down-sizing ., Indeed , we showed that a significant amount of genes underwent horizontal transfer among different mycoplasma species that share the same ruminant hosts ., In these species , the occurrence of a genetic element that can promote DNA transfer via cell-to-cell contact suggests that some mycoplasmas may have retained or acquired sexual competence ., Transferred genes were found to encode proteins that are likely to be associated with mycoplasma–host interactions ., Sharing genetic resources via horizontal gene transfer may provide mycoplasmas with a means for adapting to new niches or to new hosts and for avoiding irreversible genome erosion .

infectious diseases, microbiology, evolutionary biology, genetics and genomics, eubacteria

journal.pgen.0040002

Dissecting the Genetic Components of Adaptation of Escherichia coli to the Mouse Gut

Bacterial populations are powerful model to explore the mechanisms of evolution ., Several in vivo experiments have pointed to the possible important role of pleiotropic adaptive mutations , but their molecular basis remain in most of cases largely elusive 1–3 ., Here we have used gnotobiotic mice that offer a simplified and controlled albeit ecologically relevant experimental environment model to analyse the adaptation of E . coli MG1655 to the gut , as E . coli is usually the first colonizer of the mammalian newborn germ-free intestine 4 , 5 ., Taking advantage that this laboratory strain is entirely sequenced and easily accessible to genetic manipulations , we could design a study that allowed deciphering the beneficial effects of pleiotropic mutations during intestinal colonisation ., The mammalian intestine is a privileged physiological site to study how coevolution between hosts and the trillions of bacteria present in the microbiota has shaped the genome of each partner and promoted the development of mutualistic interactions ., Genetic adaptation to the host over the millions years of coevolution has translated into physiological regulatory pathways that are rapidly mobilized in response to intestinal colonization 6–9 ., In the microbiota , the contrast between the considerable number of species , more than a thousand , and the small number of bacterial divisions 10 , indicates that coevolution has selected bacterial genera possessing the genetic gear to adapt to the host environment , a notion supported by recent evidence that gut habitats in different host species dictate distinctive structures of intestinal bacterial communities 11 ., Yet , the intestine is a complex and highly dynamic ecosystem composed of a large diversity of niches that vary in space and time , where bacteria face a permanent adaptive challenge ., Furthermore , intestinal bacteria must be able to hurdle between their hosts across the exterior environment and for certain such as E . coli to switch between two entirely distinct natural environments ., Gnotobiotic animals that offer a simplified albeit relevant model to study reciprocal mechanisms of adaptation between bacteria and their hosts , within a few days , the host can only adapt via physiological changes , whereas bacteria can adapt both by gene regulations and adaptive mutations ., Indeed , we have previously demonstrated that adaptive mutations are central for efficient intestinal colonization by E . coli MG1655 12 ., Here we show that adaptation of this strain of E . coli during intestinal colonization entails rapid and parallel evolution in the EnvZ/OmpR two-components transduction system 13 ., The gain of fitness provided by the diverse mutations selected in this global regulator during in vivo colonization results mainly from two distinct and measurable effects on motility and permeability that are both reduced in the mutant strains selected in the gut environment ., These findings suggest that evolutionary pressures can put a diverse set of physiological functions facilitating adaptation under the control of one global regulator , and that mutations permit to adjust the scale of the physiological regulation controlled by this regulator in a given environment ., We have shown that adaptive mutations play a critical role in the success of the E . coli MG1655 strain in colonizing of the mouse gut 12 ., A possible clue to the nature of the mutation ( s ) selected during colonization ensued from our subsequent observation of bacteria with a reduced motility phenotype in the feces of all gnotobiotic mice colonized with the wild type MG1655 strain ( WT ) ( Figure 1A ) ., The colonies displayed a new small and granular morphotype ( SG ) distinct from the large and smooth morphotype ( LS ) of the WT inoculated strain ( Figure S1 ) ., SG colonies forming bacteria , undetected in the initial inocula , appeared in the feces within two days , and reached a prevalence of 90% within seven days ( Figure 1B ) ., Their phenotype remained stable when grown in vitro over many generations , indicating that it was heritable and may result from the rapid in vivo selection of mutation ( s ) ., In order to identify the potential mutations responsible for the SG morphotype , a clone forming SG colonies ( SG1 ) isolated from mouse feces two days post-colonization , was transformed with a genomic DNA plasmid library generated from the parental WT strain ., All plasmids that restored the ancestral WT LS morphotype carried the ompB locus , coding for the membrane sensor EnvZ and the transcriptional regulator OmpR of a two-component signal transduction system central to the osmolarity-dependent regulation of genetic expression 13 ., A chloramphenicol resistance gene ( cat ) , inserted downstream the ompB locus in the chromosome of the WT ancestral and the SG1 strains , co-transduced with a 95% frequency with the morphotype ( LS or SG ) , indicating that in the SG1 strain , the DNA region surrounding cat was responsible for the SG morphotype ., This region was sequenced for one SG and one LS clone harvested from the feces of each of the 8 independent mice inoculated with either MG1655 or an MG1655 E . coli strain carrying a yellow fluorescent protein ( YFP ) as reporter of fliC expression ( MG1655pfliC-YFP ) ( see below ) ., While no mutation was detected in LS clones , all SG clones displayed a different missense point mutation , seven located in envZ , and one in ompR ( Table1 ) ., The independent systematic and rapid selection of mutations in the same genes under identical experimental conditions is evidence for a strong selective advantage of the mutants during gut colonization 1 ., To confirm and estimate the relative fitness of the SG1 mutant versus the ancestral strain in the mouse gut , we performed in vivo competition experiments between strains isogenic except for the point mutation present in the envZ gene of the SG1 strain ( SG1 mutation ) and the inducible fluorescent marker ( RFP vs . GFP ) ., Prior experiments have indicated that these inducible markers do not induce any selection bias 14 ., The ratio of mutant ( GFP ) to WT ( RFP ) colonies was defined after culture of the feces and ex vivo induction of the fluorescent marker ., Competition experiments using initial ratios of mutant to WT strain of 1:1 , 1:100 and 1:1 , 000 indicated that the SG1 mutation confers a considerable fitness gain ( Figure 1C ) ., With the assumption that the mean generation time for E . coli in the gut is 60 minutes 15 , the selective advantage of the SG1 mutation was estimated to be 24% when the mutant to WT strain ratio remained under 1:10 ( Table S1 ) ., These data explained how adaptive mutations in envZ , that are likely to happen at a frequency below 10−7 , can be very rapidly selected upon colonisation with the WT strain ., The selective advantage of the SG1 mutation decreased to approximately 10% when the ratio of mutant to WT strain increased over 1:10 , indicating that the selective advantage conferred by the mutation is frequence-dependent , consistent with the observation that the WT strain is not entirely displaced in the mono-colonization experiment ( Figure 1B ) ., Importantly , the selected mutants did not exhibit the same motility phenotype as null mutations , since strains deleted for envZ , ompR or both kept the wild type LS morphotype ( Figure S1 ) ., The membrane receptor kinase-phosphatase EnvZ forms a two-component pair with its cognate response regulator , OmpR , that enable cells to sense external changes of osmolarity 13 ., The native receptor exists in two active but opposed signalling states , the OmpR kinase-dominant state and the OmpR-P phosphatase-dominant state ., The balance between the two states determines the level of intracellular OmpR-P , which in turn determines the level of transcription of the many target genes 13 ., One important bacterial function controlled by OmpR is motility , as OmpR regulates transcription of the flhDC operon , the master regulator of flagellar biosynthesis 16 ., Several mutations identical to those selected in vivo during colonization were previously shown , by in vitro mutational analysis of EnvZ activities , to switch on the EnvZ kinase-dominant state 17 , 18 ( Figure 2 ) , resulting in increased levels of phospho-OmpR and repression of the flhDC operon 16 ., Consistent with repression of flagellin expression in all SG mutants , no flagellin could be detected in cell lysates or supernatants obtained from stationary phase cultures , while the ancestral WT strain and the LS colonies ( that kept the wild-type motility phenotype after mouse colonization ) synthesised large amounts of flagellin in the same in vitro conditions ( Figure 3B ) ., We have previously shown that the WT ancestral E . coli strain induces a potent NF-κB-dependent inflammatory response in intestinal epithelial cells that hinges on the interaction of flagellin with Toll receptor 5 19 ., Consistent with impaired flagellin expression , culture supernatants of SG strains in stationary conditions , failed to induce any inflammatory signal in monolayers of epithelial cells ( Figures 3A and S2 ) ., These in vitro observations showing repression of flagellin synthesis in SG mutants were thus compatible with the observed defective motility morphotype ., This morphotype was however clearly distinct from the pin point morphotype of the ΔfliC strain lacking the gene encoding flagellin , the primary flagellar subunit ( Figure S1 ) ., In order to confirm that flagellin was downregulated by SG mutants in vivo in the intestine , germ-free mice were inoculated with an MG1655 E . coli strain carrying a yellow fluorescent protein ( YFP ) as reporter of fliC expression ., The bacterial fluorescence in the feces was monitored in the feces by flow cytometry ., Fluorescence decreased rapidly in mice inoculated with the WT strain , demonstrating in vivo down modulation of flagellin ( Figures 4 and S3 ) ., Fluorescence monitoring after plating confirmed this result ., Thus , in mice inoculated with the WT strain , the fraction of fluorescent colonies decreased to an average of 10% within 8 days , consistent with the selection of SG mutants described above ( Figure 4B ) ., Furthermore , all bacteria forming non-fluorescent colonies tested on motility plate exhibited an SG morphotype , while those forming fluorescent colonies retained the LS morphotype ( Figure 4B ) ., As OmpR/EnvZ controls many activities , we looked for other effects of the selected mutants ., The characteristic motility phenotype of the SG selected mutants could be a result of an enhanced aggregation of bacteria to each other via the production of curli fibres encoded by the csgBA operon whose expression is regulated by the OmpR regulated csgD gene 20 ., However , in contrast to the previously described ompR mutant of E . coli K12 that promotes biofilm formation via the derepression of the csgA gene 21 , none of the SG mutants exhibited changes in csgA gene expression and their biofilm formation was reduced compared to the WT strain ( data not shown ) ., Another essential function of the two-component system envZ/ompR is to modulate membrane transport and permeability in response to medium osmolarity 22 ., In particular , OmpR affects the reciprocal transcription of the small pore OmpC and large pore OmpF porins 23 , the two E . coli porins that are thought to play a central role in the adaptation of E . coli to the hyperosmotic conditions of the intestine 24 ., Consistent with mutations that switch on the OmpR kinase-dominant state of EnvZ , selected SG mutants had decreased ompF and increased ompC mRNA and membrane protein levels compared to the WT ancestral strain ( Table 1 , Figure 3C ) , i . e . a reduced permeability phenotype 23 ., Membrane permeability is central for both stress protection and nutritional competence 25 ., It has been postulated that reduced permeability would be favourable in the environmental conditions of the gut , consisting of high osmolarity , low oxygen pressure and the presence of bile salts 24 ., Indeed , all SG mutants grew much better than the ancestor in medium containing bile salts , the ancestor being entirely displaced within 7 hours of growth ( data not shown ) ., Transcriptome analysis has pointed to the potential role of the two-component EnvZ/OmpR system in the regulation of multiple genes , including genes involved in transport across membranes and cell metabolism 22 , which may perhaps promote intestinal adaptation of E . coli ., We therefore assessed the importance of flagellin repression and/or porins regulation on the parallel selection of envZ-ompR mutations ., To analyse the role of flagellin in the selection of SG mutants , germ-free mice were inoculated with either the WT or the ΔfliC strain carrying a fluorescent protein ( YFP ) as reporter of fliC expression ., Flow cytometry analysis of the feces showed that in situ fluorescence decreased faster and more extensively in mice inoculated with the WT than with the ΔfliC strain ( Figures 4A and S3 ) , a result confirmed by fluorescence monitoring after plating ( Figure 4B ) ., Thus , in mice inoculated with the ΔfliC strain , the fraction of fluorescent colonies had decreased to only 50% on day 8 as compared to 10% in mice inoculated with the WT strain and the kinetics of selection was slower ( Figure 4B ) ., Altogether , these results point to a strong impact of flagellin on the selection of EnvZ mutations ., However , mutations downregulating fliC expression could still be selected despite the absence of flagellin , presumably because of the pleiotropic effect of these mutations ., Sequencing the ompB locus in non-fluorescent clones harvested from 4 mice inoculated with the ΔfliC strain revealed missense point mutations ( Table 1 ) ., Three were located in envZ , including one identical to a mutation found in a clone isolated from a mouse inoculated with the WT strain ., The fourth one was located in the same codon of ompR as the mutation identified in a clone derived from the WT strain ( Table 1 ) ., These results show that the adaptive advantage conveyed by selected mutations is only partially flagellin-dependent , suggesting that selected mutations provide further advantage resulting from the modulation of other genes controlled by OmpR ., One likely candidate was the large porin encoding gene ompF ., Indeed we have observed that this gene expression is downmodulated by the selected envZ-ompR mutations , resulting in a reduced permeability phenotype known to be associated with increased resistance to bile salts 26 , as observed for SG mutants ., To assess the role of OmpF in the selection of EnvZ mutations , mice were inoculated with a ΔompF mutant that expresses OmpC but no OmpF protein ( Figure 3C ) and carries the YFP reporter of fliC expression ., Although the impact of OmpF deletion alone was not as strong as the one of flagellin , selection of non fluorescent mutants studied in the feces after plating was significantly less efficient than in mice colonized with the WT E . coli strain ( Figure 5 ) ., In one out of five studied mice , all non-fluorescent mutants exhibited an SG phenotype in soft agar plates ., In two other mice , the non-fluorescent colonies had a totally nonmotile ( NM ) pinpoint phenotype comparable to the ΔfliC-engineered strain ( Figure S1 ) ., In the last two mice , both SG and NM morphotypes were observed ., Sequencing the ompB locus revealed a missense mutation in envZ in all SG clones tested ( Table 1 ) ., In contrast , NM clones forming pin-point colonies had a normal envZ sequence but contain large deletions from 1 . 5 to 12 kb between the otsA and cheB loci , encompassing the flhDC operon and thereby precluding any expression of the whole flagellum operons ( Figure 6 ) ., Interestingly , all deletions had occurred immediately upstream of an Insertion Sequence ( IS1 ) located just upstream the flhDC operon , and probably reflecting an imprecise excision of the IS 27 ., The deleted genes , that all belong to the chemotaxis/motility pathway , failed to be amplified by PCR ( data not shown ) , showing that they were indeed lost rather than inserted ectopically ., These results show that in mutants with reduced permeability , the major fitness gain results from repression of gene ( s ) controlled by FlhDC , probably flagellar genes and in particular the fliC gene encoding flagellin ., To confirm this hypothesis , mice were inoculated with double ΔfliC ΔompF mutants carrying the YFP reporter of fliC expression and expressing the fluorescent CFP protein under the control of a constitutive promoter ., Strikingly , combining deletions of porins and flagellin had additive effects and almost entirely abolished the in vivo selection of EnvZ/OmpR mutants ( Figure 5 ) ., At day 11 post-inoculum , only 9% of clones were YFP-negative ., None had mutation in the ompB locus , a deletion in the flhDC region or a mutation in the pfliC-YFP construct ., These YFP-negative clones were all CFP positive and remained CFP positive during the 100 days of observation ., Since YFP and CFP were expressed at the same level in the inoculated strain , the hypothesis that YFP expression was costly for the bacteria and eliminated by mutations is unlikely ., Therefore , our results show that the selective pleiotropic advantage conferred EnvZ/OmpR mutation predominantly results from a combined effect of modulation of fliC expression and membrane permeability , but does not exclude minor additional effect ( s ) of ( an ) other as yet uncharacterized gene ( s ) under the control of EnvZ/OmpR ., Due to their high growth rate and large population size , microbes have a remarkable capacity to evolve and diversify by generation and spread of mutations that improve their fitness in a given environment 1 ., We have previously observed that within a few days a mutant strain with a high mutation rate increased in frequency to the expense of the parental commensal E . coli MG1655 strain during gut colonization ., In contrast , the mutator strain lost the competition against a clone collected from the feces of mice colonized for 40 days with the parental commensal E . coli MG1655 12 ., These results suggested that adaptive mutations enable bacteria to rapidly and efficiently cope with the drastic environmental changes encountered during gut colonization ., Our novel results identify the central role of the EnvZ/OmpR regulon in the physiological adaptation of E . coli MG1655 to the gut environment , and show that adaptive mutations in this two-component system provide an additional gear to adjust precisely the scale of the physiological regulation controlled by this regulator to the gut environment ., Furthermore our results provide the molecular basis of the beneficial effects of the pleiotropic mutations in EnvZ/OmpR in adaptation of E . coli MG1655 to the mouse gut ., Mutations in the envZ/ompR locus were systematically detected in 90% of bacteria harvested from independent mice feces within a week of colonization with WT E . coli MG1655 ., Except for one mutation in its cognate transcription factor OmpR , all mutations were found in the membrane sensor EnvZ ., The major fitness gain conferred by these mutations was confirmed by in vivo competitions between the ancestor WT strain and an isogenic mutant strain harboring the prototype SG1 envZ mutation ., The emergence of distinct point mutations at the same two-component locus in bacterial populations evolving in different colonized mice suggested a comparable impact on the physiological effects mediating the fitness gain due to these mutations ., Indeed all mutations resulted in profound repression of flagellin expression and modulation of OmpF versus OmpC porin expression yielding a reduced permeability phenotype ., This phenotype is typical of mutations that switch the phosphatase/kinase membrane sensor EnvZ toward a OmpR kinase-dominant state ., Indeed several of the missense mutations selected during in vivo colonisation were previously identified by in vitro mutational analysis as turning on this functional state 17 , 18 ., Mutations selected during colonization were not restricted to the catalytic domains of EnvZ , but were also found in the periplasmic sensor and cytoplasmic linker domains , highlighting the participation of all of the proteins domains in the control of gene regulation ( Figure 2 ) ., Interestingly in mice colonized with the ΔfliC mutant , where adaptive mutants were mainly selected on their reduced permeability phenotype , mutations were still exclusively found in the EnvZ/OmpR system , a result that underscores the prominent role of the EnvZ/ompR system in the regulation of membrane permeability of E . coli MG1655 during intestinal colonization ., Notably , colonization with the WT E . coli did not select for mutations inactivating genes specifically controlling motility or permeability ., Yet , selection of mutants with deletion of flhD/C operon was observed during colonization by the ΔompF strain , a result reminiscent of observations in streptomycin-treated mice 28 , 29 ., Clonal interference 30 thus likely prevents the selection of mutations affecting only one function , presumably associated with smaller selective value than the pleiotropic mutations in envZ/ompR modulating simultaneously functions as different as permeability and motility ., Indeed , using reporter mutant bacteria carrying a fluorescent protein under the control of the fliC promoter , we could clearly demonstrate that the selective advantage conveyed by mutations in envZ/ompR resulted from their pleiotropic and additive effects on the repression of flagellin production and OmpF porin expression ., The almost complete abolition of adaptive selection of envZ/ompR mutations in mice colonized with a double mutant E . coli strain that lacks both fliC and ompF , underscores the major contribution of the pathways controlled by envZ and ompR in the intestinal adaptation of E . coli ., The precise elucidation of the selective forces is beyond the scope of this study , but likely scenarios are briefly discussed below ., Flagellin downregulation could be selected for via its pro-inflammatory role 19 , 31–35 , via its direct energetic cost 28 , 36 , or via still non-identified mechanisms ., The fitness gain conveyed by reduced permeability was suggested by in vitro analysis indicating that , similar to ΔompF mutants , all ompR/envZ mutants grew much better in medium containing high concentrations of bile salts , a major stress factor for bacteria in the intestinal lumen ., Interestingly , it has been reported that the concentration of biliary salts in the intestinal lumen decreases upon colonization 37 , 38 ., A lower concentration of biliary salts in mice treated by streptomycin which empties the enterobacteriae niche but does not deplete completely the intestinal flora , might explain the predominant selection of mutants in the flhD/C operon in this mouse model 28 , 29 ., In E . coli , stress protection comes at the cost of nutritional competence through the regulation of membrane permeability 39 ., In the gut rich environment , bacterial nutrient intake is likely sufficient even if permeability is restrained , so that the growth rate is not significantly affected 25 ., Yet , the extent of physiological regulation allowed by wild type EnvZ/OmpR might not be optimal to respond to our experimental mice gut conditions ., Thanks to adaptive mutations in EnvZ/OmpR , the trade-off between self-preservation and nutritional competence ( SPANC balance ) might easily be switched to either better resistance or faster growth 25 ., To mutate may thus represent a complementary genetic gear to adjust precisely the scale of physiological regulation controlled by a global regulator when switching between complex environments ., Notably , the selective advantage conferred by the envZ mutations was frequency dependent , consistent with the observation that in mice colonized with the WT strain , the mutation invades rapidly and massively the population , but does not go to fixation , as a minor part of the population kept the original colony morphotype ( and genotype for envZ-ompR ) ., These results suggest a mechanism causing the coexistence of ancestral and evolved form , perhaps because the ancestral phenotype confers some advantage to colonize a specific niche ., Work is in progress to address this issue ., Experiments with microbial populations have been largely used to gain insight into the mechanics of evolution and have pointed to the possible important role of pleiotropic adaptive mutations 1 ., Thus , finding mutations in regulatory genes is a recurrent observation both in natural populations and during in vitro experimental evolution , that led to postulate that mutations affecting regulators are more likely to promote adaptation and evolution than those improving a single enzymatic step 1 , 25 , 40 ., Our results obtained in an in vivo model of bacterial evolution supports this hypothesis ., As mutations in global regulators affect the regulation of many genes , they must be pleiotropic and are thus expected to result in the expression not only of beneficial but also of detrimental traits ., The molecular mechanisms responsible for the selection of such pleiotropic mutations have therefore remained largely elusive in most systems ., A recent study in a simple ecological in vitro model 41 , has shown that adaptive mutations allow P . fluorescens to occupy a novel ecological niche at the air-liquid interface 42 ., All selected strains had pleiotropic loss-of-functions mutations in one gene encoding a putative methyl-esterase in the wsp operon 2 , 3 ., Drawing analogy with the che operon of E . coli that encodes proteins homologous to the wsp operon , the authors suggested that this protein acts in concert with a putative methyl-transferase to adjust the activity of a kinase ., The mutations may thus destroy the capacity of the pathway to fluctuate between activity states , producing instead a steady state output allowing niche specialization ., Our results , combined with previous biochemical works 17 , provide direct evidence that a distinct scenario promotes the in vivo adaptation of an E . coli MG1655 to the gut of germ-free mice ., In the case of EnvZ/ompR , the two opposed enzymatic activities are exerted by the cytoplasmic domain of EnvZ and are modulated in response to signals sensed by the external domain of the protein ., Mutations in EnvZ , that directly affect the balance between two activities , are selected because of their independent and additive effects on genes controlling flagellin expression and membrane permeability ., Dissecting the fitness gain due to these independent pathways allowed us to demonstrate that the EnvZ/OmpR global regulator orchestrates the physiological adaptation of E . coli MG1655 to the gut environment ., More generally , the observation that the EnvZ/OmpR system gathers under its control genes central to promote intestinal colonization leads us to suggest that global regulators may have arisen during evolution to optimize the coordination of genes that collaborate to adapt to a given niche ., Mutations in such global regulators may provide a complementary genetic tool that allows bacteria to extend the scale of the physiological regulation and promotes their rapid adaptation when confronted to very specific environments ., All strains were derived from the commensal flagellated E . coli K12 MG1655 sequenced strain 43 ., The MG1655 ΔfliC E . coli isogenic mutant has been described 19 ., To construct the reporter WT pfliC-YFP strain used to monitor in vivo activity of fliC promoter , sequence encoding the fluorescent protein YFP++ 44 was cloned downstream the upstream region of the fliC gene ( pfliC: from nucleotides −230 to +5 relative to the translation start ) ., The fragment ( pfliC YFP , T1T2 and cat ) was flanked by 40 nucleotides sequences homologous respectively to the 5′ and 3′ of the IS2 and IS30 insertion sequences interrupting the ybdA E . coli gene and by KpnI and SphI restriction sites and cloned in p5Y , a pUC-18-derived plasmid ., After plasmid amplification , the fragment was inserted into the ybdA gene of the MG1655 E . coli chromosome replacing the IS sequences following method already described 45 ., MG1655 ΔfliC pfliC-YFP was constructed by P1 phage co-transduction of the pfliC-YFP-v+ and the cat alleles from MG1655 pfliC-YFP into MG1655 ΔfliC strain ., The MG1655 ompB-cat and SG1 ompB-cat E . coli strains ( used to assess the link between the ompB locus and the motility phenotype ) were constructed by inserting the FRT flanked cat gene of the pKD3 plasmid 45 between the envZ and pck genes as described 45 , using PCR primers that contained a 40 bases-5′ end extension centered on the translation stops of the envZ or pck gene ., Insertion of the PCR product was monitored using primers respectively identical or complementary to the nucleotides 1562 to 1582 of pck and 1238 to 1258 of the EnvZ gene ., These strains kept the motility phenotype of the MG1655 and SG1 strains respectively ., The MG1655 ptet-GFP ompBSG1-cat and MG1655 ptet-RFP ompB-cat E . coli strains ( used to measure the relative fitness of the SG1 strain in vivo ) were constructed by introducing by P1 phage co-transduction of the ompB region from the SG1 ompB-cat strain and the cat allele into the MG1655 ptet-GFP and the MG1655 ptet-RFP strains respectively ( described in 14 ) ., The MG1655 ptet-GFP ompBSG1-cat strain was selected among granulous transductants ( SG morphotype ) in motility agar whereas the MG1655 ptet-RFP ompB-cat was selected among transductants that kept the WT motility phenotype ( LS morphotype ) ., The ΔompF , ΔompC , ΔompR , ΔenvZ , and ΔompB strains were constructed by replacing the ompF , ompC , ompR , envZ and envZ and ompR open reading frame respectively from start to stop codon by the FRT flanked cat gene of the pKD3 in the E . coli MG1655 strain following method already described 45 ., The MG1655 ΔompF pfliC-YFP strain was constructed by P1 phage co-transduction of the ΔompF and the cat alleles from MG1655 ΔompF into MG1655 pfliC-YFP p2rrnB-CFP strain ., The MG1655 ΔfliC ΔompF pfliC-YFP strain was constructed by P1 phage co-transduction of the ΔompF and the cat alleles from MG1655 ΔompF into MG1655 ΔfliC pfliC-YFP p2rrnB-CFP strain ., To construct the reporter p2rrnB-CFP , sequence encoding the fluorescent protein CFP++ was cloned upstream of the promoter p2 of the rrnB operon ( p2rrnB: from nucleotides 152 to 94 relative to the translation start of the rrsB gene ) ., The fragment ( prrnB-cfp , T1T2 and cat ) was flanked by 40 nucleotides sequences homologous respectively to the 5′ and 3′ of the IntC ( IntS ) E . coli gene and by KpnI and PacI restriction sites and cloned in a pUC-18-derived plasmid ., After plasmid amplification , the fragment was inserted into the IntC gene of the MG1655 E . coli chromosome following method already described 45 ., Genomic DNA from E . coli MG1655 strain was prepared with the Wizard Genomic DNA Preparation kit ( Promega , Charbonnières , France ) and partially digested with the Sau3AI restriction enzyme ., Fragments ranging from 2 to 6 kb were eluted from agarose gel ( Gel extraction kit , Promega ) , and cloned into BamHI-digested and dephosphorylated pACYC184 plasmid ., The purified ligation reaction was used to electro-transform DH5-α E . coli ., Transformants were selected on LB plates containing chloramphenicol ., Ligation efficiency was 95% and average size of genomic inserts 3 Kb ., Plasmids were extracted from about 1 . 5 × 104 pooled colonies ( Miniprep kit , Promega ) ., The SG1 clone was transformed with the genomic library and transformants were selected on motility plates supplemented with chloramphenicol ., The clones with a wild type motility phenotype ( LS ) were isolated and the E . coli MG1655-derived locus carried by the transforming plasmids was determined by sequencing with primers flanking the cloning site ., Sequencing of the ompB locus ( from the greB translation stop to the pck translati

Introduction, Results, Discussion, Materials and Methods

While pleiotropic adaptive mutations are thought to be central for evolution , little is known on the downstream molecular effects allowing adaptation to complex ecologically relevant environments ., Here we show that Escherichia coli MG1655 adapts rapidly to the intestine of germ-free mice by single point mutations in EnvZ/OmpR two-component signal transduction system , which controls more than 100 genes ., The selective advantage conferred by the mutations that modulate EnvZ/OmpR activities was the result of their independent and additive effects on flagellin expression and permeability ., These results obtained in vivo thus suggest that global regulators may have evolved to coordinate activities that need to be fine-tuned simultaneously during adaptation to complex environments and that mutations in such regulators permit adjustment of the boundaries of physiological adaptation when switching between two very distinct environments .

The mammalian intestine is a privileged physiological site to study how coevolution between hosts and the trillions of bacteria present in the microbiota has shaped the genome of each partner and promoted the development of mutualistic interactions ., Herein we have used germ-free mice , a simplified albeit ecologically relevant system , to analyse intestinal adaptation of a model bacterial strain , Escherichia coli MG1655 ., Our results show that single point mutations in the ompB master regulator confer a striking selective adaptive advantage ., OmpB comprises EnvZ , a transmembrane sensor with a dual kinase/phosphatase activity , and OmpR , a transcription factor controlling more than 100 target genes ., In response to environmental changes , EnvZ modulates the phosphorylation and thereby the transcriptional activity of OmpR ., We further show that the selective advantage conferred by OmpB mutations is related to their additive and independent effects on genes regulating permeability and flagellin expression , two major set of genes controlled by OmpR ., These results suggest that global regulators may have evolved to coordinate physiological activities necessary for adaptation to complex environments and that mutations offer a complementary genetic mechanism to adjust the scale of the physiological regulation controlled by these regulators in distinct environments .

ecology, immunology, microbiology, evolutionary biology, eubacteria, mus (mouse)

journal.pbio.2005512

GABAergic modulation of olfactomotor transmission in lampreys

Olfactory cues can trigger goal-directed locomotor behaviors , such as homing , predator avoidance , or food and mate searching 1–11 ., It is only recently that the neural pathways and mechanisms involved in transforming olfactory inputs into locomotor behavior were characterized for the first time in a vertebrate species , the lamprey 12 , 13 ., It consists of a specific neural pathway extending from a single glomerulus located in the medial part of the olfactory bulb ( medOB ) to the mesencephalic locomotor region ( MLR ) , with a relay in the posterior tuberculum ( PT ) 12 ., In all vertebrates , the MLR acts as a motor command center that controls locomotion via descending projections to brainstem reticulospinal ( RS ) neurons 14–22 ., This olfactomotor pathway is present throughout the life cycle of lampreys , whether in larvae , newly transformed , parasitic , or spawning animals 12 ., Yet , olfactory-induced motor behaviors can be life stage specific in lampreys ., For instance , at the parasitic stage , lampreys feed on fish that they detect using olfactory cues 23 ., Then , when sexually mature , the adults are attracted upstream by migratory pheromones released by larvae 24–26 ., Once upstream , the females are attracted to males by sex pheromones 27 , 28 ., The general organization of the lamprey olfactory system , from the periphery to the central nervous system ( CNS ) , is very similar to that of other vertebrates ., The peripheral olfactory organ is composed of a main olfactory epithelium and an accessory olfactory organ 29–31 ., Axons from olfactory sensory neurons ( OSNs ) of the olfactory epithelium terminate in the olfactory bulb ( OB ) ., As in other vertebrates , the OB can be divided in two subregions , based on their inputs ., The main olfactory bulb ( MOB ) , which occupies the whole OB except its medial part ( i . e . , the medOB ) , receives inputs from the main olfactory epithelium ., The medOB , on the other hand , receives inputs from OSNs located in the accessory olfactory organ 32–34 ., The OB of vertebrates constitutes the primary olfactory center of the CNS and , as such , filters and actively shapes sensory inputs to secondary olfactory structures 35 , 36 ., This processing of sensory inputs in the OB is driven by modulatory inputs coming from the numerous neurotransmitter systems present in the OB of vertebrates 37 , 38 ., GABA is the main inhibitory neurotransmitter in the CNS , and numerous GABAergic processes are present in the OB of several vertebrate species 39–43 ., GABAergic neurons of the OB are believed to play a critical role in olfactory processing by providing inhibition to the bulbar microcircuitry 44 ., However , their effect on the outputs of the OB and ultimately on behavior is far less understood ., Here , we hypothesized that GABAergic neurons of the OB could play a significant role in modulating transmission in the olfactomotor pathway of lampreys ., To address this , we used anatomical ( tract tracing and immunohistochemistry ) and physiological ( intracellular recordings ) techniques ., The present study showed abundant GABAergic cell bodies and processes in the OB ( n = 10 adult animals , Fig 1 ) , thus confirming the findings of Meléndez-Ferro and colleagues 43 ., GABAergic neurons were mainly observed in the central region of the OB ( internal cell layer ICL , Fig 1A and 1D and S1 Fig ) , where the most common OB interneuron type , the granule cell , was described 45 ., GABAergic processes were found all over the OB , including in and around the glomeruli of both the MOB ( Fig 1A and 1C ) and the medOB ( Fig 1A and 1B and S2 Fig ) ., To investigate the physiological role of the GABAergic circuitry in the OB , local microinjections of the GABAA receptor antagonist , gabazine , were made into restricted areas of the OB , while stimulating the olfactory nerve ( ON ) and intracellularly recording from RS neurons on the same side of the brain ., Gabazine injections ( 0 . 1 mM , 1 . 4 ± 1 . 6 nL ) in the medOB ( n = 60 synaptic responses; n = 6 neurons; n = 6 larval animals; Fig 2 ) were found to amplify synaptic responses of RS neurons to electrical stimulation of the ON ( amplitude increase of 372 . 2 ± 277 . 5%; p < 0 . 05; no statistical differences between control and washout; Fig 2B and 2C ) ., RS neurons from all four reticular nuclei ( mesencephalic reticular nucleus and anterior , middle , and posterior rhombencephalic reticular nuclei ) responded similarly as shown by calcium imaging experiments ( n = 362 neurons; n = 6 adult animals , S3 Fig ) ., Extracellular recordings of the OB further showed that responses of OB neurons to ON stimulation were greatly increased under gabazine ( n = 60 responses; n = 6 animals , S4 Fig ) , thus corroborating our previous findings ., In addition to increasing the responses of RS cells , stimulation of the ON after gabazine injection in the medOB even induced motor discharges in the ventral roots ., The neural activity consisted of rhythmic discharges alternating on both sides , a hallmark of fictive swimming ( in 58 . 1% of trials; n = 36 locomotor bouts out of 62 trials for gabazine versus 0 out of 78 for control; n = 9: three adult animals and six larval animals , Fig 3 and S1 Data ) ., Because the density of GABAergic processes seemed relatively similar in the medOB and the MOB , we hypothesized that the neural activity in the MOB could be modulated by GABA , as observed for the medOB ., To test this hypothesis , the effect of gabazine injections in the MOB on RS cell responses was examined ., As shown for the medOB , gabazine injections into the MOB ( 0 . 1 mM , 1 . 8 ± 2 . 0 nL ) enhanced the RS neuron responses to ON stimulations ( n = 60 synaptic responses; n = 6 neurons; n = 6 larval animals; amplitude increase of 174 . 4 ± 167 . 0% , p < 0 . 05; no statistical differences between control and washout; Fig 4A ) ., However , stimulation of the ON does not activate MOB neurons specifically , as it also activates medOB neurons ., To rule out any involvement of the medOB in the increased RS responses after MOB gabazine injections , the effect of an electrical stimulation of the MOB with a gabazine injection ( 0 . 1 mM , 2 . 9 ± 1 . 1 nL ) in the MOB was tested ., Under control conditions , MOB stimulation did not induce responses in RS neurons ., However , after a gabazine injection in the MOB , electrical stimulation of the MOB elicited responses in RS cells ( n = 70 synaptic responses; n = 7 neurons; n = 7 larval animals; amplitude increase of 286 . 3 ± 296 . 8%; p < 0 . 05; no statistical differences between control and washout; Fig 4B ) ., As a further control , electrical stimulation of the MOB under gabazine elicited significant responses in RS cells , even when the medOB had been surgically resected ( n = 5 larval animals , S5 Fig ) ., Furthermore , recordings of the ventral roots of the spinal cord showed that electrical stimulation of the MOB after a gabazine injection in the MOB can induce fictive swimming ( in 65 . 1% of trials; 54 locomotor bouts out of 83 trials for gabazine versus 0 out of 105 for control; n = 9 larval animals , Fig 5 and S1 Data ) ., Taken together , these findings suggest the presence of a previously unknown pathway linking the MOB to RS cells that seems to be under a strong tonic GABAergic inhibitory control ., We investigated the spatial organization of projections from the MOB that would eventually reach the RS neurons ., We injected the axonal tracer biocytin in the MOB ( n = 13 adult animals , Fig 6A1 ) and found ipsilateral axonal projections to the lateral pallium ( LPal ) , medial pallium , dorsal pallium , striatum , dorsomedial telencephalic neuropil , and habenula ., Contralateral projections were found to the OB , dorsomedial telencephalic neuropil , striatum , and LPal ., The MOB injections did not label any fibers in the PT ., Similar olfactory projections from the OB have been reported in other species of lampreys 46 , 47 , but the selective contribution from the medOB or the MOB was not investigated in these earlier studies ., The LPal appears to be a major target of neurons in the MOB , judging by the numerous labeled fibers seen to enter this region ., The fibers densely filled the outermost layer covering the entire rostro-caudal extent of the LPal ( Fig 6A2 ) ., Many fibers were also seen in the more central layers of the LPal , where the neuronal cell bodies of that structure are located ., Tracer injections in the LPal ( n = 9 adult animals , Fig 6B1 ) retrogradely labeled many neurons in the MOB without ever labeling cell bodies in the medOB ( Fig 6B2 ) ., The retrolabeled neurons in the MOB were found close to the glomeruli , but were almost never seen inside them ., Physiological experiments were then carried out to characterize the effect of the pharmacological inactivation of the LPal on the responses of RS cells to the electrical stimulation of the MOB ., Based on the results reported in Fig 4B , these experiments were carried out after removing the local GABAergic inhibition with a gabazine microinjection into the MOB ( 0 . 1 mM , 0 . 9 ± 1 . 0 nL , just prior each stimulation ) ., An injection of glutamate receptor antagonists ( 2-amino-5-phosphonopentanoic acid AP5: 0 . 5 mM , 6-cyano-7-nitroquinoxaline-2 , 3-dione CNQX: 1 mM , 5 . 2 ± 0 . 8 nL ) in the LPal strongly decreased the RS neuron responses ( amplitude decrease of 64 . 8 ± 21 . 3%; p < 0 . 05 ) , thus confirming the role of the LPal in relaying glutamatergic outputs from the MOB to locomotor control centers ( n = 50 synaptic responses; n = 5 neurons; n = 5 larval animals , Fig 6C ) ., Biocytin was injected in the LPal to examine its descending projections ., Emphasis was placed on regions known to be involved in the medial olfactomotor pathway , such as the PT and the MLR ( n = 5 adult animals , Fig 7 ) ., Numerous fibers terminated in the PT , predominantly on the ipsilateral side ( Fig 7B ) , with fibers crossing locally to the contralateral side ( arrows in Fig 7B2 ) ., At levels immediately caudal to the PT , in the rostral mesencephalon , the number of descending fibers decreased sharply ., Only a few labeled fibers continued to the level of the MLR ( Fig 7C ) , where many appeared to terminate ( Fig 7C2 ) ., More caudal levels were not investigated in the present study , but it is not excluded that some fibers continued down more caudally 48 ., Tracing experiments were carried out to further characterize the population of LPal neurons projecting to the PT and the MLR ., The organization and anatomical boundaries of the LPal in lamprey are still debated 47 , 49–56 ., In the present study , we followed the nomenclature of Northcutt and Puzdrowski 47 and Pombal and Puelles 54 ., The part of the brain that was considered to be the LPal in the present study is illustrated in S6 Fig . In this series of experiments , a solution containing Texas Red-conjugated dextran amine ( TRDA ) was injected in the MOB to label olfactory projections from the OB and a solution containing biocytin was injected in the PT ( n = 7 adult animals and 1 larval animal , Fig 8A and S7 Fig ) or the MLR ( n = 4 adult animals and 1 larval animal , Fig 9A and S7 Fig ) to retrogradely label neurons projecting to the PT or MLR ., Typical results are shown in Figs 8 and 9 and S7 Fig . Labeled cell bodies were distributed uniformly in all regions of the LPal , dorsal , ventral , rostral and caudal , when injections were made in the PT ( Fig 8A and 8B ) or the MLR ( Fig 9A and 9B ) ., The dendrites of cells often extended radially towards the outermost layer of the LPal , where secondary olfactory fibers , labeled from the MOB , are located ( Fig 8B and Fig 9B ) ., These results show that fibers originating in the MOB came in proximity with LPal neurons projecting to both the PT and the MLR , suggesting that the LPal is a relay for MOB inputs to the PT and MLR ., Electrophysiological experiments were then conducted to examine the effect of deactivating the PT and the MLR on the RS neuron responses to the electrical stimulation of the LPal ( Fig 8C and Fig 9C , respectively ) ., Glutamate antagonists were locally injected in either the PT ( AP5: 0 . 5 mM , CNQX: 1 mM , 1 . 1 ± 1 . 2 nL , Fig 8C ) or the MLR ( AP5: 0 . 5 mM , CNQX: 1 mM , 3 . 6 ± 2 . 5 nL , Fig 9C ) , and the RS neuron responses were markedly decreased ( PT: amplitude decrease of 48 . 7 ± 19 . 7%; p < 0 . 05; no statistical differences between control and washout; n = 50 synaptic responses; n = 5 neurons; n = 5 larval animals; MLR: amplitude decrease of 45 . 3 ± 21 . 7%; p < 0 . 05; n = 60 synaptic responses; n = 6 neurons; n = 6 larval animals ) ., Taken together with our previous findings , these results show that glutamatergic olfactory outputs from the MOB are relayed via the LPal to the PT and to the MLR before reaching RS cells ., The relative importance of the projection from the LPal to the PT or to the MLR was examined by counting retrogradely labeled cells in the LPal after an injection of a fluorescent tracer in the PT or the MLR ., Bilateral biocytin injections in the PT ( n = 6 adult animals ) followed by the analysis of 10 LPals revealed that , on average , 751 ± 283 LPal neurons ( per LPal ) projected to the PT ( Fig 10A ) ., Bilateral biocytin injections in the MLR ( n = 5 adult animals ) followed by the analysis of eight LPals revealed an average of 93 ± 62 neurons ( per LPal ) in these animals ( Fig 10A ) ., The size of LPal neurons projecting to the PT and MLR was measured along their long axis ., LPal PT- and MLR-projecting neurons measured on average 16 . 3 ± 3 . 0 μm ( n = 90 cells from a subset of three animals , Fig 10B ) and 15 . 4 ± 2 . 4 μm ( n = 90 cells from a subset of three animals , Fig 10B ) , respectively ., Interestingly , a few medOB neurons were systematically labeled after an MLR tracer injection ( Fig 9A ) , thus demonstrating a direct projection from the medOB to the MLR ., The lateral olfactomotor pathway ( orange pathway in Fig 11 ) may contribute significantly to the motor responses of lampreys to olfactory cues in their environment , in parallel to the previously described medial olfactomotor pathway ( green pathway in Fig 11 ) ., Lampreys , like many other animal species , display sex- and life stage–specific olfactory-induced motor behaviors 60–63 ., The neural mechanisms accounting for the behavioral variability associated with a specific neural pathway within a species are largely unknown ., However , the long-standing hypothesis that it was due to fundamental differences in brain wiring is now being challenged ( reviewed in 64 ) ., Indeed , only very subtle sex-specific differences have been found in the structure and circuitry of the brain in mammals 65–68 ., Likewise , we have shown in a previous study that a hardwired olfactomotor pathway is present in both sexes at all life stages in the sea lamprey 12 ., For this reason , we hypothesized in the present study that modulatory mechanisms acting on this pathway could play a role in the variability of the behavioral responses of lampreys to olfactory cues ., The OB is the first relay of the olfactomotor pathway ., As such , it interfaces sensory afferents with motor control centers and it is ideally located to modulate olfactory-induced motor responses in lampreys ., It has been proposed that the main function of the OB in vertebrates is the filtering and transmission of olfactory inputs 69 ., Studies in mammals and turtles have shown that the sensory inputs to the OB are modulated both at presynaptic and postsynaptic levels by two classes of local GABAergic interneurons: periglomerular and granule cells 69 ., Periglomerular cells inhibit glutamate release from primary olfactory axon terminals via a GABAB-mediated mechanism 70–73 ., On the other hand , granule cells inhibit projection neurons via a GABAA-mediated mechanism 74–78 ., Despite a rather good understanding of the cellular mechanisms responsible for the modulation of olfactory inputs , little is known about their overall effect on the OB output and , ultimately , on the resulting behavior ., Using an in vitro isolated preparation of lamprey CNS , we provide the first evidence linking cellular GABAergic modulatory mechanisms in the OB to the activation of a sensorimotor pathway producing locomotor behavior ., We showed that the lamprey OB anatomical organization is very similar to that of other vertebrates , regarding its GABAergic circuitry ., Our material confirms previous work showing that the lamprey OB contains numerous GABAergic neurons of different morphological types 43 ., The morphology and location of the GABAergic neurons suggest that they are mainly granule cells 43 , 45 , but not excluding possible periglomerular cells 43 , 51 ., We also showed that both medOB and MOB glomeruli are densely innervated with GABAergic processes ., These GABAergic processes are in close proximity to both primary olfactory axon terminals and dendrites or somata of OB projection neurons; this suggests possible pre- or postsynaptic contacts ( S2 Fig ) ., We have not formally identified types ( i . e . , axons versus dendrites ) and origin ( i . e . , intrinsic versus extrinsic ) of the GABAergic processes ., The abundant GABAergic cell bodies labeled in the OB suggest that they may be of intrinsic ( OB ) origin ( i . e . , granule cells or periglomerular cells ) , as seen in other vertebrate species 39–43 ., The lamprey granule cells are axonless 45 , as in other vertebrate species ., These processes are thus likely to be dendrites of granule cells or dendrites and axons of periglomerular cells , but some of these processes could be axons originating from neurons located in other parts of the brain ., In mammals , most neuromodulatory inputs to the OB originate from the locus coeruleus ( noradrenergic inputs ) , the nucleus of the diagonal band of Broca ( cholinergic inputs ) , and the midbrain raphe ( serotoninergic ) ( reviewed in 69 , 79 , 80 ) ., However , some cells located in the nucleus of the diagonal band of Broca are GABAergic and project to the OB 81 , 82 ., We now show that injection of the GABAA receptor antagonist , gabazine , in the OB potentiates RS cell responses to ON or OB stimulation , thus suggesting an enhancement of the olfactomotor transmission ., In the case of OB ( MOB ) stimulation , however , we cannot completely exclude that electrical stimulation of the MOB might recruit not only projection neurons but also local GABAergic interneurons , and that in such a case , an injection of gabazine might block the effect of their activation ., Under gabazine , the electrical stimulation of the ON or OB can induce fictive swimming—the in vitro corollary of swimming behavior ., Overall , these findings suggest that the GABAA antagonist gabazine increases the output of the OB ., Indeed , downstream relays of the olfactomotor pathway ( PT and MLR ) control locomotion in a graded fashion 12 , 15 , 83 ., Consequently , the increased RS cell responses observed under gabazine are likely to result from an increased drive from the OB to the PT and/or MLR ., Studies in mammals have shown that GABA acts at several locations in the OB ., OSN terminals express GABAB receptors 84–86 , which inhibit transmission from OSN axons to mitral cell primary dendrites upon release of GABA by periglomerular cells 70 , 71 , 87 , 88 ., Mitral cell dendrites express both GABAA and GABAB receptors 89–93 ., Pharmacological blockade or genetic alteration of GABAA receptors in mitral cells alters the OB γ oscillations and leads to increased ON-induced mitral cell discharges 78 , 94 ., The effect of GABAB receptor activation in these cells is less clear 93 ., Both periglomerular and granule cells release GABA on mitral cells; periglomerular cells contact mitral cell primary dendrites , whereas granule cells contact mitral cell secondary dendrites 69 , 95 ., Granule and periglomerular cells also express GABAA receptors 89 , 91 , 96 , 97 ., Genetic alteration of the GABAA receptor subtype expressed in granule cells ( i . e . , expressing the β3 subunit ) either globally or in a cell-specific manner increases the granule cell inhibition of mitral cells and results in increased OB γ oscillations 98 , 99 ., To the best of our knowledge , the effect of periglomerular cell GABAA receptor activation on mitral cell activity has not been investigated , but an inhibition of periglomerular cells leading to the disinhibition of mitral cells could be expected ., Finally , electrophysiological evidence suggests that granule cells also possess GABAB receptors whose activation modulates granule cell inhibition of mitral cells 100 ., GABA can thus depress or potentiate mitral cell activity depending on its site of action ( i . e . , OSNs axons , OB interneurons , or mitral cell ) ., However , as OB interneurons act on mitral cells via GABAA receptors , the net effect of the pharmacological blockade of GABAA receptors in all OB layers is likely to be a disinhibition of mitral cells ., This is consistent with our results in lampreys and those found in other vertebrate species 77 , 94 , 101–104 ., The presence of both tonic and phasic inhibition in the OB has been reported in fish , amphibians , and mammals 71 , 77 , 78 , 94 , 98 , 99 , 102 , 105 , 106 ., It has been suggested that tonic inhibition may modulate the strength of sensory inputs to the OB 88 or the sensitivity of second-order olfactory neurons to sensory inputs 102 ., Phasic inhibition has been shown to generate neuronal synchrony ( i . e . , oscillations ) in projection neurons 98 , 99 ., The role of these oscillations and thus of the phasic inhibition is still debated , but several studies in insects and mammals point toward a crucial role in coding olfactory information 107–109 ., Our study shows that a strong GABAergic inhibition of the OB output is present in the lamprey , one of the most basal extant vertebrate , and thus may be a common ancestral feature of the vertebrate OB ., The GABAergic modulation of the olfactomotor pathways seen in lampreys could explain some of the life stage–specific behavioral responses to olfactory cues ., For instance , migratory pheromones attract only pre-spawning adult lampreys 24–26 ., This is surprising because these pheromones evoke strong responses in OSNs at other life stages 110 ., Somehow , the activation of OSNs only leads to locomotor responses during the pre-spawning adult life stage ., Meléndez-Ferro and colleagues 43 , 111 have stated that the density of OB GABAergic cells declines significantly between the newly transformed and pre-spawning life stages ., Whether this apparent decrease in GABAergic cell density could account for some of the life stage differences is not known at present , but it could be one plausible mechanism worth investigating ., A series of recent studies have shown that a CO2-mediated water acidification significantly impairs several olfactory-driven behaviors in fish , including prey tracking , predator avoidance , alarm response , and homing 112–116 ., The mechanism at play has not been fully characterized yet , but it involves an alteration of the normal functioning of GABAA receptors , as blocking these receptors with gabazine led to a behavioral recovery 115 , 117 ., The authors of these studies proposed that a potentiation or a reversal of the GABAA receptor function ( from inhibitory to excitatory ) because of changes in anionic gradients over neuronal membranes could underlie these behavioral alterations ., Taken together , these studies show that GABAergic mechanisms also play a crucial role in modulating olfactomotor behaviors in fish ., Further studies are needed to establish whether the neural pathways and modulatory mechanisms characterized in lampreys are also present in fish and other vertebrates ., In the present study , we showed that stimulation of the MOB under gabazine led to excitatory responses in RS cells and to locomotion ., This suggests the existence of a distinct pathway from the MOB to the RS cells and the presence of a strong tonic GABAergic inhibition in the MOB ., We characterized the anatomy and physiology of this pathway ., Anatomical data showed that the LPal receives a massive projection from the MOB and projects down to both the PT and MLR ., The PT , in turn , projects to the MLR 12 , 118 , 119 ., We also showed that the MLR receives a direct projection from the medOB , in addition to the already characterized projection via the PT 12 ., The MLR then reaches the command cells for locomotion , the RS cells , via glutamatergic and cholinergic projections 120–123 ., Physiological data confirmed that the LPal relays MOB olfactory inputs to the RS cells via the PT and MLR ., This is consistent with the recent findings of Suryaranayana and colleagues 124 indicating that some LPal neurons receive monosynaptic inputs from the OB ., Interestingly , Ocaña and colleagues 48 showed that a few fibers originating in the LPal could reach RS neurons directly and that some of these could be followed as far as the first spinal segments ., This prompted the authors to conclude that the LPal possesses an efferent projection pattern similar to that of the amniote motor cortex 48 ., It would be interesting to examine if these projections from the LPal to the RS neurons and spinal cord are also involved in olfactomotor responses ., Although we cannot exclude that there may be other pathways linking olfactory centers to motor centers , our study demonstrates the existence of two distinct glutamatergic pathways linking the olfactory and motor systems in lampreys ( Fig 11 ) ., Both these pathways share a common output via the PT/MLR–RS neurons system ., However , they differ regarding their pathways from the OB to motor control centers ( i . e . , PT/MLR ) , as well as to their inputs from the periphery ., In lampreys , the main olfactory epithelium contains numerous tall , ciliated OSNs expressing the G-protein Golf , as in the main olfactory epithelium in other vertebrates 125–133 ., The OSNs of the main olfactory epithelium project their axons to the MOB , which , in turn , projects mainly to the LPal , i . e . , the putative homologue of the mammalian olfactory cortex in lampreys 134 ., This pathway is strikingly similar to the main olfactory pathway of terrestrial vertebrates and thus further supports its evolutionary conservation ., In addition to the main olfactory epithelium , lampreys possess an accessory olfactory organ 29–32 , 135–138 ., The accessory olfactory organ contains short , broad , ciliated OSNs 32 that do not express the G-protein Golf and project only to the medOB 32 , 129 ., Projection neurons of the medOB then project directly to the PT and MLR , bypassing the LPal ., Taken together , these findings show that the lamprey accessory olfactory organ constitutes a discrete olfactory subsystem ., It has even been suggested that the accessory olfactory organ represents a primordial vomeronasal system 29 , 31 , 138 ., In other vertebrates , the presence of parallel olfactory pathways conveying the information from the periphery to high-order brain olfactory centers suggests that these systems subserve different behavioral functions 139–143 ., For instance , in fish , segregated olfactory pathways , from the olfactory epithelium to the telencephalon , mediate feeding , reproductive , and alarm behaviors 139 , 142 , 144–151 ., Similarly , the main and accessory ( i . e . , vomeronasal ) systems of terrestrial vertebrates are segregated until at least the third-order neurons and their respective activation elicits different behaviors 152–155 ., Physiological evidence in lampreys also supports this hypothesis , as OB local field recordings showed that the medOB and MOB have overlapping but different response profiles to feeding cues and pheromones 33 , 34 ., Moreover , we show that the pathways from the OB to the motor control centers differ for the two olfactory subsystems ., The medOB projects directly to motor control centers , whereas the MOB projects first to the LPal before reaching motor control centers ., Not surprisingly , activation of both systems leads to locomotion ., This could be attributed to the paucity of the behavioral repertoire of lampreys compared to mammals ., However , it should be noted that reproductive , migratory , and feeding behaviors all require locomotion in lampreys ., The distinction between these two subsystems thus lies in their inputs from the periphery ( accessory olfactory organ versus main olfactory epithelium ) as well as in the involvement of the LPal in the lateral pathway ., It is tempting to propose that the medial pathway could mediate innate responses to chemical stimuli ( for example , avoidance ) , whereas the lateral pathway could be involved in olfactomotor behaviors requiring further processing and perhaps learning ( for example , olfactory navigation ) ., A similar distinction between dual “olfactory” systems exists in invertebrates 156–158 ., In mammals , it was shown that mitral cells of the MOB can develop differential responses to rewarded/unrewarded odors 159 ., It has been suggested that the dichotomy between innate responses versus learned responses may be what distinguish the main and accessory systems of terrestrial vertebrates 154 ., This hypothesis has , however , received little attention , and further studies are needed ., In conclusion , our study shows that olfactory inputs can activate the locomotor command system via two distinct glutamatergic pathways in lampreys ., To the best of our knowledge , this is the first characterization of a dual olfactory pathway , from the periphery to the motor command system , in vertebrates ., Both pathways are strongly modulated by the GABAergic circuitry of the OB that may account for some of the variability in behavioral responses to olfactory inputs in lampreys ., The existence of two segregated olfactory subsystems in one of the most basal extant vertebrates sheds light on the evolution of the olfactory system and suggests that its organization in functional clusters could constitute a common ancestral trait of vertebrates ., For all procedures , the animals were deeply anesthetized with tricaine methanesulphonate ( MS-222 , 200 mg/L , Sigma-Aldrich , Oakville , ON ) and then decapitated ., All surgical and experimental procedures conformed to the guidelines of the Canadian Council on Animal Care and were approved by the animal care and use committee of the Université de Montréal ( Protocol no . 18–018 ) , the Université du Québec à Montréal , and the University of Windsor ., Experiments were performed on 57 larval and 61 adult sea lampreys ( Petromyzon marinus ) of both sexes ., Some animals were used in more than one experiment ., Larvae were collected from the Pike River stream ( QC , Canada ) ., Adults were collected from the Great Chazy River ( NY , United States ) and were kindly provided by agents of the U . S . Fish and Wildlife Service of Vermont ., The permission to collect animals in the field was granted by the Quebecs Ministry of Natural Resources and Wildlife ( permit no . 2017-03-30-2189-16-SP ) ., All animals were kept in aerated fresh water maintained at 4–5 °C ., For all types of experiments , the animals were deeply anesthetized with tricaine methanesulphonate ( MS-222 , 200 mg/L , Sigma-Aldrich ) , decapitated caudal to the seventh branchiopore , and transferred into cold oxygenated Ringers ( 8–10 °C ) of the following composition ( in mM ) : 130 NaCl , 2 . 1 KCl , 2 . 6 CaCl2 , 1 . 8 MgCl2 , 4 . 0 HEPES , 4 . 0 dextrose , and 1 . 0 NaHCO3 , at pH 7 . 4 ., The branchial apparatus , myotomal musculature , and all soft tissues attached to the ventral side of the cranium were removed ., The dorsal part of the vertebrae and cranium were removed to expose the brain and the rostral spinal cord ., The peripheral olfactory organ was left intact with the ON still attached to the brain ., All other nerves were cut and the choroid plexus covering t

Introduction, Results, Discussion, Materials and methods

Odor-guided behaviors , including homing , predator avoidance , or food and mate searching , are ubiquitous in animals ., It is only recently that the neural substrate underlying olfactomotor behaviors in vertebrates was uncovered in lampreys ., It consists of a neural pathway extending from the medial part of the olfactory bulb ( medOB ) to locomotor control centers in the brainstem via a single relay in the caudal diencephalon ., This hardwired olfactomotor pathway is present throughout life and may be responsible for the olfactory-induced motor behaviors seen at all life stages ., We investigated modulatory mechanisms acting on this pathway by conducting anatomical ( tract tracing and immunohistochemistry ) and physiological ( intracellular recordings and calcium imaging ) experiments on lamprey brain preparations ., We show that the GABAergic circuitry of the olfactory bulb ( OB ) acts as a gatekeeper of this hardwired sensorimotor pathway ., We also demonstrate the presence of a novel olfactomotor pathway that originates in the non-medOB and consists of a projection to the lateral pallium ( LPal ) that , in turn , projects to the caudal diencephalon and to the mesencephalic locomotor region ( MLR ) ., Our results indicate that olfactory inputs can induce behavioral responses by activating brain locomotor centers via two distinct pathways that are strongly modulated by GABA in the OB ., The existence of segregated olfactory subsystems in lampreys suggests that the organization of the olfactory system in functional clusters may be a common ancestral trait of vertebrates .

Olfactory-induced behaviors ( homing , food or mate searching , etc . ) are crucial for the survival and reproduction of most animals ., A neural substrate underlying odor-induced behaviors in vertebrates was recently uncovered using a basal vertebrate model: the lamprey ., It consists of a neural pathway extending from the medial olfactory bulb , a first-order relay of olfactory information in the brain , to locomotor regions ., Here , we investigated modulatory mechanisms acting on this neural pathway ., We show that an inhibitory circuitry that releases the neurotransmitter GABA in the olfactory bulb strongly modulates motor responses to olfactory stimulation ., We also discovered and characterized a novel olfactomotor pathway that originates in the non-medial olfactory bulb and consists of a projection to the lamprey olfactory cortex that , in turn , projects to locomotor regions ., This discovery of a novel pathway linking olfactory and motor centers in the brain indicates that olfactory inputs can activate locomotor centers via two distinct pathways ., Both pathways are strongly modulated by the neurotransmitter GABA in the olfactory bulb ., The existence of segregated olfactory subsystems in lampreys sheds light on the evolution of olfactory systems in vertebrates .

medicine and health sciences, fish, brain, vertebrates, neuroscience, animals, biological locomotion, surgical and invasive medical procedures, lampreys, olfactory organs, functional electrical stimulation, animal cells, olfactory receptor neurons, agnatha, cellular neuroscience, eukaryota, olfactory bulb, cell biology, anatomy, cyclostomata, physiology, neurons, biology and life sciences, cellular types, afferent neurons, organisms

journal.pcbi.1005187

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

MinePath aims to address and cope with the aforementioned traditional pathway analysis problems and overcome the gene-set oriented visualization limitations , i . e . , what color should be assigned to a target gene when , for one phenotype it is activated by an activator source gene , and for another phenotype it is inhibited by another source gene ., MinePath fully exploits the topology as well as the underlying pathway gene regulatory relations , including the type and direction of these relations ., Having on our disposal the sub-paths resulting from the functional decomposition of a pathway and the gene expression data , MinePath proceeds to the identification of the sub-paths that functionally differentiate between the targeted phenotype classes ., The aim is the identification of those sub-paths that exhibit a high differential power to discriminate between the expression profiles of samples assigned to different phenotypes ., Existing and widely utilized pathway databases provide pathways of proved molecular value ., Relevant on-line public repositories contain a variety of information that includes not only the pathway network per se but also incorporate links and rich annotations for the respective nodes ( genes ) and edges ( regulatory relations ) ., In its current implementation MinePath utilizes the KEGG pathways repository ( www . genome . jp/kegg/pathway . html ) 11 ., KEGG pathways are widely utilized as a reference knowledge base for understanding biological pathways and the function of respective cellular processes ., MinePath reads pathways directly from their original KGML representation format ( KEGG markup language; www . genome . jp/kegg/xml ) ., It is also able to cope with the richer XGMML format ( a graph XML representation schema , also utilized by Cytoscape , wiki . cytoscape . org/XGMML ) and so , it could be easily extended to manage other relevant pathway resources like , BioCarta 12 , ReActome 13 , and Pathway Commons 14 ., Here we have to note that protein regulation may occur in both translational and post-translational levels , and KEGG encompasses and reports both protein and expression changes ., Even though MinePath cannot directly detect post-translational modifications , the quantitative relations due to differences in gene expression are a strong indicator of protein regulation , and thus identification of related sub-paths remains a powerful tool for the identification of biologically significant relations ., More details on this in section ‘Results/MinePath and mutation-based/driven or post translational modifications’ ., The main goal of this paper is to present MinePath–a pathway analysis approach that directly utilizes and exploits the underlying pathway topology and regulatory machinery , and contrast it with respective state-of-the-art approaches ., As a systematic review of pathway analysis methodologies is out of the scope of this paper , we refer the reader to relevant extensive reviews 15 , 16 , 17 and 18 ., A number of recent pathway analysis methodologies take advantage and exploit the topology and the gene regulatory relations of pathways ., Furthermore , some of the relevant tools implement and offer network visualization functionality in order to map and display the underlying regulation machinery of pathways ., Based on a literature search we identified relevant pathway analysis methodologies and tools that are presented in Table 1 ., The identified state-of-the-art methodologies and tools are presented in a unified and standardized notation , which expose their common characteristics in terms of the pathway features that they tend to utilize in their core processes ., Observing Table 1 a general remark concerns the pathway knowledge that is utilized by each methodology ., A bunch of pathway analysis approaches ( #1–8 in Table 1 ) focus on the identification of differentially expressed genes ., These approaches ignore , and do not employ in their methodology the topology and the underlying regulatory relations with an exception of the 7th and 8th method/tool in Table 1 that take into account only the pathway topology ., Another group of pathway analysis methods ( #9–31 in Table 1 ) move one-step further trying to identify discriminant pathways , even if they do not fully exploit the underlying pathway regulatory machinery ., As a general remark we may state that:, ( a ) most of the current pathway analysis tools focus mainly on the pathway enrichment characteristics of the target genes , and, ( b ) they compromise the connectivity in favor of computational simplicity since the topology and the type of pathway relations are ignored or under-represented 59 ., It is generally recognized that in order to efficiently address and to overcome the statistical barriers in traditional gene selection methodologies , the pathway topology and the underlying gene interactions should be taken into account 60 ., Even in its infancy , this approach is followed by most of the recent pathway analysis methodologies ( #32–38 in Table 1 ) ., They present a promising alternative towards the identification of the hidden underlying regulatory machinery that putatively governs and explains the expression of specific phenotypes ., Representative systems include GGEA 58 , SPIA 53 , TEAK 54 , HotNet 55 , Paradigm 56 , and PATHOME 57 ., Moreover , even if these methodologies exploit the underlying pathway regulatory machinery , they reside on ‘summing’ over the functional status of the pathway regulatory gene relations without considering the exact functional status of each pathway relation or sub-path ., Most of these approaches generate overall pathway ranks , with an exception of GGEA and Paradigm that provide respective sub-path views ., A key-component in order to indicate the predictive sub-paths and their power to differentiate between the target phenotypes is the efficient visualization of the pathway analysis results , which unfortunately most of these systems do not support ., This obstructs the inspection of results and limits the user exploratory potential ., Systems such as KEGG Atlas/Mapper 61 , WebGestalt 62 , NetworkTrial 63 , Graphite Web 64 , AltAnalyze 65 , ReactomeFIViz 66 and EnrichNet 67 visualize just the pathway genes using a color-coding schema to indicate the strength of a pathway relation ., The same holds for TEAK and GGEA that use a color-coding schema to visualize just the functional status of genes , not the functional status of sub-paths ., In its extended version , Paradigm 68 , visualizes the altered status of genes in the pathway while EnrichmentBrowser 69 R package enables the application of a range of set-based and network-based enrichment methods and provides visualization of results ., With the gene-set oriented visualization approach the problem is apparent even for small sub-paths like the single inhibition relation A—| B ( A inhibits B; A , B represent genes ) ., The inhibition relation exhibits a ‘dual’ character and could be considered as functional in a specific sample in two cases: when A is up-regulated and B is down-regulated or , when A is down-regulated and B up-regulated ., In the first case , up-regulation of the A inhibitor causes the down-regulation of B . In the second case , the down-regulation of the inhibitor in a sense ‘allows’ B to be expressed and up-regulated ., So , each of the genes should be visualized with a different color ( just to indicate its expression status ) ., The situation becomes even more complicated when one has to visualize the phenotype inclination of an interaction , for example when an inhibition relation is functional just for one phenotype and not for the other ., Coloring and visualizing the functional status of pathway relations seems a promising alternative , and this is the approach that MinePath adopts and follows ( see sections ‘Functionality and visualization capabilities of MinePath‘ for more details on the MinePath visualization conventions and functionality ) ., The MinePath web-application may be accessed by www . minepath . org ., The main goal of the comparison is to contrast MinePath with those state-of-the-art pathway analysis tools that utilize and exploit in their methodology the topology and/or the regulatory machinery of the pathways ., Note that for most of these state-of-the-art tools , the comparison could not be performed directly on the level of the identified discriminant sub-paths since most of the methodologies do not report information per sub-path ., However , we attempt such a comparison in order to assess to which extent the different approaches are able to identify and reveal biologically important regulatory pathway relations and sub-paths ( see below the sub-section ‘Identification of discriminant cancer-related regulatory relation and sub-paths‘ ) . The heterogeneity in the pathway representation formats utilized by the various pathway analysis tools constraints their thorough comparison . This is also apparent for those tools that exploit in their methodology the topology and/or the regulatory relations of pathways . Either an alternation of pathway formats or an alteration of the underlying tools’ algorithmic process is required in order to accommodate the differences 17 ., Various pathway analysis methodologies ( e . g . , Paradigm ) support the BioPAX ( level, 2 ) standard ( www . biopax . org ) to represent pathways while MinePath and other ( e . g . , GGEA and SPIA ) support the KGML KEGG standard ( www . kegg . jp/kegg/xml ) ., Therefore , we decided to conduct the comparison either with state-of-the-art pathway analysis systems that offer free implementations ( e . g . , GGEA and SPIA ) or with systems for which the original publications report results on experiments that could be also conducted with MinePath ( e . g . , PATHOME , DAVID and SPIA ) ., PATHOME is one of the most recent pathway analysis tools that compute the differential power of regulatory relations in order to assess the phenotype differential significance of the whole pathway ., In the system’s original publication 57 , PATHOME was compared with two well-known pathway analysis tools , namely GSEA and DAVID ., GSEA ( Gene Set Enrichment Analysis , www . broadinstitute . org/gsea ) follows a gene-set enrichment methodology in order to identify statistically significant phenotype differentiating gene-lists by assessing their functional enrichment in targeted pathways 32 ., DAVID ( Database for Annotation , Visualization and Integrated Discovery , david . ncifcrf . gov ) is a widely utilized web-based environment that offers a set of functional annotation tools to reveal , assess and comprehend the underlying biological meaning of genes 59 ., The comparison was made on the basis of a public Gastric Cancer ( GC ) gene expression dataset ( GSE13861 , with the Illumina HumanWG-6 v3 . 0 expression beadchip; it includes 65 primary GC frozen tissue samples and 19 normal appearing gastric tissue samples ) ., Gastric cancer is the second leading cause of cancer-related death worldwide with most of the patients to receive similar treatment , typically surgery followed by chemotherapy , as there are no reliable biomarkers to optimize therapy 97 ., For the comparison we used a reference standard of nine cancer-related pathway categories as reviewed by Vogelstein and Kinzler in 83 ., Each pathway category refers to various single KEGG pathways ( from a total of 15 ) : HIF1 ( mTOR/hsa04150 , Pathways in cancer/hsa05200 , Renal cell carcinoma/hsa05211 ) , p53 ( hsa04115 ) , RB ( Cell cycle/hsa04110 ) , Apoptosis ( Apoptosis/hsa04210 ) , GLI ( Hedgehog/hsa04340 ) , APC ( Wnt/hsa04310 ) , RTK ( ErbB/hsa04012 , Pathways in cancer/hsa05200 ) , SMAD ( TGF-β/hsa04350 ) and PI3K ( ErbB/hsa04012 , Pathways in cancer/hsa05200 , mTOR/hsa04150 , MAPK/hsa04010 , Insulin/hsa04910 , Focal adhesion/hsa04510 , Chenokine/hsa04062 , VEGF/hsa04370 ) ., In the original publication of PATHOME the authors do not provide access to the tool or the source code ., We conducted the same experiment with MinePath ( i . e . , using the same dataset and all the KEGG human pathways ) in order to compare with the results reported in 57 ., Furthermore , even though PATHOME computes the differential power of sub-paths in order to assess the significance of the whole pathway , in the original publication only the significant pathways are reported ., Under the aforementioned restrictions , the comparison is limited on the identified significant pathways ., The significant pathways were identified on the basis of FDR ( as reported in the original publication ) –FDR < 0 . 05 for PATHOME and MinePath , ( Benjamini ) FDR < 0 . 3 for DAVID , and FDR ( q-value ) < 0 . 3 for GSEA that results into 27 selected significant pathways for PATHOME , 17 for GSEA , 15 for DAVID and 28 for MinePath ., The comparison results are summarized in Table 5 ., MinePath identified as significant 5 out of the 15 reference cancer-related single KEGG pathways ( MAPK , P53 , mTOR , Wnt , Focal adhesion ) that cover 4 out of the 9 reference standard cancer-related pathway categories ( PI3K , P53 , HIF1 , APC ) ; PATHOME identified as significant 6 out of the 15 reference cancer-related KEGG pathways ( MAPK , Chenokine , Wnt , Focal adhesion , Insulin , Pathways in cancer ) that also cover 4 out of the 9 reference standard cancer-related pathway categories ( PI3K , APC , HIF1 , RTK ) ; DAVID identified as significant just one KEGG pathway ( Focal adhesion ) that covers just one cancer-related pathway category ( PI3K ) ; and GSEA just one KEGG pathway ( Cell cycle ) that covers just the RB/Cell cycle pathway category ., Furthermore , the authors reported that five genes , WNT5A , VANGL1 , SFRP2 , FZD1 and PLCB1 are up-regulated in GC cases ., Note that the APC/Wnt pathway is already validated as a pathway associated to gastric cancer 98 ., Fig 7 visualizes the discriminant functional sub-paths identified by MinePath in the Wnt pathway ., As it can be observed , MinePath is in accordance with the specific outcome reported in 83 , identifying the functional GC-related sub-path ( indicated with the green colored edges ) , WNT16 ( WNT5A ) → FZD10 ( FZD1 ) → DVL1 which , after a number of gene bindings/associations ( engaging VANGL1 , 2 ) activates LEF1 which in turn activates MYC , FOSL1 and MMP7 to enter the Cell cycle pathway ., Note also that MinePath was the only methodology that identified the P53 pathway as significant ., In order to validate and assess the ability of MinePath to cope with RNAseq gene expression data we applied it on the domain of BrCa targeting the ER phenotype ( ER+ vs . ER- ) ., The RNAseq data comes from a large scale multicenter BrCa study performed by the Sweden Cancerome Analysis Network—Breast ( SCAN-B ) Initiative 99 ( GEO accession: GSE60788 , 54 BrCa cases , 40 ER+ and 14 ER- ) ., In addition , we applied MinePath on the ‘3ER GSE2034-3494-7390’ microarray ( MA ) gene expression dataset that was used for the validation of MinePath ( section ‘ Self-assessment: MinePath for meta-analysis of gene expression studies’ ) ., Aiming to contrast between microarray and RNAseq gene expression measurements on the pathway level we focus on the ErbB pathway ., The results are illustrated in Fig 8 where , different arrow types and colors are used in order to visualize the commonalities and similarities between the two dataset types ., Different types of arrows and colors are used in order to contrast between the relations and sub-paths identified as discriminant between the RNAseq and microarray datasets; drawing of the different types of lines was done manually in order to visually contrast between the two datasets ., In general , the results from both RNAseq and microarray datasets are similar ., For the ER- phenotype , the EGFR ( epidermal growth factor receptor ) is activated by the extra-cellular factors ( direct arrow lines in red; see the explanatory legend at the top-right of the figure ) BTC for both RNAseq and microarray , HBEGF for only microarray and EREG for only RNAseq in order to enter the intra-cellular regulation by the activation of GRB2 , which in turn activates GAB1 ., The relation GAB1 → PIK3R5 is functional for both RNAseq ( red dotted line ) and microarray ( black dashed line that is also functional for the ER- phenotype ) ., Then , the functional sub-path continues with the common to the two datasets relation PIK3R5 → AKT3 and the inhibition of CDKN1B ., Similar regulations hold for the ER+ phenotype ( indicated by ‘green’ lines in Fig 8 ) ., It is notable that for both RNAseq and microarray datasets the cyclin-dependent kinase inhibitors CDKN1A and CDKN1B are inhibited and block the triggering of cell cycle events ., A finding that is of interest concerns the ‘strength’ of the regulations identified as functional and discriminant for the two datasets ., The AKT3—| CDKN1B inhibition relation covers 78 . 6% of the RNAseq ER- samples , compared with the respective 33 . 1% of the samples covered for the microarray dataset ( the percentage figures are shown in Fig 8 over the inhibition relation ) ., This may be suggestive for the superiority of RNAseq technology to measure RNA abundance more objectively because of its ability to detect low abundance transcripts and genes with higher fold-changes , as well as to avoid technical issues related to microarray hybridization 100 ., The RNAseq experiment presents a first attempt towards the comparison between different gene expression profiling technologies on the level of molecular pathways , and at the same time it demonstrates the ability of MinePath to make such research quests feasible ., Protein regulation can occur in both translational and post-translational level , and it is true that KEGG pathways engage and report both protein and expression changes ( in the respective pathway maps and XML/KGML formatted files ) ., As most gene set and pathway enrichment analysis approaches are based solely on gene expression measurements and data , they could not capture regulatory mechanisms that may not be reflected in gene expression data , such as post-translational modifications or kinetic control of biochemical reactions 101 ., Quantitative relations due to differences in gene expression however , remain a strong indicator of protein regulation , and thus a useful tool for the identification of protein relations/regulation ., Even though MinePath cannot directly detect post-translational modifications , the available information in KEGG pathways could be utilized for mapping differential gene expression and identification of relevant differential sub-paths ., Under this setting , it remains a powerful tool for the identification of indicative and biologically significant relations ., Moreover , as it is reported in a study about the connectivity of cancer co-expression networks , “… the biological meaning of co-expression changes can be interpreted in terms of modifications of cancer genome landscape … that confirms the hypothesis that loss of connectivity fingers toward genes harbouring alterations ( e . g . mutations , losses and deletions , promoter DNA methylation ) or affected by post-translational modifications ( e . g . phosphorylation , acylation , methylation , etc . ) in tumors . ” matching of multi-dimensional data with samples for each kind of mutations is suggested in order to validate the hypothesis 102 ., Under this driver , and on the basis of respective gene expression data , we assess the utility of MinePath in cases where mutations in an upstream regulatory factor can cause differential expression of target genes affecting their regulation ., Assessment is based on the application of MinePath on a study that explores the principle role of the SDF1/CXCR4 axis in the homing and engraftment of hematopoietic stem/progenitor cells ( HSPCs ) , with the proper functioning of CXCR4 downstream signaling to depend upon consistent optimal expression of both SDF-1 ligand and its receptor CXCR4 103 ., In this study , CXCR4 constitutive active mutations–CXCR4-CAMs ( N119A and N119S ) in K562 ( human immortalized myelogenous leukemia ) cell line were engineered ., These CXCR4 mutations are able to induce autonomous downstream signaling in a regulated manner ., To assess the effects of the specific CXCR4 mutations , the genome wide differential gene expression ( microarray ) profiles of three- ( 3 ) wild-type and six- ( 6 ) mutated samples were generated ( with the Agilent-027114/Custom Human Whole Genome 8x60k Microarray; GEO accession: GSE76544 ) ., The CXCR4-CAMs resample the post translational modifications ( PTMs ) involved in the active state of the CXCR4 gene product ., The task is to assess the ability of MinePath to identify and reveal potential regulatory relations and sub-paths caused by the corresponding gene expression alternations ., It was encouraging to observe that most of these relations indeed affect gene targets that are downstream of CXCR4 ., The MinePath analysis results are illustrated in Fig 9 where , the downstream CXCR4 mutation signaling and the corresponding regulatory events ( ‘red’ colored edges ) are mapped on an integrated regulatory network ., The results are in accordance with the findings reported in the original study , most of the reported in the study paper CXCR4-mutation affected pathways are ranked as significant , with ‘MAPK’ , ‘Phosphatidylinositol signaling’ and ‘Axon Guidance’ to be on the top of the reported MinePath list ., In particular , and inspecting the network in Fig 9 , the following observations could be made:, ( i ) all the reported ( in the study paper ) genes are present in the network ( shaded rectangular nodes ) ,, ( ii ) the non-shaded rectangles are genes ( GRK7 , FGR , PTK2 , MAPK14 , IGF1R , RASGRP1 , RRAS2 , RRAS ) which are not reported in the study paper , and present putative targets for further research–especially the GRK7-CXCR4 axis is of interest for future studies ,, ( iii ) a positive regulation ‘loop’ between genes PTK2 and PIK3R3 is imprinted in the network ( PTK2 and PIK3R3 are intracellular binding proteins involved in stromal contact in the bone marrow microenvironment 104 ) , a finding that is in accordance with a relevant comment in the study paper: “the differential gene expression profile of CXCR4 mutants reveals a positive loop of genes related to homing and engraftment” ., The aforementioned results and the integrated network in Fig 9 were produced by the following off-line analysis methodology:, ( i ) the file of the differential sub-paths ( as saved by MinePath ) was processed , and all the single relations of each sub-path were extracted;, ( ii ) a network with all the extracted relations was generated and imported in Cytoscape;, ( iii ) using special functionality of Cytoscape the nodes/genes ( and their synonyms ) that are reported in the study paper were retained ., The resulted integrated network , after rearranging its topology ( view ) layout , and renaming some of the nodes ( to reflect their grouping in the KEGG pathways ) is shown in Fig 9 ., The color of the edges follows the already represented coloring scheme , with ‘red’ and ‘green’ for the CXCR4-mutated and wild-type functional relations , respectively , and ‘black’ for relations which are functional for both cases ., It is in our plans to automate and encompass the presented off-line analysis methodology in MinePath towards the creation of integrated networks with differential sub-paths that range across different pathways ., Integration of heterogeneous sources represents an effective venue , as compared to working within the boundaries of a single domain ., This realization is particularly valid for the bioinformatics domain 105 ., Bioinformatics and systems biology have demonstrated that knowledge across domains can better aid relevant scientific communities in their research ., Pathway analysis methodologies that exploit the underlying regulatory machinery of pathways and the identification of phenotype differentiating sub-paths addresses and solves a typical problem of set enrichment strategies that is: the conflicting constrains between molecular pathways and gene expression data ., An example is reflected in situations where two significantly up-regulated genes increase the enrichment of the set in gene expression data , even if one of the genes acts as an inhibitor of the other ., MinePath introduces a pathway analysis methodology that directly exploits the topology as well as the underlying pathway regulatory mechanisms , including the direction and the type of the engaged regulatory relations ., This is in contrast with the traditional pathway analysis approaches that employ the so called Gene Set Analysis ( GSA ) 6 or Gene Set Enrichment Analysis ( GSEA ) 32 methodologies , with the target to identify the most significant ( with respect to the target phenotypes ) pathways ., Even if there are some differences between the two methodologies ( mainly with respect to the background statistical framework that they utilize ) their fundamental characteristic is that they face pathways not as networks but just as groups ( plain list ) of associated genes ., Both GSA/GSEA aim towards the reduction of differentially expressed gene lists ( as assessed by gene ranking and selection ) to biology relevant short lists that exhibit over-representation characteristics in targeted biological processes and molecular functions such as the ones present in pathways ., Even in the lack of differentiating gene lists , GSA/GSEA may assist the identification of phenotype associated genes by taking advantage of the fact that many genes in a gene list may exhibit changes in their expression status under different functional conditions 106 , and in some cases proved effective in improving predictive performance 15 , 107 ., Nevertheless , pathways are richer and encompass much more knowledge than just a plain list of genes , such as the topology and the involved gene regulatory relations recorded in the respective pathway networks ., This important drawback of the GSA/GSEA approaches limits their ability to capture and model the multiple roles that genes take in the various molecular pathways ., GSA/GSEA base their analysis on the cellular components ( i . e . , genes , proteins etc . ) and not on the pathway networks’ connectivity ( topology and interaction types ) just because they compromise the underlying networks’ complexity in favor of computational simplicity 60 ., Even if some of the existing pathway analysis methodologies and tools , like GSEA , take into account the topology and the underlying regulation machinery of pathways , a fundamental difference contrast them with the methodology followed by MinePath ., The difference resides in the handling of the pathway gene regulatory relations ., Most of these systems follow a scoring methodology in which , each regulatory relation is scored according to its status in the input gene expression data with activations to receive a ‘+1’ and inhibitions a ‘-1’ score , depending on their consistency with the respective gene expression sample profiles ., A final score per sub-path is calculated and a final rank score per pathway is provided—an exception holds for GGEA , which provides sub-path qualitative consistency assessments ., With such a ‘summation’ approach the risk to miss important regulations is increased ( such a case is shown in the ‘Results’ section ) ., A final remark concerns the ability of MinePath to assess the phenotype differential power of pathway sub-paths and not the respective power of single regulatory relations ., This unique feature of MinePath makes it a valuable tool for in silico molecular biology experimentation , and serves the biomedical researchers’ exploratory needs to reveal and interpret the underlying pathway regulatory mechanisms that putatively govern the expression of the target phenotypes ., The performance of MinePath was assessed using publicly available BrCa and CG gene expression data ., The results demonstrate the validity of the MinePath methodology in devising sub-path based predictive models ., It would be of interest to compare and contrast the predictive performance of MinePath with the performance of traditional differential gene expression analyses , as well as the degree of overlapping genes between different datasets and phenotypes ., Such a comparison is out of the scope of the current paper , as the main focus is on the detailed presentation of MinePath sub-path based pathway analysis methodology and its comparison with relative state-of-the-art pathway analysis methodologies ., A fair comparison with traditional gene expression analyses methodologies will require a large enough collection of diverse gene expression datasets as well as different ( algorithmic ) parameterization arrangements , and it is in our plans to set-up and conduct such a systematic and large-scale assessment study ., The comparison of MinePath with state-of-the-art pathway analysis methodologies like SPIA , GGEA , DAVID , GSEA and PATHOME highlights the value of the system , not only for its ability to identify important molecular regulations but also for its web-based implementation as well as , for its interactive visualization capabilities that facilitates the biological interpretation of the findings ., Using a meta-analysis approach on three merged BrCa ER datasets , and focusing on the well-known ErbB signaling pathway , we provided indicative evidence for the power of MinePath to identify and reveal important molecular cancer-related regulatory operations that governs the expression of specific BrCa phenotypes ., Although protein regulation can occur in both translational and post-translational level , quantitative relations of gene expression still remain a strong indicator of protein regulation , and thus a useful tool for the identification of protein relations/regulation ., As an example , MinePath was able to reveal the downstream effects and the corresponding regulatory machinery that underlies CXCR4-mutant affected genes ) ., MinePath is in active continuous development with ongoing work and planned extensions to target and include:, ( i ) automation of the CXCR4-mutant analysis methodology in order to create integrated networks with differential sub-paths that range across different pathways;, ( ii ) support multi-class gene expression data in order to differentiate between more than two target phenotypes–exploiting relevant research from the machine learning field , transformation of a multi-class problem to different two-class/binary problems seems a promising direction to follow–optimization approaches are also of relevance 108 , and will be assessed for their customization to the MinePath methodology;, ( iii ) adaptation of more pathway databases and relative pathway representation formats ( except from KEGG ) –such an extension will enable the assessment of the robustness of results across different pathway databases;, ( iv ) offer services for automated uploads of gene expression data repositories ( e . g . from Gene Expression Omnibus ( GEO ) and TCGA/Cancer Genome Atlas Research Network ( cancergenome . nih . gov ) ) ;, ( v ) provide more enriched annotations and respective links for the visualized results ( genes , relations , pathways etc ) –such an extension will ease the users to focus their inquiries on specific genes of interest ( e . g . , genes that belong to particular molecular function ) , and will enable the respective filtering and restriction of the input gene expression profiles just to these genes and, ( vi ) visualization of differential genes , e . g . , gene signatures from various studies , could be also supported ( a first attempt , to be included as a stable component of MinePath , is implemented in the application of MinePath on a study that concerns the determination of the biological relevance of transcription factor binding sites over functional pathway sub-paths 109 ) ., In gene expression studies , the quest is not only to identify genes that differentiate between phenotype classes but also to uncover putative correlations between these genes ., Such an appr

Introduction, Results, Discussion, Materials and Methods

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks , offering a promising approach towards the biological interpretation of phenotype differentiating genes ., Early pathway analysis methodologies , named as gene set analysis ( GSA ) , view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations ., These approaches , even if they achieve computational efficiency and simplicity , consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics ., Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile ., Even with this approach , assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths ., We introduce MinePath , a pathway analysis methodology that addresses and overcomes the aforementioned problems ., MinePath facilitates the decomposition of pathways into their constituent sub-paths ., Decomposition leads to the transformation of single-relations to complex regulation sub-paths ., Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power ., Assessment of differential power supports the identification of the most discriminant profiles ., In addition , MinePath assess the significance of the pathways as a whole , ranking them by their p-values ., Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach ., In contrast with many pathway analysis tools , MinePath is a web-based system ( www . minepath . org ) offering dynamic and rich pathway visualization functionality , with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination ., This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes .

It is generally recognized that using different sources of information and knowledge is better than just using a single source ., This is most profound in the post-genomics era ., On one hand , the advent of genomic high-throughput technologies realized by DNA microarray and next generation RNAseq technologies enabled a ‘systems level analyses’ by offering the ability to measure the expression status of thousands of genes in parallel ., On the other , molecular pathway networks depict the interaction of DNA segments during the transcription of genes into mRNA ., The prominent and vital role of pathways in the study of various biology processes is a major sector in contemporary biology research ., We introduce MinePath , a pathway analysis methodology that amalgamates information and knowledge from gene expression profiles and molecular pathways ., The novelty of MinePath resides in its ability to target not just the genes involved in the pathways , as most of existing methodologies and tools do , but directly their interrelations and interactions ., With this approach , the regulatory machinery that putatively governs and guides the expression of disease phenotypes can be explored and revealed .

cell death, genetic networks, gene regulation, cell processes, network analysis, genome analysis, bioassays and physiological analysis, mapk signaling cascades, research and analysis methods, computer and information sciences, gene expression, metabolic pathways, metabolism, microarrays, biochemistry, signal transduction, cell biology, phenotypes, apoptosis, genetics, biology and life sciences, genomics, cell signaling, computational biology, signaling cascades

journal.pcbi.1007111

Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints

There is an abundance of literature on the modeling , analysis , and control of epidemics ., We briefly mention three areas that are closely related to our paper , namely , mobility modeling , disease modeling , and designing interventions to control the spread of epidemics ., We refer to 11 12 for surveys on these topics ., We develop a framework for national seasonal/pandemic influenza planning using realistic datasets , a mechanistic model of disease spread , and a greedy optimization algorithm for vaccine allocation ., Our specific contributions are discussed below ., Our approach in building the national scale model involves two broad steps: Model calibration is the process of estimating parameters of the computational model that can reproduce observed characteristics in the ground truth ., In the context of epidemiology , beyond forecasting , calibrated models allow us to perform counterfactual ( i . e . , what-if ) studies , and address resource allocation questions like VaccIntDesign ., In this section , we will briefly describe our approach and the ground truth used for the two-stage calibration of the national-scale influenza model ., We begin with the assumption that the ground truth of interest y is a noisy version of the simulation model η ( ⋅ ) at some unknown input parameter configuration θ ^ ., We use a gaussian error model , which are simple and adopted widely for many applications , including epidemics 30 , 31 ., We adopt importance sampling 32 scheme to produce posterior realizations of the calibration parameters ., We begin with sampling from an easy-to-sample importance distribution Im ( θ ) ( say , uniform ) , and run the simulation model η at each of those samples ., The importance weights are computed as the ratio of the posterior distribution ( proportional to the product of likelihood and prior distribuion ) and importance distribution evaluated for each of the samples ., The samples along with the normalized weights then constitute an estimate of the posterior distribution ., Further , it is often useful to factorize the likelihood function , if possible , when the simulation model is required to be calibrated to several different criteria 31 ., One possible way is to sequentially calibrate the model to different criteria ., In addition to simplifying the computation of importance weights , the approach allows user to introduce more samples as needed using the intermediate calibrated parameter space ., More details on the statistical framework and the two-stage posterior exploration is provided in S1 Appendix ., Additional methods ., Finally , in our case , since we are interested in using a single calibrated model for the optimization study ( as against a weighted ensemble provided by the posterior distribution ) , we consider the Maximum a Posteriori ( MAP ) estimate i . e . , model configuration with highest frequency to be the calibrated model ., We now consider the problem of determining the spatial allocation of vaccines across the US to minimize a chosen objective function ., In addition to the complexity introduced by non-linear dynamics of the disease model , we also need to account for the temporal constraints imposed by vaccine production and delivery logistics ., Formally , the VaccIntDesign problem involves determining the vaccine allocation vector X that minimizes the total attack size given by f ( X ) ., This can be expressed as:, minimize X f ( X ) subject to ∑ i X i , t ≤ B t , for all t ,, where Bt is the total number of vaccines available at time t ., Our goal in the VaccIntDesign problem is then determine the amount of vaccine allocated to each patch i at time t , denoted by Xi , t ., The VaccIntDesign problem is very challenging , and its exact complexity remains open ., A strategy that has been useful in many kinds of intervention design problems is to design a greedy allocation , which selects each decision variable based on the marginal improvement to the objective function ., If the problem involves submodular maximization , such a strategy is guaranteed to give a constant factor approximation; see , e . g . , 22 23 ., In contrast , VaccIntDesign involves a minimization , and the objective functions are neither submodular or supermodular , in general ., Nevertheless , the greedy strategy is a reasonable approach for designing vaccine allocation strategies , and we study it here with an allocation step size of L . The algorithm begins with an initial zero allocation ., For each week w , the algorithm allocates the next set of L vaccines to the state s which leads to the maximum reduction in the objective value f ( X ) ., The algorithm is repeated for week w , until we exhaust Bw , and then proceed to the next week’s supply of vaccines ., Note that the computation of marginal benefit of allocating L additional vaccines to state s subject to population constraints , can be computed in parallel ., As a generalization , we have also included the lookahead duration d ( in weeks ) as an additional parameter ., This means that the potential allocations at a greedy stage of week w are evaluated by their reduction of attack size at week min ( w + d , T ) where T is the total duration of the epidemic ., While this includes the total attack size ( full lookahead , when d ≥ T ) as a special case , it also allows us to explore the resulting trade-off due to varying forecast horizons ., The detailed algorithm is provided in S1 Appendix ., Additional methods ., For the current study , we begin with the disease model calibrated to the 2014-15 influenza season ., Given the best fit model Mθ⋆ , we define the optimization study scenarios as follows: A scenario is defined by a ( v , E ) tuple and is derived by setting the vaccination efficacy to v in model Mθ⋆ and calibrating the transmissibility β to achieve national attack size of E under pro rata vaccine allocation ., We do this to simulate multiple seasons that spread spatially like the 2014-15 season , but vary in their severity ( captured by the national attack size E ) and the efficacy of seasonal vaccine ( captured by v ) ., In our study setting , we construct 12 scenarios , where v takes values in {0 . 2 , 0 . 35 , 0 . 5} and E takes values in {40 , 61 , 73 , 86} where the values are in millions of cases , corresponding to different severity levels based on past seasons of seasonal influenza ., Thus for each target attack size E , we have three scenarios , in which E is achieved by assigning vaccines at v efficacy ., In our study ( restricted to contiguous US , including DC ) , the number of states S = 49 ., Also , we set the number of weeks W = 40 , roughly the period from September to May corresponding to the influenza season ., Therefore , the allocation profile X has 1960 spatio-temporal dimensions ., The temporal constraint B is based on historical vaccine uptake schedue available from CDC FluVaxView 36 ., CDC FluVaxView provides monthly coverage estimates nationally for the past influenza seasons ., We scaled it by the national population to get a vaccine uptake schedule and converted it to the temporal constraint B . Note that CDC also provides the vaccine supply and distribution schedule 37 , however , we noticed a considerable delay between the supply and uptake schedules , so we chose to use the uptake schedule to reflect ground reality ., Current policies for vaccine interventions are designed based on a host of social and political issues , and tend to be fairly simplistic ., For instance , Department of Health and Human Services ( HHS ) directives for targeting pandemic vaccines are based on age group 5 , and the allocation of the national vaccine supply and other resources is typically done proportional to the state population ., There has been a lot of interest in developing more effective interventions , e . g . , 2 4 25 26 ., For instance , Medlock et al . 2 developed an optimal vaccination strategy for the H1N1 outbreak; their model showed a prioritization for a different age group than the ones recommended by CDC directives ., All prior methods are restricted to simple models , and only focus on non-temporal interventions in which the allocation is done ahead of time ., In reality , vaccine supply varies over time , and the real problem involves finding an allocation that respects the supply constraints and optimizes the epidemic outcomes ., Our current model can be extended in several ways ., Firstly , the model calibration process can be refined to match more detailed trajectories of influenza spread , like the ILI % time series , or the in-season burden estimates being produced by CDC starting 2018-19 season 33 ., Such approaches can then be used to do real-time forecasting and provide vaccine allocation recommendations for an ongoing influenza season ., Further , instead of selecting the MAP model for vaccination study , one could use an ensemble of calibrated models based on the posterior distribution , thus being able to quantify uncertainty in the vaccine allocation policy’s effectiveness ., Another aspect of the real-world dynamics currently not being captured in our model is that of residual immunity ., The national influenza model can be improved by taking into account the co-circulating and dominant influenza strains , as well as the strains present in the recommended vaccine for the season ., Note that while improving over pro-rata allocation , greedy algorithm , even with the lookahead duration , may lead to sub-optimal policies ., One can develop algorithms that earmark resources for regions with high spreading capacity , thus potentially improving the effectiveness of vaccine allocation ., Finally , the logistics of the supply of medical resources , such as medicines , medical equipment ( e . g . , ventilators ) , and medical staff is also very complex ., The health infrastructure is generally optimized for typical demand for such resources , and any surge , as would happen during a pandemic outbreak , would place a severe strain on hospitals ., Ajao et al . 27 show that over 50 , 000 ventilators might be needed in the event of a national influenza pandemic outbreak ., Since local and state health systems are usually unprepared for such a surge in demand , the Office of the Assistance Secretary for Preparedness and Response ( ASPR ) maintains a stockpile of mechanical ventilators in strategic locations 38 , which can be deployed during an emergency ., While existing efforts partially address the question of optimizing stockpile redistribution 28 , a mechanistic model like the one developed in this paper will help design better national-scale studies for pandemic preparedness exercises , and develop strategies for allocation of vaccines and other resources during such emergencies ., In conclusion , we have presented a national level seasonal influenza model , based on short-range and long-range mobility datasets , and used it to optimize the spatio-temporal allocation of vaccines ., For the scenario under consideration , we find that the national attack size can be reduced by up to 17% by allocating the early vaccines to regions around the origin of the epidemic ., Most states still end up with close to their overall pro-rata quota of vaccines , however , these findings demonstrate that shifting when and where these vaccines are administered has a sizable impact on the national attack size ., Achieving these optimal outcomes would require better surveillance and the ability to accelerate vaccine uptake at will , which presents multiple challenges ., However , the study shows there is ample room for improvement and this framework provides means for developing a play-book for epidemic containment .

Introduction, Materials and methods, Results, Discussion

Prophylactic interventions such as vaccine allocation are some of the most effective public health policy planning tools ., The supply of vaccines , however , is limited and an important challenge is to optimally allocate the vaccines to minimize epidemic impact ., This resource allocation question ( which we refer to as VaccIntDesign ) has multiple dimensions: when , where , to whom , etc ., Most of the existing literature in this topic deals with the latter ( to whom ) , proposing policies that prioritize individuals by age and disease risk ., However , since seasonal influenza spread has a typical spatial trend , and due to the temporal constraints enforced by the availability schedule , the when and where problems become equally , if not more , relevant ., In this paper , we study the VaccIntDesign problem in the context of seasonal influenza spread in the United States ., We develop a national scale metapopulation model for influenza that integrates both short and long distance human mobility , along with realistic data on vaccine uptake ., We also design GreedyAlloc , a greedy algorithm for allocating the vaccine supply at the state level under temporal constraints and show that such a strategy improves over the current baseline of pro-rata allocation , and the improvement is more pronounced for higher vaccine efficacy and moderate flu season intensity ., Further , the resulting strategy resembles a ring vaccination applied spatiallyacross the US .

Annual vaccination campaigns continue to be one of the prime measures which help alleviate the burden of seasonal influenza ., Due to production and logistic constraints , there is a need for prioritization policies associated with vaccine deployment ., While there is general consensus on age-based or risk-based prioritization , spatial optimization of vaccine allocation has not yet been explored in sufficient detail ., In order to do this , we develop a mechanistic model of influenza spread across the United States , and propose a greedy mechanism for spatial optimization ., We test the methodology on different realistic scenarios with temporal constraints on vaccine production .

medicine and health sciences, influenza, applied mathematics, immunology, social sciences, human mobility, simulation and modeling, vaccines, preventive medicine, algorithms, mathematics, infectious disease control, vaccination and immunization, human geography, research and analysis methods, public and occupational health, infectious diseases, geography, earth sciences, biology and life sciences, vaccine development, viral diseases, physical sciences

journal.pgen.1004548

An Otx/Nodal Regulatory Signature for Posterior Neural Development in Ascidians

Neural tissue formation is a multi-step process through which embryonic cells acquire a neural phenotype ., In vertebrate central nervous system ( CNS ) development , the first step is called neural induction ., Naive ectodermal cells undergo a binary fate decision between epidermis and neural tissue in response to endomesodermal signals that modulate the FGF , BMP and Wnt signaling pathways 1–3 ., While there may be variations between species , BMP inhibition together with FGF signaling activation are key events in neural induction ., Concomitantly or following neural induction , neural tissue is patterned along the antero-posterior and medio-lateral axes ., Acquisition of a differentiated neural phenotype involves further processes such as stabilization and reinforcement of the neural fate , specification of cellular identity and progression towards final differentiation ., Each of these steps is controlled by complex mechanisms involving a variety of molecular players 4–6 ., Non-vertebrate chordates include ascidians ( tunicates ) and amphioxus ( cephalochordates ) ., They form prototypical tadpole-like larvae with a dorsal hollow neural tube patterned similarly to vertebrates 7 , 8 ., The embryological process of neural induction also takes place in these animals but our current knowledge does not provide a unified view ., In amphioxus , BMP activation represses neural tissue formation but FGF inhibition does not abolish neural tissue formation 9 , 10 ., In ascidians by contrast , FGF is essential for neural induction while BMP inhibition does not seem to be involved 11 , 12 ., Comparative embryology within each of these groups and with vertebrates provides an outstanding opportunity to assess the diversity of regulatory strategies leading to a common shared body plan and to test models of gene regulatory network evolution proposed in other bilaterian groups 13 , 14 ., In this context , ascidians can be regarded as interesting chordate evolutionary outliers with unique developmental and genomic features ., Their mode of development , based on small cell numbers and invariant cell lineages , diverges markedly from that found in vertebrates and amphioxus 15 ., In addition , ascidians also display a fast rate of evolution with extensive genome rearrangements and compaction as well as gene losses 16 , 17 ., Ascidian genomes are thus very different from other chordate genomes ( for example , synteny and ultra conserved elements conserved between vertebrates and amphioxus are not found in ascidians ) 18 , 19 ., Finally , the high conservation of ascidian cell lineages throughout ascidian groups allows the comparison of genomically divergent ascidian embryos with a cellular level of resolution 20–22 ., The dorsal hollow neural tube of the ascidian larva is composed of three morphologically distinct regions: the sensory vesicle anteriorly , the visceral ganglion and the tail nerve cord posteriorly ( Figure 1 ) ., While there are still debates on their precise homology to vertebrate CNS domains , they are thought to be equivalent to fore/midbrain , hindbrain and spinal cord respectively 23 , 24 ., The ascidian CNS has a dual origin and specification logic ( reviewed in 25 ) ., Three separate lineages , named according to the founding blastomeres of the 8-cell stage embryo , form the ascidian CNS ( Figure 1 ) ., The A-line neural lineage originates from vegetal blastomeres and gives rise to the posterior part of the sensory vesicle and to the ventral and lateral parts of both visceral ganglion and tail nerve cord ., Ectodermal blastomeres give rise to the anterior part of the sensory vesicle ( a-line ) and to the dorsal part of the visceral ganglion and tail nerve cord ( b-line ) ., While A-line CNS is specified autonomously 26 , a- and b-line are specified through neural induction by FGF9/16/20 secreted from the vegetal hemisphere at the 16- to 32-cell stage transition 11 , 12 , 27 , 28 ., Early target genes including Otx , Nodal , Elk and Erf are expressed at the 32-cell stage in all or part of the neural precursors ( a6 . 5 and b6 . 5 blastomeres; Figures 1 and S2 ) where ERK signaling is active 11 , 29 , 30 ., Interestingly , each of these precursors also contributes to the peripheral nervous system ( PNS ) following FGF9/16/20 induction 31 , 32 ., For example , the b6 . 5 blastomere gives rise to the dorsal midline of the tail epidermis , a neurogenic territory from which the epidermal sensory neurons of the PNS form ( Figure 2A ) ., Beside the requirement of Otx for anterior neural tissue formation 33 and the key role of Nodal in A-line CNS patterning and formation of the b6 . 5 derivatives 23 , 29 , 32 , 34 , 35 , little is known for the function of these immediate target genes in neural fate acquisition or stabilization ., In order to gain insights into post-neural induction events , we focused our attention on the regulation of Msxb and Delta2 , markers of the progeny of the b6 . 5 blastomeres ., Both genes are expressed from the 64-cell stage ( after neural induction ) in the b6 . 5 progeny ( b7 . 9 and b7 . 10 blastomere pairs; Figure 2A and 36 , 37 ) and are required for further specification and differentiation of these progenitors ., Msxb is a marker of the entire b6 . 5 lineage until neurula stages , and is required for tail dorsal epidermal midline and dorsal nerve cord formation 23 , 35 ., Delta2 is involved in the specification of epidermal sensory neurons within the epidermal midline 32 , 38 ., In this study , we show that FGF signaling is necessary and sufficient for b6 . 5 fate acquisition in posterior ectoderm ., Downstream of FGF , Nodal is necessary for b6 . 5 fate ., Although it cannot induce neural tissue on its own , it is sufficient to posteriorize FGF-induced neural tissue ., This led us to search for other factors acting with Nodal downstream of FGF ., We uncovered a critical function for the transient expression of Otx in posterior neural fate acquisition ., Using this simple model of regulation , we were able to isolate b6 . 5 lineage specific enhancers for both Msxb and Delta2 ., We further show that this mode of regulation is shared with the distantly related ascidian Phallusia mammillata , strengthening our proposal that Otx , a well known regulator of anterior neural tissues in many metazoans , has been co-opted in ascidians for posterior nervous system formation ., Previous reports indicated that induced b6 . 5 fates are lost after abolition of FGF signaling 11 , 28 , 35 ., We extended these results using a pharmacological inhibitor of FGF/MEK signaling ( U0126 ) , three early markers of b6 . 5 progeny ( Msxb , Delta2 and Chordin ) and two tailbud markers of dorsal tail epidermis midline and dorsal nerve cord , Klf1/2/4 and KH . C7 . 391 respectively ( Figures 2 and S1 ) ., MEK inhibition led to a conversion of neural b6 . 5 progenitors into epidermis as demonstrated by the loss of expression of all neural markers , coupled to the ectopic expression of the epidermal marker Ap2-like2 at gastrula stages ( Figure S1 ) ., Previous reports indicated that activation of the FGF pathway in explanted ectodermal precursors leads to the induction of neural fate in cells normally fated to form epidermis , with different neural fates achieved in a-line and b-line blastomeres 11 , 12 , 27 , 32 ., We confirmed that this was also the case in whole embryos ., We treated whole embryos either with recombinant FGF protein from the 16-cell stage or overexpressed FGF9/16/20 by electroporation using the pFOG driver ( expressed from the 16-cell stage throughout the entire ectoderm 39 ) ., As expected , the epidermis marker Ap2-like2 was strongly down-regulated throughout the ectoderm ( data not shown ) ., The posterior neural markers Nodal , Msxb and Delta2 were ectopically expressed throughout the posterior ectoderm ( b4 . 2 lineage or b-line ectoderm ) , and the anterior neural marker Dmrt1 was activated throughout the anterior ectoderm ( a4 . 2 lineage or a-line ectoderm ) ( Figures 3A-H and S2 ) ., Chordin , which is normally expressed in the progeny of b6 . 5 as well as in a8 . 26 and a8 . 28 blastomere pairs ( Figure 3C ) , was expressed throughout the posterior ectoderm and in part of the anterior ectoderm in response to ectopic FGF treatment ( Figures 3C and 3G ) ., Nodal activation at the 32-cell stage was a likely direct consequence of FGF signaling ., FGF treatment activated Nodal ectopic expression in the presence of protein synthesis inhibitor ( Figures S2 ) , suggesting the absence of a transcriptional relay ., In addition , a previously identified b6 . 5-specific Nodal enhancer has the same regulatory logic as the FGF-responsive enhancer of the direct FGF target gene Otx 30 ., Msxb , Delta2 and Chordin are more likely to be indirect targets of FGF as they are activated later at the 64-cell stage ., In the following sections , we will precisely define the regulatory interactions between FGF , Nodal , Otx , Msxb , Delta2 and Chordin in the b6 . 5 lineage ., To determine the function of Nodal during b6 . 5 fate acquisition , we blocked the function of its receptor with the pharmacological inhibitor SB431542 or overexpressed the Nodal antagonist Lefty in the ectoderm using electroporation ., Both perturbations led to a loss of expression of Msxb , Delta2 and Chordin in b-line neural lineage at gastrula stages ( Figures 2B , 3I-K and S1 ) ., At later stages , expression of the dorsal tail nerve cord marker KH . C7 . 391 was lost , as was the dorsal expression of the tail midline marker Klf1/2/4 ( Figure 2B ) ., This altered genetic program was similar to that obtained in response to FGF inhibition , suggesting that Nodal acts downstream of Fgf9/16/20 in b-line neural specification ( Figure 2C ) ., Consistent with this , FGF-induced ectopic activation of Msxb , Delta2 and Chordin was suppressed by Lefty overexpression ( Figure 3M-O ) ., Nodal was however not the sole mediator of FGF action , as its inhibition was not sufficient to convert the b6 . 5 progeny into epidermis , marked by Ap2-like2 expression ( Figure S1 ) ., We next overexpressed Nodal throughout the ectoderm using the pFOG driver and analyzed marker expression in the a- and b-line ectoderm ., Ectopic expression of Chordin was observed throughout the ectoderm ( Figure 3S ) , independently of the FGF induction status of the cells ., Ectopic Chordin expression was stronger in a-line ectoderm , possibly reflecting the stronger levels detected in a8 . 26 and a8 . 28 blastomeres compared to b6 . 5 progeny in control embryos ( Figure 3C ) ., By contrast , we did not detect ectopic activation of Msxb and Delta2 in posterior ( b-line ) ectoderm ( Figure 3Q , R ) ., However , anterior neural tissue precursors ( a6 . 5 lineage ) ectopically expressed these two genes ( Figure 3Q , R ) and had reduced Dmrt1 expression ( Figure 3T ) ., These data indicate that anterior neural precursors adopted a posterior identity in response to Nodal expression ., Consistent with these observations , co-electroporation of pFOG-FGF9/16/20 and pFOG-Nodal , led to the induction of posterior neural tissue in anterior ectoderm , demarcated by the ectopic activation of both Msxb and Delta2 and by the repression of Dmrt1 ( Figure 3U , V and X ) ., The results of this section indicate that Nodal alone is required , though not sufficient , to induce neural tissue and that it can posteriorize FGF-induced neural tissue ., Interestingly , expansion of the anterior neural marker Dmrt1 to posterior b-line territories was not observed following Nodal signaling inhibition in either wild type or FGF-induced contexts ( Figure 3L , P ) ., These results are consistent with the presence of a Nodal-independent factor necessary for Dmrt1 expression and anterior neural fate acquisition in a-line ectoderm 40 , 41 ( see discussion ) ., In summary , three genes expressed downstream of FGF in the b6 . 5 progeny show different requirements regarding Nodal signaling: Chordin can be activated in the entire ectoderm while Msxb and Delta2 are positive targets of Nodal solely in FGF-induced neural cells ., The conversion of a6 . 5 anterior neural precursors into posterior neural fate upon ectopic activation of Nodal signaling ( Figure 3 Q , R ) suggests that posterior neural fates may result from the cooperation of Nodal with another FGF-target ., Otx is a conspicuous candidate since it is expressed in all neural precursors downstream of FGF signaling ( Figure S2 ) and is coexpressed with Nodal in posterior neural precursors marked by Msxb and Delta2 expression 11 , 27 ( Figures 2 , 3 and S2 ) ., We first tested the requirement of Otx in b6 . 5 fate acquisition by injecting a specific translation-blocking morpholino antisense oligonucleotide ( MO ) ., Otx morpholino injection led a full loss of Msxb and Delta2 expression at stage 10 ( Figure 4C , F ) ., The resulting embryos displayed gastrulation and neurulation defects reminiscent of FGF or Nodal signaling inhibition ., The tail midline marker Klf1/2/4 was strongly affected ( Figure 4I ) ., Dorsal tail epidermis midline staining originating from b6 . 5 was abolished while posterior-most staining ( originating from b6 . 6 lineage ) was maintained ., Ventral midline expression was also kept but the domain of expression appeared reduced in size ., Dorsal tail nerve cord did not form either as revealed by the loss of the marker KH . C7 . 391 ( Figure 4J ) ., We obtained similar results by overexpressing a dominant negative form of Otx , OtxHDenR ( a fusion protein between the Otx homeodomain and the repressor domain of Engrailed ) 42 in the ectoderm ( Figure S4 ) ., The phenotypes appeared milder probably because OtxHDenR was only targeted to the ectoderm and because of the mosaic inheritance of the transgene introduced by electroporation ., In addition , we observed that expression of the epidermal marker Ap2-like2 was unchanged following overexpression of OtxHDenR ( Figure S4 ) ., Similarly to what has been observed for Nodal inhibition , b-line neural lineage did not form neural tissue upon Otx loss-of-function but did not form epidermis either ., We next tested the effect of Otx overexpression using the pFOG driver ., Although we expected that Otx would need to cooperate with Nodal to activate Msxb and Delta2 , Otx overexpression was sufficient to activate both of these latter genes throughout the ectoderm ( Figure 4B , E ) ., When we overexpressed simultaneously Otx and Nodal throughout the ectoderm , we simply observed an addition of each molecule effect with no increase in the number of embryos ectopically expressing Msxb and Delta2 in the ectoderm ( data not shown ) ., To better understand these results , we further explored possible transcriptional interactions between Nodal and Otx that may control maintenance of their expression following the initial induction by FGF ( Figure S2 ) ., We detected robust activation of Nodal expression at the 64-cell stage when Otx was ectopically expressed ( Figure S3Aii ) ., Accordingly , Nodal expression was repressed by the overexpression of OtxHDenR ( Figure S3Aiii ) ., This interaction between Otx and Nodal was not reciprocal , since Otx expression was not changed upon modulation of Nodal signaling ( Figure S3Avi , vii ) ., Nodal signaling inhibition also prevented Nodal expression ( Figure S3Aiv ) , suggesting the existence of an autoregulatory loop on Nodal similarly to what has been described in vertebrates 43 ., The ectopic activation of Msxb and Delta2 in the ectoderm by Otx overexpression did not require the activation of Nodal , as overexpression of Lefty did not significantly block Otx effect ( Figure S3B ) ., By contrast , Nodal-mediated ectopic expression of Msxb and Delta2 in anterior neural precursors was inhibited by OtxHDenR overexpression ( Figure S3C ) ., These data demonstrate that Otx is an essential regulator of b6 . 5 lineage derived posterior neural tissue formation ., Figure 4K provides a schematic representation of the gene regulatory network acting downstream of FGF in b-line ectoderm ., We next used the above functional evidence to isolate cis-regulatory DNA regions responsible for neural marker expression in the b6 . 5 lineage ., We reasoned that the enhancer responsible for b6 . 5 lineage expression should integrate both Otx and Nodal inputs ., Nodal is a ligand which controls gene expression through the activation of the Smad2/3 nuclear effector ., A Smad2/3/Smad4 complex can directly bind DNA with low affinity through poorly defined GC rich regions or through ( C ) AGAC Smad Binding Element ( SBE ) consensus sequences 44 ., However , high affinity binding is usually achieved through association with a DNA binding cofactor ., In several instances , Fox transcription factors have been shown to fulfill this function 44–46 ., We consequently searched the Msxb locus for the co-occurrence of Otx and Fox/Smad binding sites ., We selected the core consensus sequences GGATTA for Otx , TGTTT for Fox from the Jaspar database 47 , and AGAC for Smad 44 ., We searched for regions enriched in Otx- , Fox- and Smad- core binding site motifs by first scanning , in Ciona intestinalis type A 48 , the 50 kb genomic region that includes Msxb up to its two flanking genes ., We arbitrarily chose a 300 bp window and found 15 regions that contained at least one of each motif ., To reduce the number of candidates we increased the stringency by increasing the number of the least frequent site , which is Otx ., We chose a more degenerate site for this additional motif , GATTA , as in 42 ., Adding one or two GATTA motifs yielded 7 and 4 candidate regions , respectively ., We focused on the latter 4 regions and searched whether the Ciona savignyi orthologous regions harbored a similar combination of binding sites using Vista suite 49 ., A single region matched this criterion and was named “msxb-b6 . 5 line” according to its enhancer activity ( see below ) ( Figure 5 ) ., This region is located just upstream of Msxb on a peak of conservation and contains 6 putative Otx , 5 putative Fox binding sites and 6 putative SBEs ( Figures 5A , B and S5 ) ., This region falls within a region bound in vivo by Otx at early gastrula stages as revealed by ChIP-on-Chip experiment ( Figure 5B ) 50 ., We amplified this 707 bp fragment from C . intestinalis type B genomic DNA ., The sequence obtained is very similar to the reference type A sequence but contains only 4 Fox binding sites and 5 SBEs ( Figure S5 ) ., Placed upstream of the minimal promoter of Fog and the reporter gene LacZ 39 , 51 , this fragment drove transcription throughout b6 . 5 derivatives from the early gastrula stage ( Figure 5C-E and Table S1 ) ., Thus , searching for enrichment in Otx , Fox and Smad putative binding sites in conserved non-coding genomic DNA was sufficient to isolate a region , which binds Otx in vivo at the early gastrula stage and is transcriptionally active in posterior neural precursors ., The same logic led to the identification of a Delta2 enhancer active in the b6 . 5 lineage ., A single genomic region at the Delta2 locus harbored a combination of Otx , Fox and SBE sites within 300 bp in both C . intestinalis and C . savignyi and was named “delta2-b6 . 5 line” ( Figure 5 ) ., This 392 bp long region is located within 2 kb upstream of Delta2 , harbors a strong level of conservation , contains 5 Otx sites , 3 Fox sites and 3 SBEs; and is bound in vivo by Otx ( Figures 5F-G and S6 ) ., When electroporated in C . intestinalis embryos it drove expression in b6 . 5 derivatives from early gastrula stages ( Figure 5H-J and Table S1 ) ., Overall , these results indicate that Msxb and Delta2 share similar regulatory motifs in their enhancers ., We next assayed the relative contribution of Otx , Fox and Smad binding motifs to enhancer activity in the b6 . 5 lineage , focusing on the “msxb-b6 . 5 line” enhancer ., Progressive shortening of this region on both sides ( Figure S7 and Table S1 ) identified an active 273 bp long fragment ( msxb-B ) containing 3 Otx binding motifs , 2 overlapping Fox binding motifs and 4 Smad motifs ( Figure 6A-B ) ., This fragment was still active in inverted orientation ( Msxb-B-inv ) , as expected from an enhancer ( Figure S8B ) ., Msxb-B enhancer activity was abolished when the Otx morpholino was injected and when Lefty was overexpressed ( Figure 6B-D ) ., Simultaneous mutation of the 3 Otx sites through a single nucleotide modification in the core ( GATTA\u200a=\u200a>GcTTA ) ( construct Msxb-D ) led to a partial loss of activity ( Figure 6E ) ., Since activity was not completely suppressed , we looked for potential Otx binding motifs with altered core sequence ., Interestingly , we found a GAATTA motif that corresponds to a canonical GGATTA sequence in Ciona savignyi ( Figure 6A ) ., Simultaneous mutation of this and the 3 canonical Otx sites ( GNATTA\u200a=\u200a>GNcgTA ) ( construct Msxb-I ) led to a complete loss of activity ., We next mutated the 4 conserved Smad Binding Elements ( AGAC\u200a=\u200a>ctAC ) and found these sites to be essential for Msxb-B activity ( Msxb-L construct; Figure 6A , E ) ., We finally mutated the Fox sites ., Two AAACA sites overlap in the AAACAAACA sequence ( Figure 6A ) ., We generated either a single nucleotide change that matches in the core of each Fox site ( AAACgAACA , Msxb-E ) or a single nucleotide change in each core ( AAgCAAgCA , Msxb-H ) ( Figures 6E and S8 ) ., These mutations did not affect enhancer activity ., Additional mutation ( AACA\u200a=\u200a>AgCA , Msxb-G ) of the three more degenerate AACA consensus found in the sequence , but not conserved in C . savignyi , also had no effect ( Figure S8 ) ., We then tested the effect of mutating Fox sites in the sensitized context of the Msxb-D element where 3 Otx sites are mutated and where activity is decreased ., The Msxb-F fragment ( 3 Otx sites mutated , 2 canonical Fox sites mutated ) displayed a further reduction in activity ( Figure 6E ) , suggesting that Otx and Fox sites may work together to control Msxb-B activity ., Mutational analysis indicates that Msxb regulation through the Msxb-B enhancer may involve putative Fox binding sites and requires the presence of putative Otx and Smad binding sites to be transcriptionally active in b6 . 5 derived cells ., We tested the transcriptional activity of the Ciona Msxb and Delta2 enhancers that we identified in a distantly related and genomically divergent ascidian , Phallusia mammillata ., When each construct was electroporated in P . mammillata embryos , we detected LacZ activity in dorsal tail epidermis midline , dorsal nerve cord and secondary muscle , the same territories that are stained in C . intestinalis ( Figure 7B , D and Table S2 ) ., These results suggest that the regulatory logic of these enhancers is interpreted in the same way in C . intestinalis and P . mammillata embryos ., The similar enhancer activity between these two species possibly reflects conservation of the combination of transcription factors , the trans-regulatory logic , acting upstream of Msxb and Delta2 ., We further tested this possibility by determining the expression patterns of Msxb and Delta2 in P . mammillata by in situ hybridization ( Figure S9 ) ., We observed that both genes are activated in the b6 . 5 lineage at the 64-cell stage ( b7 . 9 and b7 . 19 blastomeres ) like the C . intestinalis orthologous genes ., This expression was abolished when inhibitors of the FGF/MEK ( U0126 ) and Nodal ( SB431542 ) signaling pathways were applied to the embryos ( Figure 7G-L ) ., These results led us to search for enhancers regulating Msxb expression in P . mammillata ., Employing the same strategy we used for C . intestinalis genes , we searched the Pm-Msxb locus for regions enriched in Otx , Fox and Smad binding motifs and conserved in the sister species Phallusia fumigata ., We isolated a 587 bp fragment containing 6 Otx , 7 Fox binding motifs and 7 SBEs and located just upstream of Pm-Msxb ( Figure S10 ) ., This fragment , “Pm-msxb-b6 . 5 line” , whose sequence could not be aligned with that of “Ci-msxb-b6 . 5 line” , was active in b6 . 5 derivatives when electroporated in P . mammillata ( Figure 7F ) or C . intestinalis ( Figure 7E ) embryos ( Tables S1 and S2 ) ., Therefore , the functional knowledge acquired in C . intestinalis was sufficient to isolate an active enhancer with expected activity in another species , P . mammillata ., FGF-triggered neural induction in Ciona appears , at first glance , to be a simple inductive process whereby two blastomeres ( a6 . 5 and b6 . 5 ) receive a signal from the vegetal hemisphere and adopt a neural fate instead of an epidermal fate ( Figures 1 and 2A ) ., However , this event is tightly controlled: ectodermal cell competence is regulated 39 , 40 , embryo geometry 52 and various signaling pathways 41 also control the response of the ectoderm to the inducer ., We have shown that three FGF-dependent genes expressed in the b6 . 5 progeny from the 64-cell stage show differential regulation by Nodal signaling ., Chordin is probably directly regulated by Nodal while Msxb and Delta2 need additional inputs from Otx ., Our data provide additional connections and genomic hardwiring to a previously described network 35 ., The network of genes regulating posterior neural fate is not linear and includes several regulatory loops ( Figure 4K ) ., FGF activates at least two direct target genes , Otx and Nodal , at the 32-cell stage , which collectively regulate secondary targets ( i . e . Msxb and Delta2 at the 64-cell stage ) ., Moreover , the regulation that we have uncovered involves a transcription factor and a signaling molecule that are expressed in the same cells ., It is possible that this configuration allows very tight transcriptional control in a lineage-restricted manner using autocrine signaling ., Finally , we have uncovered additional interactions that most likely maintain gene expression in a lineage-restricted manner following initial activation ., For example , maintaining Nodal expression in the b6 . 5 progeny following FGF induction is apparently controlled both by Otx and Nodal itself ( Figures S3 and 4K ) ., The actual mode of concerted regulation of Msxb and Delta2 by Otx and Nodal at the molecular level will need further investigation ., We have proposed that the signaling molecule Nodal uses a Fox factor as a nuclear effector 44 , 53 ., This hypothesis led us to isolate three enhancers active in the b6 . 5 lineage ., However , it is very likely that omitting Fox sites in our enhancer search would have led to the same outcome since Fox consensus sites ( AAACA ) are probably very abundant in the AT-rich ascidian genomes ., Nevertheless , we observed that two overlapping Fox sites ( AAACAAACA ) are present in Msxb enhancers from both C . intestinalis and P . mammillata ( Figures S5 and S10 ) ., However , mutation of these sites in “Ci-msxb b6 . 5 line” enhancer was silent unless some Otx sites were also mutated ( Figure 6 ) ., The C . intestinalis genome encodes 29 predicted Fox factors whose expression pattern during early development has been determined 37 , 54 , but the number of candidate Fox factors ( expressed in the b6 . 5 lineage or maternally provided ) is beyond the scope of the current study ., Although we cannot exclude the involvement of Fox factors in Msxb and Delta2 regulation , we would favor an alternative scenario explaining the concerted action of Otx and Nodal ., We have shown that Smad Binding Elements ( SBEs ) are essential for msxb-B enhancer activity , and the active enhancers that we have isolated contain at least three SBEs ., We could thus conceive that Otx itself serves as a co-factor for Nodal signaling and that it would interact directly with activated Smad2/3 on the enhancer to promote transcriptional activation ., Besides activating secondary FGF targets , the function of direct FGF targets is an opened question ., Epidermal versus neural fate decision is primarily controlled by FGF signaling ., We have shown that inhibition of FGF , Nodal or Otx function abolishes b-line neural fate ., However , contrary to the inhibition of FGF , blocking Nodal or Otx function does not lead neural precursors to adopt the alternative epidermal fate ( Figures S1 and S4 ) ., These observations can be explained by two non-exclusive hypotheses: epidermis fate inhibition is achieved directly upon reception of FGF signaling or several direct FGF targets contribute to epidermis repression ., In particular , in addition to Otx and Nodal , genes such as Elk and Erf are expressed in neural progenitors and are likely direct FGF targets 30 , but their function has not been determined ., Following their activation at the 64-cell stage in the b7 . 9/10 blastomeres , Msxb and Chordin remain expressed in all daughter cells ( until mid-gastrula stages ) but Delta2 expression becomes restricted in b8 . 18/20 blastomeres , precursors of the dorsal tail midline epidermis ( Figure 2 ) ., This change in expression correlates with and may be involved in the fate restriction that occurs at early gastrula stages ., This event is crucial since it separates central nervous system ( dorsal nerve cord ) and peripheral nervous system ( dorsal tail midline epidermis ) precursors ., A similar CNS versus PNS segregation occurs at the same time in the anterior part of the embryo and involves FGF signaling 55 ., While Msxb is essential for the formation of both dorsal tail epidermis midline and dorsal nerve cord 23 , 35 , the role of the two other genes remains to be investigated ., Otx is a transcription factor expressed in the anterior nervous system , and which participates to anterior neural patterning in many bilaterians 56 , 57 ., In ascidians , a similar role has previously been ascribed to this gene in two distantly related species Ciona intestinalis and Halocynthia roretzi 21 , 27 , 33 , 35 , 42 , 58 ., The additional involvement of Otx in posterior neural tissue formation that we describe in the present study is rather unexpected ., However , the function of Otx that we have addressed corresponds to a very early phase of its dynamic expression ., Otx has been shown to be a direct target of FGF signaling at the 32-cell stage 11 ., The expression is transient ( from the 32-cell stage to the 112-cell stage ) in both anterior ( a6 . 5 lineage ) and posterior ( b6 . 5 lineage ) neural tissue precursors and precedes a new and massive expression only in the anterior neural plate ( from early gastrula stages ) ., This early phase marks neural induction in both ascidian species studied 11 , 59 , 60 ., While the onset of expression of Otx homologs in vertebrates may be broader than the prospective anterior central nervous system 61 , there is no report , to our knowledge , of participation of Otx genes in posterior nervous system formation ., We consequently propose that Otx has been co-opted in ascidians for posterior neural tissue specification ., Whether this co-option is unique to ascidians will await more functional data in invertebrate deuterostomes ., We have shown that Nodal is required for posterior neural tissue formation and that Nodal can posteriorize FGF-induced neural tissue ., Interestingly , Nodal signaling is also involved in posterior neural tissue formation in vertebrates 62–64 ., However , this is most likely indirect through the control of mesoderm specification and patterning ., Nodal signaling is rather thought to be an anti-neural pathway whose activity needs to be shut down for neural fate acquisition 65 , 66 ., Our study shows that the function of Nodal signaling in ascidians is different from vertebrates: Nodal is not incompatible with neural fate and it can directly posteriorize neural tissue ., In Ciona , Nodal expression in posterior neural precursors is the result of differential competence of animal blastomeres to respond to FGF ., This competence is controlled by FoxA-a , expressed in anterior blastomeres 35 , 40 , 41 ., When FoxA-a f

Introduction, Results, Discussion, Materials and Methods

In chordates , neural induction is the first step of a complex developmental process through which ectodermal cells acquire a neural identity ., In ascidians , FGF-mediated neural induction occurs at the 32-cell stage in two blastomere pairs , precursors respectively of anterior and posterior neural tissue ., We combined molecular embryology and cis-regulatory analysis to unveil in the ascidian Ciona intestinalis the remarkably simple proximal genetic network that controls posterior neural fate acquisition downstream of FGF ., We report that the combined action of two direct FGF targets , the TGFβ factor Nodal , acting via Smad- and Fox-binding sites , and the transcription factor Otx suffices to trigger ascidian posterior neural tissue formation ., Moreover , we found that this strategy is conserved in the distantly related ascidian Phallusia mammillata , in spite of extreme sequence divergence in the cis-regulatory sequences involved ., Our results thus highlight that the modes of gene regulatory network evolution differ with the evolutionary scale considered ., Within ascidians , developmental regulatory networks are remarkably robust to genome sequence divergence ., Between ascidians and vertebrates , major fate determinants , such as Otx and Nodal , can be co-opted into different networks ., Comparative developmental studies in ascidians with divergent genomes will thus uncover shared ascidian strategies , and contribute to a better understanding of the diversity of developmental strategies within chordates .

The Chordate phylum groups vertebrates , tunicates ( including ascidians ) and cephalochordates ( amphioxus ) ., These animals share a typical body plan characterized by the presence during embryonic life of a notochord and a dorsal neural tube ., Ascidians , however , took a significantly different evolutionary path from other chordates resulting in divergent morphological , embryological and genomic features ., Their development is fast and stereotyped with very few cells and ascidian genomes have undergone compaction and extensive rearrangements when compared to vertebrates , but also between ascidian species ., This raises the question of whether developmental mechanisms controlling typical chordate structure formation are conserved between ascidians and vertebrates ., Here , we have studied the set of ascidian genes which control the formation of the posterior part of the nervous system ., We uncovered original usages of the signaling molecule Nodal and the transcription factor Otx ., For example , Otx , which is a specific determinant of anterior identity in most metazoans , has been co-opted for the formation of the ascidian posterior nervous system ., These two factors define a regulatory signature found in enhancers of posterior neural genes in two genomically divergent ascidian species .

genome expression analysis, functional genomics, genome evolution, animals, dna transcription, gene function, organisms, animal models, developmental biology, model organisms, genome analysis, sea squirts, research and analysis methods, embryology, gene expression, comparative genomics, ciona intestinalis, gene regulatory networks, transcriptome analysis, genetics, biology and life sciences, genomics, evolutionary biology, computational biology, cell fate determination, evolutionary developmental biology

journal.pcbi.1003481

Effect of Dedifferentiation on Time to Mutation Acquisition in Stem Cell-Driven Cancers

Certain aspects of the cancer stem cell hypothesis have previously been addressed by mathematical models ., It has been shown that having a hierarchical tissue design , where a small population of stem cells maintains a transient population of differentiating cells , may slow the accumulation of mutations and protect against cancer 26–28 ., The question of whether genetic instability ( resulting in hyperactive mutation rate ) is an early or later event in mutation acquisition leading to cancer has been addressed by several groups ( see 4 for review ) ., Most mathematical models find that the onset of genetic instability should be an early event , if at least some of the mutations are neutral ., However , sequencing suggests that the mutator phenotype is expressed relatively late in cancer progression 9 ., Stem cell populations are typically small ., Hence , the dynamics of mutant cells in the stem cell population are highly sensitive to stochastic fluctuations ., A tumor begins with a single mutated cell , so there is a substantial chance of mutant extinction due to random events ., Genetic drift and stochastic clonal extinction in stem cell lineages have been experimentally demonstrated for both normal tissue stem cells 29–31 and cancer stem cells 2 in several tissue types ., Consequently , a deterministic model of mutation acquisition in stem cells will significantly underestimate the time to cancer establishment 32 ., Many models of mutation acquisition use a stochastic approach and are concerned with calculating time to emergence or fixation ( or when the number of mutant cells reaches some threshold value used in diagnosis ) of a mutant cell with fitness in a population of size ., The waiting time for cancer is often defined as the time until a particular number of mutation events have occurred in at least one cell ., Iwasa et al . 33 considered a two-stage Moran model and described conditions under which “stochastic tunneling” can occur ., ( In this phenomenon , cells with two mutations reach fixation before cells with one mutation reach fixation . ), Durrett et al . 34 obtained asymptotic estimates of waiting times until a cell with mutations first appears under the assumption of neutrality ( ) ., These models typically consider a fixed population size 5 , 23 , 25 , 35–39 ., The fixed population assumption is supposed to reflect homeostasis in the stem cell population , though how homeostasis is achieved is typically not addressed ., Although the Moran model captures the stochastic nature of mutation acquisition , this type of model is not capable of describing mutations that change the stem cell division pattern and result in possible expansion of the stem cell pool , which in turn leads to tumor growth ., Some recent models also consider mutation accumulation in exponentially growing cell populations 40–43 ., Beerenwinkel et al . 6 used the Wright-Fisher model with exponentially growing population size to look at the effect of selection on the waiting time to cancer , and they predicted that the observed genetic diversity of colorectal cancer genomes can arise under a normal mutation rate ( taken to be per cell division ) if the average selective advantage per mutation is on the order of 1% ., Similar calculations using a discrete branching process found % given 40 ., Note that increased mutation rates due to genetic instability would allow even smaller selective advantages during tumorigenesis , but neutral mutants ( ) result in waiting times that are too long compared with disease incidence ., Other groups have also concluded that for normal mutation rates and neutral mutants , mutations in multiple genes in acquired hematopoietic disorders are most likely very rare events , as acquisition of multiple mutations typically requires development times that are too long compared to disease incidence 36 ., Spencer et al . 44 and Ashkenazi et al . 45 have focused on the sequential order of mutations associated with increased rate of proliferation , decreased rate of death , increased mutation rate , and other hallmarks of cancer that must accumulate before emergence of cancer ., The sequence of mutations with the shortest waiting time to getting all the necessary mutations is considered the most likely mutational pathway 25 , 44 ., However , these models do not consider the possibility that dedifferentiation of progenitor cells can affect the time to multiple mutation acquisition ., The dividing progenitor cell population has previously been described by multi-compartment ODE models , with cells moving between compartments as they age 45–47 ., Note that in these models the exact number of different stages of differentiation is ambiguous and does not exactly correspond to mitotic events , as cells may undergo more than one division in each compartment stage 46 ., Most of these models of age-structured cell populations assume a stem cell proliferation rate that is dependent on the total number of cells and thus incorporate negative feedback as a means of achieving homeostasis 48 , 49 ., These deterministic models have focused on mechanisms that could regulate cell numbers that are necessary for homeostasis and efficient repopulation ., We use a similar mathematical approach to model the progenitor population as 49 , but we couple it to stochastic dynamics in the stem cell compartment ., Upon division a stem cell can produce zero , one , or two stem cells with probabilities , , and , respectively ( Fig . 1A ) ., The mean number of stem cell offspring is given by ., If symmetric divisions are permitted , the stem cell population can be described by a branching process with the expected number of cells at time given by ., However , a branching process either goes extinct or undergoes exponential growth , and thus it cannot capture stem cell dynamics at equilibrium ., One solution is to use a conditional branching process 50 , where the probabilities for a branching process are conditioned to the total population size remaining constant by an unspecified sampling mechanism ( i . e . , assuming that the stem cell population remains in homeostasis ) ., Some theoretical studies have previously considered the impact of the asymmetry of cell division on stem cell dynamics ., However , these stochastic models all assumed a fixed stem cell population size , either through a variant of the Moran process 35 , 51 or conditional branching process 39 ., We utilize a different approach to get a time-varying but bounded stem cell population size in our models ., We use mathematical modeling to study how the possibility of “dedifferentiation” of mutant progenitor cells into a stem cell-like state affects the waiting time to carcinogenesis ., Dividing progenitor cells have large growing populations , so we use a deterministic model to describe their evolutionary dynamics ., For stem cell populations , stochastic effects are important , because the proliferating stem cell population is typically small ., We use a stochastic model for stem cell dynamics as a boundary condition to the PDE governing differentiated cell expansion ( Fig . 1B and C . ) There is also feedback from the deterministic progenitor population to the stochastic stem cell population as a rate of “dedifferentiation” ., To assess the effect of dedifferentiation on time to carcinogenesis , we consider models for stem cell dynamics with both fixed and variable stem cell numbers ( Fig . 1D ) ., The main questions we address are: Our general compartment model can be applied to different tissues , such as colonic crypts , mammary cells , and hematopoiesis ., Extending Eq ., ( 1 ) to account for mutations between multiple subpopulations of progenitor cells ( Fig . 1C ) we obtain ( 2a ) ( 2b ) ( 2c ) Here is the mutation rate per cell per unit time and is the number of progenitor cells of “age” from the subpopulation with mutations ., We assume , and no back mutation is allowed ., Let be the number of stem cells with mutations at time ., Let be the probability of a symmetric division that gives rise to two differentiated cells , be the probability of an asymmetric division that gives rise to one stem cell and one differentiated cell , and be the probability of a symmetric division that gives rise to two stem cells ., Then ( 3 ) If we neglect mutation , the steady wave-form solutions of Eq ., 2 have the form ( 4 ) where is the average number of stem cells of type produced per division and is the age-dependent growth rate of the differentiated cell population ( Text S1 ) ., Hence , the long-term age distribution is largely determined by the functional forms of the differentiated cell birth and death rates ( Fig . S1 and S2 . ) Altered birth and death rates due to mutations can result in mutant subpopulations growing to higher plateaus in size , but the final population size will be bounded ., Our PDE system can be easily modified to have a maximal carrying capacity for each sub-population ., This does not qualitatively change the age distribution of progenitor cells ( Fig . S1 ) and does not significantly affect the fraction of -mutation cells in the total progenitor population ( Fig . S3 ) , so we do not consider it further ., To mimic a maturity switch for cellular proliferation and death , we took the proliferation and death rates of differentiated cells per unit time to be ( 5a ) ( 5b ) Here and are the maximal proliferation and death/removal rates of progenitor cells ., The age at which the proliferation switch occurs ( i . e . , half the progenitor cells stop dividing ) is given by , and the steepness of the proliferation switch is determined by ., Similarly , the age at which half the cells begin to undergo apoptosis is given by , and the steepness of the death switch is controlled by ., If , then differentiated cells between the ages of and are not replicating ( senescent ) ., Note that setting either of these values to zero results in a uniform rate of birth/death ., Effects of varying proliferation/death parameters are shown in Fig . S2 ., The parameters governing proliferation , in particular and , have much larger influence on the final differentiated cell population size than parameters governing death/removal ., The steepness of the switch does not substantially change the age distribution ., Parameters used are summarized in Table 1 ., We used parameter estimates from the human hematopoietic system because parameters for other cancers are less well known ., We used as the number of necessary mutations to develop a cancerous phenotype ., Although it has been estimated that for the human hematopoietic system there are 11 , 000–22 , 000 stem cells 59 , which give rise to all blood and immune system cells , most of these cells are quiescent and only divide when body sustains an injury and needs to repopulate the hematopoietic system ., Our model only considers actively dividing stem cells , which have been estimated by various methods to number around 100 32 , 60 ., The entire actively dividing stem cell population has previously been modeled as turning over once per year 32 , but most recent estimates have an individual stem cell dividing every 25–50 weeks 61 ., However , this is likely an over-estimate , as it is difficult to distinguish between actively dividing and quiescent stem cell populations ., We assume that an active stem cell divides every 20 weeks , which when multiplied by results in an active stem cell population turnover time of weeks ., ( The entire stem cell population including quiescent cells turns over on a much longer timescale . ), Whereas the size of the active hematopoietic stem cell pool is small , the number of progenitor cells such as granulocyte , erythroid , monocyte , and megakaryocyte colony-forming units ( CFU–GEMM ) and granulocyte and monocyte colony-forming units ( CFU–GM ) is much larger ., There are approximately CFU–GEMM cells and CFU–GM cells 62 ., There are estimates that each CFU–GEMM may contribute to hematopoiesis for an average of 60 days ( range of 40–340 days ) and that it replicates at an average rate of once every 50 days ( range of 35–285 days ) 62 ., We track the progenitor populations for weeks , and assume that their proliferative potential rapidly drops off after 10 weeks ., The maximal proliferation and death rates , and were chosen so that 100 stem cells results in progenitor cells of all ages ., Not much is known about the selective advantage provided by driver mutations for different cancer types , except that it is small ( ) ., Unless stated otherwise , we assume neutral fitness in the stem cell pool ( ) in our stochastic models throughout the paper , to focus on the effect of dedifferentiation ., We use a range of for the progenitor cells in the deterministic model ., Mutation estimates per cell division per gene range from about in normal cells to in the case of chromosomal instability 63 ., ( Note that the rate of epigenetic change has been estimated to be orders of magnitude higher than that of genetic change and could also play a role in cancer initiation 10 . ), A common value used in many mathematical models is a driver mutation rate of per division , obtained by assuming a somatic mutation rate of per gene , and about 100 genes that could be mutated to give same phenotype 40 , 45 ., In normal hematopoietic cells the mutation rate has been measured as per division 64 ., Note that in the stochastic model , which considers every cell division , the mutation rate can be used as is , but using chronological time ( i . e . , weeks or months ) means that this value should be multiplied by the average number of divisions per unit time to obtain ., ( Mutations that speed up the cell cycle will then speed up the apparent mutation rate per unit of chronological time in our progenitor model . ), The expected number of doublings from stem to progenitor cells is 46 , and the total number of progenitors cells of type is ., Using values from Table 1 , this results in cell divisions that take place over 10 weeks , so in equations ( 2 ) ., We first considered whether mutation and reproduction in the progenitor population could by itself generate a sustained population of two-mutation cancerous cells ., We thus modeled a scenario in which no stem cell mutations occur , so the boundary condition to the progenitor population system in equations ( 2 ) is simply ., Because selection in the progenitor population might favor mutants , we also assumed that progenitor cells with mutations have a proliferation rate ( Eq ., ( 5 ) ) ., This yields a steady-state age distribution of normal and mutant progenitor cells ( Fig . S2 ) ., Fig . 2 summarizes results for typical parameter values , showing that for mutant cells to be an appreciable fraction of the population , the mutation rate and proliferative advantage must both be unreasonably high ., This is true both if the total progenitor population can grow without bound ( Fig . 2a ) and if its growth is restricted ( Fig . 2b ) ., Similar findings are obtained if competition between progenitor subpopulations is included in the model ( Fig . S3 ) ., Consistent with previous work 26 , 27 , 36 , these results show stem cell dynamics cannot be ignored in considering time to carcinogenesis , so we next considered stochastic models of the stem cell population ., Our first models for stem cells did not incorporate dedifferentiation , so the dynamics were entirely governed by the stem cells ., In modeling cancer , the time to carcinogenesis can be defined as the time for a single -mutation cell to emerge , the time for -mutation cells to pass some threshold number or fraction , or the time for -mutation cells to fix in the population ., If the mutation rate is low ( such that ) , then all three definitions are similar , because the time to emergence of a successful -mutation cell is long compared to the time from emergence to fixation ., However , there is large uncertainty regarding effective mutation rates in carcinogenesis ( Table 1 ) , so the assumption of low mutation rate may not always be valid , and we thus calculated times to fixation ., We began our stem cell modeling by considering fixed population size , corresponding to strict homeostasis ., In this constant case , we could leverage several analytic results , with which our simulations agreed well ., Fig . 3A shows a typical simulation ., The full probability density distribution of time to fixation is given by Eq ., ( 12 ) and agrees well with our simulations for high mutation rates ( Fig . 3B ) ., The time to emergence of a successful mutant is of order 1/ ( ) stem-cell generations ( Eq ., ( 9 ) ) ., For normal mutation rates of per cell division , the mean time until emergence of a two-mutation cell is stem cell generations , which is very long even with a short stem cell generation time ., Because homeostasis is likely imperfect , we also considered a stochastically fluctuating stem cell population size ., We found that , without dedifferentiation , the distributions of times until fixation are very similar for models with and without fluctuations in the stem cell population size , as long as we condition on non-extinction of the stem cell population ( Fig . 3B ) ., This is true for a wide range of probabilities of asymmetric division and strengths of mean reversion ( Eq ., ( 15 ) ) ., This agrees with previous findings that demographic stochasticity does not alter fixation times of neutral mutants in a large population 65 , provided that the carrying capacities of the mutants are the same ., Our results suggest that dynamics within either the progenitor or stem cell compartments considered separately do not result in carcinogenesis in the hematopoietic system on a realistic time-scale , provided that cancer-causing mutations occur at normal mutation rates , selection advantages relative to wild-type stem cells do not appear until mutations , and the stem cell population size is constant or varies stochastically around a carrying capacity ., We thus turned our attention to coupled model systems in which progenitor cells can dedifferentiate into stem cells ., For the coupled system , we first considered stem cell homeostasis caused by strict asymmetric division in the stem cell population , so the stem cell population size remains fixed ., To model dedifferentiation in this case , we built off the Moran model and assumed that when a stem cell dies and another enters the population , the new entrant comes from the two-mutation progenitor population with probability equal to times the proportion of two-mutation cells in the progenitor population ., Otherwise the new stem cell comes from replication of another stem cell ., Roughly speaking , in this model the death of a stem cell leaves a opening in the niche , which can potentially be filled by a dedifferentiated progenitor cell ., The number of progenitor cells which can successfully dedifferentiate is controlled by the number of niche openings ( stem cell deaths ) , not by the absolute number of progenitor cells ., Typical simulation results are shown in Fig . 4A ., We found that dedifferentiation dramatically shortens the time to fixation of two-mutation cells ( Fig . 4B ) ., For small dedifferentiation rates , we also saw good agreement between our simulations and a semi-analytical approximation for the time to fixation of two-mutation cells with selective advantage ( Eq ., ( 12 ) ) ., This agreement suggests that under strict stem cell homeostasis , dedifferentiation is effectively equivalent to a growth advantage for mutant stem cells ., Distributions of times to fixation of two-mutation stem cells are plotted as a function of both dedifferentiation rate and mutation rate in Fig . 4C ., Dedifferentiation had two major effects in this model: increasing the probability that an emergent two-mutation stem cell would fix and reducing the time between emergence and fixation ., Both of these effects act only after a two-mutation cell has been generated in the stem cell population ., ( Recall that , as shown in Fig . 2 , the mutation rate and selective advantage must be unrealistically high for a nontrivial fraction of two-mutation progenitor cells to exist in the absence of underlying two-mutation stem cells . ), For all mutation rates , the distribution of times to fixation was roughly constant for dedifferentiation rates , consistent with population genetics theory that selection is only effective when the selection coefficient is greater than the reciprocal of the effective population size ., For small mutation rates , increasing beyond this threshold only marginally shortened the total time to fixation ., This is because in this case the total time to fixation is dominated by the time for a successful two-mutation cell to emerge , and dedifferentiation only reduces this time by a factor of ( Eq ., ( 9 ) ) , where is the probability of a emergent two-mutation stem cell fixing ., Under neutrality , so for our model with , dedifferentiation can shorten the time to emergence by at most a factor of 10 ., The dedifferentiation rate needed to significantly change this waiting time scales linearly with ( Fig . S4C ) ., Hence , for larger stem cell population sizes , a small dedifferentiation rate would have a larger effect ., For high mutation rates , the effect of dedifferentiation is more dramatic , because the time from emergence to fixation of two-mutation cells , which dedifferentiation also shortens , is comparable to the time to emergence ( Fig . 4D ) ., The model considered in Fig . 4 assumes that only two-mutation progenitor cells can dedifferentiate ., We also considered an alternate model in which any progenitor cell can dedifferentiate ( Text S1 ) ., In this alternate model , dedifferentiation again had little effect for ., Past that threshold the effect was substantial , because in this model dedifferentiation speeds up the time to emergence of two-mutation cells , because one-mutation cells fix much more quickly when they too can dedifferentiate ( Fig . S4D ) ., In addition , we considered the case in which the dedifferentiation rate is additionally weighted by the progenitor proliferation rate , and our results did not change qualitatively ( Text S1 , Fig . S4B ) ., Our analytical and numerical results suggest that , with intact homeostasis in the stem cell population and normal mutation rates , dedifferentiation plays a fairly minor role in speeding up the time to cancer initiation ., We thus turned to consider the case in which homeostasis is not strict ., In the previous section , we assumed that the stem cell population size was constant because homeostasis was maintained by all divisions being strictly asymmetric ., Consequently , dedifferentiated progenitor cells could only occupy newly created openings in the stem-cell niche created by a death event in the stem cell population ., Because homeostasis is likely maintained at the population level 66 , with each stem cell division producing not strictly one stem cell but rather on average one stem cell , we next considered a model in which the stem cell population could stochastically fluctuate around a carrying capacity ., In this model , stem cell homeostasis was maintained by dynamically altering the probabilities of the three possible outcomes of a stem cell division: two stem cells , one stem and one progenitor cell , or two progenitor cells ( Eq ., ( 15 ) ) ., Two-mutation progenitor cells each had a probability per unit time of dedifferentiating , and dedifferentiated cells were simply added to the stem cell pool ., Thus in this model the total influx of dedifferentiated cells depended on the total number of two-mutation progenitor cells , not on the creation of openings in the stem cell niche ., ( Note that , in our previous model with constant stem cell population size , the rate of dedifferentiation per reproduction event was denoted . To distinguish the present model , we denoted the progenitor dedifferentiation rate per cell per unit time as . ), Again , we asked whether dedifferentiation substantially speeds the time to carcinogenesis ., Fig . 5A and 5B show typical results from this model for a moderate dedifferentiation rate ., After a waiting time , the population of stems cells began to grow exponentially , because the influx of dedifferentiated two-mutation progenitor cells exceeded the capacity of stem-cell division homeostasis ., For larger dedifferentiation rates , the exponential growth rate is larger ( Fig . 5C and 5D ) , and the distribution of progenitor ages can be distorted , with many young cells , as seen in Fig . 5E and 5F ., Exponential growth eventually occurs whenever the dedifferentiation rate exceeds a threshold ., Solving self-consistently for the influx of dedifferentiated cells and the growth rates of the stem and progenitor cell populations , we obtained an integral equation for the growth ( 18 ) which provides an excellent fit to the numerical simulations ( Fig . 5 and 6A , B ) ., ( For derivation details , see Text S1 . ), Setting this growth rate to zero , we found ( 19 ) Here is probability of asymmetric stem cell division ( producing one stem and one progenitor cell ) , and is the mean time between stem cell divisions ., ( Note that if , this model reduces to the Moran model with the population size monotonically increasing due to dedifferentiation . ), Lastly , in Eq ., ( 19 ) is the average number of progenitor offspring produced by a two-mutation stem cell ., Because changes as the system attempts to maintain stem-cell homeostasis , is actually a stochastic variable that depends on the stem cell population size ., During exponential growth , because the probability of symmetric divisions that give rise to two stem cells goes to zero , and all new stem cell growth comes from dedifferentiated progenitor cells ., In Eq ., ( 19 ) , is the growth rate of two-mutation progenitor cells as a function of age , so is the number of progenitors produced by one two-mutation stem cell ., Increasing the amplification of mutant stem cells into progenitors increases the net dedifferentiation rate , lowering the threshold ., Because the threshold depends on the age distribution of the two-mutation cells , for a given ( small ) rate of dedifferentiation , evolving a mutant that proliferates faster ( increasing ) can destabilize a system in which the number of cancerous cells is stable and take it into exponential growth regime ., The dependence of the critical dedifferentiation rate on the growth-rate advantage of two-mutation progenitor cells and probability of asymmetric cell division is shown in Fig . 6B ., The critical decreases rapidly as the selective advantage of two-mutation cells increases ., Increasing or also lowers the critical dedifferentiation rate , because homeostasis is less effective when asymmetric stem cell divisions are less frequent ., Note that the exponential growth rate does not depend on the mutation rate ( Fig . S5A ) , and although the critical given by ( 19 ) needed for exponential growth is a function of the probability of asymmetric division , the actual growth rate and the time to exponential growth are not significantly affected by changing ( see Fig . S5B ) ., For dedifferentiation rates below , two-mutation stem cells eventually fix in the population , but for , the stem cell population is likely to begin exponential growth before fixation of two-mutation stem cells ., Thus in Fig . 6C and 6D we report the time to carcinogenesis as the time for the two-mutation stem cell population to exceed , the nominal carrying capacity of the stem cell compartment ., In this case of stochastic stem cell homeostasis , dedifferentiation can dramatically shorten the time to carcinogenesis , even for low mutation rates ., This is because the first two-mutation stem cell often arises not from direct mutation of a stem cell , but rather from dedifferentiation of a progenitor cell generated by mutations within the progenitor compartment ( Fig . 6E ) ., Although mutations in the progenitor compartment do not affect a large fraction of progenitors , because the number of progenitor cells is so large , the absolute number of two-mutation progenitor cells is non-negligible ., Thus even small rates of dedifferentiation can have dramatic effects ., This is in contrast to the case of strict stem cell homeostasis , in which the absolute number of two-mutation progenitor cells was unimportant , because they needed an opening in the stem cell niche to successfully dedifferentiate ., Our results show that the case of stochastically controlled stem cell homeostasis is qualitatively different from the case of strict homeostasis ., If homeostasis is controlled at the population level ( where stem cell decisions between symmetric and asymmetric division are stochastic ) , dedifferentiation can overwhelm it , leading to exponential growth of the stem cell population ., Moreover , if dedifferentiated cells do not depend on openings to colonize the stem cell niche , dedifferentiation can dramatically hasten the time to carcinogenesis , even for low mutation rates ., Progression to cancer is associated with expansion of the cancer stem cell ( CSC ) population , but the origin of these CSCs remains unclear ., Although CSCs may arise directly from adult stem cells , they may also arise from somewhat differentiated cells that have dedifferentiated and acquired stem cell-like characteristics 13 , 14 , 18 , 19 , 67 ., Stems cells replicate indefinitely , giving them a long time to accumulate the mutations that drive carcinogenesis , but the population of actively dividing stems cells ( ) is small ., Progenitor cells replicate only a small number of times , but the population of progenitor cells is typically several orders of magnitude larger than the stem cell population ., Thus , as a population , progenitors undergo many more divisions , potentially letting some of these cells acquire mutations that enable them to dedifferentiate and drive carcinogenesis ., Here , using mathematical modeling , we have shown that even a small rate of dedifferentiation may drastically shorten the time to cancer emergence , even for low mutation rates ., Recent studies suggest stem cell dynamics during homeostasis are governed by neutral competition and genetic drift 10 , 29 , 30 ., Traditionally , stem cells were thought to always undergo asymmetric division , always yielding a stem cell and a progenitor cell , resulting in a fixed stem cell population size ., This scenario is represented by our first model for stem cell dynamics , based on the popular Moran model ., It has been recently shown , however , that symmetric divisions also occur in adult stem cells and may be the predominant form of division 68 , 69 ., Moreover , cancer stem cells have been shown to undergo more symmetric divisions than normal stem cells 70 ., Little is known , however , about how the stem cell population size is regulated 29 ., Hence , in our second model for stem cell dynamics , we made the simplifying assumption of an a priori carrying capacity ., We considered a density-dependent stochastic process , in which the degree of mean reversion is controlled through the probabilities of producing zero , one , or two stem cell offspring ., In this model , the non-constant stem cell population size tends to return to the carrying capacity , because the mean number of stem cells produced per division is greater than one when and less than one when ., ( Although the stem cell population size could , in principle , be maintained by regulating apoptosis rather than biasing division , previous modeling suggests that regulating division probabilities rather than cell cycle time or removal is more important for maintaining homeostasis 46 , 71 . ), If the stem cell population size varies and is regulated by biasing division , we found two distinct regimes ., If the dedifferentiation rate is much less than a critical value , then the initial two-mutation stem cell often arises from a normal stem cell , so the time to fixation

Introduction, Models, Results, Discussion

Accumulating evidence suggests that many tumors have a hierarchical organization , with the bulk of the tumor composed of relatively differentiated short-lived progenitor cells that are maintained by a small population of undifferentiated long-lived cancer stem cells ., It is unclear , however , whether cancer stem cells originate from normal stem cells or from dedifferentiated progenitor cells ., To address this , we mathematically modeled the effect of dedifferentiation on carcinogenesis ., We considered a hybrid stochastic-deterministic model of mutation accumulation in both stem cells and progenitors , including dedifferentiation of progenitor cells to a stem cell-like state ., We performed exact computer simulations of the emergence of tumor subpopulations with two mutations , and we derived semi-analytical estimates for the waiting time distribution to fixation ., Our results suggest that dedifferentiation may play an important role in carcinogenesis , depending on how stem cell homeostasis is maintained ., If the stem cell population size is held strictly constant ( due to all divisions being asymmetric ) , we found that dedifferentiation acts like a positive selective force in the stem cell population and thus speeds carcinogenesis ., If the stem cell population size is allowed to vary stochastically with density-dependent reproduction rates ( allowing both symmetric and asymmetric divisions ) , we found that dedifferentiation beyond a critical threshold leads to exponential growth of the stem cell population ., Thus , dedifferentiation may play a crucial role , the common modeling assumption of constant stem cell population size may not be adequate , and further progress in understanding carcinogenesis demands a more detailed mechanistic understanding of stem cell homeostasis .

Recent evidence suggests that , like many normal tissues , many cancers are maintained by a small population of immortal stem cells that divide indefinitely to produce many differentiated cells ., Cancer stem cells may come directly from mutation of normal stem cells , but this route demands high mutation rates , because there are few normal stem cells ., There are , however , many differentiated cells , and mutations can cause such cells to “dedifferentiate” into a stem-like state ., We used mathematical modeling to study the effects of dedifferentiation on the time to cancer onset ., We found that the effect of dedifferentiation depends critically on how stem cell numbers are controlled by the body ., If homeostasis is very tight ( due to all divisions being asymmetric ) , then dedifferentiation has little effect , but if homeostatic control is looser ( allowing both symmetric and asymmetric divisions ) , then dedifferentiation can dramatically hasten cancer onset and lead to exponential growth of the cancer stem cell population ., Our results suggest that dedifferentiation may be a very important factor in cancer and that more study of dedifferentiation and stem cell control is necessary to understand and prevent cancer onset .

mathematics, theoretical biology, applied mathematics, biology, evolutionary biology

journal.pgen.0030159

In Vivo Validation of a Computationally Predicted Conserved Ath5 Target Gene Set

To understand regulatory networks , it is important to unravel the direct interactions of its transcriptional regulators ., For this , the corresponding transcription factor binding sites in the upstream region of the respective target genes have to be identified ., However , available approaches have not been able to overcome problems related to the fact that the transcription factor binding sites are short ( 6–20 bp ) and consequently are found very frequently , spread all over the genome ., These motifs are functional in only a small fraction of their instances 1 ., It has been suggested that epigenetic processes , in particular histone modifications , permit or prevent the access to chromatin 2 ., Cooperative binding of multiple transcription factors to combinations of motifs also account for the high selectivity in vivo ., Combinations of transcription factor binding sites have therefore been used to computationally predict regulatory modules 3–6 ., Comparative genomic approaches applied methods commonly termed “phylogenetic footprinting” 7 ., These techniques are based on the fact that functional genomic regions are under selective pressure , resulting in the evolutionary conservation of the respective sequences ., Phylogenetic footprinting identifies conserved stretches of noncoding DNA in sequence alignments of related species with limited complexity ., To apply this approach to complex genomes , the complexity can be reduced by focusing on the sequences flanking identified genes ., In closely related species , neutrally evolving sequences , as well as functionally relevant and therefore conserved sequences , result in an alignment ., Consequently , functional motifs are masked by the high degree of overall sequence similarity ., On the other hand , if the genomes are too diverged , sequence comparison may fail to detect short conserved functional motifs due to the lack of significant alignment ., Thus , the evolutionary distance of the genomes analyzed has to be considered ., To overcome these problems , we developed a novel evolutionary filtering approach that takes advantage of the increasing number of sequenced vertebrate genomes ., In a first step , we limited the complexity of closely related genomes by restricting the analysis to the upstream region of annotated genes ., Considering only those genes that contain a transcription factor binding site in this region , we subsequently performed alignments with their orthologs from closely related genomes ., In the second step , the regions of their orthologs in more diverged genomes were scanned for the presence of the motif ., This evolutionary double filtering allowed to identify—in the large number of occurrences of a short motif—the small number of evolutionarily conserved transcription factor binding sites ., We benchmarked this procedure using the available dataset for the transcription factor E2F by comparing the results with the existing chromatin immunoprecipitation ( ChIP ) on chip 8 ., Eighty-five percent of our in silico predicted targets contained in the ChIP on chip dataset were experimentally validated ., This demonstrates the predictive power of the procedure in the context of the complex human genome ., We next used our procedure to de novo identify of a set of Ath5 target genes ., The basic helix loop helix ( bHLH ) transcription factor Ath5 is a key regulator of vertebrate retinal development ., Ath5 is required for the differentiation of retinal ganglion cells ( RGCs ) , which provide the axonal link of the retina to the respective visual centers 9–11 ., Loss of ath5 function results in the absence of RGC formation in vertebrates 12–14 ., Conversely , gain of ath5 function by overexpression in the retina promotes RGC formation 15 , 16 ., So far , only a few Ath5 target genes have been identified , including Ath5 itself 17 , 18 and its binding site is only poorly defined ., We show that Ath5 interacts with its own promoter and autoregulates its own expression via binding to an extended E-box motif ( CCACCTG ) containing the consensus site recognized by bHLH transcription factors 19 ., Using this motif , we predict by phylogenetic double filtering a conserved set of target genes and experimentally validate a number of those targets in vivo ., We first experimentally defined an Ath5 binding site to be used as a signature for the computational prediction of its conserved target genes ., Ath5 had been shown to control its own expression in a conserved positive regulatory feedback loop 17 , 18 ., Since our aim is to identify conserved target genes , we also searched for motifs within the Ath5 regulatory region that are conserved throughout vertebrates ., In a comparative approach using promoterwise ( http://www . ebi . ac . uk/~birney/wise2/ ) we identified two evolutionarily conserved ( from teleosts to mammals ) extended E-box motifs ( CCACCTG ) within 2 kb of upstream sequences that in medaka fish embryos faithfully recapitulate ath5 expression in a reporter construct ( Figure 1A–1C ) ., To test the interaction of Ath5 with these conserved CCACCTG motifs , electrophoresis mobility shift assays ( EMSAs ) were performed with oligos containing the two wild-type motifs or different variants in which the motif was altered with or without affecting the E-box consensus ( see Materials and Methods ) ., We found that the presence of at least one E-box was sufficient to allow binding of Ath5 ., Binding was only abolished if the consensus E-box in both motifs was changed ( Figure S1A ) ., Furthermore , only those oligos in which one of the E-boxes was preserved competed with the wild-type probe when added in excess ( Figure S1B ) ., Those results confirm the specificity of the interaction and indicate a high affinity of Ath5 for the conserved CCACCTG motif ., To investigate the ability of Ath5 to activate its own promoter , we used cos7 cells in a luciferase transcription assay ., As previously demonstrated for chick Ath5 17 , the medaka 2-kb Ath5 promoter is also strongly activated by Ath5 in a dose-dependent manner ( Figure 1D ) ., Our mutational analysis revealed that changing one of the motifs while preserving the E-box consensus results in reduced transcriptional activation ( 2-fold versus 6 . 5-fold of the wild-type promoter; Figure 1D ) ., No activation was observed in all the other variants tested ., Furthermore , embryos injected with corresponding GFP reporter constructs , in which the E-box consensus in the two conserved motifs is disrupted , failed to express GFP in the endogenous domain ( unpublished data; see also Materials and Methods ) ., This indicates that only the identified CCACCTG motifs are efficiently recognized and bound by Ath5 ., To identify functional target sites , and , consequently a conserved target gene set of a given transcription factor in a genome-wide manner , we devised a multistep procedure that relies on the evolutionary conservation of functionally relevant transcription factor binding sites ., First , we reduce the complexity by limiting the search space to the region upstream of annotated human genes ., We subsequently search for the presence of the motif corresponding to the transcription factor binding site in a conserved region with rodents ( see Materials and Methods for the definition of conservation ) ., In a last step , we scan orthologous regions in more diverged species for the presence of the motif ., This additional filtering step is independent of any alignment , i . e . , the motif does not have to lie in a conserved stretch ., All the genes with an upstream region that passes the last filter are defined as the predicted target genes of the analyzed transcription factor ( see Text S1 for details ) ., To assess the performance of our in silico procedure , we benchmarked it using the binding site of the transcription factors E2F ( Transfac , Jaspar 20 , 21 ) by comparing our dataset with that obtained by ChIP 8 ., The details of the benchmarking procedure are described in Text S1 ., Of the 1 , 342 genes with Ensembl identification numbers that were tested by Ren et al . 8 , we predict 14 to be bound by E2F , of which 12 ( 85 . 7 % ) are correct ., This is a significant improvement over a control where genes are randomly sampled ( p-value < 0 . 00001 ) ., We note , however , that our stringent conservation requirement misses 89% of the bound genes ., Low sensitivity is , at this point , an unavoidable consequence of comparative studies that aim at high specificity using evolutionarily distant species ., Using the defined Ath5 binding site , we applied our evolutionary double filtering procedure to identify conserved Ath5 binding sites and , by this , potential Ath5 target genes ., In previous studies , the majority of conserved regulatory regions had been found within 5 kb upstream of genes 22 ., Therefore , in our search for the Ath5 binding site , we concentrated on the 5-kb upstream sequence of all annotated genes in the vertebrate genomes analyzed ., Candidate genes were thus identified by the presence of the conserved CCACCTG motif or its corresponding reverse complement within this region ., Our procedure ( Text S1 ) filtered the number of occurrences of the 7-bp Ath5 binding site from about 324 , 000 instances in the entire human genome ( Ensembl v42 , repeat masked sequences ) to 166 evolutionarily conserved sites and the corresponding genes ( Table S1 ) ., We noted that the majority of these sites are found within the first 2 kb upstream of the annotated transcriptional start site ( Figure S2 ) ., This is in contrast to the random distribution of Ath5 motifs present in the 5 kb upstream sequences of all annotated human genes and further confirms previous studies on the position of relevant regulatory elements 23 relative to the gene start ., We compared the gene ontology annotation ( GO ) 24 of the identified gene set to that of the entire annotated human genome ( Figure S2 ) ., We found an enrichment of the cellular component “nucleus” ( p = 1 . 2e−04 ) , the biological process “transcription factor activity” ( p = 1 . 40e−08 ) , and the biological function “development” ( p = 7 . 02e−12 ) ., We organized the set of predicted target genes of Ath5 into functional categories: transcription factor , neuronal function , axon guidance and growth , cell cycle and signaling , development , and others ( n = 166; Table S1 and references therein ) ., We analyzed the expression pattern of thirty predicted target genes within relevant categories ( see Table S1 ) in the medaka fish retina in comparison to ath5 expression ( Figures 2 and 3; unpublished data ) by whole mount in situ hybridization and found retinal expression for 19 of them ( Figures 2 and 3; unpublished data ) ., At the onset of retinal differentiation ( stage 27 ) 25 , all the target genes expressed in the retina show an expression overlapping with that of ath5 in the central retina ( Figure 2; unpublished data ) ., At subsequent retinal differentiation stages , the expression patterns of the different target genes can be classified into three major groups ., In the first group , the expression pattern remains entirely overlapping with that of ath5 ( Figure 2A–2J ) ., The second group is composed of genes expressed late in mature RGCs in the central retina , abutting the ath5 expression domain ( Figure 2K–2O ) ., In the third group , in addition to the GCL , late expression is also found in neurons of the inner nuclear layer ( Figure 2P–2T ) ., Analyzing the predicted target genes with respect to the GO categories we found Ath5 among the transcription factors , in agreement with its autoregulatory function , as well as a number of factors that have been implicated to function in RGC differentiation , including Brn3C ( POU4F3 , Figure 3A ) , Gfi-1 ( GFI1 , Figure 3D ) , Irx5 ( IRX5 , Figure 3E ) , Dlx2 ( DLX2 , Figure 3H ) , Dlx1 ( DLX1 ) , and Tbx2 ( TBX2 Figure 3K ) 26 ., In some cases , their involvement in differentiation and/or survival of RGCs has been well documented , such as for Brn3C and Dlx1/Dlx2 ( Figure 3A and 3H ) 16 , 27 , 28 ., The majority of the genes in the category “neuronal function” are ion channels such as the voltage dependent anion channel Vdac-2 ( Figure 3L ) ., This category also contains the RNA binding protein ELAVL3 ( HuC , ElavC ) , which has been shown to function in RGC development ( Figure 3G ) ., The category “axon guidance” contains the cell adhesion molecules CD166 ( ALCAM , Figure 3B ) , MCAM , Slit-1 ( SLIT1 , Figure 3N ) and integrin alpha-6 ( Int-α6 , ITGA6 , Figure 3O and 3P ) that play a role in axonal guidance ( see Table S1 ) ., Furthermore , this category contains genes that were not previously shown to be expressed in RGCs ., Our analysis confirmed expression in RGCs for ADAM11 ( Figure 3C ) and NN1 ( NAV1 , Figure 3J ) ., The last category includes genes involved in cell cycle regulation and cell signaling ( RAB25 , Figure 3M and MNT/ROX , Figure 3I ) ., Some of those genes , e . g . , NDRG1 and NDRG2 , play a role in cell differentiation , whereas others , e . g . , CABLES1 and CABLES2 stimulate neurite outgrowth 29 ., In conclusion , we analyzed 30 putative target genes by whole mount in situ hybridization in medaka fish and found retinal expression for 19 of them ( Figure 3; unpublished data ) ., The remaining 11 genes either showed no expression or a pattern that was not consistent with regulation by Ath5 ., Furthermore , retinal expression had already been shown in other species for five additional predicted target genes ( Table S1 ) ., Thus , out of these 35 genes analyzed , 24 ( 63% ) are expressed in a pattern consistent with their regulation by Ath5 ( Figures 2 and 3 ) ., We used ectopic Ath5 expression in the developing medaka embryo to examine the transcriptional regulation of the target genes ., To monitor ectopic Ath5 expression , a plasmid expressing Ath5 under the control of a strong and ubiquitous promoter was injected into one-cell stage embryos together with the 2kb Ath5::GFP reporter ., This results in a mosaic distribution of the cosegregating plasmids in the injected embryo 30 ., Cells expressing Ath5 ( as visualized by GFP ) also ectopically express the putative Ath5 target gene HuC , as visualized by fluorescent in situ hybridization ( Figure 4A ) ., Similar results were obtained for other target genes such as Brn3C and CD166 ( unpublished data ) ., Control embryos coinjected with the empty expression vector and the Ath5::GFP reporter did not show any colocalization of ectopic GFP with any of the target genes analyzed ( Figure 4B ) ., Ectopic overexpression of the related bHLH transcription factors Xath3 ( Xenopus NeuroM , Neurod4 ) or Xash1 ( Xenopus Ash1 ) did not result in ectopic activation of these Ath5 targets genes ( Figure S3; Table S2; Text S1; unpublished data ) ., Taken together , these experiments show that the expression of HuC , Brn3C , and CD166 is specifically activated by Ath5 ., We next analyzed whether Ath5 binds to the promoters of the predicted target genes using ChIP on chick retinal chromatin preparations 18 ., We concentrated on the chick orthologs of the target genes Dlx2 , HuC , Nn1 , and Int-α6 ( Table S1 ) 31 ., Ath5 in vivo occupancy of target sequences was found in all cases tested ( Figure 5 ) ., As a negative control , in the same extracts we found no Ath5 occupancy of the neuroM promoter , a gene also expressed in the retina but not activated by Ath5 18 ., In addition , no occupancy of the Ath5 target sequences was detected in extracts from the optic tectum , where ath5 is not expressed ( Figure 5 ) ., Our results show that our procedure efficiently identifies novel transcriptional targets of Ath5 ., Out of 35 predicted genes analyzed , 24 are expressed in a pattern consistent with regulation by Ath5 ., When tested for ectopic induction by Ath5 , in fish embryos three out of three tested genes were directly activated by ectopic Ath5 ., Finally , ChIP showed the occupancy by Ath5 of all four ( out of four ) target loci tested ., Some of these target genes have been implicated to function in RGC differentiation ., We demonstrate that Ath5 regulates the transcription of these genes and furthermore is bound to their promoter during retinogenesis ., In the work presented here we describe an approach for the identification of relevant target genes that relies on a novel computational procedure ., This in silico procedure provides predictions for functionally relevant instances of transcription factor binding sites ., This is achieved by a phylogenetic double filtering process that relies on the use of evolutionarily diverged genomes , reducing the large number of spurious motif matches , thereby selecting for the putative functional instances of the motif ., Hence , our procedure predicts only evolutionarily conserved targets ., Of crucial importance for the efficiency of the procedure is the second filtering step , where diverged genomes are analyzed for the presence of the motif in an alignment-independent way ., Recently , a comprehensive list of putative regulatory motifs was identified using annotated vertebrate genomes 23 , 32 , but this work did not identify the direct target genes linked to the motifs ., Our benchmarking analysis demonstrates that our method significantly enriched ( p < 0 . 00001 ) for true target genes of a transcription factor when compared to an experimental data set ., Thus , our procedure provides a list of putative targets that have a high probability of being relevant ., This list , as illustrated by our Ath5 target gene prediction , represents a valuable starting point for a downstream analysis of this transcriptional network ., The use of distantly related fish species for the filtering procedure also implies that the list of predicted targets contains only genes from which the regulation through the transcription factor studied has been retained from mammals to fish ., Considering the entire target gene set of a given transcription factor in one species , the nonconserved target genes will be missed using this procedure ., This loss and the apparently low sensitivity ( 89% for the benchmarking using E2F ) are intended , and are an unavoidable consequence of comparative studies aiming at high specificity using evolutionarily distant species ., With the addition of more entirely sequenced genomes resulting from the ongoing sequencing efforts of many vertebrate species , the sensitivity issue will be improved while retaining similar specificity 33 ., This will also allow clade-specific innovations to be addressed , rather than just conserved functions ., A prerequisite for using this procedure is an established binding site for the transcription factor studied ., We experimentally identified an Ath5 binding site , relying on the direct Ath5 autoregulation , which is necessary for the upregulation of its expression in RGC precursors 34 , 35 ., Based on this 7-bp Ath5 binding site , we identify 73 putative Ath5-regulated target genes ., A recent microarray study on Ath5-regulated genes 26 compared wild-type and Ath5 mutant mouse retinae ., The significant ( p = 5 × 10−5 ) but limited ( nine genes ) overlap between our data set and the microarray study is not surprising , given the different approaches used ., While our approach predicts direct targets of a given transcription factor , the microarray analysis does not distinguish between direct and more indirect responses and provides a more global view of the transcriptional differences ., This is well supported by our benchmark analysis ., More recently , in a candidate gene approach , a number of transcriptional targets shared by the transcription factors Ath5 and NeuroD in Xenopus was reported 36 ., Three out of the four Xenopus Ath5 target genes with clear orthologs in other vertebrate species were also identified by our procedure , further supporting the significance of our results ., Ath5 is one of the earliest transcription factors specifically expressed in terminally differentiating RGCs , suggesting its key position in the underlying regulatory network ., The fact that within the target genes we find a strong enrichment of the GO term “transcription factor activity” is in good accordance with this and provides further evidence for the significance of our results ., Within the predicted target genes , we find a strong enrichment of genes acting in cell cycle control , axonal guidance , and neuronal function ., Considering that Ath5 is required for the differentiation of neurons that provide axonal connectivity , this finding is in good agreement with the developmental role of Ath5 ., For example , Ath5 is upregulated shortly before final mitosis 35 , and cell cycle exit is a prerequisite for neuronal differentiation ., The suggested role of Ath5 in this process is underscored by the enrichment of target genes acting in cell cycle control ., Our target gene validation by whole mount in situ hybridization revealed a coexpression with Ath5 in 63% of the cases analyzed ., Furthermore , we show in vivo activation of targets by Ath5 ., This activation is specific for Ath5 , whereas other related bHLH transcription factors fail to activate these targets ., This strongly suggests that the identified Ath5 binding site is specifically recognized by Ath5 to activate transcription ., Furthermore , in ath5 mutant lakritz embryos the expression of all predicted target genes analyzed is absent from the retina , demonstrating their dependence on Ath5 function ( Figure S4; unpublished data ) ., Finally , target gene promoters are occupied in vivo by Ath5 at the time of retinal differentiation , as has been shown for the single established Ath5 target gene , NachR 18 ., In summary , we present a novel in silico approach that predicts target genes of a given transcription factor ., Our benchmarking and experimental application and validation on a novel binding site shows the high predictive power to identify in vivo relevant target genes ., The 5 kb upstream ( 1 , 000 bp upstream regions for the E2F benchmarking ) of all annotated genes were retrieved in Homo sapiens , Mus musculus , Rattus norvegicus , Takifugu rubripes , and Danio rerio using Ensembl version 17 ., The sequences were repeat masked and exon masked ( for possible annotated exon , upstream of the annotated gene start ) ., The gene start was considered to be the annotated start of the longest transcript for each gene ., Orthologous gene pairs were taken from the Compara database ( version 17 ) and all the possible pairs were considered; best reciprocal hits as well as Reciprocal Hit based on Synteny around ., For each human upstream sequence retrieved , the 5-kb orthologous regions in rat and mouse were identified using the downstream gene orthology mapping described above ., Pair-wise alignments between human and mouse and human and rat were done using Promoterwise 32 ., A conserved region is defined as a region with significant alignments ., A significant alignment is defined has having a promoterwise hit higher than 25 bitscore ., See 32 for the justification of such a cutoff ., A conserved site between human and mouse ( or rat ) is defined as a sequence that satisfies the motif description in both species in one position of the significant alignment ., A conserved site between human and a fish is defined as a sequence that satisfies the motif description in both species 5-kb ( or 1-kb for the E2F benchmarking ) orthologous region but is not necessarily located in a significant alignment between these two species ., The motif description can either be a discrete motif or a position weight matrix ( PWM ) ., Both the forward and reverse strand were analyzed ., The ChIP data from 8 was used to benchmark the computational procedure ., From the ChIP data , we used the 130 genes described in Table 3 in 8 as the positive set ., The corresponding Ensembl identification numbers were retrieved from the gene annotation ( 113 genes ) ., The total set corresponds to the entire array used in the experiment ( 1 , 449 genes from Table S1 , of which 1 , 342 have an Ensembl identification number ) ., The E2F PWMs ( M00516 , M00050; Transfac 20 , ) were used to search E2F target genes as described in the filtering procedure section of the Materials and Methods ., The sites were located using the perl module TFBS::pwm 37 with variable score cutoff ( ranging from 75% to 100% ) ., The sensitivity and specificity for each PWM hit cutoff was calculated by comparing the result obtained from the filtering approach to the reference data from 8 ., with TP ( true positive ) being the number of genes overlapping between the positive gene set in 8 ( 113 genes ) and the gene set from the filtering procedure ( x genes depending on the matrix cutoff ) ., FN ( false negative ) is the number of genes in the positive gene set in 8 ( 113 genes ) minus the TP ., FP ( false positive ) is the number of genes overlapping among the gene sets from the filtering approach and the negative set of 8 ( 1 , 342 − 113 ) and TN ( true negative ) is the the negative set minus FP ., Randomization: A set of genes was randomly sampled from the genes analyzed by 8 ., The number of genes in that random set corresponds to the real number of genes found by the computational procedure to overlap with the set of genes analyzed by 8 ., The overlap between the random set and the positive set of 8 was assessed and compared with the real overlap obtained using the computational procedure ., This randomization procedure was repeated 100 , 000 times ., For example , the filtering dataset using the PWM M00516 with a cutoff of 85% gave 38 candidate genes , out of which 14 overlapped with the 1 , 342 genes studied by 8 and 12 overlapped with the positive set ( 113 genes , POS ) ., We randomly picked 14 genes from the genes set studied by 8 ( 1 , 342 genes , ALL ) and calculated the overlap of this random set with the positive set ( 113 genes ) ., The procedure was repeated 100 , 000 times ., The average overlap and maximum overlap was assessed ., For each GO term identification number ( from cellular component , molecular function , biological process ) , we calculated the number of genes annotated with the GO identification number in the positive set ( 166 predicted target genes of Ath5 ) and in the entire human gene set ( Ensembl version 17 ) ., The enrichment of each GO term identification number was evaluated using hypergeometry distribution 38 ., Only GO categories with more than three genes in the positive set were further analyzed ., The positions of the Ath5 motif ( CCACCTG ) and its reverse complement motif are located on the upstream sequences of the human genes and the distance relative to the annotated start site is calculated ( in bp from the longest transcript , Ensembl version 17 ) ., The distribution of these relative positions is then analyzed for all the annotated genes in the human genome and compared with the same distribution obtained using only the 166 predicted target genes of Ath5 ( see Table S1 ) ., The Cab strain of wild-type Oryzias latipes from a closed stock at EMBL-Heidelberg was kept as described 39 ., Embryos were staged according to Iwamatsu 25 ., Zebrafish lak mutants were obtained by crosses of heterozygous lakth241 carriers ., A fragment of about 60 bp encoding medaka ath5 homolog was amplified from a 3-d-old embryo cDNA library using degenerate PCR primers ( forward ATGCARGGIYTNAAYACNGC , reverse TSICCCCAYTGIGGNACNAC ) ., The PCR conditions were: 5 cycles at 95 °C for 1 min , 50 °C for 1 min and 72 °C for 1 min , followed by 30 annealing cycles at 55 °C ., The PCR product was cloned into TOPO TA vector ( Invitrogen ) and sequenced ., Based on this sequence , we designed specific primers for amplifying the full-length cDNA using standard PCR techniques ., Full-length ath5 sequence was cloned in the eukaryotic expression vector pCS2+ for overexpression , in vitro translation , and fluorescein-labeled probe synthesis ( see below ) ., The medaka ath5 cDNA was used to screen a medaka genomic cosmid library ., The 5 kb of ath5 genomic sequence immediately 5′ of the coding region was then cloned into pGL3 ( Promega ) or into a promoterless GFP reporter ( F . Loosli and J . W . , unpublished results ) ., The second vector contains recognition sequences for I-SceI meganuclease for efficient transgenesis 40 ., Deletion constructs containing 4 , 3 , 2 , or 1 . 5 kb of 5′ ath5 genomic region were created by PCR ( primer sequences are available upon request ) ., Point mutations in the two Ath5 binding motifs were generated using the QuickChange XL kit ( Stratagene ) ., Primer sequences are as follows: WT ( GGGGGCGGGCCTCCACCTGCTGCCACCTGTTTGTCTGCTGCG ) , M ( GGGGGCGGGCCTCCAATTGCTGCCACCTGTTTGTCTGCTGCG ) , N ( GGGGGCGGGCCTCCACCTGCTGCCATATGTTTGTCTGCTGCG ) , NM ( GGGGCGGGCCTCCAATGCTGCCATATGTTTGTCTGCTGCG ) , H ( GGGGGCGGGCCTCAAGCTTCTGCCACCTGTTTGTCTGCTGCG ) , P ( GGGGGCGGGCCTCCACCTGCTGCCGATCGTTTGTCTGCTGCG ) , and HP ( GGGGGCGGGCCTCAAGCTTCTGCCGATCGTTTGTCTGCTGCG ) ., See also Table S3 ., The Tbx2 and Dlx1 fugu 5′ genomic regions were identified in Ensembl ., Two PCR products of 2 . 6 and 2 . 3 kb , containing the Ath5 binding motif , were amplified from fugu genomic DNA ( Medical Research Council , Rosalind Franklin Centre for Genomics Research ) using specific primers ( Tbx2 forward GAA CCT CAC GGT GTT GCT CAA AGG CAC and reverse CCT GTT TAT TTG GAC CCG AAA CGA GCG; Dlx1 forward TTG AAT GTG GTG ACC TTT CTG CAG AAG and reverse GGA CGG CTC CCA ATT TAA GTC GAA CTG ) and cloned into pGL3 ., All constructs were verified by sequencing ., Transgenic fish embryos were generated as previously described 40 ., As previously reported , due to the early integration of the reported construct , we observed a very low or null degree of mosaicism in the injected fish allowing the direct analysis of F0 embryos ., Identical patterns of expression were maintained in the following generations ( up to F2 ) ., Injection of reporter constructs differing in the e-box consensus led to different transgenesis efficiencies ., WT: 44/110 embryos reproduce endogenous GFP expression pattern , 40% maximum reachable transgenesis efficiency ., Variant H: 44/115 , 38%; variant P: 26/107 , 24%; and variant HP: 9/111 , 8% ., See Table S3 for primer sequences ., To test the activity of Xenopus Ath5 promoter , the pG1X5 3 . 3-kb construct 34 was injected into embryos at the one-cell stage at a concentration of 20 ng/μl , and embryos were scored 4 d later for GFP expression ., Double whole mount in situ analysis on medaka embryos was performed using a fluorescein probe for ath5 , revealed with fast red ( Roche ) ., Digoxygenin probes for the other target genes were revealed with the NBT/BCIP substrate ( Roche ) using standard protocols ., The sequences of the medaka homologs of the genes were obtained by blasting the fugu coding region on the medaka genome sequence at http://medaka . utgenome . org/ ., Partial cDNA sequences were amplified by PCR from a cDNA library and cloned with TOPO TA vector kit ( Invitrogen ) ., All the clones where confirmed by sequencing and submitted to the European Molecular Biology Laboratory ( EMBL ) database ., Primer sequences are available upon request ., Embedding and sectioning was performed according to standard procedures as described previously 41 ., Zebrafish in situs were performed using standard protocols ., The sequences for Zebrafish Brn3C , Gfi-1 , CD166 , and Adam11 orthologs were retrieved from Ensembl , sequences were amplified using standard PCR reactions from zebrafish 72 h-post-fertilization cDNA , and partial coding sequences were cloned into pCRII-TOPO vector ( Invitrogen ) , following manufacturers instructions ., Primers and constructs sequences are available upon request ., Injection of expression plasmids into one-cell stage fish embryos leads to mosaic distribution and expression with cosegregation of different constructs 29 ., Medaka embryos at the one-cell stage were injected with a solution containing 50 ng/μl of

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

So far , the computational identification of transcription factor binding sites is hampered by the complexity of vertebrate genomes ., Here we present an in silico procedure to predict target sites of a transcription factor in complex genomes using its binding site ., In a first step sequence , comparison of closely related genomes identifies the binding sites in conserved cis-regulatory regions ( phylogenetic footprinting ) ., Subsequently , more remote genomes are introduced into the comparison to identify highly conserved and therefore putatively functional binding sites ( phylogenetic filtering ) ., When applied to the binding site of atonal homolog 5 ( Ath5 or ATOH7 ) , this procedure efficiently filters evolutionarily conserved binding sites out of more than 300 , 000 instances in a vertebrate genome ., We validate a selection of the linked target genes by showing coexpression with and transcriptional regulation by Ath5 ., Finally , chromatin immunoprecipitation demonstrates the occupancy of the target gene promoters by Ath5 ., Thus , our procedure , applied to whole genomes , is a fast and predictive tool to in silico filter the target genes of a given transcription factor with defined binding site .

To establish regulatory gene networks that drive key biological processes is of crucial importance to identify the genes that are directly controlled by transcriptional regulators ., Ideally , this can be accomplished by identifying the direct transcription factor binding site in the cis-regulatory regions of the respective target genes ., However , problems related to the fact that the motifs recognized and bound by transcription factors are short ( 6–20 bp ) and consequently found very frequently and spread all over the genome , have limited this approach ., The transcription factor Ath5 is involved in the specification and differentiation of retinal ganglion cells in the developing vertebrate eye ., We show that Ath5 directly regulates its own expression by binding to a small region of its proximal promoter that contains two identical motifs ., Using this motif description , together with conservation across large evolutionary distances , we then searched in the genome for other target genes of Ath5 and predicted 166 direct target genes ., We then validated a subset of these predictions both in vitro and in vivo ., Our analysis therefore provides an example of computation prediction of transcriptional target genes ., At the same time , the genes identified represent the most comprehensive list of effectors mediating the role of Ath5 during eye development .

developmental biology, chicken, mammals, medaka, in vitro, computational biology, teleost fishes, genetics and genomics

journal.pcbi.1007023

Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations

Most human cells contain 100-10 , 000 copies of mitochondrial DNA ( mtDNA ) which are situated inside the mitochondria ., The proteins encoded by mtDNA are crucial for mitochondrial functionality , and mutations in mtDNA can cause devastating diseases 1–6 ., Heteroplasmy , the proportion of mutant mtDNA molecules in a cell , typically has to pass a certain threshold ( ∼ 60-95% ) before any biochemical defects can be observed 7–14 ., The existence of thresholds at which mutant loads begin to have an effect has profound implications for our understanding of disease onset , drawing attention to the variance dynamics of the mutant fraction in cellular populations ., As this variance increases more cells can be above threshold , and thus show pathology , even if average mutant load is unchanged ., Mitochondrial biogenesis and maintenance require cellular resources , and mitochondria are key sources of ATP and play other important metabolic roles ., The particular ‘effective cost’ that cellular control of mitochondria acts to minimise remains poorly understood: for example , both decreases 15 and increases 15 , 16 in wildtype copy numbers have been observed for different mutations as the mutant load increases ., Some studies suggest that mtDNA density is controlled 17–19 , others that total mtDNA mass 20 , 21 , or mtDNA transcription rate 22 is controlled ., Understanding mtDNA population dynamics inside cells , and how these populations react to clinical interventions , is crucial in understanding diseases 23 , 24 ., However , experimental tracking of mtDNA populations over time is challenging , necessitating predictive mathematical modelling to provide a quantitative understanding ., In parallel with efforts to elucidate cell physiological control , protein engineering methods to artificially control mtDNA heteroplasmy are making fast progress ., Two recently developed methods for cleaving DNA at specific sites involve zinc finger nucleases ( ZFNs ) and transcription activator-like effector nucleases ( TALENs ) 25–31 , which have been re-engineered to specifically cleave mutant mtDNA 32–36 ., MitoTALENs have been successfully used to reduce mutant loads in cells containing disease-related mutations , but elimination of the target mutant mtDNA was not complete 32 , 37 ., Similarly , treating cells multiple times with mtZFNs led to near-complete elimination of mutant mtDNAs 35 , 36 ., Quantitative theory for these therapeutic technologies has not yet been developed , leaving open questions about how these tools can be optimally deployed ., In this paper , we develop theory from bottom-up bioenergetic principles which allows us to study the effects of distinct cellular mtDNA control strategies , to analyse the bioenergetic cost of different mtDNA states , and to combine mtDNA control and energy-based cost to identify optimal control strategies for the cell ., Finally , we construct a model for therapeutic mtDNA control using recent experimental data 36 and highlight challenges linked to heteroplasmy variance ., We employ a linear form of mtDNA feedback control and assume each mtDNA molecule replicates and degrades according to Poisson processes with rates λ and μ , respectively ., Because control of biogenesis or autophagy yield similar behaviours 38 , we assume that the degradation rate μ is constant and that feedback control is manifest through the replication rate λ ( w , m ) , where w and m denote the number of mutant and wildtype mtDNA molecules in the cell ., To connect with experiments , we use μ ≈ 0 . 07 day−1 corresponding to a half-life of about 10 days 39 ., We only model post-mitotic cells , though our analysis can be extended to include cell divisions ., Specifically , we use a birth rate of the form:, λ ( w , m ) = μ + c 1 ( w o p t - ( w + δ m ) ) ( 1 ), where c1 > 0 , wopt > 0 and δ are constants , with wopt denoting the steady state value towards which the effective population , here defined as w + δm , is controlled ., The magnitude of c1 determines how tightly the population is controlled ., We use the term ‘mitochondrial sensing’ to describe how the cell might sense the mitochondrial population that is present ., ‘Mutant sensing’ then refers to how strongly mutants are sensed relatively to wildtypes , which is encoded in the parameter δ ., When steady state is reached ( i . e . w + δm = wopt ) , replication and degradation rates are equal ., In the absence of mutants , the resulting wildtype steady state is assumed to be optimal ., We note that assuming the existence of wopt does not imply a control based on copy number ., Other quantities related to mitochondria may be controlled instead , such as total mitochondrial mass or ATP production , their desired values being reached at an effective population size of wopt ., Thus , we define ‘mitochondrial sensing’ to refer to a wide range of mechanisms available to the cell to infer properties of its mitochondrial population , which can then be used to decide on a control action ., The deterministic dynamics resulting from this control are described in Eq ( 4 ) ., We do not include the possibility of de novo mutations but our approach can straightforwardly describe the subsequent behaviour if new mutations arise ., Our linear model shares features with the ‘relaxed replication model’ 40 , 41 ( Eq ( 5 ) ) , though is written in a simpler form ., The relaxed replication model has been used in a variety of other models 42 , 43 and has obtained experimental support 15 ., We will first investigate properties of more general control strategies , after which we return to our linear control and discuss parameterisations that optimise the energy status of the cell ., Finally , we use the linear control to fit recent experimental data involving treatment of heteroplasmic cells with mtZFNs ., Next , to find general quantitative principles underlying mitochondrial energy budgets , we build a cost function that assigns a cost to any given mtDNA state ( w , m ) and allows a general quantitative investigation of the tradeoffs in maintaining cellular mtDNA populations ., The ‘true’ energy budget of a cell with a given mitochondrial population is highly complex , involving many different metabolic processes in which mitochondria are involved 44–46 ., We provide a simpler description , focussing on ATP production as a central mitochondrial function , and removing kinetic details in favour of a coarse-grained representation , to provide qualitative rather than quantitative results ., In this work , we have built a quantitative theory bridging stochastic optimal control , costs of mtDNA populations , and gene therapies ., Our results contribute to a growing body of evidence 63–66 that the variance of mtDNA populations has important physiological and therapeutic implications independently of mean heteroplasmy , and underline that stochastic theory is required to understand this biologically and medically important quantity ., Key findings of our model ( Table 1 ) include ( I ) the identification of tradeoffs in the control of one or the other mtDNA species; ( II ) the observation that increasing mtDNA variance can lead to increased energetic costs over time and ageing even when means and demands are preserved; ( III ) intermediate heteroplasmy states can be more expensive than states homoplasmic in either mutant or wildtype; ( IV ) mutant sensing can be required to avoid an exponentially increasing cost; ( V ) sensing of cellular energetic status can be more effective than other targets like mitochondrial mass; ( VI ) reduction of mutant mtDNA alone is not always the optimal control strategy; ( VII ) high heteroplasmy variance challenges gene therapy treatments; and ( VIII ) weak , long gene therapy trajectories are more effective than short , intense ones ., Our findings hold qualitatively under the range of conditions we discuss above ., The aim of our manuscript is not to make detailed quantitative predictions and conclusions based on complex models , nor do we intend to imply that our models are the only possible models one could construct ., Rather , we aim to provide general biologically plausible models to gain qualitative insights and to comment on large-scale behaviours ., To this end , our cost function , used to illustrate some of our results , is phenomenological and contains several parameters ., Most of these are biologically interpretable , meaning their values can be obtained or estimated from the literature ., The main elements in our cost function are quite general: terms involving supply , demand , and resource ., To test the qualitative shape of our cost function , one could sort cells based on mitochondrial copy number and heteroplasmy to obtain samples at different points in ( w , m ) space ., Measurements of e . g . cell proliferation , ROS or apoptosis rates allow for the evaluation of an effective cost at each of these points ., By measuring the relative consumption rates of NADH and succinate , as well as the amount of ATP produced per glucose consumed , in identical cells exposed to different energy demands , the saturating output model may be probed ., If the parameter δ is low , i . e . mutants are sensed less , mutant copy numbers at high heteroplasmies will be higher than wildtype copy numbers at low heteroplasmies ., Experimentally , it has been observed that heteroplasmic cells can have total mtDNA copy number values that are 5-17-fold higher compared to cells homoplasmic in wildtype 67–70 ., The cell has somehow allowed these mutants to expand , which may mean that they are less tightly controlled; controls based on total energy output or mtDNA mass ( which can result in δ < 1 ) may lead to such behaviours ., A control on mtDNA mass could explain why deletion mutants are often seen to expand 71 , 72 and would also predict normal copy number levels in cells harbouring mtDNA point mutations ., Recently , it was found that samples with mtDNA indels had very high mtDNA copy number levels , but single nucleotide variants did not 73 ., We showed that heteroplasmy distributions in cell populations can provide important information about the possibility of successfully treating these cells with endonucleases ., A tissue may be harder to treat if its high mean heteroplasmy level is caused by a small percentage of dysfunctional cells ., Experimental values of mean homogenate heteroplasmy in heart tissue of patients with the 3243A>G mutation are roughly around 0 . 8 ( though ranges can be large 74–77 ) and muscle tissue often shows mosaic structures , with deficient patches of cells adjacent to healthy cells ., These examples show that it may be that , at least in some cases , high mean levels are indeed caused by a relatively low percentage of cells , meaning that there are still challenges ahead for efficiently treating these tissues ., One of the features of our cost function is that resource limitations play an important role in shaping the cost landscape ., There are indications that cellular levels of NAD ( a coenzyme involved in oxidative phosphorylation ) are limiting , and that a sufficient supply of NAD to mitochondria becomes critical 78–81 ., An increase of intracellular NAD can lead to an increase in oxygen consumption and ATP production 81 indicating that resource limitation may , at least in some cases , be a genuine constraint ., Adding various kinds of resources can significantly change mitochondrial basal respiration rate 82–84 ., Like any other model , our models have a defined range of applicability ., A key baseline assumption was using identical replication and degradation rates for mutants and wildtypes ., Various possibilities of distinct rates have been offered in the literature , including faster mutant replication rates 22 , 68 , 85–88 , lower mutant degradation rates 89 , and higher mutant degradation rates 90 , 91 ., Including such differences , and other features such as de novo mutations , degradation control , and cell divisions 38 , 64 , 92 , 93 , constitute natural extensions to our theory ., Wildtype and mutant mtDNA copy numbers are considered to have birth rate λ ( w , m ) = μ + c1 ( wopt − ( w + δm ) ) and death rate μ , leading to the following evolution equations:, d w d t = w ( λ ( w , m ) - μ ) d m d t = m ( λ ( w , m ) - μ ) ( 4 ) The corresponding stochastic system , required to e . g . describe fixation , does not have an explicit solution due to nonlinearities ., The deterministic steady state solution of Eq ( 4 ) is given by ( wss + δmss ) = wopt and represents a straight line in ( w , m ) -space ( S1A Fig ) , whose slope depends on the value of δ ., Stochastic dynamics will fluctuate around the steady state line , causing heteroplasmy to change over time until fixation of either species occurs ., This means that , over long times , a cell will reach either h = 0 or h = 1 ( in the absence of mutations ) ., When mutations do occur , a cell will always reach a state with h = 1 ( though many different mutant species may be present ) ., The relaxed replication model assumes a constant death rate μ and a birth rate of the form, λ ( w , m ) = μ w + m ( α R w o p t - ( w + η m ) + w + η m ) ( 5 ), with αR > 1 and η constants 40 , 41 ., We have renamed the parameters of the original model for convenience ., Note that both αR and η influence the mutant contribution to λ ( w , m ) ( rather than the single parameter δ in our linear model ) ., Let the cost per unit time of state ( w , m ) be denoted by C , and the cost corresponding to the steady state ( wss , mss ) by C ¯ ., Even if steady state copy numbers are constant over time ( i . e . the mean values of w and m are always equal to wss and mss ) the mean cost per unit time is generally not equal to C ¯ ., By performing a Taylor expansion , the mean cost per unit time can be written as follows:, E C ( t ) ≈ C ¯+ 1 2 ( var ( w ( t ) ) ∂ 2 C ∂ w 2 + var ( m ( t ) ) ∂ 2 C ∂ m 2 + 2 cov ( w ( t ) , m ( t ) ) ∂ 2 C ∂ w ∂ m ) ( 6 ), where EC ( t ) is the expected cost per unit time given that the trajectory starts in state ( wss , mss ) , and all partial derivatives are evaluated at steady state ., These findings imply the following: suppose all cells in a population of cells are initialised in a state with minimum cost ( corresponding to some specific number of mutant and wildtype mtDNA molecules ) ., At some later time , the mtDNA populations in the different cells will have drifted apart and even if mean copy numbers ( averaged over all cells ) of w and m are identical to their initial values , the increase in variance between cells means that the overall mean cost ( averaged over all cells ) is higher than it was initially ., We assume that the net energy supply per unit time in a state ( w , m ) , called S ( w , m ) , involves the following four terms:, ( i ) the energy output per unit time ( si ) produced by the mitochondria;, ( ii ) a maintenance cost per unit time ( ρ1 ) to maintain the mitochondria , as their presence imposes some energetic cost ( e . g . mRNA and protein synthesis ) ;, ( iii ) a building cost ( ρ2 ) for the biogenesis of new mitochondria; and, ( iv ) a degradation cost ( ρ3 ) to degrade mitochondria ., We will assume that every mtDNA molecule is associated to a particular amount of mitochondrial volume which we refer to as a ‘mitochondrion’ ( section 4 in S1 File ) ., At any time , mitochondria experience a certain energy demand and to meet this demand they need to have a certain resource consumption rate ri ( where i = w , m refers to wildtype or mutant ) ., Here we use the term ‘resource’ as an amalgamation of the substrates used for the oxidation system ., We need to specify the relationship between the power supply ( s ) and the rate of resources consumed ( ri ) by mitochondria ., We use two different models s ( ri ) which are discussed further in section 3 in S1 File s ( r w ) = ϕ ( r w - β ) s ( r w ) = 2 s m a x 1 + e - k r w - 1 ., 1 s m a x ( 7 ), where ϕ , β , k and smax are constants respectively describing the mitochondrial efficiency , a basal proton leak-like term , the saturation rate of the efficiency , and the maximum power supply ( section 4 in S1 File ) ., We assume that pathological mutants can have a deficient electron transport chain ( which may support a smaller flux leading to a lower resource consumption rate for mutants and therefore a lower ATP production rate ) and a lower energy production efficiency , leading to the following mutant energy output: ϵ2s ( ϵ1rw ) ., Here , ϵ1 , ϵ2 ∈ 0 , 1 describe the mutant resource uptake rate and the mutant energy production efficiency relative to that of a wildtype , respectively ., In the main text we set ϵ2 = 1; other values of ϵ2 are discussed in section 4 . 7 in S1 File ., The mitochondrial maintenance cost is denoted by ρ1 and corresponds to the energetic cost required to maintain the mitochondrion that contains the mtDNA ., This energetic costs involves factors like the synthesis and degradation of mitochondrial proteins and enzymes ., We assume the maintenance cost is the same for wildtype and mutant mitochondria ( though for some mutations this is quite possibly not the case ) ., The net energy supply per unit time , S ( w , m ) , then follows as Eq 3 ., To determine the value of rw for a given state ( w , m ) , we first check whether the demand D ( which we assume is a constant ) can be satisfied by supply S ( w , m ) ., If it can , we set Eq ( 3 ) equal to D and solve for rw , i . e . we assume that if possible , the mitochondria will exactly satisfy demand ., It may , however , not be possible to satisfy demand , which can be because of two reasons:, i ) there are not enough mitochondria present to produce enough energy , or, ii ) the resource supply rate , R ( a constant ) , is not enough to meet demand ., In the former case , we set rw = rmax ( a specified maximum resource consumption rate per mitochondrion ) : the mitochondria work as hard as possible to keep their energy output closest to demand ., In the latter case , we assume that the total available resource supply is shared equally between the mitochondria: r w = R w + ϵ 1 m ., Further details of the cost function are given in sections 3–5 in S1 File ., The parameters used in our cost function are summarised in S2 Table and motivated in section 4 in S1 File ., Despite our model being simple , most parameters are biologically interpretable ., Experimentally , cells are transfected with two mtZFN monomers: one which binds selectively to mutant mtDNAs , and one that binds mutants and wildtypes with equal strength 62 ., We simplify this picture by assuming an ‘effective’ mtZFN pool and use ZFN to denote its concentration ., The increase in mtDNA degradation rate caused by the mtZFNs is then assumed to be proportional to ZFN ., Nucleases are imported into the cell and then degrade over time , meaning that their concentration in the cell ( and in the mitochondria ) may be approximated by an immigration-death model:, d Z F N ( t ) d t = I ( t ) - μ z Z F N ( t ) ( 8 ), where I ( t ) and μZ are the immigration and death rates of the effective mtZFN pool , respectively ., In recent experiments 36 , nucleases are expressed for short times meaning that the immigration rate will increase sharply at the start of the treatment after which it decreases over time: we chose to model I ( t ) as an exponentially decaying function , I ( t ) = I0e−bt , where I0 denotes the initial rate directly after the treatment is initiated and b is a constant describing the duration of the treatment ., The mtZFN concentration now becomes, Z F N ( t ) = I 0 μ z - b ( e - b t - e - μ z t ) ( 9 ), which is shown for various parameter values in S8A Fig . The data we use to fit our models concerns heteroplasmy and total copy number measurements over four rounds of treatment , each treatment consisting of mtZFN transfection followed by a 28-day recovery period ., During this recovery period , total copy numbers recover their initial values due to cellular feedback control ., The increase in mtDNA death rate due to the presence of the mtZFNs , μZFN , is given by, μ Z F N ( 28 · i < t < 28 · ( i + 1 ) ) = μ + ∑ j = 0 i Z F N ( t - 28 · j ) ( 10 ), where i = 0 , 1 , 2 , 3 indicates the treatment round ., This equation is simply stating that new mtZFNs are added every 28 days ., Death rates for m and w are now assumed to be, μ ( t ) w = μ + ξ · μ Z F N ( t ) μ ( t ) m = μ + μ Z F N ( t ) ( 11 ), where μ denotes the baseline degradation rate and ξ represents treatment selectivity ( e . g . when ξ = 0 there is no off-target cleavage ) ., To fit our nuclease model to recently obtained experimental data 36 , we use Eq ( 4 ) with μ replaced by μ ( t ) w or μ ( t ) m and λ ( w , m ) given by Eq ( 1 ) :, d w d t = w c 1 ( w o p t - ( w + δ m ) ) - ξ · μ Z F N ( t ) d m d t = m c 1 ( w o p t - ( w + δ m ) ) - μ Z F N ( t ) ( 12 ) Total mtDNA copy numbers in pre-treatment 80% heteroplasmy cells were measured using quantitative PCR ( section 6 . 4 in S1 File ) and were found to be 889 ± 214 ( S . E . , n = 3 ) ., We therefore assume an initial total copy number of 900 , meaning w and m were initialized at 0 . 2 ⋅ 900 = 180 and 0 . 8 ⋅ 900 = 720 , respectively ., These evolution equations incorporate cellular feedback control as well as the nuclease treatment which occurs in cycles of 28 days ., The mtZFN degradation rate was assumed to be μz = ln ( 2 ) day−1 , corresponding to a half-life of 1 day ., This is in accord with the experimental observation that almost no mtZFN was present 4 days post-transfection ( with a half-life of 1 day , only 6% of initial copy numbers remain after 4 days ) ., MCMC inference was performed using the Python package Pymc3 , a package designed for Bayesian statistical modelling and probabilistic machine learning 94 ., A Gaussian error model was assumed , i . e . the observed heteroplasmy y i ( h ) and total copy number y i ( T ) data are given by, y i ( h ) = y ^ i ( h ) + N ( 0 , σ h 2 ) y i ( T ) = y ^ i ( T ) + N ( 0 , σ T 2 ) ( 13 ), where y ^ i ( h ) and y ^ i ( T ) denote our predicted heteroplasmy and copy number values obtained by numerically solving Eq ( 12 ) , and we allow for different noise variances for h and T ( in general , different experimental errors are expected as different methods are used to measure h and T ) ., A metropolis sampler is used for parameter estimation ., Maximum a posteriori ( MAP ) values were found to be ( I 0 , b , c 1 , ξ , δ , σ h 2 , σ T 2 ) M A P ≈ ( 122 . 82 , 46 . 68 , 1 . 90 × 10 - 4 , 0 . 72 , 1 . 26 , 0 . 061 , 0 . 10 ) ., Due to a degeneracy in our mtZFN dynamics model ( section 6 . 5 in S1 File ) the MAP values of I0 and b are not necessarily unique at large b ( details in section 6 . 5 in S1 File ) ., We explore the ability of our model to account for additional data from Ref ., 36 ( Fig 5C and 5D ) which was not included in our inference ., Using the MAP values for parameters I0 , b , c1 , δ , σ h 2 and σ T 2 ( based on the data shown in Fig 5A and 5B ) , the maximum likelihood estimate of ξ is obtained based on the additional data , using a Gaussian error model similar to Eq ( 13 ) ., This maximum likelihood value is ξ ≈ 0 . 15 .

Introduction, Results, Discussion, Methods

The dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases ., Cellular populations of mitochondria are under homeostatic control , but the details of the control mechanisms involved remain elusive ., Here , we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations , the cost to the cell of different mtDNA states , and the optimisation of therapeutic control of mtDNA populations ., This formalism yields a wealth of biological results , including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue , that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant , that heteroplasmy distribution ( not mean alone ) is crucial for the success of gene therapies , and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations .

Mitochondria , best known for their role in energy production , are crucial to the survival of most of our cells ., To respond to energetic demands and mitigate against mutational damage , cells control the mitochondrial populations within them ., However , the character of these control mechanisms remains open ., As experimental elucidation of these mechanisms is challenging , theoretical approaches can help us understand the general principles of cellular control of mitochondria in physiology and disease ., Here , we use stochastic modelling to compare control strategies by studying their impact on the dynamics of mitochondrial DNA ( mtDNA ) populations as well as their energetic burden to the cell ., We identify optimal strategies for the cell to control against mtDNA damage and preserve energy production and use this theory to explore the action of recently developed mitochondrial gene therapies , which reduce the fraction of mutant mtDNA molecules inside cells ., We show how treatment efficiency may depend on pre-treatment distributions of mutant and wildtype mtDNA molecules: treatments are less effective for tissues consisting of cells with highly varying mutant levels , and long-term , rather than short intense , gene therapies should be favoured .

genome engineering, control theory, medicine and health sciences, gene therapy, mitochondrial dna, engineering and technology, nucleases, enzymes, synthetic biology, dna-binding proteins, enzymology, plant energy production, synthetic bioengineering, control engineering, plant science, systems science, mathematics, forms of dna, heteroplasmy, dna, bioenergetics, mitochondria, molecular biology techniques, cellular structures and organelles, synthetic genomics, research and analysis methods, bioengineering, synthetic genome editing, computer and information sciences, proteins, molecular biology, clinical genetics, biochemistry, plant biochemistry, hydrolases, zinc finger nucleases, cell biology, nucleic acids, heredity, genetics, biology and life sciences, physical sciences, energy-producing organelles

journal.pcbi.1004665

Modeling the Slow CD4+ T Cell Decline in HIV-Infected Individuals

HIV-1 progression to the AIDS stage within untreated patients usually takes many years ., As HIV-1 infection progresses , the CD4+ T cell population declines slowly and the infected individual becomes progressively more susceptible to certain opportunistic infections and neoplasms ., These are particularly common when CD4+ T cells reach a level below 200 cells/ul , which defines AIDS 1–7 ., How HIV-1 infection induces progressive CD4+ T cell depletion is unclear 8 ., One explanation is that the turnover rate of CD4+ T cells is significantly increased in HIV or simian immunodeficiency virus ( SIV ) infected subjects 9 , 10 ., Therefore , massive activation of CD4+ T cells , which leads to more viral infection and cell death , might outrun the regeneration of T cells and cause progressive depletion ., Another explanation is the failure of CD4+ memory T cell homeostasis during progressive HIV infection ., This is possibly due to the destruction of the microenvironment of organs and tissues supporting T cell regeneration 3 , 11–14 ., It remains unclear whether the impaired conformation of T cell regenerative tissues leads to the regeneration failure or it is merely a pathogenic reformation caused by HIV to promote viral replication ., Mathematical models may shed light on how the complex interplay between the immune response and viral infection leads to overt immunodeficiency ., Matrajt et al . used a model to analyze the simian-human immunodeficiency virus ( SHIV ) infection data in macaques 15 ., They found that uninfected or bystander cell death accounts for the majority of CD4+ T cell death 15 ., Mohri et al . studied the turnover of CD4+ T cells and found that T cell depletion is primarily induced by increased cellular destruction rather than decreased cellular production 16 ., Kovacs et al . also showed that HIV does not impair CD4+ T cell production but increases T cell proliferation 17 ., Using a model including the activation of resting CD4+ T cells , Ribeiro et al . found that HIV infection increases both the activation rate of resting CD4+ T cells and the rates of death and proliferation of activated CD4+ T cells 18 ., Chan et al . showed that the rapid proliferation of CD4+ T cells provides more targets for infection and that preservation of CD4+ T cells in natural host monkeys is due to the limited CD4+ T cell proliferation 19 ., Thus , CD4+ T cell depletion may be caused by the massive immune activation during chronic infection ., However , a model by Yates et al . suggested that if immune activation drives T cell decline , then the predicted decline would be very fast , which is not consistent with the time scale of T cell depletion during chronic infection 20 ., The above observations and analyses may explain T cell depletion but the long-term dynamics of CD4+ T cells have been neither simulated by models nor compared with patient data ., In a recent study , Hernandez-Vargas and Middleton 21 developed a model including the infection of macrophages to explain the three stages of HIV infection ., Fast infection of CD4+ T cells can explain the CD4+ T cell and viral load dynamics in the early stages , while slow infection of macrophages may explain the dynamics in the advanced stages of infection ., Whether macrophages form a long-term reservoir causing T cell depletion and viral explosion in the later stages of infection needs further experimental investigation ., Different from apoptosis , a programmed process that results in non-inflammatory cell death , pyroptosis is a form of programmed cell death associated with antimicrobial responses during inflammation 22 ., During HIV infection , Doitsh et al . 23 , 24 found that when virus enters a CD4+ T cell that is non-permissive to viral infection , the caspase-1 pathway is triggered to induce pyroptosis , which can secrete inflammatory cytokines such as IL-1β ., These cytokines establish a chronic inflammation state and attract more CD4+ T cells to the inflamed sites , resulting in more infection and cell death ., Thus , pyroptosis generates a vicious cycle in which dying CD4+ T cells secrete inflammatory signals that attract more CD4+ T cells to be infected and die 23 ., These findings suggest that HIV-1 may use the intrinsic feature of the immune system to seek targets of infection , establish productive viral replication , and meanwhile destroy the CD4+ T cell population ., Here we developed mathematical models incorporating the effect of pyroptosis to study whether it can explain the very slow T cell depletion during HIV-1 infection ., Using the models we explored if highly active antiretroviral therapy ( HAART ) can preserve the CD4+ T cell population ., We studied the effect of CD4+ T cell proliferation and CD8+ T cell response on CD4+ decline ., We also compared our modeling prediction with clinical data obtained from patients in Rio de Janeiro , Brazil 25–28 ., At last , we probed the possible contribution of chronic inflammation associated with pyroptosis to the HIV latent reservoir persistence ., The patient data were obtained from seroconverters in 3 cohorts 25–28 ., One cohort consists of high-risk , HIV-seronegative homosexual and bisexual men who did not report injection drug use , were enrolled between July 1995 and June 1998 and seroconverted during follow-up 26 ., The other cohorts consist of seroconverters from high-risk HIV-seronegative homosexual and bisexual men patients who were enrolled from December 1998 to May 2001 in a study designed to evaluate the behavior impact of post-exposure prophylaxis 27 , and participants from the control arm of SPARTAC , a randomized trial designed to evaluate the impact of short term antiretroviral therapy on the course of primary HIV infection 28 ., The median of the CD4+ T cell data was derived from these cohort studies ., The median of the Current Study Multicenter AIDS Cohort Study ( MACS ) was obtained from the study 29 ., These patient data and medians were compared with modeling prediction ., Inflammatory cytokines released by abortively HIV-infected cells can attract more CD4+ T cells to be infected ., In the following one-compartment model , to minimize the number of variables and parameters we described the effect of pyroptosis by use of an enhanced viral infection rate because of increased availability of CD4+ T cells attracted by cytokines to the inflamed sites ., The variable T represents the population of uninfected CD4+ T cells ., They are generated at the rate λ ., Proliferation of target cells will be considered later ., The infection rate is modeled by a mass action term kVT , which is enhanced by the inflammatory cytokine ( C ) with a factor γi ., Uninfected T cells die at a per capita rate d1 ., T* is the population of productively infected T cells and their death rate is d2 ., A fraction ( f ) of new infection is assumed to be abortively infected ., The death rate of abortively infected T cells ( M* ) is d3 ., Virus ( V ) is generated by productively infected T cells with a viral production rate pv and is cleared at a rate d4 ., Inflammatory cytokines are released with a burst size ( Nc ) when an abortively infected cell dies ., Thus , Ncd3 represents the generation rate of cytokines per abortively infected cell ., The decay rate of cytokines is assumed to be d5 ., The schematic diagram of this model is shown in Fig, 1 . Parameters and values are listed in Table, 1 . In the above one-compartment model , we described the consequence of pyroptosis but did not explicitly model the cytokine-induced attraction of CD4+ T cells from elsewhere to the place where abortive infection occurs ., Below we developed another model with two compartments to include cytokine-induced T cell movement explicitly ., The model is more complicated and contains more parameters ., In the model there are two compartments: one represents the blood ( T1 ) and the other represents human lymphoid tissues ( T2 ) such as lymph nodes in which abortive infection takes place on a large scale 23 ., CD4+ T cells in compartment I ( or II ) can transport to compartment II ( or I ) at a rate σ1 ( or σ2 ) ., In blood , cytokines released during abortive infection cannot accumulate as in lymphoid tissues ., They cannot attract other immune cells to fight the infection and contribute to inflammation ., Thus , pyroptosis is assumed to take place only in lymphoid tissues ( compartment II ) , as observed in ref ., 23 ., The transportation rate σ1 from the blood to tissues is assumed to be enhanced by a factor ( 1+γrC ) due to inflammatory cytokines ( C ) released during pyroptosis in compartment II ., Viruses ( V1 and V2 ) can also transport between two compartments with the rates D2 ( V1-V2 ) and D1 ( V2-V1 ) , which depend on the difference of viral load in the two compartments ., Because the dynamics of the virus are much faster than those of infected cells , it is reasonable to assume that they are proportional to each other ., Thus , we only included the transportation of virus between compartments ., In the Supporting Information ( S1 Text and S7 Fig ) , we added the transportation of infected cells to the model and found that the model prediction is similar to the case without infected cell transportation ., All the other variables and parameters ( summarized in Table 1 ) can be defined similarly as those in the one-compartment model ( Fig 1 ) ., The schematic diagram of the two-compartment model is shown in Fig, 2 . For model simulation , we fixed most of parameters based on existing experimental data and our previous modeling studies 30–33 ., Because the CD4+ T cell level within an uninfected individual ranges normally from 500 cells/μl to 1500 cells/μl , we changed the unit to cells/ml and assumed CD4+ T cells to be 106 cells/ml before infection 34 ., The death rate ( d1 ) of uninfected CD4+ T cells is assumed to be 0 . 01 day-1 35 ., Thus , from the steady state of target cells before infection , we obtained that the generation rate ( λ ) of target cells is 106 ( 0 . 01 ) = 104 cells ml-1 day-1 ., The viral infection rate k is assumed to be 2 . 4×10−8 ml virion-1 day-1 30 ., The death rate of infected T cells is d2 = 1 day-1 36 ., We chose the parameter γi to be 2×10−4 ml molecule-1 ., The viral production rate of productively infected T cells in the one-compartment model is chosen to be 2 . 5×104 virions cell-1 day-1 37 ., As described by Doitsh et al . 23 , 24 , abortive infection accounts for 95% of the total infection ., Thus , we chose f to be 0 . 95 ., Because abortive infection mainly takes place in non-permissive quiescent T cells , we chose their death rate ( d3 ) to be 0 . 001 day-1 31 , 32 ., The burst size of cytokines is fixed to Nc = 15 molecules ., The half-life of IL-1β is about 2 . 5 hours 38 ., Thus , we chose the decay rate of cytokines to be d5 = 6 . 6 day-1 ., We also performed sensitivity tests of the modeling prediction on a number of parameters ., We fit both the one-compartment and two-compartment models to subjects with more than 10 data points 25–29 ., The root mean square ( RMS ) between model prediction and patient data is minimized for each patient ., RMS is calculated using the following formula, RMS=Σi=1n ( T ( ti ) +T* ( ti ) −T^ ( ti ) ) 2n, where T ( ti ) +T* ( ti ) represents the CD4+ T cell population level in blood at time ti predicted by the model , T^ ( ti ) is the corresponding patient data at ti ., We used T1 ( ti ) +T1* ( ti ) in the fitting for the two-compartment model ., Parameter estimates are based on the best fit that achieves the minimum RMS ., Data fitting is performed using the R programming language ., In order to statistically compare the best fits of using the two models , we calculated the Akaike information criterion ( AIC ) ., The model with a lower AIC value fits the data better from a statistical viewpoint ., The AIC is calculated using the following formula, AIC=nln ( RSSn ) +2m, RSS=∑i=1n ( T ( ti ) +T* ( ti ) −T^ ( ti ) ) 2, where n is the number of observations ( i . e . number of data points ) and m is the number of fitted parameters ., RSS is the residual sum of squares ., T ( ti ) , T* ( ti ) and T^ ( ti ) are the same as those defined in the calculation of RMS ., We obtained the 95% confidence intervals for fitted parameters using a bootstrap method 39 , where the residuals to the best fit were re-sampled 200 times ., Using the parameter values listed in Methods and initial values V ( 0 ) = 1×10−3 RNA copies/ml , T ( 0 ) = 103 cells/μl , T* ( 0 ) = 0 , M* ( 0 ) = 0 , and C ( 0 ) = 0 in the one-compartment model , we showed that the population of CD4+ count declines from 103 cells/μl to about 200 cells/μl around the 6th year after infection ( Fig 3A ) ., This is consistent with the slow time scale of T cell decline during HIV infection ., The entire T cell depletion course consists of two major phases ., The first massive depletion phase is rapid , followed by a slower chronic depletion phase ( Fig 3A ) ., The first-phase T cell decline is due to the substantial viral infection during the early stage ., If there is no infection ( k = 0 ) , then the T cell level would stabilize at the initial level ( Fig 3A ) ., The slow second-phase T cell decline is due to pyroptosis enhanced viral infection ., Without the effect of inflammatory cytokines released during pyroptosis ( i . e . γi = 0 or no inflammation in Fig 3A ) , a balance between T cell generation and viral infection is reached and the T cell population is maintained at a steady state level ., This agrees with the prediction of most viral dynamics models without treatment ., Because of pyroptosis , cytokine-enhanced viral infection breaks the balance between cellular production and viral infection , which makes the T cell level decline at a very low rate and approach the immune-deficient level after several years ( Fig 3A ) ., The viral load change was plotted in Fig 3B ., Without the effect of inflammatory cytokines , the viral load reaches a steady state level ., When there is cytokine enhanced viral infection , viral load increases very slowly during the phase of chronic infection ( Fig 3B ) ., Using a constant λ is a simple way to approximate the generation of target cells ., We included the proliferation of target cells in the model ( S1 Text ) ., Simulation with different proliferation rates is shown in S1 Fig . As the proliferation rate increases , the decline of CD4+ T cells becomes faster ., This is because more target cells lead to more abortive infection , which releases more cytokines attracting more CD4+ T cells to be infected and die ., This prediction is consistent with the observation that the level of T cell proliferation in non-pathogenic infection ( e . g . SIV infection in natural host monkeys such as sooty mangabeys or mandrills that do not develop AIDS-like diseases ) was much lower than in pathogenic infection , e . g . , SIV in rhesus macaques 40 , 41 ., This provides an additional support to the view that an attenuated rather than effective adaptive immune response preserves immune function in natural host monkeys 42 ., We performed sensitivity analysis of the CD4+ T cell decline for a number of parameters ., Fig 4 shows that the sensitivity tests on parameters k , λ , pv and γi ., S2–S5 Figs show the tests on parameters Nc , d3 , d5 , and f , respectively ., We found that the model is robust in generating the slow decline of CD4+ T cells , although the model prediction is more sensitive to three parameters k , pv and f ( see Figs 4A , 4C and S5 ) ., In the above simulation , we assumed that the viral infection enhancement parameter γi is a constant ., When the concentration of inflammatory cytokines is low , they may not be able to trigger the attraction of CD4+ T cells from elsewhere ., Thus , we simulated a scenario in which enhanced viral infection is triggered only when the level of cytokines is above a threshold value ., We chose γi to be the following step function ., It is zero if the level of cytokines is below a certain level ., The threshold value was chosen to be 2000 or 4000 molecules/ml in Fig 5A ., CD4+ T cells do not decline until the level of cytokines reaches the corresponding threshold ( Fig 5B ) ., A more realistic scenario is that γi increases gradually when the concentration of cytokines is above the threshold ., We chose γi ( C ) to be the following exponential function ., The hill coefficient ρ determines how fast γi ( C ) increases from 0 to its maximum value γi ., Both ρ and γi were fixed to 2×10−4 ml molecule-1 ., With a non-constant parameter γi ( C ) , we found that CD4+ T cells also undergo a slow decline to below 200 cells/μl ( Fig 5B and 5C ) ., Using an exponential function for γi ( C ) , the decline of CD4+ T cells is smoother than the case using a step function ., Using the one-compartment model , we studied if HAART can rescue the CD4+ T cell population ., During HAART we assumed that the viral infection rate k is reduced by a factor ( 1-ε ) , where ε is the overall drug efficacy of the treatment 32 ., The simulation shows that if the treatment effectiveness is very high , then CD4+ count can rebound to its pre-infection level ( Fig 6A ) no matter when HAART is initiated ., For lower treatment effectiveness ( e . g . ε = 0 . 6 in Fig 6B ) , the patient needs a relatively long time to restore the CD4+ T cell population ., The later HAART starts , the longer it takes for CD4+ T cell restoration ( Fig 6B ) ., When the treatment effectiveness is further lower , CD4+ T cell depletion could not be prevented ., These results suggest that HAART has the potential to rescue CD4+ T cell population , but CD4+ response depends on the effectiveness of the therapy and when the therapy is initiated ., We included CD8+ T cells in the one-compartment model to study the interaction between CD4+ T cell decline and CD8+ T cell response ., CD8+ T cells ( E ) are assumed to kill infected T cells at a rate αET* ., The activation rate of CD8+ T cells depends on the level of infected cells with a half-maximal saturation constant θ ., pE is the maximum activation rate ., CD4+ T cells play an important role in activating the adaptive immune response ., We used another saturation function T/ ( T+η ) to account for this influence ., The T* and E equations are given below ., The simulation of the model with CD8+ T cell response is shown in Fig 7 ., Parameter values are listed in Table 1 ., For comparison , we plotted the predicted T cell dynamics with and without the influence of CD4+ T cells ., In column A of Fig 7 , we performed the simulation without T/ ( T+η ) ., CD4+ T cells decline slowly and CD8+ T cells reach a steady state level ., Column B shows the simulation with the term T/ ( T+η ) ., CD8+ T cell response becomes weaker than in column A because of the slow depletion of CD4+ T cells ., CD4+ T cells decline faster because of the incapability of CD8+ T cells to control viral infection ., Inflammatory signals released during pyroptosis induce the movement of CD4+ T cells from circulation in blood to inflamed lymph nodes 43–46 ., We developed a more comprehensive model by including two cell compartments ( Fig 2 ) ., One is the blood compartment and the other is the compartment of lymphoid tissues where pyroptosis takes place ., Simulation of the two-compartment model shows that CD4+ count in blood declines from 103 cells/ul to 200 cells/ul over a long time period ( Fig 8A ) ., The viral load change in blood is also similar to that shown in Fig 3B except that T cell and viral load dynamics generated by the two-compartment model have less oscillation than by the one-compartment model ., Using the two-compartment model we also tested if HAART can rescue CD4+ T cell population ., We assumed that the drug efficacies of HAART within blood and lymph node are different ( i . e . , the viral infection rate k in compartment I is reduced by 1-ε1 and k in compartment II is reduced by 1-ε2 ) ., We found that if the drug efficacies in both compartments are high , then CD4+ T cell depletion can be prevented ( Fig 8B ) ., The time for CD4+ restoration also depends on when HAART is initiated ., However , if the drug efficacy in compartment II is relatively low ( e . g . ε2 = 0 . 4 ) compared with the high efficacy in compartment I ( e . g . ε1 = 0 . 9 ) , then CD4+ T cells decline even when HAART is initiated at the beginning of viral infection ( Fig 8C ) ., In the simulation , CD4+ T cells stabilize at 230 cells/ul after more than 30 years ( Fig 8C ) ., This result suggests that even if some lymphoid tissues might be difficult for drugs penetration ( i . e . drug sanctuary sites ) , CD4+ T cells can be maintained at a higher level in treated patients than in untreated patients ., This may explain the increased life expectancies in HIV patients treated with combination therapy 47–51 ., However , because of the CD4+ cell decline ( Fig 8C ) , life expectancy should be lower in patients with lower baseline CD4+ cell counts than in those with higher baseline counts ., This is consistent with the reported life expectancy of individuals on combination therapy in a collaborative analysis of 14 cohort studies 47 ., We compared modeling predictions with the CD4+ T cell data shown in 25–29 ., Using the one-compartment model , we fit parameters k , γi , λ , pv and fix the other parameters for each patient ., We also fit the model to the median data calculated from all the patients in the two cohort study 25 and the median data of the Current Study Multicenter AIDS Cohort Study ( MACS ) 29 ., Using the two-compartment model , we fit parameters k , γr , λ1 , pv1 to the same patient and median data ., Figs 9 and 10 show that both models provide a good fit to the long-term CD4+ T cell data in untreated HIV-1 patients ., The fit to the median data is better than the fit to individual patients based on the calculated error between modeling prediction and data ., These data fits suggest that pyroptosis induced CD4+ T cell movement during abortive infection can explain the progressive CD4+ T cell depletion observed in untreated HIV-1 patients ., Parameter estimates and their 95% confidence intervals based on the fits to the one-compartment and two-compartment models are listed in Tables 2 and 3 , respectively ., The estimate of the viral production rate pv in the one-compartment model is higher than the viral production rate pv1 in blood of the two-compartment model ( pv2 = 2000 virions per cell per day is fixed during fitting ) ., This is because in the one-compartment model 95% of infection is assumed to be abortive and only 5% of infection produces virus ., Thus , a higher value of viral production rate is needed to generate viral load with reasonable magnitude ., In the second-compartment model , although only 5% of infection produces viruses in lymphoid tissues , the target cell level is much higher in lymphoid tissues than in blood ( i . e . λ2 >> λ1 ) ., Thus , the viral production rates in the two compartments are on the same order of magnitude ., The Akaike information criterion ( AIC ) value is calculated to compare data fitting using the two models ( Tables 2 and 3 ) ., We found that for patients 11 , 38 , 44 , and median of patient data , the AIC value of using the second model is less than that of using the first model ., This suggests that the two-compartment model provides a better fit to the data for these patients from a statistical viewpoint ., IL-7 plays an important role in latently infected CD4+ T cell proliferation 52 ., It has been observed to be over expressed in inflamed tissues 53 , 54 ., Inflammatory cytokines released during cell death by pyroptosis may promote the establishment and persistence of the latent reservoir in HIV patients ., Here we included the population of latently infected CD4+ T cell ( L ) into the one-compartment model ., Latently infected CD4+ cells are produced with a fraction μ during HIV-1 infection ., They can also be maintained by proliferation which is assumed to rely on the cytokine level ( see the term 1+φC in the following equation where φ is fixed to 10−2 ml molecule-1 ) ., We chose the base proliferation rate pL to be 0 . 001 day-1 32 , which represents a limited proliferation capacity in the absence of inflammatory cytokines ., The carrying capacity of latently infected cells ( Lmax ) is fixed at 100 cells/ml 32 ., The other parameter values are listed in Table 1 ., The equations of L and T* are given below and the other equations are the same as those in the one-compartment model ., If there is no chronic inflammation ( i . e . φ = 0 in the L equation ) , then latently infected cells undergo a slow decline ( Fig 11A ) ., However , if the proliferation is enhanced by cytokines released during cell death by pyroptosis , then the latent reservoir can be maintained at a higher level ( Fig 11B ) ., This result suggests that inflammatory cytokines generated during abortive infection might contribute to the establishment of the latent reservoir and the maintenance of its size ., We also performed sensitivity test of latently infected cells on the parameter φ , the effectiveness of cytokines promoting latently infected cell proliferation ., The modeling prediction is robust to this parameter ( S6 Fig ) ., Latently infected cells can be activated by relevant antigens and become productively infected cells ., In S1 Text , we included the activation of latently infected cells in the one-compartment model ., Simulation with different values of the activation rate is shown in S8 Fig . As the activation rate increases , the size of the latent reservoir decreases ., The mechanisms underlying the slow time scale of CD4+ T cell decline in untreated HIV-1 patients remain unclear ., HIV-mediated cell death can contribute to the loss of CD4+ T cells , but quantitative image analysis suggested that infection-induced cell death could be compensated by upregulated T cell division 55 , 56 ., Some studies suggested that the destruction of bystander non-infected cells may account for the CD4+ T cell decline during disease progression 57–60 ., Immune activation might be the reason of bystander cellular demise 60 ., It drives uninfected CD4+ T cells into several rounds of division and cells are susceptible to activation-induced death 61 , 62 ., However , a mathematical model showed that the decline of CD4+ T cells would be very rapid if immune activation drives T cell depletion 20 ., Another possible reason of T cell decline might be the regeneration failure of CD4+ T cells during disease progression 3 , 11–14 ., A recent study found that about 95% of CD4+ T cells within lymph nodes die from pyroptosis and release inflammatory signals that attract more CD4+ T cells from elsewhere to be infected 23 ., HIV-1 may use this vicious infection cycle to promote disease progression and chronic T cell depletion ., In this paper , we developed mathematical models to explore whether cell death induced by pyroptosis can explain the slow time scale of CD4+ T cell decline in untreated HIV patients ., In the first model , we assumed that increased availability of target cells due to attraction by inflammatory cytokines facilitates viral infection , which drains the CD4+ T cell population slowly during chronic infection ., In the second model , we explicitly included the movement of CD4+ T cells from blood to lymphoid tissues where pyroptosis occurs ., Both models generate a very slow decline of CD4+ T cells in plasma ( Figs 3 and 8 ) , and agree with the long-term CD4+ T cell data from untreated HIV patients in several cohorts in Brazil ( Figs 9 and 10 ) ., We found that the entire CD4+ T cell decline consists of two major phases ( Fig 3 ) ., The first-phase decline is very rapid ., This decline is due to the enormous virus infection and virus-induced cell death during primary infection ., Following the first phase , CD4+ T cells partially recover because of cell regeneration and viral control by immune responses ., However , a balance cannot be established between cell generation and viral infection ., Chronic inflammatory cytokines released during pyroptosis can attract CD4+ T cells from other places to inflamed lymphoid tissues ., These cells are infected and die , resulting in a slow decline of CD4+ T cells in plasma ., These results suggest that HIV-mediated cell death causes the dramatic decline of CD4+ T cells during primary infection and that persistent chronic inflammation acts like an erosive force which gradually drains the CD4+ T cell population in plasma during chronic infection ., HAART was shown to have the potential to restore the CD4+ T cell population ( Figs 6 and 8 ) , which agrees with the robust and sustained CD4 recovery among patients remaining on therapy 63 and a normal life expectancy in patients with a good CD4 response and undetectable viral load 50 ., However , CD4 response depends on the effectiveness of the therapy , when the therapy is initiated , and whether there exist drug sanctuary sites ( Figs 6 and 8 ) ., This may explain the considerable variability in the increase of life expectancy in patients treated with combination therapy between 1996 and 2005 47 ., Our model has limitations ., First , it does not account for the spatial effect of CD4+ T cells ., Although we used a two-compartment model to describe the transportation of cells and virus between blood and lymphoid tissues , release of cytokines during cell death by pyroptosis and attraction of CD4+ T cells are mainly constrained to occur locally ., Ordinary differential equation models could not capture these features ., It would be valuable to develop spatial models that can describe the vicious cycle within lymphoid tissues ., Spatial models require precise description and parameterization of diffusion of cytokines and attraction of CD4+ T cells , and are also computationally demanding in studying T cell dynamics within blood and different lymphoid tissues ., The second limitation of our model is that we did not consider a detailed inflammatory signal transduction cascade between T cells and relevant tissues ., Recruitment of T cells to the inflamed tissue goes through several steps of immunological reaction ., Upon secretion of IL-1β , expression of adhesion molecules such as E/P-selectin and ICAM-1 on the vascular endothelium is upregulated 64 ., Binding to these molecules facilitates T cells attachment to vascular endothelium ., After attachment T cells undergo conformational changes and penetrate into the inflamed tissue 65 , 66 ., In our models , we used a very simple factor multiplied by the concentration of cytokines to describe the effect of inflammatory cytokines ., A more comprehensive model requires a detailed description of intracellular processes underlying the inflammatory signal cascade and related data for model verification ., The third limitation is that our model cannot generate viral load explosion in the later stages of HIV infection ., Assuming that all parameters are constant and that only one cell population produces virus , our model cannot describe viral explosion ., However , as CD4+ T cells drop to very low levels , the immune system cannot kill infected cells or neutralize virus effectively ., This leads to a reduction in the death rate of infected cells or viral clearance rate , and may explain the viral explosion ., Infection of other cell populations such as macrophages ( as suggested by Hernandez-Vargas and Middleton in ref . 21 ) or other viral reservoirs may also explain the dramatic viral load increase during the AIDS stage ., Our simulation shows that the latent reservoir may be maintained by chronic inflammation ., How inflammation promotes the latent reservoir persistence is not fully understood ., Some results suggested that caspase-1 can promote cellular survival ., For example , epithelial cells activate caspase-1 to enhance membrane repair in response to the pore-forming toxins to prevent proteolysis 67 ., Whether latently infected

Introduction, Methods, Results, Discussion

The progressive loss of CD4+ T cell population is the hallmark of HIV-1 infection but the mechanism underlying the slow T cell decline remains unclear ., Some recent studies suggested that pyroptosis , a form of programmed cell death triggered during abortive HIV infection , is associated with the release of inflammatory cytokines , which can attract more CD4+ T cells to be infected ., In this paper , we developed mathematical models to study whether this mechanism can explain the time scale of CD4+ T cell decline during HIV infection ., Simulations of the models showed that cytokine induced T cell movement can explain the very slow decline of CD4+ T cells within untreated patients ., The long-term CD4+ T cell dynamics predicted by the models were shown to be consistent with available data from patients in Rio de Janeiro , Brazil ., Highly active antiretroviral therapy has the potential to restore the CD4+ T cell population but CD4+ response depends on the effectiveness of the therapy , when the therapy is initiated , and whether there are drug sanctuary sites ., The model also showed that chronic inflammation induced by pyroptosis may facilitate persistence of the HIV latent reservoir by promoting homeostatic proliferation of memory CD4+ cells ., These results improve our understanding of the long-term T cell dynamics in HIV-1 infection , and support that new treatment strategies , such as the use of caspase-1 inhibitors that inhibit pyroptosis , may maintain the CD4+ T cell population and reduce the latent reservoir size .

The CD4+ T cell population within HIV-infected individuals declines slowly as disease progresses ., When CD4+ cells drop to below 200 cells/ul , the infection is usually considered to enter the late stage , i . e . , acquired immune deficiency syndrome ( AIDS ) ., CD4+ T cell depletion can take many years but the biological events underlying such slow decline are not well understood ., Some studies showed that the majority of infected T cells in lymph nodes die by pyroptosis , a form of programmed cell death , which can release inflammatory signals attracting more CD4+ T cells to be infected ., We developed mathematical models to describe this process and explored whether they can generate the long-term CD4+ T cell decline ., We showed that pyroptosis induced cell movement can explain the slow time scale of CD4+ T cell depletion and that pyroptosis may also contribute to the persistence of latently infected cells , which represent a major obstacle to HIV eradication ., The modeling prediction agrees with patient data in Rio de Janeiro , Brazil ., These results suggest that a combination of current treatment regimens and caspase-1 inhibitor that can inhibit pyroptosis might provide a new way to maintain the CD4+ T cell population and eradicate the HIV latent reservoir .

journal.pbio.1001157

LIN-44/Wnt Directs Dendrite Outgrowth through LIN-17/Frizzled in C. elegans Neurons

Correct dendrite development is essential for the establishment of neuronal connectivity and , in sensory neurons , for the detection of external stimuli ., However , the complexity and variety in morphology of dendrites has made the study of their development more challenging than that of axons ., Previous findings have shown that some axon guidance molecules can also regulate dendrite development , often with opposing effects ., For example , the guidance cue Slit can simultaneously repel axons and enhance dendrite branching and outgrowth in cortical neurons 1 ., Similarly , Semaphorin 3A , a guidance molecule that acts through the Neuropilin-1 receptor , functions as both a chemorepellent for cortical axons and a chemoattractant for dendrites within the same neurons 2 ., The differential response of axons and dendrites to Semaphorin 3A is mediated by asymmetric localization of a soluble guanylate cyclase to the dendrites 2 ., In cultured hippocampal neurons , local elevation of cAMP and reduction of cGMP in undifferentiated neurites promotes axon formation and suppresses dendrite formation , whereas the reciprocal levels of these molecules have the opposite effects 3 ., Interestingly , local upregulation of cAMP in a single neurite results in long-range inhibition of cAMP levels in all other neurites , suggesting a mechanism for the development of one axon and multiple dendrites and indicating that dendrite formation in this context is secondary to axon formation 3 ., More recently , in vivo studies have uncovered molecules that regulate dendrite development independently of the axon ., Sensory neurons in the head of C . elegans develop by anchoring their dendritic tips to the nose while the cell body migrates away , extending a dendrite ( retrograde extension ) 4 ., In the C . elegans tail motor neuron , DA9 , the extracellular guidance cue UNC-6/Netrin controls the final extension of the dendrite in an axon-independent manner through its interaction with the receptor UNC-40/DCC 5 ., In a different highly branched mechanosensory neuron , PVD , the cell-autonomous activity of the EFF-1 fusogen promotes branch retraction to retain a precise patterning of arbors during dendrite development 6 ., In a Drosophila sensory neuron ( vch1 ) , correct orientation of the dendrite is regulated by Netrin-A and its receptor Frazzled and is mediated by a migrating cap cell , which drags the tip of the dendrite into position 7 ., In all these cases , however , the cell-intrinsic molecules involved in the initial stages of dendrite formation remain elusive ., Wnt morphogens and their Frizzled receptors are highly conserved molecules with diverse functions in nervous system development 8 , 9 ., In rat and mouse hippocampal neurons , Wnt molecules promote dendritic arborization 10 , 11 , whereas in Drosophila neuronal activity regulates the remodeling of dendritic branches in a Wnt-dependent manner 12 ., In C . elegans there are five Wnt ligands , ( LIN-44 , EGL-20 , CWN-1 , CWN-2 , and MOM-2 ) and four Frizzled receptors ( LIN-17 , MIG-1 , CFZ-2 , and MOM-5 ) ., The posteriorly expressed Wnt ligand , LIN-44 , regulates neuronal polarity , axon guidance , axon termination , and synapse formation , acting mainly as a repellent through the LIN-17/Frizzled receptor on neurons in the posterior of the animal 13–17 ., Another posteriorly expressed Wnt ligand , EGL-20 , controls cell migration and axon guidance of different cells along the anterior-posterior axis of the worm 18–20 ., CWN-1 and CWN-2 , which are expressed more broadly along the anterior-posterior axis , affect neurons in the mid-body and the head of C . elegans , regulating neuron migration , axon guidance , nerve-ring placement , as well as the outgrowth and pruning of neurites 21–25 ., In this study , we show that LIN-44/Wnt initiates and guides the development of the dendrite in the PQR oxygen sensory neuron , through a mechanism that occurs prior to and independently of the formation of the axon ., In contrast to its role as a repellent in synapse formation and axon termination , in the context of PQR development LIN-44 acts as an attractant that is specific for the outgrowth of the dendrite ., The effect of LIN-44 is mediated through the LIN-17 receptor , which functions in a cell-autonomous manner ., We also identify EGL-20/Wnt and MIG-1/Frizzled as crucial molecules in PQR dendrite development ., Taken together , these findings show for the first time that Wnt signals and Frizzled receptors can promote dendrite-specific outgrowth in developing neurons in vivo ., PQR is an oxygen-sensory neuron with its cell body positioned in the posterior lumbar ganglion on the left side of the animal 26 ., PQR extends a single axon anteriorly along the ventral nerve cord and a single dendrite posteriorly towards the tail ( Figures 1D , 2A ) ., The tip of the dendrite , which is part of the left phasmid sensory organ , protrudes with its sensory cilia into the pseudocoelom ., PQR is born post-embryonically , facilitating investigation of its development in newly hatched larvae ., A gcy-36::GFP reporter was used as a selective marker for PQR , allowing visualization of its dendrite during development , starting at the L1 stage ( see Materials and Methods ) ., PQR arises as a descendant of the QL neuroblast , and subsequently migrates towards the tail ., We observed that upon reaching its final destination , at 5 . 5–6 . 5 h after hatching , PQR assumed a rounded or elliptical shape , without any neurites ( Figure 1A ) ., At 6 . 5–7 h , dendrite formation began with lamellipodia-like extensions emerging on the dorsal-posterior region of the cell body , which had become elliptical or triangular in shape ( Figure 1B ) ., At this stage , no other projections were present , indicating that dendrite outgrowth is initiated before outgrowth of the axon ., At 7–7 . 5 h , the dorsal-posterior protrusion thinned and extended into a developing dendrite with a growth cone at its distal tip , and the cell body became rounder in shape ( Figure 1C ) ., At the same time , the axon began to emerge from the ventral-anterior side of the cell , appearing as a small neurite that , unlike the dendrite , did not present a large growth cone at its tip ., By 7 . 5 h , both the dendrite and axon were visible and continued to extend to their final positions until 18 h after hatching ( L2/L3 ) ( Figure 1D ) ., PQR subsequently maintained its morphology throughout adulthood ( Figure 2A ) ., Overall , our analysis demonstrates that the PQR dendrite forms by growth cone crawling and is initiated prior to axon outgrowth ., We next used a candidate gene approach to discover the molecules regulating dendrite development in PQR ., We found that animals mutant for LIN-44/Wnt presented severe defects , with PQR dendrites that were short , absent , or misrouted in the anterior direction ( Figure 2B–D , and quantified in 2E ) ., The axon , however , appeared morphologically normal ., These defects could arise from a dendrite-specific effect or a change in neuronal polarity whereby the identity of the neurites is compromised ., To distinguish between these two possibilities we investigated whether there were any changes in the location of the presynaptic sites of PQR , which are normally on the axon ., rab-3 encodes for a vesicle-associated Ras GTPase , which localizes to presynaptic densities 27 , 28 ., Using a YFP::RAB-3 fusion protein expressed specifically in PQR ( Pgcy-36::YFP::RAB-3 ) , we found that the presynaptic sites in lin-44 mutants were largely located on the axon as in wild-type animals ( Figure 2F ) ., This suggests that the identity of the neurites is unchanged and that the PQR defect of the lin-44 mutant is dendrite-specific ., Next , we tested if the PQR dendrite defect of lin-44 mutant animals could arise from an abnormal cell division in the precursor cell ., However , we found that the asymmetric cell divisions of the PQR precursor occurred normally in the lin-44 mutant animals ( Figure S1 ) , precluding this possibility ., Finally , we investigated whether the absent and short dendrite phenotypes we observed were generated either by excessive pruning or by direct outgrowth failure ., Examination of early stages of PQR development in lin-44 mutants revealed that the dendrite often failed to form or fully extend ( Table S1 ) ; we also observed animals with dendritic growth cones developing abnormally on the anterior side of the neuron , which would explain the anteriorly misrouted dendrites observed in adults ( Table S1 ) ., Thus , our results indicate that LIN-44 acts at very early stages of PQR development by regulating proper formation of the growth cone and its extension ., The Wnt ligand LIN-44 is expressed in close proximity to the PQR neuron from four hypodermal cells ( hyp-8 , -9 , -10 , and -11 ) in the tip of the tail 29 , a position posterior to the PQR dendrite ( Figure 3A ) ., As the PQR dendrite grows towards the source of LIN-44 , we hypothesized that this molecule might act instructively as an attractive cue for the developing dendrite ., Alternatively , LIN-44 may act as a permissive cue , whereby its positional information is not essential for correct dendrite development ., To distinguish between these two possibilities , we expressed LIN-44 ectopically from regions anterior to the PQR cell body in lin-44 mutant animals , using a version of LIN-44 genomic DNA that had been engineered to contain a secretion signal sequence to ensure proper secretion from cells that do not normally produce LIN-44 16 ., Transgenic lines were generated to express LIN-44 from the myo-2 promoter 30 in the pharynx ( Pmyo-2::LIN-44 ) , or from a short fragment of the cwn-1 promoter 21 in the intestine and head neurons ( Pcwn-1::LIN-44 ) ( Figure 3A and Figures S2 , S3 ) ., When compared to lin-44 mutant animals , transgenic animals expressing LIN-44 anterior to PQR displayed a decrease in the proportion of normal dendrites and an increase in the proportion of dendrites that were misrouted in the anterior direction , towards the ectopic source of LIN-44 ( Figure 3B and Figures S2 , S3 ) ., On the contrary , expression of LIN-44 from its endogenous promoter ( Plin-44::LIN-44 ) provided strong rescue of the PQR dendrite defect of lin-44 mutant animals ( Figure 3B ) ., We next examined the ectopic expression of LIN-44 from the myo-2 promoter in the wild-type background and found that it altered the normal development of the PQR dendrite ( Figure S4 ) ., Thus , the worsening of dendrite defects observed when LIN-44 is ectopically expressed from anterior regions suggests that LIN-44 has an instructive role in PQR dendrite development , whereby it acts as an attractive cue to direct the outgrowth of the dendrite ., In wild-type C . elegans , the four tail hypodermal cells hyp-8 , -9 , -10 , and -11 express LIN-44 throughout embryogenesis and larval stages 29 ., In order to define the time period in which LIN-44 is required for normal PQR dendrite development we eliminated larval production of LIN-44 by laser ablation of the hyp-8 , -9 , -10 , and -11 hypodermal cells ., Remarkably , in adult animals that were laser-ablated as newly hatched L1 larvae , the PQR dendrite appeared to be largely unaffected ( Figure 4A ) even though the ablations were performed several hours before PQR is born in the mid-L1 stage ., This result indicates that LIN-44 expression from these hypodermal cells during embryogenesis is sufficient for the correct development of the PQR dendrite ., To further define the temporal requirement of LIN-44 we next utilized an inducible heat shock promoter to express LIN-44 ( Phsp16-2::LIN-44 ) in a lin-44 mutant background at specific times during development ., Heat shock-induced LIN-44 expression in newly hatched L1 animals partially rescued PQR dendrite defects ( Figure 4B and Figure S5 ) ., However , when animals were heat shocked later , at the time of dendrite outgrowth , no such rescue effect was observed ( Figure 4B ) , suggesting that LIN-44 expression is required prior to PQR dendrite outgrowth ., The hsp16-2 promoter drives expression broadly throughout the body of the animal , in cells that are both anterior and posterior to PQR 31 ., Thus , the dendrite rescue we observed in heat shocked animals could indicate that LIN-44 plays a permissive role , or that the ligand is produced more efficiently from regions posterior to PQR ., To further investigate this we expressed Phsp16-2::LIN-44 into a wild-type background and found that the ectopic expression of LIN-44 generated PQR defects similar to those of lin-44 mutants , confirming the instructive role of this molecule ( Figure S6 ) ., Taken together , these results suggest that a molecular pattern of LIN-44 generated prior to PQR formation , during embryonic development and early L1 , is both necessary and sufficient to instruct PQR dendrite outgrowth hours later , at which time the source of LIN-44 expression becomes dispensable ., LIN-17 is a Frizzled molecule known to function as a receptor for LIN-44 in a variety of developmental processes 14 , 16 , 17 , 29 , 32–35 ., We found that lin-17 mutants had defects resembling those of lin-44 , with PQR dendrites that were short , absent , and misrouted anteriorly ( Figure 5A ) ., lin-17 mutants also presented a strong migration defect 18 , 19 , with a high percentage ( 60% to 90% ) of PQR neurons mispositioned in anterior regions of the body ., Thus , our analysis was performed on those animals in which PQR was correctly positioned in order to eliminate any possible effect that the aberrant location may have had on PQR dendrite development ., Importantly , lin-17 mutants , like lin-44 mutants , appeared to have largely normal localization of presynapses to the axon , as visualized using the YFP::RAB-3 fusion protein expressed specifically in PQR ( Pgcy-36::YFP::RAB-3 ) , eliminating the possibility of a switch in neurite identity ( Figure 2F ) ., In addition to testing known alleles of lin-17 , we also performed a forward genetic screen and isolated a previously uncharacterized allele , vd002 , consisting of a G to A transition in position 490 of the lin-17 gene that resulted in a cysteine residue being replaced by a tyrosine residue ( Figure 5A ) ., The isolation of this mutant from an unbiased screen further supports the significance of lin-17 in this process ., To investigate whether there might be a genetic interaction between lin-17 and lin-44 with respect to PQR dendrite development , we next examined lin-17 lin-44 double mutants and found that the dendrite defects were qualitatively and quantitatively similar to those of lin-17 mutants ( Figure 6A ) ., This indicates that these two molecules function in the same genetic pathway with respect to PQR dendrite development and strongly suggests that LIN-44 acts as a ligand for LIN-17 in this process ., LIN-17 is expressed extensively and dynamically in several cells of the tail region including PQR ( Figure S7 ) 35 ., Wnt signaling through the LIN-17 receptor could occur cell-autonomously within PQR or could result from interactions with the surrounding cells ., We first tested whether LIN-17 acts cell-autonomously by expressing the wild-type lin-17 cDNA from the gcy-36 promoter , which is transcriptionally active in PQR during the final stages of its migration ., This transgene failed to rescue the dendrite defects , despite being tested at a range of different concentrations ( see Materials and Methods ) ., We therefore questioned whether LIN-17 might be required in PQR at earlier stages , before the gcy-36 promoter is transcriptionally active ., To test this possibility we used the egl-17 promoter that is highly and selectively expressed in the precursors of PQR during the L1 stage 36 , 37 to drive LIN-17 expression from the time PQR was born ., Wild-type LIN-17 cDNA expressed by the egl-17 promoter ( Pegl-17::LIN-17::YFP ) strongly rescued the PQR dendrite defects of lin-17 mutants , to levels similar to that of the endogenous promoter ( Plin-17::LIN-17::YFP ) ( Figure 5B ) ., These results suggest that LIN-17 regulates dendrite development in a cell-autonomous fashion and is required very early in development , before or during PQR migration ., The PQR dendrite is ensheathed by PHso2L , a glia cell of the left phasmid sensillum; this sensillum comprises two socket cells ( PHso1L , PHso2L ) , a sheath cell ( PHshL ) , and two sensory neurons ( PHAL and PHBL ) 26 ., Recent results in different systems have demonstrated a role of the support cells in regulating dendrite development 4 , 7 ., To determine if similar mechanisms were in place for PQR development , we next performed cell-ablation experiments whereby we selectively eliminated the socket cells or the socket cells together with the sheath cells ., PQR morphology in ablated animals was largely normal , with only a small number of animals presenting short dendrites when left and right phasmid socket cells were ablated ( 3/15 ) or when left phasmid socket and left sheath cells were ablated ( 2/19 ) ., We never observed the penetrance and variety of defects of the lin-17 mutants ., These results indicate that glial cells play a minor role in only the final stages of dendrite extension and suggest that LIN-17 does not have an effect on the PQR dendrite through these support cells ( Table S2 ) ., In addition , ablations of the phasmid neurons PHA and PHB also had no effect on PQR dendrite development ( Table S2 ) , thereby providing further evidence that the function of LIN-17 in PQR dendrite development is unlikely to be mediated by the surrounding cells ., To further understand how LIN-17 acts on the PQR dendrite , we then asked at what stage in PQR development LIN-17 was visible on the cell membrane and how LIN-17 was distributed in PQR ., Using a LIN-17::YFP functional fusion protein expressed under the control of the egl-17 promoter , we observed faint , relatively uniform localization of LIN-17 on the membrane of the QL . a cell as it was dividing into QL . aa and PQR ( unpublished data ) ., Following this division , the membrane-localized LIN-17::YFP in PQR decreased until it was barely visible at the time at which PQR had completed its posterior migration ( unpublished data ) ., This reduction in LIN-17::YFP appeared to be independent of down-regulation by the egl-17 promoter and is consistent with our previous results suggesting an early role for LIN-17 in regulating PQR dendrite outgrowth ., We suggest that ubiquitous membrane-localization of LIN-17 may be required to detect the posterior source of Wnt ligand , which acts as the directional signal for the PQR dendrite ., Multiple Wnt ligands and Frizzled receptors are known to function in basic developmental processes in C . elegans and have frequently been shown to have redundant or synergistic roles ., Although lin-44 mutants present striking PQR dendrite defects , 32% of these animals still have the ability to sprout a normal PQR dendrite , suggesting the involvement of other molecules in this process ., We therefore tested three other Wnt molecules–EGL-20 , CWN-1 , and CWN-2–for possible roles in PQR dendrite formation ., EGL-20 is expressed around the PQR cell body , in a group of epidermal and muscle cells near the anus 13 , 20 , and CWN-1 and CWN-2 are expressed to a greater extent anteriorly in the intestine , body wall muscle , and neurons in the midbody and head regions , anterior to the PQR cell body 13 , 22 , 38 ., No significant dendrite defects were observed in cwn-1 or cwn-2 single mutants ., The cwn-1 cwn-2 double mutant presented a higher percentage of ectopic processes from the cell body , and dendrite branching , compared to the single mutants , but no absent-dendrite or dendrite-misrouting defects were observed ( Table S3 ) ., This suggests that these molecules are less directly involved in development of the PQR dendrite , but are important to prevent the formation of ectopic processes ., Although the loss of cwn-1 alone caused no significant dendrite defects on PQR , when combined with the lin-44 mutation it was able to enhance the dendrite misrouting defects of lin-44 mutants ( Table S3 ) ., Thus , CWN-1 might have a minor and redundant role in PQR dendrite development ., As previously described , egl-20 mutants have a very strong Q cell migration defect 18–20 resulting in 97%–98% of animals having anteriorly positioned PQR neurons ., Restricting our analysis to those animals with PQR correctly positioned , we found that only 7% of egl-20 animals developed a normal , full-length dendrite , whereas the rest presented qualitatively similar defects to those of lin-44 and lin-17 animals , with absent , short , and anteriorly misrouted PQR dendrites ( Figure 6B ) ., egl-20 mutants presented a higher proportion of anterior dendrites , as compared to lin-44 mutant animals ( Figure 6B ) , but the PQR dendrite phenotype of the egl-20 lin-44 double mutant did not display a significant worsening of defects when compared to the egl-20 single mutant ., This suggests that egl-20 and lin-44 may interact to regulate PQR dendrite formation ( Figure 6B ) ., Furthermore , the egl-20 lin-17 double mutant was no worse than either of the single mutants ( Figure 6C ) , suggesting that LIN-17 may act as a receptor for both EGL-20 and LIN-44 ., Taken together , the above results indicate that egl-20 and lin-44 are the major regulators of PQR dendrite outgrowth , and appear to genetically interact , whereas cwn-1 plays only a minor role in the process ., To determine the possible roles of other Frizzled receptors , we also studied PQR dendrite formation in cfz-2 and mig-1 mutants ., cfz-2 mutants showed no significant defects , whereas mig-1 mutants presented 50% normal PQR dendrite ( Figure 6D , Table S3 ) ., Thus , LIN-17 appears to be the main Frizzled receptor regulating PQR dendrite formation ., To analyze functional redundancy among the Frizzleds , we tested whether mig-1 could enhance the lin-17 defect ., In the mig-1 lin-17 double mutant , there was almost a 2-fold increase in the absent-dendrite phenotype ( Figure 6D ) , indicating a possible parallel role of mig-1 in PQR dendrite formation ., Several studies across different model systems have shown that Wnts can act instructively as both attractants and repellents in neurodevelopmental processes such as axon guidance , synapse formation , and neurite outgrowth 13 , 16 , 17 , 23 , 41–43 ., Conversely , Wnt molecules can also act in a permissive manner , as non-spatial cues 14 , 15 , 20 , 22 ., Our results suggest that posteriorly expressed LIN-44 acts as an attractive cue for the PQR dendrite ., Ectopic expression of LIN-44 from the anterior side of PQR increases the tendency for dendrites to emerge and grow anteriorly , towards the source of LIN-44 ., This role of LIN-44 as an attractant in PQR dendrite development differs from its role as a repellent signal for synaptic clustering in the dorsal section of the DA9 motor neuron 16 , highlighting the distinct effect of LIN-44 on these neighbouring neurons ., The partial rescue of PQR dendrite defects by ubiquitous expression of LIN-44 from the heat shock promoter could suggest a permissive role for LIN-44 ., However , a possible alternative interpretation is that local asymmetry of the ligand is generated , providing rescue when the concentration is higher on the posterior side of PQR ., This conclusion is supported by the observations that a higher concentration of ligand ( increased length of heat shock ) is unable to increase the rescue , and that in the wild-type background heat shock-directed expression causes dendrite defects ., To be fully functional , Wnts must undergo post-translational modifications , sorting in the endoplasmic reticulum , and secretion from the cells where they are expressed 44 ., It is possible that cells that do not normally express LIN-44 have lower efficiency in regulating the proper maturation and secretion of this Wnt molecule ., Hence LIN-44 expression from the heat shock promoter may provide functional , secreted LIN-44 with variable efficiency depending on the tissue of expression ., Wnt patterning occurs during embryogenesis , at a time when many neurons are born ., Our observation that PQR forms a normal dendrite following ablation of the tail hypodermal cells at the time of hatching suggests that embryonically expressed LIN-44 provides spatial information needed by the developing PQR several hours later ., However , PQR remains receptive to heat shock misexpression of LIN-44 up until the dendrite begins developing ., It is not known how stable Wnts are in C . elegans; however , in Drosophila the Wnt Wingless ( Wg ) and the morphogen Decapentaplegic ( Dpp ) are stable for about 3 h 45 , 46 ., Wnts can also function at long distances ., In C . elegans , for example , EGL-20 has been shown to direct cell migration across half the animals body length 20 , 46 ., Similarly in Drosophila , Wg can cover 10–20 cell diameters away from its source in the developing wing 47 , 48 spreading over a distance of about 50 µm in 30 min 46 ., Our results showing an effect of LIN-44 when expressed in the pharynx from the promoter myo-2 in a region far from PQR also suggest a potential long range effect for this ligand ., Emerging evidence suggests that dendrites of sensory neurons are shaped in a variety of ways ., In contrast to dendrite development by retrograde extension , or towing by associated cells 4 , 7 , we and others 49 have observed that the dendrite of PQR forms by growth cone crawling , a mode of development more commonly seen in axons ., In LIN-44 mutants , this growth cone often fails to form , preventing the outgrowth of a dendrite ., Our results demonstrate that LIN-17 , a receptor for LIN-44 , cell-autonomously regulates the initiation and outgrowth of the PQR dendrite ., To our knowledge , a ligand-receptor pair that can specifically affect the development of a dendrite in this manner has not previously been described ., Interestingly , phasmid glia associated with the PQR dendrite do not have a major effect on its development ., It has previously been shown that lin-44 and lin-17 mutants have defects in phasmid socket glia that arise due to disrupted polarity of the T cell precursor 29 , 35 , 50 ., However , the aberrant structure of the phasmid in these mutants does not seem to be the main cause of dendrite defects , as ablation of these cells did not reproduce the mutant phenotypes ., Notably , glia appear to have some involvement in the final extension of the dendrite , as some ablated animals had short dendrites ., This is reminiscent of a previous study in which it was demonstrated that ablation of the sheath glia associated with the CEP sensory neuron in the head of C . elegans resulted in a failure of the sensory dendrite of this neuron to fully extend 51 ., Different lines of evidence suggest that LIN-17 , like LIN-44 , may be required early in development to promote normal dendrite outgrowth ., Cell-specific LIN-17 expression can rescue lin-17 dendrite defects if induced very early , before PQR is born , but has no such effect when induced later , once the cell has almost completed its migration ., Furthermore , LIN-17::YFP expression from the rescuing egl-17 promoter appeared to become extremely faint or absent by the time the dendrite began to develop ., This raises the interesting possibility that levels of LIN-17 receptor on the PQR cell surface are temporally regulated to elicit the appropriate response to Wnt ligands ., We propose a model in which the LIN-17 receptor , present at low levels on the membrane of the PQR cell from the moment it is born , detects a posterior source of LIN-44 that signals the dendrite to emerge from the posterior side of the cell ( Figure 7A , B ) ., This initial specification of the site of dendrite outgrowth appears to be an important determinant of the subsequent direction of dendrite outgrowth ., The tendency for lin-44 and lin-17 mutant dendrites to grow anteriorly from the PQR cell , rather than from random orientations ( including dorsal or ventral ) , may imply the presence of an intrinsic anterior-posterior bias of the site and direction of PQR dendrite outgrowth controlled by Wnts and Frizzleds , or the existence of a dorso-ventral dendrite outgrowth controlled by other guidance molecules still unknown ., In C . elegans , Wnts are expressed in different regions along the anterior-posterior axis ., These different Wnts have often been shown to have distinct effects on cells that are located in proximity to the respective source of Wnt expression ., Our genetic studies suggest that , similar to LIN-44 , the posteriorly expressed Wnt ligand EGL-20 also acts through the LIN-17 receptor to regulate PQR dendrite development ( Figure 7C ) , which could explain why lin-17 defects are more severe than those of lin-44 ., However , whether EGL-20 plays an instructive role in this process remains unclear ., Previous studies have also shown that both LIN-44/Wnt and EGL-20/Wnt can function through LIN-17/Frizzled; however , whether Frizzled receptors can simultaneously bind multiple Wnts , or whether Wnts can form homo- or hetero-dimers , remains unknown ., The Wnt molecules CWN-1 and CWN-2 are both expressed more broadly in the body wall muscle , intestine , ventral cord neurons , and some head neurons 13 , 21 , 22 , 38 ., Although these Wnts do not appear to directly regulate PQR dendrite development , our observation that a significant proportion of cwn-1 and cwn-2 mutants present ectopic processes on PQR suggests an indirect role in neurite pruning ., This is consistent with recent findings that identify CWN-1 and CWN-2 as key regulators of developmental pruning of the head neuron AIM 21 ., The MIG-1 receptor appears to act synergistically in a parallel pathway to LIN-17 ( Figure 7C ) ., Notably , the increase in the percentage of the absent dendrite phenotype of the lin-17 mig-1 double mutant compared with the lin-17 mutant suggests a role for MIG-1 in regulating the ability of the neuron to send out a dendrite , regardless of its direction ., Wnt morphogens have diverse functions in developmental processes across species , yet how they act with such precision on a single cell within a closely wired nervous system remains enigmatic ., As we and others have shown , spatio-temporal organization of Wnts and their Frizzled receptors must be tightly orchestrated ., The challenge now will be to gain insight into how these molecules are patterned and how they can be interpreted differently by individual cells ., Nematodes were cultured using standard methods 52 ., All experiments were performed at 18°C except where otherwise noted ., The following mutations were used: LGI , lin-17 ( n677 ) , lin-17 ( n671 ) , lin-17 ( n3091 ) , lin-17 ( vd002 ) , lin-44 ( n1792 ) , mig-1 ( e1787 ) ; LGII , cwn-1 ( ok546 ) ; LGIV , egl-20 ( n585 ) , cwn-2 ( ok895 ) ; LGIV , cfz-2 ( ok1201 ) ., Transgenes used were: kyIs417Pgcy-36::GFP , Podr-1::dsRed , kyIs403Podr-1::dsRed2 , Pflp-18::UNC-43g::dsRed2 , Pgcy-36::YFP::RAB-3 , Pgcy-36::mCFP , vdEx127Phsp16-2:LIN-44 ( 10 ng/µl ) , Pcoelomocyte::GFP ( 25 ng/µl ) , wyEx806Plin-44::signal sequence:: flag::GFP::lin-44 genomic coding::lin-44 3′UTR , odr-1::GFP , vdEx224Pcwn-1::signal sequence::flag::GFP::lin-44 genomic coding ( 20 ng/µl ) , Pcoelomocyte::GFP ( 30 ng/µl ) , vdEx235 ( Pmyo-2::signal sequence::flag::GFP::lin-44 genomic coding ( 20 ng/µl ) , Pcoelomocyte::GFP ( 30 ng/µl ) , vdEx251Podr-1::dsRed ( 30 ng/µl ) , Pegl-17::LIN-17::YFP ( 20 ng/µl ) , Pgcy-36::mCherry ( 0 . 5 ng/µl ) , vdEx133Plin-17::LIN-17::YFP ( 10 ng/µl ) , Pchs-2::dsRed ( 2 ng/µl ) , pSM ( 10 ng/µl ) , vdEx265 Plin-17::mCherry ( 20 ng/µl ) , Pegl-17::GFP ( 50 ng/µl ) ., The kyIs417 strain was generated in Cori Bargmanns lab , the kyIs403 strain was provided by Manuel Zimmer and Cori Bargmann , and the wyEx806 strain was provided by Kang Shen ., Standard molecular biology methods were used ., All constructs were cloned into

Introduction, Results, Discussion, Materials and Methods

Nervous system function requires proper development of two functional and morphological domains of neurons , axons and dendrites ., Although both these domains are equally important for signal transmission , our understanding of dendrite development remains relatively poor ., Here , we show that in C . elegans the Wnt ligand , LIN-44 , and its Frizzled receptor , LIN-17 , regulate dendrite development of the PQR oxygen sensory neuron ., In lin-44 and lin-17 mutants , PQR dendrites fail to form , display stunted growth , or are misrouted ., Manipulation of temporal and spatial expression of LIN-44 , combined with cell-ablation experiments , indicates that this molecule is patterned during embryogenesis and acts as an attractive cue to define the site from which the dendrite emerges ., Genetic interaction between lin-44 and lin-17 suggests that the LIN-44 signal is transmitted through the LIN-17 receptor , which acts cell autonomously in PQR ., Furthermore , we provide evidence that LIN-17 interacts with another Wnt molecule , EGL-20 , and functions in parallel to MIG-1/Frizzled in this process ., Taken together , our results reveal a crucial role for Wnt and Frizzled molecules in regulating dendrite development in vivo .

Neurons have distinct compartments , which include axons and dendrites ., Both of these compartments are essential for communication between neurons , as signals are received by dendrites and transmitted by axons ., Although dendrites are vital for neural connectivity , very little is known about how they are formed ., Here , we have investigated how dendrites develop in vivo by examining an oxygen sensory neuron ( PQR ) in the nematode C . elegans ., Using a genetic approach , we have discovered that Wnt proteins , a group of highly conserved secreted morphogens , interact with their canonical Frizzled receptors to control the development of the PQR dendrite ., We show that Wnt molecules act as attractive signals to determine the initiation and direction of dendrite outgrowth ., Interestingly , Wnt proteins act specifically on the dendrite without affecting the axon , suggesting that outgrowth of the dendrite can be regulated by distinct processes that are independent of axon formation ., We predict that similar mechanisms may be in place in other species owing to the conserved roles of Wnt and Frizzled molecules in development .

animal models, morphogens, developmental biology, caenorhabditis elegans, cellular neuroscience, model organisms, developmental neuroscience, neuronal morphology, axon guidance, molecular development, biology, neuroscience, neural circuit formation

journal.ppat.1007260

Expansion of commensal fungus Wallemia mellicola in the gastrointestinal mycobiota enhances the severity of allergic airway disease in mice

The gut microbiome is a dynamic ecosystem that profoundly influences immune function throughout the body 1–3 ., Commensal microorganisms are recognized by the host immune system and can alter systemic immune response or produce bioactive metabolites which are absorbed into the bloodstream and have pharmacological effect on distant organ systems 4–7 ., The commensal microbial composition of the gut can therefore have a distant effect on immune function in the lung and other organ systems; this is the concept of the gut-lung axis ., In addition to bacteria , the healthy gastrointestinal system contains a community of commensal fungi that live in the gut and continually interact with the gastrointestinal mucosa 8–10 ., As many commensal fungi require fastidious culture conditions , non-culture-based assays are essential to comprehensively profile intestinal fungal communities ., Like 16S sequencing of bacterial DNA , targeted amplicon sequencing of the first internally transcribed spacer region ( ITS1 ) of the fungal ribosomal RNA genome can be used to generate a profile of the fungal organisms in a sample and their relative abundance 11 ., Although there are several sample processing and analysis considerations distinct from 16S analysis , ITS1 sequencing has been used successfully to profile gastrointestinal fungal communities in a both mice and humans 9 , 11 ., ITS1 sequences have high variability across the phylogenetic tree and can generally be used to classify organisms to the genus and often species level ., Studies using ITS1 sequencing to profile the human gastrointestinal mycobiota generally report a diverse community containing more than 50 unique genera with substantial variation across individuals 8 ., Fungi are particularly important in asthma ., Inhaled fungi are a well-described trigger for asthma , and patients with fungal sensitization have increased asthma incidence and more severe and fatal disease compared to patients sensitized to other allergens 12 , 13 ., Interestingly , many commensal fungal species commonly found in the human gastrointestinal tract are triggers of allergic respiratory illness when inhaled such as Aspergillus , Cladosporium , Penicillium , and others 14 ., We know that avoidance of airborne fungi can improve control of asthma in some patients , but it is uncertain whether variation in the commensal fungal species which reside in the gut and continually interact with the host immune system may also alter the severity of allergic airways disease ., This is an emerging area of investigation , but tantalizingly , one recent study of human infants suggested that gut fungal dysbiosis may be more strongly associated with the development of allergic wheeze than bacterial dysbiosis 15 ., Prior studies of the intestinal mycobiota have largely focused on Candida albicans and other Candida species ., These are opportunistic pathogens that are recognized to be subject to intestinal overgrowth after exposure to oral antibacterials ., In mouse models , intestinal overgrowth of Candida can be induced after treatment with the antibiotic cefoperazone and exposure to a bolus of live yeasts 16 ., Mice with Candida overgrowth have exacerbated allergic airways disease , which has been suggested to occur via fungal secretion of prostaglandins that are absorbed into systemic circulation 4 ., Building on these studies , we previously investigated whether suppressing natural commensal fungal populations with anti-fungal drugs could suppress allergic airway disease ., We found , instead , that antifungal treatment surprisingly exacerbated allergic airways disease 17 ., Although anti-fungal therapy depleted native Candida populations , ITS1 sequencing analysis of their gastrointestinal mycobiota suggested that other , relatively drug-resistant fungal organisms increased in abundance; specifically , Aspergillus amsteoldami , Wallemia mellicola , and Epicoccum nigrum ., Further , when administered together as a cocktail by oral gavage , these three fungi exacerbated allergic airways disease ., These studies have led us now to investigate the idea that relatively modest changes in naturally occurring non-Candida intestinal fungi may influence pulmonary immune responses ., In this manuscript , our experimental results have led us to focus on Wallemia mellicola , a common environmental fungus ., W . mellicola is a ubiquitous spore forming filamentous basidiomycete that is a common component of house dust and an agent of food spoilage 18–21 ( S1A and S1B Fig ) ., W . mellicola is slow growing and may therefore be less commonly detected by culture than other commensal fungi such as Candida and Aspergillus ., Wallemia spp ., are highly xerotolerant and have been reported to secrete several toxins when grown in culture such as walleminol , walleminone , and wallimidione 22–24 ., The metabolic products generated by Wallemia residing in the mammalian intestine are unknown ., W . mellicola does not generally act as a human pathogen , but there are some reported associations between Wallemia exposure and lung disease ., Specifically , asthmatic patients have a high incidence of immune sensitization to Wallemia , and Wallemia was identified as one of a handful of environmental fungi associated with increased risk of asthma in individuals living in water damaged homes 25 , 26 ., We have previously observed that a fluconazole-induced intestinal fungal dysbiosis state enhances the severity of allergic airways disease in mice 17 ., Fluconazole therapy has complex and surprising effects on commensal gut fungal communities ., Although fluconazole moderately depletes the overall burden of commensal fungi including Candida spp ., , fungi that are relatively resistant to fluconazole expand in population ( Fig 1A ) ., Notably , this is not simply an increased relative abundance due to mismatched decline in individual species abundance compared to total fungal burden ., Rather , the absolute quantity of selected gut fungal species can increase during fluconazole therapy ., Our prior observation that fluconazole depletion of gut mycobiota exacerbates the severity of allergic airways disease suggests that previously-characterized effects of intestinal Candida overgrowth on allergic airways disease may not be restricted to Candida 17 ., To investigate further , we first sought to define conditions that generate a fungal dysbiosis in the gut by means other than inducing Candida overgrowth or repeated oral gavage with fungi ., We examined conditions required for enhancing intestinal colonization with several non-Candida commensal fungal species that are natively found in the intestinal microbiota of specific pathogen free mice ( SPF ) at our animal facility: Wallemia mellicola , Aspergillus amstelodami , and Epicoccum nigrum ., These were selected because we have previously observed that these species expand in relative population abundance in our fluconazole treated mice who develop exacerbated sensitivity to the house dust mite model of asthma 17 ., We initially attempted simply a gavage of high dose of cultured live fungi from each species into the mouse gastrointestinal tract ., This did not result in sustained expansion of the population of any fungus ., We hypothesized that resistance to expansion of the fungal populations was due to competition from other commensal gastrointestinal microbes ., Previous authors have shown that mice with an intact commensal bacterial community resist colonization with Candida albicans , but that antibiotic depletion of mouse commensal bacteria renders them vulnerable to C . albicans overgrowth after exposure 27 ., We therefore performed 7 days of treatment of mice with cefoperazone followed by one-time gavage of each fungus ( Fig 1B ) ., Cefoperazone treatment had a devastating effect on bacterial communities , depleting total commensal bacterial burden of the gut by nearly 10 , 000x fold , but gut bacterial abundance recovered to previous levels within 8 days of cessation of antibiotics ( Fig 1C ) ., A single gavage of W . mellicola into a mouse with antibiotic depleted bacterial microbiota resulted in sustained and substantial increase in the population of Wallemia above baseline ( Fig 1D ) ., The Wallemia-expanded mycobiota persisted after discontinuation of antibacterials and without any further W . mellicola gavage to support the population ., W . mellicola is present as a minor component of the commensal mycobiota in our mice at baseline , but mice treated with cefoperazone and gavaged with sterile water ( Fig 1D ) did not experience expansion of this fungus suggesting that both antibacterial depletion and exposure to a bolus of W . mellicola are necessary to generate a Wallemia-expanded dysbiosis state ., To be certain that W . mellicola can survive and grow in the mouse intestines , we further colonized germ-free mice with W . mellicola and observed that we could culture organisms from the stool after 10 days ( S1C Fig ) ., Antibiotic treatment was not similarly sufficient to allow for enhanced colonization with Epicoccum nigrum or Aspergillus amstelodami , suggesting that antibiotic depletion of gut bacteria is not universally able to facilitate expansion of a commensal fungal species ( Fig 1D ) ., These organisms may not directly compete with bacteria for their gut ecological niche , or they may compete with bacteria that are not affected by cefoperazone therapy ., We next explored the characteristics of the Wallemia-expanded mycobiota ., W . mellicola colonization was predominantly in the cecum and colon and restricted to the gastrointestinal tract with no W . mellicola detected in extra-gastrointestinal organ systems ( Fig 2A ) ., Notably , we did not detect W . mellicola by rtPCR or culture in mouse lungs ., Total gastrointestinal fungal abundance in mice with Wallemia-expanded microbiota remained similar to untreated mice , but the population of fungi had shifted such that W . mellicola had markedly increased in relative abundance ( Fig 2B and 2C ) ., This contrasts with the Candida albicans overgrowth state where the total fungal burden in the gastrointestinal tract increases by orders of magnitude after mice are subjected to a similar protocol of cefoperazone depletion of gut bacteria followed by C . albicans gavage ( Fig 2C ) ., Finally , there was no evidence to suggest that W . mellicola was behaving as an infectious pathogen in these mice ., Specifically , mice with Wallemia-expanded mycobiota showed no weight loss , behavioral changes , or stool changes throughout the experiments , and there was no histological evidence of colonic inflammation ( Fig 2D and 2E ) ., Together , these data suggest that stable W . mellicola colonization is best thought of as a model of altered intestinal fungal community rather than gastrointestinal fungal overgrowth ., We next examined whether W . mellicola and the other commensal fungi studied in these experiments are found in the human gastrointestinal tract ., Prior studies using ITS1 sequencing have detected sequences from Wallemia spp ., , Aspergillus spp ., , and Epicoccum spp ., in human gastrointestinal samples , but species-specific PCR based assay of human gastrointestinal samples for the three fungal species discussed in this manuscript has not previously been described 28–30 ., We extracted DNA from stool specimens from 9 healthy human subjects and performed rtPCR using species-specific primers to directly assess for DNA from each of the three relevant fungal species ., We detected Wallemia mellicola in 3 of 9 human samples and Aspergillus amstelodami in 7 of 9 human samples ( Fig 3A ) ., We did not detect Epicoccum nigrum DNA in any human samples in our cohort , as no amplification was observed in any sample tested by rtPCR with Epicoccum nigrum specific primers ., These results are consistent with prior studies showing that the composition of the human gut mycobiota varies across individuals and suggest that Wallemia mellicola and Aspergillus amstelodami may be capable of residing in the human gastrointestinal tract ., We next examined the three human subjects who had W . mellicola DNA detectable in their stool ., The total amount of W . mellicola DNA per stool weight was similar between Wallemia-colonized humans and SPF mice in our facility who are natively colonized with W . mellicola but less than mice who underwent the Wallemia-expanded colonization protocol ( Fig 3B ) ., This is not surprising because none of these healthy individuals carried a diagnosis of asthma or had recent antibiotic use , and we observed that antibiotic therapy was necessary to generate the Wallemia expansion dysbiosis in our mouse experiments ., Human subjects found to be colonized with Wallemia do not have enhanced total fungal burden compared to non-Wallemia-colonized subjects ( Fig 3C ) ., Having established a mouse model of sustained W . mellicola colonization of the gut , we next sought to determine whether mice with Wallemia-expanded intestinal dysbiosis have an altered immune response to inhaled aeroallergens ., We generated mice with a Wallemia-expanded mycobiota as described above by gavage of W . mellicola conidia into antibiotic-treated animals ., Control mice were housed and treated identically to the Wallemia-expanded mice including the antibiotic treatment , but they received sterile water gavage rather than W . mellicola conidia gavage ., We induced allergic airways disease in both groups by weekly intratracheal house dust mite ( HDM ) sensitization ., Mice with expanded intestinal population of W . mellicola had increased severity of allergic airways disease compared to control by multiple measures ., Wallemia-expanded mice demonstrated markedly greater bronchoalveolar lavage ( BAL ) cellularity driven primarily by increased alveolar eosinophils ( Fig 4A and 4B ) along with enhanced airways hyperresponsiveness ( AHR ) to methacholine challenge ( Fig 4C ) ., Histological analysis of the lungs showed enhanced goblet cell hyperplasia in Wallemia-expanded mice compared to controls ( Fig 4D and 4E ) , and these mice also had higher BAL levels of IL-5 and serum IgG1 to HDM detectable at the end of the experiment compared to controls by ELISA ( Fig 4F and 4G ) ., To determine whether this effect on allergic airways disease might possibly be due to the initial bolus of Wallemia conidia , rather than the sustained colonization , we compared live W . mellicola gavage to gavage with heat-killed organisms ., Heat-killed organisms did not influence allergic airways disease ( S2A Fig ) ., Similarly , we observed that an initial gavage with live W . mellicola did not influence disease if no pretreatment with antibiotics was provided to allow for sustained colonization ( S2B Fig ) ., Finally , to be certain that established colonization was essential , we delayed beginning the HDM sensitization for a week after the live W . mellicola gavage and found that the presence of Wallemia still exacerbated disease ( S2C Fig ) ., Together the data support the conclusion that enhanced colonization with live W . mellicola in the intestines exacerbates susceptibility to HDM allergic airways disease ., To begin to understand the mechanism by which expansion of gastrointestinal W . mellicola may have altered pulmonary immune response to HDM , we extracted the mediastinal lymph node from both groups and cultured lymphocytes in vitro ., Five days after in vitro restimulation with HDM , lymphocytes extracted from mice with a Wallemia-expanded mycobiota had increased percentage of CD4+ T-cells positive for Th2 cytokine IL-13 by intracellular staining and increased supernatant IL-13 concentration ( Fig 4H–4J ) ., We did not observe any differences in IFNγ or IL-17 between the two groups by either intracellular staining or supernatant cytokine levels , suggesting that the Wallemia-expanded mycobiota has little effect on Th1 and Th17 response in this setting ( S3 Fig ) ., Finally , to determine whether this augmented pulmonary immune response could be due to stimulation by direct migration of W . mellicola to the lungs , we examined for the presence of W . mellicola in the lungs by multiple methods ., We plated homogenized lung from Wallemia-expanded mice on antibacterial treated SDB agar plates and observed no growth of W . mellicola ., We also performed PCR of whole lung homogenate and no amplification was observed in any sample tested by rtPCR using W . mellicola specific primers ., Together , these results suggest that an intestinal dysbiosis state characterized by enhanced presence of W . mellicola has a distant effect on pulmonary immune response characterized by increased eosinophilic airway inflammation , goblet cell hyperplasia , and enhanced secretion of IL-13 by mediastinal lymphocytes in response to HDM ., We have shown population expansion of intestinal W . mellicola enhances the severity of allergic airways disease in response to HDM allergen challenge ., However , these experiments are insufficient to establish that intestinal Wallemia itself alters pulmonary immune response ., Rather than having a direct effect , it is possible that Wallemia population expansion may alter or suppress commensal bacteria or other fungi which are themselves responsible for altering asthma severity ., Notably , Wallemia species have been reported to secrete antibacterial compounds when grown in culture , so we hypothesized that expanded growth of Wallemia might alter bacterial community composition 31 ., To determine whether expanded W . mellicola colonization altered bacterial and fungal communities , we analyzed fecal bacterial and fungal communities in Wallemia-expanded mice via 16S and ITS1 rDNA sequencing respectively ., To control for potential cage-effects 32 , each group ( n = 8 ) was spread across 4 independent cages ., Principal coordinates analysis ( PCoA ) and pair-wise differential abundance analysis with LEfSe 33 suggested that W . mellicola expansion altered bacterial communities ( Fig 5A , 5B , S4A and S4B Fig ) ., Further , we observed changes to fungal communities in Wallemia-expanded mice in addition to the expected expanded W . mellicola population ( Fig 5C , 5D , S4C and S4D Fig ) ., However , enhanced colonization with W . mellicola did not affect levels of E . nigrum or A . amstelodami ( Fig 5E ) ., Together , the data suggest that enhanced growth of Wallemia mellicola in the gut alters intestinal bacterial and fungal populations , and this makes it difficult to conclude whether Wallemia mellicola itself is sufficient to alter allergic immune responses in the lung ., To investigate whether the presence of W . mellicola in the gastrointestinal microbiota itself is sufficient to alter airway response to allergens , we employed a gnotobiotic mouse model that offers more precise control of intestinal microflora ., Altered Schaedler Flora ( ASF ) mice are gnotobiotic animals with a stable microbiota consisting of eight defined bacterial species ., Importantly for this study , they are fungus-free ., Being colonized with bacteria , ASF mice are healthier than germ-free mice and have more mature immune systems 34–36 ., While ASF mice and germ-free mice are initially “fungal-free” , gastric gavage with live W . mellicola is sufficient to establish fungal colonization ( Fig 6A and 6B ) ., W . mellicola grows similarly in both types of animals , although the total fungal burden remains substantially lower than SPF mice from our facility ., Interestingly , W . mellicola colonization of ASF animals did not require prior antibiotic-mediated depletion of bacteria suggesting that the ASF bacteria do not substantially compete for the niche required by this fungus ., We further observed that colonization of ASF animals with W . mellicola did not alter ASF bacterial microbiota ., ASF mice have fecal bacterial levels similar to SPF mice , and colonization of ASF mice with W . mellicola did not grossly alter the total bacterial burden ( Fig 6C ) ., Upon measuring fecal levels of each of the 8 constituent ASF bacteria , we did not observe any Wallemia-induced quantitative changes in the population ( Fig 6D , S5A and S5B Fig ) , although these results do not exclude the possibility that W . mellicola may alter metabolic products produced by the ASF bacterial community ., Together the data suggest that these animals are a strong model for evaluating the specific ability of intestinal W . mellicola to influence allergic airways disease ., We next performed intratracheal HDM sensitization on these mice to induce allergic airways disease ., We compared ASF mice to ASF mice colonized with W . mellicola ., Like conventional mice with an expanded population of W . mellicola , we observed that colonizing ASF with W . mellicola increased the severity of HDM-induced allergic airways disease ., Interestingly ASF mice demonstrated a mildly blunted response to HDM at baseline compared to SPF mice , but the pattern of disease was like that observed in SPF mice with antibiotic-associated expansion of Wallemia , including increased eosinophilic airway infiltration , increased histological inflammation with hyperplasia of mucous producing goblet cells , elevated BAL IL-5 , and increased serum IgG1 to HDM ( Fig 7 ) ., The data suggest that intestinal W . mellicola is sufficient to exacerbate allergic airways disease since the effect occurs without substantial alteration to other commensal bacteria ., We have shown that altered composition of the gastrointestinal mycobiota enhances the severity of allergic airways disease with enhanced eosinophilic airway inflammation and increased IL-13 production by mediastinal lymphocytes in response to HDM allergen stimulation ., Interestingly , these effects are not due to a fungal overgrowth state where bloom of a single organism results in exponential population expansion of the total gastrointestinal fungal burden ., Rather , the W . mellicola dysbiosis described herein is a shift in the composition of the commensal fungal community that occurs without substantial increase in the total fungal burden yet still produces a significant change in pulmonary immune response to inhaled allergens ., The term “dysbiosis” is generally used to describe altered gut microbial ecosystem that results in negative host effects but is not an infectious state ., We believe that the dysbiosis state described in this manuscript is not unique to Wallemia mellicola ., Rather Wallemia mellicola dysbiosis may just be one representative example of a gut microbial pattern that alters pulmonary and systemic immune response ., Other alterations of the gastrointestinal mycobiota community characterized by expansion of different fungal species may have distinctive beneficial or harmful effects on respiratory immune function ., The mechanism by which gastrointestinal W . mellicola population expansion alters pulmonary immune function is unknown , but there are several possibilities ., W . mellicola may produce a toxin or metabolite that is absorbed , circulates systemically , then acts like a drug to modify the pulmonary immune response ., This phenomenon has been observed with several other gut commensal microorganisms such as Clostridium orbiscindens which produces a small molecule metabolite ( desaminotyrosine ) that is systemically absorbed and alters pulmonary interferon response to influenza infection and prostaglandin E2 produced by intestinal Candida as discussed earlier 4 , 5 ., Interestingly , Wallemia species have been described to secrete a variety of mycotoxins such as walleminol , walleminone , and wallimidione when cultured in vitro 22–24 ., Mycotoxins produced by other similar environmental fungal species have been shown to promote inflammatory cytokine secretion by lung alveolar macrophages 37 ., Fungal mycotoxin production is generally influenced by the environmental conditions , and further study is needed to determine whether Wallemia produces toxins or other bioactive metabolites during growth in the mammalian intestine , whether these are absorbed into systemic circulation , and whether they affect host immune cells ., It is also possible that Wallemia indirectly triggers production of different metabolites by the bacterial microbiota that then affects immune responses to challenge ., Future studies will need to be designed to address this possibility ., Alternatively , W . mellicola may be recognized by the gastrointestinal host immune system and result in differential immune cell trafficking to the lungs ., Gut commensal fungi have previously been shown to promote trafficking of different immune cell populations to non-GI organ systems ., For example , migration of RALDH+ dendritic cells to peripheral lymph nodes in young mice is enhanced specifically by the presence of certain commensal gut fungal species 38 ., W . mellicola is not generally considered to be a human pathogen , so there has been little prior study of immune response to this organism ., Wallemia is known to elicit serum IgE and IgG responses 25 , 39 , but other innate and adaptive immune responses are not well characterized ., Further study is needed to understand how W . mellicola is recognized by intestinal immune and epithelial cells and the consequences ., An important observation in this study is that oral antibiotic therapy places mice at risk for expansion of the dysbiosis-associated fungus Wallemia mellicola , but that mice with intact microbiota resist W . mellicola expansion after exposure ., Our W . mellicola dysbiosis state in mice was established under conditions like those that a human asthma patient might experience ., Wallemia are common environmental and food spoilage fungi , and therefore it is plausible that individuals may be exposed to live Wallemia conidia in their food or environment during a course of antibiotic therapy ., Cefoperazone is a third-generation cephalosporin , and antibacterial medications in this class are widely used in patients with asthma and other respiratory diseases ., Although antibacterials are not indicated for an uncomplicated asthma exacerbation , patients with severe asthma nonetheless receive frequent courses of broad spectrum antibacterial medications for a variety of indications throughout their lifetime 40 , 41 ., Furthermore , frequent courses of antibacterials , particularly early in life , have been associated with an increased incidence asthma 42 , 43 ., The concept that an intact microbiota resists colonization by pathogens such as Clostridium difficile has been previously established 44 , and we now describe that a non-pathogenic commensal fungus can expand in the face of an antibiotic depleted microbiota and enhance the severity of allergic airways disease in mice ., Intestinal fungal dysbiosis might therefore be an unrecognized but potentially important risk of each course of antibiotic therapy in patients with asthma and other respiratory disease ., Further studies are needed to determine whether a phenomenon like the Wallemia dysbiosis state described in this manuscript can occur in humans during routine broad spectrum antibacterial therapy and more generally to what extent that gut mycobiota changes in humans alters pulmonary and systemic immune function ., All experiments involving research animals were performed in accordance with the recommendations outlined in the Guide for the Care and Use of Laboratory Animals ., All research animal protocols were approved by the institutional animal use and care committee at Cedars-Sinai Medical Center ( IACUC #6670 and #5160 , PHS assurance number A3714-01 ) ., All studies involving humans and human samples were approved by the Cedars-Sinai Medical Center Institutional Review Board ( IRB #0003358 , Federalwide Assurance number 00000468 ) ., All human subjects were adults age >18 who provided written informed consent ., All specimens were assigned an anonymized sample ID with no connection to patient identifiable information ., Specimens were collected by the Cedars-Sinai MIRIAD IBD Biobank ., 7-8-week-old C57BL/6 female mice were purchased from Jackson Laboratory and housed in specific pathogen free conditions at the Cedars-Sinai animal facility ., Germ-free mice C57BL/6 mice were obtained from Taconic Farms then housed and bred in microbially sterile flexible film isolators ., A separate colony of altered Schaedler flora ( ASF ) mice was generated by gavage of live cultures of the 8 ASF bacterial species ( Taconic Farms ) into germ-free mice 35 ., These mice were subsequently housed , bred , and raised in a separate flexible film isolator that was designated exclusively for maintenance of the ASF mouse colony ., PCR based assays were performed to confirm stable presence of all 8 ASF bacterial species in subsequent generations of mice ., ASF experiments were performed in age matched cohorts of 7-8-week-old male mice who had been born in a flexible film isolator to mothers colonized with ASF bacteria ., Quality control testing including microbial culture and PCR was regularly performed on both the germ-free and ASF colonies to ensure that no contaminating microorganisms were introduced into either colony ., Notably , ASF animals were verified to be fungus-free by FungiQuant rtPCR assays of both stool and environmental samples 45 ., All animal experimental protocols were approved by the Institutional Animal Use and Care Committee ( IACUC ) at the Cedars-Sinai Medical Center ( IACUC #5160 and #6670 ) ., Fungal cultures of Wallemia mellicola ( ATCC 42694 ) , Aspergillus amstelodami ( ATCC 46362 ) , Epicoccum nigrum ( ATCC 42773 ) were obtained from American Type Culture Collection ( ATCC ) and grown on Sabouraud dextrose agar for 7–14 days at 23°C as previously described 17 ., Note that due to a recent taxonomic revision , the Wallemia strain examined in this study ( ATCC 42694 ) was previously identified as Wallemia sebi but has been reclassified as Wallemia mellicola 19 ., As documented in this manuscript , Wallemia mellicola is a naturally-occurring commensal microbe in mice and humans ., Fungal conidia suspensions were generated by flooding a Sabouraud dextrose agar plate with mature fungal culture growth with 10 mL sterile water , gently washing by pipet to dislodge conidia , then passing the spore suspension through a 40 μm filter to exclude hyphal fragments ., Conidia were centrifuged at 1600 rpm x4 minutes , resuspended in 1 mL sterile water , counted using hemocytometer , and the volume of water was adjusted to produce a 5 x 107 conidia /mL suspension for gavage ., For experiments involving heat-killed Wallemia , conidia were prepared as described and exposed to 95° C for 10 minutes then plated on Sabouraud dextrose agar to confirm non-viability ., Medicated drinking water was administered in selected experiments as follows: Fluconazole powder ( Sigma-Aldrich , PHR1160 ) was dissolved in deionized water at a concentration of 0 . 5 mg/mL or cefoperazone sodium salt ( Alfa Aesar , J65185 ) was dissolved in deionized water at a concentration of 0 . 5 mg/mL ., The medicated water was provided as the exclusive source of drinking water for mice over the duration of antibiotic therapy , and mice were permitted to drink the medicated water ad libitum ., The water was protected from light exposure by foil , and the medicated water was exchanged with freshly mixed solution every 3–5 days ., The fungal expanded colonization protocol described herein was adapted from experiments originally described by Noverr and Huffnagle 16 ., SPF mice were treated with cefoperazone drinking water for 7 days to deplete intestinal bacteria ., Mice subsequently received gavage of 5 x 106 live conidia from a single species ( Wallemia mellicola , Aspergillus amstelodami , or Epicoccum nigrum ) or 5 x 106 live yeast ( C . albicans ) suspended in 100 μL deionized water ., Antibiotic drinking water was discontinued 4 hours after gavage ., Stool collection was performed at several timepoints ( Days #1 , 7 , 14 , 21 ) and fungal DNA was extracted from stool as later described ., Stool pellets were examined for consistency and for the presence of blood ., Mice ( n = 5 per group ) first underwent the coloniza

Introduction, Results, Discussion, Materials and methods

The gastrointestinal microbiota influences immune function throughout the body ., The gut-lung axis refers to the concept that alterations of gut commensal microorganisms can have a distant effect on immune function in the lung ., Overgrowth of intestinal Candida albicans has been previously observed to exacerbate allergic airways disease in mice , but whether subtler changes in intestinal fungal microbiota can affect allergic airways disease is less clear ., In this study we have investigated the effects of the population expansion of commensal fungus Wallemia mellicola without overgrowth of the total fungal community ., Wallemia spp ., are commonly found as a minor component of the commensal gastrointestinal mycobiota in both humans and mice ., Mice with an unaltered gut microbiota community resist population expansion when gavaged with W . mellicola; however , transient antibiotic depletion of gut microbiota creates a window of opportunity for expansion of W . mellicola following delivery of live spores to the gastrointestinal tract ., This phenomenon is not universal as other commensal fungi ( Aspergillus amstelodami , Epicoccum nigrum ) do not expand when delivered to mice with antibiotic-depleted microbiota ., Mice with Wallemia-expanded gut mycobiota experienced altered pulmonary immune responses to inhaled aeroallergens ., Specifically , after induction of allergic airways disease with intratracheal house dust mite ( HDM ) antigen , mice demonstrated enhanced eosinophilic airway infiltration , airway hyperresponsiveness ( AHR ) to methacholine challenge , goblet cell hyperplasia , elevated bronchoalveolar lavage IL-5 , and enhanced serum HDM IgG1 ., This phenomenon occurred with no detectable Wallemia in the lung ., Targeted amplicon sequencing analysis of the gastrointestinal mycobiota revealed that expansion of W . mellicola in the gut was associated with additional alterations of bacterial and fungal commensal communities ., We therefore colonized fungus-free Altered Schaedler Flora ( ASF ) mice with W . mellicola ., ASF mice colonized with W . mellicola experienced enhanced severity of allergic airways disease compared to fungus-free control ASF mice without changes in bacterial community composition .

The microbiome is the ecosystem of bacteria , fungi , viruses , and other microorganisms that live in and on us ., The intestines are where most of these organisms reside , and the composition of the intestinal microbiota has a profound effect on immune system function throughout the body ., In this manuscript , we observe that expansion of a certain species of house dust fungus ( Wallemia mellicola ) can occur in the intestines of mice after they are treated with antibiotics and exposed to the fungus , but that mice with an intact and healthy intestinal microbiota resist this expansion ., After expansion of this fungal population , the mice are more prone to develop asthma-like inflammation in their lungs when exposed to allergens ., Although it is not known whether the same phenomenon can occur in people with asthma , we have also identified this fungus as a component of the microbiota of some healthy humans ., Elimination of this organism from the intestines is not as simple as taking an antifungal medication because antifungal medications can also disrupt the balance of the intestinal microbial community and may similarly worsen allergic airways disease .

antimicrobials, medicine and health sciences, microbiome, pathology and laboratory medicine, pathogens, drugs, microbiology, animal models, fungi, model organisms, antibiotics, experimental organism systems, pharmacology, fungal diseases, fungal pathogens, bacteria, microbial genomics, digestive system, research and analysis methods, infectious diseases, mycology, medical microbiology, microbial pathogens, mouse models, gastrointestinal tract, eukaryota, anatomy, genetics, microbial control, biology and life sciences, genomics, organisms

journal.pgen.1004042

Crossover Patterning by the Beam-Film Model: Analysis and Implications

Crossover ( CO ) recombination interactions occur stochastically at different positions in different meiotic nuclei ., Nonetheless , along a given chromosome , COs tend to be evenly spaced ., This interesting phenomenon implies the existence of communication along chromosomes , the nature of which is not understood ., CO patterning , commonly known as “CO interference” , was originally detected from genetic studies in Drosophila 1 , 2 ., It was found that the frequency of meiotic gametes exhibiting two crossovers close together along the same chromosome ( “double COs” ) was lower than that expected for their independent occurrence ., The implication was that occurrence of one CO ( or more correctly one CO-designation ) “interferes” with the occurrence of another CO ( CO-designation ) nearby ., We previously proposed a model for CO patterning in which macroscopic mechanical properties of chromosomes play governing roles via accumulation , relief and redistribution of stress ( Figure 1A ) 3 , 4 ., In that model , a chromosome with an array of precursor interactions comes under mechanical stress along its length ., Eventually , a first interaction “goes critical” , undergoing a stress-promoted molecular change which designates it to eventually mature as a CO ., By its intrinsic nature , this change results in local relief of stress ., That local relaxation then redistributes outward in the immediate vicinity of its nucleation point , in both directions , dissipating with distance ., A new stress distribution is thereby produced , with the stress level reduced in the vicinity of the CO-designation site , to a decreasing extent with increasing distance from its nucleation point ., This effect disfavors occurrence of additional ( stress-promoted ) CO designations in the affected region ., The spreading inhibitory signal comprises “CO interference” ., More such CO-designations may then occur , sequentially , each accompanied by spreading interference ., Each subsequent event will tend to occur in a region where the stress level remains higher , which will necessarily tend to be regions far away from prior CO-designated sites ., Thus , as more and more designation events occur , they tend to fill in the holes between prior events , ultimately producing an evenly-spaced array ., The most attractive feature of this proposed mechanism is the fact that redistribution of stress is an intrinsic feature of any mechanical system , thus comprising a built-in communication network as required for spreading CO interference ., CO-designated interactions then undergo multiple additional biochemical steps to finally become mature CO products 5 ., Precursors that do not undergo CO-designation mature to other fates , predominantly inter-homolog non-crossovers ( NCOs ) ., CO patterning by the above stress-and-stress relief mechanism can be modeled quantitatively by analogy with a known physical system that exhibits analogous behavior , giving the beam-film ( BF ) model 3 ., We note that BF model simulations can be applied to any mechanism whose effects are described by the same mathematical expressions as the beam-film case ., In such a more general formulation ( Figure 1B ) , there is again an array of precursor interactions ., That array would be acted upon by a “Designation Driving Force” ( DDF ) ., Event-designations would occur sequentially ( or nearly so ) ., Each designation would set up a spreading inhibitory effect that spreads outward in both directions , decreasing in strength with increasing distance , thereby decreasing the ability of the affected precursors to respond to the DDF ., When multiple designation/interference events occur , they would produce an evenly-spaced array ., Maturation of CO-designated and not-CO-designated interactions ensues ., The present study adds several new features to the BF simulation program and explores in further detail the predictions and implications of the BF model ( whether mechanical or general ) ., We evaluate the ability of the model to quantitatively explain experimental CO pattern data sets in budding yeast , tomato , grasshopper and Drosophila ., Our results show that the logic and mathematics of the BF model are remarkably robust in explaining experimental data ., New information of biological interest also emerges ., We then present detailed considerations of three phenomena of interest , the so-called “obligatory CO” and “CO homeostasis” , and the nature of “non-interfering COs” ., We discuss how these phenomena are explained by the BF model and show that BF predictions can very accurately explain experimental data pertaining to these effects ., Overall , the presented results show that BF simulation analysis is a useful approach for exploring experimental CO patterns ., Other applications of this analysis are presented elsewhere ., The current study has also provided new criteria for characterization of CO patterns using Coefficient of Coincidence analysis and illustrates both short-comings and useful applications of gamma distribution analysis ., Relationships of the BF model to other models are discussed ., CO data sets , whether experimental or from BF simulations , comprise descriptions of the positions of individual COs along the lengths of each of a large number of different chromosomes ( “bivalents” ) ., Each bivalent represents the outcome of CO-designation in a single meiotic nucleus; the entire data set comprises the outcomes of CO patterning for a particular chromosome in many nuclei ., BF simulations require specification of three types of parameters ( Table 1 ) ., One set describes the nature of the precursor array upon which CO-designation acts; a second set describes features of the patterning process per se; and a third precursor specifies the efficiency with which a designated event matures into a detectable CO or CO-correlated signal ., Application of the BF model to an experimental data set permits the identification of a set of parameter values for which simulated CO patterns most closely match those observed experimentally ( general strategy described and illustrated in Figure S4 ) ., Best-fit simulation analysis for data sets from yeast , Drosophila , tomato and grasshopper demonstrates that the logic and mathematics of the BF model can describe experimental CO patterns with a high degree of quantitative accuracy ., This conclusion is evident in descriptions of CoC and ED patterns as described in this section ( III ) ., Additional evidence is provided by applications and extensions of BF simulation analysis to CO homeostasis , the obligatory CO and non-interfering COs as described in sections IV–VI ., Inspection of experimental CoC relationships has also provided new information regarding the metric of CO interference in tomato and the fact that interference spreads across centromere regions ( in grasshopper , as previously described , and also in tomato and yeast ) ., Experimental evidence has revealed that variations in the level of recombination-initiating double-strand breaks ( DSBs ) are not accompanied by corresponding variations in the number of COs ., When DSB levels are either reduced or increased , CO levels are not reduced or increased commensurately 15 , 34 , 43–46 ., This phenomenon is referred to as CO homeostasis 43 ., According to the BF model , CO homeostasis is dependent upon , and in fact is a direct consequence of , CO interference ( Figure 11A ) , as proposed 43 , 46 ., In the absence of interference , the probability that a precursor will give rise to a CO is a function only of its own intrinsic properties , independent of the presence/absence of other precursors nearby ., Thus , as the number of precursors decreases , the number of COs will decrease proportionately ., In contrast , if interference is present , each individual precursor is subject to interference that emanates across its position from CO-designation events at neighboring positions ., The lower the number of precursors , the less this effect will be ., Thus , assuming a fixed level of CO interference , the frequency of COs per precursor will increase as the number of precursors decrease ., Put another way: as the density of precursors decreases , the ratio of COs to precursors increases , even though there is no change in CO interference ., Importantly , since CO homeostasis requires CO interference , its magnitude will also depend on the strength of CO interference as discussed below ., Regular segregation of homologs to opposite poles at the first meiotic division requires that they be physically connected ., During meiosis in all organisms , in at least one sex and usually both , the requisite physical connection is provided by the combined effects of a crossover between non-sister chromatids of homologs and connections between sister chromatids along the chromosome arms ., Correspondingly , in such organisms , in wild-type meiosis , every bivalent almost always acquires at least one CO 47 ., This first CO that is essential for homolog segregation is often referred to as the “obligatory CO” ., In fact , the obligatory CO is simply a biological imperative: the level of zero-CO chromosomes should be low ., The CO patterning process , by whatever mechanism , must somehow explain this feature ., In most situations , the frequency of zero-CO bivalents is extremely low ( <10−3 ) , but higher frequencies also occur in certain wild-type situations as well as in certain mutants ( below ) ., In some models for CO patterning , the obligatory CO is ensured by a specific “added” feature of the patterning process ( e . g . the King and Mortimer model; Discussion ) ., In contrast , in the beam-film model , the requirement for one CO per bivalent is satisfied as an intrinsic consequence of the basic functioning of the process , as follows ., In some organisms , a significant fraction of COs arises outside of the patterning process ., The existence of these “non-interfering” COs is most rigorously documented for budding yeast , where the number of “non-interfering” COs is ∼30% among total COs ( by compassion the number of patterned COs defined by analysis of CO-correlated Zip2/Zip3 foci with the number of total COs from genetic and microarray analyses ) ( e . g . 22 , 31 , 52 , 53; below ) ., The origin of non-interfering COs is unknown ., One possibility is that they arise from the majority subset of interactions that do not undergo CO-designation 5 ., By this model ( “Scenario 1”; Figure 14A left ) , not-CO-designated interactions would mostly mature to NCOs but sometimes would mature to COs , analogously to the situation in mitotic DSB-initiated recombinational repair 54 ., Alternatively , such COs might arise from some other set of DSBs that arise outside of the normal process , e . g . because they occur later in prophase after CO-designation is completed or earlier in prophase before patterning conditions are established ( “Scenario 2”; Figure 14A right ) ., Both scenarios can be examined using the BF simulation program ., To simulate the outcome of Scenario 1 , where non-patterned COs arise from non-designated interactions left over after patterning , a standard CO-designation BF simulation is performed to define the interfering COs; the precursors that have not undergone CO-designation are then used as the starting array of precursors for a second round of CO-designation ., In this second round , COs are randomly selected from among the precursors remaining after the first round of designation ., The COs resulting from the two simulations are then combined and the total pattern is analyzed ., To model Scenario 2 , in which non-patterned COs arise from an unrelated set of precursors , a standard CO-designation BF simulation is performed to define interfering COs ., Then a second , independent simulation is performed using a specified number of precursors that are unrelated to the first set and random selection of COs from among that precursor set ., COs generated by the two types of simulations are then again combined and analyzed ., CoC relationships for total COs ( interfering plus non-interfering ) will depend significantly on whether the precursors that give rise to the “non-interfering” COs are evenly or randomly spaced along the chromosomes ., CoC curves for total COs reflect the combined inputs of CoC relationships for interfering COs and non-interfering COs ., CoC curves for interfering COs are affected only modestly by even-versus-random spacing due to the overriding effects of CO interference ( above; e . g . Figure 14B left ) ., However , non-interfering CO relationships are a direct reflection of precursor relationships , which differ dramatically in the two cases ., For precursors , CoC\u200a=\u200a1 for random spacing and significant “interference” for even spacing; Figure 14B second from left ) ., CoC relationships for non-interfering COs alone exhibit the same features ( Figure 14B , rightmost two panels ) ., These differences are directly visible in CoC curves for total COs , with greater or lesser prominence according to the relative abundance of non-interfering COs versus interfering COs ( Figure 14C ) ., Notably , CoC relationships for Scenario 1 , where precursors exhibit the even spacing defined by BF best-fit simulations ( E\u200a=\u200a0 . 6 ) , show a qualitatively different shape than CoC relationships under Scenario 2 ., Given this framework , we defined CoC curves for total COs along yeast chromosomes IV and XV as defined by microarray analysis ( Figure 14D left panel ) ., The general shapes of these experimental curves correspond qualitatively to those predicted for emergence of non-interfering COs from an evenly-spaced precursor array , with a closer correspondence to those predicted for Scenario 1 than to those predicted for Scenario 2 ( compare Figure 14D left panel with Figure 14C ) ., This impression is further supported by BF simulations ., To model Scenario 1 , we began with the set of best-fit parameters defined for interfering COs ( Zip3 foci ) above ( Figure 6I ) and generated predicted total CoC curves , assuming that non-interfering COs comprise 30% of the total ( above ) , for each of the three possible case of non-interfering COs: Scenario 1 ( where precursors are assumed to be evenly spaced as for interfering COs ) ; and Scenario 2 with precursors assumed to be either evenly or randomly spaced ( Figure 14D , second panel from left ) ., The CoC curve for the first of these three cases has the same shape as the experimental CoC curves for total COs ( compare Figure 14D left and second from left panels ) and direct comparison shows that it gives a quite good quantitative match with the experimental curves ( Figure 14D third panel from left ) ., Scenario 2 with evenly-spaced precursors is a less good match ( Figure 14D , right panel ) ., Scenario 2 with randomly-spaced precursors ( Figure 14D , second panel from left , red ) is a quite poor match ( not shown ) ., These analyses suggest that , in yeast , non-interfering COs arise from the not-CO-designated precursors as a minority outcome of the “NCO” default pathway ( Figure 14A , Scenario 1 ) ., Many studies of CO interference characterize CO patterns by defining a gamma distribution that best describes an experimentally observed distribution of the distances between adjacent COs , often with the assumption ( implicit or explicit ) that a higher value of the gamma shape parameter ( ν ) corresponds to “stronger” CO interference ( e . g . 11 ) ., We have examined the way in which ( ν ) varies as a function of changes in the values of several BF parameters ., Variations in L or Smax increase or decrease the value of ( ν ) in correlation with increased or decreased LCOC and in opposition to the average number of COs per bivalent ( Figure 15AB , compare green line and blue/pink distributions with red and black lines ) ., This is the pattern expected for a change in the “strength of interference” ., In contrast , the value of ( ν ) is also altered by variations in M or N , which have little or no effect on LCOC; moreover , the change in ( ν ) co-varies with the change in the average number of COs per bivalent ( Figure 15CD , compare green line and blue/pink distributions with red and black lines ) ., The BF model thus implies that a change in the value of ( ν ) , e . g . in a mutant as compared to wild type , may or may not imply a change in the patterning process per se ., However , comparison of the variation in ( ν ) with the variation in average COs per bivalent can distinguish between the two possibilities , with opposing variation implying a patterning difference and co-variation implying a difference in some other feature ., This is true not only with respect to CoC and ED relationships but with respect to more detailed effects such as CO homeostasis and the obligatory CO ., These matches , and the information that emerges there-from , support the notion that the basic logic of the BF model provides a robust and useful way of thinking about CO patterning ., These matches are also specifically supportive of the proposed mechanical stress-and-stress relief mechanism ., In budding yeast:, ( i ) CO patterning has the same basic features for shorter and longer chromosomes;, ( ii ) Mlh1 is required specifically for CO maturation not for CO patterning; and, ( iii ) Precursors are evenly spaced , as shown by both CoC analysis and analysis of total ( interfering-plus-non-interfering ) COs ., In tomato ( and , to be described elsewhere , in budding yeast ) , the metric of CO interference is physical chromosome length ( µm ) not genomic length ( Mb ) ., In the case of tomato , differences in CoC relationships expressed in the two different metrics is attributable to differential packaging of heterochromatin versus euchromatin along the chromosome plus differential proportions of heterochromatic versus euchromatic regions among different chromosomes ., In tomato and yeast , as previously described for grasshopper , human and several other organisms , crossover interference spreads across centromeres with the same metric as along chromosome arms ., In budding yeast , non-interfering COs arise from evenly-spaced precursors , most probably by occasional resolution of NCO-fated precursors to the CO fate ., With respect to CO homeostasis , the importance of CO interference as a determinant in the strength of homeostasis is emphasized and BF simulations are shown to permit accurate quantitative descriptions of homeostasis ., Also , the strength of homeostasis can be seen to reflect the ratio of interference distance ( LCoC ) to the distance between adjacent precursors ., With respect to the obligatory CO , the general logic of the BF model ( Figure, 1 ) suggests that occurrence of a low level of zero-CO chromosomes is independent of CO interference ( and precursor spacing ) and is achieved by an appropriate evolved constellation of all other parameters ., Explanations can also be provided for several known cases where the level of zero-CO chromosomes is unusually high , but interference is robust , and potential explanations for other mutant phenotypes are suggested ., Importantly , the logic of the beam-film model predicts the existence of mutants that lack interference but still exhibit the obligatory CO , evidence for which will be presented elsewhere ., The central issue for CO patterning is how information is communicated along the chromosomes ., Three general types of mechanisms have been envisioned ., ( 1 ) A molecular signal spreads along the chromosomes , e . g . as in the polymerization model of King and Mortimer 55 or the “counting model” of Stahl and colleagues 20 , 35 ., ( 2 ) A biochemical reaction/diffusion process surfs along the chromosomes 56 , as recently described in detail for bacterial systems 57 , 58 ., ( 3 ) Communication occurs via redistribution of mechanical stress , as in the beam-film model 3 , 4 or via other mechanical mechanisms ( e . g . 59 ) ., The counting model can provide good explanations of experimental data; however , the underlying mechanism is contradicted by experimental findings ( 43; but see 60 ) ., No specific reaction/diffusion mechanism has been suggested thus far for CO interference ., The King and Mortimer model and the beam-film model are significantly different , in three respects ., First , in the King and Mortimer model , the final array of COs reflects the relative rates of CO-designation and polymerization ., Thus it is the kinetics of the system that governs its outcome ., In the beam-film model , where interference arises immediately after each CO-designation , kinetics does not play a role ., Second , in the King and Mortimer model , the interference signal continues to spread until it runs into another signal approaching from the opposite direction ., In the beam-film model , the interference signal is nucleated and spreads for an intrinsically limited distance , with an intrinsic tendency to dissipate with distance from its nucleation site ., Third , the King and Mortimer model envisioned that precursors were Poisson distributed among chromosomes ., As a result , significant numbers of chromosomes would initially acquire no precursors if the average number of precursors is low and thus would never give a CO , thereby giving an unacceptably high level of zero-CO chromosomes ., To compensate for this effect , the model proposed that the effect of interference was to release encountered precursors , which then rebound in regions that were not yet affected by interference ( and thus on chromosomes with no precursors ) ., This precursor turnover would ensure that all chromosomes achieved a precursor that could ultimately give a CO ., Because of this feature , the King and Mortimer model envisions that interference is required to ensure a low level of zero-CO chromosomes ( i . e . to ensure the “obligatory CO” ) ., By the beam-film model , instead , precursors do not turn over and interference is not required to ensure a low level of zero-CO chromosomes , which results instead from an appropriate constellation of other features , as described above ., The beam-film model predicts the existence of mutants that are defective in interference but do not exhibit an increase in the frequency of zero-CO chromosomes ., Yeasts SK1 strains ( Figure 6 and S7 ) are described in Table S1 ., In all strains , ZIP3 carries a MYC epitope tag; a construct expressing LacI-GFP and is integrated at either LEU2 or URA3 , and a lacO array 61 is inserted at HMR ( chromosome III ) , Scp1 ( Chromosome XV ) or Chromosome IV telomere ( SGD1522198 ) to specifically label each chromosomes by binding of LacI-GFP ., Pachytene chromosomes exhibit ∼65 foci of Zip2 , Zip3 and Msh4 , with strong colocalization of Zip3 and Msh4 foci ( 31 , 62; this work ) ., Zip2 foci 63 exhibit interference as defined by CoC relationships for random adjacent pairs of intervals 31 ., We further show here that Zip2 and Zip3 foci exhibit interference as defined by full CoC relationships along specific individual chromosomes ( Figure 6 and Figure S5 ) ., Zip2 and Zip3 foci also both occur specifically on association sites of zip1Δ chromosomes 31 , 64 ., The total number of COs per yeast nucleus as defined by microarray and genetic analysis is ∼90 22 , 52 , 65 implying that Zip2/Zip3/Msh4 foci represent 65/90\u200a=\u200a70% of the total ., Correspondingly , mutant analysis suggests that “non-interfering” COs comprise ∼30% of total COs ( e . g . 50 ) ., Additionally , BF analysis accurately explains CoC relationships for total COs on the assumption of 70% patterned COs and 30% “non-interfering” COs ( Figure 14 , Results ) ., Synchronous meiotic cultures ( SPS sporulation procedure from 66 ) were prepared and harvested at a time when pachytene nuclei are most abundant ( ∼4–5 hours ) ., Cells were spheroplasted and chromosomes spread on glass slides according to Loidl et al . and Kim et al . 67 , 68 ., Primary antibodies were mouse monoclonal anti-myc , goat polyclonal anti-Zip1 ( Santa Cruz ) and rabbit polyclonal anti-GFP ( Molecular Probes ) ., Each was diluted appropriately in the above BSA/TBS blocking buffer ., Secondary antibodies were donkey anti-mouse , donkey anti-goat , and donkey anti-rabbit IgG labeled with Alexa488 , Alexa645 or 594 and Alexa555 ( Molecular Probes ) , respectively ., Stained slides were mounted in Slow Fade Light or Prolong Gold Antifade ( Molecular Probes ) ., Spread chromosomes were visualized on an Axioplan IEmot microscope ( Zeiss ) with appropriate filters ., Images were collected using Metamorph ( Molecular Devices ) image acquisition and analysis software ., Acquired images were then analyzed with Image J software ( NIH ) , with total SC length and positions of Zip3 foci for the specifically labeled bivalent were measured from the lacO/LacIGFP-labeled end to the other end ( Figure 6B bottom ) ., For each type of chromosome analyzed ( III , IV and XV ) in each experiment , measurements were made for >300 bivalents , one from each of a corresponding number of spread nuclei ., Resulting data were transferred into an EXCEL worksheet for further analysis ., Coefficient of coincidence ( CoC ) curves were generated from SC length and Zip3 focus positions determined as described above ., Each analyzed bivalent was divided into a series of intervals of 0 . 1 µm in length ( corresponding to the resolution with which adjacent Zip3 foci can be resolved ) ., Chromosome III , IV and XV were thus usually divided into 9 , 42 and 30 intervals with equal size , respectively ., Each chromosome length was normalized to 100% and each Zip3 focus position was also normalized correspondingly ., Each Zip3 focus was then assigned to a specific interval according to its coordinate ., The total frequency of bivalents having a Zip3 focus in each interval was calculated ., For each pair of intervals , the frequency of bivalents having a Zip3 focus in both intervals was determined to give the “observed” frequency of double COs ., For each pair of intervals , the total CO frequencies for the two intervals were multiplied to give the frequency of double COs “expected” on the hypothesis of independent occurrence ., The ratio of these two values is the CoC ., Thus in each pair of intervals , CoC\u200a= ( Obs DCO ) / ( Pred DCO ) ., CoC values for all pairs of intervals can be plotted as a function of the distance between the midpoints of the two involved intervals ( “inter-interval distance” ) ., However , for all of the data shown here , the CoC values from all pairs of intervals having same inter-interval distance were averaged and this average CoC was plotted as a function of inter-interval distance ( e . g . Figure 6C and others ) ., The previous Beam Film program 3 was rewritten in MATLAB ( R2010a ) for easy use and modified to include more features as described in the text ., Extensive details regarding program structure and application are provided in the Protocol S1 section ., However , briefly , there are three options in the software that serve three different purposes: The Chorthippus L3 chiasmata data were generously provided by Gareth Jones ( University of Birmingham , UK ) ., The Drosophila X-chromosome crossover data are from 33 ., The tomato ( S . lycopersicum ) Mlh1 foci date are from 38 ( generously provided by F . Lhuissier ) ., Zip2 data in the S . cerevisiae BR background are from 31 ( generously provided by J . Fung ) .

Introduction, Results, Discussion, Materials and Methods

Crossing-over is a central feature of meiosis ., Meiotic crossover ( CO ) sites are spatially patterned along chromosomes ., CO-designation at one position disfavors subsequent CO-designation ( s ) nearby , as described by the classical phenomenon of CO interference ., If multiple designations occur , COs tend to be evenly spaced ., We have previously proposed a mechanical model by which CO patterning could occur ., The central feature of a mechanical mechanism is that communication along the chromosomes , as required for CO interference , can occur by redistribution of mechanical stress ., Here we further explore the nature of the beam-film model , its ability to quantitatively explain CO patterns in detail in several organisms , and its implications for three important patterning-related phenomena: CO homeostasis , the fact that the level of zero-CO bivalents can be low ( the “obligatory CO” ) , and the occurrence of non-interfering COs ., Relationships to other models are discussed .

Spatial patterning is a common feature of biological systems at all length scales , from molecular to multi-organismic ., Meiosis is the specialized cellular program in which a diploid cell gives rise to haploid gametes for sexual reproduction ., Crossing-over between homologous maternal and paternal chromosomes ( homologs ) is a central feature of this program , playing a role not only for increasing genetic diversity but also for ensuring regular segregation of homologs at the first meiotic division ., The distribution of crossovers ( COs ) along meiotic chromosomes is a paradigmatic example of spatial patterning ., Crossovers occur at different positions in different meiotic nuclei but , nonetheless , tend to be evenly spaced along the chromosomes ., We previously-described a mechanical “stress and stress relief” model for CO patterning with an accompanying mathematical description ( the “beam-film model” ) ., In this paper we explore the roles of mathematical parameters in this model; show that it can very accurately describe experimental data sets from several organisms , in considerably quantitative depth; and discuss implications of the model for several phenomena that are directly related to crossover patterning , including the features which can ensure that every chromosome always acquires at least one crossover .

biology

journal.pcbi.1007014

Heterogeneous susceptibility to rotavirus infection and gastroenteritis in two birth cohort studies: Parameter estimation and epidemiological implications

Rotavirus is the leading source of gastrointestinal disease burden in children globally , with nearly 10 million severe cases and 193 , 000 fatalities estimated to occur annually 1 ., One decade after their rollout in high-income settings , live oral rotavirus vaccines are currently being introduced to national immunization programs of low- and middle-income countries ( LMICs ) ., However , randomized controlled trials and post-licensure studies have reported lower vaccine efficacy and effectiveness against rotavirus gastroenteritis ( RVGE ) in LMICs compared to higher-income settings 2 , 3 ., Understanding this performance gap is essential to maximizing the impact of rotavirus vaccines where they are needed most ., Recent observational studies have investigated how factors such as oral polio vaccine co-administration 4 , 5 , exposure to breast milk antibodies 6 , environmental enteropathy 7 , and nutritional status 8 , 9 influence susceptibility of children to RVGE and performance of oral vaccines ., Variation in susceptibility among individuals within and between studies—due to these or other unmeasured risk factors—is well known to influence estimates of vaccine efficacy and effectiveness 10–13 ., Differential removal of highly-susceptible individuals to a partially-immune state constitutes a form of frailty bias or effect modification that may persist even in randomized studies 10 , 14 , 15; we use the term bias here in reference to discrepancies between common measures of association , such as hazard ratios and risk ratios , and the per-exposure biological effect of immunity ( from vaccination or natural infection ) on infection and/or disease endpoints 16 , 17 ., Demonstrations of the impact of variation in susceptibility have arisen in both experimental and theoretical studies 18 , 19 ., Refinements in our ability to characterize such variation both statistically and experimentally 20–27 , together with formalizations of per-exposure measures of intervention efficacy in trials 28–30 and observational studies 16 , 17 , have highlighted the potential for heterogeneity in susceptibility to influence epidemiologic measurements ., While the possibility of such frailty bias in rotavirus vaccine studies has been raised 15 , 31 , 32 , distinguishing its contribution to variation in estimates of vaccine protection against RVGE has been difficult given the concordance of observed patterns with multiple hypotheses 33 ., Importantly , the rate of asymptomatic infections and the distribution of risk factors across settings are not easily measured or compared 34 , and individual variation in susceptibility may be only partially attributable to known or measured risk factors ., Similarly-designed birth cohort studies undertaken in socioeconomically-distinct LMIC populations of Mexico City , Mexico and Vellore , India provide an opportunity to characterize heterogeneity in susceptibility to rotavirus infection and RVGE , and to assess its influence on estimates of immune protection 35 , 36 ., While the two studies supplied similar estimates of naturally-acquired immune protection against re-infection , differences in estimates of protection against RVGE reflected discrepancies in estimated vaccine efficacy between Latin America and South Asia 37–39 ., Whereas no children in Mexico City experienced moderate-to-severe RVGE after two or more previous infections , two previous infections were associated with only 57% protection against moderate-to-severe RVGE among children in Vellore 35 , 36 ., Paired re-analysis of the studies has provided evidence that differences owe , in part , to the influence of a subset of “high-risk” individuals in the Vellore cohort—who experienced high rates of rotavirus infection as well as high risk for RVGE given infection—and age-dependent risk for RVGE given infection 33 ., We revisited data from these studies aiming to better understand and compare the distribution of susceptibility among individuals within the two cohorts , and to explore the implications for epidemiologic analyses ., We developed a model to estimate susceptibility of children to rotavirus infection and RVGE , accounting for the natural history of rotavirus and differences between settings in transmission intensity ., We conducted statistical inference via kernel-based and Markov chain Monte Carlo inference approaches , recovering near-identical parameter estimates under the two strategies ., We used our findings to explore the influence of sources of bias underlying conventional measures of protective immunity ., Incidence of rotavirus infection and RVGE among children enrolled in the two cohorts has been described previously 33 , 35 , 36 , 40 ., Briefly , the studies enrolled 200 and 373 unvaccinated Mexican and Indian children who were followed from birth to up to 2 years and 3 years of age , respectively , yielding 3699 and 13 , 937 child-months of follow-up , and characterized the spectrum of asymptomatic to severe clinical manifestations of each rotavirus infection ., In total , 315 rotavirus infections were detected in Mexico City and 1103 were detected in Vellore , with 89 ( 28% of 315 infections ) and 282 ( 26% of 1103 infections ) episodes of RVGE occurring in the two settings , respectively ., Incidence was higher in Vellore , such that first infections occurred in 56% and 81% of Indian children by ages 6 months and one year , compared to 34% and 67% of Mexican children , respectively ( Fig 1A and 1B ) ., The proportion of infections causing RVGE declined with a higher number of previous infections ( Fig 1C and 1D ) ., However , analyses stratified by age and previous infection revealed this trend could owe to confounding by age , i . e . declining RVGE risk with older age for each of first , second , and later infections ( Fig 1E and 1F ) ., At matched ages , RVGE was more common , paradoxically , during second and later infections than first infections in Vellore ., In contrast , this trend was not apparent in Mexico City ., We developed a set of model structures addressing biological hypotheses of rotavirus natural history , based on previous studies of transmission dynamics 41–43 and secondary analysis of the birth-cohort datasets 33 ., We estimated the proportion of each cohort belonging to a “high-risk” group , and tested for evidence of variation in susceptibility to infection and/or risk of RVGE given infection among the high-risk group compared to the rest of the cohort ( see Materials and Methods ) ., We also tested whether the risk of RVGE given infection varied depending on age at time of infection , and/or the number of previous infections ., We estimated 33% ( 95% CI: 23% to 41% ) , 50% ( 42% to 57% ) , and 64% ( 55% to 70% ) reductions in the rates at which children re-acquired rotavirus after one , two , or three or more previous infections ( Table 1 ) , closely recapitulating estimated protection against re-infection in the original studies ( S2 Table ) 35 , 36 ., Our models also captured declining risk for infections to cause symptomatic RVGE at older ages ( Fig 1B ) ., The proportion of secondary and subsequent infections causing RVGE in the first year of life in Vellore closely matched expectations among children classified as a “high-risk” subset of the population ( detailed below; Fig 1F ) ., We compared the fit of models with differing assumptions about acquired immune protection against RVGE given infection ( Table 1 ) ., After accounting for declining risk of RVGE given infection at older ages , we did not identify improvements in fit ( based on values of the Akaike Information Criterion 44 ) when allowing for acquired immune protection against symptoms during second or later infections ( Model 2 ) ., Several salient differences between the two studies were reproduced in model-based predictions ., Although we predicted higher-than-observed rates of infection in Mexico City during the first six months of life , predictions accurately reflected between-setting differences in cumulative incidence by the end of the first year ( Fig 1A and 1B ) ., In addition , fitted parameters recapitulated the observation of significantly lower probabilities of RVGE during second , third , and fourth infections in Mexico City as compared to Vellore ( Fig 1C and 1D ) , despite predicting RVGE in a higher-than-observed proportion of second infections in Mexico City ., Our modeling framework partitioned the cohort populations across distinct risk groups ( R and RC ) with prevalences αM and 1–αM , respectively , in Mexico City , and αV and 1–αV in Vellore ., Because the size of the risk group and group-specific relative risk for infection and/or disease outcomes are inversely related , the relative susceptibility and prevalence of these two risk groups were not simultaneously identifiable ., We therefore estimated conditional between-group differences in susceptibility to infection ( hazard ratio ϕ ) and RVGE given infection ( relative risk ρ ) associated with particular values of αM and αV ., We reconstructed the full distribution of ϕ and ρ from the marginal distributions of {ϕ , ρ}|{αM , αV} ( see Materials and Methods ) ., Fitting a more complex model ( S1 Text ) which considered an exhaustive set of risk groups—including children with modified susceptibility to infection only or disease only—allowed us to verify the hypothesis of a linkage between children’s susceptibility to infection and disease given infection , which was suggested in previous analyses of the cohort data 33 ., This modeling approach enabled us to compare the prevalence of children with particular susceptibility levels between cohorts ( Fig 2 ) ., We estimated that 3% ( 1% to 23% ) of children in Mexico City would belong to a high-risk-stratum experiencing a ≥50% higher-than-baseline rate of acquiring rotavirus infection , compared to 13% ( 6% to 29% ) of children in Vellore ( Fig 2G ) ., A subgroup with over double the baseline rate of infection would include 2% ( 1% to 8% ) of children in Mexico City and 10% ( 5% to 18% ) of children in Vellore , while only 1% ( 0% to 2% ) of children in Mexico City and 6% ( 5% to 9% ) of children in Vellore would belong to a subgroup experiencing rates of infection ≥3-fold higher than the baseline rate ., Greater susceptibility to infection was associated with higher risk of experiencing RVGE given infection , regardless of the prevalence of the high-risk group ( Fig 2F ) ; fitting both ϕ and ρ , we identified ≥99 . 99% probability for excess risk of disease given infection within the sub-cohorts defined to have higher rates of acquiring infection ( S1 Table ) ., Joint distributions of ϕ and ρ with the size of the risk groups were indistinguishable under the original model specification and a more complex model that allowed either linked or unlinked susceptibility to infection and disease ( S1 Text , S2 Fig ) ., Our modeling approach provided a statistical basis for calculating the probability that each child belonged to the “high-risk” subgroup ( see Materials and Methods ) ., To examine the validity of these estimates , we next assessed whether a child’s estimated risk of belonging to the “high-risk” subgroup was related to host factors and exposures measured in the original studies that have previously been reported to predict risk for rotavirus infection and RVGE ( Tables 2 and 3 ) ., Male children were 26% ( 4% to 67% ) more likely to be among the “high-risk” subgroup than female children ( Table 3 ) ., Birth weight was also a predictor of being in the “high-risk” subgroup , with each log-kilogram decrease in birth weight conferring 2 . 05 ( 1 . 02 to 5 . 03 ) -fold higher probability of belonging to the “high-risk” subgroup ., However , we did not detect a significant association between susceptibility and weight at 12 months ., In each cohort as well as in the pooled analysis , children without siblings were more likely to belong to the “high-risk” subgroup than children with siblings ., In comparison to children whose mothers had completed <5 years of education , children whose mothers had completed ≥10 years of education were 25% ( 0% to 50% ) less likely to belong to the “high-risk” subgroup ., We also identified several factors predicting within-cohort variation in susceptibility that were consistent with findings in primary analyses of the studies 35 , 36 ., In Vellore , children whose household members were involved in producing bidis ( indigenous cigarettes ) —an indicator of lower household socioeconomic status—were 45% ( 10% to 122% ) more likely than other children to belong to the “high-risk” subgroup ., In Mexico City , children with a shorter duration of breastfeeding were more likely to belong to the “high-risk” subgroup , although this association did not reach conventional thresholds of statistical significance in our analysis ., Among children experiencing RVGE in the cohorts , those who experienced moderate-to-severe RVGE symptoms ( defined by a Vesikari score ≥11 ) on at least one episode were 44% ( 13% to 122% ) more likely to belong to the “high-risk” subgroup , suggesting model-based measures of susceptibility to rotavirus infection and ( any ) RVGE given infection also predicted the severity of rotavirus disease ., In addition , children who experienced higher rates of diarrheal episodes caused by pathogens other than rotavirus were more likely to belong to the “high-risk” subgroup within each cohort ., In Vellore , we also found a positive association between the incidence of acute respiratory infections and the likelihood that a child belonged to the “high-risk” subgroup; this information was not available for the Mexico City cohort ., We next conducted simulation studies ( Fig 3 ) to assess how variation in transmission intensity and in the susceptibility of children could influence estimates of naturally-acquired immune protection against rotavirus infection , RVGE , and RVGE given infection 45—as measured by the hazard ratios of infection and RVGE , and relative risk of RVGE given infection—following one , two , or three previous infections , compared to zero previous infections ., We compared estimates from in silico cohorts with differing prevalence of “high-risk” children ( α ) exposed to varying forces of infection ( Λ ) ., We accounted for susceptibility differences between risk groups by sampling from the joint , unconditional distribution of {ϕ , ρ} , thereby isolating the effect of differences in risk-group prevalence ., We did not identify a large of impact of susceptibility differences on estimates of protection against reinfection ( i . e . estimates of NE^ were similar across different levels of Λ and α ) , which may help to explain why these estimates were nearly equal in the original studies 35 , 36 ., However , we found that estimates of protection against RVGE—which were lower in primary analyses of the Vellore cohort—were expected to decline in settings with higher transmission intensity , reflecting acquisition of infection at younger , higher-risk ages ., The impacts of heterogeneity in susceptibility were outweighed by the impacts of unaccounted-for age-dependent symptom risk ., For a population exposed to transmission intensity on the order of one rotavirus infection per susceptible child-year at risk , increasing the prevalence of the “high-risk” subgroup ( α ) from 0% 50% reduced the estimate of protection against RVGE conferred by one previous infection by 9% ( –12% to 27% ) , in absolute terms ., Increasing transmission intensity to the equivalent of four infections per susceptible child-year at risk led to a reduction of 14% ( –8% to 34% ) at α = 0 and , similarly , of 12% ( –6% to 27% ) at α = 0 . 5 , in absolute terms ., Evidence of naturally-acquired immunity against rotavirus from birth-cohort studies provided an impetus toward the development of live oral rotavirus vaccines , which are now among the most effective strategies for the prevention of severe illness and deaths due to RVGE globally 46 ., However , challenges have persisted in understanding and addressing the lower protective efficacy of rotavirus vaccines in high-burden LMIC settings , which mirrors protection derived from naturally-acquired immunity 47 , 48 ., Our analysis suggests that discrepant estimates of protection may in part reflect epidemiological bias , attributable to differences between settings in transmission intensity and differential susceptibility of children to rotavirus infection and RVGE , individually and by age ., Lower estimates of protection in settings with high rotavirus burden thus reflect factors other than weaker immunity among children in LMICs ., Accounting for aspects of the natural history of rotavirus enabled us to directly compare the susceptibility of children enrolled in birth cohort studies undertaken in socioeconomically-distinct settings ., Although we estimated only modestly higher susceptibility for the average child in Vellore as compared to Mexico City , individual variation in susceptibility was considerably greater within the Vellore cohort ., We estimated that a higher proportion of children in Vellore , as compared to Mexico City , showed elevated rates of rotavirus infection as well as excess risk for RVGE given infection ., This finding can account for several unexpected features of the epidemiology of rotavirus in Vellore ., The increasing probability of RVGE in association with first , second , and later infections occurring at matched ages that we identified , particularly in Vellore ( Fig 1F ) , reflects high risk for RVGE given infection among individuals susceptible to frequent rotavirus infection ., In other words , children who experienced two or more infections before 6 months of age , or three or more infections before 12 months of age , are more likely to belong to a subgroup with pronounced susceptibility to rotavirus infection and disease , given infection ., Indeed , our analysis identified that susceptibility to rotavirus infection was positively associated with susceptibility to RVGE given infection among individual children ( S1 Table , S2 Fig ) ., While the proportion of children belonging to a “high-risk” subgroup constituted a source of epidemiologic bias in simulation studies , and was expected to lead to estimates of weaker protection against RVGE in settings with higher transmission intensity such as Vellore , the degree of bias imposed was not large ., Several other studies have recently addressed transmission-dynamic factors that may contribute to the apparent underperformance of rotavirus vaccination in high-transmission settings 32 ., Using data from the PROVIDE trial of monovalent rotavirus vaccine in Bangladesh , Rogawski and colleagues demonstrated that acquisition of naturally-acquired immunity may contribute to lower estimates of vaccine efficacy due to earlier and more frequent infection within the control arm; impacts on estimates of protection are most notable in high-transmission settings , and among children in their second year of life 15 ., Here we were able to account for the contribution of all previous infections to naturally-acquired immunity , including subclinical infections , and to account for age-specific RVGE risk ., Selection bias resulting from variation in individual susceptibility can result in further downward bias 31 , underscoring the need for per-exposure estimates of immune effectiveness such as we have sought in this analysis ., Directly comparing susceptibility between populations or settings is difficult because determinants of susceptibility are often unknown or unmeasured , and may be imperfectly characterized by measurable epidemiological risk factors ., The contributions of susceptibility and transmission intensity to disease incidence rates are not easily disentangled ., Our analysis employed a novel approach to characterize susceptibility of children in two cohorts based on a model that included known aspects of rotavirus natural history , facilitated by access to similar measurements from settings with distinct risk profiles and force of infection ., Our estimates of susceptibility appear externally valid based on their association with previously-reported risk factors for RVGE 49–52: male children , children with lower birth weight , and children whose mothers had lower educational attainment were more likely to belong to a higher-risk subset of the population in our analysis ., In Vellore , children whose households were involved in bidi work—a marker for lower socioeconomic status—were also at higher risk 36 , while in Mexico City , we observed a trend toward lower risk associated with longer breastfeeding , consistent with previous studies 53 , 54 ., In addition , we observed higher incidence of diarrhea caused by pathogens other than rotavirus among children who were found to have greater susceptibility to rotavirus ., This observation may signify the presence of environmental enteric dysfunction within the cohorts , or other sources of variation in immune status or pathogen exposure ., In Vellore , children who we estimated were more susceptible to rotavirus also experienced higher incidence of respiratory infections , as reported previously 36 ., While the associations we identify ( in particular with time- or age-specific risk factors ) do not measure causal effects in either direction , our inferences pertaining to within-cohort susceptibility are supported by the fact that children classified by the model as having “high risk” exhibit risk factors widely believed to be associated with rotavirus infection and RVGE ., These and other host factors associated with susceptibility to rotavirus infection and RVGE have also been reported to predict weaker immune responses to live enteric vaccines such as those against rotavirus ., While our model does not address variation in the strength of immune responses among individuals or across settings , 58% of Indian children versus 90% of Mexican children seroconverted after Rotarix immunization in previous studies 47 , 55 ., Nonetheless , near-equal naturally-acquired protection against re-infection was noted among children in the birth cohort studies in Vellore and Mexico City ., Our findings demonstrate that some degree of the reported variation in protection against RVGE can be attributed to epidemiological biases resulting from differential transmission intensity and differential susceptibility of children , although we found age-dependent diarrhea risk was a more important contributor to variation in estimates ., The finding that older age diminishes risk for children to experience RVGE given rotavirus infection has been suggested in previous analyses of the cohort datasets 33 ., Our simulation study demonstrates that such age-related symptom risk enhances protection against RVGE in low-transmission settings ., Deferring infections to later ages significantly reduces the risk for children to experience symptoms upon reinfection ., While the mechanisms underlying age-dependent diarrhea risk are not precisely known , the observation has been reported in mouse , rat , rabbit , and gnotobiotic piglet models of rotavirus infection 56–59 ., Age-dependent TLR3 expression and host responses to rotavirus enterotoxins contribute to this observation in mice 60 , 61 ., Other aspects of immune maturation , intestinal development , and the establishment of gut microbial communities may further drive associations between age and diarrhea risk in both humans and animals 62 ., Furthermore , the greater dehydrating effect of diarrhea in younger children with smaller body volumes may contribute to severity—and thus the reporting and diagnosis—of RVGE in early-life infections 63 ., There are several limitations to our analysis ., Whereas we assume exponentially-distributed infection times ( consistent with a constant hazard of infection ) , this provides an imperfect fit to the timing of early-life infections , particularly in the Mexico City cohort ., The departure between predictions and observations may reflect the protective effect of maternal antibodies , as reported previously 35 , 64 , or the influence of age-specific social mixing patterns on transmission 65 ., Thus , our model tended to overestimate the probability of RVGE associated with second rotavirus infections in Mexico City , although this discrepancy was not sustained for third and fourth infections ., Our analyses also do not distinguish between homotypic and heterotypic protection because we lack genotype data for serologically-detected infections , which constitute the majority of infections in both cohorts ., Although moderate-to-severe RVGE episodes are the primary endpoint of most studies evaluating vaccine efficacy and effectiveness , our analysis addressed RVGE episodes of any severity ., Only 7% of children in Mexico City experienced RVGE episodes with Vesikari score ≥11 , limiting the statistical power for analyses of moderate-to-severe RVGE ., Nonetheless , previous analyses of the studies identified similar risk factors for mild and moderate-to-severe RVGE 33; moreover , we find that children identified by our method to face higher risk for rotavirus infection and RVGE likewise experienced higher risk for moderate-to-severe manifestations of RVGE episodes ., Thus , our findings may inform the interpretation of studies with moderate-to-severe RVGE endpoints ., Our ability to account for variation in susceptibility to infection as well as disease , and indeed to identify a linkage between these traits , is a unique advantage afforded by data describing both clinically-apparent and subclinical infections ., While methods exist to account for frailty in time-to-event data 10 , 66 , as may be present in studies with only one class of endpoints ( such as serological studies of infection or clinical trials with disease endpoints ) , susceptibility to disease given infection is also of interest ., Importantly , our findings suggest that age , rather than naturally-acquired immunity , determines risk for rotavirus infections to present symptomatically , together with individual-level susceptibility factors ., Adaptations of our model to the natural history of other pathogens may facilitate similar studies in other disease-specific contexts ., Whereas other models have used continuous distributions to characterize individual susceptibility , this approach has generally relied on the ability to measure or even manipulate exposure intensity at the individual level , for instance by measuring infectious contacts or through controlled-dose challenge experiments 20–30 ., As our data presented the opportunity to compare exposure intensity between cohorts but not between individuals , we considered a simpler case of dichotomous risk groups within cohorts , and determined how the sizes of cohort-specific risk strata ( α ) were jointly distributed with the degree of risk elevation ( ϕ , ρ ) ., A priori knowledge of risk strata , for instance based on previous estimates of covariate effect sizes , presents an alternative strategy to infer distributions of individual-level susceptibility 67 ., Birth-cohort studies have been instrumental to our understanding of the natural history of rotavirus ., Uncertainties surrounding differences in the epidemiology of rotavirus in socioeconomically-distinct populations underscore the need for a theoretical basis for comparing outcomes of individual studies ., Our approach permitted assessment of how age , acquired immunity , and variation in individual susceptibility independently contributed to infection and disease risk in distinct birth cohorts , helping to resolve discrepancies in estimated protection that arose in primary analyses of the datasets ., The modeling framework we introduce here may thus have applicability to studies of other partially-immunizing pathogens ., The two birth cohort studies followed similar protocols that have been described previously 35 , 36 ., Children were enrolled at birth and followed to 24 and 36 months of age in Mexico City and Vellore , respectively ., The studies aimed to detect all rotavirus infections , both symptomatic and asymptomatic ., Rotavirus infections were detected by three approaches: ( 1 ) sera were drawn every 4 and 6 months in Mexico City and Vellore , respectively , and tested for IgA or IgG titer increases; ( 2 ) asymptomatic stool samples were collected weekly in Mexico City and every two weeks in Vellore and tested for rotavirus; and ( 3 ) diarrheal stools were collected by field workers every time mothers alerted the study teams of any change in a child’s stool pattern ( S2 Table ) ., Virus detection was performed by ELISA in Mexico City and by ELISA or real-time PCR in Vellore ., In Mexico City , 200 children were recruited and retained for 77% of the scheduled follow-up period , while our analysis of the Vellore dataset included the 373 children ( 83% of 452 enrolled ) who completed follow-up ., Data were available for 96% ( 1037/1080 ) and 99% ( 2565/2598 ) of scheduled serum tests; 97% ( 15 , 503/16 , 029 ) and 93% ( 26 , 902/28 , 906 ) of scheduled asymptomatic stool tests; and 85% ( 963/1133 ) and 99% ( 1829/1856 ) of reported diarrheal episodes in Mexico City and Vellore , respectively ., To better understand variation in susceptibility among children within each cohort , we evaluated associations between individual-level factors ( Table 2 ) and the probability for each child to belong to the high-risk ( R ) subgroup ., For each of 10 , 000 draws of θ , we measured the probability of belonging to the high-risk group , equal to P ( R|Yi , s ) for ρ>1 or 1−P ( R|Yi , s ) for ρ<1 , for each child ( in all samples , we identified ϕ>1 for ρ>1 and ϕ<1 for ρ<1 ) ., We used least-squares regression to test for associations between covariates and children’s log-transformed probability of being in the high-risk group , using estimated regression parameters to measure relative risks ( Table 3 ) ., Models included a setting term to account for differential prevalence of high-risk children ., We pooled relative risk estimates across our draws of θ to recover their distribution ., To examine potential bias in conventional estimates of naturally-acquired immune protection , we used our model of the natural history of rotavirus infection to simulate individual histories of infection and RVGE over the first three years of life , sampling from estimated parameters describing the effects of age ( β0 , β1 , β2 ) and previous infection ( ψ1 , ψ2 , ψ3 ) on susceptibility to RVGE and infection , respectively ., We conducted simulations under an external force of infection ( Λ ) ranging from 0 . 2 to 4 infections per year , assigning 0% to 50% ( α ) of children to the high-risk subgroup R; values of ϕ and ρ were drawn independently of α so that we could determine the effect of differences in the proportion of high-risk children on estimates of protection ., We sampled exponentially-distributed infection times ( calculated from time of birth or previous infection ) , and defined the occurrence of RVGE for each individual infection as a Bernoulli random variable using the model-predicted probability of RVGE given infection ., For each cohort simulation , we measured the hazard ratio for reinfection and RVGE from the incidence rate ( IRk ) of infection and RVGE after one , two , or three previous infections , relative to the IR0 from birth ., We also measured the relative risk of RVGE given reinfection among children who had experienced one , two , or three previous infections , relative to those with no previous infections , calculated from the proportion ( pk ) of infections with RVGE ., We defined estimates of natural immune efficacy ( NE^ ) as, NE^k=1−IRkIR0, ( 11 ), for protection against infection and RVGE among children who had experienced k previous infections , and, NE^k=1−pkp0, ( 12 ), for risk of RVGE given infection among children who had experienced k previous infections .

Introduction, Results, Discussion, Materials and methods

Cohort studies , randomized trials , and post-licensure studies have reported reduced natural and vaccine-derived protection against rotavirus gastroenteritis ( RVGE ) in low- and middle-income countries ., While susceptibility of children to rotavirus is known to vary within and between settings , implications for estimation of immune protection are not well understood ., We sought to re-estimate naturally-acquired protection against rotavirus infection and RVGE , and to understand how differences in susceptibility among children impacted estimates ., We re-analyzed data from studies conducted in Mexico City , Mexico and Vellore , India ., Cumulatively , 573 rotavirus-unvaccinated children experienced 1418 rotavirus infections and 371 episodes of RVGE over 17 , 636 child-months ., We developed a model that characterized susceptibility to rotavirus infection and RVGE among children , accounting for aspects of the natural history of rotavirus and differences in transmission rates between settings ., We tested whether model-generated susceptibility measurements were associated with demographic and anthropometric factors , and with the severity of RVGE symptoms ., We identified greater variation in susceptibility to rotavirus infection and RVGE in Vellore than in Mexico City ., In both cohorts , susceptibility to rotavirus infection and RVGE were associated with male sex , lower birth weight , lower maternal education , and having fewer siblings; within Vellore , susceptibility was also associated with lower socioeconomic status ., Children who were more susceptible to rotavirus also experienced higher rates of rotavirus-negative diarrhea , and higher risk of moderate-to-severe symptoms when experiencing RVGE ., Simulations suggested that discrepant estimates of naturally-acquired immunity against RVGE can be attributed , in part , to between-setting differences in susceptibility of children , but result primarily from the interaction of transmission rates with age-dependent risk for infections to cause RVGE ., We found that more children in Vellore than in Mexico City belong to a high-risk group for rotavirus infection and RVGE , and demonstrate that unmeasured individual- and age-dependent susceptibility may influence estimates of naturally-acquired immune protection against RVGE .

Differences in susceptibility can help explain why some individuals , and not others , acquire infection and exhibit symptoms when exposed to infectious disease agents ., However , it is difficult to distinguish between differences in susceptibility versus exposure in epidemiological studies ., We developed a modeling approach to distinguish transmission intensity and susceptibility in data from cohort studies of rotavirus infection among children in Mexico City , Mexico , and Vellore , India , and evaluated how these factors may have contributed to differences in estimates of naturally-acquired immune protection between the studies ., Given the same exposure , more children were at high risk of acquiring rotavirus infection , and of experiencing gastroenteritis when infected , in Vellore than in Mexico City ., The probability of belonging to this high-risk stratum was associated with well-known individual factors such as lower socioeconomic status , lower birth weight , and incidence of diarrhea due to other causes ., We also found the risk for rotavirus infections to cause symptoms declined with age , independent of acquired immunity ., These findings can , in part , account for estimates of lower protective efficacy of acquired immunity against rotavirus gastroenteritis in high-incidence settings , mirroring estimates of reduced effectiveness of live oral rotavirus vaccines in low- and middle-income countries .

children, medicine and health sciences, respiratory infections, pathology and laboratory medicine, geographical locations, pulmonology, north america, diarrhea, age groups, vaccines, research design, signs and symptoms, gastroenterology and hepatology, cohort studies, infectious disease control, families, research and analysis methods, infectious diseases, epidemiology, rotavirus infection, people and places, diagnostic medicine, population groupings, natural history of disease, viral diseases, mexico

journal.pbio.1002307

Evolutionary Conservation and Diversification of Puf RNA Binding Proteins and Their mRNA Targets

The phenotypic diversity of life on earth results not only from differences in the proteins encoded by each genome but , perhaps even more , from differences in the programs that specify where , when , under what conditions , and at what levels these proteins are expressed ., A grand challenge in biology is to understand these gene expression programs ., Uncovering the similarities in and differences between gene expression programs in related organisms can help reveal fundamental properties of these programs , how they have evolved , how they may be wired and rewired , and ultimately how they can be engineered ., The seminal step in gene expression and the focus of much current effort is the initiation of transcription through transcription factors that bind in proximity to genes and regulate the timing and magnitude of RNA synthesis ( see 1–7 for reviews ) ., Each transcription factor regulates a set of genes , numbering a few to thousands , specified by short DNA sequences that are in proximity to those genes and are recognized by that transcription factor ., One major mechanism for diversification of gene expression programs is the loss or gain of regulation by individual transcription factors , due to mutations that , respectively , disrupt or create the proximal recognition sequences ( see 8–13 for reviews ) ., The binding specificity , regulation , and targets of a transcription factor tend to be conserved over a short evolutionary timescale , but each of these properties has changed over evolution , allowing the regulatory roles of orthologous transcription factors to diverge and diversify ., Evolutionary changes in regulation at the next level of gene expression are virtually unexplored ., After transcription , each messenger RNA ( mRNA ) undergoes a functional odyssey and can be regulated at steps that include splicing , transport , localization , translation , and decay 14 ., RNA binding proteins function in each step , and each mRNA interacts with many RNA binding proteins over its lifetime 15–22 ., Each RNA binding protein can recognize a few to thousands of mRNAs , and the target sets of each individual RNA binding protein often share functional themes , encoding proteins involved in a particular biological process or localized to the same part of the cell 15 , 23–37 ., These effects can be described in terms of a model originally referred to as the “RNA operon” model in which RNA binding proteins bind to and coordinate the regulation of mRNAs encoding functionally or cytotopically related proteins 18 , 20 , 21 ., We set out to trace the evolutionary history of an RNA binding protein and how its interactions with targets change over evolution ., Identifying this natural history is a step toward understanding the critical differences between organisms , how evolution has progressed , why these differences have arisen , and how gene expression programs are “wired . ”, We chose to investigate the Puf ( Pumilio–Fem-3-binding factor ) family of RNA binding proteins , taking particular advantage of the relatively well-understood relationship between Puf protein sequences and the specific RNA sequences they recognize ( Fig 1 ) ., Puf proteins are found in most , if not all , eukaryotes 38–40 and have been implicated in regulating the decay , translation , and localization of distinct sets of functionally related RNA targets 38 , 41–43 ., For example , in Saccharomyces cerevisiae Puf3 binds and regulates hundreds of distinct RNAs transcribed from the nuclear genome that , almost without exception , encode for proteins localized to the mitochondrion 25 ., Puf3 promotes localization of its target mRNAs to the periphery of mitochondria 44–46 and can repress the expression of these mRNAs by promoting their decay 25 , 47 , 48 ., Puf3 recognizes a specific sequence element usually found in the 3 untranslated region ( 3 UTR ) of its targets ( Fig, 1 ) 25 ., S . cerevisiae Puf3 and its orthologs in Drosophila melanogaster ( Pumilio ) and Homo sapiens ( Pum1 and Pum2 ) recognize nearly identical RNA sequence motifs , but they bind to distinct sets of mRNAs that encode proteins with distinct functional themes 24 , 25 , 27 , 29 ., Fewer than 20% of the targets of the Puf3 orthologs in humans and flies are themselves orthologs 24 , 29 , and the functional themes of their mRNA targets in flies and humans starkly contrast with those for yeast Puf3 24 , 27 , 29 ., Thus , the mRNA targets of Puf3 orthologs have diverged since humans , flies , and yeast shared ancestors ., Nevertheless , bioinformatics studies have suggested that Puf targets are conserved over short timescales , underscoring the importance of these distinct interactions 60–65 ., We first systematically investigated the conservation and divergence of the RNA targets that are likely to be recognized by orthologs of S . cerevisiae Puf3 in diverse eukaryotes ., We then focused in detail on the larger family of Puf RNA binding proteins and their RNA targets in fungi , as the many sequenced fungal genomes provide the power to identify major and minor evolutionary changes in the repertoires of Puf proteins , their binding specificities , and their RNA targets ., The numerous and often concerted changes in this single family of proteins and their RNA targets provide strong corroborative evidence for the role of coordinated protein binding to sets of related mRNAs in organizing gene expression 18 , 20 , 21 ., The observed extensive evolutionary changes suggest that changes in RNA binding proteins and their interacting mRNAs are an important source of biological diversification and specialization; studies of these changes across evolutionary time may provide a powerful complement to traditional deep investigations of specific model organisms ., We searched for orthologs of S . cerevisiae Puf3 in 99 diverse eukaryotes ( S1 Text , S1 Fig , S1 Table , Materials and Methods ) and used the identified orthologs to determine the conservation of features important for RNA binding specificity ., Puf3 is a canonical Puf protein containing eight Puf repeats 39 , 40 , 66 , 67 that together fold to form a characteristic crescent shape with an RNA binding interface on the inner side ( Fig 1 ) 54 , 55 , 59 , 68–73 ., Three amino acid residues within each Puf repeat typically contact an RNA base directly and are important determinants of RNA binding specificity ( Fig 1 legend and references 49 , 54 , 55 , 59 , 68–73 ) ., The observations that Puf3 orthologs have a distinctly conserved pocket around the bound RNA and that the residues that determine RNA binding specificity are especially conserved suggest that orthologs of Puf3 recognize the same RNA sequence motifs ( S2 Text , S2 Fig ) ., This inference is consistent with experimental results from Puf3 orthologs in diverse eukaryotes 24 , 25 , 27 , 29 , 50 , 51 ., We used this insight to infer , by analysis of RNA sequences , the extent to which the RNA targets of Puf3 are conserved ., The diversity of the fungal kingdom is a result of more than one billion years of evolution 77 , and the many available sequenced genomes and their relatively low complexity render fungi accessible and powerful for evolutionary studies ., Here we synthesize the sequence data with biochemical and functional data to build a model of the evolution of Puf proteins and their targets in fungi ., The rewiring of gene expression programs plays a major role in evolution and adaption of new species ., Considerable effort has been dedicated to analyzing evolutionary changes in transcription factors and in their targets ( see 8–13 for reviews ) , but far less is known about rewiring at the level of RNA and its binding proteins ., We surveyed the evolutionary changes in one family of RNA binding proteins and their cognate recognition elements , broadly across eukaryotes and more deeply within fungi ( Figs 2 and 8 ) ., Our evidence points to the existence of mRNA targets of Puf proteins that have been maintained for hundreds of millions of years ( Figs 2 and 8 ) ., Overlaid on this conservation are numerous and remarkable changes in the number of Puf proteins , their specificity , their regulatory output , and their targets ., The substantial changes in Puf proteins and targets over evolution followed by long periods of high conservation together underscore the importance of these protein–RNA interactions for organismal adaptation and fitness ., Puf proteins represent only ~1% of all RNA binding proteins 15 , but similar rewiring of interactions between RNA binding proteins and their targets has likely been a pervasive adaptive strategy throughout evolution ., The highly conserved binding specificity of Pufs suggests that the conserved interactions between each protein and its many mRNA targets place a large constraint on binding specificity ., A change in binding specificity thus marks a period of innovation in the gene regulatory program ., In the time following Puf4 duplication in Saccharomycotina , the binding specificity of the paralogs ( Puf4 and Puf5 ) became restricted with respect to the ancestral specificity and diverged with respect to each other ( Figs 5B and 8 #3 ) ., Analogous binding and catalytic promiscuity has been proposed to have been present in ancestral enzymes that later duplicated and specialized 98–103 ., Our phylogenetic studies and evolutionary model suggest specificity changes , potential physical origins ( S15 Text ) , and support the idea that aspects of the evolution of RNA binding proteins and their targets proceeded via early promiscuous binding proteins that later underwent gene duplication and subdivision of the ancestral RNA recognition ., The observations that the conserved RNA targets of each Puf protein share functional themes and that a set of functionally-related RNA targets can switch in concert from specific interactions with one RNA-binding protein to another , provide strong support for the notion that RNA binding proteins play an important biological role in organizing and coordinating aspects of gene expression 18 , 20 , 21 ., Concerted evolutionary changes in mRNAs encoding mitochondrial organization and biogenesis proteins involved hundreds of RNA sequences , placing the same set of orthologous genes in distinct fungal lineages under the regulation of Puf3 , Puf4 , or both proteins ., The evolutionary history of changes in their post-transcriptional regulation , suggested by this analysis , provides strong evidence for the fitness advantage of coordinating the regulation of distinct sets of genes and may harbor clues to the selective pressures that led to changes in the regulatory program ., Whereas essentially all of the inferred RNA targets of Puf3 in Saccharomycotina are transcribed from nuclear genes encoding proteins with mitochondrial functions , not every ortholog of each gene we identified as encoding a Puf3 target in the Saccharomycotina contains a recognizable Puf3 binding site ., It is possible that the fitness advantage ( or disadvantage ) conferred by Puf3 regulation of each of the individual genes in this set is often small enough to allow for considerable genetic drift within the lineage ., The evolutionary plasticity that this would allow might help account for the distinct but overlapping functional and cytotopic themes shared by the targets of a given Puf protein in distinct species and lineages ., Although Saccharomycotina Puf3 is essentially monogamous in its relationship to RNAs with mitochondrial functions and has served as a “poster child” for RNA binding protein-based coordination of gene expression , the targets of other Puf proteins are functionally and cytotopically more promiscuous ., For example , Saccharomycotina Puf4 binds RNAs encoding histone and nucleolar proteins , while Pezizomycotina Puf4 binds RNAs encoding histone and mitochondrial proteins ., The RNA targets of Leotiomyceta Puf4 also encompass a broader array of cellular functions relative to the Saccharomycotina Puf3 targets , including targets with roles in energy metabolism ( through the ETC and TCA cycle ) and the proteasome ., We do not know whether these multiple themes arise because RNA binding proteins help coordinate and integrate cell status and signals between different systems or whether they represent multiple uses of the same protein for independent functions 104–106 ., It is also possible that limitations in our understanding of and ability to identify biological function could account for our inability to map mRNA targets to function in a 1:1 fashion ., Evolutionary changes in regulatory RNA–protein interactions are likely to have many similarities to the changes observed in the evolution of transcriptional control ( S12 Table ) ., By comparing the changes in transcriptional regulation ( as reflected by gain or loss of specific promoter elements ) and post-transcriptional regulation ( as reflected by gain or loss of Puf-protein recognition elements in the corresponding transcripts ) in sets of functionally related genes that share features of both transcriptional regulation and putative Puf-protein regulation , we found that the timing and likely the consequences of evolutionary changes at these two levels of regulation of a common set of genes can be distinct ( S13 Text ) ., RNA–protein interactions can thus provide an additional and independently evolvable infrastructure by which global gene expression networks can be orchestrated and reconfigured to generate phenotypic diversity ., By using systematic investigation of evolutionary changes in gene expression programs to enrich the pictures of these programs acquired from years of detailed studies of “representative” model organisms , we found compelling evidence for dramatic changes in the gene expression program at the level of RNA–RNA binding protein interactions during fungal evolution ., Mapping evolutionary changes in post-transcriptional regulation can provide new insights into the makeup , logic , and malleability of gene expression programs , and may contribute to our ability to engineer new phenotypes by rewriting or de novo design of post-transcriptional programs ., Protein sequence files and SQL tables containing ortholog information were downloaded from InParanoid 107 ( version 7 . 0 , http://inparanoid . sbc . su . se/ ) ., Genome sequences for each species were downloaded in July 2010 from the sources listed in S3 Table ., We used a two-step BLASTP search to identify putative Puf proteins in each species ., A custom BLAST database was created for each species protein sequences using makeblastdb ( part of the blast+ package from NCBI ) ., In the first step , the sequences of the Pum domains of S . cerevisiae Pufs 1–6 ( Puf1:557–913 , Puf2:511–872 , Puf3:513–871 , Puf4:539–888 , Puf5:188–596 , Puf6:133–483 ) and the complete protein sequence of S . cerevisiae Nop9 were used as a query to search for similar protein sequences in each species using blastp ( NCBI BLAST version 2 . 2 . 23 108–110 ) , using an E-value cutoff of 10−5 ., Sequences identified in the first step were then used to search for additional Puf proteins in a second step , also with an E-value cutoff of 10−5 ., In the second step only the parts of the protein sequence identified in the first step as having significant similarity to S . cerevisiae Pum domains were used ., If more than one of the query sequences from the first step was similar to a searched sequence , the similar sequence of longest length was kept ., Results from the first round yielded near-complete coverage of known Pufs from Caenorhabditis elegans , A . thaliana , and O . sativa ( 12/12 , 24/26 , and 17/19 , respectively ) 38 , 111–113 ., The second round yielded one more known Puf from A . thaliana and two from O . sativa ., Additionally , putative Pufs in these organisms were found in both rounds ( one from the first round , two from the second ) ., Two of the three additional putative Pufs contained one or more Puf repeats according to the SMART annotation tool 114 , 115 , suggesting these hits are real Puf proteins ., As our next step was to classify Puf proteins , we aimed for high coverage at the expense of a small fraction of false positives ., We classified Puf proteins as orthologs to each of the S . cerevisiae Puf proteins or to N . crassa Puf8 , a previously uncharacterized Puf that we identified and named ., We chose S . cerevisiae because of our focus on fungi in this work , and the results suggest that S . cerevisiae Pufs well represent the diversity of Puf proteins found across eukaryotes , with the exception of N . crassa Puf8 , which our initial phylogenetic analysis suggested was deleted in an ancestor of S . cerevisiae ., More than 90% of the eukaryotic Pufs and 98% of the fungal Pufs were classified as orthologs to S . cerevisiae Pufs or N . crassa Puf8 ., We classified Puf proteins based on a combination of information: reciprocal best BLAST hits , the pattern of amino acids predicted to contact RNA bases within each Puf repeat , and phylogenetic analysis ., For reciprocal best BLAST , we checked each Puf against S . cerevisiae and N . crassa Pufs ., A protein was tentatively assigned as an ortholog if it was a reciprocal best BLAST hit to at least one S . cerevisiae or N . crassa Puf protein , and the reciprocal best BLAST hit did not disagree between the S . cerevisiae Puf and its N . crassa ortholog ., A Puf protein was also tentatively assigned as an ortholog to S . cerevisiae Puf1/Puf2 , Puf3 , Puf4/Puf5 , or N . crassa Puf8 based on predicted RNA-contacting amino acids ., RNA-contacting amino acids are highly conserved but are different in distantly related Pufs ., The S . cerevisiae Puf1 and Puf2 have similar RNA-contacting amino acids , and those in S . cerevisiae Puf4 and Puf5 are identical to each other so this type of classification cannot distinguish between these two proteins ., Outside of these two pairs , the RNA-contacting amino acids are sufficiently different to allow this classification ., We performed this classification manually and note any differences between the protein and its tentatively assigned ortholog with respect to these amino acids in S1 and S2 Tables ., Puf proteins were assigned a final ortholog if the BLAST-based classification or the RNA contact classification identified a tentative ortholog and so long as the assignment from the two classification methods did not disagree ., Any Pufs not assignable by these criteria were subject to a phylogenetic analysis ., Protein sequences for S . cerevisiae Pufs , N . crassa Pufs , and the unassigned Pufs were aligned as a group using MUSCLE 116 , 117 in Geneious ( using default settings ) ., Columns with more than 50% gaps were stripped , and a maximum likelihood tree was built using PhyML 118 , 119 implemented through Geneious ( WAG substitution model , 8 substitution rate categories , best of NNI Nearest Neighbor Interchange and SPR Subtree Pruning and Regrafting search ) ., Many of the remaining Pufs were classified based on this tree ( S1 and S2 Tables ) ., In some cases , we referred back to the pattern of RNA-contacting amino acids to inform our decision ( see notes in column “unknownGroup_ML tree” in S1 and S2 Tables ) The relationship of a group of Puf proteins from worms , including C . elegans Fbf-1 and Fbf-2 , remained ambiguous ., This relationship was resolved by considering which Pufs were likely present in the ancestor of these species ., These worm Pufs tend to have eight predicted Puf repeats and are closest to Puf3 and Puf4 among S . cerevisiae Pufs ., We inferred that the Puf4 gene was deleted in an ancestor to metazoans and the choanoflagellate Monosiga brevicollis and therefore could not be orthologous to these worm Pufs ., In contrast , Puf3 is inferred to be present in the ancestor of these worms , and we had already identified other Puf3 orthologs in these species ., We assigned the worm Pufs as orthologs to Puf3 under a model that Puf3 underwent several duplications ( duplication of Puf3 and duplication of duplicates ) along the worm lineage with subsequent divergence of many of the duplicates ., For S2 and S5 Figs , protein sequences were aligned using MUSCLE 116 , 117 , as implemented through the program Geneious and using default settings ., For calculating percent identity of residues , all columns containing gaps in S . cerevisiae Puf3 were removed ., Percent identity was calculated as the percent of residues matching the most abundant residue within each column of the alignment ., Puf repeats were defined using the SMART annotation tool 114 , 115 ., The S . cerevisiae Puf3 repeats are residues 538–573 , 574–609 , 610–645 , 646–681 , 682–717 , 718–752 , 760–795 , and 809–844 ., The multiple sequence alignments and calculated percent identities are presented in S2 Dataset ., Protein sequences were mapped back to the respective genome to identify coding sequence boundaries using standalone BLAT v34 120 ( with parameters–q = prot–t = dnax ) ., BLAT output was processed to identify for each query the hit with the smallest discrepancy ( defined as the smallest difference between query and match lengths ) ., We assessed overall performance by calculating the average percent discrepancy and average coverage for the best hits ., The median across all InParanoid species for average coverage was 99 . 8% , and the average discrepancy was 0 . 2% ., Eighty of the InParanoid species had proteins mapping back to the genome with an average coverage >99% and a discrepancy <1% ., G . gallus had the lowest average coverage ( 90 . 6% ) , and G . lamblia had the highest average discrepancy ( 12 . 5% ) ., All 80 fungi had an average coverage of >99% and a discrepancy of <1% ( median: 99 . 9% coverage , 0 . 1% discrepancy ) ., The 500 nucleotides downstream ( 3 on the coding strand ) of each best BLAT hit were extracted as the 3 UTR ., We used a custom Perl script analogous to Fastcompare 63 , 74 , 75 to search for the Puf3 motif in orthologous sequence sets of two species , yielding a 2 x 2 contingency table of the number of sets that have a motif match in both species , in only one of the species , or in neither of the species ., We searched 3 UTRs of orthologs identified by InParanoid in 99 eukaryote species 107 ., The significance of ortholog sets that both have motif matches was computed by the hypergeometric test ., To control for sequence similarity expected between closely related species , we repeated the search using permutations of the Puf3 motif ( e . g . , UAACUAUAGU ) and used the hypergeometric p-value as a score to rank the Puf3 motif against all of its permutations ( n = 1119 ) ., We report a p-value if the overlap between two species for the Puf3 motif is significant after correcting the hypergeometric p-value for multiple hypothesis testing ( p < 0 . 05 after Bonferroni correction ) and if the Puf3 motif is ranked in the top 1% ( i . e . , empirical p < 0 . 01 for comparison against all permutations ) ., Phylogenetic trees were inferred using methods similar to those used previously 121–123 ., To identify proteins whose sequence has preserved the underlying phylogenetic signal , we searched for proteins that contained an ortholog to a human protein in at least 90 of the 99 species investigated herein , and that within each species contained at most two orthologs to a human protein ( 1:1 or 2:1 orthologs ) ; we identified a total of 53 sets of proteins meeting this criteria , and within each set , most species only had one ortholog for each human protein used ( 1:1 orthologs ) ., Each set of orthologs was multiply aligned using standalone MUSCLE 116 , 117 ( version 3 . 8 . 31 with default settings ) ., The alignments were concatenated , and during the concatenation process , we kept only the first ortholog encountered for each species and added a sequence of gaps where an ortholog was not found ., Columns containing more than 5% gaps were removed , yielding a final alignment with 27 , 239 columns ., A tree was inferred by maximum likelihood using standalone PhyML 118 , 119 ( version 20120412 , parameters -d aa -b 1 -m WAG -o tlr -s SPR—n_starts 10 -v e -c 8 ) ., For the phylogeny displayed , the descendants of a node were collapsed if a branch length from the ancestor node to one of the descendant nodes ( i . e . , the internode distance ) was greater than 0 . 65 ., The branches that were collapsed largely reflect uncertainty in the relationship of species diverging earliest within eukaryotes and uncertainty about the root of the tree ., The final phylogeny displayed generally agrees with the literature consensus , and points of disagreement did not affect our conclusions ., For example , N . vectensis , T . adhaerens , Capitella sp ., I , H . robusta , and L . gigantea are proposed to be basal metazoan species in the literature consensus , and the worms ( nematode , trematode ) are proposed to be grouped with the insects to the exclusion of vertebrates ., The final phylogeny with species names can be found in S20 Fig . The multiple sequence alignment and a newick-formatted tree can be found in S3 Dataset ., For fungi we identified 20 sets of proteins that across all species were 1:1 ortholog to an S . cerevisiae protein ., We allowed A . macrogynus to have multiple orthologs to each S . cerevisiae protein because its genome contains many duplicated genes ., Each set of orthologs was multiply aligned using standalone MUSCLE 116 , 117 ( version 3 . 8 . 31 with default settings ) ., The alignments were concatenated , and during the concatenation process , we kept only the first A . macrogynus sequence encountered ., Columns containing gaps were removed , yielding a final alignment with 4 , 251 columns ., An initial maximum likelihood tree was inferred using standalone PhyML 118 , 119 ( version 20120412 , parameters -d aa -b 100 -m WAG -o tlr -s SPR—n_starts 10 -v e ) ., The initial fungi phylogeny placed A . oligospora ( a species within Orbiliomycetes ) and T . melanosporum ( a species within Pezizomycetes ) together ., We suspected that this was a long-branch artifact , as it disagreed with previous studies that used a higher sampling of species within Orbiliomycetes and Pezizomycetes 88 , 92–94 ., The previous studies placed Orbiliomycetes and Pezizomycetes as separate lineages that diverged the earliest within Pezizomycotina ., Nevertheless , one study 88 disagreed with others 92–94 in terms of which lineage is most basal ( i . e . , earliest diverging ) ., We chose to constrain the topology to place Orbiliomycetes ( A . oligospora ) as the most basal lineage followed by Pezizomycetes ( T . melanosporum ) then the rest of Pezizomycotina ., This order is consistent with two of the three studies that inferred phylogenies using multiple gene sequences 92 , 94 and the study using the ultrastructure character of different species 93 ., The alternative topologies ( the one from the literature and our unconstrained topology ) lead to models in which an additional loss event is required to account for the Puf3 pattern and thus would alter details of our models but not the overall conclusions drawn ( S21 Fig ) ., We constrained the tree topology and optimized the branch lengths and rate parameters using PhyML ( with parameter -o lr ) ., The resulting tree was rooted between the species within Chyridiomycota ( A . macrogynus , B . dendrobatidis , S . punctatus ) and all other fungi , but this root should be viewed as a hypothesis ., The final phylogeny used for fungi contains discrepancies with previously published trees , but the discrepancies occur at parts of the tree where the literature itself is inconsistent ., As the alternative topologies would not affect our conclusions , we did not attempt to resolve these discrepancies ., The multiple sequence alignment and a newick-formatted tree can be found in S3 Dataset ., Protein and genome sequence data were retrieved from the sources listed in S4 Table ., We used InParanoid v4 . 1 107 , 124–126 ( default settings with no outgroup species ) to identify orthologs of S . cerevisiae or N . crassa proteins in each of the other fungi ., Tables containing orthologs can be found in S4 Dataset ., N . crassa strains were obtained from the Fungal Genetics Stock Center 127 ., Strains were the wild-type N . crassa 74-OR23-1VA ( FGSC #2489 ) 128 and knockout strains of the gene NCU06199 . 2 ( PUF1 , FGSC #13194 ) , NCU06511 . 2 ( PUF3 , FGSC #13380 ) , NCU01774 . 2 ( part of PUF4 removes N-terminus of protein , FGSC #14089 ) , NCU01775 . 2 ( part of PUF4 removes Pumilio domain , FGSC #14547 ) , NCU01760 . 2 ( PUF8 , FGSC #15499 ) , or NCU06199 . 2 ( PUF1 , FGSC #13194 129 ., The Puf4 gene was originally annotated as two separate genes , so the Neurospora knockout collection had a separate knockout strain for each of the original annotated genes ., One strain has a deletion of the sequence encoding the 5 portion of the mRNA including the predicted natural start codon ., The other deletion strain is missing the sequence encoding the 3 end of the mRNA , including the sequence that encodes the Pumilio RNA binding domain and the natural translation stop codon ., The knockout strains were homokaryons and of mating-type A . Strains were preserved long-term by resuspending conidia in sterile 7% milk , mixing with an equal volume of 50% glycerol , and storing at −80°C ., Agar race tubes were prepared in 25 mL pipets ( Falcon 352575 ) ., Pipets were filled with 13 mL of autoclaved medium containing 1X Vogels Medium , 1 . 5% agar ( BD Difco 214530 ) , and 2% of a carbon source ( sucrose , glucose , maltose , or glycerol ) ., Medium was allowed to solidify on a flat surface ., Each N . crassa strain was streaked onto 3 mL agar slants made with Vogels Medium with 2% sucrose and grown for 7–10 days at room temperature with constant exposure to indoor light ., Conidia were obtained by adding 1 mL of water to each slant , vortexing , and extracting the liquid ., Resuspended conidia ( 20 μL ) were used to inoculate a race tube through the hole made at the top of the pipet using a heated needle ., Tubes were incubated at 37°C in the dark for 24 h to allow the strains to reach a maximal growth rate , and then measurements were taken twice daily until mycelium growth neared the end of the tube ., Growth rates were calculated as a weighted average of the rates obtained between every two measurements , where the weight is the fraction of time elapsed between two given measurements ., The calculated rates from this approach displayed lower variability than those calculated from linear regression ., Measurements were obtained from two replicates for sucrose and maltose conditions , four for glycerol , and five for glucose ., Statistical significance was assessed by the two-sided t test ., Conidia were extracted in water from N . crassa strains streaked onto 3 mL agar slants made with Vogels Medium with 2% sucrose and grown for 7–10 days at room temperature with constant exposure to indoor light ., An estimate of conidia concentration was made by taking a sample , diluting 1:40 into water , and measuring the optical density at 530 nm ., An OD530 of 0 . 25 was found to correspond to approximately 108 conidia/mL in the undiluted sample ., Conidia were added to a final concentration of 106 conidia per mL into 25 mL of Vogels Medium with 2% glucose as the carbon source ., Cultures were shaken at 200 rpm in a 30°C incubator with lights on ., After 8 h ~100% of cells exhibited hyphal growth with most having germ tube lengths between 50 and 400 μm ., At this point , mycelia were collected by vacuum filtration ., Material was scraped from the filter and placed into tubes containing 0 . 5 mL of buffer AE ( 50 mM sodium acetate , 10 mM EDTA ) , 33 . 3 μL of 25% SDS , and 0 . 5 mL of acid phenol:chloroform pH 4 . 5 ( Ambion AM9720 ) then inverted to mix and flash frozen in liquid nitrogen ., RNA was isolated by hot acid phenol/chloroform extraction ., Samples were placed at 65°C in a thermomixer shaking at 1 , 400 rpm for 10 min , vortexed for 10 s , then placed back in the thermomixer for another 5 min ., Samples were cooled on ice for

Introduction, Results and Discussion, Materials and Methods

Reprogramming of a gene’s expression pattern by acquisition and loss of sequences recognized by specific regulatory RNA binding proteins may be a major mechanism in the evolution of biological regulatory programs ., We identified that RNA targets of Puf3 orthologs have been conserved over 100–500 million years of evolution in five eukaryotic lineages ., Focusing on Puf proteins and their targets across 80 fungi , we constructed a parsimonious model for their evolutionary history ., This model entails extensive and coordinated changes in the Puf targets as well as changes in the number of Puf genes and alterations of RNA binding specificity including that:, 1 ) Binding of Puf3 to more than 200 RNAs whose protein products are predominantly involved in the production and organization of mitochondrial complexes predates the origin of budding yeasts and filamentous fungi and was maintained for 500 million years , throughout the evolution of budding yeast ., 2 ) In filamentous fungi , remarkably , more than 150 of the ancestral Puf3 targets were gained by Puf4 , with one lineage maintaining both Puf3 and Puf4 as regulators and a sister lineage losing Puf3 as a regulator of these RNAs ., The decrease in gene expression of these mRNAs upon deletion of Puf4 in filamentous fungi ( N . crassa ) in contrast to the increase upon Puf3 deletion in budding yeast ( S . cerevisiae ) suggests that the output of the RNA regulatory network is different with Puf4 in filamentous fungi than with Puf3 in budding yeast ., 3 ) The coregulated Puf4 target set in filamentous fungi expanded to include mitochondrial genes involved in the tricarboxylic acid ( TCA ) cycle and other nuclear-encoded RNAs with mitochondrial function not bound by Puf3 in budding yeast , observations that provide additional evidence for substantial rewiring of post-transcriptional regulation ., 4 ) Puf3 also expanded and diversified its targets in filamentous fungi , gaining interactions with the mRNAs encoding the mitochondrial electron transport chain ( ETC ) complex I as well as hundreds of other mRNAs with nonmitochondrial functions ., The many concerted and conserved changes in the RNA targets of Puf proteins strongly support an extensive role of RNA binding proteins in coordinating gene expression , as originally proposed by Keene ., Rewiring of Puf-coordinated mRNA targets and transcriptional control of the same genes occurred at different points in evolution , suggesting that there have been distinct adaptations via RNA binding proteins and transcription factors ., The changes in Puf targets and in the Puf proteins indicate an integral involvement of RNA binding proteins and their RNA targets in the adaptation , reprogramming , and function of gene expression .

We set out to trace the evolutionary history of an RNA binding protein and how its interactions with targets change over evolution ., Identifying this natural history is a step toward understanding the critical differences between organisms and how gene expression programs are rewired during evolution ., Using bioinformatics and experimental approaches , we broadly surveyed the evolution of binding targets of a particular family of RNA binding proteins—the Puf proteins , whose protein sequences and target RNA sequences are relatively well-characterized—across 99 eukaryotic species ., We found five groups of species in which targets have been conserved for at least 100 million years and then took advantage of genome sequences from a large number of fungal species to deeply investigate the conservation and changes in Puf proteins and their RNA targets ., Our analyses identified multiple and extensive reconfigurations during the natural history of fungi and suggest that RNA binding proteins and their RNA targets are profoundly involved in evolutionary reprogramming of gene expression and help define distinct programs unique to each organism ., Continuing to uncover the natural history of RNA binding proteins and their interactions will provide a unique window into the gene expression programs of present day species and point to new ways to engineer gene expression programs .

A map of the evolutionary history of Puf proteins and their RNA targets shows that reprogramming of global gene expression programs via adaptive mutations that affect protein-RNA interactions is an important source of biological diversity.

journal.ppat.1006111

Within Host Evolution Selects for a Dominant Genotype of Mycobacterium tuberculosis while T Cells Increase Pathogen Genetic Diversity

Microbial pathogens adapt to host environments to establish replicative niches and counter immune responses ., Adaption , which commonly relies on the generation and transmission of genetic variants with increased fitness , is especially critical for obligate pathogens that must infect , replicate , and be transmitted to new hosts to survive and propagate ., While mutations emerge randomly in the genome , the accumulation and loss of variants is the result of both genetic drift and natural selection , and is influenced by transmission population bottlenecks within and between hosts ., The large reductions in population size during bottlenecks can have strong evolutionary effects , leading to erosion of genetic diversity and reduction in evolutionary potential and individual fitness through random fixation of slightly deleterious alleles1 , 2 ., The severe nature of bottlenecks implies that the success of infection depends on, 1 ) the genetic diversity of the pathogen population in the donor host and ,, 2 ) the selection of adapted genotypes during transmission ., For HIV and other RNA viruses , founder particles are biased to favor the transmission of variants associated with increased fitness3 , 4 ., The corresponding increase in fitness results from the selection and contribution of low frequency variants , and not to the effect of a single , dominant genome ., The same principle applies also to other pathogens , including certain bacteria and parasites5 6 ., Understanding within-host pathogen population diversity has important implications for drug treatment and resistance7 , 8 , and for inferring transmission networks9 , 10 , 11 and evolutionary processes12 ., Two opposing models of within-host microbial evolution have been described6: a ‘dominant-lineage’ model , in which beneficial mutations lead to unique genotypes to establish and maintain infection , and a ‘diverse-community’ model , in which minor variants rise to intermediate frequencies and coexist with major variants in the microbial population ., Here , we distinguish between these models and determine the roles of drift , bottlenecks , and selection in genetic variation of Mycobacterium tuberculosis during infection ., Members of the M . tuberculosis complex ( MTBC ) cause tuberculosis , a chronic infection transmitted by aerosol that remains a deadly disease , despite the availability of drug treatment13 ., M . tuberculosis is an obligate pathogen , and has no natural ecological niche other than its human hosts , with which it has coevolved for thousands of years14 , 15 ., Immunological control of M . tuberculosis depends on T lymphocytes , which recognize peptide fragments of bacterial proteins bound to polymorphic MHC ( HLA in humans ) molecules16 ., The MTBC is characterized by a largely clonal population structure classified into 7 main human-adapted phylogenetic lineages15 , 17 ., Despite thousands of years after divergence from a common ancestor , all MTBC lineages share identical 16S rRNA sequences and 99 . 9% nucleotide identity at the whole genome level18 ., However , M . tuberculosis can generate diversity over short and long time courses ., For example , most drug resistance determinants in the MTBC represent chromosomal mutations selected by drug exposure18 ., Moreover , population genetic analyses have highlighted that nonsynonymous single nucleotide polymorphisms ( nSNPs ) tend to accumulate in the M . tuberculosis genome at a higher rate than in related organisms19 , 17 ., Because nSNPs are often deleterious , it was suggested that M . tuberculosis undergoes random genetic drift associated with serial population bottlenecks17 ., Yet , M . tuberculosis does not show the typical signs associated with increased genetic drift18 ., Indeed , its genome has moderate numbers of insertion sequences , few pseudogenes , and no obvious other signals of extensive genome degradation ., Several findings support the notion that within-host selection plays a critical role in shaping the genome of the MTBC ., Comparison of a globally representative sample of M . tuberculosis isolates yielded evidence of strong purifying selection , with different patterns of selection related to gene function20 ., We also found that antigen conservation dominates in this pathogen , and that the vast majority of the currently known T cell epitopes are more conserved than any other part of the genome , indicating that these sequences are under strong selective pressure21 ., Although the factors driving epitope conservation in M . tuberculosis are still undetermined , these observations led to the hypothesis that human T cell recognition could play an important role by limiting genetic diversity21 ., In the context of drug exposure , although within-host selection is a source of heterogeneity , the frequency of mutations is low and their locations in the genome highly specific22 ., To better understand conservation and diversity of M . tuberculosis during infection , and to delineate the contribution of T cell selection , we combined high-density whole-genome sequencing ( WGS ) and mathematical modeling to assess the evolution of genetic diversity of M . tuberculosis in murine pulmonary infection , and in human disseminated M . bovis BCG infection ., In the widely-studied virulent strain of M . tuberculosis , H37Rv , genomic differences among stocks from individual laboratories have been reported , indicating that in vitro culture generates M . tuberculosis diversity23 ., To further understand this observation , we sequenced our laboratory stock and compared it to that of the H37Rv reference ( NC_000962 ) 24 ., Deep sequencing revealed that the stock population was heterogeneous and contained 34 polymorphisms present at various frequencies ( S1A Table ) ., Among those mutations , 25 were nonsynonymous single nucleotide changes affecting 25 proteins ( Table 1 ) ; nonsynonymous mutations were overrepresented in genes whose products are involved in metabolic pathways with potentially important in vivo functions ( observed = 12 , expected = 7; χ2 , p<0 . 05; Table 1 ) ., Notably , we observed that 28% of the bacterial population had a nSNP in the gene encoding Isocitrate lyase 1 ( Icl1 ) , an enzyme essential for allowing net carbon gain by diverting acetyl-CoA from β-oxidation of fatty acids into the glyoxylate shunt pathway25 , 26 ., Disruption of icl1 attenuates M . tuberculosis persistence and virulence in mice without affecting bacterial growth during the acute phase of infection ., An additional metabolic gene , kgd , contained an nSNP in 28% of our stock and encodes an α-ketoglutarate decarboxylase involved in an alternative pathway that generates succinate for the tricarboxylic acid cycle , which may help the pathogen cope with hypoxia27 ., Since Icl1 and Kgd are employed in oxygen- or glucose-deprived environments , we hypothesized that they do not play a key role during growth in rich culture media ., This hypothesis was supported by the predicted impact of the identified nSNPs on protein function and/or stability: amino acid substitutions in 17 ( 68% ) of the 25 mutated proteins , including Icl1 and Kgd , were predicted to be deleterious ( Table 1 ) ., Taken together , these data indicate that during in vitro growth , MTBC accumulates mutations in genes which are not relevant for in vitro growth but might be key for in vivo growth and/or transmission ., Based on our observation that T cell epitopes are the most conserved regions of the MTBC genome , we tested whether T cell immunity constrains genetic diversity during infection ., We used WGS to compare the frequency of variants in M . tuberculosis after six in vivo passages of the H37Rv stock containing sequence variants in immunocompetent wild-type ( WT ) or in T cell-deficient ( TCR β/δ-/- ) mice ( Fig 1 ) ., Following the third and sixth passages , a pool of M . tuberculosis bacteria from the lungs of each mouse was sequenced and compared to the sequence of the initial aerosol inoculum ., 5 Wild type ( C57BL/6; purple ) or 5 T cell-deficient ( TCR β/δ-/-; orange ) mice were each infected with 100 CFU of M . tuberculosis H37Rv stock ., After 6 weeks of infection , the lungs of each mouse were homogenized to prepare an inoculum used to infect the next set of mice of the same phenotype ( see Methods ) ., Genomic DNA samples of the initial inoculum and of the bacterial populations after the 3rd and 6th mouse passages were examined by WGS ., In vivo passaging allowed 14 and 18 doublings of the bacteria in lungs of WT and T cell deficient mice , respectively , followed by a ~10 , 000-fold bottleneck during infection of the next group of mice , so we were able to characterize the impact of transmission bottlenecks and in vivo selection on the genetic diversity within the inoculum population ., We monitored the frequencies of the 34 pre-existing polymorphisms in the M . tuberculosis population after the third and sixth passages in the two groups of mice ., Following three passages , 4 variants were purified out , and 2 achieved fixation in the isolates from both immunocompetent and T cell-deficient mice ., These 6 variants were present at the highest frequency ( ≥50% ) in the initial inoculum population ( S1A Table ) and the majority ( 4/6 ) were sSNPs or intergenic mutations ., In contrast , 82% ( 23/28 ) of the minor variants present in the initial inoculum and that were maintained after three passages were nSNPs ( Fig 2A ) ., The frequency of these 28 variants was decreased compared to that in the inoculum population ( average total frequencies: 25% in the inoculum , 19% in the WT mouse isolates and 20% in the T cell deficient mouse isolates; multiple t test of coverage , p<0 . 01; Fig 2 ) ., These observations indicated that mixed populations of M . tuberculosis continued to exist in both groups of mice after three passages ., However , after six passages , all of the minor variants ( 28/28 ) were lost ( polymorphic reads <5% ) in both mouse groups and each mouse was infected with a clonal M . tuberculosis population closer to the H37Rv reference sequence ( Fig 2 ) ., To infer whether some variants identified in the initial inoculum might be linked , we analyzed the changes in frequencies of each SNP after the three first passages in WT and T cell-deficient mice ., We found 5 sets of variants containing 2 to 3 SNPs each that evolved in parallel ( multiple t test of coverage , p<0 . 05 ) during in vivo passage in both mouse groups , suggesting that these mutations were linked ( S2 Table ) ., The data are not consistent with a simple dilutional bottleneck , since minor sequence variants were not progressively lost from the population ., Instead , the frequency of the minor variants was unchanged or only slightly reduced after the third passage , but were absent after the sixth passage ( S1 Table ) ., In contrast , the sequence variants that were present as a higher fraction of the initial inoculum were purified within the first 3 passages , resulting in fixation or loss ( S1 Table ) ., This pattern was independent of the presence of T cells ., To explain the observed purification dynamics , we developed a mathematical model using a system of ordinary differential equations , analyzed by dynamical system theory28 ., The model parameters were estimated from the data by the generalized profiling method29 ., The model results strongly supported that in vivo selection of M . tuberculosis populations is driven by a deterministic process for which the kinetics depend on the frequencies of variants in the initial inoculum population ( Fig 3 ) ., When the variant allele frequency in the inoculum population was less than 50% , the purifying process consisted of two steps: the first step was characterized by the competitive co-existence of minor genotypes ., This co-existence continued for a minimum of three passages ., The second step was a dynamic process , occurring during or after passage 4 , resulting in fixation of polymorphisms and purification of a single M . tuberculosis population ., In contrast , when the percentage of variants in the inoculum was equal to or greater than 50% , the bacterial population underwent a rapid purification process that occurred within the first 3 passages , leading to either elimination of these mutants or their fixation as the dominant genotype ., These results show evidence that, 1 ) M . tuberculosis evolution is deterministic and follows a “dominant lineage” model in vivo and, 2 ) is a dynamic process largely independent of the presence of T cells that cannot be explained by passive transmission bottlenecks ., To determine whether within-host selection at specific loci can also generate variation , and to examine the contribution of T cells in generating this diversity , we characterized the polymorphisms that appeared de novo in the M . tuberculosis population after infection of wild-type and T cell-deficient mice ., Genome sequencing of bacterial populations following the third mouse passages revealed 2 minor variants that were undetectable in the initial inoculum and present in the WT ( 1 mutation ) or T cell deficient mouse isolates ( 1 mutation ) ( Table 2 and S1B Table ) ., While no additional polymorphisms were detected in the T cell deficient isolates after the 6th passage , 4 mutations appeared in the genomes of the WT mouse isolates ., Among the polymorphisms identified in WT mouse isolates , 4 were nSNPs ( Table 2 ) ., By contrast , the unique mutation identified in the bacterial populations from T cell deficient mice was a synonymous SNP ( Table 2 ) ., Together , these results indicate that T cell-dependent immunity can contribute to sequence diversity in M . tuberculosis ., To further evaluate the impact of T cells on the genome diversity of M . tuberculosis during infection , we determined the average mutation rates of M . tuberculosis in both mouse groups ., Considering a similar bacterial generation time of 20 hours for both groups , we estimated that the average mutation rate of M . tuberculosis was highly reduced during prolonged infection in T cell deficient mice ( 3 . 8 x 10-9 in WT mice versus 7 . 7 x 10-10 in T cell deficient mice ) and this , despite a higher bacterial burden in T cell deficient mice ( 2 logarithms ) ., Thus , the reduction in the mutation rate calculated for the T cell deficient mouse isolates strongly supports a role for T cells in generating diversity in M . tuberculosis during prolonged infection ., To determine whether the results obtained in mice are also relevant in human infections , we examined the extent and nature of within-host selection of slow-growing mycobacteria isolated from human patients ., Since it is not possible to determine the sequence of the inhaled inoculum that establishes human tuberculosis , we took an alternative approach ., Invasive carcinoma of the urinary bladder in immunocompetent patients is commonly treated by instillation of a standardized preparation of mycobacteria ( M . bovis BCG ) ., In rare cases , disseminated BCG infection develops , and involves tissues beyond the bladder30 ., Since the BCG inoculum is defined and prepared according to pharmaceutical standards , we could be confident in the identity of the bacteria to which a patient was exposed ., We first determined the genome sequence of the inoculum used to treat bladder cancer in the United States , M . bovis BCG Tice ., Similarly to what we observed for our H37Rv inoculum , the BCG Tice inoculum population was heterogeneous and contained 6 variants present at frequencies from 3% to 66% ( S3 Table and Fig 4 ) ., Two of these variants were nSNPs present in echA6 or eccB5 ., The gene echA6 encodes a putative enoyl-CoA hydratase capable of supplying energy and carbon from fatty acid β-oxidation during starvation31 ., EccB5 is a membrane protein of the ESX-5 type VII secretion system involved in secretion of proteins and uptake of nutrients32 ., Both proteins are thus implicated in metabolic pathways used in potentially hostile environments ., To characterize the evolution of these minor variants during dissemination in humans , we sequenced M . bovis BCG isolates from 4 patients ., We found that the heterogeneity of the inoculum population was eliminated in vivo , as the four variants were either purified out or achieved fixation ( Fig 4 ) ., Together , these results reinforce the conclusion that M . tuberculosis populations follow a dominant lineage model of evolution during infection ., The combination of genomics and mathematical modeling to determine the impact of in vivo evolution on sequence diversity of M . tuberculosis yielded evidence that conservation of the M . tuberculosis genome is driven by a deterministic process leading to the selection of a dominant genotype in vivo ., By examining evolution of a heterogeneous population of M . tuberculosis from in vitro passaged bacteria during prolonged infection in mice , we found that conservation of M . tuberculosis in vivo is driven by selection forces limiting genome diversity acquired in vitro ., Even though the variants in the initial inoculum population could replicate in vivo , within-host selection limited their ability to compete with the rest of the population , and a single dominant genome eventually emerged ., These results indicate that specific mutants are purged in vivo and that a dominant lineage establishes and maintains long–term infection ., This has important implications for understanding the mechanisms of evolution of M . tuberculosis and is in contrast to what is observed during infection with the opportunistic pathogen Burkholderia dolosa6 ., After entering the airways of people with cystic fibrosis , B . dolosa establishes long-term colonization during which emergence of mutations in the population leads to diversification rather than genetic fixation , with potential for cooperative action among subclones ., Because M . tuberculosis is a human obligate pathogen that has co-evolved for thousands of years with the same unique host species , the need for variation to establish successful infections in the lung may be reduced and could even be deleterious for the pathogen population 33 ., Our results help explain the global phylogenetic structure of the MTBC ., Although the MTBC is considered clonal , each lineage is distinct and associated with a specific human population15 , leading to the hypothesis that the genetic conservation of each lineage is the result of an enduring sympatric relationship with their host ( same geographical origin ) ., Several observational studies provide evidence supporting this model ., Notably , a Swiss cohort study found that MTBC lineages tend to transmit preferentially among sympatric host populations34 ., Two other studies in Ghana offered a potential explanation for the geographical restriction of lineage 5 in West Africa , by showing association between these lineages and specific human ethnicities35 , 36 ., A dominant lineage model of evolution can also help explain the various clinical outcomes resulting from infections with strains from distinct lineages of the MTBC ., This would explain the finding that individuals from the Gambia that were infected with modern lineages 2 and 4 were more likely to progress to active disease than individuals infected with ancient lineage 6 which is endemic and likely evolved in that region37 ., The finding that within-host selection contributes to shape the M . tuberculosis genome is also supported by the increased diversity observed during M . tuberculosis growth in nutrient-rich culture media ., For decades , laboratories have maintained M . tuberculosis H37Rv ., Originally derived from a clinical strain ( H37 ) , it was recovered in 1905 from a patient with pulmonary tuberculosis ., Early records showed the capacity of H37 to adapt to different in vitro conditions leading to phenotypic dissociation between virulent and avirulent derivatives38 ., Although the mechanisms by which this dissociation occurred were unknown , it highlighted the plasticity of M . tuberculosis metabolism and its central role for virulence ., We found that in vitro culture conditions represent permissive environments for genetic drift and that these changes targeted enzymes involved in metabolic pathways dispensable during in vitro culture , but essential for optimal growth under nutrient-limiting conditions in vivo ., These results demonstrate the capacity of M . tuberculosis to generate variation under permissive conditions ., A significant finding of this study is the impact of T cells in generating diversity in M . tuberculosis populations in vivo ., By comparing genome sequences of M . tuberculosis populations isolated from WT and T cell deficient mice , we found that the presence of T cells was associated with the appearance of unique variants of the M . tuberculosis genome during prolonged in vivo infection ., The findings reported here are in contrast with the hypothesis that T cells are strictly driving M . tuberculosis epitope conservation ., Although the present results could either be due to direct recognition of specific peptide sequences by clonotypic T cells or to the indirect consequence of T cell activation and effector mechanisms , the possibility that direct recognition by T cells can contribute to sequence diversity is consistent with our recent finding that naturally occurring sequence variation in specific M . tuberculosis epitopes affects human T cell recognition21 ., In addition , the findings emphasize the point that the impact of T cell recognition , whether to promote conservation or diversity , is a function of the specific antigen/epitope and genetic locus , and that T cell recognition of distinct antigens can have different outcomes that may favor the pathogen or the host ., Overall , the results reported here reveal that purifying selection and increased genomic diversity are not two mutually exclusive processes during M . tuberculosis infection ., While the impact of purifying selection was apparent shortly after the initial infection , increased genomic diversity occurred progressively and was observed after serial infection-transmission cycles over the course of months ., Similar results were recently published that reveal evidence of both purifying selection and genome diversification in M . tuberculosis isolates obtained from distinct lesions and organs of HIV-coinfected humans that succumbed to infection39 ., Thus , in another context , prolonged infection leads to increased M . tuberculosis genetic diversity in humans despite overall purifying selection pressure ., Together , the findings in immunocompetent humans , HIV-infected humans , and mice , all indicate that although selection favors overall sequence conservation in M . tuberculosis , there are also long term forces that favor diversifying selection , most likely to adapt to a new environment or to counter new stresses ., Our findings will guide deeper investigation of the mechanisms used by M . tuberculosis to adapt and to continue to be a globally successful pathogen ., In conclusion , our findings indicate that M . tuberculosis genetic selection is driven by a deterministic process imposed by both genetic drift and within-host selection , leading to a dominant lineage mode of evolution ., Although M . tuberculosis is not rapidly mutating , our results indicate that this pathogen is capable of genetic plasticity dictated by environmental changes ., The necessity to adapt leads to selection and the contribution of dominant genotypes determined by the host ., Our results also demonstrate for the first time the impact of T cells on sequence diversity of M . tuberculosis and indicates that T cell responses are a force that can promote diversity at specific sites rather than to only maintain conservation during infection ., All animal experiments were done in accordance with procedures approved by the NYU School of Medicine Institutional Animal Care and Use Committee ( IACUC - Laboratory Animal Care Protocol #160426–01 ) ., These IACUC regulations conformed to the national guidelines provided by the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ., In an ABSL3 facility , M . tuberculosis H37Rv was cultured and used to infect 5 WT ( C57BL/6 ) or 5 T cell-deficient ( TCR β/δ-/- ) mice by the aerosol route with ~100 CFU/ mouse ., After 42 days of infection , mice were euthanized and their lungs homogenized in 5 ml of 7H9 culture medium and the bacterial population was allowed to expand during a minimal period ( ~1 week ) in vitro ., The culture from one mouse was then diluted to prepare an aerosol inoculum of 100 CFU , which was used to infect a new set of mice of the same background ., At each passage , CFU were calculated and frozen stocks were made ., Following the third ( 21 weeks ) and sixth ( 42 weeks ) mouse passages , a pool of M . tuberculosis from each mouse was used for DNA extraction and sequencing ., M . bovis BCG isolates from 4 patients were provided from the collection of the New York State Mycobacteriology Laboratory in the form of solid agar cultures ., The samples were de-identified , but were known to have been obtained from 4 different adult patients treated for bladder cancer ., For each culture , frozen stocks were made before entire plates were swept for gDNA preparation ., The pharmaceutical grade M . bovis BCG Tice strain was obtained from Theracys ., Genomic DNA was extracted using a standard kit ( Qiagen ) , and sequenced by GATC-Biotech ., Illumina single read sequencing was performed with single-read of 51 bases and a target coverage of at least 3 million high-quality bases ., On average , 9 . 5 million reads were obtained per isolate ., We used Burrows-Wheeler Aligner ( BWA ) 40 to map the reads from the genome sequences against the H37Rv NC_000962 reference sequence ., BWA outputs were analyzed and annotated using SAMtools41 , and ANNOVAR 42 ., SNPs in genes annotated as PE/PPE genes , integrases , transposases , resolvases , maturases , or phages were removed from the analysis ., Bacteria from entire plates were pooled from each lung sample and sequenced with deep coverage ., The reads were aligned to H37Rv NC_000962 reference sequence and we identified fixed mutations , appearing in all reads , and polymorphisms , appearing in only a fraction of the reads ., For study of disseminated M . bovis BCG isolates , bacteria on entire plates were pooled and the sequence reads were aligned to the M . bovis BCG Tice reference sequence ( SAMN03023974 ) ., To remove false positive polymorphic sites caused by systematic sequencing or alignment errors43 , we developed a set of thresholds and statistical tests that rejected polymorphic sites where the mutated and ancestral reads had significantly different properties ., The population sequencing approach reliably detected polymorphisms where the minor allele frequency was greater than 10% , while decreasing the cost and labor required per sample ., We considered a position to be polymorphic if it met the following quality thresholds in the given sample: minor allele frequency: more than 10% of reads supported a particular minor allele; minor allele coverage: at least 50 reads aligned in both the forward and reverse direction , and the total number of reads aligning is below the 99th percentile of covered positions in that sample; base quality: average base quality ( provided by sequencer ) was greater than 20 for both the major and minor allele calls on both the forward and reverse strand; mapping quality: average mapping quality ( provided by aligner ) was greater than 19 for reads supporting both the major and minor alleles on both the forward and reverse strand; indels: no reads aligning to that position support an indel at any position along that read; isogenic control: More than 98 . 0% of reads aligning to this genomic position in the isogenic control support a major allele; strand bias: A p-value < 0 . 01 supporting a null hypothesis that the minor allele frequency for the SNPs identified in the mouse isolates is the same for reads aligning to both the forward and reverse strand ( Fisher’s exact test ) 6 ., For some mutations identified in the inoculum population , the strand bias criterion was not taken into account if the polymorphism was confirmed after the 3rd mouse passage and purified out after the 6th mouse passage ., Minor variants identified at high frequency ( >30% ) in the bacterial populations were confirmed by Sanger sequencing of PCR amplification products using primers anchoring unique regions flanking the mutated genes ., PCR was performed using the FastStart High fidelity PCR system ( Roche ) ., The purified product was diluted and submitted with the forward and reverse primers to Genewiz for dideoxy chain termination sequencing ., BLAST was used to align the resulting sequences against the corresponding genes to confirm the presence of multiple peaks at the polymorphic positions ., To characterize the families and functions of nSNP-encoding proteins , we used the following databases: Tuberculist ( http://tuberculist . epfl . ch/ ) , KEGG ( http://www . genome . jp/kegg/ ) , UniProt ( http://www . uniprot . org/ ) and ModBase ( http://modbase . compbio . ucsf . edu/modbase-cgi/index . cgi ) ., To predict the impact of amino acid changes on protein function , we used 3 algorithms in parallel: Sift ( http://sift . jcvi . org/ ) , Polyphen-2 ( http://genetics . bwh . harvard . edu/pph2/ ) and Provean ( http://provean . jcvi . org/index . php ) ., A mutations was considered deleterious if the outputs obtained from the 3 algorithms converged toward the same conclusion ., According to the experimental results , there are two fixation states of polymorphisms for the frequency of mutated reads: one with zero representation ( denoted as F0 ) and the other with full representation ( denoted as F1 ) ., We introduced a variable y into the system to characterize the level of a decision marker that depicts the competitions between two fixation states ., We assumed that y satisfies y =a ( 1-y ) -b y , where a represents the transition rate from F0 to F1 , and b represents the transition rate from F1 to F0 ., Write y* = a/ ( a+b ) and this quantity is indeed the equilibrium solution of the equation above ., Furthermore , we assumed that the closer the value of y to y* , the more it favors F1 ., Meanwhile , the initial condition of y was set as follows: y ( 0 ) = 0 when the percentage of variant alleles in the inoculum population is below 50% , whereas y ( 0 ) = y* when the percentage of variant alleles in the inoculum population is above or equal to 50% ., The percentage of M . tuberculosis variant alleles was defined as x ., We assumed that x obeys the following dynamics: x = r ( t ) x ( 1-x ) -d ( t ) x , where the growth of x is assumed to be logistic , and r ( t ) and d ( t ) represent the time dependent per capita birth and death rates , respectively ., Let er and ed ( or Δr and Δd ) denote the baseline ( or elevation of ) birth and death rates of x , respectively ., Let Td be the decision time ., Then r ( t ) and d ( t ) can be written as: ( 1 ) r ( t ) = er+Δr , if |y*-y ( Td ) |< c and t> = Td , and r ( t ) = er otherwise; ( 2 ) d ( t ) = ed+Δd , if |y*-y ( Td ) |> = c and t> = Td , and d ( t ) = ed otherwise ., Here c defines a prescribed decision threshold ., Particularly , the fixation ( achieved at the end ) will be in favor of F1 if the distance between the value of y and y* at the decision time is close enough and is less than c , and F0 otherwise ., The proposed system of ordinary differential equations is analytically solvable ., However , only the part of the system ( i . e . x ) is observable ., Thus , the method of nonlinear least squares is not applicable to fit the model to the data ., So , we estimated the model parameters from data by employing the general profiling procedure proposed by Ramsay et al . 29 ., These parameter estimates along with estimated initial values of x component allowed us to solve the ordinary differential equations ., We assumed a

Introduction, Results, Discussion, Materials and Methods

Molecular epidemiological assessments , drug treatment optimization , and development of immunological interventions all depend on understanding pathogen adaptation and genetic variation , which differ for specific pathogens ., Mycobacterium tuberculosis is an exceptionally successful human pathogen , yet beyond knowledge that this bacterium has low overall genomic variation but acquires drug resistance mutations , little is known of the factors that drive its population genomic characteristics ., Here , we compared the genetic diversity of the bacteria that established infection to the bacterial populations obtained from infected tissues during murine M . tuberculosis pulmonary infection and human disseminated M . bovis BCG infection ., We found that new mutations accumulate during in vitro culture , but that in vivo , purifying selection against new mutations dominates , indicating that M . tuberculosis follows a dominant lineage model of evolution ., Comparing bacterial populations passaged in T cell-deficient and immunocompetent mice , we found that the presence of T cells is associated with an increase in the diversity of the M . tuberculosis genome ., Together , our findings put M . tuberculosis genetic evolution in a new perspective and clarify the impact of T cells on sequence diversity of M . tuberculosis .

Mycobacterium tuberculosis is amongst the most successful and enigmatic pathogens that has burdened humanity for thousands of years ., The success of this pathogen depends on unique strategies employed to adapt during infection ., Understanding these strategies is key to decipher the complexity of M . tuberculosis and crucial for epidemiological predictions and drug treatments ., However , little is known on the impact of transmission population bottlenecks and host immune pressures on sequence diversity of mycobacteria ., By combining deep sequencing and parallel evolution , we characterized the evolution of M . tuberculosis genetic diversity during the course of pulmonary infection in mice , and in disseminated M . bovis BCG infection in humans ., We found that under in vitro rich culture conditions , multiple adaptive mutations arise , but none of these generate lasting allele diversity in vivo ., We found that this phenomenon was not uniquely dictated by transmission bottleneck and that within host evolution contribute to select for a dominant genotype during M . tuberculosis infection ., Finally , we used a immuno-deficient mouse model to show that T cells are contributor rather than limitator of genetic diversity during infection ., Together , our results emphasize the complexity and uniqueness of M . tuberculosis and contribute understanding M . tuberculosis genetic adaptation .

blood cells, cell physiology, medicine and health sciences, immune cells, protein metabolism, immunology, population genetics, cell metabolism, mammalian genomics, population biology, bacteria, white blood cells, animal cells, t cells, actinobacteria, evolutionary genetics, animal genomics, biochemistry, cell biology, mycobacterium tuberculosis, genetics, biology and life sciences, cellular types, genomics, evolutionary biology, metabolism, organisms, mycobacterium bovis

journal.pcbi.1000288

Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing

Red blood cells are the major component of blood and with a radius of ∼4 µm and a thickness of ∼1–2 µm are sufficiently large that the effects of thermal fluctuations are typically negligible , i . e . their equilibrium diffusivity is very small ( where f is the viscous drag coefficient for a flat disk with radius 4 µm in water at room temperature 1 ) ., However , when suspensions of these soft cells are driven by pressure gradients and/or subject to shear , complex multi-particle interactions give rise to local concentration and velocity gradients which then drive fluctuating particle movements 2–4 ., Nearly all studies of whole blood to date focus on only the mean flow properties , with few notable exceptions 5 ., Since the rheology of suspensions in general is largely determined by the dynamically evolving microstructure of the suspended particles 6 , it is essential to measure both the dynamics of individual cells and the collective dynamics of cells in order to understand how the microscopic parameters and processes are related to larger scale phenomena such as jamming and clotting ., We complement the large body of work characterizing the flow of sheared and sedimenting rigid particulate suspensions 7–11 and here study the statistical dynamics of pressure-driven soft concentrated suspensions while making connections to human physiology and disease ., In particular , we provide quantitative evidence that there is heterogeneity in cellular velocity and density ., This heterogeneity may play a role in the slow flow or stasis that can lead to the collective physiological and pathological processes of coagulation or thrombosis , as Virchow noted more than 100 years ago 12 ., To investigate the short-time dynamics of flowing red blood cells we develop and use computational image processing 13 and machine learning algorithms to segment and track individual blood cells in videos captured at high spatial and temporal resolution in a microfluidic device ( Figures 1 and 2 and Videos S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 ) ., We measure individual cell trajectories comprised of more than 25 million steps across more than 500 , 000 video frames ., These measurements enable us to ask and answer questions about the variability of velocity fluctuations at the scale of individual normal and sickled red blood cells with variable shape and rigidity ., We quantify the effect of bulk flow velocity and density on the microscopic velocity fluctuations , and the role of collective behavior under pathological conditions which alter these properties ., We utilized microfluidic devices with cross-sectional area of 250 µm×12 µm , similar to the devices used to characterize the phase diagram for vaso-occlusion in an in vitro model of sickle cell disease 14 ., The 12 µm dimension of the microfluidic channels along one axis confines the cell movements in this direction; indeed the range of motion is already hydrodynamically limited by the Fahraeus effect 15 ., The primary advantage of this quasi-two-dimensional experimental geometry is the ability to visualize the cells easily , because any significant increase in the size of the channel in this direction would make the cell tracking impossible ., This small dimension changes the dynamics as compared to those of cells moving through large circular channels , owing to the effects of the relatively large shear rates in the narrow dimension and our inability to measure fluctuations along this axis , but our system nevertheless enables the characterization and measurement of the quasi-two-dimensional statistical dynamics of both normal and pathological blood flow with very high time and spatial resolution ., We chose a set of device and blood parameters relevant to human physiology and pathology in the microcirculation associated with capillaries and post-capillary venules ., We derived our quasi-two-dimensional data from the middle fifth of the 250 µm-high channel , where the narrow 12 µm thickness provides the only significant shearing direction , and this shear rate ( ∼10/sec ) is in the physiological range for the microcirculation 15 ., Figure 3a quantifies the planar fluctuations of individual blood cells in terms of the mean-squared displacement , 〈Δr2 ( τ ) 〉\u200a=\u200a〈 ( rbulk ( τ ) −rcell ( τ ) ) 2〉 where denotes a spatial average , and shows that 〈Δr2 ( τ ) 〉\u200a=\u200aDτ , with an effective diffusion constant D much larger than the equilibrium diffusivity ( ∼0 . 1 µm2/s ) ., ( See Videos S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 for examples of this diffusive behavior . ), Thus movement of a cell in relation to the bulk at one instant becomes rapidly decorrelated with its subsequent movement , except over very short times relative to the time of interaction between cells ., 〈Δr2 ( τ ) 〉 is roughly isotropic at shorter times , and then anisotropic at longer times with fluctuations parallel to the direction of flow 50% larger than perpendicular to it , a finding which is qualitatively consistent with observations of sheared and sedimenting rigid particulate suspensions 3 , 16 ., This diffusive behaviour is itself dynamical in its origin , being driven by the relative flow of fluid and cells and the boundary ., To understand this dependence , we also plotted in Figure 3b the evolution of the scaling exponent as a function of the bulk flow velocity ( Vbulk ) and red blood cell concentration for more than 700 different experiments with different blood samples ., We find that an increase in Vbulk from rest to about 50 µm/s is associated with a change in dynamics from stationary through sub-diffusive to diffusive ., However , over the pathophysiologically relevant range of densities studied ( 15%–45% ) there is no consistent effect on the nature of the statistical cell dynamics ., Figure 3b shows significant variation in this dynamical process , and only by combining measurements of a large number of cell trajectories are we able to see that the curve flattens with increasing Vbulk as α approaches 1 . 0 ., Further , in Figure 3c we show that 〈α〉∼1 . 0 , providing additional support for the conclusion that the typical flow is diffusive ., A diffusive process has a characteristic length scale λ corresponding to the mean free path that a cell travels before an interaction , and a characteristic time scale corresponding to the time between these interactions , typically given by the inverse of local shear rate , at the low Reynolds numbers typical of microvasculature flows in vivo as well as in our experiments ( where Re\u200a=\u200aO ( 0 . 01 ) ) ., Then the effective diffusivity scales as , where C is a dimensionless constant which will depend on microscopic properties such as cell shape and rigidity ., There are three length scales in the problem that can determine the effective diffusive length scale λ: cell size , cell separation , and cell distance from the boundary ., Different length scales will dominate in different limits of density , geometry , and cell size , as a cell will travel only a fraction of the inter-cellular distance before it interacts with another cell or a boundary ., In the unconfined limit where the boundary is infinitely far away , the only characteristic scale is the cell size so that , and ., This dilute limit has received the most attention to date 2 , 4 , but is far from the soft , dense , and confined suspensions we study ., The two remaining origins for this characteristic scale are:, ( i ) the distance between cells ( about 3 µm at a two-dimensional density of 33% ) which is comparable to and even smaller than the cell size;, ( ii ) the small height of our channel , 12 µm , which implies that the discoid red blood cells interact with the wall ., The cells are typically oriented with their discoid faces perpendicular to the smallest dimension of the channel ., The strong local shear ( , where 2h is the channel height ) relative to the wall leads to an effective diffusivity , where ., As has previously been shown 4 , 6 , 16 , 17 , a velocity gradient can lead to particle interactions and rearrangements in all three principal directions particularly when the shapes of the particles are non-spherical as here ., This is particularly true in our study because the particles ( cells ) are disc-like and deformable , so that the combination of shape anisotropy and the generation of normal forces via tangential interaction in soft contact can lead to diffusive motions in the measurement plane 18 ., In Figure 4a , we show this diffusive behaviour for Vbulk >∼50 µm/s ., The measured D≈8 µm2/s , and for λ ∼ 3 µm ., By sampling over times longer than , our measurements reach far enough into the asymptotic behavior of the dynamics to characterize this diffusive process ., Over shorter times , we expect a mixture of diffusive and ballistic dynamics , though this effect in our results is dominated by the fact that extremely small displacements are below our analytic sensitivity and appear as stasis ., In addition , cell velocities fluctuate because of the localized spatio-temporal fluctuations in shear rate , i . e . , ., ( See Videos S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 . ), These shear rate fluctuations could potentially also contribute to the effective diffusivity of the cells , but here we limit ourselves to the simplest mean field picture that ignores the fluctuations in the shear rate itself ., To assess the relative role of microscopic determinants such as cell shape and stiffness on this diffusive process , we investigated the behavior of blood cells from patients with sickle cell disease ., Red blood cells from these patients become stiff in deoxygenated environments as a result of the polymerization of a variant hemoglobin molecule 19 , resulting in a dramatic increase in the risk of sudden vaso-occlusive events with a poorly understood mechanism 20 ., In Figure 4b , we plot D versus Vbulk for oxygenated and deoxygenated sickle cell blood and see that for a given bulk flow rate , the stiffer cells have a smaller diffusivity ., Since , our results therefore imply that Cdeoxygenated<Coxygenated , i . e . , the stiffness of the cells influences the dynamics of a pressure-driven suspension independent of Vbulk , likely due to changes in the nature of the interactions of cells with each other , with the channel walls , or with the plasma velocity gradients ., The tangential and normal forces between two fluid-lubricated soft moving objects is a complex function of shape , separation , stiffness , relative velocity , and fluid viscosity ., Tangential interactions between soft cells lead to normal forces that push the cells away from each other , thus reducing the friction between them 18 ., Since the effective diffusion coefficient of this driven system is inversely proportional to the frictional drag , we expect the diffusion coefficient for the stiffer cells to be smaller than that for soft oxygenated cells when the flow velocity is held constant , as is observed ., Hydrodynamic interactions between red blood cells lead to velocity fluctuations and diffusive dynamics of the individual cells ., Changes in Vbulk or cellular stiffness alter D and therefore control the magnitude of velocity fluctuations ., Cellular velocity fluctuations are quantified by their mean square , , which may be interpreted in the language of the statistical physics of driven suspensions 16 , 21 as an effective suspension temperature ., Just as thermal temperature reflects the mean squared molecular velocity fluctuation , the suspension temperature reflects the mean squared cellular velocity fluctuation ., This temperature will then change with Vbulk as well as with particle stiffness ., Slower flows will have lower effective suspension temperature , as will flows of stiffer particles ., In Figure 5 , we show the measured probability distribution of δV2 for two different flow experiments and see that it has longer tails than an equilibrium Maxwell-Boltzmann distribution owing to the non-equilibrium nature of the system , consistent with observations in physical suspensions 3 , 10 ., We may nevertheless use the crude analogy of an effective temperature to characterize “hot” blood flow which has increased 〈δV2〉 and is also less likely to coagulate or “freeze” than is a “cold” blood flow where cells are not fluctuating and local stasis is more likely to arise and to persist ., Virchows Triad characterizes the conditions leading to thrombosis as stasis , endothelial dysfunction , and hypercoagulability 12 and our results offer one possible explanation for why pathological blood with stiffer cells and smaller cellular velocity fluctuations will occlude at flow rates where normal blood will not ., In conclusion , we have identified random walk-like behavior for pressure-driven dense suspensions of soft particles in quasi-two-dimensional confinement which we quantify in terms of cellular velocity fluctuations as a function of blood flow rate , shape , and stiffness ., Our results suggest that these fluctuations may be involved in the collective pathophysiological processes of occlusion and thrombosis , both of which are strongly heterogeneous in space and time ., While simple scaling ideas are suggestive , a well-defined microscopic mechanism for this process remains to be established ., This study was conducted according to the principles expressed in the Declaration of Helsinki ., The study was approved by the Institutional Review Board of Partners Healthcare Systems ( 2006-P-000066 ) ., All patients provided written informed consent for the collection of samples and subsequent analysis ., Videos were captured of blood flowing in microfluidic devices under controlled oxygen concentration ., Microfluidic fabrication and blood sample collection and handling are described in detail elsewhere 14 ., Blood flowed through channels with cross-sectional dimension of 250×12 µm and was driven by a constant pressure head ., A juxtaposted network of gas channels allowed control over the oxygen concentration within the blood channel network ., Blood samples were collected in EDTA vacutainers and had hematocrit ranging from 18% to 38% ., By changing oxygen concentration in situ , we were able to compare the oxygenated and deoxygenated behavior of the same sample and largely control for any differential contributions of the plasma ., Videos were captured at a rate of 60 frames per second , with a resolution of about 6 pixels per micron ., ( See Videos S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 for examples . ), We note that the rapid rate of deoxygenation in our studies results in little change in shape for most cells , consistent with existing understanding of heterogeneous hemoglobin polymerization , while the magnitude of the change in stiffness is expected to be more independent of deoxygenation rate 19 , 22 ., We developed morphological image processing algorithms to identify a significant fraction of the cells in captured frames of video ., See Figure 2 for examples of the segmentation approach ., All software was written in MATLAB ( The MathWorks , Natick , Mass . ) ., These algorithms implement marker-controlled watershed segmentation , described in detail in reference 13 ., Marker images were computed by identifying annular and filled cells of heuristically-determined sizes and shapes ., Annular cells were defined as fillable holes not touching the border ., Markers for these annuli were created by subtracting border-contacting high-intensity regions and performing morphologic reconstruction on the result ., This reconstruction operation used a marker image that was morphologically opened with a 5 µm line segment oriented in increments of 45 degrees ., The reconstruction was then subtracted from the border-cleared image ., The final result was dilated using a disk with radius 0 . 2 µm ., Filled cells were defined using granulometry with a circular structuring element of radius 2 µm ., Markers for these cells were selected using two transformations of this opened image: the distance transformation of the thresholded binary image followed by the h-maxima transformation with a height of 3 ., Background pixels were identified by the skeletonization of a thresholded binary image ., Previously determined cell markers were added to the binary image ., The result was eroded using a disk with radius 0 . 5 µm ., The skeletonization of this erosion was the background marker image ., Foreground and background markers were used to impose minima on the intensity gradient of the original image after background subtraction and histogram equalization ., The watershed transformation was then applied to the gradient of the intensity image ., The watershed catchment basins , or blobs , were then filtered heuristically by size , shape , and orientation of the objects convex hulls ., First-pass thresholds were determined empirically by manually segmenting several video frames in Adobe Photoshop ., Initial size limits were total convex hull area between 5 and 50 µm2 ., A measure of convex hull circularity was calculated by comparing the effective radius based on the object area to the effective radius based on the objects perimeter ., A circle has a ratio of 1 ., All other objects have ratios less than 1 ., The initial circularity threshold was set at 0 . 6 ., After an initial filtering process , video frames were re-filtered using thresholds for all morphologic characteristics based on the mean convex hull metrics with allowed variation of twice the standard deviation ., We then developed machine learning algorithms to track these segmented cells from frame to frame and to compute velocities for individual cells ., For each object segmented in each video frame , potential “child” cells were iteratively identified in the subsequent frame and ranked by changes in size , shape , and displacement ., Child cells were reassigned if a better “parent” cell was identified ., Maximum changes in x- and y-displacement were calculated based on apparent flow rates ., Y displacement was limited to 600 µm/s in either direction , and x displacement was limited to 1200 µm/s ., Maximum changes in area , perimeter length , and eccentricity were determined by manual tracking of several video frames in Adobe Photoshop as part of a validation check on the tracking algorithm ., Area was initially allowed to vary by 50% , perimeter by 50% , and eccentricity by 60% ., After all cells in a frame were tracked or determined to be un-trackable , the median inter-frame displacement was computed for all tracked objects ., Any tracking events representing displacements that were five times greater than the maximum of the median or the analytic sensitivity threshold ( 1 µm ) were excluded , and the whole frame was retracked with this tighter displacement threshold ., Tracking events which represented the extension of existing trajectories were rejected if they represented a change in cell velocity greater than twice the maximum of the median frame displacement or an analytic sensitivity threshold ., After excluding these inconsistent tracking events , the whole video frame was retracked iteratively until no trajectory extensions exceeded this threshold ., Our measured cell velocities were based on more than 25 million displacements calculated across more than 500 , 000 video frames ., We improved and measured the accuracy of our cell velocity measurements a number of different ways , including manual segmentation by an observer of selected video frames and manual tracking by an observer of selected of cells from frame to frame ., Inaccuracies in cell velocity measurements can be separated into two categories: errors in the location of a cell , and errors in the assignment of a tracking event for two identified cells ., We took a series of steps to reduce the magnitude and bias of this noise and to ensure that it does not influence our results ., We measured projected cell density first by thresholding grayscale intensity images using the MATLAB graythresh function ., We then combined this thresholded image with the foreground cell markers calculated by our segmentation algorithm ., Under steady state conditions , we would expect this density calculation to be relatively stable ., Previous studies have reported a coefficient of variation for hematocrit of 3% due to biological variation , and another 3% due to analytic variation achieved with commonly used automated hematologic analyzers 23 ., These automated analyzers work with typical volumes of ( 20 , 000 cells*1/0 . 4 total volume/cell volume*80 µm3 cell volume/cell\u200a=\u200a4×106 µm3 ) , which is about 100 times larger than the volume projected in a typical video frame ., The relationship between an actual three-dimensional volumetric density and a projected two-dimensional density depends on the orientation of the red blood cells and the depth of the flow chamber in the direction of the projection ., Under steady state conditions , our density measure is stable over time with a coefficient of variation typically between 10% and 25% .

Introduction, Results, Discussion, Methods

Blood is a dense suspension of soft non-Brownian cells of unique importance ., Physiological blood flow involves complex interactions of blood cells with each other and with the environment due to the combined effects of varying cell concentration , cell morphology , cell rheology , and confinement ., We analyze these interactions using computational morphological image analysis and machine learning algorithms to quantify the non-equilibrium fluctuations of cellular velocities in a minimal , quasi-two-dimensional microfluidic setting that enables high-resolution spatio-temporal measurements of blood cell flow ., In particular , we measure the effective hydrodynamic diffusivity of blood cells and analyze its relationship to macroscopic properties such as bulk flow velocity and density ., We also use the effective suspension temperature to distinguish the flow of normal red blood cells and pathological sickled red blood cells and suggest that this temperature may help to characterize the propensity for stasis in Virchows Triad of blood clotting and thrombosis .

Viewed from a distance , flowing blood looks like a uniform fluid , but up close the cells in the blood change their position and speed somewhat heterogeneously ., These individual cell movements may play a role in the physiology and pathophysiology of nutrient and gas transport , clotting , and diseases where normal processes go wrong ., To characterize these random motions , we need to follow individual cells in a very crowded suspension—cells usually occupy more than one-third of the volume in blood ., We have developed computer software that can separate individual cells in a crowd and track them as they flow ., We use this software to analyze blood flow at the level of the cell and find new and possibly important differences between the blood from healthy patients and the blood from patients with sickle cell disease , a disorder in which blood cells become stiff and often stop flowing ., We provide evidence that blood from patients with sickle cell disease shows decreased random cellular motions and suggest that this difference may provide a physical basis for the increased risk of occlusion in sickle cell disease .

computer science, hematology/hemoglobinopathies, pathology/pathophysiology, physics/condensed matter, physiology/cardiovascular physiology and circulation, physics/fluids, plasmas, and electric discharges, physiology/integrative physiology, biotechnology/bioengineering, mathematics/statistics, biophysics, computational biology/systems biology, radiology and medical imaging

journal.pcbi.1005073

Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes

Functional genomic approaches ( based on microarrays and next-generation sequencing ) provide a powerful means to decipher the molecular mechanisms underlying the control of development and cell differentiation , as well as deregulations thereof associated with diseases such as cancer ., Together with low-throughput experimental data , these high-throughput methods enable the delineation of large and sophisticate regulatory networks ., Understanding and predicting the behaviour of such complex networks require the use of proper mathematical modelling frameworks ., Various dynamical models have been proposed for a handful of relatively well known developmental processes , many using differential equations and referring to Drosophila development ( see e . g . 1–5 and references therein ) ., However , these modelling studies consider relatively limited numbers of regulatory components ( at most a dozen ) and require the quantitative determination of poorly documented parameters ., In this context , formal qualitative modelling approaches constitute an interesting alternative , at least as a first step towards more quantitative modelling ., In particular , logical ( Boolean or multilevel ) modelling has been applied to various regulatory and signalling networks of increasing sizes over the past decade ( see e . g . 6–19 and references therein ) ., But only few attempts were made to predict novel phenotypes , and therefore the full predictive value of the network and its usefulness to test hypotheses regarding novel genetic perturbations remain unclear ., Here , we set out to decipher the network controlling the specification of mesoderm , one of the three germ-layers , into its four main derivatives , namely visceral muscle , heart , somatic muscle , and fat body , the primordia of which are iterated in segmentally repeated units along the anterior-posterior axis of the Drosophila embryo ( Fig 1 ) ., Mesoderm specification is induced by ectodermal signals such as Decapentaplegic ( Dpp ) , which controls dorsal-ventral differentiation 20–25 , Wingless ( Wg ) , which is essential for dorsally located heart cell precursors and to the majority of somatic muscles that develop from more ventrally located cells 26–28 , and Hedgehog ( Hh ) , which specifies the visceral mesoderm dorsally and the fat body ventrally , itself characterised by the expression of Serpent ( Srp ) 29–31 ., During embryonic stages 8–10 , the mesoderm is thereby progressively specified into four different tissue primordia , each of which is characterised by the expression of specific lineage transcription factors ( Fig 2 ) 27 , 32 ., Collating all phenotypic data from the literature into a mathematical model allows to formally assess the coherence between the current view of the network with individual published results on single or multiple mutant phenotypes ., More specifically , we aim to further characterise the crucial regulatory components and interactions driving mesoderm specification ., As we mostly rely on published qualitative molecular and genetic data , we use a flexible logical modelling framework and the software GINsim ( cf . Material and methods ) , which enables the use of multilevel variables whenever justified , along with fully asynchronous updating ., Systematic simulations of the resulting logical model were then performed to, ( i ) assess the coherence and comprehensiveness of our representation of the underlying network ,, ( ii ) identify gaps in the current understanding and characterisation of mesoderm specification , and, ( iii ) ultimately predict phenotypic outcomes of novel genetic perturbations ., We demonstrate that the resulting logical model can recapitulate all known mutant phenotypes , therefore indicating that this formal representation of the network is sufficient and coherent to explain mesoderm cell fate decisions ., By running simulations on over 300 genetic mutation combinations ( many of which are double mutants with non-intuitive outcomes ) , the model could predict the phenotypic outcome for each novel mutant background , at least in terms of gene expression patterns , thereby providing new testable hypothesis that we experimentally confirmed ., This approach thus provides developmental biologists with a very useful tool kit to test novel hypotheses , which are often very difficult to carry out experimentally ., Moreover , the model provides novel insights into the underlying regulatory network driving these cell fate decisions ., To initiate this study , we performed an extensive analysis of all reported genetic and molecular data in the literature to identify the main regulatory components involved in Drosophila mesoderm specification , along with the known interactions between them ., Indeed , dozens of articles extensively cover the genetic bases of the sub-division of Drosophila mesoderm ( this is evident by the bibliographical entries linked to key regulatory components in the model file and model documentation provided as S1 Text ) ., Cis-regulatory information is sometimes available , enabling us to infer direct interactions and epistatic relations ., In particular , we relied on recent ChIP data reporting the in vivo occupancy of six key mesoderm transcription factors ( Bagpipe ( Bap ) , Biniou ( Bin ) , Dorsocross 1 , 2 and 3 ( Doc ) , Myocyte Enhancer Factor 2 ( Mef2 ) , Tinman ( Tin ) and Twist ( Twi ) ) 33–35 to assess direct interactions inferred from genetic experiments ., Encoded using the software GINsim ( Computational and experimental procedures ) , the resulting regulatory graph ( Fig 3 ) is provided in a computer readable format , along with extensive annotations ( text and links to relevant literature and database entries , see S1 File ) ., This regulatory graph encompasses 48 nodes ( including 12 input components , representing mainly ectodermal signals ) and 82 regulatory interactions ., In many cases , the definition of the logical rule associated with each node is straightforward ( e . g . when a node is the target of a unique regulator ) ., However , for more complex regulatory relationships , i . e . when multiple interactions converge on the same component , we examine the following scenarios:, ( i ) Is the presence of an inhibitor sufficient to completely or partially block gene expression ?, ( ii ) Which activator ( s ) is ( are ) sufficient to drive the expression of the target gene ?, ( iii ) Can the activators do so in the presence of repressor ( s ) ?, After several iterations , we obtained a set of logical rules consistent with all available knowledge on the regulation of each gene in the network , which further enabled the recapitulation of all published phenotypes ( see below ) , demonstrating the robustness of the model ., Before attempting to simulate the specification of the mesoderm into its four main presumptive tissues ( visceral muscle ( VM ) , heart ( H ) , somatic muscle ( SM ) , and the fat body ( FB ) , we needed to specify the patterns of gene expression expected as a result of wild type development ., Based on published data ( mainly in-situ hybridization or immunostaining assays ) , we have derived the qualitative levels of expression of the 48 network components in each of the four presumptive territories ( VM , H , SM and FB ) from the literature ( S1 Fig ) ., Only subsets of these components are crucial in the specification of each of the four tissue subtypes ., These tissue markers can be readily identified based on the phenotypes reported in loss-of-function mutant embryos , leading to severe defects in tissue formation , or following ectopic expression , often leading to specific tissue expansion ., Embryos lacking Tin , Bap or Bin , for example , do not develop VM ., Moreover , tin mutant embryos fail to develop H cells , and have severe defects in all tissues derived from the dorsal mesoderm 32 , 36 , 37 ., Overall , ten network components play such dramatic roles in specific tissues ( emphasised by bold contours for the corresponding coloured cells in the S1 Fig ) ., Note that the ventral mesoderm territory that gives rise to both SM and FB is subdivided into regions that have low ( yet significant , hence the use of the value 1 ) and high Twi expression ., Indeed , the inhibition of Notch ( N ) combined with the presence of Wg and Daughterless ( DA ) activates Twi at a higher level ( maximal level , i . e . value 2 ) , thereby delimiting a region of high Twi expression 38 , 39 ., We systematically searched for relevant information and refined the logical rules until model behaviour was found fully consistent with all published data ., To ease simulations , our regulatory graph was reduced by hiding intermediate signalling components ( components in grey in Fig 3 , see also Material and methods ) ., Provided that we do not delete any regulatory circuit , the resulting reduced model preserves the stable states of the system , which represent the different specification states ( i . e . mesoderm derivatives for wild-type or mutant situations ) ., To perform simulations , the initial values for each component must be specified , in particular for the signalling input components coming from the ectoderm ., For each of the four presumptive tissue territories , we thus have a specific input combination ( S1 Fig , left ) ., For the sake of simplicity , we set all internal nodes to zero for each wild-type initial state , with the notable exception of Twi , which was set to the value 1 ., For each territory , the target values at stage 10 were evaluated based on published data ( S1 Fig , right ) ., For example , in the region that will form VM , the initial state ( stage 8 ) is characterised by the presence of Twi , which activates Tin 40 and Mef2 41 expression ., Bap is then activated by Tin at stage 9 37 , which is followed by the activation of Bin by Bap 42 , 43 in late stage 9 embryos ., Finally ( stage 10 ) , Bap is activated at its maximum level ( value 3 ) by Cubitus Interruptus ( Ci ) and Engrailed ( En ) 27 ., To recapitulate the formation of each mesodermal tissue derivative in the wild-type situation , we thus ran four different simulations using an asynchronous updating policy ( Material and methods ) ., A detailed comparison of simulation results with experimental data led us to refine the logical rules , and sometimes even to consider additional regulators , until we converged on the regulatory graph shown in Fig 3 , along with the rules listed in S1 Table ( see also the S2 Text for more information about the delineation of the logical rules associated with Tin and Bap ) ., Our final model qualitatively recapitulates all aspects of the major events in the specification of the four main domains of the mesoderm , from stage 8 to stage 10 ( S2 Fig ) ., In parallel , we also simulated the effects of genetic perturbations reported in the literature , the results of which led to some model adjustments ., Iterating this procedure for all known mesodermal mutants led to a model that is robust and consistent with all relevant published data ., The simulated phenotypes resulting from seven selected genetic perturbations are illustrated in Fig, 4 . For example , the simulation of a wg loss-of- function ( lof ) gives rise to a loss of cardiac tissue , as observed experimentally 27 , 44 , while dpp lof gives rise to an extension of FB at the expenses of VM , mirroring previously reported experimental data 30 , 32 ., We can also simulate more complex genetic backgrounds ., For example , a double gof of dpp and hh combined with a lof of wg leads to an expansion of VM in the entire mesoderm 27 ., To date , our simulations recapitulate all mutants reported in the literature ., Although expected , since this literature information was used as input to generate the model , these results demonstrate the coherence of the model , which was based on disjoint information generated from published studies from different labs , mostly based on single mutant phenotypes , with only a small number of documented multiple genetic perturbations ., A study focused on heart or VM development , for example , often will not have examined markers for FB , yet the model can simulate the phenotypes in all four mesodermal domains ., Given the accuracy of our model to recapitulate all known published phenotypes , we reasoned that the model provides a very useful tool to perform systematic novel in silico perturbations at large scale ., In fact , some of the results obtained with the simulations described above already correspond to new predictions , as biologists typically check only subsets of markers for each mutant studied ( see in particular the tissue domains shown in yellow in Fig 4 , which correspond to situations that have not been fully analysed experimentally , and for which we obtain combinations of markers associated with different tissues ) ., Nevertheless , our aim here is to go beyond this and perform a more systematic assessment of the effects of combinations of two perturbations affecting different pathways and/or tissue markers ., Single and multiple mutants can be readily defined using GINsim ( cf . Material and methods ) , while they can often be very difficult , and sometimes impossible , to generate experimentally ., To this end , we simulated the effects of single or double perturbations in each of the four tissue domains ., The interpretation of the results generated is not trivial ., To assess these results more efficiently , we used the expression of the key lineage transcription factors for each tissue as defining signature: for VM , expression of Tin ( level 1 ) , Bap ( level 2 ) , and Bin; for H , expression of Tin ( level 2 ) , Doc , and Pannier ( Pnr ) ; for FB , expression of Srp; and for SM , expression of Twi ( level 2 ) , Pox meso ( Poxm ) , DSix4 and Zfh1 ., These tissue signatures were then matched against the stable states reached during model simulations , thereby automating the interpretation of the resulting phenotypes ., Practical considerations ( mutant strain availability ) led us to consider fourteen components ( Twi , Tin , Bap , Bin , Ci , Doc , Pan , Pnr , Slp , Srp , Mef2 , Mad , Med , Nicd ) for systematic single and pairwise combinations of loss- and gain-of function in silico perturbations ., The results of the 338 mutant simulations performed are displayed in a matrix form ( Fig 5 ) and can be browsed in a convenient searchable web archive ( S2 File ) ., This format enables an easy comparison of the effects of different perturbations , which facilitates the detection of dominant or synergic effects of different perturbations ., For example , slp lof generally shows a loss of H tissue , a result similar to that obtained for wg lof 26 , 27 ., Although some of these mutants have been partly documented experimentally , most of the double perturbations listed in Fig 5 have not been fully experimentally assessed in all four-tissue domains ., To demonstrate the usefulness and accuracy of these predictions , we experimentally tested six genetic perturbations ( two double mutants and the associated four single mutants ) , examining the effects within all four tissue domains ( Fig 6 and S3 Fig ) ., Model predictions for each of these mutants are highlighted in Fig, 5 . To examine the phenotype of each tissue , Tin , Srp , Glycogen phosphorylase ( GlyP ) and Bin were used as markers for the development of H , FB , SM and VM , respectively ., We first assessed our predictions for lof mutants of Medea ( Med ) and Sloppy-paired ( Slp ) , and the double mutant ., Medea is directly required for the induction of tinman ( Tin ) by Dpp via the tin-D dorsal mesoderm enhancer 23 ., Once expressed , Tin and Med have a direct protein-protein interaction that is required for dorsal mesoderm specification 28 ., As the heart specification requires both activation by Med-Tin and repression of the VM within the heart domain by Slp , we were interested to examine if loss of Med and Slp would completely abolished cardiac mesoderm specification and be sufficient to extend the VM territory ., For Med lof , our model simulations predict a loss of H , which is indeed what we observed experimentally ( Fig 6A ) ., Although the expression status of some genes within the VM region is changed in the mutant , the VM develops largely unperturbed , as predicted ., The simulation of slp lof also results in a loss of H , and two stable states within the SM , one leading to normal SM development , while the other state lacks some marker expression , and therefore should perturb SM development ., When we examine slp lof mutant experimentally , we observe the predicted loss of H , while SM appears largely normal , indicating that the corresponding stable state is the correct outcome ., Simulations of the double lof mutant give the combined phenotype of the two single mutants , which again qualitatively fits with experimental data , with the H even more severely affected in the double mutant ( Fig 6A , in situ for tin expression ) ., In contrast , the VM develops largely unperturbed , indicating that loss of heart , even the severe disruption seen in the double mutant , is not sufficient to lead to expansion of VM , in this genetic background ., We next tested a combination of two gof conditions , where it is not a priori obvious what the phenotypic consequence would be within the FB or SM domains ., Slp is normally expressed in the H region , where it inhibits VM development through the direct inhibition of Bap expression 32 , 45 ., Doc is expressed within the Heart domain ( segmentally repeated patches of cells within the dorsal mesoderm ) at stage 10 , where it is essential for heart development 46 , 47 ., For a gof of Doc , our simulations predicts normal H development , with minor perturbations of VM , FB and SM ., Our experimental results largely confirm these predictions , with very minor perturbations on the development of each tissue ( based on the expression of the corresponding tissue markers ) , despite the ubiquitous expression of Doc ( Fig 6B ) ., The simulation of a gof of Slp predicts a severe perturbation of VM ( yellow cell ) , characterised by the lack of expression of the key cell markers Bap ( level 1 instead of 3 ) and Bin , as expected 32 , 45 , along with a potential perturbation of FB ( obtention of two stable states , both with Srp expression ) ., When the two gof genotypes are combined , our model predicts normal H and SM specification , but a loss of VM and FB , which is exactly what we observe experimentally , as seen in the in situ shown in Fig 6B ., These results therefore demonstrate that our qualitative model can correctly predict the interaction between two gof causing a severe loss of FB ., The expansion of heart cells can be further explained by the ectopic expression of heart markers in our simulations ., The logical model presented here integrates all major genetic processes underlying the formation of four tissues during Drosophila mesoderm specification ., The model is based on the integration of extensive analysis of in vivo experimental data , especially genetic data ( patterns of gene expression and mutant phenotypes ) , partly confirmed by functional genomic data ( ChIP data for transcription factor occupancy ) ., These data were translated mathematically in terms of a regulatory graph and logical rules ., The simulation of our model qualitatively recapitulates the expression of the main lineage markers of each region from developmental stage 8 to 10 , for the wild type case , as well as for over twenty reported mutant genotypes ., This study is the first attempt to model the regulatory network controlling the specification of mesoderm during Drosophila development , and more broadly represents one of the most comprehensive developmental networks that have been modelled to date ., Mesoderm specification has been extensively studied in many species , including the sea urchin 48 ., Recently , the Davidson group developed a Boolean model that recapitulates the specification of the sea urchin endo-mesoderm in the wild-type case , as well as experimental data for three genetic perturbations 49 ., The approach of Davidsons group converge with ours in the delineation of a reference network with reliable annotations , which then serve as a scaffold to define logical rules and perform simulations ., Both approaches implement the crucial components and interactions , along with the dynamical unfolding of the corresponding developmental network in an intuitive manner ., Importantly , we demonstrate that we can not only recapitulate the known mutant phenotypes , but also predict various novel phenotypes ., In the case of our study , several regulatory mechanisms were simplified , in particular regarding the signalling pathways involved ., We have developed more complete models of most Drosophila signalling pathways 50 , but we retained simpler implementations of these pathways to keep our mesoderm specification model computationally tractable ., A limitation of this study resides in the poor documentation of specific markers associated with each type of embryonic domain ., In particular , our marker set is limited to Srp in the case of FB ., Presumably , others regulatory factors must be implicated in the specification of this tissue , which remain to be discovered ., This lack of information complicates the interpretation of mutant phenotypes ., For example , it is known that Bap lof leads to the loss of VM , but we miss information about effects on other tissues ., Although Bap is crucial for VM development , it is also expressed at later stages in H . At this point , we assume that H , SM and FB develop normally in Bap lof mutant , as no other experimental defect has been reported ., Finally , Boolean models of embryonic processes generally rely on qualitative expression data from in-situ hybridisation ., Our discrete model ( as the sea urchin model 49 ) is therefore limited to qualitative results , such as the presence or absence of a given tissue in a given presumptive territory ., Although we cannot reproduce quantitative data , such as an increase or a decrease of specific cell numbers , we can still recapitulate the presence of different cell types ., Our logical model could further serve as a scaffold to build more quantitative models when more quantitative and systematic experimental datasets will become available ., For now , the advantage of logical modelling is that models can be easily abstracted at a level subsuming missing data , which is less straightforward for more quantitative modelling frameworks , such as differential or stochastic equations ., Given the complexity of embryonic development , the shear number of parameters involved and the high inter-connected nature of regulatory networks , logical modelling offers an accurate solution that can be applied to many systems with the amount of data that is available today ., We use the multilevel logical formalism , originally proposed by René Thomas 51 , which has already been used to model various networks involved in the control of cell differentiation or proliferation ( see e . g . 6 , 8 , 9 , 11 , 12 , 19 ., In short , both the structure of a logical model and its dynamics are represented in terms of graphs ( in the sense of the graph theory ) , called regulatory graphs and state transition graphs , which are briefly described hereafter ., In a regulatory graph , the vertices ( or nodes ) represent regulatory genes or products ( transcription factors , kinases , etc . ) ., In many cases , these regulatory components can be satisfactorily represented by Boolean variables , which can take only two values , 0 or 1 , corresponding to the absence or presence of the component , respectively ., However , in some situations ( e . g . the consideration of a morphogen ) , more qualitatively different levels may be required ., The arcs ( or arrows ) connecting pairs of vertices represent regulatory interactions between components ( e . g . transcriptional activations or inhibitions , phosphorylation , etc . ) ., These arcs are usually associated with a plus ( + ) or minus ( - ) sign , denoting an activation or inhibition effect of the source node onto the target node , respectively ., When the source of an arc is associated with a multilevel variable , a threshold ( i . e . minimal level ) must be specified ., To complete this model description , logical rules ( or logical parameters ) are further defined to indicate how each component reacts to different combinations of regulatory interactions ( S2 Text and S1 Table ) ., The simulation of a logical model can be represented by a state transition graph ( STG ) , whose vertices represent logical states ( i . e . a vector encompassing values for all components ) , whereas arcs represent transitions between states enabled by the corresponding regulatory graph and logical rules ., In this work , we use an asynchronous updating mode , meaning that we consider all possible unitary transitions ( affecting only one variable at a time ) whenever there is a call to change some component value ( s ) at a given state ., One recurrent problem with logical simulations ( in particular when using asynchronous updating ) is the potential combinatory explosion of the STG when dealing with large regulatory graphs ., Consequently , it is often difficult to generate and analyse the STG for complex networks encompassing several dozens of components ., However , using proper algorithms and software tools ( see below ) , it is possible to characterise the asymptotical behaviour of the systems , which is of special interest for us here ., Indeed , attractors , especially stable states ( states with no successor ) , are usually associated with specific differentiated states ., Logical models provide a realistic description of cellular events , as they are capable of reproducing time dependent processes in a qualitative manner ( i . e . focusing on the sequential order of transitions ) ., The software GINsim ( for Gene Interaction Network simulation ) implements the logical formalism 52 ., It allows the edition , analysis and simulation of regulatory graphs ., Freely available ( http://ginsim . org ) , GINsim supports the annotation of components and interactions with free text and URLs ., Once a model is defined , the user can select a simulation mode and define a set of initial states ., GINsim can then be used to compute state transition graphs and report the stable states ., GINsim also enables the definition and the simulation of different types of mutants ( loss-of-function , ectopic gene expression , and combinations thereof ) by blocking the levels of expression of the corresponding variables in defined intervals ., To further ease the analysis of multiple perturbations , we have written a set of scripts in python , which iteratively compute the behaviour of our mesoderm specification model for each region and mutant considered , process the results and generate a synthetic web page ( cf . Results and S2 File ) ., To enable the dynamical analysis of comprehensive regulatory graphs , we take advantage of a novel reduction method implemented in GINsim ., This functionality allows the user to select components of a regulatory graph to be made implicit ., The software verifies that the proposed reduction does not fundamentally change the network topology ( elimination of regulatory circuits ) and update the logical rules for the components targeted by reduced nodes ., The original and reduced networks have the same stable states ( in terms of levels of common variables ) , while differences may appear as to their reachability 53 ., The following Drosophila lines were used: UAS-Slp and UAS-Doc lines were kindly provided by M . Frasch ( Doc line C2 46 ) ., We crossed both stocks with a marked double balancer to generate the homozygous stock x/y;UAS-Doc;UAS-Slp ., Males from the UAS-Doc , UAS-Slp and UAS-Doc;UAS-Slp lines were crossed with females carrying a homozygous twist-GAL4 driver , kindly provided by Maria Leptin ., Slp1 and Med1e loss-of-function mutations were obtained from the Bloomington stock centre ( stock numbers 5349 and 9033 ) , and crossed together to make the double loss-of-function stock , which were placed over lacZ-marked balancers ., Embryos were collected using standard procedures ., Fluorescent in situ hybridisation was performed as described previously 54 ., The following ESTs were used to generate anti-sense probes: RE01329 ( tin ) , SD07261 ( srp ) , and LD24485 ( Glyp ) , while a full length cDNA was used for bin ( gift from M . Frasch ) and lacZ ., The probes were detected with peroxidase-conjugated antibodies ( Roche ) and developed using the TSA system ( Perkin Elmer ) ., slp and Med mutant embryos were unambiguously identified based on the absence of lacZ expression from the balancer chromosome .

Introduction, Results, Discussion, Materials and Methods

Given the complexity of developmental networks , it is often difficult to predict the effect of genetic perturbations , even within coding genes ., Regulatory factors generally have pleiotropic effects , exhibit partially redundant roles , and regulate highly interconnected pathways with ample cross-talk ., Here , we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development , thereby providing a formal integration of all available genetic information from the literature ., The four main tissues derived from mesoderm correspond to alternative stable states ., We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new , often non-intuitive , loss- and gain-of-function mutations , and combinations thereof ., We further validated several novel predictions experimentally , thereby demonstrating the robustness of model ., Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions .

We delineate a logical model encompassing 48 components and 82 regulatory interactions controlling mesoderm specification during Drosophila development , thereby integrating all major genetic processes underlying the formation of four mesodermal tissues ., The model is based on in vivo genetic data , partly confirmed by functional genomic data ., Model simulations qualitatively recapitulate the expression of the main lineage markers of each mesodermal derivative , from developmental stage 8 to 10 , for the wild type case , as well as for over twenty reported mutant genotypes ., We further use this model to systematically predict the effects of over 300 loss- and gain-of-function mutations , and combinations thereof ., By generating specific mutant combinations , we validated several novel predictions experimentally demonstrating the robustness of model ., This modelling study is the first to tackle the regulatory network controlling the specification of mesoderm during Drosophila development , and more broadly deals with one of the most comprehensive developmental networks that have been modelled to date .

infographics, invertebrates, gene regulation, animals, simulation and modeling, animal models, developmental biology, drosophila melanogaster, model organisms, embryos, drosophila, research and analysis methods, embryology, computer and information sciences, gene expression, mesoderm, insects, arthropoda, data visualization, phenotypes, graphs, genetics, biology and life sciences, organisms

journal.pcbi.1004471

An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition

Many complex behaviors—human speech , playing a piano , or birdsong—consist of a set of discrete actions that can be flexibly organized into variable sequences 1–3 ., A feature of many variably sequenced behaviors is the occurrence of repetitive sub-sequences of the same action ., Examples include trills in music , repeated syllables in birdsong , and syllable/sound repetitions in stuttered speech ., A central issue in understanding how nervous systems generate complex sequences is the role of sensory feedback versus internal motor programs 4 ( Fig 1a ) ., At one extreme ( the serial chaining framework ) , the sensory feedback from one action initiates the next action in the sequence; therefore sensory feedback is critical for sequencing the actions 5 , 6 ., However , because of the delays in both motor and sensory processing in nervous systems , it has been argued that a sequence generation mechanism relying solely on sensory feedback would be too slow to account for the execution of fast sequences such as typing and speech 1 ., At the other extreme , sequences are generated by internal motor programs controlling sequence production without the use of sensory feedback 7–9 ., However , there is ample evidence that sensory feedback can affect action sequences 10–14 ., Despite the ubiquity of sequencing in behavior , the neural mechanisms of how sensory feedback interacts with internal motor programs to influence discrete actions remain largely unexplored ., Here , we study the role of sensory feedback in the production of repetitive vocal sequences using the Bengalese finch as a model system ., The Bengalese finch produces songs composed of discrete acoustic events , termed syllables , organized into variable sequences ( Fig 1b ) ., However , sequence production is not random 15 , as the transition probabilities between syllables are statistically reproducible across time 13 , 16 ., A prominent feature of the songs of several songbird species , including the Bengalese finch , is syllable repetition 15 , 17–21 ( e . g . ‘b’ in Fig 1b ) ., For a given repeated syllable , the number of consecutively produced repeats ( the repeat number ) varies ., The first order Markov process , in which the probability of repeating a syllable is constant , is a simple model for generating syllable repetitions ., Such a process produces a monotonically decreasing distribution of repeat numbers , with the most probable repeat number ( peak repeat number ) being one ( Fig 1c , black curve ) ., Indeed , many repeated syllables in the songs of the Bengalese finch do have such distributions 20 ., However , there are also repeated syllables that violate the predictions of the Markov process ., These syllables are typically long repeated , and their distributions of repeat numbers are peaked , with the most probable repeat number being much greater than one 20–23 ( Fig 1c , red curve ) ., In the songs of the Bengalese finch , the transition probabilities between syllables are altered shortly after deafening 24 , 25 or in real-time by delayed auditory feedback 13 , demonstrating that disturbing auditory feedback can disturb sequence generation ., Songbirds are prominent models for studying the neural basis of complex sequence production ., Experimental data from sensory-motor song nucleus HVC ( proper name ) of singing zebra finches have led to neural network models of the internal motor program for sequence generation that instantiate first-order Markov processes 26 ., This suggests that additional mechanisms contribute to the generation of non-Markovian distributions of repeat numbers 20 , 21 , 26 ., One possibility is that , because of sensory-motor delays , auditory feedback from the previous syllable interacts with the internal motor program to contribute to the transition dynamics for subsequent syllables 13 , 14 , 27 , 28 ., For repeated syllables , we hypothesized that the interaction of auditory-feedback and ongoing motor activity forms a positive-feedback loop that contributes to sustaining syllable repetition beyond the predictions of a Markov process ( Fig 1a ) ., However , such positive-feedback architectures are inherently unstable , prone to indefinite repetition ( i . e . perseveration ) ., Across sensory modalities , a common feature of sensory responses to repeated presentations of identical physical stimuli is a gradual decrease of response magnitude ( i . e . response adaptation ) 29 ., We therefore hypothesized that auditory inputs are subject to response adaptation , which gradually reduces the strength of the positive feedback loop over time ., Thus , an auditory-motor feedback loop with response adaptation is predicted to contribute to the generation of non-Makovian repeated syllable sequences by both pushing repeat counts beyond the expectations of a Markov process and simultaneously preventing indefinite repetitions of the syllable ., We tested these hypotheses using computational modeling combined with behavioral and electrophysiological experiments ., The critical features of our framework for repeat generation are: ( 1 ) the population of neurons generating a repeated syllable receives a source of excitatory input in addition to the recurrent excitation from the sequencing network , and ( 2 ) the strength of this input adapts over time during repeat generation ., For concreteness , we instantiate this framework as a ‘branched-chain’ network with adapting auditory feedback , and place this network in nucleus HVC ., In songbirds , HVC has been proposed to contain an internal motor program for the generation of song sequences 26 , 30–35 ., HVC sends descending motor commands for song timing to nucleus RA ( the robust nucleus of the arcopallium ) , which in turn projects to brainstem areas controlling the vocal organs 36 , 37 ( Fig 2a ) ., HVC also receives input through internal feedback loops from the brainstem 38 , via Uva ( nucleus uvaeformis ) and NIf ( the interfacial nucleus of the nidopallium ) 39 ., Experiments in the zebra finch have shown sparse sequential firing of the RA projecting HVC neurons ( HVCRA ) during singing 30 , 31 , 35 ., This has led to the hypothesis that the motor program for sequence production in HVC includes sequential “chaining” of activity , in which populations of HVCRA neurons responsible for generating a syllable drive the neuronal populations that generate subsequent syllables either directly within HVC or through the internal feedback loop 31 , 34 , 35 , 40 , 41 ( Fig 2b ) ., Our model for generating syllable sequences starts with such a synaptic chain framework ., The details of this model have been described previously 26 and are summarized in Materials and Methods ., In synaptic chain models , each syllable is encoded in a chain network of HVCRA neurons ( Fig 2b ) ., Spike propagation through the chain produces the encoded syllable by driving appropriate RA neurons ., To generate variable syllable transitions , the syllable-chains are connected into branching patterns ., At a branch point , syllable-chains compete with each other through a winner-take all mechanism mediated by the inhibitory HVC interneurons ( HVCI ) , allowing only one branch to continue the spike propagation ., The selection is probabilistic due to intrinsic neuronal noise , which provides a source of stochasticity in the winner-take-all competition ( Fig 2b ) ., In this model , syllable repetition is generated by connecting the syllable-chains to themselves at the branching points 26 , 34 ., In branched chain networks , the transitions between the syllable-chains are largely Markovian , and for repeating syllables this implies that repeat number distributions should be a decreasing function of the repeat number—in particular , the most probable ( or “peak” ) repeat number will be one 26 ( Fig 1c ) ., However , many repeated syllables in Bengalese finch song have repeat distributions that are highly non-Markovian , with peak repeat numbers much larger than one 20–23 ., This implies additional processes beyond synaptic chains contribute to generating non-Markovian repeated sequences ., Here we incorporate auditory feedback into the branching chain network model and show that , when this feedback is strong and adapting , non-Markovian repeat distributions emerge ., In HVC , as in many sensory-motor systems , including the human speech system 42 , 43 , the same neuronal populations that are responsible for the generation of the behavior also respond to the sensory consequences of that behavior , i . e . the bird’s own song ( BOS ) 14 , 44–46 ., HVC receives much of its auditory input from NIf 47–50 , which can provide real-time auditory feedback during singing ( Fig 2a ) 51 ., However , because of the time it takes to propagate motor commands to the periphery ( 30–50 ms ) and process the subsequent auditory signals ( 15–20 ms ) ( Fig 2a ) , auditory feedback is necessarily delayed relative to the motor activity that generated it 1 , 13 , 14 , 28 ., This sensory-motor delay for HVC ( 45–70 ms ) is on the order of the duration of a syllable , making it possible for auditory feedback to influence HVC motor programs and the transition dynamics between syllables 13 , 14 , 27 ( Fig 2a ) ., We first tested the feasibility of this mechanism using biophysically detailed neural network models ., To illustrate this model , we focus on generating sequences of the form ‘abnc’ , where syllable ‘a’ transitions to syllable ‘b’ , ‘b’ repeats a variable number of times ( n ) , and transitions to ‘c’ ( e . g . ‘abbbbbbbc’ ) ., For concreteness , we model the adapting input as an auditory feedback signal to the network , though in principle this adapting input could reflect recurrent circuit-activity that is non-sensory ., To incorporate auditory feedback into the previous model , each HVCRA neuron in chain-b is contacted by excitatory synapses carrying auditory inputs triggered by the production of syllable ‘b’ ( Fig 2c ) ., We assume that the auditory synapses are made by axons from NIf , which is a major source of auditory input to HVC 47–50 and is selective to BOS 49 ., When auditory feedback is present , the auditory synapses receive spikes from a Poisson process , assumed to be from the population of NIf neurons responding to syllable ‘b’ ( Materials and Methods ) ( Fig 2c ) ., The auditory synapses are subject to short-term synaptic depression , resulting in gradual adaptation of responses to repeated inputs 52 , 53 ., Specifically , due to the synaptic depression , the average strength of the auditory inputs to chain-b decreases exponentially during the repeats of syllable ‘b’ ( Materials and Methods ) ., In Fig 3 , we show results from an example network in which the auditory input to chain-b is strong and the spiking dynamics produce repeats of syllable ‘b’ with large repeat numbers ., A spike raster for a standard single run of the network is shown in Fig 3a ., Once spiking was initiated in chain-a ( through external current injection ) , spikes propagated through chain-a , and activated chain-b ., Chain-b repeated a variable number of times before the spike activity exited to chain-c and stopped once it reached the end of chain-c ., As chain-b continued to repeat , the synapses carrying the feedback signal weakened over time due to adaptation ( Fig 3b ) ., Analyzing multiple trials , we find that the probability of chain-b transitioning to itself ( repeat probability ) also decreases over time , though the repeat probability is only meaningful at the transition times—i . e . when the activity reaches the end of chain-b ( Fig 3c ) ., Examining the feedback strength at these transition times across the same trials allowed us to understand how the instantaneous feedback strength affects the repeat probability ( Fig 3d ) ., Not surprisingly , we found that the repeat probability increases with the strengths of the auditory synapses ., Repeat probability pr as a function of the feedback strength could be well fit with the sigmoidal function ( Fig 3d , red curve ), p r ( A ) = 1 - c 1 + η A ν , ( 1 ), where A > 0 represents the strength of the auditory synapses , η , ν > 0 are parameters controlling the shape of the curve , and 0 < c < 1 is a parameter for the repeat probability when there is no auditory feedback ( i . e . A = 0 ) , which is determined by the connection strengths of the network at the branching point ., Note that , when the auditory input A = 0 , the repeat probability is pr = 1 − c , and conversely , as A is large , pr approaches 1 ., Initially , the strong auditory feedback biases the network toward repeating and so the repeat probability is close to 1 ., If the strong excitatory input resulting from auditory feedback were constant , the network would perseverate on repeating syllable ‘b’ indefinitely ( a result of the positive feedback loop ) ., However , because of the short-term synaptic depression , the auditory input to chain-b when syllable ‘b’ repeats decreases exponentially over time ( Fig 3b , red line; time-constant of τ = 148 ms for this particular network ) ., Even so , the repeat probability stays close to 1 as long as the auditory input is strong enough ., Further weakening of the feedback reduces the repeat probability more significantly , making repeat-ending transitions to chain-c more likely ., For this network , this process produced a repeat number distribution peaked at 6 , as shown in Fig 3e ., These results demonstrate that branched-chain networks receiving adapting excitatory inputs can generate repeat distributions that are non-Markovian ., The repeat number distributions from our network model can be described using a simple statistical model with a small number of parameters ., In our network model , the gradual reduction of excitatory drive from auditory feedback as a syllable is repeated reduces the probability that the syllable transitions to itself , and thus reduces the repeat probability ., Eq ( 1 ) describes the dependence of the repeat probability pr on the auditory input strength , A . The synaptic depression model tells us how A changes with time ., Sampling this at the transition times describes how A changes with the repeat number , n ., At the end of the nth repeat of the syllable , A reduces to, A ( n ) = a 0 e - n T / τ , ( 2 ), where a0 is the initial strength of the auditory feedback , τ is the time constant of the input decay , and T is the duration of the syllable ., Combining this with the dependence of the repeat probability on A , shown in Eq ( 1 ) , we find that the repeat probability after the nth repetition of the syllable is given by, p r ( n ) = 1 - c 1 + η a 0 ν e - n ν T / τ = 1 - c 1 + a b n , ( 3 ), where a = η a 0 ν and b = e−νT/τ ., Therefore , there are effectively three parameters ( a , b and c ) for how pr depends on n ., We call Eq ( 3 ) the sigmoidal adaptation model of repeat probability ., The network sequence dynamics can be represented with a state transition model , in which a single state corresponds to the repeating chain ., The state can transition to itself with a probability pr ( n ) given by Eq ( 3 ) , or exit the state with probability 1 − pr ( n ) ., This single state transition model can accurately fit the repeat number distributions generated by the network simulations with varying parameters , as shown in Fig 4a ( all fit errors below their respective benchmark errors , which characterize the fitting errors expected from the finiteness of the data set—see Materials and Methods ) ., This model contains the Markov model and a previously described ‘geometric adaptation’ model 20 as special cases ( Materials and Methods ) ., Both of these models fail to fit the simulated data , even when a large number of states/parameters are used ( Fig 4b and 4c ) ., On the other hand , we have shown that the sigmoidal model provides an accurate fit with a single state and a small number of parameters ., Therefore , relative to other statistical models , the single-state transition model with sigmoidal adaptation parsimoniously and accurately replicates the syllable repetition statistics of our network model ., Using the single state transition model with sigmoidal adaptation , we explored how peak repeat numbers depend on the initial feedback strength and the adaptation strength ( defined by the related parameter , α , in the synaptic depression model , Materials and Methods ) ( Fig 4d ) ., Here we see that , for a given adaptation strength , there is a threshold feedback strength at which the peak repeat number is greater than 1 , and this threshold increases with increasing adaptation strength ., This demarcates the transition between Markovian ( peak repeat number = 1 ) and non-Markovian ( peak repeat number > 1 ) repeat distributions ( black-to-red transition in ( Fig 4d ) ) ., Further increases in the feedback strength result in larger peak repeat numbers ., Conversely , for a given feedback strength , increasing the adaptation strength results in a reduction of the peak repeat number ., Together , these results demonstrate that a large range of peak repeat numbers can be generated through various combinations of feedback and adaptation strengths , and suggest that there is a threshold feedback strength required to produce non-Markovian repeat distributions ., To see whether the non-Markovian repeat distributions generated with our network model can accurately describe syllable repeat number distributions of actual Bengalese finch songs , we recorded and analyzed the songs of 32 Bengalese finches ., We identified the song syllables and obtained the syllable sequences ( Materials and Methods ) ., Our data set contains more than 82 , 000 instances of 281 unique syllables , of which 71 are repeating syllables ., Since the simulations of the network model are slow , we used the single state transition model with sigmoidal adaptation to fit the repeat number distributions for these syllables ., As demonstrated above , the statistical model ( Eq ( 3 ) ) captures the essential features of our network model , and succinctly represents the repeat number distributions produced by the network simulations ., In Fig 5a , we show six examples of Bengalese finch repeat count histograms ( grey bars ) with different peak repeat counts ( peak repeat count increases across plots i-vi . ) , and the best-fit model distributions ( red lines ) ., These examples show a range of distribution peaks and shapes , from small peak numbers with long rightward tails ( i ) , to large peak numbers with tight , symmetric tails ., Interestingly , we found that three repeated syllables ( out of 71 ) had clear double-peaked distributions , with a prominent peak at repeat number 1 and another peak far away ( two of which are displayed in panels ii and vi ) ., These double peaked distributions cannot be explained with a single state transition model ., A simple explanation is that the single peak and the broad peak are generated by two separate states ( or neural substrate ) , as postulated in Jin & Kozhevinov ( the “many-to-one mapping” from multiple chains in HVC to the same syllable type ) 20 ., Here we removed the single peak at repeat number 1 for these three syllables and only analyzed the longer repeat parts ., The state transition with sigmoidal adaptation model does an excellent job of fitting the wide variety of peaks and shapes of the repeat distributions found in the Bengalese finches ., The results comparing the fit errors from the sigmoidal adaption model to benchmark errors across all 71 repeating syllables are shown in Fig 5b ( Materials and Methods; see also 20 ) ., The vast majority of fit errors from the feedback adaptation model are below their respective benchmark errors ( 86% of fit errors below the benchmark error ) , demonstrating that the model does an excellent job of fitting the diverse shapes of Bengalese finch song repeat number distributions ., Therefore , the single state transition model with sigmoidal adaptation , and by extension the branched-chain model with adaptive auditory feedback , can successfully describe the syllable repeat number distributions in Bengalese finch songs ., In our framework , auditory feedback from the previous syllable arrives in HVC at a time appropriate to provide driving excitatory input to HVC neurons that generate the upcoming syllable ., For repeated syllables , this creates a positive feedback loop which is responsible for generating peak repeat numbers greater than 1 ( adaptation drives the process to extinction ) ., Therefore , a key prediction is that without auditory-feedback driven excitatory input , the peak-repeat number should shift toward 1 ., To test this prediction , we deafened six Bengalese finches by bilateral removal of the cochlea , and analyzed the songs before and soon after they were deafened ( 2–4 days ) ( Materials and Methods ) ., We found that deafening greatly reduces the peak repeat-counts ., For example , in Fig 6a , we display spectrograms and rectified amplitude waveforms of the song from one bird prior to deafening ( top ) and soon after deafening ( 2–3 days post-deafening ) ., We see that deafening reduces the number of times that the syllable ( red-dashed box ) is repeated ., The time course of repeat generation from this bird is examined in more detail in Fig 6b , where we plot the median repeat counts per song of the syllable from Fig 6a before deafening ( black ) and after deafening ( red ) ., Here we see that , even in the first songs recorded post-deafening , there is a marked decrease in the produced number of repeats ., This data further exemplifies that repeat counts per song is generally stable across bouts of singing within a day both before and after deafening ., Across days , repeat counts continued to slowly decline with time since deafening , though the co-occurrence of acoustic degradation of syllables makes these later effects difficult to interpret 24 , 54 ., Nonetheless , the rapidity of the effect of deafening underscores the acute function of auditory feedback in the generation of repeated syllables ., Similar results were seen across the other repeated syllables ., Fig 6c shows the repeat number distributions for two additional birds before ( black ) and after ( red ) deafening ., In these cases , deafening resulted in repeat number distributions that monotonically decayed ., The peak repeat numbers pre and post deafening for all 19 syllables in our data set are presented in Fig 6d ., Across the 19 repeated syllables from 6 birds , deafening significantly reduced the number of consecutively produced repeated syllables ( Fig 6d , p < 0 . 01 , sign-rank test , N = 19 , medians demarcated in red , overlapping points are vertically shifted ) , although there was variability in the effect magnitude: the effect of deafening appeared larger for the repeat with larger initial repeat number ( compare upper and lower panels of Fig 6c ) ., This suggests that the degree to which deafening reduces peak repeat number depends on the initial repeat number ., We examined the change in peak repeat number resulting from deafening as a function of the peak repeat number before deafening ( Fig 6e , red dots correspond to data from individual syllables , overlapping points are horizontally offset for visual display ) ., We found that the magnitude of decrease in peak repeat numbers after deafening grows progressively larger for syllables with greater peak repeat numbers before deafening ( R2 = 0 . 81 , p < 10−7 , N = 19 ) ., This suggests that repeated syllables with larger repeat numbers are progressively more dependent upon auditory feedback for repeat production ., Interestingly , after two days of hearing loss , one of the deafened Bengalese finches in our experiments had a repeat that was minimally affected by deafening , and several birds retained peak repeat number around 2 , not all the way to 1 as predicted for a Markov process ( Fig 6d ) ., None-the-less , these deafening results are consistent with the hypothesis that the generation of repeated syllables is driven , in-part , by a positive-feedback loop caused by excitatory auditory input during singing ., A key prediction of the adaptive feedback model for repeat generation is that auditory responses of HVC neurons should decline over the course of repeated presentations of the same syllable ., To test this hypothesis , we examined the properties of HVC auditory responses to repeated syllables in sedated birds ( Materials and Methods ) ., An example recording from an HVC multi-unit site in response to playback of the bird’s own song ( BOS ) stimulus is presented in Fig 7a , which displays the stimulus oscillogram ( top ) , and the average spike rate in response to the stimulus ( bottom ) ., Multiple renditions of the repeated syllable are demarcated by red-dashed boxes , and we see that the evoked HVC auditory responses to repeated versions of the same syllable gradually declined ., The example presented above suggests that auditory responses to repeated presentations of the same syllable adapt over time ., However , in the context of BOS stimuli , the natural variations that occur in syllable acoustics , inter-syllable gap timing , and in the identity of the preceding sequence , make it difficult to directly compare responses to different syllables in a repeated sequence ., Therefore , to examine how responses to repeated syllables are affected by the length and identity of the preceding sequence , for each bird we constructed a stimulus set of long , pseudo-randomly ordered sequences of syllables ( 10 , 000 syllables in the stimulus , one prototype per unique syllable , median of all inter-syllable gaps used for each inter-syllable gap , derived from the corpus of each bird’s songs , Materials and Methods ) ., This stimulus allows a systematic investigation of how auditory responses to acoustically identical syllables depend on the length and syllabic composition of the preceding sequence 28 ., Auditory responses at 18 multi-unit recordings sites in HVC from 6 birds were collected for this data set , which contained 40 unique syllables ., Of these 40 syllables , 6 syllables in 4 birds ( with 11 recording sites ) were found to naturally repeat ., We used these stimuli to systematically examine how auditory responses to a repeated syllable depend on the number of preceding repeated syllables ., We found that HVC auditory responses gradually declined to repeated presentations of the same syllable ., In Fig 7b , for each uniquely repeated syllable ( different syllables are colored from grey-to-red with increasing max repeat number ) , we plot the average normalized auditory response ( mean ±s . e . across sites ) to that syllable ( e . g . ‘b’ ) as a function of the repeat number ( e . g . repeat number 5 corresponds to the last ‘b’ in ‘bbbbb’ ) ., Across HVC recordings sites and repeated syllables , the response to the last syllable declined as the number of preceding repeated syllables increased ( R2 = 0 . 523 , p < 10−10 , N = 24 , slope = -5% ) ., Thus , auditory responses to repeated syllables gradually adapt as the number of preceding repeated syllables increases , providing confirmation of a key functional mechanism of the network model ., To generate non-Markovian repeat distributions , we have proposed that the sequence generation circuitry is driven , in part , by auditory feedback that provides excitatory drive to sensory-motor neurons that control sequencing ., Specifically , auditory feedback from the previous syllable arrives in HVC at a time appropriate to provide driving excitatory input to neurons that generate the upcoming syllable ., This predicts that if HVC auditory responses are positively modulated by sound amplitude , feedback associated with louder syllables should provide stronger drive to the motor units , and thus generate longer strings of repeated syllables for a given rate of adaptation ., This logic is supported by the sigmoidal adaptation model , which predicts a threshold auditory feedback strength at which the peak repeat number becomes greater than one ( i . e . non-Markovian , Fig 4b ) ., Behaviorally , this predicts that non-Markovian sequences of repeated syllables should be composed of the loudest syllables in the bird’s repertoire ., We tested this behavioral prediction by comparing the amplitudes of Bengalese finch vocalizations based on their repeat structure ., Fig 8a plots the rectified amplitude waveforms ( mean ±s . d . ) of a few consecutively produced repetitions of a non-Markovian repeated syllable ( black ) , a Markovian repeated syllable ( red ) , and ‘introductory’ note ( grey ) from one bird ., The non-Markovian repeated syllable is qualitatively louder than the other repeated vocalizations in the birds’ repertoire ., To quantitatively test this prediction , we measured the peak amplitude of the 281 unique syllables in our data set , and normalized this to the minimum peak amplitude across syllables ( Materials and Methods ) ., We categorized each syllable in our data set according to whether it was an introductory note ( Intro ) , a non-repeated syllable ( NR: repeats = 0 ) , a Markovian repeated syllable ( MR: peak repeat number = 1 ) , or a non-Markovian repeated syllable ( nMR: peak repeat number > 1 ) ., In Fig 8b , we plot the mean ±s . e . of the normalized peak amplitudes of these syllable groups across the data set ., As exemplified by the data in Fig 8a , we found that non-Markovian repeated syllables were significantly louder than the other vocalizations in a bird’s repertoire ( ***: p < 10−3 , **: p < 10−2 , sign-rank test , Bonferroni corrected for m = 3 comparisons ) ., Therefore , syllables with non-Markovian repeat distributions are typically the loudest vocalizations produced by a bird ., If amplitude is a contributing factor to repeat generation , then HVC auditory responses should be positively modulated by syllable amplitude ., However , previous work in the avian primary auditory system has found a population of neurons that is insensitive to sound intensity 55 , and amplitude normalized auditory responses have been utilized in previous models of sequence encoding in HVC auditory responses 56 ., Therefore , we first examined whether auditory responses were positively modulated by syllable amplitude ., To make recordings from different sites/birds comparable , we normalized both the syllable amplitudes ( relative to mean ) and auditory responses ( relative to minimum ) ., The scatter plot in Fig 8c plots the normalized syllable amplitudes vs . the normalized auditory responses ( averaged across sites within a bird ) , for the 40 syllables in in our data set 28 ., We found a modest but significant positive correlation between auditory responses and syllable amplitude ( R2 = 0 . 30;p < 10−3 , N = 40 syllables ) ., We next examined whether the increased amplitude of repeated syllables resulted in increased HVC auditory response to these syllables ., We performed a paired comparison of normalized auditory responses to non-repeated syllables ( NR ) and non-Markovian repeated syllables ( nMR ) at the 11 sites where auditory responses to repeated syllables were collected ( Fig 8d ) ., We found that repeated syllables had significantly larger auditory responses than non-repeated syllables ( p < 0 . 01 , sign-rank test , N = 11 sites ) ., Thus , HVC auditory responses are sensitive to syllable amplitude , and repeated syllables elicit larger auditory responses than non-repeated syllables , likely due to being the loudest syllables that a bird sings ., Therefore , the strong auditory feedback associated with these loud repeated syllables may be a key contributor to their non-Markovian repeat distributions ., We have provided converging evidence that adapting auditory feedback directly contributes to the generation of long repetitive vocal sequences with non-Markovian repeat number distributions in the Bengalese finch ., A branching chain network model with adapting auditory feedback to the repeating syllable-chains produces repeat number distributions similar to those observed in the Bengalese finch songs ., From the networ

Introduction, Results, Discussion, Materials and Methods

Consecutive repetition of actions is common in behavioral sequences ., Although integration of sensory feedback with internal motor programs is important for sequence generation , if and how feedback contributes to repetitive actions is poorly understood ., Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds ., We propose that auditory signals provide positive feedback to ongoing motor commands , but this influence decays as feedback weakens from response adaptation during syllable repetitions ., Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song ., We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC ., Together , our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations , and suggest sensory adaptation is important for feedback control of motor sequences .

Repetitions are common in animal vocalizations ., Songs of many songbirds contain syllables that repeat a variable number of times , with non-Markovian distributions of repeat counts ., The neural mechanism underlying such syllable repetitions is unknown ., In this work , we show that auditory feedback plays an important role in sustaining syllable repetitions in the Bengalese finch ., Deafening reduces syllable repetitions and skews the repeat number distribution towards short repeats ., These effects are explained with our computational model , which suggests that syllable repeats are initially sustained by auditory feedback to the neural networks that drive the syllable production ., The feedback strength weakens as the syllable repeats , increasing the likelihood that the syllable repetition stops ., Neural recordings confirm such adaptation of auditory feedback to the auditory-motor circuit in the Bengalese finch ., Our results suggests that sensory feedback can directly impact repetitions in motor sequences , and may provide insights into neural mechanisms of speech disorders such as stuttering .

journal.pcbi.1006945

Chemical features mining provides new descriptive structure-odor relationships

Around the turn of the century , with its acknowledgement as an object of science by the Nobel society 1 the hidden sense associated with the perception of odorant chemicals , hitherto considered superfluous to cognition , became a focus of study in its own right ., Odors are emitted by food , which is a source of pleasure 2; they also influence our relations with others 3 ., The olfactory percept encoded in odorant chemicals contributes to our emotional balance and wellbeing: olfactory impairment jeopardizes this equilibrium 4 , 5 ., Neuroscientific studies have revealed that odor perception is the consequence of a complex phenomenon rooted in the chemical properties of a volatile molecule ( described by multiple physicochemical descriptors ) further detected by our olfactory receptors in the nasal cavity 6 ., A neural signal is then transmitted to central olfactory brain structures 7 ., At this stage , a complete neural representation , called “odor” is generated and then , it can be described semantically by various types of perceptual qualities ( e . g . , musky , fruity , floral , woody etc . ) ., While it is generally agreed that the physicochemical characteristics of odorants affect the olfactory percept , no simple and/or universal rule governing this Structure Odor Relationship ( SOR ) has yet been identified ., Why does one odorant smell of rose and another smell of lemon ?, Given the fact that the totality of the odorant message was encoded within the chemical structure , chemists have tried for a long time to identify relationships between chemical properties and odors ., Topological descriptors , eventually associated with electronic properties or molecular flexibility , have been tentatively connected to odorant descriptors ., For instance , molecules carrying a sulfur atom and/or having low molecular weight or low structural complexity are often rated as unpleasant 8–10 ., In addition to the hedonic valence of odors , others have looked for predictive models describing odor perception and quality ( see 11–14 ) ., Indeed , this was the aim of a crowd-sourced challenge recently proposed by IBM Research and Sage called DREAM Olfaction Prediction Challenge ., The challenge resulted in several models that were able to predict pleasantness and intensity as well as 8 out of 19 semantic descriptors ( namely “garlic” , “fish” , “sweet” , “fruit” , “burnt” , “spices” , “flower” and “sour” ) with an average correlation of predictions across all models above 0 . 5 15 ., Although these investigations brought evidence that chemical features of odorants can be linked to odor perception , the stimulus-percept problem raised a number of issues ., For instance , the stimulus-percept relationship is generally viewed as bijective in that one physicochemical rule describes or predicts one quality ., However , some cases suggest the existence of more than a single rule to relate chemistry and perception ., Indeed , chemicals belonging to different families can trigger a “camphor” or a musky smell 16 ., On the other hand , a single chiral center can render a compound odorless or shift its perceived odor completely , as is the case for ( + ) and ( - ) -carvone 17 ., These examples strengthen the notion that the connections between the chemical space and the perceptual space are subtler than previously thought with multiple physicochemical rules describing a given quality ., At best , the bijective SOR rules may be only be applicable to a very small fraction of the chemical space , with the remaining part of the perceptual space being best described using a multiple rules approach ., The complexity of available databases , they include both thousands of chemical properties and a large heterogeneity in perceptual descriptions , 18–21 means that the manual generation of multiple rules is not feasible ., In other words , to better understand the stimulus-percept issue in olfaction , there is a clear need to extract knowledge automatically and in an intelligible manner ., Such an approach is positioned upstream of predictive modeling since it will enable modeling that extracts descriptive rules from the data that link subgroups belonging to both chemical and perceptual spaces ., The main aim of our study was to develop such a computational framework to discover new descriptive structure-odor relationships ., To achieve this , we first set up a large database containing more than 1600 odorant molecules described by both physicochemical properties and olfactory qualities ., We then developed an original methodology based on the discovery of physicochemical descriptions distinguishing between a group of objects given a target or class label , namely odor qualities ., This approach has been widely studied in Artificial Intelligence ( AI ) , data mining and machine learning ., Specifically , supervised descriptive rules were formalized through subgroup discovery , emerging pattern/contrast-sets mining 22 ., In all cases , we face a set of objects associated with descriptions and these objects are related to one or several class labels ., This new pattern mining method , a variant of redescription mining 23 , allows the discovery of pairs consisting of a description ( of physicochemical properties ) and a label ( or sub-set of labels , olfactory qualities ) ., The strength of the rule ( SOR in our application ) is evaluated through a new quality-control measure detailed in the Methods section ., We designed and set up a database describing odorant molecules by both their perceptual and physicochemical properties ., Here , data from different sources were extracted and grouped:, ( i ) for odorant identification and olfactory qualities , we referred respectively to the PubChem website ( https://pubchem . ncbi . nlm . nih . gov/ ) and the textbook by Arctander 24;, ( ii ) for physicochemical properties , we referred to the Dragon software package ( http://www . talete . mi . it/index . htm ) ., Olfactory qualities were thus gathered from the book “Perfume and Flavor Chemicals” , published in 1969 by Steffen Arctander ., In this book , Arctander gives a complete description , including olfactory and trigeminal qualities as well as flavors , of 3102 odorants ( detailed physicochemical properties of 1689 odorants among these 3102 odorants were retrieved , see below ) ., These odorants were further identified by chemical name , molecular weight and corresponding olfactory qualities ., Here , the 74 olfactory qualities selected by Chastrette and colleagues 25 were used as a reference list ., These qualities were selected in a study of the whole of Arctander’s book by excluding those that did not provide qualitative olfactory information and those that were the least frequent ., Note that before selecting this source , we ran a comparison with other existing Atlases and websites used for research , teaching and applicative purposes: specifically , the Dravnieks Atlas 26 , the Boelens Atlas ( see 27 ) , and the Flavornet website ( http://www . flavornet . org ) ., These sources ( atlases , book and website ) were compared along a series of parameters ( the comparison took into account all odorants for which we collected CID numbers ) ., The first parameter of interest was the number of molecules studied in the source , and was respectively 1689 , 138 , 263 , and 660 for the Arctander , the Dravnieks , the Boelens and the Flavornet ( here , only molecules for which we found a PubChem Compound Identification or CID are taken into account ) ., The second parameter was the number of evaluators ( and their expertise level ) who smelled the compounds and provided the olfactory qualities: one trained evaluator for the Arctander , a large panel of evaluators for the Dravnieks ( although there seems to be a large heterogeneity in the expert profile of these panelists , and little information as to the extent of training that panelists were given ) , six trained evaluators for the Boelens , and no information is given regarding the panelists for the Flavornet website ., Third , when considering the way olfactory qualities were collected in the source , both the Arctander and the Flavornet used a binary format ( presence/absence of quality ) , and both the Dravnieks and the Boelens used a scale of intensity or agreement ., Fourth , we compared the number of olfactory qualities used in each atlas/book/website and observed the following distribution ( the average number of qualities per molecule is in brackets ) : 74 ( 2 . 88 ) for the Arctander , 146 ( 29 . 99 ) for the Dravnieks , 30 ( 12 . 86 ) for the Boelens , and 197 ( 2 . 72 ) for the Flavornet ., Note also that the minimum ( and the maximum ) number of qualities for one molecule was: Arctander ( min: 1; max: 10 ) , Dravnieks ( min: 5; max: 52 ) , Boelens ( min: 0; max: 22 ) , Flavornet ( min: 1; max: 5 ) ., Thus , this analysis showed that whereas some sources are characterized by a large number of molecules ( e . g . Arctander and Flavornet ) , others contain only a limited number of odorants ( e . g . Boelens and Dravnieks ) ., Moreover , there is great heterogeneity between these different sources with regards to the number and the degree of expertise of the evaluators ., Some sources involve a large number of evaluators but with heterogeneous profiles ( e . g . Dravnieks ) and others involve a limited number of experts ( e . g . Boelens and Arctander ) ., Finally , whereas some sources have , on average , between 10 and 30 qualities per odorant ( e . g . Boelens and Dravnieks ) , the average number is around three for others ( e . g . Arctander and Flavornet ) ., In view of these parameters , and because the descriptive approach used in this study requires a large database , we used the Arctander book because it contained the highest number of odorant molecules ( 1689 ) and a reasonable number of qualities per odorant ( 2 . 88 on average ) ., Odorant physicochemical properties were then obtained using Dragon , a software application that enables the calculation of 4885 molecular descriptors ( Talete ) ., Descriptors included in our dataset ranged from the simplest atom types , functional groups and fragment counts , to topological and geometrical descriptors ., As Dragon requires 3D structure files , these were collected from the PubChem website ( https://pubchem . ncbi . nlm . nih . gov ) by using the compound identifier number of each odorant ( CID ) ., Individual odorant CIDs were obtained by using the CAS Registry Number and/or the chemical name of the odorant as an entry in the PubChem website ., In total , 1689 CIDs were found for the 3102 odorants ., In the following section , we study the set M of odorant molecules that are described by n physicochemical properties denoted F . Each property fi ∈ F is a function that associates a real value with a molecule: fi: M → image ( fi ) with image ( fi ) an interval of R . The olfactory qualities are denoted by O and class is a mapping that associates a subset of O to a molecule: class: M → 2O ., Here , we developed an original subgroup discovery approach to mine descriptive rules that specifically characterize subsets of olfactory qualities ( O ) ., The specificity of this approach is intended to be able to extract rules with several olfactory qualities as targets , and also to treat unbalanced classes robustly , i . e . , the fact that some olfactory qualities are very rare ( e . g . “musty” ) compared to others ( e . g . “fruity” ) ., Subgroup discovery is a generic data mining method aimed at discovering regions in the data that stand out with respect to a given target ., We instantiated this framework in order to identify the conditions on some odorant physicochemical properties that are strongly associated with olfactory qualities ., A structure odor rule ( SORule ) , denoted D → Q , is defined by a physico- chemical description D and a set of olfactory qualities Q ⊆ O . The description is a set of n intervals D = ⟨x1 , y1 , x2 , y2 , … , xn , yn⟩ , each being a restriction on the value image of its corresponding physicochemical property: xi , yi ⊆ image ( fi ) ., The molecules whose values on physicochemical descriptors belong to the intervals of the description D are members of the coverage of D:, coverage ( D ) ={m∈M∀i=1…n , xi≤fi ( m ) ≤yi}, We count the number of molecules in the coverage with support ( D ) = |coverage ( D ) | ., The quality of a rule is evaluated with respect to the olfactory qualities of the molecules in its coverage ., First , the precision measure gives the proportion of the molecules of the coverage of D that also have ( part of ) the olfactory qualities Q:, P ( D→Q ) =|{m∈coverage ( D ) class ( m ) ⊆Q}|support ( D ), This is the percentage of times the rule is triggered for molecules whose qualities are in Q . On the other hand , it is also important to know if the rule covers all the molecules of quality Q . This is what the recall measure evaluates:, R ( D→Q ) =|{m∈coverage ( D ) class ( m ) ⊆Q}||{m∈Mclass ( m ) ⊆Q}|, These two measures behave in opposite ways: when one increases , the other decreases ., One way to globally evaluate a rule is to use the F1 measure , the harmonic mean between the precision and recall measures:, F1 ( D→Q ) =2P ( D→Q ) R ( D→Q ) P ( D→Q ) +R ( D→Q ), As mentioned above , the olfactory qualities are more or less frequent in the data ., To take that into account , the Fβ measure gives more importance to the precision measure for rare olfactory qualities , while favoring the recall measure for frequent qualities:, Fβ ( D→Q ) = ( 1+β ( support ( Q ) ) P ( D→Q ) R ( D→Q ) β ( support ( Q ) ) P ( D→Q ) +R ( D→Q ), with support ( Q ) = |{m ∈ M |class ( m ) ⊆ Q}| and, β ( x ) = ( 0 . 5× ( 1+tanh ( xβ-xlβ ) ) ) 2, Here , the terms xBeta and lBeta are determinant in choosing the appropriate sigmoid model , and are values that can be set by the experimenter ., Given that , our approach aims to discover rules D → Q whose support support ( D ) is greater than a threshold minSupp and with |Q| is lower or equal to a value maxQual ., Those parameters make it possible to identify rules that are supported by sufficient odorant molecules , and also that are specific to a small set of olfactory qualities ., The maxQual parameter enforces that the right-hand side of the rule contains a limited number of olfactory qualities to be interpretable by the analyst ., Similarly , a maxProp parameter allows to limit the number of ( physicochemical ) conditions in the left-hand side of the rules ., To illustrate the previous definitions , let us consider the toy olfactory dataset given in Table 1 ., This dataset contains 6 molecules identified by their IDs M = {1 , 2 , 3 , 4 , 5 , 6} ., Each molecule is described by its molecular weight MW , its number of atoms nAt and its number of carbon atoms nC , that is , F = {MW , nAt , nC} ., Besides , the molecules are also associated with their olfactory qualities among O = {fruity , vanillin , woody} ., Let us consider the description, D=⟨128 , 151 , 23 , 29 , 9 , 12⟩, Its coverage is coverage ( D ) = {2 , 3 , 5 , 6} ., If we consider the odorant quality Q = {vanillin} , as there is 2 molecules of coverage ( D ) with this quality , the precision of the rule is equal to:, P ( D→Q ) =24, As there are 3 molecules in the whole dataset with that quality , the recall of the rule is:, R ( D→Q ) =23, Its F1 measure is thus equal to:, F1 ( D→Q ) =227, Detailed information regarding the principle of the algorithm are provided as S1 Text ., Our olfactory dataset includes 1689 molecules described by 74 olfactory qualities ., The dataset is multi-labeled , each molecule being associated with one or several olfactory qualities ., On average , each molecule refers to 2 . 88 olfactory qualities among the 74 possible labels ., Moreover , the frequency of olfactory qualities across odorants is unbalanced: on average a quality is used in 65 . 79 molecules ( standard deviation: 105 . 28 ) , the maximum is reached for the “fruity” quality ( used in 570 molecules ) , the minimum for musty ( used in only 2 molecules ) ., Fig 1 illustrates the entire building process of the database ., Fig 2 presents a world cloud of the 74 olfactory qualities ., With regard to the physicochemical properties , our original database contained more than 4000 physicochemical features ., For the purpose of a rational approach where features can be interpreted on a chemical basis , we selected attributes that were relevant , but more importantly easily interpretable ., This approach is strongly inspired by the so-called 3D-olfactophore , where such easily interpretable features computed on odorants sharing the same olfactory percept are gathered in the 3 dimensions of space ., Such features are typically Hydrogen bond donor/acceptor , Aromatic cycle , Charged atom , etc ., This methodology is typically useful for molecular scientists to learn about structure-property relationships and design new molecules which fulfill the properties of these olfactophores 28 ., Here the features we used were a series of physico-chemical properties ., Thus , we selected constitutional , topological and chemical descriptors that represent molecular features which can be easily interpreted and extrapolated for further predictive models ., They include the following categories: constitutional indices ( n = 29; ex . “Molecular weight” ) , ring descriptors ( n = 7; ex . “Number of rings” ) , functional group counts ( n = 40; ex . “Number of esters” ) , molecular properties ( n = 6; ex . “Topological polar surface area” ) ., To select these descriptors , we screened the whole set of descriptors proposed by Dragon ., We carefully selected descriptors able to provide information interpretable by any molecular scientist ., The cost of selecting interpretable descriptors is a reduction in the description of the dataset ., To evaluate the loss of information on the variance of a given molecular dataset , descriptors were computed on a set of 2620 odorants provided by Saito and colleagues 29 ., Finally , 347 descriptors remained after filtering the following: correlated ( above 0 . 85 ) , constant for the whole dataset ( no variation across parameters ) , not available for the whole dataset ., After the dimensionality reduction , our selected 82 descriptors accounted for 37 . 2% of the original variance ., When choosing randomly 82 descriptors within this set of 347 , the variance always falls below 25% , suggesting that our descriptors performed quite well at describing a molecular set with a certain degree of variability ., Finally , when projecting the entire set of molecules on to the two first components of a PCA , the dataset remains well split and molecules were still distinguishable ., First , the physicochemical rules were generated for each of the 74 qualities based on the 82 descriptors ., This was done using the following parameters: maxoutput ( 100 ) , beamwidth ( 30 ) , MaxQual ( 1 ) , MaxProperties ( 8 ) , max Supp ( 700 ) , XBeta ( 110 ) , IBeta ( 20 ) , and four different minSupp ( 5 , 10 , 20 and 30 ) ( see Methods section and S1 Text for a detailed definition of these parameters ) ., Second , an algorithm search for the best rules or combination of rules ( with a maximum of 12 rules ) for each of the 74 qualities and the four different minSupp ( from 5 to 30 ) ., At this stage , the rules or combination of rules were ranked as a function of their Precision ., Here , to evaluate the best rule or combination of rules that can describe each quality , we calculated for each rule ( or combination of rules ) the distance ( Euclidian ) from the “ideal” situation defined as the data-point with an error of “0” ( error was calculated as one minus precision ) and the best recall ( value of 1 in the y-axis , meaning that all molecules that belong to the quality are described by these physicochemical rules ) ., The point ( s ) with the smallest distance was ( were ) selected as the best rule or combination of rules for a given quality ., From this selection , we built a list of rules and/or combination of rules for each quality ( see S1 Table ) ., We showed that around 90% of the olfactory qualities were described by 1 to 6 rules and 66% ( 49 qualities among 74 ) were described by 3 , 4 or 5 rules ( see Fig 3a ) ., Moreover , for the same quality , different rules or combinations of rules were selected because their distance to the “ideal” situation ( recall: 1; error:, 0 ) was the same ( see an example in Fig 3b ) ., Fig 3c shows an example of the chemical structure of the molecules described by the same quality ( jasmine here ) and rules/combinations of rules ., To compare olfactory qualities according to their description by physicochemical rules , we plotted all physicochemical rules ( and/or combination of rules ) of each quality in a 2D space comprising error ( x-axis ) and recall ( y-axis ) ( Fig 4 ) ., As can be seen , whereas some qualities were close to the “ideal” situation others were very far ., First , 38 qualities ( 51 . 35% , named “Group 1” ) exhibited an error rate lower than 0 . 5 and a recall greater than ( or equal to ) 0 . 5 ( sulfuraceous , vanillin , phenolic , musk , sandalwood , almond , orange-blossom , jasmine , hay , tarry , smoky , lilac , piney , camphor , grape , anisic , buttery , gassy , fatty , waxy , acid , minty , aromatic , mossy , violet , citrus , peppery , caramelic , medicinal , tobacco , pear , lily , sour , orange , animal , honey , hyacinth , rose ) ., Second , 17 qualities ( 22 . 97% , named “Group 2” ) exhibited an error rate lower than 0 . 5 but a recall lower than 0 . 5 ( amber , geranium , metallic , fruity , pineapple , ethereal , plum , woody , balsamic , creamy , green , berry , oily , spicy , floral , winey , herbaceous ) ., Third , 18 qualities ( 24 . 32% , named “Group 3” ) showed an error rate greater than ( or equal to ) 0 . 5 and a recall greater than ( or equal to ) 0 . 5 ( leathery , aldehydic , mushroom , coco , mimosa , tea , nut , root , peachy , earthy , powdery , orris , apple , leafy , apricot , musty , brandy , narcissus ) ., Fourth , one quality ( 1 . 35% , named “Group 4” ) showed an error rate greater than ( or equal to ) 0 . 5 and a recall lower than 0 . 5 ( banana ) ., To further examine whether the generated physicochemical rules were specific to a given perceptual quality , in other words whether they provided a good and relevant model , we used Bootstrap confidence intervals to evaluate whether the generated F-measure of the rules/models was significative ., Here , knowing that a given set of rules covers X molecules , we sampled 100 , 000 sets of X molecules ( with replacement ) and calculated the F-measure of each sample according to the studied quality ., Next , the confidence intervals ( CI: 99% ) of these sets were computed ., Afterwards , the F-measure of the set of discovered rules was compared to this CI ., Results showed that for all 74 qualities , the F-measure was significant in that its value was outside ( and greater ) the CI at 99% ., Finally , to examine how the model built with 82 physicochemical descriptors performed compared to a model built with all 4000 descriptors , we calculated the F-measure for each quality ( computed on the basis of all sets of rules ) in both types of models ., Results showed that , on average , the F-measure was significantly greater ( p<0 . 0001 ) in the model with 82 physicochemical descriptors ( mean = 0 . 592 , SEM = 0 . 012 ) compared to the model with all 4000 descriptors ( mean = 0 . 487 , SEM = 0 . 011 ) , reflecting that the use of a small but explicative and intelligible set of descriptors enhances performance ., To sum up , we provide here a computational framework that enables the automatic extraction , from a complex and heterogeneous dataset , descriptive rules linking subgroups in a chemical space onto subgroups in a perceptual space ., As can be seen in Fig 3a , only 3 qualities could be best described by a single physicochemical rule whereas more than two thirds of the qualities needed between 3 and 5 rules to be described ., When dealing with the confidence of the rules , a gradient was observed whereby some rules were associated with a good rate of recall and minimum rate of error , whereas other rules exhibited a lower confidence in describing olfactory qualities ., Note that all the generated rules are available to the reader in S1 Table ., The computational approach that we developed is available at the following address: https://projet . liris . cnrs . fr/olfamine/ Here , we analyzed some of the best-known qualities in the field of olfactory evaluation , namely fruity , floral , woody , camphor , earthy , spicy , fatty ., The analysis of the rules and combinations of rules ( see S1 Table ) , shows that the number of rules is quite high for these qualities ranging from six ( floral ) , seven ( camphor , earthy ) , eight ( spicy , woody ) , nine ( fatty ) to twelve ( fruity ) ., From a physicochemical point of view , translated into interpretable rules , the floral quality is characterized by either aromatic and strongly hydrophobic molecules or non-aromatic and moderately hydrophobic odorants ., For camphor , molecules are rather small in size , moderately hydrophobic , and eventually cyclic ., The earthy quality is characterized by moderately hydrophobic molecules with unsaturations ., The spicy quality is characterized by rather rigid molecules , eventually aromatic ., Woody quality includes hydrophobic molecules , rather not cyclic nor aromatic ., For the fatty , the molecules have a larger carbon-chain skeleton which is highly hydropobic with aldehyde or acid functions ., Finally , for the fruity quality , molecules are described as having moderate hydrophobicity and being medium to large in size ., To push the interpretation further , we examined qualities associated with generated physicochemical rules with the highest level of confidence ., Here , we attempted, ( i ) to understand the rules based on a priori knowledge and, ( ii ) to examine whether the rules could raise new scientific assumptions ., We analyzed a total of eleven qualities corresponding to the first quartile of the distribution of all rules ., Based on the Euclidian distance to the “ideal” situation; 473 rules were generated by our analysis ( see Fig 4 ) ., These qualities were: sulfuraceous , vanillin , phenolic , musk , sandalwood , almond , orange-blossom , jasmine , hay , tarry , smoky ., The “sulfuraceous” quality was described as follows: R1: 0 . 0<nCsp2<0 . 0 0 . 0<nHAcc<0 . 0 11 . 611<Se<22 . 069 144 . 039<SAtot<222 . 269 0 . 0<TPSA ( Tot ) <50 . 6; R2: 1 . 0<nS<2 . 0 1 . 0<nC<6 . 0 0 . 0<N%<0 . 0 25 . 0<C%<33 . 3 38 . 8<TPSA ( Tot ) <64 . 18; R3: 1 . 0<nS<2 . 0 -0 . 264<Hy<0 . 323 102 . 715<SAtot<222 . 269 0 . 0<O%<6 . 3 ., These descriptions suggest , somewhat intuitively , that sulfuraceous odorants encompass molecules with one or two sulfur atoms and are moderately heavy , with a maximum of six carbon atoms ., Four rules defined the “phenolic” quality: R1: 216 . 155<SAtot<218 . 661 0 . 0<nCrs<0 . 0 0 . 0<nOHp<0 . 0 30 . 4<C%<45 . 0 0 . 0<Ui<2 . 322; R2: 1 . 117<Mi<1 . 118 -0 . 768<Hy<-0 . 158 0 . 0<nR = Ct<0 . 0 43 . 5<H%<50 . 0 0 . 0<nOxiranes<0 . 0 0 . 0<nR = Cp<0 . 0; R3: 2 . 807<Uc<2 . 807 3 . 0<nCp<5 . 0 0 . 4<ARR<0 . 545 2 . 0<Ui<2 . 0 -0 . 888<Hy<-0 . 277 37 . 8<C%<40 . 0 0 . 0<nOHt<0 . 0 0 . 0<nOHp<0 . 0; R4: 0 . 6<ARR<0 . 75 1 . 0<nArOH<2 . 0 2 . 807<Uc<3 . 17 170 . 356<SAtot<222 . 475 0 . 893<MLOGP<2 . 778 0 . 0<nArCO<0 . 0 ., Thus , odorants having a “phenolic” quality are of moderate size , with few unsaturations and low hydrophilicity ( and high lipophilicity ) ., It can be regarded as a cyclic molecule ., A good consistency is observed between the 4 rules ., For “vanillin” , the following rules were observed: R1: 0 . 5<ARR<0 . 545 3 . 0<nCb-<4 . 0 3 . 0<nHAcc<3 . 0 1 . 0<nArOR<2 . 0 0 . 0<nR = Cp<0 . 0 0 . 0<nArCO<0 . 0 38 . 1<C%<46 . 2; R2: 3 . 0<nCb-<3 . 0 3 . 0<nO<3 . 0 0 . 0<nArCOOR<0 . 0 -0 . 727<Hy<0 . 66 42 . 1<H%<50 . 0 0 . 0<nArCO<0 . 0 38 . 1<C%<42 . 3; R3: 2 . 0<nCsp3<2 . 0 1 . 0<nArOR<2 . 0 0 . 699<MLOGP<1 . 75 0 . 0<nArCOOR<0 . 0 0 . 0<nArCO<0 . 0 2 . 0<nCb-<4 . 0 ., These descriptions suggest that odorants belonging to this group are mostly cyclic molecule ( like the prototypical molecule vanillin ) , with 3 Hydrogen bond acceptors branched on saturated carbons atoms on an aromatic cycle ., When considering the “musk” quality , the following rules emerged: R1: 3 . 72<MLOGP<4 . 045 2 . 0<nCrs<15 . 0 1 . 0<nCIC<1 . 0 333 . 936<SAtot<436 . 545; R2: 4 . 0<nCb-<6 . 0 33 . 0<nBT<47 . 0 0 . 0<nCbH<2 . 0; R3: 0 . 0<RBN<0 . 0 11 . 0<nCs<16 . 0; R4: 238 . 46<MW<270 . 41 57 . 1<H%<63 . 8 402 . 5<SAtot<440 . 301 0 . 0<nR07<0 . 0 -0 . 931<Hy<-0 . 763 0 . 0<ARR<0 . 316 0 . 0<RBN<12 . 0 0 . 0<nCt<3 . 0 ., Musky molecules are heavy and hydrophobic compounds ., This is reflected by a rather large logP , surface area or molecular weight ., From a general point of view , these descriptors reflect well the features of musky odorants ., For the “sandalwood” quality , two rules were observed: R1: 3 . 0<nCrt<5 . 0 1 . 0<nHDon<1 . 0 0 . 0<nR04<0 . 0 1 . 0<nCrq<2 . 0; R2: 3 . 0<nCrt<5 . 0 1 . 0<nHDon<1 . 0 -0 . 429<Hy<-0 . 325 2 . 0<nR05<3 . 0 ., Sandalwood odorants are quite diverse and minor modifications within their structure can abolish the sandalwood note ., The rules which are mined here correspond to models which are very simple and hardly capture the subtlety of this odorant family 28 ., The description presented here corresponds to the prototypic beta-santalol structure which has a campholenic skeleton ., The “almond” quality was described by four rules: R1: 0 . 0<nCp<0 . 0 152 . 443<SAtot<165 . 41 1 . 0<nO<2 . 0 2 . 0<Ui<2 . 585; R2: 0 . 706<ARR<0 . 8 0 . 0<nArCO<0 . 0 1 . 0<nO<1 . 0 3 . 0<Uc<3 . 807 0 . 143<MLOGP<3 . 571 -0 . 917<Hy<-0 . 71 0 . 0<nCb-<2 . 0; R3: 1 . 0<nH<5 . 0 0 . 0<nOxiranes<0 . 0 1 . 0<nHAcc<3 . 0 1 . 0<nN<2 . 0 23 . 79<TPSA ( Tot ) <90 . 27 0 . 0<O%<14 . 3 0 . 0<ARR<0 . 75; R4: 1 . 0<nArCHO<1 . 0 11 . 0<nBT<20 . 0 45 . 0<C%<47 . 1 -0 . 864<Hy<-0 . 668 1 . 0<nHAcc<2 . 0 ., These descriptions suggest that odorants evoking an almond-like quality are compounds bearing at least one oxygen and/or other hydrogen bond-accepting atom but also bearing an aromatic cycle ., This means that the structure bears several unsaturations ., These chemicals are thus relatively small and can be compared to the prototypical structure of benzaldehyde ., Four physicochemical rules described the “orange-blossom” quality: R1: 10 . 0<nCsp2<10 . 0 9 . 23<TPSA ( Tot ) <58 . 89; R2: 1 . 0<nArNH2<1 . 0 213 . 361<SAtot<326 . 286 0 . 0<nR = Cs<0 . 0 0 . 0<nCt<0 . 0 37 . 9<C%<51 . 5; R3: 0 . 773<ARR<0 . 857 39 . 4<H%<45 . 5 9 . 23<TPSA ( Tot ) <52 . 32 3 . 0<nCb-<5 . 0; R4: 47 . 243<Se<53 . 454 4 . 0<nCbH<9 . 0 3 . 287<MLOGP<5 . 007 3 . 0<nHAcc<4 . 0 0 . 231<ARR<0 . 462 ., These descriptions characterize very diverse structures ranging from very small to medium or large compounds ., As a general rule , one can note the presence of unsaturations , consistent with a terpenic structure , associated with a quite hydrophobic feature ., The “jasmine” quality was described by six rules: R1: 12 . 0<nC<13 . 0 43 . 37<TPSA ( Tot ) <44 . 76; R2: 336 . 137<SAtot<337 . 327 0 . 0<nR = Cs<0 . 0; R3: 7 . 0<nCsp2<8 . 0 1 . 0<nCb-<1 . 0 2 . 0<nCp<3 . 0 50 . 0<H%<53 . 3 4 . 0<nCsp3<5 . 0 1 . 0<nCs<3 . 0 1 . 0<nRCOOR<1 . 0; R4: 1 . 0<nCb-<1 . 0 2 . 034<MLOGP<2 . 386 2 . 0<nHet<3 . 0 7 . 0<nCsp2<8 . 0 1 . 0<nCp<2 . 0 -0 . 807<Hy<-0 . 727 0 . 0<nArCOOR<0 . 0 0 . 0<nArOR<0 . 0; R5: 5 . 0<RBN<6 . 0 1 . 0<nRCO<1 . 0 291 . 434<SAtot<350 . 346 10 . 0<nC<13 . 0 0 . 0<nArCO<0 . 0; R6: 1 . 0<nR = Ct<1 . 0 4 . 0<nCs<8 . 0 2 . 0<nCconj<4 . 0 0 . 0<nCt<0 . 0 -0 . 912<Hy<-0 . 873 ., This rule characterizes, ( i )

Introduction, Methods, Results, Discussion

An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants ., In other words , why do some odorants smell like fruits and others like flowers ?, While the so-called stimulus-percept issue was resolved in the field of color vision some time ago , the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day ., Although a series of investigations have demonstrated that this relationship exists , the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication ., One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality , whereby two very different molecules can evoke a similar odor ., Moreover , the available datasets are often large and heterogeneous , thus rendering the generation of multiple rules without any use of a computational approach overly complex ., We considered these two issues in the present paper ., First , we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities ., Second , we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties ., Third , we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible ., Taken together , our findings provide significant new insights into the relationship between stimulus and percept in olfaction ., In addition , by automatically extracting new knowledge linking chemistry of odorants and psychology of smells , our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling .

An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts ., This is known as the stimulus-percept problem ., Here , we set out to better understand this issue by combining knowledge about the chemistry and cognition of smells with computational olfaction ., We also assumed that not only one , but several physicochemical models may describe a given olfactory quality ., To achieve this aim , a first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities ( e . g . fruity , woody ) ., A second challenge consisted in developing a computational model enabling the discrimination of olfactory qualities based on these chemical features ., By meeting these 2 challenges , we provided for several olfactory qualities new chemical models describing why an odorant molecule smells fruity or woody ( among others ) ., For most qualities , multiple ( rather than a single ) chemical models were generated ., These findings provide new elements of knowledge about the relationship between odorant chemistry and perception ., They also make it possible to envisage concrete applications in the aroma and fragrance field where chemical characterization of smells is an important step in the design of new products .

smell, chemical compounds, statistics, social sciences, neuroscience, data mining, perception, physicochemical properties, cognitive psychology, scientists, forecasting, odorants, mathematics, materials science, information technology, science and technology workforce, physical chemistry, chemical properties, research and analysis methods, physical properties, computer and information sciences, mathematical and statistical techniques, chemistry, physics, people and places, professions, psychology, science policy, careers in research, biology and life sciences, population groupings, materials, physical sciences, sensory perception, cognitive science, phenols, statistical methods

journal.pgen.1004192

Protein Quantitative Trait Loci Identify Novel Candidates Modulating Cellular Response to Chemotherapy

Pharmacogenomics aims to identify clinically actionable markers associated with response or toxicity; for oncology , evaluating genotype-phenotype relationships is particularly important because non-response and adverse events associated with chemotherapy can be life-threatening ., Drug response and toxicity are thought to be multi-genic traits requiring whole genome studies to capture the most relevant variants ., To complement clinical data and enhance discovery of genetic variants associated with sensitivity to drugs using a whole genome approach , we and others ( reviewed by Wheeler and Dolan 1 ) have developed cell-based models using International HapMap lymphoblastoid cell lines ( LCLs ) ., The genetic and expression environment for these cells has been well characterized thus allowing for genome-wide association studies ( GWAS ) and functional follow-up studies ., Genetic variants associated with a given chemotherapeutic discovered in the LCL pharmacogenomic model have been replicated in clinical trials , arguably the most relevant system for biomedical science 2 , 3 , 4 , 5 , 6 ., In addition to their value in pharmacogenomics discovery 7 , 8 , 9 , 10 , 11 , LCLs have had broad utility as a discovery tool for genetic markers associated with many functional phenotypes , including: gene expression 12 , 13 , 14 , 15 , 16; modified cytosines 17; variation in mRNA decay rates across individuals 18; DNase hypersensitivity 19; and baseline micro RNA levels 20 ., In addition , the LCL model has been used to identify genetic markers of inflammatory cell death 21 , bipolar disorder 22 , and response to serotonin reuptake inhibitors 23 , 24 ., Therefore , incorporating protein expression information into an existing dataset of genetic , epigenetic , mRNA expression , and drug sensitivity has the potential to identify novel candidates and mechanisms relevant to pharmacologic traits ., Previously , we reported that SNPs associated with inter-individual variation in cytotoxicity of chemotherapeutic agents in LCLs are enriched in expression quantitative trait loci ( eQTLs ) and separately , enrichment was observed for eQTLs associated with ten or more target genes 25 ., SNPs that overlapped between preclinical LCL studies and outcomes of patients treated with the same drug were also enriched in eQTLs 2 ., An implicit assumption in these analyses and studies of other complex traits is that mRNA transcript abundances are a suitable proxy measurement for their corresponding protein levels ., However , recent data has demonstrated poor overall correlations between mRNA and protein expression 26 , 27 , 28 , 29 , 30 ., To investigate the role of genomics in protein expression and the role protein expression plays in altering pharmacologic responses , we employed the micro-western array ( MWA ) 31 , a method that is approximately 1000-fold more sensitive and has an ∼100-fold greater dynamic range than standard mass spectrometry methods and requires ∼200-fold less sample and antibody than standard immunoblotting methods 32 , 33 ., After screening 4 , 366 previously unvalidated antibodies targeting 1 , 848 transcription factors ( TFs ) and 200 well-validated antibodies targeting cell signaling proteins , we used MWAs and reverse phase protein arrays ( RPPAs ) to collect protein data regarding 441 protein isoforms from 68 HapMap Yoruba ( YRI ) LCLs ., Baseline protein levels were evaluated for their correlations with cellular sensitivity to cisplatin and paclitaxel , two of the most widely-used and successful chemotherapeutics worldwide that are mechanistically distinct 34 , 35 , 36 ., The measurement of proteins in HapMap LCLs is of great value to complement the extensive publicly available genetic information already available on these cell lines ., Although LCLs are not tumor cells , upon transformation they are likely to have changes in pathways that control cell cycle and cell proliferation , which are relevant pathways for anti-cancer drugs ., Furthermore , we identified genetic variants associated with chemotherapeutic sensitivity that acted through their effect on protein levels ., We observed an enrichment of pQTLs in genome variants associated with pharmacologic phenotypes ., We combined this information to identify proteins relevant for pharmacologic phenotypes through multiple independent SNPs throughout the genome ., Prior to our global analysis , a pilot study consisting of three independent biological replicates of six cell lines demonstrated significant variation not only among protein levels from different individuals , but also among cells thawed and propagated independently from the same individual ., Based on a significant thaw effect explaining 3 . 75% of global protein expression variation ( p\u200a=\u200a0 . 01 , F test ) , we measured baseline , steady-state protein levels from three independent thaws ( thawed simultaneously ) from each of 68 unrelated YRI LCLs to have a more accurate estimate of inter-individual variation in protein expression ., These measurements were evaluated with both fixed effect ( by averaging the three thaws ) and mixed effect ( by incorporating a random thaw effect per individual ) models ., Mixed effect modeling ( MEM ) allowed us to gain additional power from multiple measurements compared with simply averaging across the biological replicates in a linear model ( Figure 1a ) ., Relationships identified by fixed effect that had conflicting trends ( i . e . positive and negative associations ) across biological replicates were more likely to be false positives ( Figure 1b ) than the observations that were reproducible by MEM ( across biological replicates ) ( Figure 1c ) ; we therefore considered the MEM to be the more robust approach and used this method for all subsequent estimates of protein-drug associations ., Cell growth inhibition and caspase 3/7 activation were measured following treatment of 68 unrelated YRI LCLs with cisplatin ( 5 µM ) or paclitaxel ( 12 . 5 nM ) ., Notably , the correlation between cytotoxicity and apoptosis was greater for paclitaxel ( r2\u200a=\u200a0 . 35 ) than cisplatin ( r2\u200a=\u200a0 . 04 ) , indicating that apoptotic cell death was a larger contributor to paclitaxel-mediated cell growth inhibition compared with cisplatin ( Figure S1 ) ., We also assessed the effect of date of cell thaw on cellular phenotypes and found a significant correlation across two independent thaws ( Figure S2; p<0 . 0001 and r2>0 . 28 for cytotoxicity , p<0 . 003 and r2>0 . 38 for apoptosis ) ., From a starting pool of 4 , 366 antibodies , 198 antibodies producing a single predominant signal at the predicted molecular weight were carried forward for population-level quantification with the RPPA approach and 243 antibodies that displayed at least one band the size of the targeted protein isoform of interest with a signal-to-noise ratio ≥3 ( but additional bands ) were selected for subsequent population-level quantification by MWAs ., We quantified the expression of 441 proteins across the same set of 68 individual LCLs for which we measured responses to chemotherapeutic agents ., At an FDR of 20% , 64 proteins were associated with one or more of the four drug phenotypes ., At p<0 . 05 , 52 and 60 protein levels were associated with paclitaxel-induced apoptosis and cytotoxicity , respectively , and 47 and 39 proteins were associated with cisplatin-induced apoptosis and cytotoxicity , respectively ., Table S2 details these nominal associations for each phenotype and Table 1 highlights the top three associations for each phenotype ., We compared the overlap between the two drugs and identified four proteins that were unique to the apoptotic pathway including CDKN2B , PDK1 , TFB1M and ZNF132 ., EP300 was the only protein exclusively associated with cytotoxicity for both drugs ., This observation implies that loss of cell viability in response to these two drugs occurs through distinct mechanisms ., Using hierarchical clustering of the drug-protein effect sizes , seven significant clusters were defined by permutation analysis ( p<0 . 001 ) ( Figure 2a ) ., We were unable to identify any significantly enriched pathways due to the limited and biased background set of proteins evaluated; however , we did observe proteins of similar function within the clusters ., Protein levels in cluster one ( Figure 2b ) were associated with increased resistance to both drugs when measured for either phenotype ., Proteins in this cluster included many known metabolism-regulating proteins , DNA damage response factors , proteins associated with innate immune response , and transcription factors associated with various stages of developmental biology ., Metabolism-regulating proteins included mTor , p70S6K ( T421/S424 ) , Gab1 ( Y627 ) , GSK3beta , and ONECUT2 ., DNA damage-related proteins in cluster one included apoptosis antagonizing transcription factor ( AATF ) and structural maintenance of chromosomes protein 1A ( SMC1A ) ., Proteins with known associations to immune response included several ubiquitin ligases such as TRIM13 and TRIM26 ., Protein levels in cluster 3 ( Figure 2c ) were associated with increased cellular sensitivity to both cisplatin and paclitaxel phenotypes and included many proteins related to calcium signaling: phospholipase C gamma 2 ( PLCG2 ) , c-Src ( SRC ) and focal adhesion kinase ( FAK ) ., Other proteins in cluster three included the tumor suppressor p15ink4b ( CDKN2B ) , estrogen receptor beta ( ESR2 ) , beta actin ( BACT ) , alpha tubulin ( TUBA ) , and several transcription factors including c-MYC ( MYC ) , Hairless homolog ( HR ) , H6 family homeobox 1 ( HMX1 ) , and ETS-related transcription factor Elf-4 ( ELF4 ) ., Protein levels in cluster 7 ( Figure 2d ) were associated more strongly with cellular sensitivity/resistance to drug cytotoxicity as compared with drug-induced apoptosis ., Drug-induced cytotoxicity is a broad phenotype that includes cellular processes such as necrosis , cell death through apoptotic and non-apoptotic pathways , cell cycle arrest , and damaged cells undergoing DNA repair 37 , whereas caspase 3/7 activation represents a specific process of cell death ., Upon evaluation of all proteins with a genome-wide significant pQTL , we identified one protein that was also associated with paclitaxel-induced apoptosis ., The trans pQTL on chromosome 16 , rs6834 , was significantly correlated ( p\u200a=\u200a2 . 66×10−15 ) with death inducer-obliterator 1 ( DIDO1 ) protein levels ( Figure 3a ) ., DIDO1 was in cluster 3 ( Figure 2c ) , indicating that increased baseline levels conferred greater cellular sensitivity to both chemotherapeutic agents ., DIDO1 protein levels were significantly correlated with paclitaxel-induced apoptosis ( p\u200a=\u200a0 . 01 r2\u200a=\u200a0 . 02; Figure 3b ) ., However , the DIDO1 pQTL was not significantly associated with paclitaxel-induced apoptosis ( p\u200a=\u200a0 . 25 , Figure 3c ) ., Despite the lack of statistical significance ( likely because of small sample size ) , the directionality was consistent with the observed protein relationship: cells containing two C alleles had lower levels of DIDO1 and lower paclitaxel-induced caspase 3/7 activation ., DIDO1 mRNA levels were not associated with paclitaxel apoptosis ( p>0 . 05 ) , suggesting that this relationship was protein-specific ., Using RNA interference , we performed gene knockdowns in YRI LCLs and examined the effect of knockdown on paclitaxel-induced cytotoxicity and apoptosis ., Three different LCLs were nucleofected with siRNA against DIDO1 ., Although knockdown levels varied considerably , the maximal degrees of protein knockdown observed for 24 or 48 hours in 18522 , 18853 , and 19192 , were 20% , 48% , and 59% , respectively ., When we pooled data from all cell lines and experiments using a MEM , knockdown of DIDO1 resulted in a significant ( p\u200a=\u200a0 . 005 ) decrease in paclitaxel-induced caspase activity ., On average , paclitaxel-induced apoptosis was decreased by 11 . 9% in cells following knockdown of DIDO1 ( Figure 3d ) ., Using the pQTLs and eQTLs ( unadjusted p<10−4 ) from the genes included in our protein dataset , we evaluated enrichment with paclitaxel and cisplatin-induced cytotoxicity and apoptosis associated SNPs at unadjusted p<10−3 ( Figure 4 ) ., For cisplatin , only the apoptosis phenotype demonstrated pQTL enrichment ( p<0 . 001 ) ( Figure 4a , 4b , left panels ) ., Conversely , both paclitaxel phenotypes demonstrated pQTL enrichment ( Figure 4c , 4d ) ., When evaluating eQTLs , only cisplatin cytotoxicity showed enrichment for eQTLs ( Figure 4b ) ., However , when evaluating all expressed genes , eQTLs showed enrichment for all drugs and phenotypes except for cisplatin-induced apoptosis ( data not shown ) ., Using both cell growth inhibition and apoptosis as cellular phenotypes , we identified pQTLs ( defined at p<10−4 ) associated with these phenotypes at p<0 . 001 ., From that overlap of pQTLs , we then analyzed the relationship between target protein levels and the respective drug phenotype ( p≤0 . 05 ) ( Figure 5 ) ., Overlapping GWAS signals identified five proteins for cisplatin phenotypes and 21 proteins for paclitaxel phenotypes ( Table S3 ) ., For each phenotype , we also identified individual lists of proteins-pQTL pairs that both associate with cisplatin or paclitaxel phenotypes ( Table S4 ) ., For cisplatin GWAS , there were 79 pQTLs targeting 27 proteins for cytotoxicity and 169 pQTLs targeting 27 proteins for apoptosis ., For paclitaxel GWAS , there were 107 pQTLs targeting 38 proteins for cytotoxicity and 119 pQTLs targeting 42 proteins for apoptosis ., Interestingly , the protein SRC was implicated through all four phenotypes ., We prioritized proteins for functional studies using the apoptosis relationship for paclitaxel and the cytotoxicity relationship for cisplatin ., Among the five proteins whose baseline expression levels associated with cisplatin cytotoxicity and apoptosis , we found structural maintenance of chromosomes 1A ( SMC1A ) to have the most significant relationship with cytotoxicity ( p\u200a=\u200a0 . 005 , r2\u200a=\u200a0 . 039 ) ( Figure 6a and 6b ) ., We therefore selected it for further functional validation ., SMC1A did not associate with either cisplatin phenotype at the mRNA level suggesting that this was a protein-specific relationship ., Because more proteins were associated with paclitaxel-mediated apoptosis and cytotoxicity phenotypes , we prioritized functional follow-up based on a combination of p-values and q-values ( to correct for multiple hypothesis testing ) ., At p<0 . 005 , five proteins were significantly associated with paclitaxel-induced apoptosis ., Zinc finger protein 569 ( ZNF569 ) ( Figure 6c , 6d ) had the lowest association q value ., At the mRNA level , ZNF569 had a weak correlation with paclitaxel-induced apoptosis ( p\u200a=\u200a0 . 04 , r2\u200a=\u200a0 . 06 ) , but no relationship with paclitaxel-induced cytotoxicity ., Table 2 lists the pQTLs that implicated SMC1A with the two cisplatin phenotypes and ZNF569 with the two paclitaxel phenotypes ., We observed a different set of SNPs associated with each protein-drug pair that also associated with either apoptosis or cytotoxicity ( Table 2 ) ., Because independent pQTLs associated with the drug-induced phenotypes , we functionally validated the relationship of these proteins with their respective drug-induced phenotypes ., We selected three LCLs ( 18502 , 19138 , 19201 ) with mid to high protein expression and performed siRNA nucleofection ., We assessed knockdown at 24 and 48 hours post nucleofection ., Knockdown of SMC1A protein levels varied across the cell lines; we did not observe more than 57% , 71% , and 62% protein knockdown for 18502 , 19138 , and 19201 , respectively , for either time point ., Using a MEM to examine the effect across cell lines , we determined that knockdown of SMC1A resulted in a 19% increase in apoptosis ( p\u200a=\u200a0 . 0002 ) and a 10 . 4% decrease in cell survival ( p\u200a=\u200a0 . 009 ) in response to cisplatin ( Figure 7a ) ., Knockdown of ZNF569 protein levels varied across the cell lines , but we observed no more than 45% , 58% , and 54% protein knockdown across 18502 , 19138 , and 19201 , respectively , for either time point ., Using a MEM to combine the effect across cell lines , knockdown of ZNF569 resulted in a 9 . 9% average reduction in apoptosis ( p\u200a=\u200a0 . 002 ) and a 26 . 8% increase in cell growth inhibition ( p\u200a=\u200a0 . 0001 ) ( Figure 7b ) in response to paclitaxel ., Because growth rate has been previously identified as a heritable trait that is relevant in pharmacologic studies , we evaluated the relationship between steady state protein levels and intrinsic growth rate 38 for the proteins measured ., Approximately 10% ( 45/441 ) of the proteins were correlated with growth at p<0 . 05 ( Table 3 ) ., Notably , SMC1A protein levels were significantly correlated with growth rate ( p\u200a=\u200a0 . 0007 ) , whereas ZNF569 protein levels were not ( p>0 . 05 ) ( Figure S3 ) ., When we adjusted for growth rate , the association of SMC1A protein levels with cisplatin phenotypes was no longer significant ( p>0 . 05 ) ., In this study , we evaluated 4 , 366 antibodies targeting 2 , 048 unique proteins ., From this set , we identified antibodies targeting 441 protein isoforms expressed at baseline in LCLs and quantified them across three biological samples from 68 YRI LCLs ., The use of multiple biological samples allowed us to implement mixed effects modeling to increase the robustness of our observations ., Many protein expression levels were correlated with sensitivity to two cellular phenotypes ( cytotoxicity and apoptosis ) of two chemotherapeutic agents: paclitaxel and cisplatin ., We validated one such finding through knockdown of DIDO1 in three LCLs , which resulted in a decrease in paclitaxel-induced apoptosis ., Quantitative trait loci for pharmacologic phenotypes were compared to quantitative trait loci for protein expression to better understand the functional significance of genetic variants contributing to inter-individual variability in drug response ., For each drug , we identified overlapping and unique sets of genetic variants associated with protein expression that were also correlated with drug-induced apoptosis and cytotoxicity ., We further validated two such proteins through gene knockdown and concomitant modulation of cellular sensitivity to drug treatment: SMC1A levels were associated with resistance to cisplatin treatment , and ZNF569 levels were associated with sensitivity to paclitaxel treatment ., This study illustrates the utility of applying a highly-sensitive , novel , antibody-based technology to simultaneously measure many proteins across a large set of individuals ., Using this method , we identified hundreds of novel genome loci that uniquely influence the expression of proteins that ultimately influence the sensitivity of cells to chemotherapeutic agents through both caspase 3/7 activation and other pathways leading to loss of cell viability ., We evaluated protein expression in the International HapMap LCLs because these samples have previously been used for many studies relating genetics to gene expression 14 , 16 , 39 and cellular phenotypes 1 , thus allowing us to perform comprehensive studies of genetics , protein expression , and pharmacology ., LCLs are immortalized B-lymphocytes and , as a result , represent “non-cancerous” cells that may provide us with important protein targets for ameliorating bone marrow suppression ., However they also have some of the pathways relevant to anti-cancer drugs ., We specifically chose the YRI population because of their greater genetic diversity relative to other populations ., We expect that this data will have wide applicability to other genetic and pharmacological studies because of the important addition of protein levels to other studies ., Whereas polymorphisms in coding regions that affect amino acid composition would seem to have the greatest effect on drug response , genetic variation that affects transcript abundance level has also been shown to affect drug response 25 ., A disproportionate number of drug response associated SNPs in a broad array of chemotherapeutic agents are eQTLs and are associated with the transcriptional expression level of multiple genes 25 ., However , our work has demonstrated poor global correlations between inter-individual mRNA and protein levels ( unpublished data ) ., Therefore , functional annotation of pharmacologic SNPs and their relationships with proteins may result in important new discoveries as it has in this study ., We note that 46 , 863 of the 121 , 484 trans pQTLs identified at P<10−4 are also cis-acting eQTLs ( within 1 Mb upstream of the transcription start state to 1 Mb downstream of the transcription end site ) for at least one of the 18 , 227 gene models quantified by RNA-Seq at P<0 . 05 ., This proportion ( 38 . 6% ) is statistically enriched compared with the proportion of all single nucleotide variants genome-wide that are cis-eQTLs ( 36 . 6% , Fishers exact test P<2 . 2×10−16 , odds ratio\u200a=\u200a1 . 09 ) , suggesting that cis-acting may contribute to some extent to underlying trans-genetic regulation of protein levels ., Because we performed multiple analyses to examine overlap and enrichment of protein and drug QTL , the p-value thresholds used in this study were more permissive relative to that typically used for genome-wide analyses ., By contrast to various chemotherapeutics that exhibit GWAS enrichment in eQTLs 25 , paclitaxel GWAS results were not enriched in eQTLs; however , we identified enrichment in pQTLs for both paclitaxel-induced apoptosis and cytotoxicity phenotypes ., Therefore , genetic variants associated with the level of a protein appear to be more important for sensitivity to this drug than mRNA regulatory variants ., We functionally validated one of these observations , DIDO1 , by siRNA knockdown ., DIDO1 is a tyrosine phosphorylated transcription factor that is localized to the nucleus 40 ., DIDO1 was also found within cluster 3 , which contained proteins with increased baseline levels correlating with greater cytotoxicity and apoptosis to each chemotherapeutic agent tested ., DIDO1 is generally believed to function through apoptosis-related processes; however , it has also been suggested to function in mitotic division based on gene overexpression in mice 41 ., This proposed function provides a clear mechanistic connection to paclitaxel , a drug that kills cells through microtubule inhibition ., Both paclitaxel and cisplatin have been in use for decades , and significant effort has been expended to identify strategies that result in increased tumor sensitivity to these agents , including targeting the activity of drug resistance pathways ., However , this approach is only successful if the cancerous and non-cancerous cells differ in their response to modulation ., Improving the therapeutic index for patients occurs if the “modulating agent” increases the sensitivity of chemotherapy in the tumor while decreasing toxicity in non-tumor tissues ., This study offers an opportunity to identify the relationship between transcription factors and signaling molecules and drug sensitivities in a non-tumor environment ., For example , high levels of proteins identified in cluster 3 were associated with greater sensitivity to both cisplatin and paclitaxel; yet several of these proteins including c-Src 42 , 43 and c-Myc 44 , 45 have been shown to be overexpressed in tumor cells and their expression correlates with paclitaxel or cisplatin resistance ., c-Src tyrosine kinase is overexpressed in a high proportion of ovarian cancers and ovarian cancer cell lines ., Its inhibition , either pharmacologically or through gene knockdown , results in an increase in sensitivity of ovarian cancer cells to paclitaxel and cisplatin 43 ., The increased cytotoxicity in response to c-Src inhibition was associated with a large increase in processing and activation of caspase-3 ., Our data support these proteins as potential drug targets , because reducing their levels in LCLs would result in lower sensitivity to the toxic effects of cisplatin and paclitaxel in contrast to cancerous cells ., We anticipate that this dataset will therefore have great utility for the development of novel modulators of chemotherapy ., Although LCLs are a more likely model for toxicity , we identified several relationships that have been recapitulated in tumor response ., Signal transducer and activator of transcription 3 ( STAT3 ) had the strongest negative associations with cisplatin- and paclitaxel-induced apoptosis , suggesting high levels of STAT3 protein conveyed drug resistance ., STAT3 mRNA expression has previously been reported to be associated with cisplatin resistance in many cancer types , including head and neck 46 , small cell lung carcinoma 47 , and human epidermoid cancer cells 48 , in which the CRE/ATF binding elements in the STAT3 promoter were shown to be important for mediating cisplatin resistance ., STAT3 mRNA expression has also been implicated in paclitaxel resistance ., Knockdown of STAT3 conveyed sensitivity to paclitaxel in lung cancer cell lines 49 ., STAT3 has been hypothesized as a potential target to modulate paclitaxel sensitivity in cancer patients 50 ., PTEN is also an example of same direction of effect in LCLs and cancer cells , however unlike STAT3 , increased levels of PTEN convey sensitivity ., Recent studies have demonstrated that PTEN has the ability to enhance cancer cell sensitivity to particular anticancer agents ., PTEN might reverse the chemoresistance of human ovarian cancer cells to cisplatin through inactivation of the PI3K/AKT cell survival pathway and may serve as a potential molecular target for the treatment of chemoresistant ovarian cancer 51 ., SMC1A is part of the multi-protein cohesion complex required for sister chromatid cohesion ., This cohesion complex has been shown to interact with the BRCA1 DNA repair protein and has been shown to be phosphorylated by ATM , a serine/threonine kinase activated by DNA double-strand breaks 52 ., The cohesion complex has also been shown to be important for expression regulation and genomic stability 53 ., Mutations in SMC1A have been shown to cause Cornelia de Lange syndrome , a multisystem developmental disorder with defects ranging from limb formations to cardiac , gastrointestinal , growth and cognitive systems 53 ., Coding variants have also been identified in colon cancer 54 and have been implicated in impairing cellular response to toxic treatment 55 ., Accumulated SMC1A protein has been linked to bortezomib-induced cell death , demonstrating its relevance for another chemotherapeutic agent 56 , but this is the first study implicating SMC1A for cisplatin-induced cellular response ., Recently , Wip1 , an important signaling protein in cellular growth following DNA damage , has been identified as an upstream regulator of SMC1A 57 , further suggesting an important role for this protein in cancer and chemotherapeutic response ., SMC1A has also been linked to cellular growth rate and was identified within cluster one which included proteins whose levels were associated with reduced cytotoxicity and apoptosis phenotypes across both drugs ., Another protein we functionally validated associated with paclitaxel , ZNF569 , was a notable candidate because it has been functionally implicated as a transcriptional repressor that suppresses MAPK signaling 58 ., Because of the importance of MAPK signaling in breast cancer 59 and the common use of paclitaxel as a breast cancer therapy 60 , this association presents an interesting biological mechanism and potential therapeutic marker ., ZNF569 is supported in our data as a transcriptional suppressor of MAPK signaling , because lower ZNF569 protein levels were correlated with increased cellular survival ., In addition , ZNF569 was also found in the cluster of proteins that negatively correlated more strongly with cytotoxicity than apoptosis for both drugs , perhaps indicating a role for ZNF569 in cell growth inhibition unrelated to caspase 3/7 activation ., Notably , this study focused on two widely used but mechanistically distinct agents ., By examining two distinct cell phenotypes , cell growth inhibition and caspase 3/7 activation , our study identified proteins associated with different cell signaling pathways responsible for cell growth inhibition ., Although our study did not reveal candidates with strikingly high effect sizes that were predictive of drug sensitivity , it revealed many unique proteins whose expression levels were correlated with phenotypic measurements for a single drug ., This observation is consistent with multiple proteins contributing small influences to drug sensitivity ., The protein data collected in this study allowed us to gain a new understanding of the potential mechanisms and pathways relevant for cell viability and the genetic variants regulating those proteins ., Interpreting GWAS results continues to present challenges; increasingly , eQTL studies are being used to inform 25 , 61 , 62 interpretation of these results and are the focus of expanded studies to understand biological mechanisms 63 , 64 ., These association tests have been extended to other functional units in the genome from microRNAs 20 to DNA hypersensitivity sites 19 and modified cytosines 17 ., The main factor limiting the inclusion of proteins in GWAS studies has been the lack of a reliable , high-throughput methodology to quantify them across populations of individuals ., The approach described in this study , including the newly developed microwestern array 32 , has started to bridge that technological gap 33 , and this study demonstrates the utility of targeted protein-omic datasets to understand cellular phenotypes and genomic studies ., YRI LCLs derived from unrelated individuals from the population residing in Ibadan , Nigeria ( n\u200a=\u200a68 ) were chosen for consistency with publicly available mRNA expression data on a single population 16 ., LCLs were cultured in RPMI 1640 media containing 20 mM L-glutamine and either 15% fetal bovine serum ( Hyclone , Logan , UT ) for baseline protein quantification , cisplatin and paclitaxel apoptosis and cisplatin cytotoxicity experiments or bovine growth serum ( Hyclone , Logan , UT ) for paclitaxel cytotoxicity experiments ., Cell lines were diluted three times per week at a concentration of 300 , 000–350 , 000 cells/mL and maintained in a 37°C , 5% CO2 humidified incubator ., Medium and components were purchased from Cellgro ( Herndon , VA ) ., Drug-induced apoptosis and cytotoxicity phenotypes were determined at 5 µM cisplatin and 12 . 5 nM paclitaxel ., Both drugs were prepared as described previously: cisplatin 65 and paclitaxel 66 ., The cytotoxic effect of cisplatin 65 and paclitaxel 66 was determined using a short-term cellular growth inhibition assay , and the apoptotic effect was measured using a caspase 3/7 activity detection reagent Caspase-Glo 3/7 ( Promega Corporation , Madison , WI ) ., Three independent thaws constituting biological replicates of 68 unrelated YRI cell lines were propagated and pelleted ( 5 . 1 million cells per pellet ) ., Cells were spun at 400 RPM , aspirated , and washed in ice-cold PBS ., This process was repeated twice and then the pellets snap frozen in liquid nitrogen and placed at −80 degrees ., Total protein was extracted by re-suspension in 1 . 0 mL of 1 . 5% SDS lysis buffer ( 240 mM Tris-acetate , 1 . 5% w/v SDS , 0 . 5% w/v glycerol , 5 mM EDTA ) containing 50 mM DTT , protease inhibitors ( 1 µg/mL aprotinin , 1 µg/mL leupeptin , 1 µg/mL pepstatin ) , and phosphatase inhibitors ( 1 mM sodium orthovanadate , 10 mM β-glycerophosphate ) ., To ensure complete protein denaturation , samples were boiled for 10 min , sonicated for 10 min ( alternating 30 s on , 30 s off ) with a Bioruptor ( Diagenode ) , and con

Introduction, Results, Discussion, Materials and Methods

Annotating and interpreting the results of genome-wide association studies ( GWAS ) remains challenging ., Assigning function to genetic variants as expression quantitative trait loci is an expanding and useful approach , but focuses exclusively on mRNA rather than protein levels ., Many variants remain without annotation ., To address this problem , we measured the steady state abundance of 441 human signaling and transcription factor proteins from 68 Yoruba HapMap lymphoblastoid cell lines to identify novel relationships between inter-individual protein levels , genetic variants , and sensitivity to chemotherapeutic agents ., Proteins were measured using micro-western and reverse phase protein arrays from three independent cell line thaws to permit mixed effect modeling of protein biological replicates ., We observed enrichment of protein quantitative trait loci ( pQTLs ) for cellular sensitivity to two commonly used chemotherapeutics: cisplatin and paclitaxel ., We functionally validated the target protein of a genome-wide significant trans-pQTL for its relevance in paclitaxel-induced apoptosis ., GWAS overlap results of drug-induced apoptosis and cytotoxicity for paclitaxel and cisplatin revealed unique SNPs associated with the pharmacologic traits ( at p<0 . 001 ) ., Interestingly , GWAS SNPs from various regions of the genome implicated the same target protein ( p<0 . 0001 ) that correlated with drug induced cytotoxicity or apoptosis ( p≤0 . 05 ) ., Two genes were functionally validated for association with drug response using siRNA: SMC1A with cisplatin response and ZNF569 with paclitaxel response ., This work allows pharmacogenomic discovery to progress from the transcriptome to the proteome and offers potential for identification of new therapeutic targets ., This approach , linking targeted proteomic data to variation in pharmacologic response , can be generalized to other studies evaluating genotype-phenotype relationships and provide insight into chemotherapeutic mechanisms .

The central dogma of biology explains that DNA is transcribed to mRNA that is further translated into protein ., Many genome-wide studies have implicated genetic variation that influences gene expression and that ultimately affect downstream complex traits including response to drugs ., However , because of technical limitations , few studies have evaluated the contribution of genetic variation on protein expression and ensuing effects on downstream phenotypes ., To overcome this challenge , we used a novel technology to simultaneously measure the baseline expression of 441 proteins in lymphoblastoid cell lines and compared them with publicly available genetic data ., To further illustrate the utility of this approach , we compared protein-level measurements with chemotherapeutic induced apoptosis and cell-growth inhibition data ., This study demonstrates the importance of using protein information to understand the functional consequences of genetic variants identified in genome-wide association studies ., This protein data set will also have broad utility for understanding the relationship between other genome-wide studies of complex traits .

biotechnology, genome-wide association studies, genome expression analysis, genome complexity, genomics, genome analysis, transcriptome analysis, genetics, biology and life sciences, genomic medicine, computational biology, pharmacogenomics

journal.pcbi.1006226

Mapping DNA sequence to transcription factor binding energy in vivo

High-throughput sequencing allows us to sequence the genome of nearly any species at will ., The amount of genomic data available is already enormous and will only continue to grow ., However , this mass of data is largely uninformative without appropriate methods of analyzing it ., Despite decades of research , much genomic data still defies our efforts to interpret it ., It is particularly challenging to interpret non-coding DNA such as intergenic regulatory regions ., We can infer the locations of some transcription start sites and transcription factor binding sites , but these inferences tell us little about the functional role of these putative sites ., In order to better interpret these types of sequences , we need a better understanding of how sequence elements control gene expression ., A deep understanding of the relationship between DNA sequence and gene expression would enable one to, a ) predict the binding strengths of novel transcription factor binding sites and, b ) design regulatory sequences de novo for synthetic biology applications ., An important avenue for developing this level of understanding is to propose models that map sequence to function and to perform experiments that test these models ., One challenge that has made it difficult to develop quantitative sequence-function mappings is the fact that an extremely small portion of known regulatory sequences are understood on a quantitative biophysical level ., Over half of the genes in Escherichia coli , which is arguably the best-understood model organism , lack any regulatory annotation ( see RegulonDB 1 ) ., Those operons whose regulation is well described ( e . g . the lac , rel , and mar operons 2–4 ) required decades of study involving laborious genetic and biochemical experiments 5 ., A wide variety of new techniques have been proposed and implemented to simplify the process of determining how a gene is regulated ., Chromatin immunoprecipitation ( ChIP ) based methods such as ChIP-chip and ChIP-seq make it possible to determine the genome-wide binding locations of individual transcription factors of interest ., Massively parallel reporter assays ( MPRAs ) have made it possible to read out transcription factor binding position and occupancy in vivo with base-pair resolution , and provide a means for analyzing additional features such as “insulator” sequences 6–8 ., In vitro methods based on protein-binding microarrays 9 , SELEX 10–12 , MITOMI 13–15 , and binding assays performed in high-throughput sequencing flow cells 16 , 17 have made it possible to measure transcription factor affinity to a broad array of possible binding sites and can also account for features such as flanking sequences 15 , 18 , 19 ., However , in vitro methods cannot fully account for the in vivo consequences of binding site context and interactions with other proteins ., Current in vivo methods for measuring transcription factor binding affinities , such as bacterial one-hybrid 20 , 21 , require a restructuring of the promoter so that it no longer resembles its genomic counterpart ., Additionally , efforts to computationally ascertain the locations of transcription factor binding sites frequently produce false positives 22 , 23 ., Furthermore , a common assumption underlying many of these methods is that transcription factor occupancy in the vicinity of a promoter implies regulation , but it has been shown that occupancy cannot always accurately predict the effect of a transcription factor on gene regulation 24 , 25 ., As these examples show , it remains challenging to integrate multiple aspects of transcription factor binding into a cohesive understanding of gene regulation that would allow for predictive models that map sequence to function ., In previous work we showed how an MPRA called Sort-Seq can be used on virgin promoters to identify regulatory architectures 26 ., The current work takes the logical and critical next step of rigorously examining how reliable the Sort-Seq results are as a foundation for predicting and controlling transcription with single-nucleotide resolution ., In Ref ., 27 , we showed that the MPRA Sort-Seq 28 , combined with a simple linear model for protein-DNA binding specificity , can be used to accurately predict the binding energies of multiple RNAP binding site mutants , serving as a jumping off point for the use of such models as a quantitative tool in synthetic biology ., Here we apply this technique to transcription factor binding sites in an effort to better understand how transcription factors interact with regulatory DNA under different conditions ., Specifically , we use Sort-Seq to map sequence to binding energy for a repressor-operator interaction , and we rigorously characterize the variables that must be considered in order to obtain an accurate mapping between DNA sequence and binding energy ., We then use our sequence-energy mapping to design a series of operators with a hierarchy of controlled binding energies measured in absolute energy units ( kBT ) ., To demonstrate our control over these operators and their associated regulatory logic , we use these characterized binding sites to design a wide range of induction responses with different phenotypic properties such as leakiness , dynamic range and EC50 ., Next , we focus our attention on the synergy between mutations in the amino acid sequence of transcription factors and their corresponding binding sites ., Finally , we show the broader reach of these results by exploring how binding site position and regulatory context can change the DNA-protein sequence specificity for multiple different transcription factors ., A major goal of this study was to show that one can use Sort-Seq to precisely map DNA sequence to binding energy for a transcription factor binding site , thus making it possible to predict and manipulate transcriptional activity in vivo ., While numerous in vitro studies have successfully mapped sequence to affinity 9–17 , 29 , 30 , and some in vivo studies have used methods such as bacterial one-hybrid to provide such mappings as well 20 , 21 , these studies are limited because they do not reflect the actual wild-type arrangement of regulatory elements , thus potentially missing vital regulatory information ., Moreover , while position-weight matrices ( PWMs ) derived from genomic data have traditionally been used to ascertain in vivo sequence specificities , it can be difficult to convert these specificities into quantitative binding energy mappings due to the relatively small number of sequences that are used to generate these PWMs ., Sort-Seq has previously been shown to be a promising technique for mapping protein binding sequences to binding energies ., In Ref ., 27 , binding energy predictions for RNAP were made from an energy matrix generated in Ref ., 28 that used the wild-type lac promoter as a reference sequence ( i . e . the sequence that was mutated to perform Sort-Seq ) ., Here , we design experiments that use the Sort-Seq technique described in 28 with the specific intent of creating energy matrices with maximum predictive power ( see Fig 1 ) , and we test the predictions from these matrices against measured binding energies ., We show that such predictive matrices can be produced for multiple transcription factors ( e . g . XylR , PurR , and LacI ) implicated in an array of regulatory architectures ., To thoroughly test the accuracy of our predictive matrices , we begin with promoters that employ “simple repression , ” in which a repressor binds to an operator such that it occludes RNAP binding , thereby preventing transcription and repressing the gene 31 ., As a model for how sequence-energy mappings might be used for transcription factor binding sites in simple repression architectures , we interrogate the binding specificity of the lac repressor ( LacI ) ., LacI was chosen for this role because it is well-characterized and has known binding sites in only one operon within the genome , making it an ideal choice for this kind of systematic and rigorous analysis ., We create three distinct energy matrices in which each of the natural lac operators ( O1 , O2 , or O3 2 ) acts as the reference sequence ., S1 Fig lists the wild-type sequences for these simple repression constructs ., As described in Fig 1 , to perform Sort-Seq we start by mutating the promoter at a rate of ∼ 10% ., Here we mutate both the RNAP binding site and the operator , starting with either O1 , O2 , or O3 for the operator sequence ., While our analysis focuses on the operators themselves , mutating the RNAP site as well aids in model-fitting as described in S1 Text ., We place the promoters upstream of a fluorescent reporter gene and create a plasmid library of these constructs ., We transform this plasmid library into a population of E . coli in which lacI and lacZYA have been deleted , but lacI has been reintroduced to the genome with a synthetic RBS that allows us to precisely control the LacI copy number within the cell , as described in Ref ., 32 ., We require at least 106 transformants for each plasmid library to ensure sufficient library diversity ., Then , we use fluorescence-activated cell sorting ( FACS ) to sort E . coli containing these plasmids into four bins based on their expression levels ., We perform high-throughput sequencing on the libraries from each bin ., For the majority of our analysis , we split the sequencing results into three separate “replicates . ”, As discussed in S1 Text , this provides a level of variation that is comparable to multi-day biological replicates ., Once we have a set of promoter sequences with corresponding expression levels , we use these data to infer an energy matrix for the transcription factor binding site that assigns energy values ( in arbitrary units ) to each base at each sequence position ., We use Markov Chain Monte Carlo ( MCMC ) with a Metropolis-Hastings algorithm to infer a set of energy matrix values that maximizes the mutual information between the predicted binding energies of our sequences and their corresponding expression bins ., Briefly , this algorithm proceeds as follows: 1 ) a set of energy matrix parameters is proposed for each base at each operator position , 2 ) the proposed matrix is used to calculate the binding energies ( in arbitrary units ) of the set of binding site sequences , and 3 ) this set of energy parameters is accepted or rejected with some probability ., If it is rejected , a new set of energy parameters is proposed , some distance away from the previous parameter set ., As discussed in detail in Ref ., 33 , the probability of accepting a proposal is derived from the probability distribution p ( data|model ) ∝ 2NI ( ε ( σ ) ;μ ) , where N is the number of data points and I ( ε ( σ ) ; μ ) represents the mutual information between the energy prediction ε ( σ ) for the promoter sequence σ and the expression bin μ ., In other words , the set of proposed values is accepted or rejected according to how well the associated binding energy predictions explain the observed distribution of sequences in expression bins ., After this algorithm has been iterated 30000 times ( after 10000 repeats for a “burn-in” period ) , the energy matrix values are then determined by calculating the sample mean of the parameter values that were accepted ., Once an energy matrix has been inferred , the matrix is fixed such that the matrix elements corresponding to the reference sequence are set at 0 , and the other matrix elements at each sequence position are calculated relative to this reference element ., The reference sequence is the operator sequence that serves as the “wild-type” in each Sort-Seq experiment–that is , the sequence away from which the library sequences are mutated ., For more details on this procedure , see S1 Text ., The energy matrices that result from the procedure described above are given in arbitrary energy units ., To convert these arbitrary units into absolute energy units , we also perform Bayesian parameter estimation using MCMC to determine the scaling factor that should be applied to the energy matrix to convert each position into kBT energy units ( we note that 1 kBT ≈ 0 . 62 kcal/mol at T = 37°C ) ., This inference procedure is highly similar to the procedure outlined above , but where before we maximized the mutual information between predicted binding energies and expression bin for a full set of energy matrix parameters , here we maximize the mutual information between predicted expression and expression bin for a single parameter , which is the scaling factor that converts a matrix into absolute energy units ., This scaling factor is a “diffeomorphic mode” of the model that cannot be inferred by direct mutual information maximization , but can be inferred when incorporated into a more complex model in which other sequence elements are also varied 34 ., We make use of the assumption that gene expression will be proportional to the probability that RNAP is bound to the promoter , pbound , which is given by, p b o u n d = P N N S e - β Δ ε P 1 + P N N S e - β Δ ε P + 2 R N N S e - β Δ ε R , ( 1 ), where P is the number of RNAP in the cell , ΔεP is the RNAP binding energy , R is the repressor copy number , NNS is the number of nonspecific binding sites in the genome , and ΔεR is the repressor binding energy ., As noted in S1 Text , our inference procedure also requires that we infer an energy matrix and scaling factor for the RNAP binding site ., We do not note these values in this work , as our focus is on sequence-energy mappings for transcription factor binding sites ., We note also that we can write the repressor binding energy as ΔεR = αεmat + Δεwt , where εmat is the energy value obtained by summing the matrix elements associated with a sequence , Δεwt is the binding energy associated with the reference sequence , and α is the desired scaling factor that converts the matrix values into kBT units ., To obtain a value for α , an MCMC algorithm is used where we maximize the mutual information I ( pbound , μ ) between the values of pbound calculated for each binding site sequence and the expression bin into which that sequence was sorted ., For more information on this process , see S1 Text ., See S2 Text for a comparison to other methods for obtaining the scaling factor ., A reference sequence refers to the sequence which serves as the “wild-type” for each experiment ., For each library , the promoter is mutated relative to its reference sequence ., Additionally , when assigning binding energies to an energy matrix , all binding energies are calculated relative to the reference sequence ., One might assume that Sort-Seq experiments should reveal the same binding specificity regardless of the reference sequence used to produce the library , provided that the transcription factor does not change ., To test this possibility , we generated energy matrices using three different reference sequences , all of which are binding sites for LacI ., For our reference sequences we use the three natural E . coli lac operators ( O1 = AATTGTGAGCGGATAACAATT , O2 = AAATGTGAGCGAGTAACAACC , and O3 = GGCAGTGAGCGCAACGCAATT ) ., For our primary analysis we use single-point energy matrix models ., These models assume that each nucleotide position within a binding site contributes independently to the binding energy ( see S3 Text for predictions using higher-order models ) ., Each operator has a distinct LacI binding energy , with O1 being the strongest at -15 . 3 kBT , O2 being the second strongest at -13 . 9 kBT , and O3 being the weakest at -9 . 7 kBT 32 ., The operator sequences are rather dissimilar to each other , with O2 having 5 mutations relative to O1 and O3 having 8 mutations relative to O1 ( and 11 mutations relative to O2 ) ., For each library , the average operator sequence has only 2 mutations relative to the reference sequence ., As a result , a library generated with O1 as the reference sequence is unlikely to share any mutant sequences with a library generated with O2 or O3 as the reference sequence ., Here we assess whether dissimilar mutant libraries generated from different reference sequences produce similar energy matrices and sequence logos from their respective Sort-Seq data sets ., We obtain energy matrix models by following the Sort-Seq procedure outlined above , splitting our Sort-Seq data into three separate groups of nonoverlapping sequences to produce matrix replicates , as discussed in S1 Text ., We then infer an energy matrix for each replicate ., In Fig 2 we show energy matrices composed of the mean energy value at each matrix position , and the corresponding sequence logos ., As shown in Fig 2A , the three operators each produce qualitatively similar energy matrices , with the left side of the binding site showing greater sequence dependence than the right side , as evidenced by the larger magnitude of the binding energies assigned to each matrix position ., Note that we set the binding energy of the reference sequence to 0 kBT for these energy matrices , so that the binding energies assigned to each possible mutation are calculated relative to the reference sequence ., For all energy matrices , positions 4-10 show the greatest sequence preference ., This preference is reflected in the natural lac operator sequences themselves , as the bases from 4-10 are conserved in each of the operators ., Notably , the majority of mutations available to O1 incur a penalty to binding energy , while many of the mutations available to O3 enhance the binding energy ., This is consistent with the observation that O1 has a strong binding energy while O3 has a weak binding energy ., When the energy matrices are used to produce sequence logos as in Fig 2B ( see Ref . 35 for an explanation of the mathematics used to relate binding energies to base-pair frequencies , and Ref . 36 for a discussion of sequence logos themselves ) , we see a consistent preference for a slightly asymmetric binding site , reflecting the fact that LacI is known to bind asymmetrically to its operators 37 ., Additionally , clear differences arise for the different operators ., While the sequence logos derived from O1 and O2 indicate very similar sequence preferences , the preferred sequence suggested by the O3 sequence logo differs in some prominent positions ., In S4 Text we note that weaker binding sites exhibit a greater variation in the quality of their sequence logos; thus it may be that the O3 binding site is simply too weak to provide an informative sequence logo ., In Fig 2C–2E we plot the energy values from each matrix against one another to show how the energy matrices compare to one another quantitatively ., For ease of comparison , here the energy values are fixed so that the mean energy value at each sequence position is 0 kBT ., We see that replicates of energy matrices with an O1 reference sequence are highly similar to each other with a Pearson’s correlation coefficient of r = 0 . 95 , as calculated from the mean energy matrix values ( Fig 2C ) ., However , this similarity deteriorates somewhat as the reference sequence diverges from O1 , with a value of r = 0 . 85 for an O2-derived matrix ( Fig 2D ) and r = 0 . 48 for an O3-derived matrix ( Fig 2E ) ., Thus , while energy matrices derived from different reference sequences may be qualitatively similar , there are notable quantitative dissimilarities between these matrices ., In S2 Fig we also represent each matrix as an energy logo as introduced in Ref ., 38 and used in Ref ., 30 to represent the sequence specificity of LacI determined from in vitro experiments ., In S3 Fig we show how energy matrix values inferred for O1 in this work compare with the energy matrix values inferred for O1 using an in vitro technique in Ref ., 30 ., We find that the values from these two studies generally agree , particularly for lower energy values ., Any differences may be due to the different experimental procedures used in these studies ., The energy matrices obtained via Sort-Seq should allow us to map sequence to phenotype ., The relevant phenotype for simple repression constructs is the degree to which the system is repressed , which can be measured using the fold-change ., We define fold-change as the ratio of expression in a repressed system to expression in a system with no repressors , as described by the equation, fold-change = expression ( R ) expression ( R=0 ) ., ( 2 ), where R is the repressor copy number ., As discussed in further detail elsewhere 31 , 32 , the fold-change can also be computed using a thermodynamic model given by, fold-change = 1 1 + 2 R N N S e - β Δ ε R , ( 3 ), where the factor of 2 in “2R” indicates that for the case of LacI , each LacI tetramer has two heads and can essentially be counted as two repressors ., NNS is the number of nonspecific binding sites available in the genome ( ∼ 4 . 6 × 106 in E . coli ) and ΔεR is the operator binding energy ., We note that this model makes the simplifying assumption that the RNAP binds weakly to the promoter ., We find that for the lacUV5 promoter , this assumption holds for RNAP copy number P ≲ 1000 ., From Ref ., 39 we know that the relevant sigma factor , RpoD , has a copy number of 650 ± 100 in the growth condition used here ( M9 + 0 . 5% glucose at 37°C ) ., In principle , the energy matrix models shown in Fig 2 can be used to predict the binding energy of an operator mutant ., To explore the ability of energy matrices to predict the effects of mutations on operator binding strength , we designed a number of mutant operators with 1 , 2 , or 3 mutations relative to the O1 operator ., Experimentally-determined values for the binding energies of these mutants could then be compared against values predicted by our LacI energy matrices ., To obtain experimental values for mutant binding energies we start with chromosomally-integrated simple repression constructs for each mutant , which were incorporated into strains with LacI tetramer copy numbers of R = 11 ± 1 , 30 ± 10 , 62 ± 15 , 130 ± 20 , 610 ± 80 , and 870 ± 170 ., These copy numbers were inferred from quantitative Western blot measurements in Ref ., 32 , and the error in these copy numbers denotes the standard deviation of at least three Western blot replicates ., We determined the fold-change by measuring the YFP fluorescence levels of each strain by flow cytometry and substituting them into Eq 2 ., We determine each mutant’s binding energy , ΔεR , by performing a single-parameter fit of Eq 3 to the resulting data via nonlinear regression ., Fig 3A shows several fold-change values for 1 bp , 2 bp , and 3 bp mutants overlaid with these fitted curves ( the remaining fold-change data are shown in S2–S4 Figs ) ., The energy matrices derived from Sort-Seq can be used to predict the value of ΔεR associated with a given operator mutant , as discussed in detail in S1 Text ., To make these predictions , we use energy matrices produced using Sort-Seq data where O1 is the reference sequence and repressor copy number R = 130 ., As before , we split the Sort-Seq data into three groups to produce three replicate energy matrices ., We make our predictions using each of these replicate matrices in order to obtain a mean value and standard deviation for the predicted ΔεR for each operator mutant ., We note that any error in the predictions can be caused by error in the energy matrices themselves or in the inferred scaling factors ., Fig 3B shows how binding energy values measured by fitting to repressor titration data compare to values predicted using energy matrices that were produced using O1 as a reference sequence ., For single base pair mutations most predictions perform well and are accurate to within 1 kBT , with many predictions differing from the measured values by less than 0 . 5 kBT ., Predictions are less accurate for 2 bp or 3 bp mutations , although the majority of these predictions are still within 1 . 5 kBT of the measured value ., To give a sense of the consequences of an incorrect energy prediction , a prediction error of ±1 kBT can alter the expected fold-change of a simple repression architecture by a magnitude of approximately 0 − 0 . 25 , depending on the binding site’s binding energy and the repressor copy number R . The quality of matrix predictions appears to degrade as mutants deviate farther from the wild-type sequence used to generate the energy matrix ., To evaluate predictions for a broader range of deviations from the energy matrix , we made predictions from two energy matrices: the mean energy matrix using O1 as a reference sequence with repressor copy number R = 130 , and the mean energy matrix using O2 as a reference sequence with R = 130 ., This allowed us to access predictions for operators that are mutated by several base pairs relative to the matrix ., In Fig 3C we show how prediction error , defined as the discrepancy in kBT between a predicted and measured energy value , varies depending on the number of mutations relative to the wild-type binding site sequence ., We find that predictions remain relatively accurate for mutants that differ by up to 4 bp relative to the wild-type sequence , with median deviations of ∼ 1 . 0 kBT or less from the measured binding energy ., Other studies have noted that energy matrix models that don’t account for epistatic interactions fail to accurately predict binding energies for mutants with multiple mutations relative to the reference sequence 29 , 40 ., Thus we find that the relatively low errors depicted in Fig 3C exceed expectations for what a single-point energy matrix model can achieve ., We note that energy matrix quality , as measured by the accuracy of its predictions , may be affected by factors such as repressor copy number or wild-type transcription factor binding energy ., Changes in growth state can be expected to affect the number of transcription factors present in the cell ( although transcription factors appear to be less sensitive to growth state than the general proteome 39 ) , so it is important to determine how sensitive our approach is to changes in repressor copy number ., Additionally , we expect that at some repressor copy numbers and binding site strengths , the binding site may be either fully saturated with repressor or entirely unbound by repressor , which may also affect energy matrix quality ., In S4 Text , we assess whether energy matrix quality is affected by the LacI copy number of the background strain , and find that it has little effect on matrix quality ., We also compare predictions made from energy matrices with different reference sequences ( i . e . O1 , O2 , or O3 ) , and find that using O1 as a reference sequence produces the most accurate energy matrices , while using O3 produces energy matrices that are almost entirely non-predictive ., In S5 Text , we consider whether better energy matrices are made using libraries in which the entire promoter is mutated or only the operator is mutated ., We find that mutating the operator alone can provide more accurate energy matrices , though one must fit energy matrix predictions to binding energy measurements in order to convert these matrices into kBT units ., Our predictive energy matrices suggest a promising strategy for addressing the challenge of genetic circuit design , which has typically relied on trial and error to achieve specific outputs 41 , 42 ., By contrast , previous studies have shown how thermodynamic models can be used to predict gene outputs given a set of inputs 31 , 32 , which can suggest appropriate inputs to produce a desired output ., For example , the key inputs for the fold-change Eq 3 are repressor copy number R and repressor-operator binding energy ΔεR , and one can use Eq 3 to determine a set of R and ΔεR values that can be used to target a desired fold-change response ., Energy matrix predictions can be used to design operator sequences with a particular value of ΔεR , thereby making it possible to tune genetic circuits and target specific phenotypes ., As shown in Fig 3B , mutating an operator by as little as one base pair can provide a broad range of ΔεR values that can be predicted accurately ., One particularly useful class of simple genetic circuit , which can be layered with other genetic components to create complex logic 43 , is inducible simple repression 44–47 ., In such a system , an allosteric repressor can switch between an active form , which binds to an operator with high affinity , and an inactive form , which has a low affinity to the operator ., An inducer may bind to the repressor and stabilize the repressor’s inactive form , thereby reducing the probability that the repressor will bind to the operator and increasing the probability that RNAP will bind and initiate transcription ., The result is that an inducible system can access a broad range of fold-change values simply by tuning the concentration of inducer ., As discussed in Ref ., 48 , the fold-change of an inducible simple repression circuit can be described by the equation, fold-change ( c ) = ( 1 + ( 1 + c K A ) n ( 1 + c K A ) n + e - β Δ ε A I ( 1 + c K I ) n 2 R N N S e - β Δ ε R ) - 1 , ( 4 ), where c is the concentration of inducer , n is the number of inducer binding sites on the repressor , KA and KI are the dissociation constants of the inducer and repressor when the repressor is in its active or inactive state , respectively , and ΔεAI is the difference in free energy between the repressor’s active and inactive states ., In Ref ., 48 we determined that these values are K A = 139 - 22 + 29 μ M , K I = 0 ., 53 - 0 ., 04 + 0 ., 04 μ M , and ΔεAI = 4 . 5 kBT for LacI with the inducer IPTG ., Where noted , superscripts and subscripts indicate the upper and lower bounds for the 95th percentile of the parameter value distributions ., There are n = 2 inducer binding sites on each LacI dimer ., We note that while we use Eq 4 here to represent induction of LacI by IPTG , this equation is general and can be used for any inducible system that utilizes simple repression ., We can use these parameter values for the lac-based system considered here to explore how tuning the operator-repressor binding energy ΔεR can alter the induction response when an effector ( i . e . IPTG ) is introduced to the system ., Importantly , our sequence-energy mapping provides a straightforward avenue for tuning ΔεR by altering the binding sequence rather than mutating the repressor itself , which is much more difficult to characterize ., We note that an induction response can be described by a number of key phenotypic parameters ., The leakiness is the minimum fold-change when no inducer is present , given by fold- change ( c → 0 ) ( Eq 1 in S6 Text ) ., The saturation is the maximum fold-change when inducer is present at saturating concentrations , given by fold- change ( c → ∞ ) ( Eq 2 in S6 Text ) ., The dynamic range is the difference between the saturation and leakiness , and represents the magnitude of the induction response ( Eq 4 in S6 Text ) ., The EC50 is the inducer concentration at which the fold-change is equal to the midpoint of the induction response ( Eq 6 in S6 Text ) ., Full expressions for these parameters are shown in S6 Text ., Fig 4A and 4B show how these phenotypic parameters vary with ΔεR given the values of KA , KI , and ΔεAI listed above and the repressor copy number R = 130 ., We can see that there are inherent trade-offs between phenotypic parameter values ., For instance , in this particular system one cannot tune ΔεR to obtain a small dynamic range ( e . g . a dynamic range of 0 . 1 ) while also having an intermediate leakiness value ( e . g . a leakiness of 0 . 4 ) ., Rather , one must design an induction response by choosing from the available phenotypes , or else alter the system by tuning additional parameters such as KA and KI , which requires mutating the protein itself or using a different transcription factor altogether as in Ref ., 41 ., To show how energy matrices can be used to design specific induction responses , we chose six of our single base-pair mutants with a range of predicted binding

Introduction, Results, Discussion, Methods

Despite the central importance of transcriptional regulation in biology , it has proven difficult to determine the regulatory mechanisms of individual genes , let alone entire gene networks ., It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase ., In this work , we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods ( massively parallel reporter assays ) to formulate quantitative models that map a transcription factor binding site’s DNA sequence to transcription factor-DNA binding energy ., We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values ., We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts ., Specifically , we show that our models can be used to design specific induction responses , analyze the effects of amino acid mutations on DNA sequence preference , and determine how regulatory context affects a transcription factor’s sequence specificity .

It has been said that we live in the “genomic era , ” a time where we can readily sequence full genomes at will ., However , it remains difficult to interpret much of the information within a genome ., This is especially true of non-coding sequences such as promoters , which contain a number of features such as transcription factor binding sites that determine how genes are regulated ., There is no straightforward regulatory “code” that tells us how transcription factor binding sites are organized within a promoter ., In this work we examine how DNA sequence determines one of the most important features of a promoter , the strength with which a transcription factor binds to its DNA binding site ., We discuss an approach to modeling DNA sequence-specific transcription factor binding energies in vivo using a massively parellel reporter assay ., We develop models that allow us to predict the binding energy between a transcription factor and a mutated version of its binding site ., We then show that this modeling technique can be used to address a number of scientific and design questions , such as engineering the behavior of genetic circuit elements or examining how transcription factors and their binding sites co-evolve .

chemical characterization, gene regulation, regulatory proteins, dna-binding proteins, dna transcription, transcription factors, sequence motif analysis, research and analysis methods, sequence analysis, transcriptional control, bioinformatics, proteins, gene expression, binding analysis, biochemistry, dna sequence analysis, database and informatics methods, genetics, biology and life sciences

journal.pbio.0050117

Unmasking Activation of the Zygotic Genome Using Chromosomal Deletions in the Drosophila Embryo

Embryonic development is controlled by a complex interaction between maternal and zygotic activities ., Although maternal transcripts and proteins are deposited in the egg during oogenesis , the activation of the zygotic genome starts at different stages in different animals and is concomitant with the degradation of a fraction of maternally supplied transcripts 1–3 ., Thus , during the maternal-to-zygotic transition ( MZT ) , the embryo undergoes an extensive remodeling of gene expression and must integrate post-transcriptional regulatory mechanisms , which are the only ones operating during the previous maternal stages , with transcriptional regulation of its own genome ., How this is achieved is poorly understood ., The concomitant degradation of maternal transcripts and activation of zygotic transcription has made it difficult in any animal to interpret changes in gene expression 4–6 ., Whereas an increase in gene expression levels can be interpreted as a sign of zygotic transcription , a decrease or absence of change is also consistent with zygotic gene activation if it is accompanied by maternal mRNA degradation ., One way to test whether a particular RNA is supplied maternally or zygotically is to compare its levels in embryos that have or do not have the corresponding DNA template ., Under these conditions , differences in expression level indicate the relative maternal and zygotic contribution ., Drosophila melanogaster offers the unique opportunity to perform such an experiment for the entire genome , as it is possible to use chromosomal rearrangements to produce embryos that lack specific arms or even entire chromosomes 7 , 8 ., Such embryos develop normally until cycle 14 and then show defects characteristic of the chromosomal region deleted ., The results of such experiments suggest that the Drosophila embryo develops under the control of maternally provided proteins until nuclear division 13 ., This stage , usually referred to as the mid-blastula transition ( MBT ) , defines the point from which development comes to be controlled by the zygotes own genome 1 ., The first morphological signs of the zygotic genome appear with the cellularization of the cortically migrating nuclei and the beginning of gastrulation ., From a transcriptional point of view , the zygotic genome is silent until nuclear cycle 9–10 9 ., In the germline , this quiescence is maintained until later stages of development , arguing for specific regulation between the soma and the germline 10 ., The molecular mechanisms linking the nuclear cycles to the activation of transcription are unknown and may involve the chromosomal squelching of negative regulators of transcription , as has been proposed for the Xenopus embryo 3 ., Chromatin-based mechanisms may also play a role ., In the mouse embryo , for example , at least one cycle of DNA replication is required to change the methylation state of the chromatin to a transcriptionally competent conformation 11 ., However , in none of these organisms have the molecular players actually regulating activation of the zygotic genome been identified ., Because such regulators must be maternally provided , they are not easily identifiable in genetic screens ., On the other hand , the recent technological advances in genomics and bioinformatics may offer alternative strategies for elucidating this mechanism , especially if the identification of cis-regulatory elements can be coupled to a biochemical characterization of the factors that bind to them ., Here we took advantage of the phenotype generated by the removal of specific genes acting during cellularization to identify embryos lacking defined chromosomal arms , and analyzed their expression profiles using microarrays ., Because this strategy allows discrimination between transcriptional and post-transcriptional regulation of gene expression , we describe here the first complete analysis of the MZT during animal development ., Earlier attempts to identify zygotically active genes in Drosophila relied on comparing mRNA levels at cycle 14 with those from unfertilized eggs or early 0–1-h-old embryos 12 ., Although zygotic transcription begins already at earlier nuclear cycles ( 9–10 ) , we also started our analysis by focusing on cycle 14 because this stage represents the earliest time point at which the mutant phenotypes associated with the deletion of each specific chromosome can be recognized ., The time-course characterization of earlier time points will be presented in the section describing the activation of the zygotic genome ., The temporal resolution of our measurements is at 1-h intervals covering the first 3 h of embryogenesis: ( 1 ) unfertilized eggs , ( 2 ) 0–1 h ( cycles 1 to 10 ) , ( 3 ) 1–2 h ( cycles 10 to 13 ) , and ( 4 ) 2–3 h ( cycle 14 ) ., Figure 1A plots the levels of mRNAs from visually staged 0–1-h eggs with those that have developed to cycle 14 ( 2–3 h ) ., In principle , this type of measurement allows identification of the following categories of transcripts: ( 1 ) purely zygotic ( transcripts that are not expressed at 0–1 h and are detected as present at 2–3 h ) , ( 2 ) maternal+zygotic ( transcripts that are present at 0–1 h and whose level increases at 2–3 h ) , and ( 3 ) maternal or maternal+zygotic ( transcripts that are present at 0–1 h and whose level either does not change or decreases in level at 2–3 h ) ., Transcripts expressed at the same level in both collections lie on the diagonal ( Figure 1 ) ., A large fraction of transcripts deviates from the diagonal and are present at increased or decreased levels in cycle 14 ., Although mRNAs that increase can be most simply explained by new transcription , the existence of mRNAs whose levels go down suggests that post-transcriptional regulation may be too complex to make judgments about the maternal or zygotic source of a transcript based on measured mRNA levels alone ., The decrease or stability in the level of mRNAs may reflect a complex balance between activation and degradation ., Even the identification of purely zygotic transcripts can be problematic if the designation is based only on measurements at 2–3 h being above the background at 0–1, h . To address this problem , we undertook a genetic approach based on chromosomal deletions ( in embryos that had developed exactly to the same stage ) coupled to microarray analysis ., We sought to evaluate the traditional interpretation of gene expression measurements , which considers up-regulated transcripts as zygotic , stable transcripts as maternal , and down-regulated transcripts as maternal-degraded ( Figure 1B , model ) ., The left arm of the second chromosome represents approximately 20% of the entire genome and is predicted to contain approximately 2 , 500 open reading frames ( BDGP4 annotation; Berkeley Drosophila Genome Project , http://www . fruitfly . org/ ) ., We compared mRNAs from embryos that lack the left arm of the second chromosome with similarly staged wild-type embryos ., Such 2L− embryos can be recognized by their distinctive halo of lipid-rich cortical cytoplasm during cellularization 13 , at the precise moment when major zygotic transcription begins ., Figure 1C and 1D plot the result of this experiment ., Most mRNAs have similar levels in both collections , and lie on a diagonal ( Figure 1 ) ., Deviations tend to be located towards the lower left of the diagonal , indicating that certain mRNAs are less abundant in the 2L− collection ., There is a small number of mRNAs whose level increases when the 2L arm is removed ., Altogether these changes can represent direct and/or indirect responses to the ablation of the 2L arm ., Although primary responses must involve genes located on 2L , secondary responses are expected to be randomly distributed on the three major chromosomes ., We plotted the chromosomal location of down-regulated and up-regulated genes at different cut-offs ( Figure 1E and 1F ) ., At a stringent fold-change cut-off value of ten , the number of deviant mRNAs is small , and all of them represent mRNAs that are less abundant than in wild type ., Approximately 90% of these mRNAs are encoded by genes located on 2L , indicating that they are normally supplied by zygotic transcription: removal of 2L eliminates the DNA templates for such transcripts , and the transcripts are not made ., As we decrease the fold-change cut-off , the number of genes that deviate from the diagonal increases ., A 2-fold cut-off identifies 378 genes on 2L whose levels depend on the presence of that chromosomal arm in the embryos ., A 2-fold difference signifies that at least 50% of the total number of transcripts for each of these genes , present at cycle 14 , are derived from zygotic rather than maternal transcription ., The observation that even at this cut-off , approximately 60% of down-regulated genes are located on 2L strongly validates this procedure ., Indeed , if the observed changes were due to random fluctuations of mRNA levels , such changes would be distributed over the entire genome , and 20% of them would be located on 2L ., It should be noted that , in principle , the down-regulated transcripts might also include maternal mRNAs whose stability is regulated by zygotic transcription ., However , the enrichment on 2L suggests that this applies to a very small fraction of genes ., We therefore classify all down-regulated transcripts ( on the deleted arm ) as zygotic ., The remaining 631 genes that are located on 2L and detected in cycle 14 embryos are not dependent on the presence of the left arm of the second chromosome in the embryo , and must therefore be supplied by maternal transcription ., At the 2-fold cut-off , a second class of affected mRNAs appears ., These mRNAs are expressed at a higher or lower level than the wild-type controls , and they mapped to other regions of the genome ., We interpret these mRNAs as gene products whose levels depend indirectly on the left arm of the second chromosome ., We therefore name these genes “secondary targets” of 2L removal ., They may be targets of transcription factors encoded on 2L whose expression at cycle 14 depends on the presence of that arm ., Alternatively , they might be post-transcriptionally regulated maternal transcripts whose stability or degradation depends on zygotic transcription ., In order to discriminate between these mechanisms , we screened the entire genome and determined the maternal and zygotic contribution for each individual gene ., Using additional chromosomal rearrangements , we extended the analysis described in detail above for 2L to the rest of the genome , analyzing mRNA populations present in embryos deficient for the X chromosome , the entire second chromosome , or the entire third chromosome ., In most cases , hybridizations were performed in quadruplicate using different batches of embryos ., Mutant embryos were recognized under a compound microscope based on their specific abnormalities associated with defects in nuclear morphology , in actin-myosin dynamics , and organelle transport: nullo ( chromosome X ) 14 , halo ( Chromosome 2 ) 13 , and bottleneck ( Chromosome 3 ) 15 ., These three phenotypes appear synchronously as the embryo enters cycle 14 , thus allowing a precise staging protocol ( Figure 2A–2J ) ., In each case , we were able to identify mRNAs encoded on the deleted chromosome and whose levels depend directly on the presence of that chromosome ., These mRNAs thus appear to be predominantly supplied by zygotic transcription ( Figure 2K ) ., For all subsequent analyses , we defined the class of down-regulated genes to be those genes with a fold-change of at least three and a p-value less than 0 . 001 ., In this range , between 60% to 80% of down-regulated genes map to the chromosomes removed ., A more stringent cut-off would have increased specificity at the expense of secondary targets ., In addition , we have built a simple online database , which provides access to the entire dataset at any ( user-specified ) fold-change and/or p-value cut-off ( http://rd . plos . org/pbio . 0050117 ) ., A 3-fold cut-off identifies all mRNAs that are at least 67% supplied by zygotic transcription at cycle 14 ., Combining the data from all four manipulations , we estimate that such zygotically active genes represent about 18% of the genes detectable at cycle 14 ,, i . e ., , 1 , 158 genes distributed on all four chromosomes ( Table S1 ) ., The remaining mRNA species appear to be supplied predominantly by maternal transcription ., When looking at the entire dataset , zygotically active genes appear to be uniformly distributed throughout the genome ., Each chromosomal manipulation also identified apparent secondary targets that mapped to other chromosomes ., Similar to the results obtained from the 2L− experiments , levels of such mRNAs deviated at most 2- to 3-fold in either the positive or the negative direction from wild-type ( WT ) mRNAs ., To test whether these genes were in fact transcriptional targets of genes on the removed chromosome , we asked whether third chromosomal or X chromosomal genes identified in the 2L chromosomal screen as secondary targets behaved as primary targets when the third chromosome or X was removed ., This was true for 62% of down-regulated and 29% of up-regulated genes ., Our four experiments identified a total number of 778 secondary targets of which only 28% are zygotic ( Table S2 ) ., The remaining 72% ( 563 ) are mostly maternally supplied ., We conclude from these observations that the expression level of most zygotically active genes was not influenced by other loci , and changed significantly only when the chromosome encoding them was removed ., The identification of 563 non-zygotic mRNAs ( Table S3 ) whose level changed in response to the removal of a specific chromosome must represent post-transcriptional regulation of maternal transcripts ., The stability or degradation of these transcripts may be regulated by transcription of certain factors ( coding for RNA-binding proteins or regulatory RNAs ) on the chromosomes that are removed ., In agreement with this interpretation is the observation that ablation of each chromosome or chromosomal arm results in the misregulation of distinct targets ., Thus , transcription at multiple loci regulates the stability of distinct maternal transcripts ., For example , the degradation of String and Twine , two cell cycle regulators involved in timing the MZT 16 , is regulated by zygotic transcription on the X and second chromosomes , respectively ( Table S2 ) ., Next , we characterized the relative contribution of maternal transcripts to the total cycle 14 expression level of zygotically active genes ., We compared the mRNA levels of 1 , 158 zygotic genes at 0–1 h with that observed at 2–3 h ( Figure 3A ) ., In one third of the cases , transcripts could not be detected in 0–1-h embryos , and increased over the 2-h period that follows fertilization ., Expression of these genes is therefore purely zygotic: all transcripts detected at cycle 14 are produced by transcription in the embryo itself ( Table S4 ) ., Almost all of these transcripts ( ~90% , 300 out of 334 ) would have been detected as purely zygotic using the simple criterion “absent at 0–1 h–present at 2–3 h” ( Table S5 ) ., On the other hand , if only this latter criterion had been used , an additional 268 genes would be scored as purely zygotic , even though the level of these transcripts does not change significantly when the chromosome harboring them is removed ., When used on its own , the “absent-present” filter may be unreliable because it identifies zygotically active genes by comparing expression measurements at one stage with background levels at another ., Our double-filter approach ( change in response to the deletion of the DNA template + a “present-absent” value ) yields a more stringent and accurate estimate of purely zygotic transcripts ., The remaining two thirds of the 1 , 158 zygotic genes were present in unfertilized eggs ( Table S6 ) ., Because the overall levels of theses mRNAs either did not change significantly or decreased between 0 h and 3 h , the dependence of cycle 14 levels on zygotic transcription implies the specific degradation of maternal transcripts before that time ., Thus , we conclude that an increase in gene expression over time is not a sufficient criterion to identify zygotic genes ., To follow the stability of maternal transcripts ( which is obscured by the presence of newly supplied zygotic transcripts in WT embryos ) , we compared mRNA levels from early 0–1-h embryos ( WT ) with mRNA levels from embryos missing each chromosome , hand-selected from the same stock during cycle 14 ., The initial analysis was restricted to genes on the left arm of the second chromosome ( Figure 3B ) ; therefore , all 2L mRNAs detected at either stage must be supplied maternally ., The relative change in expression levels between 0–1 h and 2–3 h provides a measure of their stability during that period ., Consistent with their strictly maternal source , none of the 1 , 009 transcripts from 2L increased significantly between 0 h and 3, h . Approximately 65% remained constant , and 35% dropped more than 3-fold ., We extended this analysis to the rest of the genome and estimated that of 6 , 485 total maternally supplied genes , 2 , 110 ( 33% ) go down significantly by cycle 14 ., In 646 cases , the maternal degradation was at least in part compensated by zygotic transcription ( Table S7 ) ., A representative list of maternal and zygotic transcripts known to be degraded or induced during the MZT and detected by our analysis is shown in Table 1 ., One third of the zygotic transcripts we have identified are not expressed maternally and can be considered purely zygotic genes ., These genes are enriched for transcription factors ( “transcription factor activity” Gene Ontology ( GO ) category , p < 10−9 ) ., This may reflect the necessity of timing the activity of genes regulating the establishment of cell identity during differentiation ., The remaining two thirds , those with maternal contribution , are not significantly enriched in any specific functional class ., This raises the question as to why the embryo transcribes genes when the corresponding maternal transcripts are present ., Two possible scenarios can be envisaged: ( 1 ) maternal transcripts must also be supplied by zygotic transcription , because they are degraded very quickly ( i . e . , they have short half-lives ) , and ( 2 ) zygotic transcription offers some advantages , such as precise spatial patterning , differential processing ( e . g . , splice variants ) , or intracellular localization ., In the latter scenario , maternal mRNAs would be specifically degraded to ensure that zygotic transcripts are the only source of these genes at cycle 14 ., Using data downloaded from the BDGP in situ database , we asked whether the zygotic genes we have identified are expressed in specific patterns at cycle 14 ( Figure 3D ) ., A total of 241 of the genes we found to be zygotic are annotated in the database ., Of those , 59% were expressed in discrete patterns at cycle 14 ., Among the total number of genes in the database ( 1 , 227 ) , only 27% were patterned at cycle 14 ., Thus , the zygotic genes we identified are enriched more than 2-fold ( p < 10−32 ) in patterned expression compared to what would be expected by chance ., Even among the 143 zygotic genes that initially had uniform maternal component , 29% evolved to patterned expression by cycle 14 , a situation occurring for only 11% of the genes in the entire in situ database ( p < 10−8 ) ., Because the expression level of these genes was either stable or decreased during the MZT , we conclude that coupling maternal degradation with zygotic transcription is part of the patterning mechanism ., Indeed , one third of the genes expressed in patterns at cycle 14 required both zygotic transcription and degradation of uniform maternal mRNAs ( Figure 3D ) ., We then asked whether the different categories defined above share common genomic regulatory elements , which could explain the behavior of an individual gene during the MZT ., We first investigated whether down-regulated maternal genes have over-represented motifs in their 3′ UTRs ., A total of 1 , 095 maternal genes with annotated 3′ UTRs decreased in levels significantly between 0–1 h and 2–3, h . As shown in Figure 3C , we found several short sequences that are significantly enriched within these 3′ UTRs , compared to the entire set of annotated 3′ UTRs ., None of them matched the 5′ extremity of any of the 78 known microRNAs ( miRNAs ) in D . melanogaster ., A similar conclusion was drawn also by studying transcript stability in unfertilized eggs 17 ., The sequences we found can be divided into two families , based on sequence similarity ., The first family contains a UUGUU core , which resembles the target site for the PUF family of RNA-binding proteins ( whose unique representative in the D . melanogaster genome is Pumilio ) ., To further investigate the role of Pumilio in maternal mRNA degradation , we compared our down-regulated maternal genes to the list of 135 targets of Pumilio in fly embryos 18 ., Although these targets do not all contain exactly the same sequence , 118 of the 135 target genes were identified as maternal in our experiments , and 63 of these ( 53% ) were also down-regulated between 0–1 h and 2–3, h . On the other hand , only 23% of maternal genes decreased globally ., Therefore , Pumilio targets are very significantly over-represented in maternal down-regulated genes ( p < 10−12 ) ., Sequences from the second family match the AU-rich element ( canonically defined as UAUUUAU ) , a known mediator of mRNA degradation 19 ., Interestingly , an RNA interference ( RNAi ) -based screen performed in Drosophila S2 cells has suggested that several components of the miRNA processing pathway are required for degradation of AU-rich element–containing mRNAs 20 ., We then investigated whether the zygotic transcripts share common DNA regulatory motifs in their upstream regions ., We identified a highly over-represented 7-nucleotide–long sequence ( CAGGTAG , which from now on we will refer to as the 7mer ) and several of its variants within the 2 kilobase ( kb ) upstream regions of purely zygotic genes ( Figure 3C ) ., This motif has been previously identified in the upstream region of sisterless A and B , and Sex-lethal , three genes involved in sex determination that are expressed early during embryogenesis 21 ., A more recent study identified this motif upstream of other genes expressed prior to cycle 14 , thus suggesting a more general regulatory function 22 ., The results described above are intriguing because the 7mer we found is present upstream of only a fraction of the zygotic genes at cycle 14 ., Although the major activation of the zygotic genome occurs at cycle 14 , earlier reports indicated signs of zygotic transcription as early as cycle 10 when the embryonic DNA is still engaged in fast cycles of S-phases and mitoses without interphases 23 ., We therefore asked whether the 7mer represents a general feature of genes expressed prior to cycle 14 and , in general , whether the zygotic genes we have identified are transcribed altogether during cycle 10 or whether different classes of transcripts respond differently to the embryonic cycles and DNA content ., We compared the expression profile of unfertilized eggs , 0–1-h freshly fertilized eggs ( pre-pole cell formation , cycles 1–9 ) and 1–2-h embryos ( post-pole cell formation and pre-cellularization , cycles 10–13 ) ., No significant change in expression levels was observed between unfertilized eggs and the 0–1-h eggs , indicating that neither transcription nor degradation has occurred ( Figure S2 ) ., Importantly , in these experiments , we analyzed unfertilized eggs that had been aged for 1 h at most ., Therefore , our results do not contradict previous reports describing the degradation of a subset of maternal transcripts in unfertilized eggs 17 , 24 since , in those studies , unfertilized eggs were aged for longer periods of time , and degradation was observed after 2 h , peaking between 2 and 4, h . Between the 0–1-h to 1–2-h collections , a single group of 59 genes was significantly up-regulated ( Figure 4A ) ., These genes ( including Snail , Zen , and Nullo , see Table S8 for a complete list ) are expressed even prior to the gap and pair-rule genes , which in our measurements do not yet show significant increased levels at this time point ., Expression of gap and pair-rule transcripts was detected at 2–3 h ( Table S1 ) , arguing that their transcripts accumulate with a slower kinetic ., When searching for over-represented motifs in the 2-kb upstream regions of these genes , we found the same motif as for the pure zygotic genes , along with other overlapping or slightly distinct variants ( Figure 4B ) ., We found 91 . 5% of the 59 genes have at least one copy of any of these variants , whereas the expectation based on all genes in the genome is 40% ( p < 10−15 ) ., Moreover , 28 . 8% of the 59 genes have four or more non-overlapping copies of these sequences , a situation occurring for only 1 . 6% of the Drosophila genes ( p < 10−16 ) ., Thus we conclude that the activation of the zygotic genome starts from genes containing this motif ., Interestingly , the occurrences of the 7mer within the 2-kb upstream regions tend to be much closer to the transcription start site than expected by chance ( Figure 4C ) ., Finally , we asked whether these genes share some additional features that increase the overall fitness of gene expression prior to cycle 14 ., We found that 70% of these genes do not contain introns ( Figure 4A ) ., Since intronless genes represent only 20% of the Drosophila genome , this result suggests an important selective advantage for the transcription of intronless genes in concomitance with fast-cycling nuclei ., The identification of a single highly over-represented cis-element in the 5′ region of the early zygotic genes suggests the existence of a single trans-acting factor involved in timing the activation of the zygotic genome ., If such a factor exists , it is most likely maternally provided and loaded into the egg during oogenesis ., To identify this factor , we undertook a biochemical approach ., We performed sequential DNA affinity chromatography ( see Materials and Methods for details ) using the 7mer or , as negative control , the upstream activation sequence ( UAS ) ( the consensus binding site of the yeast trans-activator GAL4 ) ., The result of this experiment is shown in Figure 5A ., Only one band was detected in the 7mer elute , and no specific band was detected in the UAS control elute ., Mass spectrometry sequencing identified this protein as the Bicoid stability factor ( BSF ) , and Western blotting analysis confirmed this result ( unpublished data ) ., BSF has been previously identified as a Bicoid mRNA binding protein involved in regulating the stability of Bicoid transcripts during oogenesis 25 ., Our data suggest an additional transcriptional function for BSF in the embryo , and indeed , the human homolog of BSF has been shown to function as a transcriptional regulator 26 ., In order to address the specificity of the 7mer/BSF interaction , BSF was expressed in rabbit reticulocyte in the presence of 35S methionine , and the binding to the 7mer or to a mutated oligo ( in which the two GG at position 3 and 4 were mutated to TT ) was tested ., In vitro–synthesized BSF bound directly and specifically to the 7mer , and only background signal was retained on the beads coupled to the mutated oligo ( Figure 5B ) ., Next , we analyzed the subcellular distribution of BSF in the embryo using immunostaining and confocal microscopy imaging ., BSF was localized to both the cytoplasm as well as the nuclei of the blastoderm epithelium ( Figure 5C and 5D ) ., In the germ cells ( pole cells ) , which at this stage are transcriptionally silent , BSF was retained in cytoplasmic puncta ( Figure 5E and 5F ) ., Thus , BSF is differentially compartmentalized between the soma and the germ line , and this compartmentalization may be important to maintain the transcriptional quiescence in the germline ., To test the function of BSF in the early embryo , it is necessary to remove the maternal contribution ., ( BSF transcripts are maternally provided and the protein is expressed during oogenesis 25 . ), To perform this experiment , we produced germline clones using a P element insertion that maps in the BSF open reading frame and is homozygous lethal ., Flies containing such clones failed to lay eggs , and the ovaries were arrested at a very early stage of development , indicating that BSF is required also during oogenesis ., This made it impossible to test the function of BSF in the early embryo ., Therefore , we took an alternative approach with the aim to functionally characterize the activity of the 7mer ., We considered two possible scenarios ., One possibility is that the 7mer may have enhancer activity , sufficient to drive transcription on its own ., Alternatively , it may play a permissive role by functioning in a combinatorial fashion with additional factors ., To discriminate between these two possibilities , we set up conditions to measure gene expression using an assay based on the UAS/GAL4 system 27 ., We generated embryos expressing green fluorescent protein ( GFP ) under the control of the UAS–heat shock minimal promoter either with or without five copies of the 7mer , and followed GFP expression using video microscopy ., GFP was not detected in embryos unless GAL4 was also provided ., Strikingly , the presence of the 7mer led to a more than 4-fold increase in the expression of GFP compared to controls ( transgene without the 7mer ) , as shown in Figure 6A and 6B ., Next , we asked how early this stimulatory activity could be detected ., We analyzed GFP transcripts using fluorescent in situ hybridization ( FISH ) ., This technology allows the visualization of nascent transcripts as they arise from the site of transcription 28 ., Because we crossed males carrying the GFP transgene to females providing GAL4 , only one chromosome in the embryo is expected to transcribe GFP ., In agreement with this prediction , we detected only one major transcription focus , appearing as an individual dot , per nucleus ( Figure 6C and 6D ) ., We observed an increase in the number and size of dots at each nuclear division when the 7mer was present ( Figure 6D ) ., This difference could be detected as early as cycle 11 ( Figure S1 ) ., By cycle 14 , images are characterized by a high signal-to-noise ratio and showed an approximately 1 . 7-fold increase in the number of dots per embryo ( Figure 6E ) ., Thus , the presence of CAGGTAG increases the number of nuclei that are actually engaged in transcription ., Because the size of each dot is also larger ( Figure 6F ) , each dot most likely contains more transcripts ., If this interpretation is correct , then it should be possible to quantify this difference by measuring the total amount of GFP transcripts ., Embryos were harvested either at the stage when the earliest GFP transcripts were expressed ( cycle 10 to 13 ) or at cycle 14 ., Total RNA was extracted and subjected to reverse-transcription PCR ( RT-PCR ) ( Figure 6G ) ., As a staging control , we followed the expression of Snail , a known zygotic gene ., We detected 7mer-driven transcription as early as cycle 12 ., In the absence of this motif , no GFP expression was detected ., By cycle 14 , we observed a 2-fold increase in GFP expression , which is in agreement with the FISH quantification ., As a control , we also followed Snail mRNA , which was expressed at similar levels in both conditions , and its expression increased from cycle 12 to 14 ., Altogether , these results show that the CAGGTAG motif functions as an enhancer that cannot drive transcription on its own ( Figure 6C ) , but can activate expression prior to cycle 14 , in combination with a transcriptional activator ., I

Introduction, Results, Discussion, Materials and Methods

During the maternal-to-zygotic transition , a developing embryo integrates post-transcriptional regulation of maternal mRNAs with transcriptional activation of its own genome ., By combining chromosomal ablation in Drosophila with microarray analysis , we characterized the basis of this integration ., We show that the expression profile for at least one third of zygotically active genes is coupled to the concomitant degradation of the corresponding maternal mRNAs ., The embryo uses transcription and degradation to generate localized patterns of expression , and zygotic transcription to degrade distinct classes of maternal transcripts ., Although degradation does not appear to involve a simple regulatory code , the activation of the zygotic genome starts from intronless genes sharing a common cis-element ., This cis-element interacts with a single protein , the Bicoid stability factor , and acts as a potent enhancer capable of timing the activity of an exogenous transactivator ., We propose that this regulatory mode links morphogen gradients with temporal regulation during the maternal-to-zygotic transition .

Embryonic development is controlled by a complex interaction between maternal and zygotic activities ., Maternal messenger RNAs and proteins are deposited in the unfertilized egg during oogenesis; after fertilization , the activation of the zygotic genome is accompanied by the degradation of a fraction of maternally supplied transcripts ., This switch from maternal to zygotic control of development is characterized by a dramatic remodeling of gene expression , and represents a universal regulatory point during animal development ., Because it is not usually possible to identify which genomes are contributing to these transcriptional changes , we have used chromosomal ablation to determine maternal versus zygotic contribution for each mRNA detectable on microarray in the Drosophila blastoderm ., This has allowed us to distinguish transcriptional and post-transcriptional modes of regulation and to identify common cis-regulatory elements associated with different classes of transcripts ., Our analysis revealed that although mRNA degradation does not involve a simple regulatory code , the activation of the zygotic genome is based on a simple mechanism , which links morphogen gradients with temporal regulation ., It will be interesting to address whether similar mechanisms also operate in other animals .

developmental biology, drosophila, genetics and genomics

As the egg develops into the embryo, maternal mRNAs are degraded and new genes activated. By using chromosomal ablation inDrosophila, the authors characterized the basis of this integration.

journal.ppat.1001166

Phylodynamics and Human-Mediated Dispersal of a Zoonotic Virus

Every year approximately 55 , 000 people die from rabies 1 ., Over 99% of these deaths occur in developing countries where rabies virus ( RABV; negative-sense RNA virus , family Rhabdoviridae ) is endemic in the domestic dog 2 ., Rabies has been neglected across much of Asia and Africa , despite becoming an increasing problem in the recent decades 1 , 3 , 4 ., Although the history of rabies in Africa prior to the 20th century is uncertain 5 , the currently circulating dog rabies virus is thought to have emerged during the 19th and 20th centuries 6 , 7 ., Despite the importance of dogs as vectors for human rabies , little is known about the spatial and temporal dynamics of rabies in this major reservoir species , or the processes responsible for its maintenance in specific geographic localities ., In particular , the role of human activities in mediating the spread of dog RABV is unclear , nor is it known how landscape characteristics , including human infrastructures such as roads , affect RABV dispersal within dog populations ., However , such information is critical to revealing the determinants of RABV transmission and hence for its control in the domestic dog ., We used a recently developed probabilistic approach 8 to determine the spatial and temporal dynamics of dog RABV transmission from a large-scale gene sequence study ., We encode different phylogeographic scenarios of viral spread , as well as different landscape features , in a model-based approach , and choose among these models in a quantitatively rigorous fashion ., Our focus was on dog populations in North Africa where RABV has been endemic for more than a century , and our key aim was to determine how ecological , anthropogenic and evolutionary dynamics shape the spatial distribution and spread of this important zoonotic pathogen 9 , 10 , 11 , 12 ., We first inferred the evolutionary history of 287 RABV sequences ( 3080 nt; encompassing the whole N , P and intergenic G-L region ) sampled from Algeria , Morocco , Tunisia and the Spanish territories from North Africa ( Ceuta and Melilla ) between 1986 and 2008 ., All these viruses are assigned to the Africa 1 genotype ( relevant epidemiological information for all RABV isolates analysed in this study is presented in Table S1 in Supporting Information S1 ) ., We estimated the timescale of this evolutionary history using a Bayesian Markov chain Monte Carlo ( MCMC ) approach 13 ., The most recent common ancestor of all the North African RABV sampled here was estimated to have existed between 1878–1945 , supporting previous suggestions that dog RABV was periodically responsible for local sporadic epidemics in the middle of the 19th century 14 , and that rabies became enzootic in this entire region during the 20th century ., More generally , this timescale is consistent with the expanding European colonial influence in North Africa 7 ., This analysis also revealed distinct phylogenetic lineages in Algeria , Morocco and Tunisia , indicating that viruses generally grouped according to their country of origin ( Figure 1 ) ., This result is unexpected if the virus is only dispersed through the local movement of animals as observed in wildlife rabies 9 , 10 as these would not respect geo-political boundaries ., Indeed , we found only a few exceptions to the country-specific clustering , such as two Algerian sequences within the Moroccan clade and four Moroccan sequences in the Algerian clade ., The Africa 1 clade is therefore consistent with the general phylogeographic pattern observed for dog RABV at reasonably large geographic scales; a series of spatially distinct clusters that experience relatively little contact among them 6 , 7 ., To analyze intra-country patterns of viral transmission in more detail , we considered a stochastic diffusion process among the 20 ( Algeria ) and 28 ( Morocco ) sampling localities for which most data were available ( Figure 2 and Figure S1 in Supporting Information S1 ) ., We quantified the degree of spatial admixture using a modified Association Index ( AI , 8 , 15 ) , and by summarizing the number of inferred transitions to each location within Algeria and Morocco ( Table, 1 ) based on an analysis in which rates of diffusion between each pair of locations were estimated ., Although these analyses reveal that there is still significant spatial structure within each country ( p<0 . 001 ) , the AIs are considerably higher ( 0 . 67 0 . 62–0 . 73 and 0 . 55 0 . 51–0 . 63 for Algeria and Morocco , respectively ) than those found for rabies at a larger spatial scale ( e . g . , 0 . 087 0 . 043–0 . 132 for the Africa 2 lineage in Central and West Africa ) 8 , indicating weaker spatial structure at the within-country level ., The summaries of transitions to each location generally identify multiple independent introductions of viruses in each location from which several samples were obtained ( Table 1 ) ., Overall , the number of independent transitions to densely sampled locations is lower in Morocco than Algeria , in agreement with the lower AI for Morocco ., Taken together with the strong spatial structure across countries , these results suggest that a relatively fluid RABV diffusion process within countries is restricted by geopolitical boundaries at larger scales ., To identify the factors that may explain RABV spread , we incorporated several potential predictors as relative diffusion rates among each pair of locations , and tested these against equal rates of diffusion ., Specifically , we considered geographical distances ( great-circle distances ) , human population size , road distances and spatial accessibility measures ., Road distances were derived from transport network data ( Figure, 2 ) and demonstrated a strong correlation with great-circle distances ( r\u200a=\u200a0 . 96 ) ., More detailed landscape features , which may imply multiple , direct and indirect pathways connecting the different localities sampled in this study , were represented by accessibility data ., These data reflect the travel time to the nearest major city using road/track-based travel 16 and were less correlated with geographical distances ( r\u200a=\u200a0 . 61 ) ., We employed circuit theory to translate the accessibility landscape into an origin-destination distance matrix ( the so-called ‘isolation by resistance model’ ) 17 ., We also tested a simple gravity model of viral spread that , in the absence of real dog population sizes for the locations involved , was based on human population sizes for the discrete as a proxy ., Finally , we also used population sizes in a landscape approach , similar to accessibility measures , to construct a population surface matrix 18 ., Marginal likelihood estimates of the model fit of these different predictors suggested that RABV spatial dynamics are best described by road distances ( Table 2 ) ., This was consistent across both countries and again supports human-assisted dispersal of rabies-infected dogs ., As expected by their high correlation , geographical distances provided only a marginally lower fit compared to road distances ., Only the population surface provided inconsistent results between both countries; whereas this model competes with road distances in Algeria , the population surface did not provide a good fit to the Moroccan data ( Figure S2 in Supporting Information S1 ) ., Although accessibility did not seem to explain RABV diffusion as well , we note that all samples were obtained from relatively accessible parts of Morocco and Algeria ., To quantify and compare the dissemination process with previous results , we estimated the rate of RABV gene flow among the sampled isolates using ‘Markov jump’ counts 19 of location state transitions and their reward-associated distances between locations across each branch ., The posterior average rate of viral gene flow among localities estimated for Algeria was 26 km/yr ( 95% highest probability density interval: 18–34 ) and 33 ( 23–43 ) km/yr based on great circle distances and road distances , respectively ., Somewhat higher viral gene flow rate estimates were obtained for Morocco with 42 ( 26–58 ) km/yr and 51 ( 34–72 ) km/yr for great circle distances and road distances , respectively ., We note that the rates of viral gene flow estimated here are highly dependent of the scale of sampling such that comparison may only prove useful at the same geographic scale ., However , these estimates were 2 . 7 to 4 . 4 times higher than those recorded in established enzootic situations in wildlife animals 9 , again suggestive of human-mediated transmission ., Although it is theoretically possible that these relatively high rates reflect epidemic waves periodically moving through this geographical region 20 , particularly since similar rates have been observed in wild carnivores during epidemic spread 9 , 10 , such waves were not observed in the geographical areas studied here and where the virus appears to be largely enzootic ., The occasional mixing of sequences from different locations at the tips of the inferred tree ( Figure, 1 ) is suggestive of long distance spread in relatively little time ( 6 months to one year ) ., To quantify such rapid and long distance spread , we summarized the posterior distribution of distances covered along individual branches ( Figure 3 ) ., We focused on branches along which inferred location state changes occurred in a time period of less than 1 year , and between 1 and 2 years ., As a control , we analysed the branches without inferred state changes; as expected these all had negligible Markov jump count distances ( not shown ) ., Across the posterior distribution of trees , we observed between 5 and 13 branches per tree that have a time length less than 2 years and cover a distance of more than 200 km , and 12 to 13 branches that cover a distance of about 100 km ( Figure 3 ) ., Importantly , our ability to clearly detect long-distance movement is limited to branches representing short evolutionary times; longer branches could also harbour such events , providing an explanation for the relatively high average rates of viral gene flow ., The rates of viral gene flow we estimate among the sampled isolates from Algeria and Morocco contrast with those of spatial RABV movement in an African dog population that should experience very limited human-mediated dissemination of rabid dogs 21 ., In this case , the spatial dispersal of single RABV infections was estimated to be predominantly less than 2 km ( and always smaller than 20 km ) ., Considering that the average incubation period of RABV is between 22 to 29 days 20 , 21 , 22 , 23 , 24 , it is clear that such long distances as those recorded in our study could only be achieved with at least some human intervention ., To investigate more formally how the RABV distribution we observe in Algeria and Morocco contrasts with the patterns of spread we would expect from transmission dynamics in African dogs alone ( i . e . without human intervention ) , we simulated a phylogeodynamic process based on epidemiological parameters obtained from detailed analyses of rabies transmission biology 20 , 21 ., In particular , we considered epidemiologically informed virus movement over all evolutionary histories in the posterior distribution resulting from our phylodynamic inference ( see Supplementary Information ) ., In our spatial simulation we analyze cases in which, ( i ) each new infection takes a random direction in continuous space , or, ( ii ) subsequent infections consistently take the same direction ( Figure 4 ) ., Although the latter may not be very realistic , it should resemble virus movement along roads ., For both Algeria and Morocco , spatial diffusion is initiated at the centre of the sampling locations , such that the process has the largest probability to cover these locations a priori ., When assuming up to one year of movement these simulations clearly show that RABV could not have spread to the same extent as shown by the current sampling in Algeria and Morocco if the virus was simply being transmitted by dog dispersal alone ., Even if we enforce a year of successive RABV transmissions in the same direction , which is highly implausible given the observed dynamics of dog RABV in a local setting 21 , the simulations still do not attain the observed spatial RABV spread ., In addition , the distances realized by dispersal in random directions along branches less than 2 years were all less than 60 km , which is far more restricted than estimated for the real data ( Figure 3 ) ., Rabies is a prime example of an infectious disease in which dispersal can be exacerbated by animal movement mediated by humans ., This is illustrated by raccoon rabies in Virginia , USA 25 , dog rabies in Indonesia in Flores Island 26 , in Bali ( F . X . Meslin , Personal communication ) and in parts of Europe 27 ., Each epidemic resulted in enormous expenditure on rabies post exposure prophylaxis in humans and animal vaccination programs 28 , 29 , 30 , 31 ., Importantly , our study allows us to quantify rates of viral gene flow among sampled dog isolates ( between 18 and 72 km/yr ) in a mixed geographic and socio-economic landscape , such as those characterized by Algeria and Morocco where there is currently little dog vaccination ., In addition , our analysis suggests that the human-mediated dispersal of infected dogs is likely to continue to play a major role in the transmission of RABV in geographical areas where it has been present for many years ., Indeed , our observations of administrative borders that restrict a relatively fluid pattern of spread , the occasional long-distance movement of viruses to particular countries , and the fit between spatial dynamics and road distances , all point to the displacement of rabies-infected dogs by humans ., Understanding the frequency and distance of movements of potentially infected animals is of paramount importance in predicting the spread of viral infections 32 , 33 ., In addition , such information has important implications for disease control; understanding the conditions under which the containment of wildlife 34 and dog rabies can reliably be achieved will assist in the long term goal of eliminating animal RABV ., In particular , that humans mediate the transmission of RABV among dogs in North Africa requires that intervention procedures are implemented more rapidly than in situations in which humans play little or no role in viral transmission ., The high cost associated with surveillance underscores the importance of sampling design and the development of cost-effective monitoring and testing approaches 9 , 12 , 35 ., In addition , this study illustrates the power of phylogeographic approaches 8 to identify the factors responsible for the spread of major animal and zoonotic pathogens ., By integrating spatial dynamics with temporal inferences , the Bayesian analysis utilized here constitutes a powerful new tool that may complement traditional epidemiological methods in studying the effects of human behaviour on the evolution of zoonotic viruses ., The Office of Veterinary Public Health Services of the different countries coordinates follow-up per animal bites ., The respective state health departments ( in conjunction with qualified laboratories that they designate ) conduct all collection , observations , and euthanization ( if necessary ) of animals suspected of rabies , according to established national standardized protocols ., A total of 287 isolates sampled from Morocco , Algeria , Tunisia and Spain ( Ceuta and Melilla ) were collected by the authors within the framework of these qualified laboratories , and sequenced ., These samples were collected from dead animals suspected of rabies so that submission for laboratory rabies diagnosis is mandatory ., Spatial co-ordinates and time of sampling , covering a period of 22 years ( 1986–2007 ) , were available for the majority of these isolates ., Relevant epidemiological information and GenBank accession numbers for all RABV isolates analysed in this study are presented in Table S1 in Supporting Information S1 ., All the locations sampled in this study experienced cases every year ., As such , our sampling does not focus on areas that have been free of rabies in the recent past ., To perform a reliable evolutionary analysis of dog RABV circulating in North Africa , we aimed at selecting a genetic region with sufficient phylogenetic information ., To this end , we sequenced a total of 3080 nt encompassing the N , P and intergenic G-L region ., Total RNA from the original brain samples was extracted using Trizol reagent ( Invitrogen ) according to the manufacturers instructions ., RT-PCRs and sequencing reactions were performed as described previously 6 , 36 ., All sequences obtained have been deposited in GenBank ( accession numbers GU798102–GU798962 ) ., Additional primers used in this study were N1280 ( 5′- AGTCAGTTCTAATCATCAAGC-3′ ) , M138 ( 5′-AAGTTCCTYATGTTYTTCTTGC-3′ ) , G ( 5′GACTTGGGTCTCCCGAACTGGGG-3′ ) and L ( 5′-CAA AGG AGA GTT GAG ATT GTA GTC-3′ ) , at positions 1348–1368 , 2632–2653 , 4666–4688 and 5512–5535 , respectively , of the lyssavirus genome 37 ., Multiple sequence alignment was performed using MUSCLE available through the Muscle web interface ( http://www . ebi . ac . uk/Tools/muscle/index . html ) 38 ., All alignments are available from the authors on request ., To investigate the evolutionary relationships and time to common ancestry among RABV lineages circulating in north Africa , we reconstructed the phylogenetic history for the entire data set using Bayesian Markov chain Monte Carlo ( MCMC ) analysis implemented in the BEAST package 13 ., BEAST incorporates sampling time information to estimate evolutionary rates and a posterior distribution of time-scaled trees ., We employed a GTR model of nucleotide substitution with gamma-distributed rate variation among sites and a relaxed ( uncorrelated log-normal ) molecular clock model 39 ., We specified a Bayesian skyline plot model as flexible tree prior 40 ., All chains were run for a sufficient length and convergence was diagnosed using Tracer ( http://tree . bio . ed . ac . uk/software/tracer/ ) ignoring 10% of the chain as burn-in ., Evolutionary history was summarized using an annotated Maximum Clade Credibility ( MCC ) phylogenetic tree ., Posterior probability values provide an assessment of the degree of support for each node on the tree ., To reconstruct the spatial dynamics of dog-associated RABV spread and investigate the role of different diffusion predictors in shaping the epidemic in both Morocco and Algeria , we extracted two data sets with their specific spatial and temporal co-ordinates:, ( i ) A total of 117 Algerian sequences ( 3080 nt ) collected from dogs in 20 cities over 7 years ( from 2001 to 2008 ) , and, ( ii ) a total of 133 Moroccan sequences ( 3080 nt ) sampled from dogs in 28 cities between 2004 and 2008 ., For all these isolates precise dates ( month ) of sampling and geographical localities ( city ) are available ( Table S2 in Supporting Information S1 ) ., As an additional component in the fully probabilitistic Bayesian inference framework , we consider a discretized diffusion process among the sampling locations in both countries , formalized as a continuous time Markov chain ( CTMC ) model 8 ., A CTMC is fully characterized using a matrix that describes the rate of movement from location state i to j for every pair of locations ., To efficiently estimate the diffusion process from a single observation ( a single location realization for each sequence ) , we restrict the parameterization to a sparse set of rates that adequately explains the phylogeographic dispersal process using Bayesian Stochastic search variable selection ( BSSVS ) ., This BSSVS procedure also allows us to employ Bayes factor testing in the identification of the most parsimonious description of the diffusion process 8 ., We used a modified Association Index ( AI ) to assess the degree of spatial structure in the phylogeographic data 8 , 15 ., This reports the posterior distribution of association values relative to those obtained by randomizing the tip locations ., In addition , we summarize the number of transitions to each sampling location in the posterior tree distribution based on the location realizations at the nodes ., The latter provide a conservative estimate of the number of independent introductions in each location ., To quantify the dissemination process , we estimated the rate of rabies spread among the sampled isolates using ‘Markov jump’ counts 19 of location state transitions for all possible states along the phylogeny ., Markov jump counts measure the expected number of transitions along each branch conditional on the observed data ., By multiplying the expected number of transitions between each pair of locations by the geographical distance between these two locations , we arrive at the expected distance travelled within the time elapsed on each branch ., This approach , implemented in BEAGLE 41 a library that can be used in conjunction with BEAST ) , integrates over all uncertainty in the evolutionary tree and offers a degree of robustness to model misspecification 42 ., To test different scenarios of phylogeographic diffusion , we fix the CTMC relative rate parameters to the normalized pairwise location measures that represent different diffusion predictors and perform Bayesian model selection using marginal likelihood approximations 43 ., We consider;, ( i ) geographical distances , specifically great-circle distances that represent the shortest path on the surface of the Earth between two points ,, ( ii ) human population size , obtained from http://en . wikipedia . org/ and http://www . mongabay . com ( Table S3 in Supporting Information S1 , rates between each pair of locations were fixed to the normalized products of the population sizes ) ,, ( iii ) road distances ,, ( iv ) a gravity model ,, ( v ) spatial accessibility , and, ( vi ) ‘population conductivity’ measures ., The accessibility estimates are derived from a range of spatial data sets , road type and network data 44 , satellite derived and cover information , settlement database locations and sizes , and satellite derived topography ., They are combined to create a ‘friction surface’ where each 1×1 km square represents the difficulty ( or travel time ) in crossing it ., These estimates provide a representation of the difficulty in travel between all the locations ., Using a circuit theory approach , an origin-destination distance matrix was estimated from this accessibility landscape 17 ., A simple gravity model was constructed by fixing the rates to the normalized product of the population sizes divided by the great circle distance between the locations involved ., As an alternative , population sizes were also mapped in a landscape ( Figure S2 in Supporting Information S1 ) 18 and again translated to an origin-destination distance matrix using circuit theory ., To assess model fit , marginal likelihood approximations are obtained using an importance sampling estimator 43 , 45 , which employs a mixture of model prior and posterior samples 46 ., To contrast the spatial distribution of our rabies samples in Algeria and Morocco with the patterns of spread we would expect from local transmission dynamics in African dogs as the sole maintenance population , we performed a simulation analysis that integrates phylodynamic and epidemiological parameters ., Specifically , we consider a spatial process based on epidemiological parameters obtained from a detailed analysis of rabies transmission biology in African dogs 20 , and simulate virus movement accordingly over all evolutionary histories in the posterior distribution resulting from our phylodynamic inference ., The latter characterizes the successful ancestral transmission history of the viruses we sampled and provides a time-scale for the spatial process we would like to simulate ., For each tree in the posterior distribution , we consider the ancestral virus at the root to start spreading from the mid-point of our available samples ( average of longitudes and latitudes ) ., We recursively visit all branches from root to tip , each time simulating a number of successive infections , which jointly encompass the entire time length for each branch ., Each time interval t ( in days ) between successive infections follows: ( 1 ) where a is a random incubation time , b is the random period of infectiousness , and f is a random fraction drawn from a uniform0 , 1 distribution ., Following the results of the comprehensive study by Hampson et al . 21 , we consider ( 2 ) and ( 3 ) Each new infection is moved a random distance d ( in m ) away from its source case; the distribution for d follows a previously described spatial infection kernel 21: ( 4 ) In the spatial simulation process , we assume that each new distance takes a random direction in continuous space , but we also explore subsequent infections consistently taking the same direction ., To achieve a realistic distribution in the relevant geographic area , we prohibit new infections to invade water areas ., The tree heights used for simulation for Algeria and Morocco were 33 ( 23–46 ) years and 28 ( 18–39 ) years respectively ( as estimated from the country-specific data ) , whereas the tree lengths encompassed 623 ( 490–789 ) years and 532 ( 367–706 ) years respectively ., This simulation procedure yields location realizations in continuous space for all tips and all trees in the posterior distribution ., We summarize this spatial distribution using two-dimensional contours ., Because the spatial simulation for each tree in the posterior distribution may cover a different area , it is important to note that the contour representing the process over all trees depicts the maximum area that can be covered for the set of epidemiological parameters we consider .

Introduction, Results/Discussion, Materials and Methods

Understanding the role of humans in the dispersal of predominately animal pathogens is essential for their control ., We used newly developed Bayesian phylogeographic methods to unravel the dynamics and determinants of the spread of dog rabies virus ( RABV ) in North Africa ., Each of the countries studied exhibited largely disconnected spatial dynamics with major geo-political boundaries acting as barriers to gene flow ., Road distances proved to be better predictors of the movement of dog RABV than accessibility or raw geographical distance , with occasional long distance and rapid spread within each of these countries ., Using simulations that bridge phylodynamics and spatial epidemiology , we demonstrate that the contemporary viral distribution extends beyond that expected for RABV transmission in African dog populations ., These results are strongly supportive of human-mediated dispersal , and demonstrate how an integrated phylogeographic approach will turn viral genetic data into a powerful asset for characterizing , predicting , and potentially controlling the spatial spread of pathogens .

At least 15 million doses of anti-rabies post-exposure prophylaxis are administered annually worldwide , and an estimated 55 , 000 people die of rabies every year ., Over 99% of these deaths occur in developing countries , predominantly in Asia and in Africa where rabies is endemic in domestic dogs ., Despite the global health burden due to rabies , little is known about the patterns of the spread of dog rabies in these endemic regions ., We used recently developed Bayesian analytical methods to unravel the dynamics and determinants of the spatial diffusion of dog rabies viruses in North Africa based on viral genetic data ., Our analysis reveals a combination of restricted spread across administrative borders , the occasional long-distance movement of rabies viruses , and a strong fit between spatial spread of the virus and road distances between localities ., Together , these data indicate that by transporting dogs , humans have played a key role in the dispersal of a major animal pathogen ., Our studies therefore provide essential new information on the transmission dynamics of rabies in Africa , and in doing so will greatly assist in future intervention strategies .

infectious diseases/infectious diseases of the nervous system, evolutionary biology/microbial evolution and genomics, computational biology/evolutionary modeling, infectious diseases/viral infections, public health and epidemiology/epidemiology, public health and epidemiology/infectious diseases, computational biology/ecosystem modeling, infectious diseases/tropical and travel-associated diseases

journal.pgen.1008337

A first genetic portrait of synaptonemal complex variation

In most species that reproduce sexually , homologous chromosomes must undergo recombination to segregate properly during meiosis 1–4 ., Recombination diversifies offspring genomes , shaping evolution and genomic patterns of variation in populations 5–7 ., Despite these functional roles for recombination , its frequency varies markedly—on both an evolutionary scale ( within and between species ) , and across genomic scales ( ranging from kilobases to chromosomes ) 8–12 ., This variation has implications for human health: too few or too many crossovers can lead to infertility , miscarriage , or birth defects 3 , 13 , 14 ., Significant progress on two fronts is laying the foundation for discovering mechanisms responsible for recombination rate differences between individuals ., First , work with genetic model organisms is revealing in increasing detail the molecular and cellular processes that lead to crossovers 15–18 ., Second , genes and genomic regions that confer standing differences in recombination rate among individuals are being identified through association or linkage mapping 19–32 ., Heritable variation in recombination rate is caused by mutations affecting one or more of the steps in the recombination pathway that culminate in crossover formation ., Consequently , genetic dissection of inter-individual differences in these formative processes should be especially revealing about how recombination rate evolves ., A promising intermediate phenotype to target for such investigations is chromosome synapsis ., Synapsis between homologous chromosomes is mediated by the synaptonemal complex ( SC ) , a meiosis-specific supra-molecular protein structure 33 , 34 ., Synapsis and the SC are intimately linked to crossovers ., Formation of the SC begins at sites of programmed double-strand breaks ( DSBs ) early in meiosis 35–37 ., In several species , these DSB sites have been shown to mediate the homology search that precedes chromosome pairing; as meiosis progresses , a small subset of DSBs are repaired as crossovers 18 , 38 , 39 ., During synapsis , the DNA in each chromosome is organized into an array of loops with the SC serving as the central axis that maintains the tight alignment among homologs 40 , 41 ., Mutations that affect SC structure distort recombination patterns , in addition to chromosome pairing and segregation 42–47 ., The length of the SC axis is a quantitative characteristic of synapsis that is strongly associated with recombination rate ., Variation in SC length reflects differences in the degree of chromatin compaction and interaction between homologs 48–50 ., Environmental factors , including temperature , can simultaneously alter SC length and the frequency of recombination 51 , 52 ., In humans , both SC length and crossover number are higher in oocytes than in spermatocytes 53 , 54 ., More broadly , SC length and recombination rate are correlated across multiple mammalian species 55–57 , suggesting that variation in SC length and variation in crossover number share a common genetic basis ., While the SC has been studied from a mechanistic perspective 33 , 34 , 50 , the genetic basis of standing variation in this fundamental meiotic phenotype remains unknown ., To fill this notable gap , we combined a novel high-throughput digital image analysis technique and immunofluorescent cytology with the power of complex trait mapping ., Genetic dissection of SC length variation in thousands of spermatocytes taken from two mouse intercrosses reveals genomic regions responsible for the evolution of SC structure ., These two intercrosses ( Fig 1 ) represent divergence on two different timescales: one between lines diverged by over 300 , 000 years 58 , referred to here as a cross between subspecies , and one between lines that diverged much more recently 59 , referred to here as a cross within subspecies ., By combining the genetic analysis of SC length and crossover number in individuals from the same dataset , we uncover important evolutionary and genetic connections , including evidence for a common genetic mechanism underlying variation in these two meiotic traits ., To characterize the SC in 9 , 532 spermatocyte images , we developed a digital image analysis algorithm that automatically estimates total SC length ., The algorithm uses image processing techniques to transform each image into a single-pixel-wide wireframe representation ., SC length is then determined by counting the number of pixels in this representation and scaling this value to the level of magnification ( see Methods for details ) ., Fig 2 shows two representative images of spermatocytes , one from each cross , and their progression through the image analysis algorithm ., To assess the accuracy of our algorithm , we first applied it to a set of test images ., We randomly selected five F2 individuals from each of the two intercrosses and manually measured SC length in the 284 images of spermatocytes from these individuals ., These measurements were compared to those produced by the algorithm for the same set of images ., SC lengths estimated using our algorithm reliably matched those obtained by manual tracing in our test set for spermatocytes from both crosses ( overall R2 = 0 . 83; castCAST × muscPWD , R2 = 0 . 74; domGI × domWSB , R2 = 0 . 81; Fig 3 ) ., We also compared the mean SC length of spermatocytes from the same individual and found even better agreement between the algorithm and manual measurements ( overall R2 = 0 . 97 ) ., Motivated by this high concordance , we applied the algorithm to images of spermatocytes from: 5 castCAST , 5 muscPWD , 289 castCAST × muscPWD F2s , 4 domGI , 6 domWSB , and 229 domGI × domWSB F2s ., muscPWD spermatocytes have longer SCs than castCAST spermatocytes ( muscPWD mean = 174 . 2 μm , SE = 0 . 7 μm; castCAST mean = 150 . 4 μm , SE = 0 . 4 μm; t-test p < 0 . 05 ) ., SC length is continuously distributed among F2s from this intercross , with a mean close to the mid-parent value ., These characteristics mirror those for the mean number of MLH1 foci in the same set of individuals ( S1 Fig and 25 ) ., domGI spermatocytes have longer SCs than domWSB spermatocytes ( domGI mean = 139 . 6 μm , SE: 1 . 6 μm; domWSB mean = 131 . 8 μm , SE: 1 . 4 μm; t-test p < 0 . 05 ) ., Approximately half of the F2s from this intercross have mean SC lengths beyond the parental means , a pattern that resembles the distribution of MLH1 foci in the same set of individuals ( S1 Fig; 63 ) ., The continuous distributions of mean SC length among spermatocytes from individuals in both intercrosses suggest that SC length is a complex trait controlled by multiple loci ., To identify quantitative trait loci ( QTL ) driving evolution of the SC between and within subspecies , we conducted genome-wide QTL scans using mean SC length as the phenotype ., We found three QTL responsible for the SC length difference between muscPWD and castCAST on chromosomes X , 3 and 4 ( Fig 4A; Table 1 ) ., muscPWD alleles at QTL on chromosomes 3 and 4 increase mean SC length in an additive manner ., In contrast , the muscPWD allele at the X-linked QTL decreases SC length , acting in opposition to the phenotypic difference between strains ., The summed additive effects of QTL on chromosomes 3 and 4 explain 29 . 2% of the SC length difference between the two strains ., Collectively , the three QTL explain 28 . 5% of the F2 variance ., This percentage is substantially less than the 74% of phenotypic variance explained by QTL for MLH1 count in this same cross 25 , though variance explained by the SC length QTL may be underestimated as this trait is more difficult to measure ( see Discussion ) ., Chromosomes X , 3 and 4 had been found to harbor QTL for mean MLH1 count 25 ., The 1 . 5-LOD intervals of QTL for SC length and MLH1 count overlap on chromosomes X and 4 ( Table 2 ) , indicating that a single locus in each of these intervals could affect both traits ., Alleles at both of these QTL affect SC length and MLH1 count in similar ways ( Fig 5A ) ., In contrast , allelic effects at the chromosome 3 QTL differ for SC length ( alleles act additively ) and MLH1 count ( castCAST allele acts dominantly ) ( Fig 5A ) ., This difference in phenotypic effects , along with distinct 1 . 5 LOD intervals , indicates separate QTL on chromosome 3: one locus for SC length ( peak LOD = 104 Mb , 68–129 Mb ) and a second , more distal locus for MLH1 count ( peak LOD = 150 Mb , 1 . 5-LOD interval 133–160 Mb ) ., We discovered two QTL responsible for the SC length difference between domGI and domWSB on chromosomes X and 5 ( Fig 4B; Table 1 ) ., Together , these loci account for 15 . 2% of the phenotypic difference between strains ( and explain 12 . 8% of the F2 variance ) ., The 1 . 5-LOD interval for the QTL on chromosome 5 overlaps with a QTL for MLH1 count 63 ., QTL effects differ between traits: heterozygotes show reduced SC length compared to homozygotes , whereas alleles act additively to shape MLH1 count ( Fig 5B ) ., The X-linked QTL for SC length appears not to affect MLH1 count 63 ., None of the QTL for SC length differences within subspecies overlaps with QTL for SC length differences between subspecies ., To identify candidate genes and mutations for SC evolution , we combined available information from spermatocyte expression data and gene ontology ( GO ) with the QTL intervals for SC length ., We focused on QTL that explained the difference in SC length between domGI and domWSB because of the relatively lower sequence divergence between these strains ., We found 301 candidate genes within the 1 . 5 LOD interval of the QTL on chromosomes X and 5; 11 of these were compelling candidates ( table in S1 Data ) ., We filtered the initial candidate list by looking for an association with recombination GO terms ( search strings: “recomb” , “synapton” , and “meio” ) and increased expression during meiosis in transcriptomic experiments 64 , 65 ., To further refine this list , we considered only those genes with a single nucleotide polymorphism ( SNP ) between domGI and domWSB that yielded either a nonsynonymous change or a change to a known or inferred transcription factor binding site ., We identified two strong candidate genes on chromosome 5 from the whole-genome sequence comparison , Hfm1 and Rnf212 , and several candidate mutations within them ( Table 3 ) ., Variants in both candidate genes have been previously associated with variation in genome-wide recombination rate in mammals 19 , 26 , 30 , 66–68 , and both genes are known to be involved in chromosome synapsis 69 , 70 ( see Discussion ) ., To examine genetic connections between SC length and crossover number , we compared SC lengths to MLH1 counts obtained for the same spermatocytes 25 , 63 ., SC length and the number of MLH1 foci are positively correlated across spermatocytes in both F2 intercrosses ( Fig 6 ) ., This correlation is significantly stronger in spermatocytes from the castCAST × muscPWD cross ( Pearson’s r = 0 . 38 , 95% CI = 0 . 36 , 0 . 40 ) compared to those from the domGI × domWSB cross ( r = 0 . 15 , CI = 0 . 12 , 0 . 19 ) ., We examined the ratio of these two phenotypic values from F2 individuals in the following analyses to consider the genetic connection between SC length and the number of crossovers ., Loci that control this ratio may play a role in controlling crossover interference , the observation from a variety of species that crossovers are spaced more regularly than expected if they occur independently 71 ., The ratio of mean SC length to mean MLH1 count ( SC/CO ratio ) ( S3 Fig ) varies more among F2s from the castCAST × muscPWD cross ( castCAST mean: 6 . 5 ± 0 . 03 μm / focus , muscPWD: 5 . 8 ± 0 . 04 , F2s: 6 . 4 ± 0 . 52 ) than among F2s from the domGI × domWSB cross ( domGI: 6 . 0 ± 0 . 1 μm / focus , domWSB: 6 . 1 ± 0 . 12 , F2s: 6 . 0 ± 0 . 30 μm / focus ) ., This mirrors the greater divergence of both traits in the inter-subspecific cross ., Treating the transformation of the two trait values as an individual trait , we scanned for associated QTL in both crosses ., We identified three QTL that influence the SC/CO ratio ., These loci overlap with QTL previously identified in single-trait analyses ( Table 2; S4 Fig ) ., The interval on chromosome 5 from the domGI × domWSB cross is notable , however ., The LOD score for SC/CO ratio at this locus is substantially higher than the score for either of the two traits analyzed separately ( Fig 7 ) ., Further , this QTL explains 12 . 5% of the variation in SC/CO ratio among domGI × domWSB F2s , substantially more than the 6 . 1% and 6 . 8% of variation this interval explains for MLH1 count and SC length , respectively ., We interpret these results as evidence that the QTL on chromosome 5 is not only pleiotropic , but responsible for the level of covariation between the two traits ., The domGI allele at this locus dominantly reduces the SC/CO ratio among the domGI × domWSB F2s ( Fig 7B ) ., To further characterize pleiotropic QTL and identify steps of the recombination pathway they modulate , we performed mediation analysis 72–75 ., This approach assesses whether the relationship between two variables exists because of the indirect effect of a third , mediating variable ., For example , a genetic interval associated with the number of MLH1 foci may actually be mediated by this interval’s effect on SC length; accounting for the effect of this mediator would reduce or wholly abolish the interval’s association with the number of MLH1 foci ., We tested two models of mediation for each putatively pleiotropic locus—one for each trait acting as a mediator of the other ., Mediation was evaluated for each QTL by comparing four regression models of QTL effects on the two traits ., Model 1 QTL → SC length YSC = β0 + βSCXQTL + ε Model 2 QTL → CO count YCO = β0 + βCOXQTL + ε Model 3 QTL → CO count → SC length YSC = β0 + β′SCXQTL + αCOXCO + ε Model 4 QTL → SC length → CO count YCO = β0 + β′COXQTL + αSCXSC + ε In this regression framework , β0 represents the intercept for each model , ε represents the error term , βSC and βCO are the regression coefficients for the QTL in an unmediated model , β′SC and β′CO are the coefficients for the QTL in a mediated model , and αSC and αCO are the coefficients for each trait when acting as a mediator for the other trait ., We found significant evidence for mediation effects ( p < 0 . 01 ) at QTL on chromosomes 4 and X in the castCAST × muscPWD cross ( Table 4 ) ., Both models of mediation are supported at these loci ., The effects of QTL on SC length are significantly mediated by their effects on the number of MLH1 foci , and likewise , the effects of QTL on MLH1 count are significantly mediated by their effects on SC length ., We estimated the proportion of QTL effects explained by the mediating variable for each of the two models as, fSC= ( βSC−β′SC ) /βSC, fXO= ( βCO−β′CO ) /βCO, For the QTL on the X chromosome , mediation is much stronger in the model where the QTL effect on SC length is mediated by its effect on the number of MLH1 foci ( fSC = 0 . 70 ± 0 . 28 , fCO = 0 . 15 ± 0 . 09 ) ., In contrast , the proportion of effects mediated at the QTL on chromosome 4 is more similar between the two models ( fSC = 0 . 21 ± 0 . 31 , fCO = 0 . 50 ± 0 . 44 ) ., We performed analyses on the sensitivity of these findings to differences in measurement error—which can reduce the strength of evidence for mediation—by adding Gaussian noise to the observations ., Evidence for mediation and the estimated proportions of effects mediated were found to be negligibly affected by differences in measurement error between the two traits ( see Materials and Methods and S1 Methods for details ) ., We report here the first loci known to be involved in the evolution of SC length , establishing a genetic basis for natural variation in this fundamental meiotic structure ., Distinct genomic regions are responsible for evolutionary differences in SC length between and within subspecies of house mice ., Several of these loci also control the genome-wide number of crossovers; this pleiotropy partially explains the correlation between SC length and recombination rate widely observed in cytological studies ., More broadly , our results demonstrate the power of genetically dissecting natural variation in multiple aspects of the meiotic program to understand differences in recombination rate that exist among organisms ., Our findings point to genetic mechanisms involved in the evolution of SC structure and recombination rate ., We took advantage of knowledge of recombination pathways and low levels of sequence variation within M . m ., domesticus to nominate strong candidate genes for evolution of the SC ., Two genes seem especially worthy of consideration in the chromosome 5 interval that affects SC length , crossover number , and the SC/CO ratio ., Hfm1 ( also known as Mer3 ) is a DNA helicase required for the completion of chromosome synapsis and crossover formation in mouse; Hfm1 knockout mice produce an SC that fails to assemble along the full length of the chromosome axis 69 ., Nonsynonymous variants in Hfm1 have been associated with inter-individual differences in genome-wide recombination rate in cattle and humans 66 , 68 ., Rnf212 is another strong candidate gene for the chromosome 5 QTL ., Rnf212 is a SUMO ubiquitin ligase that selectively localizes to a subset of recombination sites along the central region of the SC , coupling synapsis to formation of crossover-specific protein complexes 70 ., Rnf212 stabilizes crossover precursors and helps determine whether each recombination site becomes a crossover 70 , 76 ., Variants at Rnf212 also contribute to inter-individual differences in genome-wide recombination rate in red deer 77 , Soay sheep 30 , cattle 26 , 66 , and humans 19 , 22 , 78 , 79 ., Two amino acid substitutions in Hfm1 and one in Rnf212 are predicted to have strongly deleterious fitness effects , suggesting they should be prioritized for further evaluation ., The strength of support for these candidate genes , along with the highly specific nature of the phenotypes ( SC length and crossover number ) , should motivate functional testing using genome editing or other approaches ., Joint consideration of SC length and crossover number provided additional clues about the genetic connections between these phenotypes ., Models of the relationship between these two traits have concentrated on the order of molecular events surrounding synapsis and recombination 80–82 ., We took two approaches to investigating the covariation of SC length and crossover number , focusing on their disparity within and between subspecies ., Our first approach treated the ratio of SC length to the number of crossovers as its own trait ., Inter-individual differences in this ratio have previously been documented in human spermatocytes 83 ., Could upstream control of this ratio be modulating differences in both traits simultaneously ?, We discovered a locus with such an effect in the domGI × domWSB cross ., As expected , loci with a large effect on the SC/CO ratio also affect each trait individually ., However , one of the QTL for this ratio ( on chromosome 5 ) had a substantially increased signal—higher LOD , narrower confidence interval , and greater percentage of variance explained—relative to its effect on any single phenotype ., These results suggest that the SC/CO ratio is closer to the mechanism of this QTL’s action for both phenotypes ., We conclude that SC length and crossover number are causally linked , in part by the action of this locus ., Changes in the length of the SC reflect differences in chromatin packing during meiosis 48–50 ., Our observation that the number of crossovers per unit of SC length differs between and within subspecies therefore indicates that the relative spacing of crossovers has evolved in house mice , a species in which crossover interference is strong 84 , 85 ., To our knowledge , the QTL on chromosome 5 is only the second locus suggested to contribute to standing variation in interference ( see 31 ) ., The SC has been shown to play a critical role in establishing proper interference 86 , 87 ., A recent model of the SC as a liquid crystal suggests the SC is a conduit for partitioning crossovers by transmitting an interference signal across the chromosome 88 ., Our second approach to investigating covariation of SC length and crossover number was to perform mediation analysis on loci that appeared pleiotropic ., This approach yielded strong evidence for pleiotropy at QTL on chromosomes 4 and X in the muscPWD × castCAST cross ., The direction of effects at these loci ( longer SC and more crossovers ) , and their concordance with the overall correlation between the traits , suggests their participation in a singular underlying mechanism ., However , the relative magnitude of mediation effects at these two loci indicates that they likely contribute to different elements in the pathway leading to crossover formation ., The coordinated variation of SC length , crossover number , and DSBs ( along with other molecular intermediaries of recombination ) in comparative studies across mammals 57 and mouse strains 89 has been used to suggest that variation in recombination rate is established at the earliest stages of meiosis ., Because MLH1 is recruited to crossovers after the SC is completely assembled , the stronger mediation of SC length by crossover number at the X-linked QTL is consistent with an effect on an early decision , perhaps by participating in the crossover/non-crossover decision before synapsis 90 , 91 ., In contrast , the direction of mediation at the QTL on chromosome 4 is consistent with a later action ., Stronger mediation of crossover count by SC length at this locus suggests an effect on crossover formation subsequent to the completion of SC assembly , a potential subset of crossovers marked by MLH1 foci 18 , 92 ., Our results indicate that , despite the covariation of these two meiotic phenotypes , natural variation in recombination rates appears to be established at multiple stages in the recombination pathway , at least in mice ., We found that the X chromosome explained variation in SC length both within and between subspecies ., No other chromosome had significant QTL from both crosses ., Previous studies identified an important role for the X chromosome in the evolution of recombination rate between subspecies of house mice 23 , 25 , 28 , 93 ., Our findings extend the contributions of the X chromosome to the evolution of a second meiotic trait connected to recombination: the SC ., One theory for the disproportionate role of the X chromosome in recombination rate variation focuses on the possibility of antagonistic sexual conflict 94–96 ., If different optima for recombination rates exist between males and females , the X chromosome may accumulate rate modifiers in an evolutionary arms race ., Male and female mice also show contrasting patterns of divergence in recombination rate , consistent with such a hypothesis 25 , 97 ., Comparing QTL locations among the two crosses also provides clues about the timescale of SC length evolution and the origins of the causative mutations ., The absence of co-localizing QTL between the two crosses is somewhat surprising given the relatively recent divergence of the mouse subspecies 58 , 98 ., We expect some of the genetic variation responsible for differences in SC length should be from alleles that arose before subspecies divergence but that have remained polymorphic within subspecies ., A comparison between mapping experiments , as in the crosses presented here , should reveal these ancestrally polymorphic alleles as shared , co-localizing QTL ., While our experiments lacked the power to identify more than a handful of SC length QTL , that none were shared may hint at their rapid or recent accumulation ., If mutations responsible for SC length divergence are sorted in the lineages we considered , it would imply that they arose during the last few hundred thousand generations ( i . e . , in the time since the subspecies diverged ) ., Genetic mapping experiments with other strains of house mice would help to pinpoint the temporal origins of the QTL alleles we discovered ., Our conclusions are tempered by a few limitations of our experimental design ., The SC is a spatially and temporally dynamic structure 99–101 , for which our measurements of length are an incomplete summary ., SC length was harder to estimate than crossover count , which complicated joint genetic analysis of the two traits ( though our sensitivity analysis suggests robustness of mediation inferences ) ., Since MLH1 foci only mark those crossovers produced by the interference-dependent pathway ( the vast majority in mice 18 , 102 ) , the connection between SC length and non-interfering crossovers was not accessible with our experimental approach ., Because our estimates of SC length were summed across all chromosomes , we could not determine whether each QTL affects multiple chromosomes ( in trans ) or only its own chromosomal region ( in cis ) ., Nevertheless , we can be certain that the pleiotropic QTL on the X chromosome acts in trans since our crossover measurements were restricted to autosomes in male meiotic cells ., Meiosis and recombination in females may operate differently ., While QTL mapping is a powerful approach to identify loci responsible for variation , its resolution is limited by the number of recombinant offspring and analyses tend to overestimate the effect size of significant loci 103–105 ., Higher mapping resolution could reveal that some of our apparently pleiotropic QTL are instead composed of multiple , closely linked loci ., This first genetic portrait of natural variation in SC length raises key questions about the evolution of this fundamental meiotic trait ., How is SC length related to fitness ?, Does natural selection target SC length through its effects on meiotic chromatin organization , recombination rate , or some other trait ?, Does divergence of SC length constrain or accelerate recombination rate divergence ?, Our findings should motivate incorporation of SC length into comparative studies of recombination rate evolution as well as genetic dissections of shifts in the underlying pathways ., All animal care and experimental protocols were approved by the University of Wisconsin Animal Care and Use Committee ( Protocol #M005388 , #V005209 ) ., Laboratory mice were euthanized by trained personnel via CO2 inhalation ., Data presented in this study was gathered using images of spermatocytes from F2 males from two separate intercrosses ., In the muscPWD × castCAST cross , 315 F2 males were sacrificed at approximately 10 weeks of age; 289 were included in the final analysis ., The second cross , domGI × domWSB , included 315 F2 males sacrificed at approximately 16 weeks of age; 229 were included in the final analysis ., Additional details on cross design and animal husbandry can be found in 25 and 106 ., Details on the preparation of spermatocyte spreads and immunostaining can be found in 63 and 25 ., Here , we briefly summarize the shared steps taken to arrive at stained spermatocyte images ., Seminiferous tubules were extracted from the testis of sacrificed males and incubated in hypotonic buffer ., The macerated tubules were then ripped apart to liberate spermatocytes ., The cellular slurry was fixed onto a glass slide with a paraformaldehyde solution and allowed to dry ., These prepared slides were incubated with primary antibodies against MLH1 , a mismatch repair protein that localizes to sites of meiotic crossover , and SYCP3 , an essential structural element spanning the synaptonemal complex ., After several wash steps , the slides were then incubated with a set of secondary antibodies tagged with fluorophores , at 488 nm and 568 nm for MLH1 and SYCP3 respectively , and then mounted for visualization ., Slides from the muscPWD × castCAST cross were imaged on a Zeiss Axioskop microscope with an AxioCam HRc camera ., Images from this cross were stored as . tiff files with a resolution of 1030×1300 pixels and 150 pixels per inch ., Slides from the domGI × domWSB cross were imaged on a Zeiss Axioplan 2 microscope with an AxioCam HR3 camera ., Images from this cross were stored as . tiff files at either 1030×1300 or 1388×1040 at 150 pixels per inch ., In both cases , images were captured with a 100× objective lens ., All images were manually curated and only cells with a clearly condensed , full set of 20 bivalents were included ., Images of cells with obvious defects or damage from handling were omitted ., We utilized techniques from computer vision to determine the total length of the SC in each spermatocyte from captured immunofluorescent images ., We applied algorithms for image processing and analysis as implemented in the scikit-image package for Python 3 ( scikit-image . org 107 ) ., For each image , we first isolated the red channel , which contains information from the fluorescence of secondary antibodies against anti-SYCP3 at 568 nm ., The image gradient on the isolated channel was then taken with a 3 pixel-wide disk structuring element ., This creates an image where regions of contrast , or edges , are enhanced ., Otsu’s method 108 was applied to the image gradient , a clustering technique on pixels that reduces the grayscale gradient image to a binary image ., Spurious pixels were removed by applying a morphological opening operator , followed by a morphological closing , with a 4 pixel-wide square structuring element ., Finally , the cleaned binary image was reduced to a single-pixel wide representation with the skeletonize algorithm 109 implemented in scikit-image ., The total number of pixels in this single-pixel wide representation was taken as the total SC length for a spermatocyte ., The reliability of this technique was assessed by comparison to measurements made by manual tracing ., Performance was evaluated in a test set of 217 spermatocyte images , from 5 muscPWD × castCAST F2s and 5 domGI × domWSB F2s ., Mice from the muscPWD × castCAST cross were genotyped at 295 SNPs using the Sequenom iPLEX MassARRAY system 25 , 110 ., Of these , 222 SNPs with Mendelian segregation patterns were retained for the QTL analysis ., These markers were monomorphic for different alleles in the parents and consistent with the expected genotypic ratio of 1:2:1 among F2s ., Mice from the domGI × domWSB cross were genotyped at 77 , 808 markers on the Mega Mouse Universal Genotyping Array ( MegaMUGA 32 , 111 ) ., Of these , 11 , 833 SNPs with Mendelian segregation patterns were retained for the QTL analysis ., All QTL analyses were performed in R ( v . 3 . 3 . 3 ) 112 with the R/qtl package ( v . 1 . 40–8 ) 113 ., Representative total SC length for each individual was calculated by taking the mean SC length among spermatocyte images from that individual ., Individuals were represented by a median of 18 spermatocyte images and those represented by fewer than 5 images were omitted from the analysis ., Haley-Knott regression 114 was performed on data from both crosses to identify QTL for variation in mean SC length ., Individuals were weighted by the number of spermatocyte observations , and cross direction was included as an additive covariate ., Thresholds for significance were determined by permutation , with genome-wide α = 0 . 05 , and established from 1000 replicates for each cross ., Phenotypic means and allelic effects for QTL were estimated at the position of peak LOD with the effectplot function in R/qtl ., Percent variance explained by each QTL was estimated under a multiple-QTL model , including all significant intervals from single-QTL scans for each respective trait , using the fitqtl function ., We tested multiple QTL models by applying a forward/backwards stepwise search algorithm with penalized LOD scores 115 , implemented using the stepwise function in R/qtl ., We also evaluated models including epistas

Introduction, Results, Discussion, Materials and methods

The synaptonemal complex ( SC ) is a proteinaceous scaffold required for synapsis and recombination between homologous chromosomes during meiosis ., Although the SC has been linked to differences in genome-wide crossover rates , the genetic basis of standing variation in SC structure remains unknown ., To investigate the possibility that recombination evolves through changes to the SC , we characterized the genetic architecture of SC divergence on two evolutionary timescales ., Applying a novel digital image analysis technique to spermatocyte spreads , we measured total SC length in 9 , 532 spermatocytes from recombinant offspring of wild-derived mouse strains with differences in this fundamental meiotic trait ., Using this large dataset , we identified the first known genomic regions involved in the evolution of SC length ., Distinct loci affect total SC length divergence between and within subspecies , with the X chromosome contributing to both ., Joint genetic analysis of MLH1 foci—immunofluorescent markers of crossovers—from the same spermatocytes revealed that two of the identified loci also confer differences in the genome-wide recombination rate ., Causal mediation analysis suggested that one pleiotropic locus acts early in meiosis to designate crossovers prior to SC assembly , whereas a second locus primarily shapes crossover number through its effect on SC length ., One genomic interval shapes the relationship between SC length and recombination rate , likely modulating the strength of crossover interference ., Our findings pinpoint SC formation as a key step in the evolution of recombination and demonstrate the power of genetic mapping on standing variation in the context of the recombination pathway .

During the first stages of meiosis , the chromosome axes are organized along a protein scaffold in preparation for recombination and their subsequent segregation ., This scaffold , known as the synaptonemal complex ( SC ) , is critical for the regular progression of recombination ., A complex relationship exists between the organization of the SC , the frequency of recombination , and the likelihood of improper chromosome segregation ., In this study , we investigate the genetics of synaptonemal complex variation in the house mouse and connect it with variation in the rate of recombination ., We found five loci and several compelling candidate genes responsible for the evolution of synaptonemal complex length within and between mouse subspecies ., Several of these loci also affect recombination rate , and our joint analyses of the phenotypes suggest an order by which their effects manifest within the recombination pathway ., Our results show that evolution of SC length is crucial to recombination rate divergence ., Our work here also demonstrates that genetic analysis of additional meiotic phenotypes can help explain the evolution of recombination , a fundamental evolutionary force .

spermatocytes, quantitative trait loci, population genetics, germ cells, dna, population biology, sperm, homologous recombination, sex chromosomes, genetic polymorphism, animal cells, chromosome biology, x chromosomes, evolutionary genetics, genetic loci, biochemistry, cell biology, nucleic acids, genetics, biology and life sciences, cellular types, dna recombination, evolutionary biology, chromosomes

journal.pgen.1002221

Genetic Architecture of Aluminum Tolerance in Rice (Oryza sativa) Determined through Genome-Wide Association Analysis and QTL Mapping

Aluminum ( Al ) toxicity is the major constraint to crop productivity on acid soils , which comprise over 50% of the worlds arable land 1 ., Under highly acidic soil conditions ( pH<5 . 0 ) , Al is solubilized into the soil solution as Al3+ , which is highly phytotoxic , causing a rapid inhibition of root growth that leads to a reduced and stunted root system , thus having a direct effect on the ability of a plant to acquire both water and nutrients ., Cereal crops ( Poaceae ) have been a primary focus of Al tolerance research 2 ., This research has demonstrated that levels of Al tolerance vary widely both within and between species 3–8 ., Of the major cereal species that have been extensively studied ( rice , maize , wheat , barley and sorghum ) , rice demonstrates superior Al tolerance under both field and hydroponic conditions 3 , 8 ., Although rice is 6–10 times more Al tolerant than other cereals , very little is known about the genes underlying this tolerance ., Based on its high level of Al tolerance and numerous genetic and genomic resources , rice provides a good model for studying the genetics and physiology of Al tolerance ., In wheat , sorghum , and barley , Al tolerance is inherited as a simple trait , controlled by one or a few genes 9–11 ., However , in maize , rice , and Arabidopsis , tolerance is quantitatively inherited 12 , 13 ., Al tolerance genes have been cloned in wheat and sorghum ., The wheat resistance gene , ALMT1 , encodes an Al-activated malate transporter 14 ., The sorghum resistance gene , SbMATE , encodes a member of the multidrug and toxic compound- extrusion ( MATE ) family and is an Al-activated , root citrate efflux transporter 15–17 ., Four mutant genes that lead to Al sensitivity in rice have recently been cloned , STAR1 ( Sensitive to Al rhizotoxicity1 ) , STAR2 ( Sensitive to Al rhizotoxicity2 ) , ART1 ( Aluminum rhizotoxicity 1 ) , and Nrat1 ( Nramp aluminum transporter 1 ) 18–20 ., The products of STAR1 and STAR2 are expressed mainly in the roots and are components of a bacterial-type ATP binding cassette ( ABC ) transporter ., Both are transcriptionally activated by exposure to Al and loss of function of either gene results in hypersensitivity to Al ., STAR1 and STAR2 are similar to two Al sensitive mutants in Arabidopsis , als1 and als3 , also encoding ABC transporters 21 , 22 ., ART1 is a novel C2H2-type zinc finger transcription factor that interacts with the promoter region of STAR1 ., ART1 is reported to regulate at least 30 down-stream genes , some of which are involved in Al detoxification and serve as strong candidate genes controlling rice Al tolerance 19 ., Nrat1 is one of the genes that is regulated by ART1 and was recently demonstrated to be an Al transporter that is localized to the root cell plasma membrane 18 , 20 ., It is hypothesized that Nrat1 confers Al tolerance by transporting Al into the cell and reducing the concentration of Al in the cell wall 20 ., None of the four cloned rice genes described above have been demonstrated to be involved in natural genetic variation of Al tolerance in rice and only one ( Nrat1 ) maps to a previously reported Al tolerance QTL 23 , suggesting that these genes may be involved in basal Al tolerance 19 , 20 , 24 ., A more thorough analysis is necessary to determine whether there might be natural variation associated with these loci that would help trace their evolutionary origins and clarify their contribution to the high levels of Al tolerance observed in rice ., Seven QTL studies on Al tolerance have been reported in rice using 6 different inter- and intra-specific mapping populations 13 , 25–29 ., Together , these studies report a total of 33 QTLs , located on all 12 chromosomes , with three intervals ( on chromosomes 1 , 3 , and 9 ) being detected in multiple studies ., In all of the QTL studies , Al tolerance was estimated based on relative root growth ( RRG ) , and specifically on inhibition of the growth ( elongation ) of the longest root ( elongation of the longest root in Al treatment/root growth of controls ) ., Rice has a very fine and fibrous root system without dominant seminal roots ., We recently showed that there is a weak correlation between rice Al tolerance based on RRG of the longest root and RRG of the total root system ( R2\u200a=\u200a0 . 17 ) 8 ., This raises the question whether mapping Al tolerance QTL using total root and longest root RRG indices independently might identify novel loci , helping to integrate QTL studies with studies based on induced mutations ., Historically , O . sativa has been classified into two varietal groups , Indica and Japonica , based on morphological characteristics , ecological adaptation , crossing ability and geographic origin 30 ., These two varietal groups are believed to represent independent domestications from a pre-differentiated ancestral gene pool ( O . rufipogon ) , followed by significant gene flow among and between subpopulations 17 , 31–39 ., These two varietal groups ( names are italicized with an upper case first letter , i . e . , Indica and Japonica ) have been further divided into five major subpopulations ( subpopulation names are italicized using all lower-case letters ) ( indica , aus , tropical japonica , temperate japonica , and aromatic group V ) based on DNA markers ( SSR , SNPs , indels ) 40–42 ., Genotypes that share <80% ancestry across subpopulations or varietal groups are classified as admixed varieties 42 , while smaller groups adapted to specific ecosystems may be recognized as upland , deep water , or floating varieties 43 , 44 ., Upland varieties , which are generally grown at high altitudes on dry ( non-irrigated ) soils , are those most commonly exposed to acidic , Al-toxic soil conditions ., These varieties are almost invariably of tropical japonica origin , suggesting a priori that the tropical japonica subpopulation would be a likely source of superior alleles for Al tolerance in rice ., Diverse panels of O . sativa are reported to have similar , or slightly elevated levels of linkage disequilibrium ( LD ) compared to species such as Arabidopsis , maize and human ., The average extent of LD in rice has been estimated at between 50–500 kb 45–49 , depending on the germplasm evaluated , compared to 10–250 kb in Arabidopsis and human 50–57 , 100–500 kb in commercial elite maize inbreds and 1–2 kb in diverse maize landraces 58 , 59 ., The inbreeding nature of O . sativa , coupled with its demographic history , are major determinants of genome-wide patterns of LD ., Strong selective pressure over the course of rice domestication has also lead to deep population substructure ( Fst\u200a=\u200a0 . 23 to 0 . 57 ) 40 , 42 , which sets it apart from Arabidopsis , in which population structure is gradual across geographic distances 60 , 61 ., Population substructure can lead to false-positives in association mapping studies , and must be taken into account 61–63 ., The mixed-model has been demonstrated to work well in both maize and Arabidopsis 61 , 63 , and it has also shown its ability to greatly reduce the false positive rates in rice when used within a single subpopulation 64 , though it may introduce false negatives when used on a diversity panel representing all domesticated subpopulations 65 ., A diversity panel consisting of 413 O . sativa accessions , representing the genetic diversity of the primary gene pool of domesticated rice 66 , was recently genotyped with 44 , 000 SNPs ( ∼10 SNPs/kb ) 65 , 67 , 68 as the basis for GWA studies ., The slow decay of LD , while facilitating GWA analysis , limits the resolution of association mapping in rice ., The first targeted association mapping study in rice 45 demonstrated that LD decay in the aus subpopulation was approximately 90 kb ( ∼5 genes ) in a region on chromosome 5 containing the xa5 resistance gene ., LD is expected to decay more quickly in O . rufipogon ( <50 kb , or 1–3 genes ) 48 , providing higher resolution for LD mapping , and more slowly in the japonica subpopulations 47–49 ., Nonetheless , when compared to the resolution of a typical QTL study ( 250 lines ) ( ∼10–20 cM resolution , where 1 cM\u200a=\u200a∼250 kb ) , association mapping is expected to provide between 10–200 times higher resolution for a population of similar size as long as sufficient marker density is obtained to exploit the historical recombination ., Thus , an association mapping study that uses markers densities similar to a QTL study will not have the increased resolution and will increase the risk of type-2 error ., For both GWA and QTL analysis in rice , fine-mapping and/or mutant analysis is generally required to identify the gene ( s ) underlying a QTL of interest ., However , the fine-mapping phase can generally be focused on a smaller target region following GWA analysis ., In this study , the genetic architecture of rice Al tolerance was investigated via bi-parental QTL analysis in two mapping populations using relative root growth of the longest root , the primary root system , and the total root system quantified with the digital root phenotyping methods described previously for rice Al tolerance 8 ., Subsequently , genome wide association ( GWA ) analysis was undertaken using 36 , 901 high quality SNPs that had been genotyped on the rice diversity panel 65 ., Regions identified by GWA were compared with regions identified as QTLs in bi-parental mapping populations for both this and previous studies , as well as with Al sensitive mutants and/or candidate genes ., Phenotypic outliers identified in the diversity panel were further investigated to identify regions of subpopulation-admixture that accounted for extreme Al tolerance phenotypes ., Three hundred eighty three diverse O . sativa accessions from the rice diversity panel 42 , 67 ( Table S1 ) were evaluated for Al tolerance using an Al3+ activity of 160 µM in a hydroponic nutrient solution ., This Al3+ activity had been previously determined to be optimal for evaluating a wide range of Al tolerance in diverse rice germplasm 8 ., In the diversity panel , Al tolerance , measured as the relative root growth of the total root system ( TRG-RRG ) , was normally distributed around a mean of 0 . 59 +/−0 . 24 ( SD ) and ranged from 0 . 03–1 . 35 ( Figure 1A ) ., Some varieties were inhibited by as much as 97% , while 16 varieties ( representing three subpopulations ) showed enhanced root growth in the presence of 160 µM Al3+ ( Table S1 ) ., When accessions were grouped based on varietal group ( >80% ancestry ) the Japonica varietal group ( consisting of the temperate japonica , tropical japonica and aromatic subpopulations ) was significantly more Al tolerant than the Indica varietal group ( indica and aus subpopulations ) ( p<0 . 0001 ) ( Figure 1B ) ., The Japonica varieties had a mean Al tolerance value of RRG\u200a=\u200a0 . 72 , an interquartile range of 0 . 61–0 . 82 , and ranged from 0 . 13–1 . 35 ., The Indica varieties had a mean Al tolerance value of RRG\u200a=\u200a0 . 36 , an interquartile range of 0 . 27–0 . 43 , and ranged from 0 . 03–1 . 15 ( Figure 1B ) ., Eleven accessions were classified as “admixed” between varietal groups , and these had a mean Al tolerance equal to the mean of all 372 accessions ( TRG-RRG\u200a=\u200a0 . 59 ) with >80% ancestry to either varietal group ., A one-way ANOVA demonstrated that subpopulation explained 57% of the phenotypic variation observed for Al tolerance ( TRG-RRG ) among the 274 accessions that carried a subpopulation classification ., Despite the differences in mean TRG-RRG between subpopulations , considerable variation was also detected within each subpopulation ( Figure S1 ) ., Two immortalized QTL mapping populations were analyzed for Al tolerance ., One consisted of 134 recombinant inbred lines ( RIL ) derived from the cross IR64/Azucena 69 , and the other was comprised of 78 backcross inbred lines ( BIL ) derived from the cross Nipponbare/Kasalath//Nipponbare 70 ., These populations were used to evaluate Al tolerance using three different indices of relative root growth ( RRG ) , ( 1 ) longest root growth ( LRG-RRG ) , ( 2 ) primary root growth ( PGR-RRG ) and ( 3 ) total root growth ( TRG-RRG ) ( see Materials and Methods for details ) ., The phenotypic distribution was approximately normal for each population , no matter which root screening index was used ( illustrated for TRG-RRG in Figure S2A and S2B ) ., The QTL mapping populations allowed us to determine which of the three root evaluation methods would be most useful for evaluating the diversity panel as a whole ., The method of phenotyping , specifically , the RRG index used to estimate Al tolerance , directly impacted the significance of QTLs detected by composite interval mapping ( Figure 2A–2C and Figure S3A–S3C ) ., In the RIL population , three Al tolerance ( Alt ) QTL were detected using total root growth ( the TRG-RRG index ) , AltTRG1 . 1 on chromosome 1 , AltTRG2 . 1 on chromosome 2 , and AltTRG12 . 1 on chromosome 12 ( Figure 2A–2C Table 1 ) ., The Azucena allele conferred increased tolerance at the loci on chromosomes 1 and 12 and reduced tolerance at the locus on chromosome 2 ., QTLs were detected in the same positions on chromosomes 1 and 12 using RRG based on primary root growth ( the PRG-RRG index ) , although with lower LOD scores ( Figure 2A–2C; Table 1 ) ., Using longest root growth ( the LRG-RRG index ) , a single QTL was detected on chromosome 9 , AltLRG9 . 1 , and this QTL was not detected when the other root indices were used ., The major QTL on chromosome 12 ( AltTRG12 . 1 ) , which explained >19% of the variation in Al tolerance based on TRG-RRG , is located between 2 . 69–5 . 10 Mb and encompasses the Al sensitive rice mutant art1 , which is located at 3 . 59 Mb 19 ., In the BIL population , two QTL were detected using the TRG index , AltTRG1 . 2 on chromosome 1 , which co-localized with the AltTRG1 . 1 QTL identified in the RIL population , and AltTRG12 . 2 on chromosome 12 , which did not overlap with the AltTRG12 . 1 identified in the RIL population ( Figure 2A–2C , Figure S3A–S3C , Table 1 ) ., The Nipponbare allele conferred tolerance at the chromosome 1 locus and the Kasalath allele conferred tolerance at the AltTRG12 . 2 locus ., No QTLs were detected on chromosome 2 in the BIL population ., Using the PRG-RRG index , one QTL was detected on chromosome 6 , where the Kasalath allele conferred resistance ., No QTLs were detected using the LRG-RRG index in the BIL population ., The Al tolerance index used for evaluating the phenotype directly affected both the identity and the significance of the QTLs detected ., Al tolerance index-specific QTLs were detected in both populations and no QTL locus was detected across all three indices ., Based on number of QTL detected , significance of QTL , and variance explained by the QTL , total root growth ( TRG ) proved to be the single most powerful Al tolerance index ., However , rice QTLs detected using different evaluation methods are likely to confer Al tolerance by different mechanisms , such as tolerance of primary , secondary , lateral , or all roots , and thus they are complementary and together provide a robust evaluation of the genetic architecture of Al tolerance than any single index alone ., To identify Al tolerance loci based on genome-wide association ( GWA ) mapping , we used an existing genotypic dataset consisting of 36 , 901 SNPs 65 , and the total root growth ( TRG-RRG ) Al tolerance phenotype generated on 373 O . sativa accessions over the course of this study ., GWA mapping was conducted , using SNPs with a MAF>0 . 05 , across all 373 genotypes as well as independently within the indica , aus , temperate japonica , and tropical japonica subpopulations ( Figure 3 ) ., The Efficient Mixed-Model Association ( EMMA ) 71 model was used in each analysis ( both within and across subpopulations ) to correct for confounding effects due to subpopulation structure and relatedness between individuals ., As the subpopulation structure was highly correlated with Al tolerance , it was observed that analyzing all samples ( 373 ) together with the EMMA model resulted in an overcorrection ( causing type 2 error ) and a corresponding reduction in SNP significance ( Figure S4 ) ., To address this problem , a PCA approach was also employed when analyzing all ( 373 ) samples together ., However , the PCA approach resulted in a slight under-correction for population structure ( Figure S4 ) , demonstrating that results from each GWA method has limitations when used across all germplasm in this highly structured diversity panel ., A total of ∼48 distinct Al tolerance genomic regions were identified by GWA mapping ( Figure 3 ) ., Twenty-one regions were detected ( p<0 . 0001 ) across all ( 373 ) accessions using the PCA model ( Figure 3 ) , while only two SNPs were above the significance threshold when all ( 373 ) accessions were analyzed together using the EMMA model ( Figure 3 ) , both of which were also detected by PCA ., The threshold of p<1 . 0E-04 was determined based on the upper-limit false discovery rate ( FDR ) , determined from the candidate genes in the same approach as in Li et al . 72 ( Table S2 ) ., Thirty-two regions were significantly associated with Al tolerance in the indica subpopulation ( Figure 3 ) , including five regions that were also detected across all ( 373 ) samples using the PCA model ., In the aus subpopulation , a single , highly significant , region was detected on chromosome 2 that was unique to this subpopulation and contained the Nrat1 candidate gene LOC_Os02g03900 ( Figure 3 ) ., No significant SNPs ( MAF>0 . 05 ) were detected in the temperate japonica or tropical japonica subpopulations ., The GWA mapping results indicate that the majority of significant loci are subpopulation-specific and that phenotypic variation for Al tolerance within given subpopulations is largely controlled by alleles that are unique to that subpopulation ., SNPs identified by GWA were also compared to a set of 46 a priori candidate genes as well as to positions of QTL regions identified through bi-parental mapping ( this study and previous reports ) ( Table 1 and Figure 3 ) ., Two regions of highly significant SNP clusters , one within the aus ( 8 SNPs; p\u200a=\u200a2 . 8E-07 ) subpopulation on chr ., 2 and one within the indica ( 32 SNPs; p\u200a=\u200a2 . 9E-07 ) subpopulation on chr ., 3 , co-localized to previously reported QTLs in populations in which an aus and indica parent served as the susceptible parents , respectively 17 , 23 ., The list of 46 a-priori Al tolerance candidate genes ( Table, 2 ) was compiled based on published information on Al sensitive mutants from rice and Arabidopsis 20–22 , 24 , cloned Al tolerance genes from wheat and sorghum 14 , 15 , expression profiles from Al treated maize and rice roots 19 , 73 , and an association study on specific candidate Al tolerance genes of maize 74 ., Significant SNPs ( p<1 . 0E-04 ) within a 200 kb window of the a priori candidate genes were enriched 2 . 4 times compared to other SNPs ( p>0 . 0001 ) outside of the a priori and QTL regions ., The 200 kb window was selected to fall within the estimated window of LD decay in rice ( ∼50–500 kb 45–49 and the upper-limit false discovery rate for the a priori genes was 42% ., In addition , four of the 46 gene candidates ( ∼9% ) were located within a 200 kb window enriched for GWA SNPs in this study ( Figure 3 and Table 2 ) ., One of the candidate genes ( Nrat1 ) on chr ., 2 , co-localized with both GWA SNPs and a previously reported QTL ( Figure 3 ) ., The relationship between the four candidates that co-localized with GWA SNPs are discussed in order of their positions on the rice genome below ., A cluster of eight highly significant SNPs ( p-values\u200a=\u200a2 . 3×10−5–2 . 8×10−7 ) on chromosome 2 between 1 . 536 Mb–1 . 675 Mb was associated with Al tolerance within the aus subpopulation ( Figure 3 and Table 2 ) ., Previously , a QTL had been reported in the same location ( 0 . 536–1 . 9 Mb ) where the susceptible parent was of aus origin 26 ., The LD decay in the aus subpopulation at this region was calculated to be 150 kb and a strong candidate gene was identified within the target region ., The gene ( LOC_Os02g03900 located at 1 . 66 Mb ) encodes a Nramp6 metal transporter and was demonstrated to have altered expression patterns in Al-treated roots of the Al sensitive art1 rice mutant 19 ., This Nramp6 metal transporter was recently reported as Nrat1 , a plasma membrane-located transporter for Al with enhanced sensitivity to Al in the knockout mutant 20 ., As was the case with the ART1 gene itself , the Nrat1 metal transporter has not been associated with natural variation for Al tolerance prior to this study ., On chromosome 5 , a significant region was detected across all samples ( 373 genotypes ) by PCA , co-localizing with the STAR2 gene ( LOC_Os05g02750 ) ( Figure 3 and Table 2 ) ., The LD decay across this region was estimated at >500 kb , and encompassed two significant regions detected across all samples ( PCA ) , one of which was also detected within the indica subpopulation ., STAR2 is the rice ortholog of the Arabidopsis Al sensitive mutant als3 21 ., It encodes the transmembrane domain of a bacterial-type ATP binding cassette ( ABC ) transporter and the star2 mutant is Al sensitive 24 ., STAR2 was also found to be part of a gene network showing altered expression in response to Al in the art1 mutant compared to the ART1 wild type 19 ., This study provides the first evidence that there may be natural variation for Al tolerance in rice at the STAR2 locus; however it is important to recognize that the PCA approach may under-correct for the effect of subpopulation in this study , thus it will be necessary to confirm the effect of the STAR2 alleles identified in this diversity panel ., A significant GWAS region identified in the indica subpopulation on chromosome 7 co-localized with LOC_Os07g34520 , a rice ortholog of a maize isocitrate lyase a priori candidate gene associated with Al tolerance in maize 73 , 74 ., The LD decay across this region within the indica subpopulation was 250 kb ., Three highly significant regions detected within indica were further investigated to identify whether any clear Al tolerance candidate genes were located within these SNP clusters ., The first region was a cluster of 32 significant SNPs ( p\u200a=\u200a3 . 0E-7 ) between 28 . 782–27 . 863 Mb on chr ., 3 that co-localized with a previously reported QTL ( Nguyen et al . , 2002 ) ., Two clear candidates were identified among the 13 genes in this cluster; a nucleobase-ascorbate transporter ( LOC_Os03g48810 ) and a chloride channel protein ( LOC_Os03g48940 ) ., The second region was a 10 SNP cluster ( p\u200a=\u200a9 . 3E-12 ) between 26 . 986–27 . 479 Mb on chr ., 7 ., Of the 80 genes in this region , 34 of which were retrotransposons , there were three strong candidate genes; a glycosyl transferase protein ( LOC_Os07g45260 ) , a cytochrome P450 protein ( LOC_Os07g45290 ) and a zing finger RING type protein ( LOC_Os07g45350 ) ., This region on chr ., 7 was also identified in the introgression analysis as a localized introgressed region from Japonica into the highly tolerant Indica outliers ( discussed below ) ., The third region was an 8 SNP cluster between 4 . 892–5 . 164 Mb on chr ., 11 ., Among the 48 genes in this region , there were two major classes of candidate genes observed , including 12 F-box proteins and a zinc finger CCHC protein ., We chose to further investigate the variation in and around the Nrat1 gene on chromosome 2 because multiple independent lines of evidence supported the existence of a gene ( s ) in this region responsible for a significant portion of the variation for Al tolerance in rice ., Evidence included a strong GWA peak in the aus subpopulation , a previously reported QTL 26 , and the localization of the Nrat1 Al transporter gene ., Using the 44 K SNP data , LD in this region was calculated to be ∼150 kb in the aus subpopulation and 11 distinct haplotypes were observed in the entire diversity panel across a 139 kb region around the Nrat1 gene ( 1 . 536 Mb–1 . 675 Mb on chr ., 2 ) ( Figure 4A ) ., Haplotype 1 ( Hap . 1 ) , which was unique to the aus subpopulation , was found in 8 Al sensitive aus accessions and one Al sensitive aus/indica admixed line ., These 9 genotypes were among the least Al tolerant ( 7th percentile , mean RRG\u200a=\u200a0 . 16 ) of the 373 accessions screened ( Table S1 ) ., Haplotype 1 explained 40% of the phenotypic variation for Al tolerance within the aus subpopulation ( Figure S5 ) ., In addition , four aus accessions that were highly or moderately Al tolerant were found to contain a tropical japonica introgression across this region ( described in the section on Introgression analysis below ) ., Haplotype 2 ( Hap ., 2 ) was found in one aus and one indica accession , and was most similar to Hap ., 1 , differing at only 2/14 SNPs ( Figure 4A ) ., The two lines containing haplotype 2 had very different levels of Al tolerance; the aus variety , Kasalath ( ID 85 ) , was highly susceptible , with a RRG\u200a=\u200a0 . 2 , while the indica variety , Taducan ( ID 163 ) , was tolerant , with a RRG\u200a=\u200a0 . 8 , suggesting that this extensive 14-SNP haplotype across the 139 kb region was not predictive of Al tolerance ., However , when the haplotype was built using only the four SNPs immediately flanking the Nrat1 gene , a group of 16 accessions sharing the same haplotype at these four SNPs was clearly identified ., These 16 accessions , included the 10 susceptible aus accessions ( including one aus/indica admixed line ) carrying haplotype 1 and haplotype 2 and six indica accessions ( of varying Al tolerance ) carrying haplotype 2 and haplotype 3 ( Figure 4A ) ., To determine if the four-SNP haplotype flanking the Nrat1 gene could be further resolved , we focused more deeply on the Nrat1 gene itself ., We sequenced all 13 exons ( including introns ) of Nrat1 ( 1874 bp ) in 26 susceptible and tolerant varieties representing the aus , indica , tropical japonica and temperate japonica subpopulations ( Figure 4B ) ., The accessions carried haplotypes 1 , 2 , 3 , 6 and 11 , as described in Figure 4A; where haplotype 1 was aus-specific and corresponded to the most sensitive group of accessions in the diversity panel; haplotype 2 was found in phenotypically divergent aus and indica accessions as described above; haplotype 3 was found in moderately tolerant indica varieties; haplotype 6 , which appeared to be the ancestral haplotype , was the most common haplotype in all subpopulations and was associated with moderately high levels of tolerance; and haplotype 11 , which was found in a majority of tropical japonica varieties , all of which were Al tolerant ., Based on the 22 SNPs and/or indels identified across the 1 , 874 bp of Nrat1 sequence , highly resolved , gene haplotypes were constructed ( Figure 4B ) ., The gene haplotypes corresponded fairly well to the extended haplotype groups that had been constructed using the data from the 44 K SNP chip , except in the case of haplotype 2 , where varieties differed at 10/22 ( 45% ) of the SNPs across the Nrat1 gene ., This fully resolved haplotype at the Nrat1 gene resulted in the susceptible Kasalath clustering with the other highly susceptible aus varieties and the tolerant Taducan clustering with other highly tolerant varieties ( Figure 4 ) ., Three non-synonymous SNPs ( polymorphisms 4 , 16 ,, 17 ) were shared among the 9 highly susceptible aus accessions ., When the Eukaryotic Linear Motif resource ( http://elm . eu . org ) was used to identify functional sites in the Nrat1 gene , polymorphism 16 was identified as a functional site where a C→T SNP caused an amino acid change from valine→alanine ( amino acid 500 ) ., This protein site was predicted to be involved in PKA-type AGC kinase phosphorylation , with the functional site spanning amino acids 497–503 ., Thus , polymorphism 16 was identified as a strong functional polymorphism candidate underlying natural variation in Nrat1 ., The fact that polymorphism 16 was also observed in two Al tolerant temperate japonica and one moderately tolerant tropical japonica accession ( haplotype 11 ) suggested that SNP 16 alone was not predictive of Al tolerance ., However , a combination of polymorphisms 4 , 16 , and 17 was entirely predictive of Al susceptibility ., This study demonstrates the power of whole genome association analysis to integrate divergent pieces of evidence from independent bi-parental and mutant studies , enabling us to associate gene-based diversity with germplasm resources and natural variation that is of immediate use to plant breeders ., There is a clear difference in the degree of Al tolerance found in the Japonica varietal group and the Indica varietal group , with the 10th percentile of Al tolerance of Japonica ( 0 . 53 ) being nearly equal to the 90th percentile of Indica ( 0 . 55 ) ( Figure 1B ) ., However , there are clear outliers within each varietal group ., Five Indica accessions are highly Al tolerant ( ID 30 , 66 , 142 , 163 , 337 ) , ranging from 2 . 1–3 . 2 times the mean Indica Al tolerance , and three Japonica accessions ( ID 12 , 52 , 112 ) are highly susceptible , each approximately 0 . 19 of the mean Japonica Al tolerance ( Figure 1B and Table S1 ) ., To determine if these outliers were the result of introgressions across varietal groups , we calculated the allele ancestry of 5 , 467 SNPs distributed throughout the genome and identified specific genomic regions where historical Indica×Japonica admixture was detected only in the respective Indica or Japonica outlier lines ., To do this , Japonica introgressions identified in highly Al tolerant Indica lines were used to query all other Indica accessions and only those Japonica introgressions that were uniquely present in the highly Al tolerant outlier Indica lines were considered as candidate regions underlying the outlier phenotype ., When the five Indica outliers were used for this analysis , a few , well-defined regions comprising 2 . 4–4 . 9% of the genome corresponded to regions of Japonica introgression ( Table 3 ) ., In the case of the three highly Al susceptible Japonica varieties , the genetic background was highly heterogeneous and the small number of lines precluded doing any admixture analysis ., Therefore , the admixture analysis was conducted only on the five highly tolerant Indica outliers ., In the five outlier Indica accessions , 6 Japonica introgressions ( median size\u200a=\u200a780 kb ) were identified that were specific only to these 5 lines ., Three of these introgressions were present in two genotypes , two of the introgressions were present in three genotypes , and one introgression was present in four of the outliers ( Table 3 ) ., Three introgressions encompass SNPs identified by GWA analysis and two co-localized with bi-parental QTL ., The introgression that was present in four of the indica outlier genotypes was located on chromosome 7 between 27 . 05–28 . 62 Mb and contained 94 annotated genes ., This introgression included a cluster of GWA SNPs that were highly significant within the indica subpopulation ( p\u200a=\u200a2 . 6×10−5 , MAF\u200a=\u200a0 . 10 ) and was one of the top 100 most significant SNPs identified when the diversity panel as a whole was analyzed ., In this study , we utilized bi-parental QTL mapping and GWA analysis to examine the genetic architecture of Al tolerance in rice and to identify Al tolerance loci ., Phenotyping of the diversity panel provided valuable information about the range and distribution of Al tolerance in O . sativa and offered new insights into the evolution of the trait ., The mean Al tolerance in Japonica was twice that of Indica ( p<0 . 0001 ) , and 57% of the phenotypic variation was explained by subpopulation ., The relative degree of Al tolerance in the five subpopulations ( temperate japonica>tropical japonica>aromat

Introduction, Results, Discussion, Materials and Methods

Aluminum ( Al ) toxicity is a primary limitation to crop productivity on acid soils , and rice has been demonstrated to be significantly more Al tolerant than other cereal crops ., However , the mechanisms of rice Al tolerance are largely unknown , and no genes underlying natural variation have been reported ., We screened 383 diverse rice accessions , conducted a genome-wide association ( GWA ) study , and conducted QTL mapping in two bi-parental populations using three estimates of Al tolerance based on root growth ., Subpopulation structure explained 57% of the phenotypic variation , and the mean Al tolerance in Japonica was twice that of Indica ., Forty-eight regions associated with Al tolerance were identified by GWA analysis , most of which were subpopulation-specific ., Four of these regions co-localized with a priori candidate genes , and two highly significant regions co-localized with previously identified QTLs ., Three regions corresponding to induced Al-sensitive rice mutants ( ART1 , STAR2 , Nrat1 ) were identified through bi-parental QTL mapping or GWA to be involved in natural variation for Al tolerance ., Haplotype analysis around the Nrat1 gene identified susceptible and tolerant haplotypes explaining 40% of the Al tolerance variation within the aus subpopulation , and sequence analysis of Nrat1 identified a trio of non-synonymous mutations predictive of Al sensitivity in our diversity panel ., GWA analysis discovered more phenotype–genotype associations and provided higher resolution , but QTL mapping identified critical rare and/or subpopulation-specific alleles not detected by GWA analysis ., Mapping using Indica/Japonica populations identified QTLs associated with transgressive variation where alleles from a susceptible aus or indica parent enhanced Al tolerance in a tolerant Japonica background ., This work supports the hypothesis that selectively introgressing alleles across subpopulations is an efficient approach for trait enhancement in plant breeding programs and demonstrates the fundamental importance of subpopulation in interpreting and manipulating the genetics of complex traits in rice .

While rice ( Oryza sativa ) is significantly more Al tolerant than other cereals , no genes underlying Al tolerance in rice have been reported ., Using genome-wide association ( GWA ) and bi-parental QTL mapping , we investigated the genetic architecture of Al tolerance in rice ., Japonica varieties were twice as Al tolerant as indica and aus varieties ., Overall , 57% of the phenotypic variation was correlated with subpopulation , consistent with observations that different genes and genomic regions were associated with Al tolerance in different subpopulations ., Four regions identified by GWA co-localized with a priori candidate genes , and two highly significant regions co-localized with previously identified quantitative trait loci ( QTL ) ., Haplotype and sequence analysis around the candidate gene , Nrat1 , identified a susceptible haplotype explaining 40% of the Al tolerance variation within the aus subpopulation and three non-synonymous mutations within Nrat1 that were predictive of Al sensitivity ., Using Indica × Japonica mapping populations , we identified QTLs associated with transgressive variation where alleles from a susceptible indica or aus parent enhanced Al tolerance in a tolerant japonica background ., This work demonstrates the importance of subpopulation in interpreting and manipulating complex traits in rice and provides a roadmap for breeders aiming to capture genetic value from phenotypically inferior lines .

biotechnology, marker-assisted selection, agricultural biotechnology, agriculture

journal.pntd.0000547

Molecular Characterization of the Schistosoma mansoni Zinc Finger Protein SmZF1 as a Transcription Factor

Schistosomiasis is a disease caused by trematode worms , mainly Schistosoma mansoni , S . haematobium and S . japonicum ., According to World Health Organization , this parasitic disease affects 200 million people throughout the world 1 ., Although the level of schistosome-associated morbidity is unclear , some recent studies have demonstrated that the illness is a more serious problem than it was previously thought to be 2 , 3 ., Therefore , emphasis should be focused on mechanisms that could not only prevent , but also cure schistosomiasis ., A useful approach to fight the disease should include infrastructure and educational components , as well as the development of vaccines and new drugs 4 ., Luckily we are living a special moment , with the recent publication of both S . mansoni 5 and S . japonicum 6 genomes , which will bring to the scientific community an enormous amount of data to be mined in the search for new therapeutic targets and vaccine development ., Lastly , additional effort should also be dedicated to studies regarding the biology and development of the parasite ., During its life cycle , S . mansoni is exposed to different environmental conditions: water , intermediate molluscan host , and a definitive vertebrate host ., As a consequence , this parasite suffers many transformations in its morphology and physiology , and , as such , represents an interesting but challenging biological system to investigate gene regulation processes 7–9 ., A variety of publications have focused on the identification and characterization of S . mansoni stage- , tissue- and sex-specific/abundant proteins and their coding genes 10–14 , which may uncover hidden aspects of parasite biology and thus provide useful leads for the development of novel intervention strategies 7 ., In a primary analysis of the S . mansoni transcriptome , Verjovski-Almeida and colleagues suggested that the number of differentially expressed genes could reach as many as 1000 for each stage 15 ., In more recent publications , in which analyses of gene expression were carried out using microarray , SAGE ( Serial Analysis of Gene Expression ) and proteomic experiments , the authors confirmed a number of sex- and stage-specific , differentially expressed genes 8 , 16–26 ., In order to better understand the transcriptional regulation of S . mansoni genes , it is necessary to identify new transcription factors , coactivators/corepressors and chromatin remodeling factors that control this molecular process , along with regulatory elements in the promoter region of genes 9 ., Several efforts to describe new transcription factors in this parasite have been made 27–31 , but given the complexity of its life cycle there are still many components to be discovered and characterized ., Zinc finger motifs are found in several proteins amongst eukaryotic organisms and are key proteins for transcription regulation 32–34 ., SmZF1 is a S . mansoni 19 kDa protein ( GenBank accession number AAG38587 ) containing three C2H2 type zinc finger motifs ., Its cDNA was casually isolated from an immune screening of a S . mansoni adult worm lambda gt11 expression library using an anti-tegumental serum ., The transcript coding for SmZF1 was also detected by PCR amplification in egg , cercaria , schistosomulum and adult worm cDNA libraries , suggesting that the protein is essential for metabolism during different stages of the parasite life cycle 35 ., In a previous work , we used a recombinant SmZF1 protein in EMSA experiments to investigate its binding capacity/specificity for DNA and RNA oligonucleotides ., SmZF1 was found to bind both double and single-stranded DNA , as well as RNA oligonucleotides , but with about 10-fold lower affinity ., Although we noticed that SmZF1 recognized DNA and RNA oligonucleotides not containing putative target sites , the protein bound preferentially to the ones containing the sequence 5′-CGAGGGAGT-3′ ( oligonucleotide D1-3DNA ) ., Furthermore , unrelated oligonucleotides were not able to abolish this interaction ., Taken together , these initial results suggested that SmZF1 may act as a putative transcription factor in S . mansoni 36 ., In order to better characterize the biological function of the SmZF1 protein , in this study we proposed to:, ( i ) verify the subcellular localization of SmZF1 in the cells of S . mansoni , as well as in mammalian COS-7 cells expressing a recombinant YFP ( Yellow Fluorescent Protein ) -SmZF1 protein;, ( ii ) test the ability of SmZF1 to activate or repress gene transcription ., The results described herein define SmZF1 as a S . mansoni nuclear protein capable of activating gene transcription ., In order to obtain anti-SmZF1 antibodies , the MBP ( Maltose Binding Protein ) portion of a MBP-SmZF1 recombinant protein 36 was cleaved using Factor Xa protease ( New England Biolabs , Ipswitch , MA , USA ) ., The cleavage reaction was carried out for 48 h at 4°C in a 1∶25 enzyme: protein proportion ., After digestion and fractionation by electrophoresis , a Coomassie blue-stained protein band ( 450 µg ) , representing the SmZF1 portion of the recombinant protein was excised from a 10% SDS-PAGE , homogenized with PBS ( Phosphate Buffered Saline – 130 mM NaCl , 2 mM KCl , 8 mM Na2HPO4 , 1 mM KH2PO4 ) , then emulsified with Complete Freund Adjuvant and used for the primary intramuscular injection into a rabbit or with Incomplete Freund Adjuvant for the two subsequent boosts ( 15 and 30 days after the first immunization ) ., Pre-immune serum was obtained before the first immunization and rabbit serum containing anti-SmZF1 antibodies was collected 15 days after the third immunization ., S . mansoni adult worms used in this study were recovered from perfused mice ., Lung-stage schistosomula were prepared according to Harrop and Wilson 37 ., Cercariae were obtained from Biomphalaria glabrata by exposing the infected snails to light for 2 h to induce shedding of parasites ., Sections of Omnifix ( AnCon Genetics Inc . , Melville , NY , USA ) fixed , paraffin-embedded adult male or female worms were deparaffinized using xylol , hydrated with an ethanol series , washed in PBS and then incubated in a blocking solution ( 0 . 05% Tween 20 , 1% w/v BSA ( Bovine Serum Albumin ) in PBS pH 7 . 2 ) overnight at 4°C ., Samples were reacted for 1 h with either the anti-SmZF1 or a control , pre-immune rabbit serum , both diluted 1∶30 in 10x diluted blocking solution ., Sections were then washed in PBS and reacted for 1 h with a 1∶400 diluted goat anti-rabbit IgG-Cy-5 conjugate ( Jackson Immunoresearch Laboratories Inc . , West Grove , PA , USA ) in 10x diluted blocking solution , which also contained Alexa Fluor 488 phalloidin ( Invitrogen , Carlsbad , CA , USA ) diluted 1∶100 to stain actin microfilaments ( except for adult male worms ) ., Afterwards , samples were washed , incubated for 10 min with 1∶3000 diluted propidium iodide ( Sigma-Aldrich , St . Louis , MO , USA ) in 10x diluted blocking solution to stain nuclei and then washed with PBS ., For experiments using cercariae and lung-stage schistosomula , a whole-mount protocol was chosen ., Omnifix fixed cercariae were treated with a permeabilizing solution ( 0 . 1% Triton X-100 , 1% w/v BSA and 0 . 1% w/v sodium azide in PBS pH 7 . 4 ) for 3 h at 4°C under constant agitation ., Subsequent immunostaining steps used the same solution and condition ., Samples were incubated overnight with the anti-SmZF1 antibody diluted 1∶90 , washed several times and reacted for 4 h with the goat anti-rabbit IgG-Cy-5 conjugate diluted 1∶1200 in solution containing Alexa Fluor 488 phalloidin ( 1∶500 ) ., The cercariae were then incubated for 20 min with propidium iodide diluted 1∶6000 and washed once more ., The schistosomulum immunohistochemistry assays were carried out as with cercaria , with the following modifications: lung stage schistosomula were treated with permeabilizing solution overnight and then incubated with the anti-SmZF1 antibody ( 1∶90 ) for 2 h ., The secondary antibody was used at a 1∶1000 dilution , and the phalloidin at a 1∶100 dilution for 2 h ., Samples ( adult male and female worms , schistosomula and cercariae ) were prepared with a mounting solution ( 90% glycerol , 10% tris-HCl 1 M , pH 8 . 0 ) and the fluorescence images were captured with a Carl Zeiss LSM 510 META confocal microscope using a 63x oil-immersion objective lens in the Center of Electron Microscopy ( CEMEL-ICB/UFMG ) ., Images were analyzed with Zeiss LSM Image Browser software and edited with Adobe Photoshop CS ., All research protocols involving mice used in the course of this study were reviewed and approved by the local Ethics Committee on Animal Care at Universidade Federal de Minas Gerais ( CETEA – UFMG N° 023/05 ) ., Adult worms recovered from perfused mice were manually separated and pooled according to their sex ., Total RNA of both male and female worms was extracted using Trizol reagent ( Invitrogen ) and treated with DNase using Ilustra RNAspin Mini RNA Isolation Kit ( GE Healthcare , Waukesha , WI , USA ) according to the manufacturers instructions ., RNA was then quantified using a NanoDrop Spectrophotomer ND-1000 ( Thermo Scientific , Waltham , MA , USA ) ., cDNA was synthesized using 0 . 3 to 1 . 0 µg total RNA and Superscript III First-Strand Synthesis SuperMix for qRT-PCR ( Invitrogen ) according to the manufacturers protocol ., For q-PCR reactions , the primers SmZF1_real2_forw ( 5′–ACTTCTCTCAGAAATCCAGCCT–3′ ) and SmZF1_real2_rev ( 5′–TGGAGAGGATTATACAATCTGGTT–3′ ) were used at a 600 nM initial concentration ., The S . mansoni glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) gene ( primers GAPDH_forw 5′–TCGTTGAGTCTACTGGAGTCTTTACG–3′ and GAPDH_rev 5′–AATATGAGCCTGAGCTTTATCAATGG–3′ ) was used as an endogenous control in order to normalize relative amounts of total RNA ., GAPDH primers were used at a 900 nM initial concentration ., The amplicon sizes were 96 bp and 65 bp for SmZF1 and GAPDH , respectively ., q-PCR reaction mixtures consisting of 2 . 5 µl of cDNA , 12 . 5 µl of Power SYBR Green PCR Master Mix ( Applied Biosystems , Foster City , CA ) and 5 µl of each primer in a total volume of 25 µl were added to 48-Well Optical Reaction Plates for amplification and quantification in a StepOne Real-Time PCR System ( Applied Biosystems ) ., Each q-PCR run was performed with two internal controls in order to assess both potential genomic DNA contaminations ( i . e . , no reverse transcriptase added in the cDNA synthesis ) and purity of the reagents used ( i . e . , no cDNA added ) ., Dissociation curve standard analyses were performed at the end of each assay to certify the specific amplifying of targets ., For each set of primers , both male and female conditions ( including negative controls ) were run in three technical replicates ., The experiment was repeated two times ( biological replicates ) and the delta-delta Ct method 38 was used in order to make a relative quantification comparing male and female transcript levels ., Due to the nonparametric distribution of data , statistical analysis of delta-delta Ct values was performed using the Mann-Whitney U-test with significance set at P<0 . 05 ., The SmZF1 cDNA was PCR amplified in a reaction mixture prepared in a 50 µL final volume containing 25 ng of template DNA , 0 . 2 pmol µL−1 of each primer ( SmZF1-start-Xba: 5′–CAGTCTAGAACTTTAACTATGGAATT-3′ and SmZF1-stop-Apa: 5′-CAGGGGCCCCATCCGGAAAGGCTTGAGA-3′ , or SmZF1-start-Sac: 5′-CAGGAGCTCACTTTAACTATGGAATT-3′ and SmZF1-stop-Hind: 5′-CAGAAGCTTCATCCGGAAAGGCTTGAGA-3′ ) , 200 mM dNTPs and 5 U of Taq DNA polymerase ( Phoneutria , Belo Horizonte , MG , Brazil ) in the appropriate buffer ( 50 mM KCl , 10 mM Tris-HCl pH 8 . 4 , 0 . 1% Triton X-100 , 1 . 5 mM MgCl2 ) ., The fragments obtained were double-digested with XbaI and ApaI or SacI and HindIII restriction enzymes ( New England Biolabs ) and purified using a Wizard SV Gel and PCR Clean-up System ( Promega , Madison , WI , USA ) following the manufacturers instructions ., The fragments were then inserted , respectively , into the commercial vectors pCDNA4/TO/myc-His ( Invitrogen ) or pEYFP-c1 ( Clontech , Mountain View , CA , USA ) , generating the constructions pCDNA4-SmZF1 and pEYFP-SmZF1 , which express the recombinant proteins SmZF1-myc tag and YFP-SmZF1 , respectively ., In addition , the viral thymidine kinase ( tk ) promoter region was inserted ( NheI/BglII ) into the commercial vector pGL3-basic ( Promega ) , generating the vector pGL3-tk-luc , with the luciferase ( luc ) reporter gene under control of the thymidine kinase promoter ., Subsequently , an oligonucleotide containing four repetitions of the putative SmZF1 DNA binding site , D1-3DNA 36 , was inserted ( KpnI/NheI ) upstream of the minimal tk promoter , producing the vector pGL3-zf-tk-luc ., The oligonucleotide sequence was as follows: 5′-CAGGAAACAGCTATGACCGGCGAGGGAGTGATCGGCGAGGGAGTGATCGGCGAGGGAGTGATCGGCGAGGGAGTGTCGTGACTGGGAAAACCCTGGCG-3′ ( specific binding sites D1-3DNA are indicated in bold ) ., Ligation products were used to transform the E . coli DH5a strain and the rescued plasmids were sequenced using 10 pmol of appropriate primers ( for constructions based on pCDNA4/TO/myc-His: CMV-fow 5′-CGCAAATGGGCGGTAGGCGTG–3′ and BGH-rev 5′-TAGAAGGCACAGTCGAGG–3′ , for constructions based on pEYFP-c1: YFP-fow 5′-TTTTGCTCACAGGTTCT–3′ and YFP-rev 5′-GCCGTAGGTGGCATCGCC–3′ , for constructions based on pGL3-basic: GLprimer2 5′-CTTTATGTTTTTGGCGTCTTCCA-3′ and RVprimer3 5′-CTAGCAAAATAGGCTGTCCC-3′ ) , 4 µL of DYEnamic ET Dye Terminator Kit – MegaBACE ( GE Healthcare ) and 300 ng of DNA ., The sequencing products were analyzed in the MegaBACE 1000 DNA Sequencer ( GE Healthcare ) ., The above plasmid constructs were used either to transfect or co-transfect COS-7 cells using Lipofectamine™ 2000 Transfection Reagent ( Invitrogen ) , according to the manufacturers protocol ., COS-7 cells were maintained at 37°C , 5% CO2 in Dulbeccos modified Eagles medium ( Invitrogen ) supplemented with 10% fetal bovine serum and 1% glutamine ( Invitrogen ) ., The plasmids pEYFP-c1 ( control ) or pEYFP-SmZF1 were transfected ( as above ) into COS-7 cells for transient protein expression studies ., Forty-eight hours after transfection the culture medium was carefully removed and cells were fixed ( 15 min ) with 3% paraformaldehyde in PBS , washed and then quenched using PBS plus 10 mM NH4Cl ( 10 min ) ., Cells were washed three times with PBS and incubated for 7 min with 0 . 1% Triton X-100 ., After another wash in PBS , COS-7 cells nuclei were stained ( 4 min ) with 5 µL of 1 mM Hoechst 33342 dye ( Sigma-Aldrich ) ., The fluorescence was directly observed using a confocal microscope ( Carl Zeiss LSM 510 META , 200x ) equipped with a Photometrics Quantix CCD camera controlled by MetaMorph imaging software ( MDS Analytical Technologies , Downingtown , PA , USA ) ., For Western blot assays , COS-7 cells ( 0 . 5×106 ) transfected either with pCDNA4-SmZF1 or pEYFP-SmZF1 and control cells transfected either with pEYFP or pCDNA were washed and resuspended in 200 µL of cold TNE ( 150 mM NaCl , 50 mM Tris-HCl pH 7 . 5 and 1 mM ethylenediaminetetraacetic acid ( EDTA ) ) ., A 50 µL aliquot of cells was centrifuged ( 700 g , 4 min , 4°C ) and the pellet resuspended in 50 µL of 2x SDS gel-loading buffer ( 100 mM Tris-HCl pH 6 . 8 , 200 mM dithiothreitol , 4% SDS , 0 . 2% bromophenol blue , 20% glycerol ) and boiled for 5 min , generating the total extract ., The remaining 150 µL of cells was centrifuged ( 700 g , 4 min , 4°C ) and the pellet resuspended in 40 µL of lysis buffer ( 10 mM Tris-HCl pH 7 . 5 , 10 mM NaCl , 2 mM MgCl2 , 1 mM phenylmethylsulphonylfluoride ( PMSF ) , one dissolved tablet of Complete Protease Inhibitor Cocktail ( Roche , Basel , Switzerland ) , 1 mM Na3VO4 and 1 mM NaF ) plus 100 µL of 1% Nonidet P-40 ( Sigma-Aldrich ) in 50 mM Tris-HCl pH 7 . 5 ., Samples were incubated in an ice bath for 10 min and centrifuged ( 700 g , 4 min , 4°C ) ., Ninety-five microliters of 5x SDS gel-loading buffer was added to the supernatant , which was boiled for 5 min , generating the cytoplasmic fraction ., The pellet was washed twice with cold TNE , centrifuged ( 700 g , 4 min , 4°C ) , resuspended in 50 µL of 2x SDS gel-loading buffer and boiled for 15 min , generating the nuclear fraction ., COS-7 total , cytoplasmic and nuclear extracts , normalized at equal volume percentage , were separated using 10% SDS-PAGE and blotted ( 2 h , 20 mA ) onto nitrocellulose membranes ( Whatman GmbH , Dassel , Germany ) using a semi-dry blot system ( GE healthcare ) ., Antibody reactions were performed as described by Koritschoner and colleagues 39 ., Briefly , membranes were blocked overnight in TBS ( 25 mM Tris-HCl pH 7 . 4 , 137 mM NaCl , 5 mM KCl , 0 . 6 mM Na2HPO4 , 0 . 7 mM CaCl2 , 0 . 5 mM MgCl2 ) plus 1 mM EDTA , 1 mM Na3VO4 , 0 . 05% Tween-20 and 3% BSA followed by two washes with 100 mM Tris-HCl pH 8 . 0 , 200 mM NaCl , 0 . 2% Tween-20 ( wash buffer ) ., Samples were reacted with anti-myc , anti-GFP or anti-c-erbB-2 ( 1∶1000 ) peroxidase conjugated antibodies ( BD Biosciences , Franklin Lakes , NJ , USA ) in blocking buffer for 1 h ., Subsequently , blots were washed and developed with ECL enhanced chemiluminescence reagents ( GE Healthcare ) and exposed to X-ray film ., The exclusively cytoplasmic protein c-erbB-2 was used as a quality control for extracts ., For the electrophoretic mobility shift assay ( EMSA ) , 20 pmol of the D1-3DNA oligonucleotide ( 5′-CGAGGGAGT-3′ ) was incubated with 1 µg of the total extract of COS-7 cells transfected with plasmids pEYFP-c1 ( control ) or pEYFP-SmZF1 ., Extracts were produced as follows: cells ( 0 . 5×106 ) were washed in PBS and resuspended in 100 µL of TDGK solution ( 20 mM Tris-HCl pH 7 . 5 , 2 mM dithiothreitol , 400 mM KCl , 5 µg/ml leupeptin , 5 µg/ml aprotinin , 20% glycerol , 0 . 5 mM PMSF , 1 mM Na3VO4 ) ., Samples were maintained on ice for 30 min , centrifuged ( 15000 g , 20 min , 4°C ) and then the supernatant was collected ., Protein concentrations were measured and normalized as previously described 36 ., The extract/DNA binding reactions were carried out in a final volume of 15 µL of binding solution ( 4 mM Tris-HCl pH 8 . 0 , 40 mM NaCl , 1 mM ZnSO4 , 4 mM MgCl2 , 5% glycerol ) for 15 min at 4°C ., For supershift reactions , the DNA/extracts mixture was incubated , as above , with 1 µL of anti-GFP or 2 µL of anti-SmZF1 antibodies ., After incubations , samples were fractionated in a 4% non-denaturing polyacrylamide gel in TBE buffer ( 89 mM Tris-borate pH 8 . 0 , 2 mM EDTA ) , at a constant 25 mA at 4°C , to separate the bound complex from the free oligonucleotides ., The resulting gels were stained with VISTRA Green DNA specific dye ( GE Healthcare ) , according to the manufacturers protocol ., Plasmid DNA co-transfections of COS-7 cells were carried out in 24-well plates ( Corning Inc . , Corning , NY , USA ) ., The day before transfection , 8×104 COS-7 cells were plated in 0 . 5 ml of medium/well ., For each well , 2 µl of LipofectamineTM 2000 Transfection Reagent were mixed with 1 . 2 µg of the plasmid DNA of interest and 300 ng of TK-Renilla reporter plasmid in serum-free Opti-MEM ( Invitrogen ) to allow the formation of DNA-LipofectamineTM 2000 Transfection Reagent complexes ., The complexes were added to the respective wells and mixed by gently rocking the plate back and forth ., Cells were incubated in a 5% CO2 incubator at 37°C for 48 h and then lysed with 60 µl of reporter lysis buffer ( Promega ) ., Luciferase activity ( Relative Light Units – RLU ) was assayed with 20 µl of lysate and 80 µl of luciferase assay reagent ( Promega ) in a TD20/20 luminometer ( Promega ) using a 10 s measurement period ., Each transfection was performed in triplicate ., Transfection efficiency was normalized to TK-Renilla luciferase reporter plasmid ., Statistical analysis of the data was carried out with Minitab Version 1 . 4 using Students t test with Welchs correction ., Only p values<0 . 05 were considered as significant ., SmZF1 ( GenBank accession AF316828 ) was initially identified during a screen of an adult worm S . mansoni cDNA library 35 ., Although it has also been detected in cDNA libraries of other developmental stages of this parasite ( i . e . , egg , 3 h schistosomulum and cercaria ) , the biological function of the protein coded by this gene remains to be elucidated ., The SmZF1 protein contains three C2H2-type zinc finger motifs and binds specific DNA oligonucleotides , as do similar nuclear proteins involved in gene transcriptional regulation 35 , 36 ., Therefore , to investigate whether SmFZ1 is present in the nucleus , where it could act as a transcription factor , we decided to verify its subcellular localization at diverse S . mansoni life stages ., We carried out in situ immunohistochemistry experiments using an anti-SmZF1 antibody on S . mansoni collected at various stages during its life cycle ., Western blot assays using the recombinant SmZF1 protein previously separated from its MBP portion , as well as fractionated extracts form adult worms revealed that this polyclonal antibody is specific to SmZF1 ( Supporting information , Figure S1 ) ., The immunohistochemistry assays showed that SmZF1 protein localizes in the cells nuclei of adult male worms ( Figures 1A–D ) , cercariae ( Figures 1K–N ) and lung stage schistosomula ( Figures 1P–S ) ., Although we have performed three different experiments in which we analyzed various paraffin sections of female adult worms , the protein could not be detected in this stage using this technique ( Figures 1F–I and Supporting information , Figure S2 ) ., No SmZF1 staining was observed in the negative controls ( Figures 1E , J , O , T ) in which only the rabbit pre-immune serum was used ., These results suggest that SmZF1 is a S . mansoni protein present in the nuclei of cells from diverse developmental stages where it may act as a transcription factor ., Plus , SmZF1 expression might be sex-specific since it could not be detected in adult female worms ., We were unable to confirm the results from the immunohistochemistry experiments showing differences in expression of SmZF1 between male and female by Western blot , since nuclear protein extraction from single sex pooled S . mansoni worms did not provide sufficient material necessary for SmZF1 detection ., Therefore , we decided to verify gene expression by comparing the transcript levels between adult male and female worms ., Total RNA extraction was performed in separate pools of male or female worms and q-PCR analyses were carried out using primers specifically designed for SmZF1 amplification ., We detected no difference in SmZF1 expression ( p\u200a=\u200a0 . 22 ) between male and female worms when comparing the amplification profile , indicating that the SmZF1 mRNA is equally present in both genders ( data not shown ) ., These results suggest that although the SmZF1 gene is transcribed in female worms , a post-transcriptional regulatory mechanism could be occurring to block SmZF1 protein production in adult female worms ., After demonstrating the nuclear localization of SmZF1 in S . mansoni cells , the next step in the protein characterization was to heterologously express it in a mammalian system to test its ability to activate the transcription of a reporter gene ., To accomplish this , we initially transfected COS-7 cells with the pEYFP-SmZF1 construction and forty-eight hours after transfection , we verified the presence of the YFP-SmZF1 recombinant protein mainly in the cells nuclei using fluorescence microscopy ., However , a low level of fluorescent staining remained in the cytoplasm ., In some cases , the protein was also visualized as fibrous material in the perinuclear region , probably associated with the cytoskeleton or Golgi complex ., The YFP protein ( negative control ) was visualized diffusely distributed throughout the cells area ( Figure 2A ) ., Since part of the fusion protein still remained in the cytoplasm of the cells , a second construction lacking YFP ( SmZF1-myc tag ) was used to confirm the SmZF1 nuclear localization in mammalian cells ., Western blot assays using equal amounts of total , cytoplasmic and nuclear extracts of COS-7 cells expressing the proteins YFP , YFP-SmZF1 or SmZF1-myc tag were performed ., Fractions were analyzed using either anti-GFP ( which also recognizes YFP ) or anti-myc antibodies ( Figure 2B ) ., The results corroborated those obtained by fluorescence microscopy ( Figure 2A ) , showing that YFP-SmZF1 is present in both nuclear and cytoplasmic extracts , with a slight enrichment of the protein in the nuclear extract ( Figure 2B ) ., However , the recombinant protein SmZF1-myc tag is only present in the nuclear COS-7 extract , suggesting that YFP may be interfering in the transport of the fusion protein to the nucleus ., The quality of the fractionation was confirmed by the localization of the cytoplasmic protein c-erbB-2 in the total and cytoplasmic fractions only ( Figure 2B ) ., In previous experiments using purified recombinant SmZF1 protein expressed in bacteria , we demonstrated the nucleic acid binding ability and specificity of SmZF1 , its preference for DNA as compared to RNA , and its putative best DNA binding sequence ( D1-3DNA ) 36 ., To verify whether the recombinant protein YFP-SmZF1 expressed in mammalian COS-7 cells was able to interact with D1-3DNA binding site in a manner comparable to its recombinant prokaryotic counterpart , EMSA assays were performed ., Total extracts of COS-7 cells transfected with either pEYFP-c1 or pEYFP-SmZF1 , expressing YFP or YFP-SmZF1 , respectively , were incubated with the D1-3DNA oligonucleotide ., To confirm the SmZF1/D1-3DNA interaction , supershift assays using anti-GFP and anti-SmZF1 antibodies were also performed ., Extracts of cells expressing the YFP-SmZF1 recombinant protein were able to shift the oligonucleotide migration in the gel ( Figure 3 , lane 5 ) ., Additionally , both anti-GFP and anti-SmZF1 antibodies were able to supershift D1-3DNA migration , confirming that the YFP-SmZF1 protein was responsible for the oligonucleotide binding ( Figure 3 , lanes 6 and 7 ) ., Extracts of cells expressing only the YFP protein ( Figure 3 , lanes 2–4 ) , as well as anti-GFP and anti-SmZF1 antibodies ( Figure 3 , lanes 8 and 9 ) , were not able to shift the D1-3DNA migration ., Although new vectors which will allow transfection of schistosome cells are under development 40–42 , it is still not possible to continuously cultivate schistosome cells lineages in vitro ., Accordingly , some authors describe the use of mammalian cells to study aspects of S . mansoni gene regulation processes , such as testing transcription factor activities or mapping promoter regions of genes 28 , 43 , 44 ., Thus , a luciferase system assay in COS-7 mammalian cells expressing YFP-SmZF1 fusion protein was used here to test SmZF1ability to regulate gene transcription . COS-7 cells co-transfected with the expression vector pEYFP-SmZF1 and the construction pGL3-zf-tk-luc , which contains four repetitions of the SmZF1 D1-3DNA binding site and a thymidine kinase minimal promoter upstream of the luciferase coding gene , were able to increase gene transcription by 2-fold ( p≤0 . 003 ) when compared to negative controls , using the Students t test ( Figure 4 ) ., These results suggested that SmZF1 positively affects the transcriptional activity of the minimal thymidine kinase promoter in COS-7 cells ., Schistosomiais is one of 13 neglected tropical diseases that together affect 1 billion people worldwide ., The disease is considered the second most socioeconomically devastating parasitic disease , the first being malaria 45 ., According to Chirac and Torreele , in the past 30 years the number of drugs which target these neglected diseases is about 1% of all the new chemical entities commercialized by the pharmaceutical industry 46 ., S . mansoni presents a variety of interesting biological regulatory processes , such as transcriptional control , which can be used to allow its adaptation to the diverse biotic and abiotic environments 8 ., Description of genes expressed in a stage- or sex-specific manner may help to elucidate the events used by the parasite to deal with these potentially adverse conditions ., In turn , this information may also help to develop suitable vaccines and chemotherapeutic drugs against this organism 7 ., As stated in the recent and high quality review on schistosome genomics by Han and colleagues 47 , some potential drug targets should include proteins involved in DNA replication , transcription and repair systems ., This suggestion is also corroborated by a chemogenomics screening approach described as part of the up-to-date S . mansoni genomic analysis , in which the authors used a strategy to find significant matches between parasite proteins and proteins known to be targets for drugs in humans and human pathogens ., That study revealed 26 putative S . mansoni protein targets and their potential drugs ., Of these 26 targets , three proteins are involved in DNA metabolism and two others are involved in chromatin modification ( histone deacetylase 1 and 3 ) 5 ., These two examples emphasize the importance of nuclear proteins as potential drug targets ., According to the authors of the S . mansoni transcriptome project 48 , 2 . 4% of the categorized ESTs ( Expressed Sequence Tags ) under the Molecular Function in Gene Ontology ( GO ) encode transcriptional regulators ., A search for conserved domains using the Pfam database in a subset of those transcripts showed that 5% of them consist of zinc fingers of the C2H2 group 48 ., Moreover , most of the 15 Pfam domains found were from proteins involved in either intercellular communication or transcriptional regulation ., These findings reinforce the importance of this class of regulatory proteins for S . mansoni biology ., In addition , using the SAGE approach , Ojopi and colleagues found that 9 . 7% of the most abundant genes ( genes containing more than 500 tags ) from S . mansoni adult worms comprise those from the nucleic acid binding GO functional category 49 ., The present study defines the SmZF1 protein as a S . mansoni transcription factor ., SmZF1 is a C2H2 zinc finger protein able to specifically bind to RNA and DNA , but with higher affinity for DNA molecules ., Its transcript was identified in the cercaria , egg , schistosomulum and adult worm stages , suggesting its importance as a regulatory protein 35 , 36 ., To define SmZF1 activity as a transcription factor , we first verified its subcellular localization , since this class of proteins is preferentially located or able to go to the cell nucleus , this import being a central step to regulate gene transcription 50 , 51 ., In silico analyses of the SmZF1 amino acid sequence did not predict any classical potential nuclear localization signal ( NLS ) , but did reveal positively charged amino acids within the zinc finger motifs 35 ., It has been demonstrated that zinc finger motifs are sufficient and sometimes essential for nuclear localization of ZF proteins , even without any canonical NLS detected in their amino acid sequences 51 , 52 ., Moreover , it is well known that small proteins ( <40 Kda ) , like SmZF1 , are sometimes able to passively diffuse into the nucleus 50 ., Immunohistochemical analysis of the diverse parasite developmental stages demonstrated that SmZF1 was indeed localized in the nucleus of S . mansoni cercariae , schistosomula and adult male worms ., This confirms previous results obtained by SmZF1 cDNA amplification 35 and reinforces our hypothesis that the protein is a transcription factor ., An unexpected result was the lack of detection of SmZF1 protein in adult female worms when assayed by this technique ., This differs from available transcriptome data , given the existence of one EST sequence ( GenBank accession number BF936884 ) derived from an adult female worm cDNA library presenting 99% identity with SmZF1 ., Also , studies using oligonucleotide microarrays in which the SmZF1 sequen

Introduction, Materials and Methods, Results, Discussion

During its development , the parasite Schistosoma mansoni is exposed to different environments and undergoes many morphological and physiological transformations as a result of profound changes in gene expression ., Characterization of proteins involved in the regulation of these processes is of importance for the understanding of schistosome biology ., Proteins containing zinc finger motifs usually participate in regulatory processes and are considered the major class of transcription factors in eukaryotes ., It has already been shown , by EMSA ( Eletrophoretic Mobility Shift Assay ) , that SmZF1 , a S . mansoni zinc finger ( ZF ) protein , specifically binds both DNA and RNA oligonucleotides ., This suggests that this protein might act as a transcription factor in the parasite ., In this study we extended the characterization of SmZF1 by determining its subcellular localization and by verifying its ability to regulate gene transcription ., We performed immunohistochemistry assays using adult male and female worms , cercariae and schistosomula to analyze the distribution pattern of SmZF1 and verified that the protein is mainly detected in the cells nuclei of all tested life cycle stages except for adult female worms ., Also , SmZF1 was heterologously expressed in mammalian COS-7 cells to produce the recombinant protein YFP-SmZF1 , which was mainly detected in the nucleus of the cells by confocal microscopy and Western blot assays ., To evaluate the ability of this protein to regulate gene transcription , cells expressing YFP-SmZF1 were tested in a luciferase reporter system ., In this system , the luciferase gene is downstream of a minimal promoter , upstream of which a DNA region containing four copies of the SmZF1 putative best binding site ( D1-3DNA ) was inserted ., SmZF1 increased the reporter gene transcription by two fold ( p≤0 . 003 ) only when its specific binding site was present ., Taken together , these results strongly support the hypothesis that SmZF1 acts as a transcription factor in S . mansoni .

Schistosomes are parasites that exhibit a complex life cycle during which they progress through many morphological and physiological transformations ., These transformations are likely accompanied by alterations in gene expression , making genetic regulation important for parasite development ., Here we describe a Schistosoma mansoni protein ( SmZF1 ) that may act as a parasite transcription factor ., These factors are key proteins for gene regulation ., We have previously demonstrated that SmZF1 is able to bind DNA and that its mRNA is present at different stages during the parasite life cycle ., In this study we aimed to define if this protein can function as a transcription factor in S . mansoni ., SmZF1 was detected in the nucleus of adult male worms , cercariae and schistosomula cells ., It was not , however , observed in female cells , suggesting it to be gender specific ., We used mammalian cells expressing recombinant SmZF1 to analyze if SmZF1 protein is able to activate/repress gene transcription and demonstrated that it increased the expression of a reporter gene by two-fold ., The results obtained confirm SmZF1 as a S . mansoni transcription factor .

molecular biology/transcription initiation and activation, genetics and genomics/gene expression, genetics and genomics/functional genomics, genetics and genomics/gene function, cell biology/gene expression

journal.ppat.1000882

Galectin-9/TIM-3 Interaction Regulates Virus-Specific Primary and Memory CD8+ T Cell Response

Adaptive immune responses to foreign antigens require precise regulation ., If not , excessive bystander damage to host tissues may occur and an unlimited reaction could erode the size of the repertoire , limiting responses to other antigens ., It is evident that the host possesses several mechanisms that control the size , composition and duration of immune reactions 1 ., In consequence , after the primary response most cells die leaving a memory population that represents a fraction of the cells that responded initially to the antigen ., Moreover , these memory cells rarely account for >10% of the total antigen reactive repertoire 2 ., In some circumstances , it would be desirable to expand the size of the memory population and perhaps extend the durability of effector cell activity , since this could improve immunity to certain pathogens ., HIV is such an example 3 ., Accordingly , understanding how immune responses are constrained could provide clues to reverse the process and improve the efficacy of vaccines ., One family of host proteins that plays multiple roles in innate and adaptive immunity is the galectin proteins ., These glycan binding proteins either form lattices on cell surfaces or react with specific receptors and trigger a variety of responses that include apoptosis and changes of cell function 4 ., Some galectins bind to the surface of pathogens and this may influence pathogen infectivity and survival 5 ., For instance , binding of Gal-1 to HIV increases infectivity of the virus for macrophages 5 , 6 ., At least two family members , Gal-1 and Gal-9 , may play an effective role in terminating the acute inflammatory response as well as restricting the extent of chronic lesions in autoimmune and allergic reactions 4 , 7 ., Recently , we showed that chronic inflammatory reactions caused by HSV infection of the eye was limited by Gal-9 binding to its specific receptor TIM-3 8 ., This interaction led to inhibitory effects on effector T cells , as well as the expansion of regulatory T ( Treg ) cell activity ., Currently , it is not clear what role galectins play at regulating acute immune responses to virus infections and whether manipulating galectin binding to their receptors can influence the magnitude and effectiveness of anti-viral immunity ., In this communication , we investigate the influence of Gal-9 binding to its receptor TIM-3 on the size and quality of CD8+ T cell mediated immunity to a virus infection ., We demonstrate that Gal-9 acts to limit the extent of CD8+ T cell immunity to HSV infection ., In support of this , we show that animals unable to produce Gal-9 , because of gene knockout , develop acute and memory responses to HSV that are of greater magnitude and better quality than those that occur in normal infected animals ., We also make what we believe is the novel observation that infusion of normal infected mice with α-lactose , the sugar that binds to the carbohydrate-binding domain of Gal-9 limiting its TIM-3 receptor engagement 9 , also caused a more elevated and higher quality CD8+ T cell response to HSV ., Such sugar treated infected mice also had expanded populations of memory CD8+ T cells ., The mechanisms responsible for the outcome of the Gal-9/TIM-3 interaction in normal infected mice involved both inhibitory effects on TIM-3+ T effector cells , as well as the promotion of Foxp3+ regulatory T cell activity ., Our results indicate that manipulating galectin signals , as can be achieved using appropriate sugars , may represent a convenient and inexpensive approach to enhance acute and memory responses to infectious agents and might be useful to improve responses to some vaccines ., All animals were housed in Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) -approved animal facilities ., Institutional Animal Care and Use Committee ( IACUC ) , The University of Tennessee , Knoxville approved all experimental protocols and experiments were performed adhering to protocols created by the committee ., Female 5–6-wk-old C57BL/6 and congenic Thy1 . 1+ B6 . PL ( H-2b ) mice were purchased from Harlan Sprague-Dawley and Jackson Laboratory , respectively and housed in the animal facilities at the University of Tennessee , Knoxville ., Galectin-9 KO ( Gal-9 KO ) mice were kindly provided by GalPharma Co ., Ltd , Japan ., Dr . Thandi Onami kindly provided P14 LCMV transgenic mice ., Foxp3-GFP knock-in mice were kindly provided by Dr . Mohammed Oukka of Harvard Medical School ., HSV-1 strain KOS was grown in Vero cells obtained from American Type Culture Collection ., The harvested virus was titrated and stored in aliquots at −80°C until further use ., Conjugated antibodies purchased from BD Bioscience were anti-CD8α , anti-CD25c anti-CD62 ligand ( CD62L ) , anti-CD44 , anti-CD69 , anti-IFN-γ , anti-TNF-α , anti-IL-2 , anti-CD25 ( 7D4 ) , anti-KLRG1 , anti-CD80 , anti-CD86 , anti-CD11b , anti-CD11c , anti-CD40 , anti-MHC class II and anti-CD103 ., PE and APC conjugated TIM-3 antibodies were purchased from R&D Systems ., Intra-nuclear Foxp3 staining was performed using a kit from ebioscience according to the instructions ., Carboxyfluorescein Succinimidyl ester ( CFSE ) was obtained from Molecular Probes ., HSV gB498–505 peptide ( SSIEFARL ) was supplied by Genescript ., PE- and APC-Kb-gB tetramer was kindly provided by NIH tetramer core facility , Emory University , Atlanta , GA ., Recombinant galectin-9 and anti-galectin-9 antibodies ( clone1A2 and 108A2 ) were provided by Gal Pharma , Japan ., Recombinant galectin-3 was obtained from R & D systems ., α-Lactose was purchased from Acros organics ( Geel , Belgium ) ., The antibody-stained cells were acquired with a FACS Calibur ( BD Biosciences ) and the data were analyzed using the FlowJo software ( Tree Star , OR ) ., Sorting of cells was performed by using a FACS Vantage system ., Mice were infected in footpad ( FP ) with 2 . 5×105 PFU of HSV KOS in 30 µl volume ., For memory recall response studies , animals were challenged with the same amount of virus in the footpads after a period of at least 30 days ., For primary responses , infected animals were divided randomly into different groups ., Mice in one group were treated ip with 125 µg of Gal-9 twice daily starting from day 3 until day 5 post infection ( p . i ) ., Similarly animals in other groups were treated twice daily starting from day 3 until day 5 with various doses ( 27 mM , 137 mM , 277 mM and 416 mM ) of α-lactose solution made in PBS ., For some memory experiments , animals were provided with 277 mM of α-lactose solution in drinking water for 10 days ., 12 hrs after the last treatment animals were sacrificed and their isolated lymphoid organs were made into single cell suspension for flow cytometric analysis ., The quantification of HSV-1 in footpad tissue was done as previously reported 10 ., Briefly , the mice were sacrificed at the indicated time p . i . , the footpad surface was cleaned with 70% isopropyl alcohol , cut and were stored in RPMI without serum and 2-mercaptoethanol at -80°C until use ., Tissues were disrupted by chopping with scissors and homogenized by using a homogenizer ( Pellet Pestle mortar; Kontes ) and centrifuged ., The supernatant was used to assess viral titers on Vero cells ., Finally , plaques were visualized after staining with crystal violet ., Different numbers of cells obtained from pooled spleen and Lymph nodes ( LN ) of Thy1 . 1 LCMV ( P14 ) transgenic animals were transferred into Thy1 . 2 C57BL/6 animals that were then infected with HSV-KOS in footpads ., At 5dpi , LCMVgp33-Tet+ and Kb-gB-Tet+ cells were analyzed for the expression of TIM-3 ., The control animals not transferred with any cell type were similarly infected with HSV KOS ., Similarly , for some other experiments , cells obtained from pooled LNs and spleens of B6 Thy1 . 1 animals were labeled with 2 . 5 µM of CFSE ., 107 of these cells were transferred into B6 Thy1 . 2 wild type and galectin-9 knockout animals ., Alternatively , CD8+ T cells obtained from Gal-9 KO and WT animals ( both Thy1 . 2 ) were CFSE labeled and transferred into Thy1 . 1 C57BL/6 animals ., After 24 hrs of transfer , recipient animals were infected with 2 . 5×105 PFU of HSV 1 KOS ., After five days of infection LNs and spleens were isolated and processed to make single cell suspensions ., The dilution of CFSE was analyzed flow cytometrically in Thy1 . 1+ or Thy1 . 2+CD8+ T cells ., CD8+ T cell proliferation was evaluated after in vitro stimulation of splenocytes and draining LN cells obtained from HSV infected mice with MHC class I ( H-2Kb ) –restricted SSIEFARL peptide ., Briefly , the cells were stimulated with SSIEFARL peptide ( 1 µg/ml ) for 3 days in the presence of 100 U/ml of IL-2 ., 3H Thymidine ( 1 µCi/well ) was added to each well 18 h before harvest ., Harvested cells were measured for radioactivity using a β-scintillation counter ( Inotech ) ., The in vivo CTL assay was done as reported earlier 11 ., Splenocytes from naive B6 mice were used as target cells and split into two equal populations ., One was pulsed with 2 . 5 µg gB498–505 peptide for 45 min at 37°C and then labeled with a high concentration ( 2 . 5 µm ) of CFSE ., The other population was un-pulsed and labeled with a low concentration of CFSE ( 0 . 25 µm ) ., Equal numbers of cells from each population ( 107 ) were mixed together and adoptively transferred i . v . into naïve and HSV-1–infected mice ., Splenocytes and peripheral blood were collected 2 h after adoptive transfer from recipient mice , erythrocytes were lysed , and cell suspensions were analyzed by FACS Calibur system ., Each population was distinguished by their respective fluorescence intensity ., Assuming that the number of peptide-pulsed cells injected is equivalent to the number of no peptide-pulsed cells injected , the percentage of killing of target cells in uninfected animals was determined as: The percentage killing of target cells in infected animals was calculated as: To enumerate the number of IFN-γ , TNF-α and IL-2 producing T cells , intracellular cytokine staining was performed as previously described 12 ., In brief , 106 freshly isolated splenocytes were cultured in U bottom 96-well plates ., Cells were left untreated or stimulated with SSIEFARL peptide ( 2 µg/ml ) , and incubated for 5 h at 37°Cin 5% CO2 ., Brefeldin A ( 10 µg/ml ) was added for the duration of the culture period to facilitate intracellular cytokine accumulation ., After this period , cell surface staining was performed , followed by intracellular cytokine staining using a Cytofix/Cytoperm kit ( BD PharMingen ) as per the manufacturers recommendations ., The Ab used were anti–IFN-γ ( clone XMG1 . 2 ) , anti-TNF-α ( clone MP6-XT22 ) , and anti-IL-2 ( clone JES6-5H4 ) ., The fixed cells were re-suspended in FACS buffer ( PBS with 3% heat inactivated serum ) and analyzed flow cytometrically ., PLNs single cell suspension isolated 6 days pi from HSV infected animals were incubated for 5 hrs with varying concentrations of Gal-9 or Gal-3 in the absence or the presence of α-lactose in 96-well flat-bottom plates in humidified incubators in the presence of 5% CO2 ., After the incubation period was over , cells were stained for annexin V using a kit from BD Biosciences ., Additionally , cells were also co-stained for CD8 , TIM-3 , Kb-gB-Tet ., Stained cells were analyzed immediately by flow cytometry ., At 6 dpi , Foxp3+CD4+ T cells were FACS sorted on a FACS Vantage system to the extent of 97 . 8% purity from HSV infected Foxp3-GFP-Knock-In animals that were either untreated or treated with 200 µl of 277 mM α-lactose i . p . from day 3 to day 5 ., Foxp3+ T cells were then cultured with anti-CD3 stimulated CFSE labeled CD4+CD25− Thy1 . 1 ( MACS purified ) and T cell depleted irradiated splenocytes for three days ., Thereafter , the extent of CFSE dilution in Thy1 . 1+CD4+ T cells was analyzed flow cytometrically ., Lymph node and spleen homogenate samples ( 10 µg/lane ) were resolved on 15% SDS-PAGE and transferred electrophoretically on to a PVDF membrane ( Bio-Rad ) ., The membrane was blocked overnight with 5% BSA and washed 5 times with TBS containing 0 . 05% Tween-20 ( TBST ) and incubated with biotinylated anti-galectin antibody ( R&D Systems ) at a concentration of 0 . 5 µg/ml diluted in TBST for 1 hr at room temperature ., The membrane was washed 5 times with TBST and incubated with streptavidin-HRP antibody ( Pierce ) at a dilution of 1∶10000 for 1 hr at room temperature ., The membrane was developed with chemiluminescent substrate ( Immobilon western chemiluminescent HRP substrate , Millipore ) and the image was taken on CL-XPosure X-ray film ( Thermo scientific ) ., Ninety-six-well microplates were coated with capture antibody at a concentration of 3000 ng/ml ( 100 µl/well , anti-galectin-9 , GalPharma Co . Ltd , Japan ) ., After incubation overnight at 4°C , the wells were washed three times with PBST ( PBS containing 0 . 05% Tween-20 ) and blocked with 5% BSA for 2 h at RT ., The wells were washed three times with PBST and Lymph node , spleen homogenate samples ( 100 µl ) were added to the wells and incubated at RT for 2 h and aspirated ., The wells were then washed with wash buffer ., Biotinylated anti galectin detection antibody ( 1;10000 , R&D Systems ) diluted in reagent diluent ( R&D Systems , PBS , 5% Tween 20 , 2% goat serum ) was added to each well and incubated at RT for 1, h . The wells were then washed three times and 100 µl of streptavidin–horseradish peroxidase ( 1∶10000 dilution ) added and incubated for 1 hr at room temperature ., The plate was washed thrice and developed with TMB ( R&D systems ) substrate and the absorbance of each sample was determined at 450 nm ., A standard curve ranging from 5 µg to 156 . 25 ng of recombinant galectin-9 ( Gal Pharma , Japan ) was generated to calculate the galectin-9 concentration in the unknown samples ., Most of the analyses for determining the level of significance were performed using Students t test by Graphpad software ., Values of p 0 . 001 ( *** ) , p 0 . 01 ( ** ) , and p 0 . 05 ( * ) were considered significant ., Results are expressed as mean ± SEM or otherwise stated ., CD8+ T cells isolated from the draining popliteal LNs ( PLN ) and spleens of HSV infected animals were analyzed for TIM-3 expression at different times pi ., As is evident in Fig 1A , whereas few CD8+ T cells isolated from LNs of uninfected animals expressed TIM-3 , after HSV infection TIM-3+ cells were numerous ., This increase in TIM-3+CD8+ T cells was evident at day 3 pi in the PLN and day 4 in the spleen ( Figure S1 ) and peak frequencies ( Fig 1A and B ) , as well as total numbers ( Fig 1C ) , were present on days 6 ., The majority of TIM-3+ T cells isolated from infected animals were CD44hi and CD62Llo suggesting that only activated or effector T cells expressed surface TIM-3+ ( Fig 1D ) ., Using tetramers and the ICCS assay to detect SSIEFARL specific CD8+ T cells ( this represents the immunodominant response to HSV in C57BL6 mice 13 , almost all cells were TIM-3+ at the peak response time ( Fig 1E and F ) ., Additionally , the frequency of IFN-γ-producing peptide-specific T cells was comparable for both KLRG1+ and TIM-3+ cells , further indicating that TIM-3 is a marker for effector cells ( Fig 1G ) ., The additional TIM-3+CD8+ T cells that were not SSIEFARL specific would likely be recognizing other HSV antigens , but this could not be verified for lack of additional reagents ., However , it was also conceivable that some of the TIM-3+ cells were not HSV specific and represented a bystander-activated population , which has been advocated to occur in some HSV-induced immunopathological lesions 14 ., To address the involvement of bystander activation different numbers of P14 transgenic CD8+ T cells ( these recognize the gp33 peptide of LCMV 15 and are not known to show any cross-reactivity with HSV ) , were transferred into B6 animals that were subsequently footpad infected with HSV ., After 5 days , the expression of TIM-3 was analyzed on Thy1 . 1+gp33-tetramer+ and Kb-gB Tet+ CD8+ T cells ., As is evident from Fig 2A-C , we failed to detect any P14 T cells that expressed TIM-3 on their surface , but in the same animals almost all of the responding HSV specific cells detected by the Kb-gB-tetramer were TIM-3+ ., These data add no support for bystander mechanisms causing TIM-3 expression on CD8+ T cells ., It likely means that all TIM-3+CD8+ T cells were reacting with viral antigens , but this needs to be formally shown ., The results of previous in vitro studies have revealed that Gal-9 binding to TIM-3 receptors on some , although not all , T cell subsets causes them to undergo apoptosis 8 , 16 ., To test the fate of the CD8+TIM3+ population expanded by HSV infection to Gal-9 exposure , PLN cells were collected 6 days pi and exposed in vitro for 5 hrs to a range of concentrations of Gal-9 ., Subsequently , the cells were analyzed by FACS for changes in the expression levels of TIM-3 and annexin V , the latter indicative of apoptosis 17 ., As shown in Fig 3A , approximately 15% of CD8+ T cells were TIM-3+ at the onset of culture and this percentage did not change significantly in the absence of Gal-9 ., However , Gal-9 addition ( at1 . 0 µM ) caused a loss of almost all cells that were TIM-3+ ( Fig 3A upper panel ) ., Baseline levels of annexin V+ cells also did not change significantly in the absence of Gal-9 ( or in the presence of Gal-3 , as shown in Fig 3E-F ) ., However , when optimal amounts of Gal-9 were present , annexin V+ cells increased 15–20% beyond baseline numbers ( Fig 3A upper panel and 3B ) ., This number roughly correlated with the fraction of TIM-3+CD8+ T cells that disappeared upon Gal-9 exposure ., Under the conditions used , we failed to detect significant numbers of TIM-3+ annexin V+ cells , although at earlier time points some such cells can be demonstrated ( data not shown ) ., In additional experiments , we measured the effects of Gal-9 addition on the fate of Tet+ TIM-3+ CD8+ T cells , almost all of which as described previously were TIM-3+ ., As shown in Fig 3C ( upper panel ) and 3D , the great majority of Tet+ TIM-3+ T cells were lost after Gal-9 exposure and there was a large increase in annexin V+ T cells ., In other experiments with Gal-9 treated cultures , we investigated the effects of adding an excess of α-lactose , the sugar that binds to the carbohydrate binding domain of Gal-9 and reduces Gal-9 binding to TIM-3 9 , 16 ., In such experiments , lactose addition served to prevent the disappearance of most TIM-3+ as well as Tet+CD8+ T cells and also reduced the increase in annexin V+ cells ( Fig 3A , lower panel and 3B as well as 3C , lower panel and 3D ) ., Taken together , our results imply that Gal-9 binding to TIM-3+ effectors caused their death by apoptosis ., It is also conceivable that binding of Gal-9 to TIM-3 masks its detection or causes TIM-3 down regulation , but such effects have not been noted in other systems ., The observation that most of the CD8+ T cells expanded by HSV infection express TIM-3 , along with in vitro observations that exposure to Gal-9 caused the deletion of TIM-3+CD8+ T cells , could indicate that endogenously produced Gal-9 acts likewise and serves to limit the magnitude of the anti-HSV CD8+ T cell response ., In consequence , we determined if endogenous levels of Gal-9 could be demonstrated in lymphoid extracts of HSV infected animals , as well as to measure if these levels changed at different time points after infection ., The quantification of Gal-9 was done by both western blotting and ELISA ( Fig 3H and I and Figure S2 ) ., Basal levels of Gal-9 were detectable in control lymphoid extracts and levels were moderately increased 2 days p . i . A greater increase ( 2–2 . 5 fold ) was also observed in 7 days pi samples ( Fig 3H ) ., Accordingly , a consequence of HSV infection is an increase in endogenous Gal-9 levels , particularly around the peak time of CD8+ T cell responses ., If indeed endogenous Gal-9 production acts to constrain the magnitude of the CD8+ T cell responses , then conceivably mice unable to produce Gal-9 due to gene knockout ( KO ) would respond with higher responses to HSV than WT animals ., To evaluate this concept , age and gender matched WT and Gal-9 KO animals were infected via the footpads with HSV and the magnitude of CD8+ T cell responses were compared 5–6 days later ( Fig 4 ) ., As shown at day 5 . 5 days pi , the frequencies ( Fig 4A , B and E for Tet+ cells and Fig 4D and E for cytokine producing cells ) and absolute numbers ( Fig 4F ) of virus-specific CD8+ T cells in the PLN as measured by tetramers and the ICCS assay , were increased up to 3 fold in the Gal-9 KO compared to WT mice ., The expression levels of CD44 were higher and CD62L lower on CD8+ T cells from Gal-9 KO animals compared to WT ( Fig 4C ) ., This indicates that the more cells from KO animals were in activated state than those from WT animals ., In experiments to compare the responses to HSV infection of Gal-9 and WT mice at different times pi , increased responsiveness of similar magnitude was observed at all times tested although there was a trend of greater differences after day 10dpi ( Figure S3 ) ., To provide evidence for a change in the quality of the CD8+ T cell response caused by the absence of Gal-9 , we measured and compared mean fluorescence intensities ( MFI ) values for some cytokines as well as the proportions of responder T cells that produced two compared to a single cytokine ., These properties have been advocated to indicate the presence of high quality T cells 18 ., Our results demonstrate that in Gal-9 KO mice there was significantly increased frequencies ( Fig 4D and E ) and numbers ( Fig 4F and G ) of IFN-γ +TNF-α + T cells , as well as a higher frequency of cells that had high MFI levels ( Fig 4H ) compared to WT mice ., Additionally , IFN-γ +IL-2+ T cell numbers were also increased in Gal-9 KO as compared to WT animals ( Figure S4 ) ., To measure the functional significance of the differential CD8+ T cell responses to HSV in Gal-9 KO and WT mice , in vivo cytotoxicity assays were performed after the peak time responses on 11 dpi ., In these experiments animals received mixed transfers of 107 CFSE labeled splenocytes that were pulsed with the MHC class I restricted SSIEFARL-peptide of HSV 1 ( CFSEhi ) along with 107 un-pulsed cells ( CFSElo ) ., To ensure the equal distribution in vivo of peptide pulsed and un-pulsed cells , the same numbers of both cell types were transferred into uninfected animals ., After 2 hrs of transfer , spleens and blood samples were collected and analyzed by flow cytometry to measure differential killing of targets in WT and Gal-9 KO animals ., Minimal killing of peptide pulsed splenocytes was seen in uninfected WT or the Gal-9 KO animals ( data not shown ) ., As is evident in Fig 4M and N , in the absence of Gal-9 , CD8+ T cells were 3 to 4 times more effective at killing targets as compared to what occurred in WT animals ., Accordingly , this indicates that endogenously produced Gal-9 may act to limit the effectiveness of virus-specific CD8+ T cell responses in vivo ., In other experiments , the ability of Gal-9 KO and WT animals were compared for their efficiency at clearing infectious virus from the infected footpad ., As shown in Fig 4I , levels of virus in footpad homogenates measured at day 5 . 5 pi were 1 . 5 log lower on average in Gal-9 KO compared to samples from WT ., Differences were also evident at other time points ( data not shown ) ., The results of both in vivo cytotoxicity and viral clearance experiments indicate that Gal-9 KO animals have more effective immunity to HSV ., Comparisons of the magnitude and quality of HSV specific CD8+ T cell responses of Gal-9 KO and WT animals were also made 32 and 60 days pi to record memory phase effects ., The results of a sample experiment , done at 60 days pi , are recorded in Fig 5 ., As in the acute phase , the memory responses of Gal-9 animals exceeded those of WT animals ., Thus , the frequencies ( Fig 5A ) and total numbers ( Fig 5C ) of CD8+ and Kb-gB Tet+ T cells were increased by approximately 2 . 5 fold in Gal-9 KO , as compared to those of WT animals ., In addition , the frequency and numbers of peptide-specific CD8+ T cells that produced both IFN-γ and TNF-α cytokines was also significantly higher in the spleens of Gal-9 KO population than in WT ( Fig 5B-D ) indicative of higher quality memory responses ., Accordingly , in the absence of Gal-9 , memory CD8+ T cell responses to HSV were increased ., Some WT and Gal-9 KO animals primed 32 days previously were re-infected in the footpad with HSV and the response in the PLN measured 2 . 5 days later by tetramers , ICCS and ex-vivo proliferative assays ., Once again , the frequencies ( Fig 5E and G ) and numbers ( Fig 5H ) of Kb-gB-Tet+ and cytokine producing cells ( Fig 5F-H ) as well as peptide-specific proliferative responses ( Fig 5J ) were greater in Gal-9 KO as compared to WT animals ., Although , the ratio of double vs single cytokine producers were not significantly different , the cells obtained from Gal-9 KO animals did show higher MFI for IFN-γ than those of WT animals ( Fig 5I ) ., Taken together the results of comparisons of the immune responses to HSV in Gal-9 KO and WT animals at the memory stage also revealed that Gal-9 might act in normal animals to limit the magnitude and efficiency of CD8+ T cell responses ., Our observation that Gal-9 KO mice develop superior CD8+ T cell responses to HSV infection could be explained by the Gal-9 KO CD8+ T cells being intrinsically more responsive or being less inhibited when Gal-9 is absent in the environment ., To further evaluate the situation , two types of experiments were done ., In one approach , Thy1 . 2 naïve Gal-9 KO or WT CFSE labeled LN cells were transferred into Thy1 . 1 animals that were then infected via the footpad with HSV ., In such experiments , no significant differences in the extent of proliferation by Gal-9 KO and WT CD8+ T cells could be observed ( Fig 6A-C ) ., This indicates that intrinsic reactivity differences between Gal-9 KO and WT T cells did not account for enhanced responses of Gal-9 KO animals ., In another approach , CFSE labeled naive splenocytes from Thy1 . 1 animals were transferred either into WT or Gal-9 KO animals that were subsequently infected via the footpad with HSV ., Cell proliferation was measured by CFSE dilution in the donor cells and the total numbers of Kb-gB Tet+ donor cells that had accumulated in the PLN after 5 days were quantified ., The donor cells proliferated to a similar degree in both recipient types ( Fig 6D ) , but the accumulation of Kb-gB Tet+ specific cells 5 day later was approximately 4 fold higher in the Gal-9 KO than in WT animals ( Fig 6E ) ., In an additional in vitro experiment to compare the proliferative potential of Gal-9 KO and WT CD8+ T cells , CFSE labeled purified CD8+ T cells were stimulated with plate-bound anti-CD3 and anti-CD28 mAb for three days and the extent of CFSE dilution was measured ., As shown in Fig 6F , both CD8+ T cell populations proliferated equally well ., These ex-vivo and in-vivo results were consistent with the extrinsic environment accounting for the observed differences in the responsiveness of CD8+ T cells in Gal-9 KO and WT animals ., A likely explanation for the better CD8+ T cell response to HSV of Gal-9 KO mice compared to WT mice is that the specific TIM-3+CD8+ T cell effectors will not undergo apoptosis in the absence of Gal-9 ., However , a contribution by the Foxp3+ regulatory T cell ( Tregs ) response could also be part of the explanation ., In support of this we showed that compared to WT mice , Gal-9 KO animals had fewer Tregs ( Fig 6G and H ) , especially those that were CD103+ ( Fig 6K ) and TIM-3+ ( Fig 6L ) ., Furthermore , the expression levels of Foxp3 ( Fig 6G , I and J ) and activation markers , such as CD103 , TIM-3 and CD44 , were lower on Tregs isolated from Gal-9 KO mice compared to those from WT animals ( Fig 6K-M ) ., Since , the CD8+ T cell response to HSV can be inhibited by Tregs 19 , 20 , having fewer of these cells , especially in the activated state , may also help explain why Gal-9 KO animals responded better than WT to HSV ., That , Gal-9 KO mice have less natural Foxp3+ Tregs than WT animals after immunization with non-viral antigens has been reported by others 21 ., Further evidence that endogenously produced Gal-9 may be acting in vivo to limit the extent of antiviral CD8+ T cell responses was obtained by infusing an excess of the sugar that binds to Gal-9 and which interferes with its binding to the TIM-3 receptor ., To this end , HSV infected animals were infused ip twice daily with 200 µl of α-lactose solutions ( 27 mM , 137 mM , 277 mM , 416 mM ) starting from day 3 until day 5 ., Subsequently , virus-specific CD8+ T cell responses were quantified 12 hr later and compared to untreated controls ., As shown in Fig 7A-F , mice infused with 277 mM α-lactose had significantly enhanced and higher quality CD8+ T cells responses than untreated controls ., The effect was dose dependant with the 277 mM lactose solution giving the maximal effect ( Fig 7A ) ., With this dose , the numbers of gB498–505 peptide stimulated IFN-γ producing CD8+ T cells at peak response times were increased 2–3 fold in the PLN and >3 fold in the spleen compared to control animals ( Fig 7B , D and E ) ., A similar pattern of results was revealed using Kb-gB Tet+ , TIM-3 and KLRG-1 to measure results ( Fig 7C ) ., Interestingly , the α-lactose recipients also had a small but significantly higher ratio of double:single cytokine producing cells indicative of better quality responses ( Fig 7F ) ., We also quantified the viral loads in the footpads of HSV infected animals that were either treated with α-lactose or PBS alone ., Viral loads were less ( p≤0 . 05 ) in α-lactose treated animals at day 6 pi than in controls ( Fig 7G ) ., Virus-specific CD8+ T cell responses were also compared at 32 days pi in α-lactose treated and control animals in limited studies ., A small , but significant increase in the frequencies ( Fig 7H ) as well as numbers ( Fig 7I ) of Kb-gB Tet+T cells , IFN-γ+ and TNFα+ -producing CD8+ T cells ( Fig 7J-L ) was observed in the α-lactose treated animals ., No significant changes in the number of double cytokine-producing cells between groups were detected ., We also measured the phenotype and function of the Foxp3+ Treg isolated from control and α-lactose treated animals at day 5 . 5 pi ( Fig 8A-F ) ., The mean fluorescence intensities of Foxp3 , CD103 and TIM-3 expression were decreased in Treg isolated from α-lactose treated animals as compared to control animals ( Fig 8C and F ) ., Additionally , the frequencies and the total number per PLN of CD103+ ( Fig 8A and B ) and TIM-3+ ( Fig 8D and E ) Foxp3+CD4+ T cells was also reduced in α-lactose treated animals as compared to controls ., Furthermore , the in vitro suppressive activity of Foxp3+CD4+ T cells isolated from α-lactose treated animals was also reduced compared to controls when compared by in vitro suppression assays ., These data indicate that inhibition of Gal-9 binding resulting in a decline of Treg function ( Fig 8G ) ., Taken together , our results with α-lactose infusion to block Gal-9 activity support the concept that endogenously produced Gal-9 acts to reduce the magnitude and function of anti-HSV CD8+ T cell responses ., The effect was in part mediated by effects on Treg responses ., The previous studies all relate to the influence of endogenous Gal-9 binding to the TIM-3 receptor on virus reactive CD8+ T cells ., In this experiment , we measured the outcome of adding exogenous Gal-9 on the magnitude of the anti-HSV CD8+ T cell response in HSV infected WT mice ., Animals were given 125 µg ip of Gal-9 during the expansion phase ( from day 3 to day 5 pi ) and virus-specific CD8+ T cell responses were compared with untreated animals 12 hrs after the last injection ., As shown i

Introduction, Materials and Methods, Results, Discussion

In this communication , we demonstrate that galectin ( Gal ) -9 acts to constrain CD8+ T cell immunity to Herpes Simplex Virus ( HSV ) infection ., In support of this , we show that animals unable to produce Gal-9 , because of gene knockout , develop acute and memory responses to HSV that are of greater magnitude and better quality than those that occur in normal infected animals ., Interestingly , infusion of normal infected mice with α-lactose , the sugar that binds to the carbohydrate-binding domain of Gal-9 limiting its engagement of T cell immunoglobulin and mucin ( TIM-3 ) receptors , also caused a more elevated and higher quality CD8+ T cell response to HSV particularly in the acute phase ., Such sugar treated infected mice also had expanded populations of effector as well as memory CD8+ T cells ., The increased effector T cell responses led to significantly more efficient virus control ., The mechanisms responsible for the outcome of the Gal-9/TIM-3 interaction in normal infected mice involved direct inhibitory effects on TIM-3+ CD8+ T effector cells as well as the promotion of Foxp3+ regulatory T cell activity ., Our results indicate that manipulating galectin signals , as can be achieved using appropriate sugars , may represent a convenient and inexpensive approach to enhance acute and memory responses to a virus infection .

Adaptive immune responses to foreign antigens require precise regulation , otherwise excessive bystander damage to host tissues may occur and responses to other antigens could be compromised ., Some galectin proteins binding to receptors on cells of the immune system form part of the regulatory system , although this topic has received scant attention as it relates to antiviral control ., In this report , we evaluate the role of the galectin-9/TIM-3 receptor pathway at regulating acute and memory CD8+ T cell responses to herpes simplex virus ( HSV ) infection ., We demonstrate that CD8+ T cell responses to HSV were significantly increased in magnitude and improved in quality in mice unable to produce galectin-9 because of gene knockout compared to wild type controls ., Furthermore , inhibiting the galectin-9 mediated response to TIM-3 using the sugar alpha-lactose that binds to Gal-9 led to a similar response pattern ., The influence of galectin-9 to constrain CD8+ T cell responses involved direct inhibitory effects on the T effectors as well as the promotion of regulatory T cell responses ., Our results indicate that manipulating the interaction of galectins with receptors using simple sugars may represent a convenient and inexpensive approach to enhance T cell responses to virus infections , and could prove useful to increase the efficacy of some vaccines .

immunology, immunology/immunity to infections

journal.pntd.0003876

Development of a Novel Rabies Simulation Model for Application in a Non-endemic Environment

Rabies is among the most lethal infectious diseases , present on all populated continents except Australia 1 ., The domestic dog remains the most important vector worldwide , causing >95% of all human cases 2–4 ., Despite availability of an effective vaccine for more than a century and repeated demonstration that vaccinating the domestic dog population is the most effective way to eliminate the disease 5–8 , rabies remains endemic in large areas in Africa and Asia ., Moreover , the disease has ( re ) emerged in areas previously free ( such as Bhutan 9 , 10 , Indonesia 11 , 12 , and the Central African Republic 13 ) ., Rabies continues to spread through the Indonesian archipelago via human mediated domestic dog movements 11 , 12 , 14 , most recently through the previously rabies-free province of Maluku in eastern Indonesia 11 , 15 ., The risk of incursion into rabies-free areas − Timor , Irian Jaya , Papua New Guinea ( PNG ) and northern Australia − is therefore high ., Possible incursion scenarios into Australia include yachts or fishing boats hosting latently rabies infected dogs traveling from Indonesian islands to remote areas in northern Australia 16 ., Also , close cultural ties between PNG and Torres Strait Island communities exist , increasing the risk of movements of dogs incubation rabies from PNG to Australia , if an incursion in PNG occurs 16 ., In these regions large , free-roaming domestic dog populations 17 , 18 increase the risk of rabies establishment , which would subsequently impact human and wildlife populations ., Because there are no historical precedents , the spread and final impact of such rabies incursions is difficult to estimate ., However , such knowledge is critical to informing preparedness and response plans prior to an incursion , and to design the most effective strategies ., Descriptions and applications of several rabies models in wildlife 19–24 , domestic dogs 5 , 7 , 25–28 or a combination of these 8 , 29 have been published ., All have been based on empirical field data in rabies endemic regions and typically aim to inform policy on reducing rabies prevalence and thus impacts ., However , for a region in which rabies is exotic , predictions of the effectiveness of different interventions following the initial detection of rabies are more relevant ., An issue is how rabies behaves when introduced to a previously free population , particular the effect of rabies on contact rates and biting rates ., Evidence on these behaviour changes from previous rabies incursion may serve as an approximation but is typically vague and therefore equivocal ., To our knowledge , epidemic models simulating rabies invasion in regions never exposed to rabies do not exist , a barrier to rabies preparedness planning ., Here , we describe the development of a novel simulation model of rabies epidemics in domestic , free-roaming dog populations in remote Indigenous communities in northern Australia–as an example of the potential scenario in many regions of the world where rabies is absent but where the risk of a rabies incursion is present and its spread is likely due to large populations of free-roaming domestic dogs ., Results of a systematic sensitivity analysis are also presented and model application options are discussed ., Data collection required to estimate model parameters has been approved by the Human Ethical Committee of the University of Sydney , grant no . 2013/757 and the Animal Ethical Committee of the University of Sydney , grant no ., N00/7-2013/2/6015 ., The rabies simulation model development was based on data from two distinct regions in northern Australia , the Northern Peninsula Area ( NPA ) of Cape York , Queensland and Elcho Island , the Top End of the Northern Territory ., Characteristics of the two study sites are presented elsewhere 18 ., Briefly , in the NPA five Aboriginal and Torres Strait Islander communities are located in close proximity ( 2−4 km ) to each other ., On Elcho Island one larger Aboriginal community is present ., The dogs are typically owned but unrestrained and build a large population in all communities ( human:dog ratio 2 . 7−8 . 8 per community , Table 1 ) 18 ., The dog population in the NPA − which informs the simulation model − is based on the most recent dog census conducted by the NPA Regional Council in 2009 ., As such information was not available from Elcho Island , the number of dogs are calculated based on the average human:dog ratio of the NPA communities and official human census data from Elcho Island in 2011 ( http://www . censusdata . abs . gov . au/census_services/getproduct/census/2011/quickstat/SSC30094 ) and similar household sizes as in the NPA are assumed ., The model developed is stochastic , spatially explicit , based on individual dog data and assumes a daily simulation time unit ., It starts with the introduction of a latently ( non-clinical ) infected dog and ends when no infected dog remains ., The exact location of each dog’s home is known and a closed dog population within the region is assumed , but dog movements between regional communities are simulated ., In the model , 429 and 410 dogs in 175 and 163 households are included in the NPA and on Elcho Island , respectively ( 137−451 and 68−142 households per km2 , respectively , Table 1 ) ., The two regions are simulated separately ., The average number of dogs per dog holding household ranges from 1 . 9–3 . 2 per community ., Parameter value definition is a critical component in modelling studies , driving the outcome of any simulation or mathematical model ., While some parameter values can be taken from the literature ( e . g . disease or vaccine related parameters ) , other parameter depend on the settings in the specific environment in which the model was developed or applied ., Seven out of 37 ( 19% ) parameter values of the model presented here are sourced from the literature ( those of rabies virus and vaccine related parameters ) and 23 ( 62% ) are based on assumptions ( S1 Table ) ., The latter can further be classified as experimental parameters ( parameters defining control strategy implementation as e . g . delay in starting control strategies or vaccination coverage , 16/37 43% ) and parameters for which value information are currently lacking ( e . g . bite probability given a contact , owner compliance to cease dog movements , 7/37 19% ) ., The remaining seven ( 19% ) parameter values were estimated based on our field collected data , including contact data within and between communities and mean distance between households used for dog confinement strategy ( S1 Table ) ., Data used to calculate the distance kernel function applied for contact rates between dogs of different households was derived from a large scale GPS study on 69 domestic dogs in all of the six communities ( S5 and S6 Tables ) 18 ., The number of contacts between each pair of dogs within the same community was extracted using the definition of contact being within 20 meters during the same minute ., As the model runs on a daily basis , the contact information was converted into a binary variable with two dogs having at least one ( 1 ) or no ( 0 ) contacts within 24 hours ., This binary outcome was analysed by a logistic regression model with the known distance between the two dogs’ homes as the explanatory variable ., The outcome variables estimated by the logistic regression ( intercept α , coefficient β and standard error of the coefficient βse ) were further used to build the distance kernel ( Eqs 1 and 2 ) ., Daily contact probability of two dogs living in the same household was estimated based on the same dataset plus similar data collected during the post-wet season ( monsoon ) in the same communities ., One of 31 ( 3% ) pairs of dog living in the same household was not observed to have at least one contact per 24 hours ., This within-household contact probability was implemented as a uniform distributed parameter with 97% as the mean ., Four parameters defining the dog movements between communities − both short term and permanent − were estimated from questionnaire data collected in the NPA ( S2 Table , approved by the Human Ethical Committee of the University of Sydney , # 2013/757 ) together with observations of short term movements by GPS and of permanent movements of dog owners from one NPA community to another during a year ., Twenty-nine dog owners were interviewed in September 2013 and one in September 2014 , including questions on frequency of dog movements to other NPA communities due to pig hunting or other trips ( e . g . visits or work ) ., A daily movement probability of 0 . 058 per dog was calculated from these reported data , while from the study we observed that only 8 of 81 ( 10% ) dogs were moved during an observation period of 6 . 2 days , resulting in daily movement probability of 0 . 016 per dog ., Combining these reported and observed data and giving twice the weight to observed data , 0 . 03 was defined as the beta-pert distribution mode for daily short term movement probability per dog; 2- and 0 . 5-fold values were used for the minimum and maximum limits of the distribution , respectively ., The duration of the short term movements were derived from the questionnaire in which all hunters reported trips of one to two days and observations from the GPS study in which all 8 dogs stayed less than one day in the community visited ., The frequency of permanent movements was estimated from questionnaire data and observations of permanent movements during September 2013 and September 2014 ., Dog owners reported that 18% ( 6/33 ) of the NPA dogs originated from a different community within the NPA , which resulted in an estimated probability of permanent movements of 1 . 64*10−4 per dog per day assuming an average dog life of three years ., In addition , owners of 6% ( 3/49 ) of the dogs were observed to have permanently moved between NPA communities during the year , resulting in a probability of daily movements of 1 . 64*10−4 per dog ., The sum ( 3 . 3*10−4 ) was selected as the mode of the beta-pert distributed daily probability of permanent movements per dog , with 0 . 5 and 2-fold values for minimum and maximum ., Finally , the destination community for both permanent and short term movements was observed to be more frequently a neighbouring community than any other; consequently a neighbouring community as the destination was assumed to be twice as likely as for any other community in the NPA ., The median distance between each household and its closest neighbour–used in the model to truncate the distance kernel for the dog confinement control strategy–were estimated for each community separately using the coordinates of all households per community ., Household coordinates were derived from Google Earth ( http://earth . google . com/ ) where placemarks were set on all private dwellings , identified with the help of community maps ., The distances between each household and its closest neighbour were calculated and the mean per community implemented in the model as a fixed value parameter ., In the first step of the sensitivity analysis ( SA ) , all model parameters used for six different control strategies were tested using the strategy’s default values:, a ) vaccination with 70% coverage either pre-emptively ( PV ) or reactive ( RV ) ;, b ) culling of dogs contacted by a rabid dog ( CC ) or reactive culling ( RC ) with culling levels of 80 and 50% , respectively;, c ) dog confinement plus movement bans between communities ( MB ) with 80% and 90% compliance , respectively; and, d ) a non-intervention strategy ( NI ) ., For all of these strategies , including NI , culling of dogs detected rabid ( DC ) was applied ., The number of index dogs was set to 1 and randomly selected in the region ., For each of the 12 combinations of the two regions ( NPA and Elcho Island ) and six control strategies , 1000 model repetitions were simulated ., For stochastic parameters , the mode or mean ( for beta-pert distributed and uniform distributed parameters , respectively ) was allowed to range between ±25% around the default value while the difference between the minimum and maximum values was kept fixed ( no variation of the distributions’ shape; S3 Table , S1 Fig ) ., Deterministic parameters were allowed to vary ±25% around the default value ., Variation of 25% has been chosen to allow enough variation for parameters with wide distributions ( e . g . the infectious period ) and avoid too large distinction between the lower and upper limit of narrow distributed parameters ( e . g . the rabies transmission probability given a bite ) ., The distance kernel was tested using three different shapes representing a minimal kernel and increased probabilities for short and long distance contacts , respectively ( S2 Fig ) ., The influence of the index community within the NPA region was investigated by defining one of the five communities hosting the index dog ., To explore the sensitivity of the model on the weighting matrix to choose the destination community for between-community movements , an alternative matrix was tested beside the default with equal chance for all communities to be selected as the destination ., The values of all parameters tested were randomly selected from the described ranges so that for each simulation , an individual set of parameter value combination was chosen ., For each of the 12 region-strategy combinations , linear multivariable regression analysis was modelled with the outbreak duration and–where applicable–outbreak size as the response variable and the parameter values as explanatory variables ., For the stochastic parameters the mode ( beta-pert distribution ) and mean ( uniform distribution ) values were modelled as explanatory variable values ., Correlations between the parameter values were explored using Kendall’s tau correlation , Chi-Square and Wilcoxon Rank Sum test for two continuous , two categorical and a continuous and categorical parameter , respectively ., Because the assumption of a normally distributed response variable was not always met ( S3 Fig ) , logistic regression following the same principle was modelled defining an outbreak with a duration or size above the median as 1 and as 0 otherwise ., Based on both the linear and logistic regression analyses , parameters were defined to be highly ( statistically significant p-values < 0 . 05 in ≥ ¾ of all tested regression models ) , low ( statistically significant p-values < 0 . 05 in < ¼ of all tested regression models ) or moderate ( otherwise ) sensitive to the outbreak duration and size ., Additionally , scatter plots of the outbreak duration and size over the range of each parameter value were visually analysed and correlations were calculated between outbreak duration and size and parameter values with continuous scale ., A correlation of >|0 . 1| was considered as a threshold to distinguish between sensitive and non-sensitive parameters ., Parameters found to be highly sensitive in either of the regression analyses during the first step of the SA were further explored in a second step to identify the influence of their mode or mean ( default , large , small ) and shape ( default , narrow , wide ) on the model’s outcome ., For the beta-pert and uniform distributed parameters nine combinations per parameter with the three values of mode or mean ( default and ±10% ) , and three values of difference to the minimum and maximum ( default and ±10% ) of the distribution were defined ( S4A–S4C Fig ) ., For the vaccine efficacy parameter , the variation of 10% had to be reduced to 4% , which was the highest variability still ensuring a maximal value < 1 , a requirement for probabilities ( S4D Fig ) ., For each parameter , 1000 repetitions were simulated for the same 12 region-strategy combinations described in step 1 of the SA ( in case of the vaccine efficacy only the scenarios for RV and PV ) , where one of the nine options was randomly chosen while all other parameters in the model were kept at their default value ., The distance kernel , defined by the three variables α , β and βse , was explored by varying the three variables around a default value ±50% , resulting in 27 combinations ( S5 Fig ) ., Six thousand repetitions were simulated for all 12 region-strategy combinations with a randomly selected distance kernel out of the 27 options while all other model parameters were kept at their default values ., The outcome was analysed visually comparing boxplots of the outbreak duration and number of rabid dogs ., A critical question for stochastic models is always , how many repetitions are required to sufficiently reflect the variability of the model ?, The coefficient of variation ( CV = standard deviation/mean ) of model outputs’ mean has been proposed as a measurement to determine the critical number of repetitions required 30 , 31 ., The CV of the estimated mean of outcome of interest ( e . g . outbreak size or duration ) over n model simulations is expected to approach 0 for infinite sample sizes n and a threshold of the CV of 15% has proposed to predict outputs with acceptable precision 30 ., We used the same approach , but reduced the CV threshold value to 3% ., To demonstrate the model’s functionality for the different control strategies , 1000 outbreaks were simulated for the default strategies:, 1 . reactive vaccination with 70% coverage at the household level ( RV ) ;, 2 . reactive culling with 50% of the dogs culled in affected communities ( RC ) ; and, 3 . dog confinement between and within communities with 90 and 80% compliance , respectively ( MB ) ., Culling of dogs detected rabid ( DC ) was applied for all of these strategies ., The model was simulated in both regions , NPA and Elcho Island , separately ., As outcome measures , the epidemic duration and size , i . e . the number of rabid dog and the number of dead dogs ( including rabid and culled dogs ) were calculated and visually compared between the different scenarios ., Two measures of outbreak size are the cumulative number of rabid dogs and cumulative number of dead dogs ( due to rabies plus culled ) ., The outbreak duration is defined as the number of days from the introduction of the latently infected index dog until the death of the last infectious dog ., The simulation model resulted in plausible results comparing outputs for the three different default control strategies ( Fig 4 ) ., Rabies spreads through the communities in a wave pattern and , depending on the control strategy , can kill the entire dog population ( S6 Fig illustrates an example epidemic curve ) ., The reactive culling ( RC ) strategy reduces the number of rabid dogs; however the number of dead dogs is only slightly less than the total dog population ., For the reactive vaccination ( RV ) strategy the number of rabid dogs ( equal to the number of dead dogs ) showed higher variability among the 1000 model simulations compared to RC , but in both regions , the median of RV was lower than for RC ., Obviously , vaccination saves the dogs from death in contrast to culling strategies ., The movement ban ( MB ) strategy showed a slight decrease of the outbreak size in the region of Elcho Island whereas no effect was observed in the NPA ., However , it was found to be the strategy with the longest durations of outbreaks , demonstrating that movement bans ( if not 100% compliant ) only slow the speed of spread rather than reducing its size ., The reduction in the number of movements between communities for MB was obvious , decreasing from a median of 52 ( RV ) and 46 ( RC ) to 19 ( S7 Fig ) ., Overall , outbreak duration ranged from 1−20 months ( median 6 . 7 months ) and was more homogenous between interventions than the outbreak size ( Fig 4 ) ., Outbreaks lasting for one month did not spread beyond the index dog ., For the RV strategy , the vaccination coverage was set at 70% of the households , producing dog level vaccination coverage of 59−75% and 56−76% for the NPA and Elcho Island region , respectively ., The control strategy with the largest number of simulations required ( n = 490 ) to capture the variability in the model’s output with the defined CV threshold of 3% was found to be the MB strategy in the NPA ( S8 Fig ) ., The number of secondary cases was reported for every rabid dog over the duration of the outbreak ., From these records , the basic reproductive ratio R0 was calculated and defined as the mean number of secondary cases for dogs becoming infectious within the first phase–i . e . up to its peak–of an epidemic ., The peak of the epidemic is defined as the day with the highest number of newly infectious animals over the entire outbreak ., R0 ranged from 0−6 . 1 ( median 1 . 8 ) for RV , 0−6 . 1 ( median 1 . 7 ) for RC and 0−5 . 7 ( median 1 . 7 ) for MB ( S9 Fig ) with an overall median of 1 . 7 ( Fig 5A ) ., The epidemic peak was reached on average after 93 days ( Fig 5B ) with a mean of 17 newly infected dogs ( Fig 5C ) ., The number of secondary cases derived from each index dog was highly variable and ranged from 0 to 79 ( median of 25 ) for NPA and 4 to 106 ( 40 ) for Elcho Island ., Over the duration of the outbreak , the effective reproductive ratio Rt and the number of dogs in the susceptible population decreased in a wave pattern ( S10 Fig ) ., The value of 1 for mean Rt is reached during the second or third wave ., This reflects that Rt depends on the dogs remaining in the population and finally the outbreak dies out because there are no susceptible dogs left that are close enough to the infectious dogs ., The simulation model outputs were highly sensitive to seven parameters: incubation period ( G1 in S4 Table ) , transmission probability given a bite ( G8 ) , distance kernel ( G5 ) , bite probability given a contact between dogs of different households ( G7 ) , vaccine efficacy ( V5 ) , index community ( G12 ) and delay in starting the control strategy of movement restrictions between communities ( B2 ) ., The same sensitive parameters , in addition to the detection delay of the first clinical case , were also identified via correlation tests , with the exception of B2 ., These outcomes were also confirmed by scatterplots , which express particularly dependencies between the outbreak duration and the incubation period , distance kernel and index community ( S11 Fig ) ., Significant correlations between parameters included in the regression analyses were only observed between categorical and continuous parameters where 2 . 1−12 . 1% ( mean 5 . 4% ) of all parameter combinations resulted in Wilcoxon Rank Sum test p-values <0 . 05 ., The influence of this subset of parameters was further explored in step 2 of the SA , with the exception of the index community and the delay in commencing movement restrictions because these two parameters directly relate to incursion and intervention scenarios ., For all parameters , except the distance kernel , it was found that both the mode and mean ( for beta-pert and uniform distributions , respectively ) and the shapes influence outbreak duration and number of rabid dogs ( S12 Fig ) ., The mean and mode were found to have a greater impact , particularly for the incubation period and rabies transmission probability ., For the distance kernel , the regression coefficient β was most influential on both the number of rabid dogs and the outbreak duration , followed by βse ( standard error of β ) , particularly for the Elcho Island region ( S13 Fig ) ., Outputs were less sensitive to the intercept α ., The model described herein provides insights into short-term rabies epidemics occurring within a small spatial extent in previously rabies-free regions ., This is of crucial value for contingency planning in areas where rabies is exotic and the model fills a gap in the published literature on rabies models ., The example of Bali , Indonesia demonstrates the impact of a rabies incursion on an under-prepared region 12 ., Late detection of the disease , lack of surveillance strategies and an unsuccessful initial response ( focused on dog culling ) resulted in island-wide disease spread and high impacts on both dog and human populations 7 , 12 , 32 ., Another example in Indonesia–comparable to communities in northern Australia with a high density of free-roaming dogs and limited veterinary health services − is the island of Flores in East Nusa Tengarra province 14 , 33 , 34 ., There , one or more latently infected dogs were introduced , developed clinical rabies and transmitted the disease to local dogs ., The disease consequently spread throughout island with a considerable impact on dogs and humans ., This is another example of a rabies invasion in a new area with very severe impact ., Another novel aspect of the current model is the inclusion of individual susceptible and rabid dogs modelled within a continuous spatial dimension , an approach previously used to simulate highly infectious diseases of livestock ( e . g . foot-and-mouth-disease 35 , 36 ) but not rabies ., To date , published dog rabies models have been based on mathematical differential equations 5 , 24 , 25 , 27 or spatially explicit models simulating the spread of rabies within grids 7 ., Our approach has several advantages , including stochasticity to capture epidemic variability , incorporation of detailed population structure to better represent real target regions of interest , and detailed spatio-temporal model outputs ., By simulating three default control measures–vaccination , culling and dog confinement–our model produced plausible results , suggesting it adequately captures how an epidemic of an infectious disease with a relatively long incubation period would develop in a previously uninfected population ., The disease spread temporally in wave-like patterns , peaking on average about three months after an incursion ., The average R0 that was estimated from the model outputs was 1 . 7 , consistent with previous estimates of rabies spread in endemic countries 16 , 37 and estimated from the Bali outbreak 7 ., For the calculation of R0 we considered rabid dogs up to the peak of the epidemic , while peak has been defined as the day during the epidemic with the highest number of newly rabies infectious animals ., It has been demonstrated that both , clustering within the susceptible population and high number of repetitive contacts among individuals in the population–thus a non-random mixing situation–affects the dynamics of disease spread via depletion of susceptible population within a cluster and therefore also affects R0 38 ., A re-evaluation of R0 of a H1N1 infection revealed that it was overestimated during the early stage of the outbreak when only cases within a cluster has been considered rather than the population-wide epidemic 39 ., In our model , random mixing of infectious and susceptible animals has to be rejected as rabies transmission depends on the distance between infectious and susceptible animals ., However , neither fixed repetitive contacts ( as in network based models ) nor significant clusters within communities are included in the model ., The only functionally relevant cluster structure is present in the region of the NPA with the five communities building distinct clusters ., We respected the cluster structure in the R0 calculation by not only considering cases to the peak within the first cluster , i . e . the community of the index dog , but all cases occurring before to the epidemic peak within the total population at risk in the respective region ., As illustrative examples we simulated the most often discussed and applied control strategies for dog rabies , namely vaccination and culling of dogs as a reactive action , as well as dog movement restrictions ., According to the model , the only beneficial measure ( based on outbreak size ) is vaccination ., This is consistent with a range of studies in regions where a rabies incursion has been observed: culling as a single control measure was unsuccessful 7 , 12 , 14 , 33 , whereas vaccination was demonstrated to be a successful strategy to control recent rabies invasions 7 , 10 , 32 , 40 ., However , success of vaccination campaigns obviously depends on the vaccination coverage , as the example of a unsuccessful rabies control via low level vaccination coverage demonstrated in Flores 34 ., Also , culling can lead to an eradication of timely detected outbreaks , as for example in region in Bhutan 9 , however might be impractical because of non-acceptance , depending on culture and religion of the community ., In Australian Indigenous communities , culling of dogs is unlikely to be culturally acceptable ., According to our model , movement bans as a single strategy does not seem to be sufficient to reduce rabies spread from one community to another nor within a community–at least for the simulated dog owner compliance ( 80–90% ) that was simulated here ., Movement bans would culturally also be difficult to implement as travel with dogs between communities and regions is common ., These results are similar for both regions , the NPA and Elcho Island ., Within the NPA , rabies epidemics were able to sustain after incursion in each the five communities , as it does for the one community on Elcho Island , identifying the here considered regions and communities equally susceptible ., Targeting surveillance should therefore be based on information revealed by risk assessment pathways exploring high risk regions for a rabies incursion ., Further detailed simulations exploring combinations of response measures and their threshold values for effectiveness–including the effect of surveillance intensity 40–is warranted as future research ., The non-intervention strategy was implemented in the model and applied during the sensitivity analysis as a “baseline” to quantify the benefit of other control strategies , although it will most certainly never be observed in the field ., Keeping in mind the assumption of a closed dog population with no influx of new dogs ( neither birth nor immigration ) , the high densities of dogs that roam freely in the modelled community and the assumption of an infectious period of up to 12 days combined with a fully naïve population , it is expected that the epidemic will eventually kill the modelled dog population , in the case of no intervention applied ., Model outputs were sensitive to the assumed distance kernel for rabies transmission between dogs from different households ., This highlights that the distance kernels should be empirically developed for the particular regions in which the model is to be applied ., In our case , the kernel was estimated from roaming dog data collected within the actual study regions 18 ., Model outputs ( size and duration of the epidemics ) were also sensitive to the assumed rabies incubation period and probability of rabies transmission given a bite ., These parameters were derived from previously published field observations in Africa ( S1 Table ) , and are likely applicable to many situations , as is the vaccine efficacy parameter ., For the probability of bite given a contact between dogs , no specific published data could be found ., We assumed this parameter to be >50% due to the aggressiveness that rabies can cause 41 , but we also included a large range of uncertainty ( 60–80% ) ., Critical parameter values ( as for contact rates and the population structure ) in this model are informed by data collected in the field ., This guarantees the fit of the model to the intended environment of application , although limitations still do occur ., Data informing the distance kernel were based on the roaming behaviour of healthy dogs 18 ., A rabid dog might change its normal roaming behaviour , as for example reported for rabid racoons in New Jersey ( USA ) which moved over significantly larger distances than healthy racoons 42 ., Apparently the effect of rabies on the roaming behaviour of domestic dogs has not been reported , but considering the observed changes in racoon behaviour , our model might underestimate the spread of rabies ., When comparing contact dista

Introduction, Materials and Methods, Results, Discussion

Domestic dog rabies is an endemic disease in large parts of the developing world and also epidemic in previously free regions ., For example , it continues to spread in eastern Indonesia and currently threatens adjacent rabies-free regions with high densities of free-roaming dogs , including remote northern Australia ., Mathematical and simulation disease models are useful tools to provide insights on the most effective control strategies and to inform policy decisions ., Existing rabies models typically focus on long-term control programs in endemic countries ., However , simulation models describing the dog rabies incursion scenario in regions where rabies is still exotic are lacking ., We here describe such a stochastic , spatially explicit rabies simulation model that is based on individual dog information collected in two remote regions in northern Australia ., Illustrative simulations produced plausible results with epidemic characteristics expected for rabies outbreaks in disease free regions ( mean R0 1 . 7 , epidemic peak 97 days post-incursion , vaccination as the most effective response strategy ) ., Systematic sensitivity analysis identified that model outcomes were most sensitive to seven of the 30 model parameters tested ., This model is suitable for exploring rabies spread and control before an incursion in populations of largely free-roaming dogs that live close together with their owners ., It can be used for ad-hoc contingency or response planning prior to and shortly after incursion of dog rabies in previously free regions ., One challenge that remains is model parameterisation , particularly how dogs’ roaming and contacts and biting behaviours change following a rabies incursion in a previously rabies free population .

Rabies in domestic dog populations still causes >50 , 000 human deaths worldwide each year ., While its eradication by vaccination of the reservoir population ( dogs and wildlife ) was successful in many parts of the world , it is still present in the developing world and continues to spread to new regions ., Theoretical rabies models supporting control plans do exist for rabies endemic regions; however these models usually provide information for long-term programs ., Here , we describe a novel rabies simulation model for application in rabies-free regions experiencing an incursion ., The model simulates a rabies outbreak in the free-ranging dog population in remote indigenous communities in northern Australia ., Vaccination , dog density reduction and dog confinement are implemented as control strategies ., Model outputs suggest that the outbreak lasts for an average of 7 months and typically spreads through all communities of the region ., Dog vaccination was found to be the most effective response strategy ., The model produces plausible results and can be used to provide information for ad-hoc response planning before and shortly after rabies incursion .

journal.pgen.1007428

Coordinated regulation of core and accessory genes in the multipartite genome of Sinorhizobium fredii

Prokaryotes play important roles in recycling nutrients and forming pathogenic or mutualistic associations with eukaryotes ., It has been established that many ecologically important processes are differentially mediated by prokaryotes at the strain level 1 ., This is partially explained by the fact that even closely related strains of bacteria and archaea can have great differences in their genomes due to a high rate of turnover in gene content , so that there are core genes shared by all members of a taxonomic group and accessory genes present in only a subset of the members 2 , 3 ., However , it is still puzzling why and how prokaryotes maintain such a high degree of genome content variability 4 ., It is widely accepted that certain accessory genes can benefit their host by conferring the ability to occupy new niches , despite the existence of putative junk genes in the pangenome 4 , 5 ., However , it is largely unexplored to what extent these accessory functions are linked with the core regulatory network during the development of adaptations to new ecological niches ., Soil bacteria able to form nitrogen-fixing nodules on legumes , collectively called rhizobia , have global impacts on sustainable agriculture and the nitrogen cycle ., These facultative microsymbionts need a cluster of key symbiosis genes called nod/nif/fix , which are located on a horizontally transferable plasmid or a genomic island , to establish a mutualistic interaction with legume plants 6–10 ., The ability to form nitrogen-fixing nodules on legumes has been reported for hundreds of species in alpha- and beta-proteobacteria 11 ., Among the 122 complete genome sequences from twelve genera of rhizobia available in the GenBank database ( on March , 30th 2018 ) , 107 genomes from eleven genera have two or more DNA molecules , a genome architecture described as a multipartite genome ., This multipartite organization is found in approximately 11% of 1 , 708 bacterial genomes analyzed in a recent study 12 ., Each DNA molecule with a separate origin of replication in bacterial genomes is referred to as a replicon ., The largest replicon , with most of the core genes , is known as a chromosome , while megaplasmids ( above 350 kb in size ) and plasmids refer to replicons lacking core genes and are characterized with significantly biased signatures such as GC content and dinucleotide composition compared to the chromosome 12 , 13 ., The term “chromid” was recently introduced to refer to a replicon with plasmid-type maintenance and replication systems , but carrying some core genes and having sequence signatures more similar to chromosomes than plasmids and megaplasmids 12 , 13 ., Accumulating evidence has suggested distinct roles of different replicons in rhizobial adaptations to either saprophytic or symbiotic conditions 14–17 , though the coordinated regulation of core and accessory functions in these multipartite genomes is largely unexplored ., A multipartite genome , composed of at least a chromosome , a chromid , and a megaplasmid ( the symbiosis plasmid ) , is present in most sequenced genomes within the Sinorhizobium genus , which includes microsymbionts associated with the important legume crops alfalfa and soybean 18–20 ., The chromid genes in Sinorhizobium associated with the same legume host show a higher differentiation level compared to the other two replicons 21 , 22 ., In contrast to the symbiosis plasmid , which shows evidence of horizontal gene transfer , the chromid core genes have a phylogeny generally congruent with that of chromosomal core genes 21 ., An engineered chromosome containing essential core genes transferred from the chromid is sufficient for growth of a model microorganism Sinorhizobium meliloti in a sterile bulk soil environment 16 ., Metabolic modeling suggests that the chromosome of S . meliloti also contributes to fitness in rhizosphere , and the chromid shows a greater fitness contribution in the rhizosphere than in bulk soil 15 , 22 ., By contrast , transcriptomics studies of free-living and symbiotic Sinorhizobium strains have demonstrated a specific up-regulation of many genes on the symbiosis plasmid within legume nodules , where core functions are generally down-regulated consistent with the growth arrest status of nitrogen-fixing rhizobia 23–25 ., However , scattered genetic evidence suggests that genes located on the chromosome and the chromid can also contribute to the integration and optimization of symbiotic functions in diverse rhizobia including Sinorhizobium 25–30 ., It has been proposed that the rhizobium-legume symbiosis requires optimization through a long-term evolutionary process involving integration of lineage-specific accessory genes ( those genes only present in a limited subset of related strains , species or genera ) with the regulatory network of core genomes 26 , 31 , but there is little direct evidence as yet 30 , 32 ., There is a need for omics-based comparative analyses of the variation in the contents , regulation and integration of core and accessory genes under different conditions ., In this study , we investigate how core and accessory genes are organized and integrated in the multipartite genome of the soybean microsymbiont , Sinorhizobium fredii ., To this end , complete genome sequences were obtained for S . fredii CCBAU45436 and CCBAU25509 , which have an overlapping host range ., The genes of these two genomes were divided into four hierarchical core/accessory subsets based on comparative genomics analyses with ten published genomes of Sinorhizobium spp ., Then the global transcriptomic profiles of the two test strains were determined at exponential and stationary phases in free-living cultures , and at the symbiotic stage within the nodules of cultivated and wild soybeans ., By analyzing this transcriptomic and genomic information , we obtained a global integration pattern of core and accessory genes under different conditions , and identified novel genes involved in symbiotic adaptations ., These findings will be discussed in the more general context of the organization and evolution of the prokaryotic pangenome in relation to ecological adaptations ., S . fredii CCBAU45436 and CCBAU25509 ( Fig 1A ) , which are effective microsymbionts of local soybean cultivars grown in northern China 33 , induced normal nitrogen-fixing nodules and non-fixing nodule-like structures , respectively , on the roots of soybean accession C08 ( Fig 1B ) , which is a close relative of the sequenced soybean cultivar Williams 82 34 , 35 ., They both established nitrogen-fixing nodules on the wild soybean accession W05 ( Fig 1B ) , which has recently been sequenced 36 ., Complete genome sequences for CCBAU45436 and CCBAU25509 were first obtained by assembling Illumina data generated previously 26 ., In this study , full assembly of these genomes were achieved by new PacBio and Ion Torrent sequencing data ( S1 Table ) , and Sanger sequencing of PCR products was used to fill assembly gaps when necessary ., The general features of CCBAU45436 and CCBAU25509 genomes are summarized in S2 Table ., CCBAU25509 has a typical tripartite genome , consisting of a chromosome ( cSF25509; 4 . 20 Mb ) , a chromid ( pSF25509b; 2 . 21 Mb ) and a symbiosis plasmid ( pSF25509a; 0 . 40 Mb ) ., In the CCBAU45436 genome , two additional smaller plasmids , pSF45436d ( 0 . 20 Mb ) and pSF45436e ( 0 . 17 Mb ) were also found besides the chromosome ( cSF45436; 4 . 16 Mb ) , the chromid ( pSF45436b; 1 . 96 Mb ) and the symbiosis plasmid ( pSF45436a; 0 . 42 Mb ) ., By including ten published genomes of Sinorhizobium ( Fig 1A and S1 Fig ) , the gene homologs shared by CCBAU45436 and CCBAU25509 were each divided into three hierarchical core subsets ( Fig 2A ) : subset I , gene homologs present in all Sinorhizobium strains; subset II , those present in all S . fredii strains excluding subset I; subset III , those shared by CCBUA45436 and CCBAU25509 but not present in all S . fredii strains , i . e . excluding subsets I and II ., The remaining accessory genes of CCBAU45436 or CCBAU25509 were defined as subset IV ., As expected , genes within each of these hierarchical core/accessory subsets were unevenly distributed on different replicons in the two strains ( Fig 2B and S3 Table; Pearson’s chi-square test , P < 0 . 001 ) ., Around 80% of the subset I genes were concentrated on chromosomes ., Genes within subsets II and IV were overrepresented on chromids ., The symbiosis plasmids were characterized by their enrichment with the subset III genes ( 58%-59% genes on the symbiosis plasmid ) and to a lesser extent with the subset II genes ( 23%-25% ) ., Two replicons ( pSF45436d and pSF45436e ) specific to CCBAU45436 were extremely enriched with the subset IV genes ( 69 . 3% and 84 . 6% ) ., To investigate how core and accessory genes with biased replicon distributions were integrated during adaptations , we used RNA-seq to obtain transcriptomes of the two test strains under three conditions: ( 1 ) free-living culture in the mid-log phase ( non-stress ) , ( 2 ) free-living culture in the nutrient-starved stationary phase ( abiotic stress ) , and ( 3 ) symbiotic bacteroids within the nodules of cultivated and/or wild soybeans ( biotic stress ) ( S4 Table ) ., For convenience , genes were classified into four expression levels ( Level_1-Level_4 ) using arbitrary cut-offs at the first , second and third quartiles of the expression profiles based on the RPKM ( reads per kilobase per million mapped reads ) value of each gene under test condition ., The distribution of these genes across different transcriptional levels under test conditions was analyzed for each replicon ( Fig 3 and S2 Fig ) ., On the chromosomes and chromids , the proportion of genes expressed at levels higher than the first quartile ( above Level_1 ) decreased along with reduced gene conservation levels ( from subset I to subset IV ) under all test conditions ( Fig 3 and S2 Fig ) ., This phenomenon can also be found in the transcriptional profiles of symbiosis plasmid genes under symbiotic conditions but not in free-living cultures , particularly for highly expressed genes ( Level_4 ) ., There was generally an increased number of highly expressed genes ( Level_4 ) in subsets I-IV of the symbiosis plasmid in legume nodules compared to free-living cultures ., By contrast , the proportion of high-expressed ( Level_4 ) subset I genes on the chromosome was notably reduced under symbiotic conditions and in the stationary phase compared to that of mid-log phase ., The chromid genes did not exhibit drastic changes in the proportions of different transcriptional levels under test conditions , except a notable increase of highly expressed genes ( Level_4 ) at the stationary phase compared to the mid-log phase ., Although transcriptional levels showed a strong dependence on both the replicon location and the conservation levels , log-linear analysis indicated that replicon and core/accessory status were independently related to gene expression levels ( all P < 0 . 001 ) ., To further investigate how genes within different hierarchical core/accessory subsets would respond to different growth conditions , dendrograms based on gene expression distance ( GE distance , defined in Materials and Methods ) were constructed ., When we examined the expression profiles of shared genes within each of subset I , subset II and subset III , the profiles of the two strains were closely matched with respect to growth phases and symbiotic conditions ( Fig 4A–4C ) , while the expression profiles of the strain-specific genes ( subset IV ) were , inevitably , clustered by strain ( Fig 4D ) ., The overall picture is that , for all gene subsets , expression in nodules is more similar to expression in exponential phase than in stationary phase and , for all subsets that they share , the difference between the two strains is less than the effect of growth conditions ., Although similar condition-dependent clustering patterns were observed for subsets I-III ( Fig 4A–4C ) , the average gene expression level under each condition decreased with reduced gene conservation level ( from subset I to subset IV ) ( Fig 4E ) ., Moreover , the higher expression plasticity ( gene expression variance among conditions ) was observed for the more conserved subsets ( Fig 4F ) , and subset IV showed the least variance in expression plasticity ., As expected , further analyses of the differentially expressed genes ( DEGs , Log2R > 1 . 732 , FDR < 0 . 001 ) based on pairwise comparisons showed that DEGs were significantly enriched in subset I and/or subset II , while depleted in subset III and/or subset IV ( S5 Table , all P < 0 . 05 ) ., It is noteworthy that up-regulated and down-regulated genes had distinct enrichment patterns across the core/accessory subsets ( S3 Fig and S5 Table ) ., Genes down-regulated at the stationary phase or in the symbiotic nodules compared to the mid-log phase were enriched in subset I ( the genus core genes ) , while the up-regulated ones were enriched in subsets II and III ( the genus accessory genes shared by the two test strains ) ( Pearson’s chi-square test , all P < 0 . 05 ) ., These results provided another line of strong evidence for differential roles of core genes with different conservation levels during environmental adaptation ., To further dissect this phenomenon , we then examined the condition-dependent co-expressed genes ., Genes could be divided into four groups based on k-means clustering of their transcriptional profiles ( Gr . 1-4; Fig 5A ) ., Gr . 4 consisted of genes constitutively expressed or non-expressed under all conditions , while Gr . 1 , Gr . 2 and Gr . 3 consisted of those up-regulated at mid-log phase , stationary phase and symbiotic stage in nodules respectively ( Fig 5A ) ., Genes within different condition-dependent groups were unevenly distributed in the hierarchical core/accessory subsets ( Fig 5B ) ., Gr . 4 was overrepresented within subsets III and IV ( Pearson’s chi-square test , all P < 0 . 001 ) ., Gr . 1 genes were enriched in subset I , Gr . 3 genes in subsets II-IV , while Gr . 2 genes in none of them ( Fig 5B ) ., Among different replicons , the chromosomes and symbiosis plasmids were enriched with Gr . 1 genes and Gr . 3 genes , respectively , while both Gr . 2 and Gr . 3 genes were overrepresented on the chromids ( Fig 5C ) , indicating a replicon-dependent gene regulation under test conditions ., Functional annotations of genes within Gr . 1-4 were further analyzed regarding COG categories ., Gr ., 1 , Gr . 2 and Gr . 3 were respectively enriched in the COG category J ( translation , ribosomal structure and biogenesis ) , S/W ( S: function unknown; W: extracellular structures ) and P/X ( P: inorganic ion transport and metabolism; X: mobilome: prophages , transposons ) ( Fig 5D ) ., Among the 4 , 931 single-copy orthologous genes shared by CCBAU45436 and CCBAU25509 , the DEGs between these two strains ( 151 at the mid-log phase , 292 at the stationary phase , and 197 within the nodules of G . soja W05; Log2R > 1 . 732 , FDR < 0 . 001 ) were significantly enriched in the hierarchical core/accessory subset III ( Fig 6A and S6 Table ) ., This provides further evidence that the differential regulation of intraspecies accessory genes may contribute to bacterial diversification ., Consistent with results described above that genes within different hierarchical core/accessory subsets exhibited a biased replicon distribution pattern ( Fig 2 ) , the strain-dependent DEGs were significantly enriched on the chromids , and the non-symbiosis plasmid pSF45436d ( Fig 6B & S6 Table ) ., The biased distribution of condition-dependent co-expressed genes and strain-dependent DEGs with respect to core/accessory genomes and replicons raised the question of whether accessory genes have been integrated in a replicon-dependent way among S . fredii strains ., Therefore , we investigated the gene connectivity ( co-expression of gene pairs ) within or between replicons in gene co-expression networks constructed from the transcriptional profiles of S . fredii CCBAU45436 and CCBAU25509 ( described in Materials and Methods ) ., When the genes from all replicons were pooled together , a significant decrease in gene connectivity was revealed in parallel with the decreasing conservation level of the genes ( from subset I to subset III ) ( Fig 7A and S4 Fig ) ., This correlation was observed on chromosomes and symbiosis plasmids , but not on chromids and other plasmids ( pSF45436d/e ) ( Fig 7A and S4 Fig ) ., A larger fraction ( 68% ) of chromid genes were linked to the chromosome than were the symbiosis plasmid genes ( 36% ) ( Fig 7B and S4 Fig ) , indicating that chromids are more closely associated with chromosomes than symbiosis plasmids in terms of transcriptional regulation ., On the other hand , the symbiosis plasmid possessed a larger fraction ( 46% ) of within-replicon gene connectivity than the chromid ( 23% ) ( Fig 7B and S4 Fig ) , and most of the within-replicon gene connectivity on the symbiosis plasmid was linked to genes required to support symbiotic nitrogen fixation , such as nif and fix genes ( S5 Fig ) ., Nevertheless , more than half ( 54% ) of the gene connectivity associated with the symbiosis plasmid was between-replicon ( Fig 7B and S4 Fig ) ., Both the typical symbiosis genes with high within-replicon gene connectivity and certain genes with low within-replicon gene connectivity can show a high level of between-replicon gene connectivity ( S5 Fig ) ., These genes with between-replicon connectivity could be interesting candidates for further functional analyses of the optimization of symbiosis ., CCBAU45436 can form effective nodules on both the wild soybean , G . soja W05 , and the cultivated soybean , G . max C08 ., This allowed us to investigate the potentially adaptive transcriptional profiles of rhizobia in the nodules of a cultivated soybean compared to those in wild soybean nodules ., There were 42 and 77 genes down-regulated and up-regulated , respectively , in CCBAU45436 bacteroids within C08 nodules compared to those in W05 nodules ( Log2R > 1 , FDR < 0 . 001; S1 Dataset ) ., These DEGs were slightly enriched in the subset II ( harboring 24 . 4% of DEGs and 14 . 9% of the total number of genes; Pearson’s chi-square test , P < 0 . 05 ) but were not enriched in any one of the replicons ., To uncover potential candidate genes essential for host adaptation , we constructed mutants for ten representative genes ( S6 Fig and S7 Table ) that were up-regulated in C08 nodules compared to W05 nodules ., These representative genes were among those with the highest log2R values and covered the four conservation levels ( subsets I-IV; S1 Dataset ) ., Eight of the mutants exhibited indistinguishable symbiotic phenotypes on both W05 and C08 compared to the wild type ( S8 Table ) , but ΔznuA and mdtA::pVO had significant effects ( Table 1 ) ., The Sinorhizobium core genes znuA/B/C ( in subset I ) encode the conserved zinc transporter components , and the in-frame deletion mutant of znuA ( ΔznuA ) formed a reduced number of nodules ( 34 . 9% - 48 . 4% , respectively , compared to wild type , P < 0 . 01 ) on both W05 and C08 , but with higher fresh weight per nodule ( 167% - 247% of wild type , respectively , P < 0 . 05 ) ( Table 1 and S7 Fig ) ., C08 plants nodulated by ΔznuA had lower leaf chlorophyll content , 80 . 7% of that from C8 soybean plants inoculated with the wild-type strain ( P < 0 . 0001 ) , which was not significantly different from the uninoculated control ( Table 1 and S7 Fig ) ., However , the same ΔznuA mutant was still fully effective in supporting the growth of W05 ( Table 1 and S7 Fig ) ., The mutant for mdtA , which is found together with mdtB/C in an operon that encodes a putative multi-drug efflux system , was ineffective on both W05 and C08 as indicated by the significantly reduced chlorophyll content of these host leaves compared to those from plants inoculated with the wild-type strain ( Table 1 and S7 Fig ) ., Notably , the mdtA mutant induced many root bumps on C08 but not on W05 ( S7 Fig ) and the mdt operon is present in CCBAU45436 but not in CCBAU25509 ( i . e . it is in subset IV ) ., Both znu and mdt operons are located on the chromosome ., The transferable symbiosis island or symbiosis plasmid is the major reason for an ever increasing collection of rhizobial germplasm associated with diverse legumes 8–11 , 37 ., The increased contribution of genes on symbiosis plasmids and dramatically reduced contribution of chromosomal genes to the transcriptomes of nitrogen-fixing bacteroids within nodules were observed for both of the S . fredii strains in this study ( Fig 3 and S2 Fig ) and in previous transcriptomic studies of S . meliloti 1021 and S . fredii NGR234 24 , 38 , 39 ., Notably , genes on the symbiosis plasmids of CCBAU45436 and CCBAU25509 that were highly expressed ( Level_4 ) in nodules included genes belonging to pangenome subsets I-IV ( Fig 3 and S2 Fig ) ., These findings support a model that the symbiosis plasmid harbors genes of different conservation levels that contribute to symbiotic adaptation ., However , a higher level of between-replicon connectivity than within-replicon connectivity was observed for symbiosis plasmids in the co-expression networks ( Fig 7B and S4 Fig ) ., Key genes involved in nitrogen fixation ( nif/fix ) have a considerable degree of both within- and between-replicon gene connectivity ( S5 Fig ) ., Genes involved in inorganic ion transport and metabolism ( COG category P ) , and those belonging to the COG category X ( mobilome: prophages , transposons ) were found to be up-regulated within nodules ( Fig 5D ) ., Indeed , some transporters provide elements ( such as iron , molybdenum , and sulfur ) essential for nitrogenase activity 25 , 40 , 41 ., The high-affinity transporters for phosphate and zinc were required by S . fredii to effectively fix nitrogen in soybean nodules 25 ., Genes encoding these transporters , and many of those directly involved in nitrogen-fixation , such as nifH/D/K , belong to COG category P . The activation of mobile elements under symbiotic conditions has been widely observed in many transcriptome analyses 24 , 28 , 42 , and was recently found to have an important role in the adaptive evolution of rhizobial symbiotic compatibility 17 ., The conserved znu and accessory mdt of CCBAU45436 contributed to symbiotic adaptation to G . max C08 , but to a lesser extent to the symbiosis with G . soja W05 ( Table 1 and S7 Fig ) ., The zinc transporter encoded by znu can import zinc under low-zinc conditions 43 , 44 ., This indicates possibly different nodule environments of W05 and C08 with respect to the zinc ion concentration ., Although the mdtA mutant did not induce pseudonodules ( root bumps ) on W05 ( S7 Fig ) , mdt contributed to the symbiotic efficiency of CCBAU45436 on W05 ( Table 1 ) ., A reasonable explanation might be that genes other than mdt have been recruited by CCBAU25509 to optimize its symbiosis with W05 ., This view is supported by our recent finding that strain-specific accessory genes can be recruited by different Sinorhizobium strains in optimization of symbiosis with the same legume host 27 ., Since both znu and mdt are located on the chromosome , this suggests that chromosomal core and accessory genes can be recruited by S . fredii to optimize the symbiotic functions in a host-dependent manner ., These results increase our understanding of the integration of key symbiosis genes with the diverse genomic backgrounds of rhizobia as characterized by their large phylogenetic diversity 31 , 32 ., Co-expression analysis of the two S . fredii strains under different conditions unveiled a higher level of gene connectivity between chromids and chromosomes than that between symbiosis plasmids and chromosomes ( Fig 7B and S4 Fig ) ., This is in line with the computational prediction of the regulatory network in S . meliloti , i . e . the preference for cross-regulation between the chromosome and chromid , as opposed to the symbiosis plasmid 45 ., A recent study of the S . meliloti metabolome revealed that removal of the chromid has a larger effect on the metabolome than loss of the symbiosis plasmid 46 ., These findings support the hypothesis of the ancient integration of chromid functions with those on the chromosome 13 ., Indeed , some essential genes can be found on the chromid , but not on the symbiosis plasmid , of Sinorhizobium strains 16 , 47 , 48 ., Moreover , in contrast to genes on symbiosis plasmids , chromid core genes are more likely to have a congruent phylogeny with that of the species tree of Sinorhizobium 21 ., It was reported that chromids contribute to the intraspecies differentiation of S . meliloti strains 22 ., This is in line with the enrichment of strain-specific genes ( subset IV ) on chromids of the two S . fredii strains ., Here we reveal that the chromid gene pool also makes a significant contribution to inter-species differentiation in Sinorhizobium , as approximately 38 . 7% of the subset II are located on the chromids of S . fredii ., When the transcriptional profiles of single-copy genes were compared between CCBAU45436 and CCBAU25509 , DEGs were significantly enriched on chromids under all test conditions ( Fig 6B ) ., It has been demonstrated in Escherichia coli that strain-dependent DEGs were more polymorphic or divergent than other genes , indicating the role of differential gene regulation in bacterial diversification 49 , 50 ., These findings indicate that the expression pattern of genes on chromids may evolve relatively rapidly , which echoes a report that genes evolve faster on chromids than on chromosomes 51 ., Those genes up-regulated at stationary phase were enriched on chromids of the two S . fredii strains , and were over represented with genes of unknown function and those involved in modifying extracellular structures , indicating a role of chromids in stress adaptation ., Notably , the average level of gene connectivity for chromid genes was generally lower than that for those from chromosome and symbiosis plasmids under test conditions ( Fig 7A and S4 Fig ) ., This may be due to a critical role of chromids in intra- and inter-species diversification and in adaptation to more diverse niches 15 , 16 that were not effectively covered in this study ., In line with this view , the chromid of S . meliloti was enriched with genes that were up-regulated under osmotic stress conditions 52 ., Moreover , genetic and metabolic modelling studies show that the chromosome alone is sufficient for the growth of S . meliloti in sterile soil , while the chromid may confer more specialized functions in the rhizosphere 15 , 16 ., Likewise , among six extrachromosomal replicons including the symbiosis plasmid pRL10 of R . leguminosarum Rlv3841 , many genes of pRL8 are specifically up-regulated in the rhizosphere of pea , but not in that of alfalfa and sugar beet 14 , indicating a contribution by pRL8 to host-specific fitness ., Therefore , in addition to the well-known symbiosis plasmid essential for symbiotic adaptation , extra-chromosomal replicons including chromids may offer rhizobia novel adaptations that are needed in soils and rhizospheres characterized by highly fluctuating levels of nutrients and stress factors ., The transcriptional profiles of pangenome subsets I-III exhibited a strong condition-dependent clustering pattern ( Fig 4A–4C ) rather than a strain-dependent one as observed for the subset IV ( Fig 4D ) ., These results are consistent with the recent comparative transcriptomic analyses of E . coli strains under free-living conditions , which revealed that the gene expression distances of core genes between strains were mainly dependent on the culture conditions rather than phylogenetic relatedness 50 , though a later independent study also identified a large number of strain-dependent transcripts in addition to condition-dependent ones 49 ., Distinct characteristics of test conditions among different studies may exert variable strength of influence on clustering patterns ., Earlier transcriptomic studies of E . coli strains under free-living conditions revealed a positive correlation between ortholog frequency ( % E . coli genomes exhibiting gene ) and expression level 50 ., In our study , the average expression level of a gene under each test condition ( free-living or symbiotic ) is positively related to its conservation level in four hierarchical subsets of the S . fredii pangenome ( Fig 4E ) from strain-specific to genus core ., The most recently acquired genes , such as those of subset IV , showed the lowest variation in expression levels between different conditions , whereas the more conserved subsets III , II and I exhibited increasing expression plasticity ( Fig 4F ) ., Moreover , the more conserved a gene is , the higher its level of gene connectivity in the co-expression network ( Fig 7A and S4 Fig ) ., These findings highlight that transcriptional regulation contributes to the development of the more conserved pangenome subsets , and the newer pangenome members are less intensively integrated with the core regulation network involved in environmental adaptations ., It has been hypothesized that the prokaryotic pangenome mainly results from adaptive , not neutral , evolution 4 , and this appears to be true at least for the subsets I-III of the S . fredii pangenome ., For those newly acquired genes with few interaction partners in the pangenome , earlier bioinformatics analysis suggests that they may take many million years to be integrated into regulatory interaction networks 53 ., Prokaryotic core and accessory genome components are analogous to the operating system and applications ( apps ) of smartphone 54 ., This work provides further evidence of the organization , regulation and integration of apps with the operating system in the prokaryotic multipartite genome of S . fredii ., We demonstrated that the average level of gene expression , the variation of gene expression between environments , and the gene connectivity degree within co-expression networks are positively related to the conservation level of a gene ., There are replicon biases in genes of different conservation levels , in genes up-regulated under specific conditions , and in the connectivity of genes within co-expression networks ., Moreover , chromosomal loci znu and mdt operons were identified as novel players in host-specific adaptations , which are generally thought to be the domain of the symbiosis plasmid ., These findings shed new light on our understanding of the coordinated regulation of core and accessory genes of rhizobia , facultative microsymbionts of legumes ., Similar strategy can be used to study other prokaryotes , which are subject to diverse stimuli in the ever-changing circumstances ., S . fredii strains were cultured at 28°C in tryptone-yeast extract ( TY ) medium 55 , and E . coli strains at 37°C in Luria-Bertani ( LB ) medium ., When required , the media were supplemented with the appropriate antibiotics at final concentrations of 30 μg/ml for nalidixic acid , 10 μg/ml for trimethoprim , 10 μg/ml for tetracycline , 50 μg/ml for kanamycin , and 30 μg/ml for gentamicin ., Plant growth and inoculation was performed according to the method previously described 25 ., Seeds of G . max C08 were surface-sterilized by successive treatments with 95% ethanol for 30 sec and 3% ( w/v ) NaClO for 5 min , and were then washed 6 times by autoclaved deionized water ., For seeds of G . soja W05 , a pre-treating step in concentrated sulfuric acid for 2 min was needed before the surface-sterilization ., The surface-sterilized seeds were germinated on 0 . 6% agar plates in the dark at 28°C for 36–48 hours ., Then , germinated seeds were planted in vermiculite wetted with low-N nutrient solution in Leonard jars 56 and were inoculated with 1 ml of physiological saline suspension ( OD600 = 0 . 2 ) of rhizobia per plant ., Plants were grown at 24°C with 12-h day and night cycles for 30 days ., Nodules for bacteroid isolation or RNA ex

Introduction, Results, Discussion, Materials and Methods

Prokaryotes benefit from having accessory genes , but it is unclear how accessory genes can be linked with the core regulatory network when developing adaptations to new niches ., Here we determined hierarchical core/accessory subsets in the multipartite pangenome ( composed of genes from the chromosome , chromid and plasmids ) of the soybean microsymbiont Sinorhizobium fredii by comparing twelve Sinorhizobium genomes ., Transcriptomes of two S . fredii strains at mid-log and stationary growth phases and in symbiotic conditions were obtained ., The average level of gene expression , variation of expression between different conditions , and gene connectivity within the co-expression network were positively correlated with the gene conservation level from strain-specific accessory genes to genus core ., Condition-dependent transcriptomes exhibited adaptive transcriptional changes in pangenome subsets shared by the two strains , while strain-dependent transcriptomes were enriched with accessory genes on the chromid ., Proportionally more chromid genes than plasmid genes were co-expressed with chromosomal genes , while plasmid genes had a higher within-replicon connectivity in expression than chromid ones ., However , key nitrogen fixation genes on the symbiosis plasmid were characterized by high connectivity in both within- and between-replicon analyses ., Among those genes with host-specific upregulation patterns , chromosomal znu and mdt operons , encoding a conserved high-affinity zinc transporter and an accessory multi-drug efflux system , respectively , were experimentally demonstrated to be involved in host-specific symbiotic adaptation ., These findings highlight the importance of integrative regulation of hierarchical core/accessory components in the multipartite genome of bacteria during niche adaptation and in shaping the prokaryotic pangenome in the long run .

Prokaryotic pangenomes are characterized by a high rate of turnover in gene content , with core genes shared by all members of a taxonomic group and accessory genes present in only a subset of the members ., Accessory functions could serve as an arsenal enabling prokaryotes to develop adaptations to new niches ., Therefore , prokaryotic core and accessory components are analogous to the operating system and applications ( apps ) of smartphones ., However , it is puzzling how these accessory functions are linked with the core regulatory network in prokaryotes during niche adaptations ., Here we address this question by investigating the adaptive regulation of hierarchical core/accessory subsets in the multipartite pangenome ( chromosome , chromid and plasmid ) of Sinorhizobium fredii , which is a facultative microsymbiont of soybeans ., The level and variation of gene expression , and gene connectivity revealed in transcriptomes under free-living and symbiotic conditions are positively correlated with the gene conservation level , i . e . from strain-specific accessory genes to genus core ., Replicon-dependent organization and adaptive regulation of hierarchical core/accessory subsets suggest distinct roles of different replicons not only in environmental adaptation but also intra- and inter-species differentiation ., Among core and accessory genes with host-specific upregulation patterns , we experimentally identified novel symbiotic players involved in host-specific adaptation .

sequencing techniques, symbiosis, gene regulation, plasmids, genome analysis, genetic elements, forms of dna, crops, dna, molecular biology techniques, rna sequencing, research and analysis methods, crop science, gene expression, comparative genomics, molecular biology, agriculture, biochemistry, soybean, nucleic acids, genetics, transcriptome analysis, biology and life sciences, species interactions, genomics, mobile genetic elements, computational biology

journal.pntd.0003120

A Model for a Chikungunya Outbreak in a Rural Cambodian Setting: Implications for Disease Control in Uninfected Areas

Chikungunya virus ( CHIKV ) belongs to the genus Alphavirus ., It is a mosquito-borne pathogen transmitted by the Aedes mosquitoes ., In humans , the virus causes an acute illness with symptoms including fever , headaches , rash and arthralgia 1 ., The virus was first identified in Africa , during an outbreak in Tanzania in the 1950s 2 ., In Africa the virus is maintained in a mainly sylvatic cycle , being spread among wild primates by forest dwelling mosquitoes 3 , 4 ., In contrast , in Asia the virus is spread between humans and the primary vector Aedes aegypti 5 ., Chikungunya was first recorded in Asia in Thailand in 1958 6 ., Large epidemics were recorded throughout Asia in countries including Cambodia , Vietnam , Burma , Sri Lanka , India , Indonesia and the Philippines , before the virus virtually disappeared following the 1973 outbreak in India 5 ., A large outbreak in Kenya in 2004 initiated a resurgence of the virus leading to widespread infection in the Indian Ocean islands of the Comoros , Seychelles , Mauritius and the French islands of Mayotte and La Réunion ., The epidemiology of the virus changed , with the major vector on La Réunion identified as Aedes albopictus 7 ., At the time , this was the largest documented outbreak , with over 266 , 000 cases estimated to have occurred 8 ., Sizeable undocumented outbreaks were also observed in Asia and India during the 1960s but exact case numbers are unavailable 5 ., Large outbreaks were detected in India in late 2005 9 , followed by outbreaks in Southeast Asia ( Thailand , Singapore and Malaysia ) in 2006 10 ., In 2011 , a strain from the Asian lineage was reported in the Pacific island of New Caledonia , the first cases of chikungunya in this part of the world 11 ., Countries in Europe , Asia and North America documented imported cases associated with travellers returning from India and the Indian Ocean islands 4 , 12–14 ., In 2007 the first chikungunya epidemic in a temperate country was recorded in the region of Emilia-Romagna in north-eastern Italy 15 ., The virus was assumed to have been imported by a traveller from an infected region of India 16 and established itself in the local Aedes albopictus population , first detected in 1990 17 ., In September 2010 , two autochthonous cases were documented in the southern French city of Fréjus where the Aedes albopictus vector is present 18 ., There was no documented transmission beyond these two cases; whether the aggressive surveillance and control efforts implemented around those cases had a significant impact is unknown ., In Cambodia , the virus was first detected in 1961 , when the Asian genotype was circulating in the region 19 ., From 2000 , CHIKV serologies were performed at the Institut Pasteur in Cambodia on the samples collected by the dengue national surveillance and control program ., The virus was first detected in Battambang province ( Thai border ) in 2011 and , since then , new cases have been reported in the country following a northwest-southeast direction 20 ., Unlike dengue fever , which has been extensively modelled , chikungunya has only started to receive attention since its reemergence in 2005 ., Mathematical models have been developed to describe detailed mosquito dynamics and the host-vector interactions 21 , 22 ., A primary focus has been on determining the reproduction number , , of an epidemic , which is defined as the number of secondary infections from an infected host in a completely susceptible population 23 ., The standard approach has been to fit a dynamic model , with varying levels of detail describing the mosquito life cycle , to the epidemic curve ., Such an approach has yielded various estimates for the La Réunion epidemic ., Dumont and Chiroleu 24 obtained a value of depending on the location on the island ., They also considered the inclusion of increased mosquito mortality due to infection , yielding estimates of 25 ., Considering seasonal fluctuations in the vector population , Bacaer 26 estimated a reproduction number of 3 . 4 ., More recently an estimate as high as 4 . 1 has been obtained 27 ., A vastly different approach was adopted by Boëlle et al 28 , who constructed the generation interval of chikungunya based on the gonotrophic cycle of the causative mosquito , obtaining a best estimate of 3 . 7 , with a range of 2–11 ., A temperature-dependent host-vector model was fitted to the 2007 Italian outbreak by Poletti et al 29 , estimating an of 3 . 3 with a range of 1 . 8–6 ., Finally , the risk of chikungunya infection in an endemic dengue region was estimated to be 64% that of dengue with an of 1 . 22 30 ., The scale of imported cases into previously unaffected countries ( e . g . UK , France , Hong Kong , USA 4 ) observed during the recent resurgence of chikungunya in the Indian Ocean has caused great concern due to the presence of a competent vector ( Aedes albopictus ) in many of these regions 31 ., The threat of disease introduction is further compounded by the apparent ease at which the infection was established in the local Italian albopictus population during the 2007 outbreak ., The urgent need to establish adequate monitoring and mosquito control programs in vulnerable countries is particularly highlighted by the recent outbreak in Singapore , in which 1059 cases were recorded in 2013 32 , despite a history of successful control measures to curb the transmission of this disease 33 ., Recent work on the spatio-temporal spread of chikungunya through an immunologically naive population driven by asymptomatic individuals 34 underlines the risk of unknowingly importing the infection into new regions ., With little information available in the early stages of an epidemic , estimates of the reproduction number are commonly used to inform public health decision makers and methods to obtain accurate estimates in newly infected regions are thus required to effectively assess the public health preparedness needs , the impact , and success of control measures ., The impact of asymptomatic cases and biologically-confirmed symptomatic cases with an undocumented date of onset is investigated in this paper ., In March 2012 , a local outbreak of chikungunya fever was reported in the rural village of Trapeang Roka in the Kampong Speu Province , Cambodia 35 ., Chikungunya infection was confirmed by laboratory analysis allowing the identification of both asymptomatic and unreported cases ., We formulate a stochastic model to describe the temporal dynamics of the outbreak and estimate the reproduction number by fitting the model to the recorded epidemic curve ., The inclusion of biologically-confirmed cases undocumented by date of onset , which do not appear on the epidemic curve , allowed a more accurate estimate of the reproduction number to be obtained , in comparison to that obtained when such cases are excluded ., This is the first attempt to apply such a stochastic model to a relatively isolated village typical of the Cambodian rural habitat , presenting the unique opportunity to consider the introduction of the virus into a comparatively closed and immunologically naive population ., The data collection protocol , implemented on March 26 2012 , in Trapeang Roka village was validated by the Cambodian Ministry of Health ., Informed consent was obtained in writing from all adults in the Khmer language and parents were asked to sign for their children ., An outbreak investigation was conducted on the 26th of March , 2012 , after reports of illness , consisting of fever and rash , among residents of Trapeang Roka village were confirmed by blood samples to be CHIKV infection 35 ., The population of the village was estimated to be 695 individuals , living in 134 houses ., The investigation protocol was validated by the Cambodian Ministry of Health ., As part of the investigation , 98 houses were visited and 425 people were interviewed ., Adults were asked for their consent and the consent for their children ., All the people in the visited houses were asked to complete a standardized questionnaire in the Khmer language ., Questions were about demographic data ( such as age and sex ) , socio-economical data ( such as occupation and level of education ) and clinical data during the previous 6 weeks , corresponding to the time period since the rains occurred ., Clinical data included observed symptoms ( skin rash , joint pain , temperature ) and the date of symptom onset ., Blood was collected on blotting paper for each individual ., A venous blood sample was performed on febrile individuals ., Samples were sent to the Institut Pasteur in Cambodia ., Serology by IgM-Capture Enzyme-Linked Immunosorbent Assay ( MAC-ELISA ) to detect IgM against CHIKV was performed on dry blood spots 36 ., No follow up blood test was performed on sero-negative people to observe seroconversion ., The blood of febrile individuals was tested by RT-PCR for CHIKV 37 ., The chikungunya variant E1-226V strain was identified 20 ., A positive case was defined as a person who had at least one sample which tested positive for CHIKV ( IgM serology and/or RT-PCR ) ., Serologic testing was also performed for flavivirus antibodies ., Anti-dengue virus or anti-Japanese encephalitis virus antibodies were detected in 20 CHIKV seropositive people ., The epidemic curve was built with the number of biologically-confirmed CHIKV cases per day determined from the date of fever onset , which was self-reported in the questionnaire ., The data spanned a period of 48 days between February 7 and March 25 inclusive , the the final laboratory confirmed cases of chikungunya , with a clinical onset , detected on March 24 , Figure, 1 . It has been documented previously that chikungunya outbreaks often follow large rainfall episodes 38–40 , which result in a surge in the local mosquito population 41–44 ., The dominant mosquito population identified in the region during an entomologic assessment performed on March 29-30 was Ae ., aegypti 35 ., The rains arrived on February 14 and persisted for 2 days ., Following the rains and the associated increase in water availability for oviposition , the time delay between the rains and the epidemic gaining momentum ( approximately 16–18 days ) is consistent with the duration of the larval/pupal stages ( 12 . 5 days ) 45 and the minimum egg incubation period ( 3 days ) 46 ., Data collection took place on March 26 , and it is likely that there were some cases after this date ., The 98 houses visited were randomly located throughout the village , with longitude and latitude recorded for all but 10 houses ( Figure 2 ) ., Of the 425 individuals interviewed , 190 laboratory confirmed cases of chikungunya were detected , 5% of which were asymptomatic ., The date of symptom onset was recorded for 138 of the confirmed cases and 52 individuals were either asymptomatic ( 10 individuals ) or could not recall the date of symptom onset ., The outbreak consisted of an initial period , between epidemic days 1 and 25 , during which sporadic cases occurred but with no consistent growth pattern ., It is noted that the outbreak itself struck houses at random throughout the village and was not spatially restricted to a particular region ( Figure 2 ) ., The high incidence observed in four houses located in the north of the village can be attributed to their above average household sizes in the range 6–13 individuals per house ( village average 4 . 3 ) ., A single infectious mosquito in such a house has many hosts available for feeding and would thus be capable of transmitting the infection to a greater number of individuals ., As the data collection occurred 7 weeks after the index case , it is possible that the 42 individuals who failed to recall their specific infection details , despite testing positive for infection , may have been infected in the earlier period of the epidemic ., Another possibility is that these individuals suffered minor illness and , as such , could not recall specific details ., The location of these individuals , undocumented by date of onset , does not form an isolated cluster within the village and they are distributed randomly among the houses surveyed , Figure, 3 . Finally , we considered any bias that may have caused people to not report dates of symptom onset , Figure, 4 . We found that gender did not play a role , with both men and women equally likely to report infections ., Age also did not appear to be a significant factor , with only slightly lower reporting rates amoung 31–50 year old groups ., Surprisingly , individuals with a secondary level education were less likely ( 22% ) to report their infection in comparison to those with no schooling ( 32% ) or a primary level education ( 29% ) ., Students and homemakers were more likely to report symptoms , a fact that could perhaps be attributed to their increased likelihood of being present during the data collection campaign ., In fact , this could possibly also explain the increased reporting rates amoung middle aged people with secondary level education ., This indicates that the time of day the data collection takes place may play a factor and is likely to omit people working outside the home or village ., The disease dynamics are modelled by considering both host and vector populations explicitly ., The human population is divided into susceptible ( ) , exposed ( ) , infectious ( ) and recovered ( ) individuals ., The outbreak was short ( 7 weeks ) relative to the human lifespan and the total human population is taken to be constant , , and it is assumed that the exposed population is not infectious ., The data did not record the date of symptom onset for 52 laboratory confirmed cases ., Of these 52 individuals , 10 were asymptomatic and the remainder could not recall the exact date of onset , possibly due to the seven week lapse between the epidemic outbreak and the data collection campaign ., Nonetheless , these cases will be incorporated into the model as these individuals are also capable of transmitting the infection ., Firstly , it is assumed that , following the latent phase , both asymptomatic and symptomatic cases become infectious at the same time ., It is also assumed that the latent and incubation periods coincide , so that exposed individuals are not infectious to biting mosquitoes ., This may not be strictly true for chikungunya , but a definitive consensus on the relative lengths of these infection states has not been reached to date 47 ., Unlike directly transmitted diseases , such as influenza or measles , the absence of symptoms does not necessarily decrease the likelihood of transmitting the infection ., There is a possibility that a lower viral load in asymptomatic individuals may decrease their ability to transmit the virus to susceptible mosquitoes , however , these is no evidence to confirm this theory and , in fact , the difference in viral loads observed between symptomatic and asymptomatic individuals have been shown to not be statistically significant 48 ., In addition , there is documented evidence of virus transmission from symptomatic seropositive primates to seronegative animals via experimental mosquito bites 49 at viremic levels detected in asymptomatic humans ., Furthermore , viremia levels in asymptomatic cases are sufficiently high as to cause widespread concern for possible contamination of donated blood supplies 16 , 50 ., Other factors could also lead to differences in the transmission potential of individuals ., For example , self-imposed quarantine of clinical cases could reduce the mobility of symptomatic people and reduce transmission to other houses in the village ., Therefore , we assume that both infectious states transmit the virus at the same rate but will consider a reduced transmission rate for asymptomatic individuals in a sensitivity analysis ., However , the asymptomatic cases , lacking overt clinical presentations , avoid detection and act as silent spreaders within the population ., The symptomatic cases with undocumented dates of onset , while overtly presenting clinical symptoms , are also not visible on the epidemic curve but are equally likely to infect a susceptible mosquito ., To this end , the infectious compartment is separated into three sub-compartments , individuals who are asymptomatic , those who are symptomatic but undocumented by date of onset and those who are symptomatic and documented by date of onset ., The total number of infectious individuals can thus be written as ., Finally , it is assumed that the recovery rates for each of the infectious states are identical ., All of the above assumptions are commonly used in dynamic models for chikungunya fever 22 , 27 ., The stochastic nature of the infection process becomes important in small populations or when the number of infectious individuals is relatively small 51 ., In a village of less than 1000 individuals a stochastic modelling framework is appropriate and is adopted herein ., The deterministic equations , from which the stochastic model can be easily derived , for the human population are The susceptibility of a human to infection following a bite from an infectious mosquito is denoted by ., and are the human incubation and infectious periods respectively , and is the average daily biting rate of the mosquito ., The proportion of infected individuals who develop symptoms is denoted by and is the proportion of symptomatic individuals who are documented by date of onset ., Following Dumont et al 24 , 25 , the adult female mosquito population is divided into susceptible ( ) , exposed ( ) and infectious ( ) mosquitoes ., A larval compartment ( ) is included to describe the dynamics of the immature mosquito populations ( Figure 5 ) ., The mosquito dynamics are described by where is the susceptibility of the mosquito to infection after biting a symptomatic infectious human and , similarly , is the susceptibility of the mosquito to infection after biting an asymptomatic human ., The transmission rates will be treated as equal , , but a lower transmission from asymptomatic individuals will be considered later in a sensitivity analysis ., is the mosquito latent period and is the adult mosquito natural death rate ., It is assumed that mosquitoes do not recover from infection ., is the maturation rate of the immature mosquito population ., is the average number of female eggs laid per day per adult female mosquito and is the carrying capacity , the maximum population of immature mosquitoes that can be sustained by the available resources ., is the natural larval mortality rate ., The basic reproduction number can be easily calculated from the next generation matrix 24 to obtain For the stochastic version of the model , all the continuous variables become discrete numbers and each compartmental transition becomes a distinct event with an associated rate ., There are 16 distinct events in the stochastic model which are listed in Table, 1 . The human population is set to the sample size , ., The latent period in the human population is the time from a mosquito bite to the onset of infectiousness , which for the clinical cases is assumed to coincide with the onset of symptoms ., The incubation period for chikungunya can range from 2 days to 12 days , with a mean of approximately 3–7 days 1 , 8 , 28 , 47 , 52 ., The value days is used in this work , which is comparable with values used in other modelling studies 24 , 25 , 29 ., The duration of infection for chikungunya is typically in the range 1–7 days 5 , 53 but symptoms can persist for several weeks 8 ., The data collection campaign conducted in the village recorded both the date of fever onset and the date the fever resolved ., The mean febrile period was calculated as 4 . 28 days with a standard deviation of 2 . 5 ., The duration of the infectious period was thus taken as days ., The proportions and are estimated from the data to be ( yielding 10 asymptomatic individuals in an infectious population of 190 ) and ( yielding 138 cases which are documented by date of onset out of a total of 180 symptomatic cases . ) Biting rates for Ae ., aegypti have been measured in laboratory settings with average values of 0 . 7 bites per day 54 , 55 ., However , they have been shown to be opportunistic feeders with biting frequency increasing with host availability 56 ., Taking a conservative estimate , we limited host availability to 12 hours per day and the resulting biting rate is approximately 54 ., Laboratory experiments , in which Ae ., aegypti were infected orally with chikungunya variant E1-226V , detected the virus in the salivary glands 2 days after infection 57 , indicating a mean extrinsic incubation period of days ., The susceptibility of mosquitoes to infection following a blood meal has been extensively studied ., Ae ., aegypti mosquitoes challenged with a strain of the virus from the La Réunion epidemic displayed infection rates of 88 . 5% to 90 . 7% 57 ., Ae ., aegypti from the French West Indies and French Guiana , infected by blood with a titre of showed infection rates from 88 . 9% to 100% , but as low as 37 . 6–62% when infected with a titre of 58 ., Girod et al found that infection rates were found to depend heavily on the housing density of the region where the mosquito was captured , with dense housing yielding an infection rate of 56 . 8% and diffuse housing a rate of 38 . 2% ., However , these results were for mosquitoes collected from chikungunya-free regions , where local transmission has not been documented to date ., Mosquitoes from Cameroon and Vietnam , exhibited infection rates of 37 . 1–84 . 8% and 66 . 5–99 . 6% respectively , when challenged with several viral strains 59 ., In particular , when challenged with the East/Central/South African strain ( 06 . 117 ) , which was identified in this outbreak 35 , the Cameroon and Vietnamese mosquitoes displayed infection rates of 64 . 8% and 78 . 3% respectively ., Mourya et al 45 found that , at and relative humidity 70–80% , the rate of infection was 61 . 82% ., Such values are comparable with conditions in the relevant region of Cambodia during March , with a long-term average from 1981 to 2013 yielding a temperature of ( range ) and relative humidity in the range 75–83% ., A conservative value of is taken in this work ., No studies have been conducted on human susceptibility to infection following a bite from an infected mosquito and the value of will be estimated from the epidemic curve ., All other parameters are intrinsically linked with the mosquito life cycle ., The lifespan of the female Ae ., aegypti mosquito under various temperatures has been measured in a laboratory setting 45 ., Observations indicate that the adult female has a mean survival duration of 43 . 7 days at but this is considerably reduced to 18 . 17 days at temperatures up to ., However , the controlled environment of such laboratory studies will undoubtedly overestimate the life span ., Mark-release-recapture studies performed on wild Ae ., aegypti populations in Rio De Janeiro during the wet season with temperatures in the range estimate an average life expectancy ( ALE ) of 3–16 days 60 , with this being limited to 1 . 9–5 days in high income neighbourhoods 61 ., Similar studies in Kenya calculate a mean survival of 9 to 10 . 7 days 62 , 63 ., Studies in Northern Australia , performed in the temperature range , found the probability of daily survival ( PDS ) to be 0 . 86–0 . 91 64 , which yields an ALE in the range 6 . 6–10 . 6 days ( using the relation 65 ) ., A value of days is used in this analysis ., A study in Malaysia found that Ae ., aegypti produced an average of 86 eggs per oviposition 66 ., The mean gonotrophic cycle length was found to be 3 days , which yields an average of approximately 3 . 3 cycles in a 10 day lifespan and a lifetime total of 286 eggs ., Thus , the breeding rate per female mosquito is approximately eggs per day ., Assuming approximately half of all eggs laid result in the emergence of a female mosquito 45 then the breeding rate is eggs per day ., Finally , for temperatures in the range , 29 . 6% of eggs fail to hatch 67 yielding per day ., Laboratory measurements by Mourya et al 45 for the duration of larval stages indicated a length of days to the emergence of the adult mosquito ., Furthermore , they found that larvae and pupae experience 2% and 6 . 63% daily mortality rates respectively ., In the wild , larval mortality will be dependent on many factors such as the destruction of breeding sites , moisture levels , temperature and interspecific competition 68 ., As such , we take the upper limit of the mortality range and set ., Finally , following Dumont et al 24 , the carrying capacity of the immature mosquito population is taken to be a multiple of the human population , where is the total number of immature mosquitoes per human ., Surveys performed in Cambodia during the months August to October in areas at high-risk for dengue outbreaks found that the number of pupae in households was highly correlated with the adult mosquito population 69 ., The mean pupae density was 16 . 4 per house , with a distribution ranging from 5 . 2/house in the rural area of Takeo province and up to 56 . 9/house in a rural area of Battambang , both comparable to the study site ., In rural areas the pupae per person index is 3 . 6 and this was found to be independent of the human population density and the distribution of water containers 69 ., We assume that the number of larva per person can be inferred from this; taking into account a 2% larval mortality rate and a 50% male-female ratio 45 , we obtain larva per person at the start of the outbreak ., All parameters used in the simulations are summarised in Table, 2 . The epidemic curve , Figure 1 , indicates the presence of a single documented symptomatic case on epidemic day 1 ( February 7 ) yielding an initial condition with ., Following Dumont et al 24 , 25 , the mosquito abundance is taken to be dependent on the total human population present , such that initial mosquito populations are taken aswhere is the number of adult female mosquitoes per human ., For a typical village in South East Asia , consisting of wooden houses , measurements indicate a population of 14 . 2 mosquitoes per house 70 ., The village in the present study has the same construction characteristics and consists of a total of 134 houses , yielding an average of 2 . 7 mosquitoes per person ., Furthermore , investigations found that , in December , the percentage of female mosquitoes was 42% 71 ., Thus , the number of female mosquitoes per human in the village is approximately ., It is assumed that there is no pre-existing immunity in the village population as no other chikungunya outbreaks had been recorded in the village or in Cambodia for 50 years , as attested by the attack rate which remained at 50% until the age of 50 , after which it dropped dramatically 35 ., The stochastic process detailed in Table 1 is implemented using Gillespies tau-leap algorithm , such that in a given time interval the event rates are calculated and the number of transitions in the interval are evaluated ., For each realisation of the model , the number of new symptomatic cases documented by date of onset on a given day is determined fromwhich , in the deterministic framework , corresponds to the integraland where denotes the individuals who have recovered from a symptomatic infection and who were documented by date of onset ., The objective function , where is the number of newly documented cases on day given by the data and is the mean of realisations of the model , was minimized using the patternsearch routine included in the Matlab Global Optimization Toolbox 72 , 73 ., The number of realisations was chosen to ensure convergence ., Each stochastic realisation is permitted to run for a maximum of 1 year or until the population is infection free , such that the following condition is satisfied: For the realisations where the duration of the simulated epidemic exceeds that of the observed epidemic it is assumed that no cases occurred after March 24 such that the data is padded with zeros to enable calculation of the objective function ., 95% confidence intervals were calculated by performing a latin hypercube sampling of the parameter space using 1000 samples and minimizing the objective function using the deterministic version of the model ., On inspection of the epidemic curve , Figure 1 , it can be seen that additional symptomatic cases of chikungunya fever were recorded from as little as 2 days after the initial index case on February 7 ., As such , given the extrinsic ( days ) and intrinsic ( days ) incubation periods , it is not possible that the recorded index case could have caused secondary infections so rapidly ., Therefore , the data itself indicates that this was not the true index case ., It is possible that either this individual incorrectly recorded the date of symptom onset ( collected 7 weeks after the event ) or the infection was already present in the population before February 7 ., Infection with another pathogen is also possible , however , the index case tested negative for other flaviviruses ., We adopt the hypothesis that the infection was already present in the population on February 7 and to reflect this the initial conditions which are considered herein are Fitting the stochastic model to the data yields estimates of the initial infected human population of ( 95% C . I . 0 . 08–1 . 08 ) , ( 95% C . I . 0 . 27–5 . 24 ) and ( 95% C . I . 2 . 68–9 . 46 ) ., The infected mosquito populations were estimated as ( 95% C . I . 0 . 29–5 . 28 ) and ( 95% C . I . 0 . 25–4 . 26 ) ., The results corroborate our earlier observation that infection was certainly more widely spread within the population on the recorded outbreak day ( February 7 ) and indicate that the infection was present in at least 8 other members of the population ., These infectious individuals , being asymptomatic or undocumented by date of symptom onset , would have escaped detection and could account for the symptomatic cases observed on epidemic days 4 and 5 ., Many of the villagers worked outside of the village raising the possibility that these early secondary cases were in fact imported from other local infected regions ., However , it is unlikely that such a large number of cases were simultaneously imported into an uninfected village ., Furthermore , our model estimates that approximately 3 infected mosquitoes were also circulating within the village on February 7 ., Due to the limited flight range of the causative mosquito ( maximum of approximately 500 m 74 ) they could have been infected outside and transported by road into the village and thus seeded the epidemic ., Alternatively , they could have been infected within the village ., In the latter scenario , the duration of the extrinsic incubation period indicates that the infectious mosquitoes were infected at least 2 days before February 7 ., This indicates that a visitor to the village or a local who was either asymptomatic or undocumented by date of symptom onset may have imported the infection , which was then established in the local mosquito population , leading to the recorded index case ., Finally , the data fitting procedure estimated the human susceptibility to infection as ( 95% C . I . 0 . 41–0 . 45 ) , which yields an estimate for in Trapeang Roka village in February-March 2012 of 6 . 46 ( 95% C . I . 6 . 24 , 6 . 78 ) ., The daily cases are plotted , along with the epidemic curve , in Figure 6 ., An initial slow rise of the simulated epidemic can be observed which mirrors the true epidemic progression ., However , the simulated epidemic does not wane near epidemic day 20 on February 27 ( see epidemic curve in Figure 1 ) ., The timing of the epidemic peak shows a good comparison with the data and the simulated epidemic starts to decline in line with the epidemic curve ., In addition , an eigendecompositio

Introduction, Methods, Results, Discussion

Following almost 30 years of relative silence , chikungunya fever reemerged in Kenya in 2004 ., It subsequently spread to the islands of the Indian Ocean , reaching Southeast Asia in 2006 ., The virus was first detected in Cambodia in 2011 and a large outbreak occurred in the village of Trapeang Roka Kampong Speu Province in March 2012 , in which 44% of the villagers had a recent infection biologically confirmed ., The epidemic curve was constructed from the number of biologically-confirmed CHIKV cases per day determined from the date of fever onset , which was self-reported during a data collection campaign conducted in the village after the outbreak ., All individuals participating in the campaign had infections confirmed by laboratory analysis , allowing for the identification of asymptomatic cases and those with an unreported date of fever onset ., We develop a stochastic model explicitly including such cases , all of whom do not appear on the epidemic curve ., We estimate the basic reproduction number of the outbreak to be 6 . 46 ( 95% C . I . 6 . 24 , 6 . 78 ) ., We show that this estimate is particularly sensitive to changes in the biting rate and mosquito longevity ., Our model also indicates that the infection was more widespread within the population on the reported epidemic start date ., We show that the exclusion of asymptomatic cases and cases with undocumented onset dates can lead to an underestimation of the reproduction number which , in turn , could negatively impact control strategies implemented by public health authorities ., We highlight the need for properly documenting newly emerging pathogens in immunologically naive populations and the importance of identifying the route of disease introduction .

During the recent resurgence of chikungunya , the scale of imported cases into previously unaffected countries has caused great concern due to the presence of a competent vector ( Aedes albopictus ) in many of these regions ., This study describes a mathematical model for a chikungunya outbreak in the rural Cambodian village of Trapeang Roka , where a chikungunya epidemic was recorded and documented in March 2012 ., The outbreak data is unique , in that all infections were confirmed by laboratory analysis , enabling the identification of asymptomatic individuals , in addition to individuals who failed to report details of their infection ., A stochastic model , partitioning the infectious population into three distinct classes , is implemented using Gillespies algorithm ., We show that the incorporation of both biologically-confirmed symptomatic cases undocumented by date of fever onset and asymptomatic cases yields a higher estimate of the reproduction number ., Our results highlight how reproduction numbers could be underestimated by limiting analysis to the epidemic curve ., Carefully documenting cases and performing laboratory testing in cluster regions , such as the village considered here , could provide a more comprehensive insight into the true infection dynamics .

biology and life sciences, population biology, medicine and health sciences, epidemiology

journal.pcbi.1002844

Molecular Mechanism of Allosteric Communication in Hsp70 Revealed by Molecular Dynamics Simulations

Heat shock proteins ( HSPs ) are essential macromolecules involved in housekeeping cellular activities , whose expression levels can be modulated in response to environmental conditions ., The Hsp70 family of proteins plays essential roles in maintaining cellular protein homeostasis ., Under normal conditions , Hsp70 can fold nascent polypeptides as they emerge from ribosomes or refold misfolded proteins , regulate the stability and activity of specific proteins and solubilize aggregates 1 , 2 ., Hsp70 is also involved in protein degradation , ubiquitination , assembly and disassembly of oligomeric complexes and translocation of proteins across membranes 2 , 3 , 4 ., Under stress conditions , increased expression of Hsp70 helps to preserve and recover the correct functional structure of client proteins by binding to denatured conformations 1 ., Given its involvement in many cellular control and regulation processes , recent studies have shown a key role of Hsp70 in several diseases: some of these , for instance several cancer types ( breast , endometrial , oral , colorectal , prostate cancers , and certain leukemias ) are associated with overactivity/overexpression of the chaperone 5 ., Defects in Hsp70s activity and consequent abnormal protein misfolding and accumulation are involved in neurodegenerative diseases , such as Alzheimer , Parkinson , and Huntington 5 , and in aging processes 6 , 7 ., This evidence points to Hsp70 as an interesting drug target 5 , 7 , 8 ., From the structural viewpoint , members of the Hsp70 family are composed of two domains connected by a highly conserved 14 residue-linker: a ∼44 kDa N-terminal nucleotide binding domain ( NBD ) , with ATPase activity , and a ∼25 kDa substrate binding domain ( SBD ) , which binds peptides 2 , 4 ( Figure 1 ) ., The NBD consists of lobe I and lobe II , which in turn can be divided into subdomains: IA ( residues 1–37 and 120–171 ) and IB ( residues 38–119 ) , IIA ( residues 172–227 and 311–368 ) and IIB ( residues 228–310 ) ., Domains IB and IIB are connected by flexible hinges to IA and IIA respectively and regulate the access to the nucleotide binding site ., The NBD terminal helix ( residues 369–383 ) is localized between the two lobes and connects the NBD to the inter-domain linker ., The SBD also contains two subdomains , a β-sandwich base ( βSBD ) and a domain made of 5 α-helixes ( A to E ) ( αSBD ) forming a lid over the polypeptide binding site 1 , 9 ., The βSBD loops protrude upwards forming a deep hydrophobic cavity closed up by helix B , where peptides can bind in a linear conformation 2 ., In Hsp70 the relative three-dimensional arrangement of the two domains is regulated by the presence of a specific nucleotide and their activities are coupled through allosteric mechanisms: the specific nucleotide bound to the NBD regulates the SBD conformation required for peptide binding 10 , 11 ., The crystal structure of bacterial Hsp70 , DnaK , in the ADP-bound closed conformation 9 displays the two domains completely separated by the linker in a flexible and extended solvent exposed conformation ., In contrast , the X-ray structure of yeast Hsp110 , a structural homolog of Hsp70 , shows an ATP-bound open conformation 12 ., In this conformation , the αSBD is widely open with respect to the βSBD 2 , and the linker folds in a β-strand localized in a hydrophobic binding pocket between the IA and IIA subdomains of the NBD , thus docking the SBD to the NBD 1 , 7 ., In the ATP-bound state , association and dissociation rates for substrates are high , while substrates affinity is low ., After ATP hydrolysis to ADP the affinity for substrates is high , while substrate exchange rate is low 2 ., Experimental evidence has established the allosteric coupling between NBD and SBD and the essential role of the linker in this mechanism 10 , 13 ., The linker transduces allosteric signals in both directions: polypeptide binding in the SBD can also transmit changes to the NBD , increasing the ATP hydrolysis rate 1 ., Allosteric coupling between the two domains is absent in Hsp110 2 , in spite of the high structural similarity of the two proteins ., The molecular determinants for the presence or absence of allosteric coupling in these families of proteins are still poorly understood and they represent a significant challenge and an opportunity to structure-based drug design 14 ., In this study , we aim at elucidating the atomic origins of the allosteric communication in Hsp70 protein family in comparison with non-allosteric Hsp110 by means of molecular dynamics ( MD ) ., By simulating several protein-nucleotide complexes in a fully solvated environment and applying a set of structural and dynamical analyses specifically developed for the study of allosteric systems 15 , 16 , 17 , 18 , we aim to gain insights into the mechanisms of nucleotide-induced signal propagation in Hsp70 and identify functional hotspots involved in the response to ATP and ADP in different conformational states of the protein ., To this end , we simulated multiple MD trajectories of Hsp70 and Hsp110 proteins in complex with ATP , ADP and in the apo form ( total simulation time: 1 . 9 microseconds ) ., The all-atom detail is maintained throughout the analysis , with the aim of relating the observed large-scale motions and conformational changes to their atomistic physico-chemical origin ., The comparison between the allosteric and the non-allosteric species allows determining the interactions and specific clusters of residues that are responsible for the different long-range , nucleotide-driven structural effects at the SBD domain ., Finally , coarse-grained elastic network models ( ENM ) are used to investigate the conformational transition mechanisms , and the results are critically discussed with respect to the ones from all-atom simulations ., In the previous sections we identified significant differences in the global structure and dynamics of DnaK and Sse1 in response to ATP or ADP binding ., In particular , we observed that the non-allosteric protein is not modulated by the ligand exchange at the lobe I-lobe II interface ., In this section we aim to gain residue-based insight into the structural and dynamical rearrangement leading to the allosteric signal between the nucleotide binding site , the linker region and the SBD in the different ligand states of DnaK and compare them to Sse1 ., The residue-based modulation is analyzed by considering the time-dependent dynamical evolution of geometric strain and the average conformational mobility to identify mechanical hinges ., Geometric strain is a measure of the time-dependent local deformation of the structure with respect to the average conformation ( see Materials and Methods for details ) ., Although not measuring the energy involved in the deformation , this quantity monitors protein areas undergoing significant microscopic rearrangements ., Namely , regions involved in conformational changes show strain peaks , which can be related to local structural changes during the structural transition in response to a nucleotide ., To complement the dynamical analysis , the network of ligand-modulated hydrogen bonds connecting nucleotide binding site and SBD and supporting the allosteric communication is analyzed ., The all-atom picture of the conformational transition between open and closed state of DnaK in the presence of ADP , and in the opposite direction in the presence of ATP , presented in the previous sections , consists of two subsequent conformational events occurring in a well-defined order: namely , in the closing transition one has detachment/local unfolding of αSBD , followed by the displacement of βSBD , while in the opening transition the two steps are reversed ( first step is onset of docking of βSBD mediated by the loop 210-linker interaction , followed by unfolding at αSBD ) ., The conformational changes from open to closed structure or vice-versa are compared to those obtained with a coarse grained approach based on Elastic Network Models ., By imposing the known DnaK start and end conformation and using the PATH-ENM tool from the AD-ENM Web Server 26 , we simulate a transition pathway connecting the two states , based on the most representative normal modes of each conformation ., Interestingly , the order of events observed in both directions corresponds to what suggested by our all-atom approach ., See Supplemental Information for details ( Figure S7 ) ., On the other hand , the correspondence between all-atom and coarse-grained approach is lost when comparing the internal dynamics of Sse1 and of the homology modeled open DnaK ., The three most relevant normal modes of the two proteins , calculated by means of the AD-ENM server 26 show a significant similarity ( Figure S8 ) as expected because of the structural homology between the two proteins ., Also , the prediction of hinges by means of Anisotropic Network Model ( ANM ) web server HingeProt 27 , 28 locates hinge residues at essentially the same positions ( 393 , 520 , 510 , 537 , DnaK numbering ) in all cases ., Therefore , no indication of differentiated ligand-based modulation of the dynamics of Sse1 and DnaK can be obtained by applying ENM-based methods to these systems , despite the fact that the coarse-grained methods catch the global displacements highlighted by the all atom analyses ., The aim of this study was to investigate by MD simulations the molecular basis of allosteric communication mechanisms in DnaK in contrast with the non-allosteric behavior of Sse1 ., The conformational transition induced by ATP on the closed state of DnaK , that is the linker docking to the hydrophobic binding pocket at the NBD and the αSBD opening , consists of significant subdomain rearrangements ., Computational investigations of large-scale structural changes usually rely on coarse-grained models , which , by making use of simplified protein representations and , in some cases , of the knowledge of the initial and final states of the transition , can efficiently model sizeable rearrangements and provide useful information on the underlying mechanisms ., Elastic Network Models can be used to retrieve collective , functionally relevant motions 27 , 28 , 29 around the specific structures on which the Hamiltonian of the system is built and allow to predict possible transition pathways between two given conformations , based on native fluctuations ., Recently , a coarse-grained approach based on UNRES MD simulations 22 was used to model the full conformational transition in Hsp70 , by posing distance constraints on the NBD subdomains that simulate the presence or absence of bound ATP ., The motion of SBD and NBD domains was shown to be modulated and , interestingly , the occupancy of open and closed SBD states was found to correlate with the “ligand” presence , in agreement with experimental data ., Overall , coarse-grained methods can shed light on ligand-activated modulation of protein motions , since the latter are largely determined by the structural organization of the native state ., However , such models lack by definition the atomic detail that is required to understand the finely tuned physico chemical origin underlying ligand-based modulation ., Similarly , differences arising from sequence divergence in homologues , such as the ones observed between Hsp70/DnaK and Hsp110/Sse1 , cannot easily accounted for by coarse-grained methods ., The question addressed in the present work , namely to elucidate the molecular mechanism underlying Hsp70 allostery in comparison to its non allosteric homologue Hsp110 , can therefore take advantage of full atomic detail , as shown in the previous section in comparison to ENM results ., On the other hand , a well-known limitation of all-atom MD simulations is sampling ., Large conformational rearrangements may be out of reach for a single 100 ns MD simulation run ., Enhanced sampling methods like accelerated MD 30 , as well as non-dynamical energy optimization pathways strategies 31 , 32 can provide a high resolution model for a complex structural transition with higher efficiency than unbiased MD ., As an alternative , information on the global conformational changes may be inferred from standard MD trajectories by extrapolating collective motions inducing the transition by means of PCA methods ., Such an approach was recently attempted for Hsp70 by Nicolai et al . 21 with the aim of defining the dynamic modes involved in the transition ., With a complementary point of view , in this paper we have applied a set of recently developed methods 15 , 16 , 17 , 18 that allow us to identify the relevant residues which are involved in the early onset of a conformational change in DnaK ., We comparatively analyze the structural and dynamical changes that occur at the single residue level on the ns scale and relate these to the complete conformational transition ., The underlying reasoning is that transitions can be triggered or favored by networks of interconnected residues that respond to specific signals ( ligand binding , exchange or even covalent modifications ) by changing their dynamic states ., The link between residue-level changes in protein dynamics and long-range propagation of allosteric signals has been probed by NMR analysis 33 , 34 , 35 ., Therefore , even if we are still unable to observe full conformational changes , the theoretical identification , coupled to validation against experimental data , of functionally relevant residues throughout the structure of the protein , in explicitly distinct ligand states , helps to shed light on the molecular determinants of allostery in different proteins of the same family ., In our study , while the progress of the opening transition in ATP-bound closed DnaK is observed only at an initial stage , the onset of the opposite closing transition occurring in the modeled open DnaK , with the αSBD detaching from the NBD domain , is detected in our MD trajectories to a significant extent ., In both directions the transition is the result of consistent microscopic modifications , such as spatial rearrangements or dynamical modulation of specific residues , which work as rigid units and flexible hinges and respond to the specific ligand ., The comparison between DnaK and Sse1 , where such conformational changes are not observed , helps validate our observations and provides a model for the allosteric mechanism in Hsp70 , identifying relevant structural residue hotspots at the atomic level ., Our combined analysis identifies two pathways transmitting the ligand encoded signal: loop 195 , interacting with ATP , appears as the most relevant sensor and induces a dynamical modulation at loop 210 ., In parallel , the coordination with K67 and E168 stabilizes the hydrogen bond network that connects the binding site , through domain IA , to the C-terminal residues and the linker ., The combined effect of such interactions results in the stiffening of the interface between lobe I and lobe II and induces the conformational rearrangement of loop 210 ., The stabilization of the interface between lobes I and II and with the linker is in turn reflected by the increased coordination of the βSBD with the NBD ., In the presence of ADP , the increased linker mobility , due to the loose coordination between the nucleotide and C-terminal end of NBD , as well as the relative mobility of lobes IA and IIA , stimulates motion through a hinge located at residue D390 that is propagated to the βSBD and sets up its rigid movement ., Interestingly , the latter is coupled to an increased mobility of NBD subdomain IIB ., This picture is supported by the dynamical and sequence-based comparison with the non-allosteric Sse1 ., In particular , loop 195 and loop 210 shows a different sequence composition in Sse1 ., This is likely to induce a different dynamical behavior , as pointed out in the Results section ., In Sse1 the persistence of interactions between loop 195 and nucleotide after ATP-ADP exchange , as well as the increased solvent protection of loop 210 through the adjacent rigid loop 180 traps the structure of the interface between domains IA and IIA of the ATP state in a stable conformation ., Available mutational data confirm the relevance of the interactions between loop 195 and nucleotide ., Mutants of G195 disrupt the ATP-induced structural dynamics 36 , while T196 mutants have a reduced ATPase activity 36 ., Also , the network originating from K67 is known to be essential for ATPase activity and for inter-domain communication , and mutants of this position display a reduced ATPase activity 37 ., The same holds for E168 mutants 36 , 37 which induce impairment of interdomain communication 13 , 36 , uncoupling ATP activity and substrate release ., Recent NMR work 10 has identified the protein hotspots , constituting the allosteric network in NBD , that respond to the nucleotide exchange with a conformational transition by means of chemical shift perturbation ., The identified network consists of the linker and the IIA β-sheet connecting loop 210 and loop 195 , which is in agreement with our simulations results ., Interestingly , our flexibility analysis investigates motions on the ns-scale , when conformational changes have not completely occurred yet ., It is worth noting that the relative flexibility increase at loop 210 and at the linker in presence of ATP compared to ADP point towards an activation of the same region ., Our dynamical results can also be qualitatively compared to hydrogen exchange data , reporting on general flexibility properties 38 ., Regions that undergo a modulation of protection to hydrogen exchange , in particular NBD linker and SBD residues 400–500 , are affected in our simulation by a significant change in the dynamical pattern that may anticipate the structural rearrangements ., Flexibility changes upon nucleotide exchange , in the non-allosteric Sse1 , are differently distributed on the protein structure , do not involve the interface between NBD and SBD and are in general less intense ., These observations support the relevance of the dynamical modulation to the Hsp70 allostery ., The binding site of the ATPase-stimulating cochaperone DnaJ has recently been reported to be highly dynamical and located along the β-strand 220 39 ., DnaJ is hypothesized to bind to the open Hsp70 structure and enhance ATP hydrolysis , thereby favoring the substrate- and ADP-bound conformation ., Considering the open DnaK state simulation in the presence of ATP , we observed increased rigidity along strand 220 , which propagates to domain IIB ., The bound cochaperone can enhance this rigidity and improve the coordination between lobes I and II , hence promoting ATP-hydrolysis ., Moreover , the Gestwicki group identified small molecules that bind to the cleft between IA and IIA and work as stimulating effectors of ATP hydrolysis in synergism with DnaJ 40 ., One of these compounds is found to bind to the same β-strand involved in DnaJ interaction , which further confirms the main functional role played by this protein region ., According to our simulations , the coordination of domain IIA and IIB is modulated by the presence of ATP in both DnaK simulations ., The motion of subdomain IIB with ADP is due to the enhanced mobility of strand 220 and of residues 308 , as a consequence of the reduced interaction between loop 195 and nucleotide upon ATP-ADP exchange , inducing the opening of the binding clamp ., The effect of the nucleotide exchange on the modulation of the subdomain arrangement in the NDB has been object of experimental and computational investigation ., In particular , MD simulations 41 confirmed by NMR data on a homologue system 42 , showed that nucleotide exchange AMPPNP-ADP induces a rotation of domain IIB coupled to the opening of the nucleotide-binding pocket ., In agreement with these results , we observe that ADP significantly affects the mobility of IIB by increasing the flexibility of two hinges , residues 225 and 308 , and hence opening the binding clamp ., Moreover , a reduced connection of domain IIB is displayed also by the absence of interaction between ADP and residue R342 at the interface between domain IIA and IIB , in both conformations , open and closed ., Mutational studies 36 show that residue substitutions around hinge 225 affect the ATPase rate , although they do not perturb the in vivo refolding function ., Binding of small molecules in the vicinity of this region in the ADP state and not in the ATP state strongly suggest a ligand-induced modulation of the binding area 43 ., The consistency between our dynamical data and experimental results suggests that the dynamical effect induced by ligands on MD-compatible time scales can shed light on the molecular basis of allosteric mechanism coupled to large structural rearrangements ., A systematic comparison between Hsp70 and its non-allosteric homologue Hsp110 to single out sequence determinants of Hsp70 allostery , by means of Statistical Coupling Analysis ( SCA ) , was recently performed 19 ., A sparse network of coevolving residues that differentiate Hsp70 and Hsp110 families has been identified and hence these residues have been suggested to be necessary for determining the allosteric mechanism in Hsp70 ., We have focused instead on the mechanistic differences between DnaK and Sse1 with an all-atom approach and have identified those protein segments that display a different dynamical behavior in the two proteins , either increasing the coordination between nucleotide binding site and SBD or acting as flexible hinges ., The hotspots we identified , comprising the β-strands 220 , loop 195 , loop 210 and 180 in lobe II and the hydrogen bond network connecting nucleotide to the linker , provide the minimal mechanistic unit that can be considered allosterically responsive ., The comparison with the set of residues provided by Smock et al . returns a common group of residues , including E168 , loop 195 , T151 , K152 , N412 and T413 ., As expected due to the differences in the nature of the analysis , the two sets do not fully overlap , since some amino acids involved in the allosteric mechanism may be conserved between Hsp110 and Hsp70 ., Also , not every residue of the SCA sector is expected to be directly essential for the allosteric function per se , but might be relevant to ensure stability and compensate for other functionally relevant mutations ., In contrast to the SCA analysis , no strongly responsive regions were identified on the β-sheet body of βSBD , but only on its loop regions ., This discrepancy might also be influenced by the absence of a peptide substrate in our simulations ., Overall , by crossing the dynamical and the sequence information , a subset of critical positions affecting allosteric communication can be promptly identified and rationalized in a mechanistic perspective ., By complementing sequence and structural-dynamical analysis one could hence define and explain the minimal required set of mutations that abolish allostery in Hsp70 ., More generally , by applying our dynamical analysis approach in a comparative study of an allosteric and a non-allosteric system , we demonstrated that including functional dynamics , internal residue-residue coordination , and protein flexibility information , could help unveil ligand-responsive regions and possible binding sites of a protein with allosteric properties , which may not be immediately evident in a single-structure representation ., This offers the opportunity of modulating protein function by specifically addressing regions crucial for the functional dynamics of the protein through specific mutations or small molecules targeting allosteric sites different from the classical binding site targets ., As initial structure of DnaK , an Hsp70 homolog , the X-ray structure of E . coli DnaK ( PDB ID: 2KHO 9 ) in complex with ADP-Mg2+ was employed ., ATP complex was built by substituting ADP with ATP-Mg2+ coordinates obtained from 2EA8 structure 44 ., Hsp110 X-ray structure of S . cerevisiae ( PDB ID: 3C7N_A 12 ) in complex with ATP-Mg2+ was utilized as starting point ., For the ADP complex , ligand coordinates were substituted with ADP-Mg2+ obtained from 1S3X 45 ., The apo forms were obtained removing ligand coordinates ., The DnaK open conformation homology model , bound to ATP , was obtained from a previous study 19 ., ADP complexes and apo structure were built as previously described ., All complexes were solvated in a triclinic box of SPC water keeping a minimum distance of 1 nm between the solute and each face of the box ., This results in about 100 . 000 water molecules included in the DnaK and Sse1 ., Total charge was neutralized with Na+ ions added to the simulation box at random positions ., Molecular dynamics simulations were performed with Gromacs 4 . 0 package 46 , employing the GROMOS96 ( ff43a1 ) force field 47 ., All complexes were energy relaxed with 1000 step of steepest-descent energy minimization ., MD simulations were performed using the LINCS algorithm 48 to constrain bond lengths and periodic boundary conditions were applied in all directions ., Long-range electrostatic forces were treated using the Fast Particle-Mesh Ewald method ( PME ) 49 ., Van der Waals forces and Coulomb potential were treated using a cut-off of 0 . 9 nm and the simulation time step was set to 2 fs ., An initial velocity obtained according to a Maxwell distribution at 300 K was given to all the atoms ., All simulations were run in NVT environment employing V-rescale as temperature coupling algorithm , with reference temperature set at 300 K . Three independent simulations were run for both DnaK and Sse1 ., The total simulation time was 200 ns for ADP and apo states ., Production runs for ATP complexes were extended to 225 ns and 210 ns for DnaK and Sse1 , respectively ., To evaluate the effects of ligand bound on single residues and on protein domains and the intrinsic differences between DnaK and Sse1 , different analyses were carried out on the equilibrated trajectories .

Introduction, Results, Discussion, Materials and Methods

Investigating ligand-regulated allosteric coupling between protein domains is fundamental to understand cell-life regulation ., The Hsp70 family of chaperones represents an example of proteins in which ATP binding and hydrolysis at the Nucleotide Binding Domain ( NBD ) modulate substrate recognition at the Substrate Binding Domain ( SBD ) ., Herein , a comparative analysis of an allosteric ( Hsp70-DnaK ) and a non-allosteric structural homolog ( Hsp110-Sse1 ) of the Hsp70 family is carried out through molecular dynamics simulations , starting from different conformations and ligand-states ., Analysis of ligand-dependent modulation of internal fluctuations and local deformation patterns highlights the structural and dynamical changes occurring at residue level upon ATP-ADP exchange , which are connected to the conformational transition between closed and open structures ., By identifying the dynamically responsive protein regions and specific cross-domain hydrogen-bonding patterns that differentiate Hsp70 from Hsp110 as a function of the nucleotide , we propose a molecular mechanism for the allosteric signal propagation of the ATP-encoded conformational signal .

Allostery , or the capability of proteins to respond to ligand binding events with a variation in structure or dynamics at a distant site , is a common feature for biomolecular function and regulation in a large number of proteins ., Intra-protein connections and inter-residue coordinations underlie allosteric mechanisms and react to binding primarily through a finely tuned modulation of motions and structures at the microscopic scale ., Hence , all-atom molecular dynamics simulations are suitable to investigate the molecular basis of allostery ., Moreover , understanding intra-protein communication pathways at atomistic resolutions offers unique opportunities in rational drug design ., Proteins of the Hsp70 family are allosteric molecular chaperones involved in maintaining cellular protein homeostasis ., These proteins are involved in several types of cancer , neurodegenerative diseases , aging and infections and are therefore pharmaceutically relevant targets ., In this work we have analyzed , by multiple molecular dynamics simulations , the long-range dynamical and conformational effects of ligands bound to Hsp70 , and found relevant differences in comparison to the known non-allosteric structural homolog Hsp110 ., The resulting model of the mechanism of allosteric propagation offers the opportunity of identifying on-pathway allosteric druggable sites , which we propose could guide rational drug-design efforts targeting Hsp70 .

physics, biochemistry, chemistry, biology, computational biology, chemical biology, biophysics

journal.pgen.1005510

Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood

High-throughput metabolomics experiments using mass spectrometry platforms are becoming an integral part of clinical and systems biology research ., Profiling of amino acids and acylcarnitine species in dried whole blood samples of newborns is used worldwide in neonatal screening programs to identify rare inborn errors of metabolism 1 ., These diseases are generally caused by rare mutations , leading to loss of function of an enzyme that catalyzes the biochemical reaction of the respective trait ., Recently , many of the amino acid and fatty acid metabolites utilized in newborn screening were also implicated in common complex diseases of adults such as cardiovascular disease , insulin resistance and obesity ., Exemplarily , obesity is accompanied by an increase in circulating levels of multiple amino acids , including branched chain amino acids 2 , 3 , and in type 2 diabetics , altered levels of acylcarnitines were described 4 , 5 ., Amino acids and acylcarnitines show substantial inter-individual variation 6 and a strong genetic contribution to their blood concentrations has been reported 7 ., Thus , the integration of genetic and metabolic profiling holds the promise for providing novel insights into the regulation of metabolic homeostasis in health and disease ., Indeed , recent studies have identified common genetic variants associated with a variety of circulating metabolites in serum , plasma or urine using different analytical platforms ( LC-MS/MS , NMR ) 8–24 ., However , the complexity of the metabolome cannot be captured by a single technology ., Since differences in metabolite abundance have been described between plasma and whole blood 25 , we hypothesized that additional genetic determinants affecting the blood metabolome are yet to be discovered ., Thus , we performed an integrated study combining genetics , gene expression and metabolom data ( see S1 Fig for the study design ) ., We applied a targeted LC-MS/MS method to measure the abundance of amino acids and acylcarnitines in dried whole blood spots of 2 , 107 individuals and performed genome-wide association analysis ., Top findings were replicated in a second independent European Caucasian cohort of 923 Sorbs ., Further , going beyond plain genetic associations , we integrated analyses of mRNA levels in leukocytes to establish causal links between genetic variations , gene-expression levels and metabolites ., Finally , we explored whether SNP-metabolite associations identified in our study overlap with previously identified genetic loci for other complex traits or diseases ., Quantitative concentrations of 26 amino acids , 36 acylcarnitines and 34 metabolite ratios were determined in dried whole blood spots of 2 , 107 participants of the LIFE Leipzig Heart Study using LC-MS/MS ., Metabolites and their ratios reflect metabolic function of various biochemical pathways e . g . urea cycle , branched chain amino acid metabolism or cellular fatty acid oxidation ( see S1 Table for complete list of phenotypes and their categories ) ., We performed a genome wide association study ( 2 , 619 , 023 SNPs ) for whole blood metabolites and identified 2 , 261 SNP-metabolite associations ( 119 after pruning ) with p-values <10-7 ., These associations comprise 42 metabolites ( including 19 ratios ) and 866 SNPs ( 54 lead-SNPs after pruning ) at 25 unique genomic locations ( Fig 1 , S2 Table ) ., QQ-plots and regional association plots for all loci demonstrating valid quality control are presented in the supplemental material ( S2 and S3 Figs ) ., Next , replication of top SNPs was sought in an independent cohort of 923 individuals from the Sorb study , where genome-wide SNP and metabolite datasets were available ., Good proxies ( r2>0 . 8 ) for replication analysis in the Sorbs were available for 858 ( 99 . 1% ) of our 866 top-SNPs , covering 21 of the 25 identified loci and comprising 2 , 227 associations ( well-imputed proxies were not available for the loci at 1q32 . 3 , 3p24 . 1 , 5p15 . 2 , 20q13 . 2 , see S3 Table for complete results ) ., We observed identical directions of effects for 2 , 133 ( 95 . 8% ) combinations of SNPs and metabolites in the replication cohort , resulting in a replication rate of 88 . 3% , when applying a FDR ( false discovery rate ) of 5% ( Fig 2 ) ., Replicated lead-SNPs were distributed over 14 of the 21 genomic loci eligible for replication analysis ( Table 1; see S3 Table for results of non-replicated loci ) ., In addition , we considered associations at locus #4 ( 2q34 ) with glycine and locus #14 ( 12q24 . 31 ) with C4 as validated results , since these loci were already reported in other GWAS for serum metabolites 8 , 9 , 13–15 ., Moreover , non-lead-SNPs at 12q24 . 31 were replicated in the Sorbs at FDR 5% level ., None of the other non-replicated loci or loci without proxies in the Sorb study achieved a p-value <10−8 in our initial GWAS ., In total , our study led to the identification of 16 unique , validated loci for 36 whole blood metabolites ( Table 1 ) ., At six of the 16 loci we identified associations for blood metabolites for the first time i . e . these loci represent novel findings of our study ., Also , we successfully validated ten loci previously reported for serum , plasma , and urine metabolites ( Table 1 and S4 Table ) ., At three of these loci , associated metabolites were different from those previously reported ., In detail , at locus #3 ( 2p13 . 1 ) we detected associations with Arg and related metabolite ratios , whereas earlier associations were reported for plasma N-acetylornithine and related compounds 8 , 13 , 14 , 16 ., Further , at loci #11 ( 9q34 . 11 ) and #15 ( 15q22 . 2 ) , we identified associations with methylmalonyl-carnitine , whereas earlier studies reported associations involving the isobaric compound succinyl-carnitine 13 , 14 ., To investigate if associated variants have gene regulatory effects , we analyzed our validated lead-SNPs for correlations with gene expression in peripheral blood mononuclear cells ( PBMC ) ., Transcriptome data ( 28 , 295 eligible transcripts ) was available for 2 , 112 subjects of the LIFE Leipzig Heart study ., At an FDR of 5% , 132 eQTLs were identified for 38 of the 45 validated lead-SNPs , affecting the expression of 69 transcripts ., Explained variances of eQTLs ranged between 0 . 4% ( corresponding p-value = 3 . 9x10-3 ) and 28 . 0% ( corresponding p-value = 8 . 0x10-153 , S5 Table ) ., We observed eQTLs at 14 of the 16 validated loci , including the six novel loci identified in our study ( Fig 3 and S7 Fig , Table 2 ) ., All 14 loci included lead-SNPs with cis-regulatory effects on gene expression ., In addition , novel loci #2 ( 1q44 ) and #12 ( 19q11 ) , as well as reported locus #14 ( 12q24 ) also included trans-regulated eQTLs ., The trans-eQTLs at locus #2 ( 1q44 ) regulating JAM3 expression were inter-chromosomal and particularly strong , explaining about 13 . 0% of variance ( Fig 3 and S7 Fig , Table 2 ) ., We next aimed to assess whether changes in expression of identified eQTL genes can explain observed SNP-metabolite associations in our study ., Therefore , we analyzed the relationship between expression levels of these genes and metabolites ., We found 40 study-wide significant associations between gene expressions and metabolites , corresponding to 9 loci and 18 eQTL transcripts ( 16 unique genes , see Table 3 and S6 Table ) ., We then integrated information from SNP-metabolite ( mQTL ) , SNP-gene expression ( eQTL ) and expression-metabolite associations to form association triangles ., A triangle is defined by a triple of SNP , transcript and metabolite showing pair-wise associations ( see methods for details ) ., We constructed a network of all pairs of associations and their strengths ( see Fig 4 ) to illustrate the multiple relationships between associated genetic loci , genes and metabolites ., An interactive html-document to explore the network is provided as supplement material ( S4 Fig ) ., Certain overlaps with previously reported molecular interactions exist ., These known relationships are summarized in S11 Table ., We identified 177 relations containing 21 unique primary associations between features analysed in our study ., Additionally , we identified 16 unique molecules potentially connecting features analysed in our study ., As expected , these molecules include Proinsulin and Ubiquitin ., Association triangles were further used to test whether variances in gene expression are causally related to variances of metabolite levels ., We discovered 38 association triangles mapping to six unique loci including the two novel loci #2 and #10 at 1q44 and 8q24 . 3 , respectively ( S7 Table ) ., To estimate the number of such triangles identified by chance , we performed a comprehensive permutation analysis including mQTL , eQTL and expression-metabolite association analysis ( S8 Fig ) ., From this , the empirical likelihood of the reported six triangles obtained by chance was estimated to be <1x10-15 ., Particularly , in only two of 100 permutations we obtained a single triangle while in 98 of our 100 permutations , no triangles were observed ., Next , we used Mendelian randomization to establish a causal link between gene expression and the metabolite ., We identified 15 metabolite-gene pairs included in 36 triangles ( S7 Table ) ., Next , we investigated whether identified eQTLs explained a significant part of the SNP-metabolite association which we could demonstrate for a total of five loci ( Table 4 ) ., Strongest causal effects were found for novel locus #10 at 8q24 . 3 associated with several Aspartic acid traits ( strongest causal effect for ratio Aspartic acid / Acetylcarnitine via cis-regulation of PPP1R16A ) and locus #11 at 9q34 . 11 associated with MMA via PPP2R4 ., Finally , we explored whether SNP-metabolite associations identified in our study overlap with genetic loci for clinically relevant traits published in the National Human Genome Research Institute ( NHGRI ) GWAS Catalog ., At nine of the 16 validated loci , metabolite associated SNPs matched SNPs previously associated with clinical traits or diseases ( S9 Table ) ., We observed associations with platelet and red blood cell properties at three loci associated with acylcarnitines in our study ( 1q44 ( C18 ) , 10q11 ( C3 ) and 15q22 ( MMA ) ) 26–28 ., Further , we found that several of our variants were associated with clinical chemistry traits , e . g . fibrinogen ( 2q34 ) 29 , homocysteine ( 2q34 ) 30 and traits reflecting lipid metabolism ( HDL-cholesterol at 2q34 and 15q22 ) 31 , purine catabolism ( uric acid at 10q21 ) 32 , and kidney function ( creatinine at 2p13 and 2q34 ) 33 ., At the 2p13 and 2q34 loci , reported associations for creatinine were also linked to chronic kidney disease 34 ., In addition , variants at the 2q34 locus for glycine also convey risk for non-small cell lung cancer 35 ., Interestingly , recent studies described a key role for glycine in cancer cell proliferation and tumorigenesis 36 , 37 ., Further , metabolite associations at 3q27 ( C5OH+HMG ) , 5q31 ( AC-total ) , 9q34 ( MMA ) and 15q22 ( MMA ) overlapped with associations for Parkinson’s Disease 38 , Asthma 39 , Hypersomnia 40 and orofacial cleft 41 , respectively ., These co-localizations may implicate a shared genetic basis ( pleiotropy ) between complex traits and aid in forming new hypothesis regarding molecular pathomechanisms ., At two of the six newly identified loci ( 6q23 , ARG1 and 21q22 , HLCS ) , rare variants are known to cause autosomal recessive inborn errors of metabolism , providing a strong biological plausibility for the SNP-metabolite associations ., Mutations in ARG1 ( 6q23 ) , encoding arginase , the enzyme which catalyzes the hydrolysis of arginine , are the cause of Argininemia ( OMIM #207800 ) ., Here , we report common variants of ARG1 to be associated with arginine levels ., Likewise , defects in HLCS ( 21q22 ) are responsible for holocarboxylase synthetase deficiency ( OMIM #253270 ) with affected individuals displaying elevated levels of C5OH+HMG ., In line with this observation , the lead SNP at the HLCS locus exhibited a strong cis-eQTL and the allele responsible for higher HLCS expression was associated with lower C5OH+HMG levels ., A third novel locus ( #8; 6q21 ) associated with multiple acylcarnitines ( lead phenotype: AC-total ) also contained a gene with direct biochemical relationship to the associated metabolites , namely SLC22A16 , encoding an organic cation/ carnitine transporter ., Gene expression of SLC22A16 was regulated in cis at this locus , but SLC22A16 gene expression was not correlated with acyl-carnitine concentrations in whole blood ., In fact , the strongest SNP metabolite association at this locus was observed for a non-synonymous coding SNP ( rs12210538 ) in SLC22A16 , which is predicted to be damaging by Polyphen and SIFT 44 , 45 ., These findings suggest that associations at 6q21 are more likely driven by this non-synonymous coding mutation than by gene expression of SLC22A16 ., The remaining three novel loci relate to candidate genes with no prior connection to metabolism to the best of our knowledge ., For the locus at 10q11 . 21 , associated with C2 and C3 , we observed cis-effects on ANUBL1 and FAM21C expression , but gene expressions of both transcripts were not correlated with either C2 or C3 ., Thus , additional work will be required to explore the causal link between genetic variation at the 10q11 . 21 locus and C2 and C3 blood concentrations ., At novel locus 8q24 . 3 , integration of SNP , eQTL and gene-expression data let to the identification of PPP1R16 as putative causal gene for the association with aspartic acid and corresponding ratios ( lead phenotype: alanine / aspartic acid ) ., While we detected strong cis-effects on expression of two local genes , PPP1R16A and LRRC14 , only the eQTL of PPP1R16A partly explained the observed SNP-phenotype associations ., Future studies need to address how PPP1R16A , a gene involved in signal transduction 46 , may be affecting blood levels of aspartic acid ., Finally , we identified JAM3 encoding the junctional adhesion molecule C ( JAM-C ) as a novel candidate gene of acylcarnitine metabolism ., Top associated SNP rs3811444 ( 1q44 ) exhibited an exceptionally strong trans-eQTL for JAM3 , located at 11q25 ., This trans effect was also described by other eQTL studies 47 ., Gene expression of JAM3 correlated with several long chain acyl-carnitines ( i . e . C16 ) and explained a significant part of the SNP-metabolite association ., JAM-C participates in cell-cell adhesion , leukocyte transmigration and platelet activation ., The soluble form of JAM-C has been shown to mediate angiogenesis 48 ., Homozygous mutations in JAM3 cause hemorrhagic destruction of the brain , subependymal calcification , and congenital cataracts ( HDBSCC , OMIM #613730 ) ., At present , the potential functional role of JAM3 in acyl-carnitine metabolism remains elusive ., In addition to the identification of novel loci , we replicated and extended functional evidence for SNP-metabolite associations at ten loci previously described in GWAS for serum or plasma metabolites ( Table 1 ) ., The majority of these loci contain highly plausible candidate genes based on their biologic function in metabolism ( MCCC1 , ETFDH , SLC22A4/5 , ACADM , ACADS , CPS1 , CRAT ) ., Rare loss of function mutations in these genes cause Mendelian inborn errors of metabolism and measuring the respective marker metabolites in whole blood spots is part of neonatal screening programs throughout the world 1 ., Here , we validated common variants located in non-coding DNA with modest effect sizes on blood metabolites ., Additionally , we found blood eQTLs for MCCC1 , ETFDH , SLC22A4/5 , ACADM , and CRAT ., This is in line with evidence from other complex genetic traits , demonstrating that most associations for common variants arise in non-coding DNA and emphasizes the importance of regulatory variants in modulating gene expression 49 , 50 ., A striking example is the ACADM locus , where SNPs have been associated with C8 and C10 levels 13 , 14 , 20 , 21 ., In our study , gene-expression of ACADM was associated with C8 and C10 blood levels and we showed for the first time that this relationship was causal explaining a part of the observed SNP association ., In conclusion , our study expanded the current knowledge on the genetic regulation of human blood metabolites by adding six novel genetic loci ., Furthermore , by integrative analysis of SNP , gene expression and metabolite data , we derived mechanistic insights into the molecular regulation of blood metabolites ., At several loci , we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for other complex traits and diseases , pointing towards potential pathomechanisms via metabolic alterations ., Additional functional studies are required to elucidate the cellular mechanisms how the discovered candidate genes affect metabolic pathways and relate to disease pathology ., LIFE Leipzig Heart is an observational study in a Central European population designed to analyze genetic and non-genetic risk factors of atherosclerosis and related vascular and metabolic phenotypes 51 ., Patients undergoing first-time diagnostic coronary angiography due to suspected stable CAD with previously untreated coronary arteries , patients with stable left main coronary artery disease and patients with acute myocardial infarction were recruited ., The latter were excluded for the present analysis ., The study meets the ethical standards of the Declaration of Helsinki ., It has been approved by the Ethics Committee of the Medical Faculty of the University of Leipzig , Germany ( Reg . No 276–2005 ) and is registered at ClinicalTrials . gov ( NCT00497887 ) ., Written informed consent including agreement with genetic analyses was obtained from all participants ., In this analysis , we considered a total of 2 , 464 individuals ., From these , 2 , 107 had complete genotype , metabolite and covariate data qualifying them for GWAS analysis ( descriptive statistics can be found in S9 Table ) ., A subset of 1 , 856 individuals had complete data of genotypes , gene expression , metabolites and covariates ., These individuals were used for integrative analyses ( see study design , S1 Fig ) ., The Sorbs were recruited from the self-contained Sorbs population in Germany 52–54 ., All individuals were at fasting state ., Phenotyping included standardized questionnaires for past medical history and family history , collection of anthropometric data ( weight , height , waist-to-hip ratio ) and results from an oral glucose tolerance test ., A complete set of high-quality genotype data , metabolites and covariates was available for 923 subjects ( S9 Table ) ., The study was approved by the ethics committee of the University of Leipzig and all subjects gave written informed consent before taking part in the study ., An overview of the study design is presented in S1 Fig . In brief , we first performed a genome-wide metabolite quantitative trait ( mQTL ) analysis in the LIFE Leipzig Heart cohort , with replication of the top-SNPs in the Sorbs cohort ., Following this two-stage design , we applied a liberal cut-off of 1 . 0x10-7 for the initial GWAS to identify candidate loci ., A stringent cut-off is applied at the replication stage where we control the ( study-wide ) FDR at 5% based on permutation analysis 55 ., This accounts for the correlation structure of individuals , SNPs and metabolites and the multiple testing issue ( for details see below section “Genome-wide association analysis and SNP replication” ) ., Functional relevance of identified loci was studied in the LIFE Leipzig Heart cohort by analyzing expression quantitative traits ( eQTL ) and gene expression-metabolite associations followed by causal inference regarding discovered associations ., Venous blood samples were obtained from all study participants and 40μl of native EDTA whole blood were spotted on filter paper WS 903 ( Schleicher and Schüll , Germany ) in the LIFE Leipzig Heart study ., In the Sorb cohort , 40μl cell suspension obtained after plasma centrifugation ( 10 min at 3500 x g ) were spotted on filter paper ., All blood spots were stored at -80°C after 3 hours of drying until mass spectrometric analysis ., Sample pretreatment and measurement is described elsewhere 56–58 ., In brief , 3 . 0 mm diameter dried blood spot punches ( containing 3 μL whole blood ) were extracted with methanol containing isotope labelled standards ., After sample extraction and derivatization , analysis was performed on an API 2000 tandem mass spectrometer ( Applied Biosystems , Germany ) ., Quantification of 26 amino acids , free carnitine and 34 acylcarnitines including related metabolites was performed using ChemoView 1 . 4 . 2 software ( Applied Biosystems , Germany ) ., Samples were analysed within 23 analytical batches with two quality controls samples in each batch ., Mean inter-assay coefficients of variation were below 11% for amino acids and below 19% for acylcarnitines ., Further , using these 61 directly measured analytes , we derived a number of biologically relevant sums ( n = 1 , total acylcarnitine ) and ratios ( n = 34 ) to assess reaction equilibria within physiological pathways and processes ( e . g . Fischer’s ratio 59 ) ., Consequently , a total of 96 quantities were analyzed as GWAS traits ., A list of metabolites and quantities is presented in S1 Table ., Metabolites with more than 20 percent of values below detection limit were dichotomized for analysis ( below detection limit versus above detection limit ) ., This applies for the metabolites C5:1 , C6DC , C14OH , C16OH , MeGlut , C18:1OH , C18:2OH , C18OH and C20:3 ., Quantities were arsinh-transformed ( area sinus hyperbolicus ) which is close to a log-transformation for large values but does not emphasize differences between small values and can operate on values of zero ., Transformed quantities were approximately normal distributed ., Values outside of the Interval Mean ± 5*SD were considered as outliers and were removed to stabilize subsequent regression analysis ., We previously analysed a variety of factors influencing blood metabolites ., Age , sex , diabetes and fasting status show pronounced effects on several metabolites while log-BMI , smoking and some blood traits showed effects on selected metabolites ., Therefore , we decided to adjust our analyses for these potential confounders ., Genome-wide association analyses for blood 96 metabolites was performed in the LIFE Leipzig Heart samples ( N = 2 , 107 with complete phenotypes , covariates and high-quality genotypes ) ., Associations were tested by linear regression models using gene-doses of imputed SNPs ., We adjusted for age , sex , log-BMI , diabetes status , smoking status , fasting status , haematocrit , platelet count , white blood cell count and the first three genetic principal components ., Results revealed no signs of genomic inflation ( maximum lambda equal 1 . 018 , see S10 Table ) ., To avoid reporting of redundant SNP information , the top-SNP list was ordered according to minimal p-values and pruned applying a linkage disequilibrium cut-off of r2<0 . 3 ., Replication analysis was performed in the independent cohort of Sorbs ( N = 923 with complete genotype and metabolite data ) and for all combinations of SNPs and metabolites achieving a p-value of <10−7 in our first stage GWAS ., Based on our unpruned GWAS top-list , we retrieved all SNPs within a ±50kB environment which were successfully imputed in the Sorbs ( IMPUTE-info score>0 . 3 in both , 500K and 6 . 0 subsample ) ., Then , on the basis of the LIFE Leipzig Heart data , we assessed which of these SNPs are the best proxies of the corresponding top-SNPs to pair GWAS top-SNPs with optimal proxies of good quality within the Sorbs study ., Associations between pairs of proxies and metabolites were again analyzed using linear regression analyses of gene-doses ., Here , we adjusted for age , sex , log-BMI , diabetes status , smoking status , haematocrit , platelet count , white blood cell count and the relatedness structure ( 52 , 64 , 65 , function “polygenic” of the “GenABEL” package of R was used to deal with the relatedness structure 63 ) ., Since test statistics are correlated due to LD between SNPs and correlations between metabolites , we decided to control the false-discovery rate ( FDR ) at 5% rather than family-wise error rates ., Null-distribution for q-value calculation was determined by permutation analysis ., For this purpose , 1000 random permutations of the links between SNPs and metabolites were analyzed ., We compared our results with published GWAS hits on the basis of the GWAS catalogue ( http://www . genome . gov/gwastudies/ , date of download March , 4th , 2014 ) ., Required LD information was derived from HapMap3 ( release 28 ) and 1000genomes project ( release 20110521 version 3 f , restricted to SNPs with a MAF ≥ 1% ) ., In addition , further evidence from published mQTL studies was manually included in this analysis to assess novelty of our results ., A total of 13 studies were analyzed 8 , 9 , 12–21 , 23 ( see also S4 Table ) ., A locus was considered as novel if none of its SNPs were in linkage disequilibrium ( r2>0 . 3 ) with any published mQTL hit reaching study-wide significance as defined by the authors of the corresponding publication ., To increase relevance , we did not match the associated metabolic phenotypes between our study and the published ones , i . e . our approach of considering loci as novel is conservative ., In complete analogy to this analysis , we determined whether our top hits are associated with other traits for which results are published in the GWAS catalogue as well as those reported in two GWAS on plasma lipids 10 , 31 ., These traits could point toward other causal or pleiotropic effects ., If applicable , information on genetic disorders related to our loci were retrieved from OMIM ( http://omin . org ) ., Peripheral blood mononuclear cells were isolated in the LIFE Leipzig Heart cohort using Cell Preparation Tubes ( CPT , Becton Dickinson ) as previously described 66 ., Total RNA was extracted using TRIzol reagent ( Invitrogen ) and quantified with an UV-Vis spectrophotometer ( NanoDrop , Thermo Fisher ) ., 500 ng RNA per sample were ethanol precipitated with GlycoBlue ( Invitrogen ) as carrier and dissolved at a concentration of 50–300 ng/μl prior to probe synthesis ., N = 2 , 501 samples were hybridised to Illumina HT-12 v4 Expression BeadChips ( Illumina , San Diego , CA , USA ) in batches of 48 and scanned on the Illumina HiScan instrument according to the manufacturer’s specifications 60 ., Documentation of sample processing included batch information at any processing step allowing adjustment in subsequent data analysis ., Raw data of all 47 , 323 probes was extracted by Illumina GenomeStudio , 47 , 308 probes could be successfully imputed in all samples ., Data was further processed within R/ Bioconductor R 67 ., Individuals having an extreme number of expressed genes ( defined as median ± 3 interquartile ranges ( IQR ) of the cohort’s values ) were excluded ., Transcripts that were not expressed according to Illumina’s internal cut-off as implemented in the “lumi” Bioconductor package ( p ≤ 0 . 05 in at least 5% of all samples ) were excluded from further analysis ., Expression values were quantile-normalised and log2-transformed 68 ., For further outlier detection , we calculated the Euclidian distance between all individuals and an artificial individual which was defined as the average of samples after removing 10% samples farthest away from the average of all samples ., Individuals with a distance larger than median + 3 IQR were excluded ., Furthermore , we defined for each individual a combined quantitative measure combining quality control features available for HT-12 v4 ( i . e . ratio of levels of perfect-match vs . mismatch control probes , mean signal of perfect-match control probes , mean of negative control probes and labelling-control probes , ratios of high-concentrated , medium-concentrated and low-concentrated control-probes , mean of house-keeping genes , Euclidian distances of expression values , number of expressed genes , mean signal strength of biotin-control-probes ) ., We calculated Mahalanobis-distance between all individuals and an artificial individual having average values for these quality control features ., Individuals with a distance larger than median + 3 IQR were excluded ., Transcript levels were adjusted for known batch effects using an empirical Bayes method as described 69 and residualised for age , sex , monocyte counts and lymphocyte counts ., Additionally , we calculated principal components of the expression data and residualised for the first five principal components of expression data to account for unmeasured batch effects 70 ., Pre-processing resulted in 28 , 295 expression probes corresponding to 19 , 519 genes ., Chromosomal mapping of expression probes and assignment of gene names was done using information as reported by the manufacturer ( HumanHT-12_V4_0_R2_15002873_B ) ., After quality control , combined SNP and gene-expression data were available for a total of 2 , 112 individuals , from which 1 , 856 had been included in the GWAS ., eQTL analysis of the pruned GWAS top-list was performed by linear regression analysis of gene-doses using the R add-on package Matrix eQTL 71 ., EQTLs were considered as cis-regulated if the distance between SNP and the centre of the associated expression probe was not larger than 1 Mb , otherwise they were considered as trans-regulated ., Cis- and trans- specific significance thresholds were derived by a Benjamini-Hochberg ( B-H ) procedure implemented in Matrix eQTL ., For our data , cis associations with a p-value up to 0 . 0039 and trans-associations with a p-value up to 3 . 6x10-14 were considered study-wide significant at FDR<5% ., B-H q-values were empirically confirmed by 100 permutation tests ( permutation of SNP and gene-expression profiles ) ., Further details can be found elsewhere 72 ., Association analysis of gene-expression and metabolites was performed in 1 , 957 individuals for which both information as well as covariates were available ( 1 , 856 of these individuals had been included in the GWAS ) ., Again , we adjusted for age , sex , log-BMI , diabetes status , smoking status , fasting status , haematocrit , platelet count , white blood cell count ., FDR was controlled at 5% ., As we observed multiple relationships between genetic loci , gene-expressions , and metabolites , we visualized all associations found at FDR 5% in a network ., Previously published relations were identified by mapping genetic loci , genes , and metabolites from mQTL , eQTL , and gene-expression-metabolite association analysis to QIAGEN’s Ingenuity Pathway Analysis ( IPA , QIAGEN Redwood City , www . qiagen . com/ingenuity ) , as of May , 2015 ) ., This database includes , among many other information , data on genome-wide protein-protein interactions , activation / co-localization and enzymatic reactions ., Significantly associated SNPs were represented by the three most proximal genes and metabolite ratios by the individual nominator and denominator ., For a more detailed characterization of the observed SNP-metabolite associations , we integrated genotype , gene expression and metabolite data to construct association triangles ., A triangle is defined as a SNP that is significantly associated with both , a certain expression probe and a certain metabolite ., Thereby , the expression probe must be also associated with the metabolite ., For this purpose , we first determined the top associated SNP per locus , its corresponding best associated metabolite and eQTLs of that SNP ( FDR = 5% , see above ) ., Resulting triples of SNP , transcript level of eQTL and metabolite level were restricted t

Introduction, Results, Discussion, Materials and Methods

Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism ., Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults ., Thus , the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases ., We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids , acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2 , 107 adults and performed genome-wide association ( GWA ) to identify genetic modifiers of metabolite concentrations ., We discovered and replicated six novel loci associated with blood levels of total acylcarnitine , arginine ( both on chromosome 6; rs12210538 , rs17657775 ) , propionylcarnitine ( chromosome 10; rs12779637 ) , 2-hydroxyisovalerylcarnitine ( chromosome 21; rs1571700 ) , stearoylcarnitine ( chromosome 1; rs3811444 ) , and aspartic acid traits ( chromosome 8; rs750472 ) ., Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels , we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines , ARG1 for arginine , HLCS for 2-hydroxyisovalerylcarnitine , JAM3 for stearoylcarnitine via a trans-effect at chromosome 1 , and PPP1R16A for aspartic acid traits ., Further , we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma , serum or urine ., In conclusion , our integrative analysis of SNP , gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism ., At several loci , we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases ., These results form a strong rationale for subsequent functional and disease-related studies .

Human metabolite levels differ between individuals due to environmental and genetic factors ., In the present work , we analyzed whole blood levels of amino acids and acylcarnitines , reflecting disease relevant metabolic pathways , in a cohort of 2 , 107 individuals ., We then performed a genome wide association analysis to discover genetic variants influencing metabolism ., Thereby , we discovered six novel regions in the genome and confirmed ten regions previously found to be associated with metabolites in plasma , serum or urine ., Subsequently , we analyzed whether these variants regulate gene-expression in peripheral mononuclear cells and at several loci we identified novel causal relations between SNPs , gene-expression and metabolite levels ., These findings help explaining the functional mechanisms by which associated genetic variants regulate metabolism ., Finally , several SNPs associated with blood metabolites in our study overlap with previously identified loci for human diseases ( e . g . kidney disease ) , suggesting a shared genetic basis or pathomechanisms involving metabolic alterations ., The identified loci are strong candidates for future functional studies directed to understand human metabolism and pathogenesis of related diseases .

journal.pgen.1004775

Genomic Evidence of Rapid and Stable Adaptive Oscillations over Seasonal Time Scales in Drosophila

All organisms live in environments that vary through time and such environmental heterogeneity can impose highly variable selection pressures on populations ., In this situation , an allele may be beneficial during one environmental regime and subsequently deleterious during another ., Such an allele would be subject to short bursts of directional selection , alternately being favored and disfavored ., When this situation occurs in diploids , the heterozygote can have a higher geometric mean fitness than either homozygote and allelic variation at this locus could be maintained for long periods despite being subject to directional selection at any given time 1–8 ., This situation is referred to as marginal overdominance and is a form of balancing selection ., There is substantial evidence for the maintenance of phenotypic and genetic variation by temporally variable selection in a variety of organisms ., For instance , evolutionary response to rapid changes in selection pressures has been demonstrated for morphological and life-history traits in mammals 9 , 10 , birds 11–13 , plants 14 , invertebrates 15–24 , and others ( reviewed in 25 , 26 ) ., Chromosomal inversions and allozyme alleles in a variety of drosophilids vary among seasons 27–33 suggesting that these polymorphisms confer differential fitness in alternating seasons ., Further , in some species of drosophilids , life-history 34 , 35 , morphological 36 , 37 and stress tolerance traits 38 , 39 also fluctuate seasonally suggesting that these traits respond to seasonal shifts in selection pressures ., Although theoretical models suggest that temporal variation in selection pressures can maintain fitness-related genetic variation in populations 1–8 and empirical evidence from a variety of species 9–39 demonstrates that variation in selection pressures over short time periods does alter phenotypes and allele frequencies , we still lack a basic understanding of many fundamental questions about the genetics and evolutionary history of alleles that undergo rapid adaptation in response to temporal variation in selection pressures ., Specifically , we do not know how many loci respond to temporally variable selection within a population , the strength of selection at each locus , nor the effects of such strong selection on neutral genetic differentiation through time ., We do not know whether adaptation at loci that respond to temporally variable selection is predictable nor do we know the relationship between loci that respond to temporally variable selection and spatially varying selection ., Finally , it is unclear whether rapid adaptation to temporally variable selection pressures is primarily fueled by young alleles that constantly enter the population but cannot be maintained for long periods of time or , rather , by old alleles that have possibly been maintained by variable selection associated with environmental heterogeneity despite short bursts of strong directional selection ., To address these questions , we estimated allele frequencies genome-wide from samples of D . melanogaster individuals collected along a broad latitudinal cline in North America and in the spring and fall over three consecutive years in a single temperate orchard ., We demonstrate that samples of flies collected in a single Pennsylvania orchard over the course of several years are as differentiated as populations separated by 5–10° latitude ., We identify hundreds of polymorphisms that are subject to strong , temporally varying selection and argue that genetic draft 40 in the wake of rapid , multilocus adaptation is sufficient to explain the high degree of genetic turnover that we observe in this population over several years ., We examine the genome-wide relationship between spatial and temporal variation in allele frequencies and find that spatial genetic differentiation , but not clinality per se , in allele frequency is a good predictor of temporal variation in allele frequency ., Moreover , at SNPs subject to seasonal fluctuations in selection pressures , northern populations are more similar to spring populations than southern ones are ., Next , we show that allele frequencies at SNPs subject to seasonal fluctuations in selection pressures become more ‘spring-like’ ( i . e . , they move towards the average spring frequency ) immediately following a hard frost event and that seasonally variably SNPs tend to be associated with two seasonally variable phenotypes , chill coma recovery time and starvation tolerance ., Finally , we demonstrate that some of the loci that respond to temporal variation in selection pressures are likely ancient , balanced polymorphisms that predate the split of D . melanogaster from its sister species , D . simulans ., Taken together , our results are consistent with a model in which temporally variable selection maintains fitness-related genetic variation at hundreds of loci throughout the genome for millions of generations if not millions of years ., To test for the genomic signatures of balancing selection caused by seasonal fluctuations in selection pressures , we performed whole genome , pooled resequencing of samples of male flies collected in the spring and fall over three consecutive years ( 2009–2011 ) in a temperate , Pennsylvanian orchard ., We contrast changes in allele frequencies through time with estimates of allele frequencies we made from five additional populations spanning Florida to Maine along the east coast of North America over a number of years ( 2003–2010 ) largely during periods of peak abundance of D . melanogaster ( Fig . 1A , Table S1 ) ., From each population and time point , we sampled approximately 50–100 flies and resequenced each sample to average read depth of 20–200× coverage ( Table S1 , and see Text S1 ) ., Estimates of allele frequency using this sampling design have been shown to be highly accurate 40 ., As a point of departure and to provide context for understanding the magnitude of genetic variation through the seasons , we first examined genetic differentiation along the cline ( Fig . 1B , Fig . S1A ) ., We calculated genome-wide average FST among pairs of populations ( excluding Pennsylvanian populations; hereafter ‘spatial FST’ ) as well as the proportion of SNPs where average spatial FST between a pair of populations is greater than expected by chance conditional on our sampling design and assuming panmixia using allele frequency estimates of 500 , 000 common polymorphisms ( Table S1 ) ., Genome-wide average spatial FST ( Fig . 1B ) as well as the proportion of SNPs where spatial FST is greater than expected by chance ( Fig . S1A ) is positively correlated with geographic distance ( r\u200a=\u200a0 . 75; p\u200a=\u200a7e-5 ) , a pattern consistent with isolation by distance 41 ., Pooled resequencing did identify polymorphisms in or near genes previously shown to be clinal in North American populations ( see Text S1 ) demonstrating that clines are stable over multiple years ., This suggests that populations sampled along the cline represent resident populations , and further confirms that our pooled resequencing design gives accurate estimates of allele frequencies 42 ., Next , we calculated genome-wide average FST between samples collected through time in the Pennsylvanian population ( ‘temporal FST’ ) as well as the proportion of SNPs where average temporal FST is greater than expected by chance given our sampling design and assuming no allele frequency change through time ( Fig . 1C , Fig . S1B ) ., Genome-wide average temporal FST ( Fig . 1C ) as well as the proportion of SNPs where the observed temporal FST is greater than expected by chance ( Fig . S1B ) increases with the difference in time between samples ., The temporal FST increases non-linearly with duration of time between samples ( slopelog-log\u200a=\u200a0 . 59 , plog-log slope\u200a=\u200a1\u200a=\u200a0 . 0004 , df\u200a=\u200a19 ) ., Genome-wide average temporal FST appears to asymptote by ∼7 months , corresponding to the duration of time between fall samples and the subsequent spring sample ., Remarkably , samples of the Pennsylvanian population collected one to three years apart are as differentiated as populations separated by 5–10° latitude , demonstrating high genetic turnover through time ., We sought to identify alleles whose frequency consistently and repeatedly oscillated between spring and fall over three years with the assumption that these polymorphisms would be the most likely to be adaptively responding to selection pressures that oscillate between the seasons ., We identified seasonally variable polymorphisms that had a large and recurrent deviation from spring to fall around the average frequency using a generalized linear model ( GLM ) of allele frequency change as a function of season ( spring or fall ) that took into account read depth and the number of sampled chromosomes ( see Materials and Methods for details ) ., Of the ∼500 , 000 common SNPs tested , we identified approximately 1750 sites that cycle approximately 20% in frequency between spring and fall at FDR less than 0 . 3 ( hereafter ‘seasonal SNPs’; Fig . 2A , Fig . S2A ) ., Statistically significant changes in allele frequency of this magnitude at seasonal SNPs correspond to selection coefficients of 5–50% per locus per generation ( Fig . 2B , see Materials and Methods ) , assuming 10 generations per summer or 1–2 generations per winter ., Given the statistical power of our experiment ( Fig . 2B ) , we estimate there may be as many as 10 times as many sites that could cycle either directly in response to seasonally varying selection or could be linked to seasonal SNPs ., Our rationale for focusing on the1750 seasonal SNPs at the FDR of 0 . 3 is that we are seeking to assess general molecular and evolutionary features of polymorphisms that may underlie rapid adaptive evolution in response to seasonal fluctuations in selection pressure ., To assess these general features and enrichments , we require a sufficient number of true positive SNPs while maintaining as low a false positive rate as possible ., Reducing FDR rates to lower values yielded an insufficient number of polymorphisms to assess enrichments with adequate precision ( FDR of 10% yields 11 SNPs; FDR cutoff of 20% yields 200 SNPs ) ., We note that our estimation of ∼1750 seasonal SNPs and their associated FDR should only be taken as a rough estimate of the number of seasonally varying SNPs: variance in linkage disequilibrium through the genome , heterscedasticity due to possible demographic events , limited statistical , unbalanced sampling of flies and variance in read-depth among samples , and modeling assumptions will affect our ability to infer the exact number of seasonally varying SNPs ., One way to address some of these issues ( e . g . , heteroscedasticity ) is to model allele frequency change through time with generalized linear mixed-effect ( GLMM ) or general estimation equation ( GEE ) models that account , to varying degrees , for the structured , time-series nature of our data ., Seasonal SNPs inferred with these models are highly congruent with seasonal SNPs inferred using a simple GLM ( Fig . S2D , E ) and q-q plots of the distribution of p-values from GLM , GLMM and GEE models suggest that GLM and GLMM modeling strategies fit the bulk of the genome well , with GEE models appearing to be anti-conservative ( Fig . S2B , C ) ., However , the identification of a statistical excess of seasonally oscillating SNPs by any modeling strategy will be subject to a number of assumptions that will almost certainly be violated in some way or another and such violations could possibly lead to an increased false-positive rate ., Because the false positive and false negative rates are inherently difficult to estimate , we adopt an empirical strategy to demonstrate that the seasonal SNPs identified though a simple GLM are not a random sample of SNPs but rather are enriched for true positive SNPs that directly underlie the adaptive response to seasonal fluctuations selection pressure ., The identified seasonal SNPs are enriched for many signatures consistent with natural selection relative to control SNPs that are matched for several biologically and experimentally relevant parameters such as chromosome , recombination rate , allele frequency , and SNP quality coupled with a rigorous blocked-bootstrap procedure that accounts for the spatial distribution of seasonal SNPs along the chromosome ( see Materials and Methods and Table S3 ) ., We now proceed to demonstrate these enrichments ., Seasonal SNPs are enriched among functional genetic elements ., These polymorphisms are likely to be in genic ( i . e . , 3′ and 5′ UTR , synonymous and non-synonymous , and long-intron SNPs; p\u200a=\u200a0 . 054 ) and coding regions ( synonymous and non-synonymous; p<0 . 002 ) and are enriched among synonymous ( p<0 . 002 ) , non-synonymous ( p\u200a=\u200a0 . 002 ) and 3′ UTR ( p\u200a=\u200a0 . 024 , Fig . 2C ) relative to control , putatively neutral polymorphisms in short-introns 43 ., The p-values of the enrichment tests were calculated after controlling for the spatial distribution of seasonal SNPs along the chromosome using a block bootstrap procedure coupled with the identification of paired control SNPs matched for several key genomic features ( Table S3 ) , such as recombination rate , average allele frequency in the Pennsylvanian orchard , chromosome , and SNP quality ( see ‘Block Bootstrap’ section in Materials and Methods ) ., Enrichment of adaptively oscillating polymorphisms among these genetic elements , including synonymous sites , suggests that these SNPs may affect organismal form and function through modification of protein function , translation rates , or mRNA expression and stability 43 , 44 ., Next , we show that rapid shifts in allele frequency at seasonal SNPs perturb allele frequencies at nearby SNPs ., Adaptively oscillating polymorphisms are in regions of elevated temporal FST ( Fig . 2D ) and the elevation of temporal FST decays , on average , by ∼500 bp , consistent with patterns of linkage disequilibrium in D . melanogaster 45 ., Elevation of temporal FST within 500 bp of seasonal SNPs could contribute to high levels of genome-wide average FST through time ( Fig . 1C ) ., However , excluding SNPs within 500 bp of seasonal SNPs did not change patterns of genome-wide differentiation through time suggesting that genome-wide patterns of FST through time are not driven by the seasonal SNPs themselves nor the SNPs in their immediate vicinity ( Fig . S3 ) ., Seasonal SNPs are spread throughout the genome ( Fig . 3A ) and there is a 95% chance of finding at least one seasonal SNP per megabase of the euchromatic genome ., This result suggests that seasonal SNPs are not exclusively concentrated in any single region ( such as an inversion ) nor distributed among a small number of regions ( such as a limited number of genes ) ., Although seasonal SNPs are distributed throughout the genome , their distribution is over-dispersed ., To assess this , we calculated the number of seasonal SNPs per 1000 SNPs under investigation in non-overlapping windows of 1000 SNPs ., If seasonal SNPs are homogeneously distributed throughout the genome , the rate of seasonal SNPs/1000 SNPs should follow a Poisson distribution with mean equal to the variance ., After accounting for heterogeneity in recombination rate throughout the genome ( see Materials and Methods ) , we find that the variance in the rate of seasonal SNPs is ∼2 . 3 times greater than expected under a Poisson distribution ( p<10−10 ) implying that some regions have an excess of seasonal SNPs and some have a deficit of seasonal SNPs ., The overdispersion of seasonal SNPs throughout the genome could be caused by several factors including variation in the density of functional elements , multiple functional and clustered seasonal SNPs , variance in the age of seasonal SNPs , or inversion status ., In general , we find no evidence that seasonal SNPs are enriched among large , cosmopolitan inversions segregating in North American populations ( p>0 . 05 , Fig . S4 ) , with only one inversion , In3R ( Mo ) , marginally enriched for seasonal SNPs ( p\u200a=\u200a0 . 02 , with p\u200a=\u200a0 . 18 after Bonferroni correction for multiple testing ) ., In addition , seasonal SNPs are significantly more common in the Pennsylvanian orchard population than polymorphisms perfectly linked 46 to large cosmopolitan inversions ( Fig . 2E ) and polymorphisms linked to inversions do not vary between seasons ( Fig . 2E , p>0 . 05 ) , including those linked to In3R ( Mo ) ., Therefore , enrichment of seasonal SNPs within In3R ( Mo ) , if present , is most likely due to increased linkage disequilibrium caused by decreased recombination surrounding this inversion 47 ., Taken together , these results indicate that the inversions themselves do not cycle seasonally in the Pennsylvanian population in any appreciable manner ( Fig . 2E ) and suggests that adaptive evolution to seasonal variation in selection pressures may be highly polygenic ., To test the hypothesis that spatially varying selection pressures along the latitudinal cline reflect seasonally varying selection pressures in the Pennsylvanian population , we examined the relationship between temporal and spatial variation in allele frequencies ., To quantify spatial variation in allele frequency , we calculated two statistics ., First , we estimated average pairwise FST among all populations for each SNP ( ‘spatial FST’ ) ., Second , we estimated clinality for each SNP by calculating the per-SNP false discovery rate ( FDR ) of the relationship between allele frequency and latitude using a generalized linear model that takes into account read depth and the number of sampled chromosomes ( hereafter ‘clinal q-value’ ) ., Spatial FST and clinal q-value are highly correlated ( r\u200a=\u200a0 . 63 , p<1e-10; Fig . S5 ) demonstrating that most , but not all , spatial variation along the latitudinal cline is represented by monotonic changes in allele frequency between northern and southern populations ., We calculated the number of clinally varying polymorphisms ( clinal q-value<0 . 1 ) and the number of adaptively oscillating polymorphisms per common segregating SNP ( average , North American MAF>0 . 15 ) per megabase of the genome ( Fig . 3A ) ., Approximately one out of every three common polymorphisms varies with latitude with FDR<0 . 1 ( i . e . , clinal q-value<0 . 1 ) whereas only one out of every three thousand polymorphisms varies predictably between seasons with seasonal FDR<0 . 3 ( Fig . 3A ) ., Although our ability to detect clinal SNPs at FDR<0 . 1 is greater than our ability to detect seasonal SNPs at FDR<0 . 3 ( cf . Fig . 2B , Fig . S6 ) , differences in power cannot explain the three order of magnitude difference in the number of detected clinal and seasonal SNPs ( cf . Fig . 2B , Fig . S6 ) ., Next , we formally tested whether seasonal SNPs are enriched among spatially varying SNPs ., Spatially varying SNPs , as defined by spatial FST , are more likely to be seasonal SNPs than expected by chance ( Fig . 3B ) , and the odds of this enrichment increases with increasing spatial differentiation ., In contrast , we cannot reject the null hypothesis of no enrichment of seasonal SNPs among clinal SNPs as defined by clinal q-value ( Fig . 3C ) ., The observed differences in the enrichment of seasonal SNPs among SNPs with high spatial FST and low clinal q-value may reflect aspects of our sampling design and differences in the evolutionary forces that shape allele frequencies through time and space ., We sampled flies along the East Coast during different years and at different points of time relative to the progression of the growing season in each population ( Table S1 ) ., Thus , in each sampled clinal population , seasonal SNPs would be at different points in their adaptive trajectory ., Consequently , seasonal SNPs would not likely have exceedingly low clinal q-values , a statistic which reflects the deviation of observed allele frequencies from the predicted value as estimated by a GLM ., Rather , seasonal SNPs would likely be highly differentiated along the cline ( i . e . , have a large spatial FST ) ., SNPs with low clinal q-values , therefore , represent those SNPs that do not change in frequency between seasons and possibly reflect long-term demographic processes or adaptation to selection pressures that vary clinally , but not seasonally ., Because of the relationship between spatial differentiation and seasonal variation in allele frequencies ( Fig . 3B ) and because of parallels between spatial and seasonal variation in climate , we hypothesized that northern populations should be more ‘spring-like’ and southern populations should be more ‘fall-like’ in allele frequencies at the seasonal SNPs ., To test this hypothesis , we calculated the absolute difference in allele frequencies for each population sampled along the cline with the average spring and fall allele frequency estimates for the Pennsylvanian population for all seasonal SNPs ., Indeed , allele frequency estimates at seasonal SNPs from high latitude populations are more similar to spring Pennsylvanian populations and those from low latitude are more similar to fall populations ( Fig . 3D ) demonstrating that latitudinally varying selection pressures at least partially reflect seasonally varying selection pressures ., In the late fall of 2011 , about two weeks after our 2011 fall sample was collected , a hard frost occurred in the Pennsylvanian orchard ( Fig . 4A ) ., We were able to obtain a sample of D . melanogaster approximately one week after the frost and we estimated allele frequencies genome-wide from this sample ., We hypothesized that allele frequencies at seasonal SNPs would predictably change following the frost event and would become more ‘spring-like . ’ To test this hypothesis , we calculated the probability that post-frost allele frequencies at seasonal SNPs overshoot the long-term average allele frequency ( i . e . , become more ‘spring-like’ ) ., We also estimated this probability for control polymorphisms , matched to adaptively oscillating polymorphisms by several characteristics ( Table S3 ) including , importantly , difference in allele frequency between the long-term average and the pre-frost allele frequency ., This later control is essential given that some shift in the ‘spring-like’ direction is expected here simply by chance due to regression to the mean ., The probability that seasonal SNPs overshoot the long-term average allele frequency is ∼43% , whereas only ∼35% of control polymorphisms overshoot the long-term average ., This significant excess of adaptively oscillating polymorphisms that become more ‘spring-like’ following the frost event ( Fig ., 4B; log2 ( OR ) =\u200a0 . 48 , p<0 . 002 ) suggests that these SNPs respond to acute changes in climate and that cold temperatures associated with winter is one selective force acting on this population shaping allele frequencies between seasons ., Chill-coma recovery time and starvation tolerance are two phenotypes that vary seasonally in drosophilid populations 48–53 ., Accordingly , we hypothesized that the winter-favored allele at seasonal SNPs would be associated with decreased chill-coma recovery time and increased starvation tolerance ., To test this hypothesis , we used allele frequency data from previously published tail-based mapping of chill-coma recovery time and starvation tolerance 54 ., We show that the winter favored allele at seasonal SNPs is more likely to be associated with fast chill coma recovery time than expected by chance across a range of GWAS p-values ( Fig . 5A ) ., A similar analysis of starvation tolerance was equivocal but the general pattern is that the winter-adaptive allele is associated with increased starvation tolerance ( Fig . 5B ) ., Balancing selection caused by variation in selection pressures through time can in principle maintain allelic variation at adaptively oscillating loci and elevate levels of neutral diversity surrounding these balanced polymorphisms ., Thus , if seasonal variation in selection pressures promotes balanced polymorphisms we hypothesized that seasonal SNPs would be old and present in regions of elevated polymorphism ., We tested the hypothesis that seasonal SNPs are old by first examining their allele frequencies in a broad survey of African D . melanogaster populations 55 ., Approximately 5% of seasonal SNPs are rare in Africa ( MAF<0 . 01 ) , however these SNPs are not more likely to be rare in Africa than control polymorphisms ( log2 ( odds ratio ) =\u200a0 . 96; p\u200a=\u200a0 . 328 ) ., Interestingly , for seasonal SNPs where one allele is rare in Africa , the summer favored alleles are more likely to be rare in Africa than winter favored alleles ( log2 ( odds ratio ) =\u200a0 . 475; p\u200a=\u200a0 . 018 ) ., Because the vast majority of seasonal SNPs segregate in Africa , it appears that adaptation to temperate environments , and particularly winter conditions , relies primarily on old , standing genetic variation ., Balancing selection acts to maintain alleles at intermediate frequencies for long periods of time and , in some instances , can maintain polymorphism across species boundaries 56 , 57 ., We examined whether seasonal SNPs showed signatures of long-term balancing selection by examining patterns of polymorphism surrounding orthologous regions in D . simulans , the sister species to D . melanogaster ., We note that the following analyses are conservative because we underestimate D . simulans diversity given the small number ( <6 ) of D . simulans haplotypes used ., First , we demonstrate that seasonal SNPs are approximately 1 . 5 times more likely to be polymorphic and share the same two alleles identical by state in both species relative to control SNPs ., This pattern is observed for all seasonal SNPs ( Fig . 6 , p<0 . 002 ) and for seasonal SNPs residing in genes ( Fig . 6 , p<0 . 002 ) ., The increased probability of shared polymorphism between D . melanogaster and D . simulans at seasonal SNPs could , in principle , be driven by an over-representation of synonymous , genic SNPs ( Fig . 2C ) ., Unless synonymous SNPs are in four-fold degenerate positions , certain mutations may cause them to be non-synonymous thereby limiting the number of possible neutral allelic states and increasing the probability of shared polymorphism between species ., However , adaptively oscillating SNPs that do not reside in synonymous sites are also more likely than expected by chance to be polymorphic and share the same two alleles by state in D . melanogaster and D . simulans ( Fig . 6 , p\u200a=\u200a0 . 014 ) ., The co-occurrence of shared polymorphism between D . melanogaster and D . simulans could result from three evolutionary mechanisms ., First , trans-specific polymorphisms could result from adaptive introgression ., This scenario seems implausible given the high degree of pre- and post-zygotic isolating mechanisms between these two species 58 , 59 ., Furthermore , if trans-specific polymorphisms resulted from recent adaptive introgression we would expect average pairwise divergence between D . melanogaster and D . simulans surrounding seasonal SNPs to be smaller than at control SNPs ., However , there is no significant difference in estimates of divergence between seasonal and control SNPs ( p\u200a=\u200a0 . 7 for windows ±250 bp ) ., Second , trans-specific polymorphisms could result from convergent adaptive evolution ., Finally , trans-specific polymorphisms could be millions of years old 60 , predating the divergence of D . melanogaster from D . simulans ., While we cannot differentiate these latter two mechanisms , we postulate that the most parsimonious explanation is that trans-specific seasonal SNPs predate the divergence of these two sister species ., Despite empirical support for the conclusion that seasonal SNPs show many signatures consistent with adaptive response to seasonally variable selection , drift , caused by cyclic population booms and busts , or migration from neighboring demes are alternative mechanisms that could drastically perturb allele frequencies in the Pennsylvanian population and could generate some of the genome-wide patterns we observe ., We address these possibilities here and conclude that neither cyclic changes in population size nor seasonal migration can plausibly explain the extent of genome-wide genetic differentiation through time , the observed number of seasonal SNPs , nor the enrichment of seasonal SNPs among many distinct genomic features ( e . g . , Figs . 2–6 ) ., At the same time , we also show through several simulation approaches that rapid adaptive evolution in response to seasonal fluctuations in selection pressure is sufficient to explain patterns of allele frequency change through time ., Furthermore , we discuss how large-scale migration is internally inconsistent with certain aspects of our data ., Taken together , we conclude that rapid adaptive evolution to seasonally variable selection is required to explain the patterns of allele frequency change through time at seasonal SNPs and at linked neutral loci that we observe in our dataset ., First , we assessed the possibility that extensive drift caused by population contraction every winter 31 , 61 , 62 could generate genome-wide patterns of genetic differentiation through time observed in our data ., To do so , we conducted forward genetic simulations that model biologically plausible variation in population size and included loci that cycle in frequency due to variable selection pressures 63 ., For these simulations , we modeled a 20 Mb chromosome with constant recombination rate of 2 cM/Mb , representing the genome-wide average recombination rate in D . melanogaster 64 ., We simulated population contraction to one of various minimum , ‘overwintering’ population sizes followed by exponential growth over 10 generations in the ‘summer’ to a fixed maximum population size ., In these models , we included various numbers of loci that respond to seasonally varying selection ., Selection coefficients for each locus were set such that allele frequencies at selected sites oscillated by ∼20% , between 60 and 40% , representing the average change in allele frequency we actually see between spring and fall at seasonal SNPs ., Finally , we placed 500 neutral loci randomly along the simulated chromosome and measured FST at these neutral loci between three ‘spring’ ( i . e . , first generation of population expansion ) and ‘fall’ ( last generation of population expansion ) samples ., See Materials and Methods for more details these models ., In the absence of seasonal selection , these forward simulations suggest that overwintering Ne would have to be exceedingly low ( ∼20; Fig . 7A ) to generate levels of FST between spring and fall as high as we observe in our data ( arrow in Fig . 1C ) ., However , with overwintering Ne of 200 and 5–10 seasonally adaptive SNPs per chromosome arm , simulated FST at neutral loci is on the order of 0 . 002 ( Fig . 7A ) , which we observe in our data ( arrow in Fig . 1C ) ., While we do not know overwintering population size , we speculate it could be on the order of 200 flies or likely substantially larger 61 , 62 and conclude that at least 25–50 ( 5–10 per main chromosome arm ) loci are sufficient to generate patterns of differentiation we observe through time ., Note that increasing the overwintering population size requires concomitant increase in number of seasonally selected loci ., We regard overwintering population sizes of ∼20 flies to be inconsistent with certain aspects of our data and also implausible given what we know about the biology of the species ., First , such a severe population contraction would result in reduction of genetic diversity , particularly for low frequency alleles ., However , the observed allele frequency spectrum between fall and the following spring samples is similar and spring samples do not exhibit the expected loss of low frequency polymorphisms that would result from a popu

Introduction, Results/Discussion, Materials and Methods

In many species , genomic data have revealed pervasive adaptive evolution indicated by the fixation of beneficial alleles ., However , when selection pressures are highly variable along a species range or through time adaptive alleles may persist at intermediate frequencies for long periods ., So called “balanced polymorphisms” have long been understood to be an important component of standing genetic variation , yet direct evidence of the strength of balancing selection and the stability and prevalence of balanced polymorphisms has remained elusive ., We hypothesized that environmental fluctuations among seasons in a North American orchard would impose temporally variable selection on Drosophila melanogaster that would drive repeatable adaptive oscillations at balanced polymorphisms ., We identified hundreds of polymorphisms whose frequency oscillates among seasons and argue that these loci are subject to strong , temporally variable selection ., We show that these polymorphisms respond to acute and persistent changes in climate and are associated in predictable ways with seasonally variable phenotypes ., In addition , our results suggest that adaptively oscillating polymorphisms are likely millions of years old , with some possibly predating the divergence between D . melanogaster and D . simulans ., Taken together , our results are consistent with a model of balancing selection wherein rapid temporal fluctuations in climate over generational time promotes adaptive genetic diversity at loci underlying polygenic variation in fitness related phenotypes .

Herein , we investigate the genomic basis of rapid adaptive evolution in response to seasonal fluctuations in the environment ., We identify hundreds of polymorphisms ( seasonal SNPs ) that undergo dramatic shifts in allele frequency – on average between 40 and 60% – and oscillate between seasons repeatedly over multiple years , likely inducing high levels of genome-wide genetic differentiation ., We provide evidence that seasonal SNPs are functional , being both sensitive to an acute frost event and associated with two stress tolerance traits ., Finally , we show that some seasonal SNPs are possibly ancient balanced polymorphisms ., Taken together , our results suggest that environmental heterogeneity can promote the long-term persistence of functional polymorphisms within populations that fuels fast directional adaptive response at any one time .

biochemistry, evolutionary ecology, ecology, phenotypes, evolutionary processes, ecological economics, genetics, biology and life sciences, dna, population genetics, evolutionary biology, evolutionary genetics, genetic loci

journal.pgen.1006799

Evolutionary forces affecting synonymous variations in plant genomes

Base composition strongly varies across and within plant genomes 1 ., This is especially striking at the coding sequence level for synonymous sites where highly contrasted patterns are observed ., Most Gymnosperms , basal Angiosperms and Eudicots have relatively GC-poor and homogeneous genomes ., In contrast , Monocot species present a much wider range of variation from GC-poor species to GC-rich and highly heterogeneous ones , some with bimodal GC content distribution among genes , these differences being mainly driven by GC content at third codon position ( GC3 ) 1 ., Commelinids ( a group containing palm trees , banana and grasses , among others ) have particularly GC-rich and heterogeneous genomes but GC-richness and bimodality have been showed to be ancestral to Monocots , suggesting erosion of GC content in some lineages and maintenance in others 2 ., As a consequence , in most species , synonymous codons are not used in equal frequency with some codons more frequently used than others , a feature that is called the codon usage bias reviewed in 3 ., This is true even in relatively homogeneous genomes such as in Arabidopsis thaliana e . g . 4 ., Which forces drive the evolution of genome base composition and codon usage is still under debate ., Mutational processes can contribute to observed variations between species and within genomes e . g . 5 ., However , mutation can hardly explain a strong bias towards G and C bases , as it is biased towards A and T in most organisms studied so far Chapter 6 in 6 ., Selection on codon usage ( SCU ) has thus appeared as one of the key forces shaping codon usage as it has been demonstrated in many organisms both in prokaryotes and eukaryotes reviewed in 3 ., Codon bias can thus result from the balance between mutation , natural selection and genetic drift 7 ., The main cause for SCU is likely that preferred codons increase the accuracy and/or the efficiency of translation but other mechanisms involving mRNA stability , protein folding , splicing regulation and robustness to translational errors could also play a role 3 , 8 , 9 ., In some species , SCU appears to be very weak or inexistent , typically when effective sizes are small 10 , as typically assumed for mammals but see 8 ., However , mammalian genomes exhibit strong variations in base composition , the so-called isochore structure 11 , which are mainly driven by GC-biased gene conversion ( gBGC ) 12 ., gBGC is a neutral recombination-associated process favouring the fixation of G and C ( hereafter S for strong ) over A and T ( hereafter W for weak ) alleles because of biased mismatch repair following heteroduplex formation during meiosis 13 ., Although gBGC is a neutral process–i . e . the fate of S vs . W alleles is not driven by their effect on fitness—gBGC induces a transmission dynamic during reproduction identical to natural selection for population genetics 14 ., Therefore , we here refer to it as a “selective-like” process as opposed to mutation and drift ., gBGC has been experimentally demonstrated in yeast 15 , 16 , humans 17 , 18 , birds 19 and rice 20 ., Many indirect genomic evidences also supported gBGC in eukaryotes 21 , 22 and even recently in some prokaryotes 23 , although it seems to be weak or absent in some species as Drosophila 24 where selection on codon usage predominates 25 , 26 , 27 , 28 ., In plants , both SCU 4 , 29 , 30 and gBGC 21 , 31 , 32 have been documented , but how their magnitudes and relative strength vary among species remains unclear ., Recently , it has been proposed that the wide variations in genic GC content distribution observed in Angiosperms could be explained by the interaction between gene structure , recombination pattern and gBGC 33 ., Increasing evidence suggests that in various organisms , including plants , recombination occurs preferentially in promoter regions of genes , or near transcription initiation sites 34 , 35 , 36 ., This generates a 5’-3’ recombination gradient , and consequently a gBGC gradient , which could explain the 5’-3’ GC content gradient observed in GC-rich species , such as Commelinids 1 , 2 ., A mechanistic consequence is that short genes , especially with no or few introns , are on average GC-richer 37 ., A stronger gBGC gradient and/or a higher proportion of short genes would increase the average GC content and simple changes in the gBGC gradient can explain a wide range of GC content distribution from unimodal to bimodal ones 33 ., So far , the magnitude of gBGC and SCU has been quantified only in a handful of plant species 29 , 30 , 32 , 38 ., As in other species studied , weak SCU and gBGC intensities were estimated ., The population-scale coefficients , 4Nes or 4Neb , are usually of the order of 1 , where Ne is the effective population size and s and b the intensity of SCU and gBGC respectively 26 , 29 , 30 , 32 , 38 , 39 ., However , high gBGC values ( 4Neb > 10 ) have been estimated in the close vicinity of recombination hotspots in mammals 38 , 40 and across the entire honeybee genome 41 ., Differences in population-scale intensities can be due to variation in Ne and/or in s or b ., For gBGC , b is the product of the recombination rate r and the basal conversion rate per recombination event , b0 ., Within a genome , variations in gBGC intensities are mainly due to variation in recombination rate e . g . 38 ., Among species , b0 can also vary ., For instance , b was estimated to be 2 . 5 times lower in honeybees than in humans but recombination rate is more than 18 times higher 41 , suggesting that b0 could be 45 times lower in honeybees than in humans ., The very intense population-scale gBGC in honeybees is thus explained by the combination of a large Ne and extremely high recombination rates 41 ., Several methods have been developed to estimate the intensity of SCU and gBGC , either from polymorphism data alone , or from the combination of polymorphism and divergence data e . g . 26 , 27 , 38 ., These methods rely on the fact that preferred codons ( for SCU ) or GC alleles ( for gBGC ) are expected to segregate with higher frequency than neutral and un-preferred or AT alleles , fitting a population genetics model with selection or gBGC to the different site frequency spectra ( SFS ) ., As demography affects SFS , it must be taken into account in the model ., Moreover , mutations must be polarized , i . e . the ancestral or derived state of mutations must be determined using one or several outgroup species ., Otherwise , selection or gBGC can be estimated from the shape of the folded SFS by assuming equilibrium base composition 42 or allowing only recent change in base composition e . g . 25 , 26 , 27 , which is not the case in mammals 43 and some Monocots 2 , for example ., As errors in the polarization of mutations can lead to spurious signatures of selection or gBGC 44 , this issue must also be taken into account ., We specifically address the following questions:, ( i ) do neutral or selective forces mainly affect base composition ?, ( ii ) if active , what are the intensities of gBGC and SCU and how do they vary across species ?, ( iii ) are the average gBGC and the 5’-3’ gBGC gradient stronger in GC-rich genomes ?, To do so we used and extended the recent method developed by Glémin et al . 38 that controls for both demography and polarization errors ., We applied it to a large population genomic dataset of 11 species spread across the Angiosperm phylogeny to detect and quantify the forces affecting synonymous positions ., Our results show that base composition is far from mutation-drift equilibrium in most studied genomes , that gBGC is a widespread process being the major force acting on synonymous sites , overwhelming the effect of SCU and contributing to explain the difference between GC-rich ( Commelinids , here ) and GC-poor genomes ( Eudicots and yam , here ) ., We focused our analyses on 11 plant species spread across the Angiosperm phylogeny with contrasted base composition and mating systems ( Fig 1 and Table 1 ) ., To survey the wide variation observed in Monocots , and in line with the sampling of a previous study 2 , we sampled one basal Monocots ( Dioscorea abyssinica , yam ) , two non-grass Commelinids ( Musa acuminata , banana and Elaeis guineensis , palm tree ) and three grasses with contrasted mating system ( Pennisetum glaucum , pearl millet , Sorghum bicolor , sorghum and Triticum monococcum , einkorn wheat ) ., In Eudicots , both Rosids ( Theobroma cacao , cacao and Vitis vinifera , grapevine ) and Asterids ( Coffea canephora , coffee tree , Olea europaea , olive tree and Solanum pimpinellifolium , tomato ) are represented ., For practical reasons cultivated species have been chosen but we only sampled wild individuals over the species range , except for palm tree for which cultivated individuals were sampled ( See S1 Table for sampling details ) ., In this species cultivation is very recent without real domestication process ( 19th century 45 ) ., For each species , we used RNA-seq techniques to sequence the transcriptome of about ten individuals plus two individuals from two outgroup species , giving a total of 130 individual transcriptomes ., Using transcriptomes has been shown to be a useful approach for comparative population genomics with no or minor bias for genome wide comparison 46 , 47 ., When a well-annotated reference genome was available ( see Material and methods ) , we used it as a reference for read mapping ., Otherwise we used a de novo transcriptome assembly already obtained for these species ( focal + outgroups ) 48 ( Table 1 and S2 Table ) ., After quality trimming and mapping of the raw reads , we kept contigs with at least one read mapped for every individual , giving between more than 24 , 000 ( P . glaucum ) and 45 , 000 ( in O . europaea ) contigs per species ( Table 1 ) ., This initial dataset was used for gene expression analyses ( see below ) ., Genotype calling and filtering of paralogous sequences were performed using the read2snp software 47 for each species separately , and coding sequence regions were extracted ( see Material and methods ) ., The resulting datasets were used to compute nucleotide diversity statistics that did not require any outgroup information ., The number of identified SNPs varies from 4 , 409 in T . monococcum ( which suffered from the lowest depth of sequencing ) to 115 , 483 in C . canephora ., Variations in the numbers of SNPs also revealed the large variation in polymorphism levels with πS ranging from 0 . 17% in E . guineensis to 1 . 22% in M . acuminata ., The level of constraints on proteins , as measured by the πN/πS ratio , varies between 0 . 122 in T . monococcum and 0 . 261 in E . guineensis ( Table 2 ) ., For the analyses requiring polarized SNPs , we also added orthologous sequences from two outgroups to each sequence alignment of the focal species individuals ( see Material and methods ) ., The number of polarized SNPs ranged from 3 , 253 in S . pimpinellifolium to 89 , 793 in M . acuminata ., Other details about the datasets are given in Table 2 ., Overall , although the dataset does not represent the full transcriptome of each species it allows large-scale comparative analyses ., We first looked at base composition: GC3 varies from 0 . 38 to 0 . 44 in Eudicots and from 0 . 46 to 0 . 56 in Monocots ( Table 2 ) ., As observed in previous studies 2 , 43 , these values tend to be lower than genome wide averages ( when available ) but the relative differences in base composition among species were conserved , notably the GC-poorness of Eudicots compared to Monocots ., Grass species exhibited a bimodal GC3 distribution except T . monococcum where bimodality was not apparent ( S1 Fig ) ., This is likely because the sequencing depth was lower for this species so that GC-rich genes ( most likely short ones 37 ) have been under sampled ., We also characterized codon usage in each species by computing the Relative Synonymous Codon Usage ( RSCU ) for every codon as the frequency of a particular codon normalised by the frequency of the amino acid it codes for ( S3 Table , S2 Fig ) ., Patterns of RSCU were relatively consistent between species but reflected differences of GC content between them , notably a higher usage of G or C-ending codons in GC-rich species ., In order to evaluate the possible effect of selection on codon usage , we defined the sets of preferred ( P ) and un-preferred ( U ) codons for each species ., The fitness consequences of using optimal or suboptimal codons should be higher in highly expressed genes , causing the usage of optimal codons to increase with gene expression ( and that of non-optimal ones to decrease ) ., Thus , we defined preferred ( or un-preferred ) codons as codons for which RSCU increases ( or decreases ) with gene expression as in 49 ( see Materials & methods for more details ) ., S3 Table shows detailed results for each species ., In contrast with genome-wide codon usage , nearly all species showed a bias towards preferred codons ending in G or C ( Table 2 , Fig 2 and S3 Table ) , only P . glaucum and S . bicolor showing a more balanced AT/GC sharing of codon preference ., Preferences for two-fold degenerated codons were highly conserved across species , with only GC-ending preferred codon except for aspartic acid and tyrosine in P . glaucum ( Fig 2 , S3 Table ) ., Preferences for other amino acids were slightly more labile but there were always one preferred GC-ending and one un-preferred AT-ending codon common to all species ., Frequency of optimal codons of a gene ( Fop , i . e . the frequency of preferred codons 50 ) , increased with expression as expected but the difference in Fop between the most highly and most lowly expressed genes was weak to moderate ( from ~5% in C . canephora to 15% in T . monococcum and M . acuminata ) and tended to be higher in Commelinid species ( Fig 3 ) ., Because most preferred codons ended with G or C , GC3 and expression were also positively correlated in all species ., To determine which forces affect variation in base composition and codon usage among species , we first evaluated whether base composition at synonymous sites was at mutation-drift equilibrium ., Glémin et al . 38 showed that the asymmetry of the distribution of non-polarized GC allele frequencies ( measured by the skewness coefficient of the distribution ) was a robust test of this equilibrium ., This statistic is not affected by possible polarization errors ( see later for more on polarization errors ) ., A skewness coefficient equal to 0 is expected under equilibrium whereas negative ( or positive ) values mean higher ( or lower ) GC content than expected under mutation-drift equilibrium ., The same rationale can be applied to codon frequencies ., We found that GC content and the frequency of preferred codons were significantly higher than predicted by mutational effects in all species , with the exception of coffee , which interestingly showed a lower GC content than expected under mutation-drift balance ( Table 3 ) ., As base composition equilibrates slowly under mutation pressure 33 , non-equilibrium conditions could be due to long-term changes in mutational patterns ., To test further whether selective-like forces can explain the excess of GC and preferred codons , we developed a modified MacDonald Kreitman test 51 comparing W→S ( or U→P ) to S→W ( or P→U ) polymorphic and divergent sites ( Material & Methods and S1 Text ) ., SNPs and fixed mutations ( substitutions ) were polarized by parsimony using two outgroup taxa for each focal species ., We built contingency tables by counting the number of polymorphic or divergent sites for each of the two mutational categories ., From these contingency tables , we computed neutrality , NI , 52 and direction of selection , DoS , 53 indices ., In the case of selective-like forces favouring the fixation of W→S or U→P mutation , NI values are expected to be lower than 1 and DoS values to be positive ., P-values were computed from a Chi-squared test on the contingency tables ., NI was lower than 1 and DoS positive in all species except S . pimpinellifolium ( Table 3 ) , indicating that selective-like forces drove the fixation of GC and preferred codon alleles ., In P . glaucum , although significant , the departure from the neutral expectation for GC content is minute , which can be explained by very weak gBGC but also by a recent increase in its intensity ( see Results below and S1 Text ) ., Overall , this analysis showed that in most species selective-like forces tended to drive base and codon composition away from their mutational equilibrium ., Selection and gBGC are the two known alternatives whose effects have to be distinguished ., Although they may have different mechanistic causes and biological consequences , selection and gBGC leave similar evolutionary footprints and are not easy to disentangle , especially in species where most preferred codons end in G or C ( Table 2 ) ., We first applied correlative approaches to try to disentangle both processes ., Then we tried to quantify their respective intensities ., Under the SCU hypothesis , departure from neutrality should be stronger for highly expressed genes and/or genes with strongly biased codon composition ., Under the gBGC hypothesis , departure from neutrality should increase with recombination rates ., However , recombination data was not available in our datasets ., As gBGC leads to an increase in GC content , departure from neutrality should thus also increases with GC content ., We split synonymous SNPs and substitutions into eight groups of same size according to their GC3 or their gene expression level ( measured by the mean RPKM values across all individuals of a given population ) , and computed the NI and DoS indices for each category based on W/S or U/P changes ., For all species except D . abyssinica and S . bicolor , we found a strong positive ( or negative ) correlation between GC3 and DoS ( or NI ) , indicating a stronger bias in favour of S alleles in GC-rich genes ( Fig 4 ) ., In contrast , the relationship between expression level and DoS or NI measured on codon usage was weaker , with more variable and on average lower correlation coefficients ( Fig 4 ) ., These results tend to point out gBGC as a stronger force than SCU affecting synonymous variations in our datasets ., We then split our datasets into four independent categories based on two GC3 groups crossed by two expression level groups to test which factor has the strongest effect on the bias towards S or P alleles ., The rationale is that SCU should make the bias towards P alleles increase with gene expression independently of GC3 ., On the other hand , gBGC should increase the bias towards S alleles with GC3 independently of gene expression ., We found that DoS clearly increased with GC3 in all species for both lowly and highly expressed genes , with the exception of D . abyssinica and S . bicolor where it decreased for lowly expressed genes , and S . pimpinellifolium where there was little change for lowly expressed genes ., On the other hand , the effect of expression on DoS was inconsistent or only weak in most species ( Fig 5 ) ., These results confirm that the effect of gBGC appears stronger than the effect of SCU ., To evaluate further the forces affecting base composition we estimated the intensity of selection ( S = 4Nes ) and gBGC ( B = 4Neb ) from site frequency spectra ( SFS ) ., SFS for all species are represented in S3 Fig . We used the method recently developed by Glémin et al . 38 that takes SNP polarization errors into account , which avoids observing spurious signature of selection or gBGC ., As mentioned above , the observed pattern in P . glaucum ( excess of GC content but almost no departure from neutrality according to the NI and DoS indices , see Table, 3 ) suggests a recent change in the intensity of selection and/or gBGC ., Also , transition to selfing , which usually can be very recent in plants 54 , could have effectively shut down gBGC in the recent past due to a deficit in heterozygous positions ., To capture these possible changes of fixation bias through time , we extended the model of 38 by combining frequency spectra and divergence estimates as summarized on Fig 6 ( and see S2 Text for full details ) ., Divergence is determined by both mutation and selection/gBGC so it is not possible to disentangle these two factors from the divergence data alone ., However , if we assume constant and identical mutation bias at the polymorphism and the divergence level , this leave enough degrees of freedom to fit an additional S or B parameter ., Thus , we assumed a single mutation bias but two different selection/gBGC intensities , one fitted on polymorphism data and the other on divergence ., We evaluated the statistical significance of the shift in intensity by a likelihood ratio test with the model where the two intensities were equal ( i . e . no change over time ) ., Simulations showed that not taking polarization errors into account can bias selection/gBGC estimates as already shown in 38 and also leads to spurious detection of changes in selection/gBGC intensities ( S2 Text ) ., Simulations also showed that the estimated differences between the two intensities were often underestimated ., This is expected as B values estimated in the model correspond to averages over the conditions that mutations have experienced during their lifetime ( drift and gBGC/selection intensities ) , so it depends on when changes occurred ., However , the method accurately retrieved the appropriate weighted averages for B0 and B1 and efficiently accommodates for demographic variations ( see S2 Text ) ., Overall , the test of heterogeneity of selection/gBGC is a conservative approach ., If we relax the assumption of constant mutational bias , changes in both bias and selection/gBGC are no more identifiable ., Recent S/B estimates are not affected but ancestral estimates are underestimated ( resp . overestimated ) when mutation bias decreases ( resp . increases ) ., However , the method is still powerful to detect departure from a constant regime of selection/mutation/drift equilibrium ( S2 Text ) ., We applied the method to the total frequency spectra , either for W/S or U/P polymorphisms and substitutions ., In all species , significant ( at the 5% level ) gBGC or SCU were detected but at low intensity ( B or S < 1 , Table 4 ) ., In four species ( P . glaucum , E . guineensis , D . abyssinica and V . vinifera ) we found significant differences between ancestral and recent intensities for gBGC and/or SCU ., In particular , the recent significant increase in gBGC in P . glaucum ( from 0 . 224 to 0 . 524 , Table, 4 ) can explain why NI is very close to one ( or DoS close to zero ) ( see above and S1 Text ) ., On average , Monocots , especially Commelinids species tended to exhibit stronger gBGC than Eudicots and B tended to increase with mean GC3 , but no relationship is significant with only 11 species when either B0 or B1 are used ., However , using the constant B estimates ( S4 Table ) , weakly significant relationships were found for the difference between Commelinids and other species ( Wilcoxon test: p-value = 0 . 0519 ) and the correlation between B and GC3 ( ρSpearman = 0 . 691 , p-value = 0 . 023 ) ., No significant relationship was found for SCU ., No significant relationship between B or S and πS was found either ., As the two processes are entangled , it is difficult to properly and separately estimate their respective intensities ., To do so , we developed a second extension of the method of 38 ., Combining the two processes , nine kinds of mutations can occur ( see S2 Text ) ., By assuming that selection and gBGC act additively , it is in theory possible to estimate separately the two effects ., We fit a general model to the nine SFS and the nine substitution counts , with a constant mutation bias , two B and two S values ., The details of the model are reported in S2 Text ., Simulations showed that the method could efficiently estimate both gBGC and SCU but tended to slightly underestimate recent gBGC and overestimate recent SCU ( S2 Text ) ., When the distributions of SNPs and substitutions are highly unbalanced ( typically S/P and W/U states are confounded and there are very few WS-PU and SW-UP mutations ) , it is more difficult to detect both effects with a significant level ( S2 Text ) ., Finally , if assignation of codon preference is not perfect , typically for four-fold and six-fold degenerated codons , this could also underestimate SCU and reduce the power to detect it , especially for highly unbalanced dataset for which it is anyway inherently difficult to distinguish gBGC and SCU ( see S2 Text ) ., For both selection and gBGC and both ancestral and recent periods , we either fixed the value to 0 or let it be freely estimated , leading to 16 different models ., For each species , the best model according to AIC criteria ( see Methods ) is given in Table 5 while all results are given in S5 Table ., In six species the model with only gBGC was the best one , this could also include M . acuminata where it was not possible to disentangle between gBGC and SCU ., For three species , the best model included both gBGC and SCU and only S . pimpinellifolium appeared to be affected by SCU but not gBGC ., If codon preferences were perfectly determined , this result is expected to be robust and conservative because simulations suggest that SCU is slightly more easily detected than gBGC ., If there were some errors in codon preference identification , this can partly explain that SCU was less often detected ., However , the species for which SCU was not detected did not present the most unbalanced codon preference ( see Table 2 ) and identification error rate should have been rather high ( >20% see S2 Text ) to strongly bias results ., Overall , this confirms that synonymous sites are widely affected by gBGC in the studied plant species and that SCU either only plays a minor role or is partly masked by the effect of gBGC ., This method also allowed us to estimate mutation bias ., As already observed in most species , mutation was biased towards AT alleles , with a bias slightly ranging from 1 . 6 to 2 . 2 ( Table 4 ) , which is of the same order as what was found in humans 38 , 55 ., Interestingly , C . canephora was again an exception with almost no mutational bias ( λ = 1 . 05 ) ., So far , we considered either global effects at the transcriptome scale or variations among genes belonging to different categories ., However , most plant species exhibit a more or less pronounced gradient in base composition from 5’ to 3’ 1 , which is strongly linked to exon-intron structure 37 ., In particular , in some species the first exon is much GC-richer than other exons ., Moreover , it has been proposed that this gradient could be due to a gBGC gradient associated with a recombination gradient 33 ., To quantitatively test this hypothesis , we separated SNPs and fixed derived mutations as a function of their position along genes ., The best choice would have been to split them according to exon ranking 37 ., However , as exon annotation was lacking ( or imprecise ) for most species in our datasets , we split contigs into two sets: the first 252 base pairs , corresponding to the median length of the first exon in Arabidopsis , banana and rice ( Gramene database 56 ) , used as a proxy for the first exon , and the rest of the contig ., We then estimated B on these two sets of contigs ., Some imprecision in the “first exon” definition and variation in transcript length among species reduced the power of this analysis and results should be interpreted with caution ., However , we did not expect that it could create artifactual B gradient as the use of a stringent criterion reinforced the observed patterns despite reducing datasets ( see below ) ., For all species except D . abyssinica and S . pimpinellifolium , the ancestral B was higher in the first part than in the rest of contigs ., The signature was less clear for recent B as far less values were significant ., Ancestral and recent B were not significantly different in most species ( S6 Table ) ., To illustrate the global pattern , Fig 7 shows average gBGC gradients for all species , i . e . assuming the same ancestral and recent B values ., Interestingly , while there was no clear taxonomic effect on global gBGC estimates ( Table 4 ) , there was a sharp difference between Commelinid species and the others for the first part of contigs ( Wilcoxon test p-value = 0 . 030 , Fig 7C ) , in agreement with the strong 5’– 3’ GC gradient observed in these species 1 , 2 ., B values and GC3 tended to be positively correlated on the first part of contigs ( ρSpearman = 0 . 591 , p-value = 0 . 061 ) but not significantly in the rest ( ρSpearman = 0 . 382 , p-value = 0 . 248 ) ., These analyses were performed on all contigs but some of them do not start by a start codon ., We restricted the analyses to the subset of contigs starting by a start codon and we found very similar results with stronger statistical support: in the first exon , B was significantly higher in Commelinids than in other species ( Wilcoxon test p-value = 0 . 0043 ) and B values and GC3 were significantly and positively correlated both on the first part of contigs ( ρSpearman = 0 . 80 , p-value = 0 . 0052 ) and in the rest of contigs ( ρSpearman = 0 . 70 , p-value = 0 . 0208 ) ( S6 Table and S4 Fig ) ., In line with previous results showing that first exons contribute to most of the variation in GC content among species 2 , 33 , 37 , these results show that species also mostly differ in their gBGC intensities in the first part of genes ., It has already been shown that base composition in grass genomes is not at mutation-drift equilibrium with both gBGC and selection increasing GC content despite mutational bias toward A/T 31 ., Our results demonstrate that even in GC-poor genomes base composition is not at mutation-drift equilibrium , implying that selective-like forces are widespread in all the 11 plant species we studied ., In all species , either the skewness and/or the DoS/NI statistics show evidence of departure from equilibrium and purely neutral evolution ( Table 3 ) ., All species except C . canephora have higher GC content than predicted by mutational effect alone , which could be explained by a mutation/gBGC ( or selection ) /drift balance ., The case of C . canephora remains intriguing ., Mutation seems not to be biased towards AT as observed in all mutation accumulation experiments reviewed in 57 and through indirect methods 58 ., So far , GC biased mutation has only been observed in the bacteria Burkholderia cenocepacia 57 ., However , despite no apparent or very weak AT mutational bias and evidence of both recent and ancestral gBGC ( Table 4 ) , GC content is rather low ( GC3 = 0 . 42 , Table 2 ) and lower than expected under mutation pressure alone ( 1/ ( 1+λ ) = 0 . 49 ) as revealed by the positive skewness statistics ( Table 3 ) ., Preferred codons mostly end in G or C ( Table 2 ) so that SCU is not a possible explanation for this low GC content ., Rather , a recent change in mutation bias is a more probable explanation ., Using B0 = 0 . 154 or B1 = 0 . 243 ( Table 4 ) , a mutational bias of 1 . 61 or 1 . 76 would be necessary to reach the observed GC3 ( = 0 . 42 ) ., Such values are in the same range as observed for the other species ., D . abyssinica is another intriguing case where DoS decreases with GC content , contrary to other species ( Fig 4 ) ., We currently have no clear hypothesis to explain this pattern and it should be viewed with caution because DoS is estimated with few substitutions in this species but it would be compatible with an increase in AT mutation bias with GC content ., Further investigation of mutational patterns in these species would be useful to understand better these two intriguing cases ., Beyond departure from equilibrium , comparison of a

Introduction, Results, Discussion, Conclusion, Materials & methods

Base composition is highly variable among and within plant genomes , especially at third codon positions , ranging from GC-poor and homogeneous species to GC-rich and highly heterogeneous ones ( particularly Monocots ) ., Consequently , synonymous codon usage is biased in most species , even when base composition is relatively homogeneous ., The causes of these variations are still under debate , with three main forces being possibly involved: mutational bias , selection and GC-biased gene conversion ( gBGC ) ., So far , both selection and gBGC have been detected in some species but how their relative strength varies among and within species remains unclear ., Population genetics approaches allow to jointly estimating the intensity of selection , gBGC and mutational bias ., We extended a recently developed method and applied it to a large population genomic dataset based on transcriptome sequencing of 11 angiosperm species spread across the phylogeny ., We found that at synonymous positions , base composition is far from mutation-drift equilibrium in most genomes and that gBGC is a widespread and stronger process than selection ., gBGC could strongly contribute to base composition variation among plant species , implying that it should be taken into account in plant genome analyses , especially for GC-rich ones .

In protein coding genes , base composition strongly varies within and among plant genomes , especially at positions where changes do not alter the coded protein ( synonymous variations ) ., Some species , such as the model plant Arabidopsis thaliana , are relatively GC-poor and homogeneous while others , such as grasses , are highly heterogeneous and GC-rich ., The causes of these variations are still debated: are they mainly due to selective or neutral processes ?, Answering to this question is important to correctly infer whether variations in base composition may have functional roles or not ., We extended a population genetics method to jointly estimate the different forces that may affect synonymous variations and applied it to genomic datasets in 11 flowering plant species ., We found that GC-biased gene conversion , a neutral process associated with recombination that mimics selection by favouring G and C bases , is a widespread and stronger process than selection and that it could explain the large variation in base composition observed in plant genomes ., Our results bear implications for analysing plant genomes and for correctly interpreting what could be functional or not .

biotechnology, taxonomy, plant genomes, plant taxonomy, mutation, substitution mutation, plant science, data management, phylogenetics, genome analysis, plant genomics, plants, flowering plants, computer and information sciences, gene expression, monocotyledons, plant genetics, evolutionary systematics, transcriptome analysis, genetics, biology and life sciences, genomics, evolutionary biology, plant biotechnology, computational biology, organisms

journal.pbio.1000280

Experimental Evolution of a Plant Pathogen into a Legume Symbiont

Bacteria known as rhizobia have evolved a mutualistic endosymbiosis of major ecological importance with legumes that contributes ca ., 25% of global nitrogen cycling ., Rhizobia induce the formation on legumes of root nodules that they colonize intracellularly 1 and in which they fix nitrogen to the benefit of the plant ., Rhizobia are taxonomically , metabolically , and genetically diverse soil bacteria 2 , 3 ., They are currently distributed in 12 genera of α- and β-proteobacteria intermixed with saprophytes and pathogens ., The occurrence of rhizobia in several distant genera is thought to have originated from repeated and independent events of horizontal transfer of key symbiotic functions in non symbiotic bacterial genomes 2 , 4 ., Symbiotic plasmid/island transfer has been proven both in the field and in the lab 5 , 6 ., However , horizontal gene transfer cannot solely account for the wide biodiversity of rhizobia , since only a few recipient bacteria—phylogenetically close to existing rhizobia 5–8—turned into nitrogen-fixing legume symbionts ., Which phylogenetic , genetic , or ecological barriers restrict evolution of symbiotic properties and how these barriers are overcome have not been investigated so far ., Experimental evolution 9 coupled with genome resequencing 10 is a powerful approach to address the evolution of rhizobia ., Ralstonia solanacearum and Cupriavidus taiwanensis are plant-associated β-proteobacteria with drastically different lifestyles ., R . solanacearum is a typical root-infecting pathogen of over 200 host plant species ., It intercellularly invades root tissues and heavily colonizes the vascular system , where excessive production of extracellular polysaccharides blocks water traffic , causing wilting 11 , 12 ., Cupriavidus taiwanensis is the major nitrogen-fixing symbiont of Mimosa spp ., in Asia 13 , 14 ( see Figure 1A ) ., Due to their phylogenetic and genomic distance ( Figure S1 ) , C . taiwanensis and R . solanacearum are ideally suited to act as symbiotic gene provider and recipient , respectively , in experimental evolution ., Here , we report on the experimental evolution of R . solanacearum carrying the symbiotic plasmid of C . taiwanensis into Mimosa-nodulating and -infecting symbionts ., Two types of key adaptive mutations are described that are crucial for the transition from pathogenicity to mutualism ., One allows nodulation to occur , whereas the other allows intracellular infection of plant cells , a very rare event in plant-associated bacteria ., To generate our starting material , we transferred the 0 . 55-Mb symbiotic plasmid pRalta of C . taiwanensis LMG19424 into R . solanacearum strain GMI1000 , generating the Ralstonia chimeric strain CBM124 ., pRalta carries nitrogen-fixation genes and a full complement of nodulation genes required for the synthesis of lipochitooligosaccharide Nod factors ( NFs ) 15 that trigger the plant developmental program of nodule organogenesis 16 ., Nevertheless , CBM124 was unable to nodulate the C . taiwanensis legume host Mimosa pudica and retained the pathogenic properties of R . solanacearum , i . e . , pathogenicity on Arabidopsis thaliana and hypersensitive response ( HR ) induction on tobacco ( Figure S2 ) ., Note that M . pudica is not a host plant for R . solanacearum ., Several lines of evidence indicated that CBM124 had a symbiotic potential that , for an unknown reason , could not be expressed ., First , a nodB-lacZ transcriptional fusion was induced by the nod-inducer luteolin in a similar way in CBM124 and in C . taiwanensis ( Table S1 ) ., Second , mass spectrometry analysis demonstrated that CBM124 produced NFs structurally identical to those of C . taiwanensis 15 ( Figure S3 ) ., Third , CBM124 induced root hair proliferation and deformations on M . pudica , typical of those induced by NFs ( see below ) , indicating that CBM124-produced NFs were active ., To isolate clones expressing symbiotic potential , we took advantage of specific traits of the rhizobium–legume symbiosis ,, ( i ) legume plants act as a trap by selecting rare , nodulation-proficient mutants in an otherwise non-nodulating population 17 ,, ( ii ) a single bacterium enters and multiplies within the nodule 18 , which implies that a rare nodulation-conferring mutation in a population is rapidly fixed , and, ( iii ) nodulation , infection , and nitrogen fixation , are phenotypically clear-cut symbiotic stages ., Both the original chimera CBM124 and a gentamicin-resistant derivative , CBM124GenR , were used to repeatedly inoculate sets of ca ., 500 M . pudica seedlings grown in nitrogen-free conditions , as previously described 13 ., Whereas no nodules were obtained using CBM124 as an inoculum , three nodules , which appeared 3–4 wk after inoculation , were recovered from three independent CBM124GenR inoculation experiments ., One bacterial clone was isolated from each nodule , generating CBM212 , CBM349 , and CBM356 ., These three clones nodulated M . pudica with different kinetics and efficiencies ( Figure 1 ) ., Their nodulation ability was , however , reduced relative to C . taiwanensis ( Figure 1D and Figure S4 ) , and all three clones were unable to fix nitrogen ( Fix− ) ., We re-sequenced the three experimentally evolved clones as well as their immediate ancestor , CBM124GenR , using paired-end Illumina/Solexa sequencing technology ( http://www . illumina . com/ ) ., Sequence data were mapped to the reference genome ( 6 . 37 Mb ) based on the known genome sequences of R . solanacearum GMI1000 19 and C . taiwanensis LMG19424 15 , and analyzed using the SNIPER software ( S . Cruveiller and C . Medigue , unpublished data ) ., We identified indels , SNPs ( single nucleotide polymorphisms ) , and large deletions in the evolved clones relative to the CBM124GenR ancestor ( Table S2 ) ., Among them , we focused on a large deletion as well as three SNPs that affected the HrpG-controlled virulence pathway in all three clones ( Table 1 ) ., We confirmed the deletion and the SNPs by PCR amplification and Sanger resequencing ., The ca ., 33-kb deletion ( Rsp0128–Rsp0154 ) of the R . solanacearum chromosome 2 removed 27 genes , including the pme gene coding for a pectin methylesterase involved in virulence and genes encoding a putative type II secretion system ., This deletion was reconstructed in the chimera CBM124 by using the cre-lox system ( see Material and Methods ) ., The resulting strain did not nodulate M . pudica , indicating that this deletion either was not adaptive or alone could not account for nodulation ., This region probably corresponds to an unstable region of the genome ., The regulatory protein HrpG controls the expression of many virulence determinants in R . solanacearum 20 ., These include a type III secretion machinery ( T3SS ) and associate effector proteins that are regulated via the intermediate regulator hrpB 21 as well as a large ensemble of genes that are modulated by hrpG in an unidentified circuitry 20 ., A stop mutation in the hrcV gene , which encodes a structural inner membrane protein at the base of the T3SS apparatus 22 , was observed in CBM356 , whereas both CBM212 and CBM349 harboured a stop mutation in the master regulator hrpG gene itself ( Table 1 ) ., Consistently , all three clones exhibited a typical T3SS-defective phenotype , i . e . , loss of HR induction on tobacco leaves ( Figure S2 ) ., Although C . taiwanensis also possesses a T3SS of unknown function , it is not located on pRalta , thus ruling out the possibility that the impact on nodulation of the R . solanacearum virulence pathway was due to a modulation of indigenous C . taiwanensis T3SS ., To assess the possible role of hrcV and hrpG gene inactivation in M . pudica nodulation , we inactivated the hrcV and hrpG genes in the original Ralstonia chimeric strain CBM124 ., Both CBM125 ( hrcV ) and CBM664 ( ΔhrpG ) were indeed found to nodulate M . pudica ., Nonpolar disruption of hrcS , another T3SS structural gene , as well as independent hrpG inactivation by site-directed Tn5 mutation in the CBM124 background further confirmed the role of the T3SS and the hrpG gene in nodulation of M . pudica ., The hrcV and hrpG mutants had symbiotic behaviours similar to that of the hrcV and hrpG evolved clones , respectively ( Figure 1C and 1D ) ., Like most rhizobia , C . taiwanensis invades Mimosa roots by means of transcellular infection threads ( ITs ) , which are initiated from microcolonies entrapped within the curled root hairs known as shepherds crooks 14 ( Figure S5 ) ., Later on , ITs elongate into emerging nodules delivering bacteria into the plant cells ., Each infected cell houses thousands of symbiosomes composed of internalized bacteria ( called bacteroids ) surrounded by plant-derived peribacteroid membranes ., Mature Mimosa nodules induced by C . taiwanensis have the typical histology of indeterminate nodules , i . e . , a single distal persistent meristem and peripheral vascular bundles ( Figure S5 ) ., The ancestral chimeras CBM124 and CBM124GenR promoted root hair proliferation and deformations as well as shepherds crooks ., However , these chimeras showed a clear defect in IT initiation and elongation ( Figure 2A and Figure S6 ) ., In contrast , well-elongated ITs were observed with the hrcV mutant CBM125 ( Figure 2B ) ., Nodules formed , which displayed the typical nodule structure ( Figure S7 ) , although often of irregular shape compared to those induced by C . taiwanensis ., However the hrcV mutant only partially and extracellularly invaded the nodule ( Figure 2C and 2D ) ., A two-step inoculation experiment using differently labelled ( gfp and lacZ ) strains confirmed that extracellular bacteria inside nodules originated from ITs and did not result from intercellular penetration of bacteria from the nodule surface ., A necrotic dark brown zone around which bacteria were distributed was often observed in the distal part of the infected zone of the nodules ( Figure S7 ) ., Plant cell wall thickening next to extracellular bacteria was also suggestive of a plant structural defence response ., This could act as a physical barrier to intracellular infection ., In a similar way , the evolved clone CBM356 ( hrcV ) was able to form elongated ITs but did not permit invasion of nodule cells ( Figure S7 ) , strongly indicating that the hrcV mutation indeed accounted for the symbiotic phenotype of CBM356 ., We observed that a double mutant of the PopF1 and PopF2 translocons , which do not inhibit the formation of the T3SS apparatus in R . solanacearum but are required for protein effector injection in plant cells 23 , had a similar phenotype ( Figure S4 ) , thus suggesting that a T3SS effector ( s ) is involved in blocking nodulation and early infection ., Most interestingly , some rhizobia have been shown to use specialized host-targeting type III or type IV secretion systems to either extend or restrict legume host range ( reviewed in 24 ) ., Expression of these secretion systems is coordinated to nodulation gene expression ., Effectors have been identified that can either be rhizobium specific or pathogen related ., They have been proposed to modulate host ( signalling ) pathways , including plant-defence reactions triggered by the presence of infecting rhizobia 24 ., Because R . solanacearum has more than 70 effectors 21 , identification of the effector ( s ) responsible for blocking nodulation requires further work ., Either nodulation is inhibited by effector-triggered immunity 25 or a T3SS effector ( s ) specifically interferes with the NF-signalling pathway ., The hrpG mutant of CBM124 ( CBM664 ) , as well as the hrpG evolved clones CBM212 and CBM349 , formed nodules on M . pudica that looked similar to those induced by C . taiwanensis ( Figure S8 ) ., In young nodules , plant cells were massively intracellularly invaded ( Figure 3A and 3B , and Figure S8 ) , although the infected zone was restricted , compared to N2-fixing nodules formed by C . taiwanensis ., Intracellular bacteria were surrounded by a peribacteroid membrane forming typical symbiosomes ( Figure 3C ) ., Nodules , however , showed early signs of degeneration generally 3 wk postinoculation , i . e . , loss of cell-to-cell contact , cytoplasmic structure desegregation of nodule cells and degradation of the internalized bacteria ( Figure S8 ) ., A few extracellular bacteria were found in nodules formed by the hrpG chimeric mutant and CBM212 and CBM349 clones ( Figure 3B ) , which is never seen with C . taiwanensis ., In these cases , no plant cell wall thickening could be observed in proximity to extracellular bacteria , suggesting that they did not induce plant defence reactions ., To summarize , hrpG mutants and evolved clones were able to intracellularly invade nodule cells , contrary to hrcV mutants , although bacteroids were impaired for long-term maintenance ., The regulatory gene hrpG thus controls one or several T3SS-independent functions interfering with plant cell entry ., In plant-associated bacteria , massive intracellular infection is restricted to nodule bacteria ., Hence , there is a paradox between the rarity of intracellular infection in plants and the ease with which this trait was acquired by a strictly extracellular pathogen ., Mechanisms of plant cell entry in C . taiwanensis and in rhizobia in general are largely unknown , although it has been established that surface polysaccharides play a key role in host invasion 1 ., Identification of the gene ( s ) downstream of hrpG controlling intracellular infection should shed light to this key , but still obscure , step of the symbiotic interaction ., How rhizobia have emerged is a fascinating , but so far only partly documented , question ., Although pioneering work 15 y ago established the role of lateral transfer in rhizobia evolution 5 , 6 , we and others 26 , 27 have observed that in many instances , transfer of symbiotic loci did not increase symbiotic competence ., Here , we show that a recipient genome—that is not immediately converted to a rhizobium upon transfer of a symbiotic plasmid—could rapidly evolve two specific symbiotic traits , i . e . , nodulation and intracellular infection , under plant selection pressure ., Although in our case , nitrogen fixation—and hence mutualism—was not achieved and evolved clones could be considered as cheaters 28 , evolution of nodulation and infection capacities is the first step in the evolutionary process of reciprocal cooperation 29 ., Extant rhizobial lineages diverged long before they acquired symbiotic properties 30 , i . e . , after legumes appeared on earth 60 million years ago ., Our results show that adaptive genomic changes indeed allow effective dissemination of symbiotic traits over large phylogenetic and ecological distances ., The fact that a single gene played a major role in the shift from extracellular pathogenesis to endosymbiosis reinforces previous reports that global regulators are preferred targets for evolution 31 and supports fluid boundaries between parasitism and mutualism ., Our knowledge of the rhizobium–legume symbiosis mainly comes from gene inactivation studies ., Although a gain-of-function approach was first initiated ca ., 25 y ago on Agrobacterium 8 , 26 , 32 and used thereafter 7 , 33 , the experimental evolution approach we describe here is novel , as it consists of the progressive and dynamic acquisition of symbiotic ability under plant selection pressure ., Evolved clones gained symbiotic traits to different degrees , allowing for a future fine dissection of unexplored aspects of nodulation and intracellular infection ., Serial in planta passages using the nodulating clones described here as ancestors should allow improvement of their symbiotic capacities , i . e . , bacteroid maintenance and possibly nitrogen fixation ., Other symbiotic stages , such as rhizosphere colonization , host specificity of nodulation , and nitrogen fixation , could similarly benefit from coupled experimental evolution and genome resequencing approaches ., Bacterial strains and plasmids used in this work are listed in Tables 2 and, 3 . C . taiwanensis strains were grown at 28°C on TY medium supplemented with 6 mM CaCl2 or quarter-strength minimal medium ( MM ) 34 supplemented with 10 mM disodium succinate and vitamin solution ( 1 µg/ml nicotinic acid , 1 µg/ml thiamine hydrochloride , 1 µg/ml pyridoxine hydrochloride , 100 µg/ml myo-inositol , 1 µg/ml calcium pantothenate , 1 µg/ml riboflavin , 1 µg/ml ascorbic acid , 1 µg/ml folic acid , 1 µg/ml cyanocobalamin , 1 µg/ml D-biotin ) ., R . solanacearum strains were grown at 28°C on rich BG medium 35 or MM supplemented with 28 mM glucose ., Antibiotics were used at the following concentrations ( in micrograms per millilitre ) : streptomycin 600 , spectinomycin 40 , trimethoprim 100 , tetracycline 10 , gentamicin 25 , chloramphenicol 50 for E . coli and 200 for C . taiwanensis , and kanamycin 50 for E . coli , and 30 for R . solanacearum ., Transfer of pRalta to R . solanacearum was performed in three consecutive conjugation steps ., Step, 1 . C . taiwanensis CBM832 was randomly transposon mutagenised using pMH1801 possessing the Tn5-B13S transposon which carries the mob site ( oriT ) , an npt-sacB-sacR cassette and Tet-resistance ., Step, 2 . Mutants were selected on TY supplemented with Tet and Str , and the helper plasmid , RP4-7 , was individually introduced into each C . taiwanensis mutant ., Step, 3 . C . taiwanensis::Tn5-B13S mutants carrying RP4-7 were then conjugated with R . solanacearum ., Transconjugants were selected on MM supplemented with glucose and Tet ., One Tn5-B13S mutagenised C . taiwanensis clone , CBM61 , was successful in producing Tet-resistant R . solanacearum transconjugants ., A selected transconjugant , CBM62 , was verified as R . solanacearum containing pRalta by 16SrDNA and nifH gene amplification , and a seemingly intact pRalta was confirmed by a modified Eckhardt gel analysis 36 ., The Tn5-B13S insertion in pRalta of CBM62 was found located within a putative transposase ( see DNA Manipulation ) , and thus had not disrupted any gene essential for symbiosis , as confirmed by nodulation tests and microscopic observation of the mutagenised C . taiwanensis strain CBM61 used as donor for pRalta transfer ., The Tn5-B13S , which contains sacRsacB genes that might interfere with plant tests , was exchanged in CBM62 with a trimethoprim ( Tri ) resistance cassette ( see DNA Manipulation ) , giving rise to the Ralstonia chimeric strain GMI1000 ( pRalta::Tri ) , or CBM124 ., The ancestral strain CBM124GenR was obtained by natural transformation 35 of CBM124 with genomic DNA from the R . solanacearum GRS412 strain ( containing the GenR plasmid pCZ367 inserted in the Rsp1236 gene ) ., Correct insertion of pCZ367 in CBM124GenR was verified by using a primer located upstream of the inactivated gene and a primer located in the lacZ gene of pCZ367 ., Transfer of pRalta::Tn5-B13S from CBM61 to R . solanacearum mutants was performed as indicated in step, 3 . To construct CBM351 , a CBM124 derivative deleted for the Rsp0128–Rsp0154 region , PCR fragments from the Rsp0125 and Rsp0157 genes ( Rsp0126 , Rsp0127 , Rsp0155 and Rsp0156 are transposases ) were amplified using oCBM494–oCBM495 and oCBM496–oCBM497 as primers and cloned into the EcoRI/NcoI and SacI/SacII restriction sites of pCM184 , respectively ., The modified plasmid was introduced into CBM124 by conjugation ., Transconjugants resistant to kanamycin and sensitive to tetracycline were screened ., The replacement of the Rsp0126–Rsp0156 region by the kanamycin resistance cassette in strain CBM351 was verified by PCR ., To construct CBM125 , a hrcV mutant of CBM124 , pRalta::Tn5-B13S , was transferred by conjugation from C . taiwanensis CBM61 to the R . solanacearum hrcV mutant GMI1694 ., The Tn5-B13S transposon was then replaced by the trimethoprim resistance cassette as described above ., To construct CBM142 and CBM145 , the hrcS mutation and the popF1 popF2 double mutation were introduced into CBM124 by natural transformation 35 of CBM124 with genomic DNA from the R . solanacearum hrcS mutant GMI1596 and the popF1 popF2 double-mutant GMI1667 , respectively ., The presence of an inserted cassette in hrcS , popF1 , and popF2 was verified by PCR ., To construct the hrpG mutants , CBM663 and CBM664 , two different methods were used ., First the CBM124 strain was transformed with genomic DNA from R . solanacearum hrpG::Tn5-B20 mutant GMI1425 ., Transformants were selected on BG medium supplemented with trimethoprim and kanamycin ., The Tn5-B20 insertion in hrpG was verified by PCR in strain CBM663 ., Second , PCR fragments upstream and downstream from hrpG were amplified using oCBM622–oCBM623 and oCBM624–oCBM625 as primers and cloned into the EcoRI/KpnI and SacII/HpaI restriction sites of pCM184 , respectively ., The resulting plasmid was introduced into CBM124 by conjugation ., Transconjugants resistant to kanamycin and sensitive to tetracycline were screened ., The replacement of hrpG by the kanamycin resistance cassette was verified by PCR in strain CBM664 ., Primers used for DNA amplification are listed in Table S3 ., To determine the precise location of the Tn5-B13S insertion point in pRalta of CBM61 and CBM62 , tail-PCR was performed with arbitrary primer AD1 or AD4 37 in combination with three sequential Tn5-specific primers designed from the terminal arms of the Tn5 transposon , oCBM183 , oCBM184 , and oCBM185 ., For Tn5-B13S insertion exchange by TriR cassette , a 2-kb PCR fragment , corresponding to approximately 1 kb each side of the Tn5-B13S insertion point , was amplified from LMG19424 using primers oCBM196 and oCBM198 and cloned into pGEM-Teasy ( Promega ) ., The TriR cassette isolated from p34E-Tp digested by BamHI was then introduced in the BglII site of the fragment , generating pMG02 ., This BglII site was located only 6 bp from the Tn5-B13S insertion point in CBM62 ., ScaI linearized pMG02 DNA was used to transform naturally competent R . solanacearum chimeric strains containing pRalta::Tn5-B13S ., The exchange of Tn5-B13S with the trimethoprim cassette was verified by establishing that the strain had lost resistance to tetracycline and could grow on 5% sucrose ., For the construction of pCBM01 , the promoter region of nodB was amplified using oCBM203 and oCBM211 as primers and cloned into pGEM-Teasy ( Promega ) , cleaved from pGEM-Teasy with HindIII and PstI , and then directionally cloned into the same sites of the lacZ transcriptional fusion in pCZ388 ., pCBM01 was introduced in C . taiwanensis and R . solanacearum strains by conjugation ., The lacZ- and gfp-derived strains were obtained by natural transformation with genomic DNA from strains GMI1485 and GMI1600 , respectively ., Sequence data production was performed by the C . E . A/IG/Genoscope ( Evry ) ., Paired-end libraries were prepared following the protocol recommended by Illumina Inc . ( http://www . illumina . com ) ., For each strain , more than 5 million paired-end reads ( L\u200a=\u200a72 bp\u200a=\u200a2×36 bp ) were generated with Genome Analyzer sequencing system , leading to a ca ., 60× total coverage of the reference genome ( Table S4 ) ., Taking advantage of the local production of raw sequencing data , a bioinformatic pipeline called SNiPer ( S . Cruveiller and C . Medigue , unpublished data ) and based on ssaha2 alignment software ( Sequence Search and Alignment by Hashing Algorithm 38 has been implemented ., This pipeline allows the detection of small variations ( SNPs and InDels ) between a collection of short reads and a reference sequence , this latter being either a consensus produced by assemblers or a previously published one ., SNiPer is a shell script that automatically sets the alignments parameters depending on the kind of reads ( ABI-3730/454-GSFLX/Solexa/SOLiD ) being used , launches the various parts of the detection pipeline , and controls for all tasks having been completed without errors ., The detection of SNPs and indels is achieved in four main steps: ( 1 ) The data preparation , which consists in, ( i ) the conversion of sequencing raw data ( i . e . , reads files ) into Sanger Institute FastQ formatted files;, ( ii ) the removal of duplicated reads ( quite common when using Solexa platform ) so as to keep exactly one copy of each read; and, ( iii ) the split of paired-ends reads into single-end reads when required ., ( 2 ) Reads mapping onto a reference molecule using the ssaha2 package 38 ., This package combines the SSAHA searching algorithm ( sequence information is encoded in a perfect hash function ) aiming at identifying regions of high similarity , and the cross_match sequence alignment program ( http://www . phrap . org/phredphrapconsed . html ) , which aligns these regions afterwards using a banded Smith-Waterman-Gotoh algorithm 39 , 40 ., ( 3 ) Based on the characteristics of reads alignments onto the reference molecule , a file containing the lists of all possible events is generated ., ( 4 ) Each event is then scored so as to keep only significant ones ., This score takes into account the reference base coverage ( i . e . , the number of reads mapping a given location ) and the quality of bases of reads displaying a change at that particular location as well ., The ca ., 5 million paired-end reads were split into single reads and mapped on the reference genome ( the two replicons of Ralstonia solanacearum GMI1000 RefSeq acc . NC_003295 . fna and NC_003296 . fna for the chromosome and the megaplasmid respectively+the nodulation plasmid of Cupriavidus taiwanensis LMG19424 RefSeq acc . NC_010529 . fna ) using SNiPer ., Among the 10 million single reads , around 7 millions were successfully mapped , leading to an effective coverage of the three reference molecules higher than 30× ( Table S4 ) , hence warranting a reliable detection of changes ., The remaining unmapped reads ( 3 million on average ) correspond to reads that could neither be mapped unambiguously ( i . e . , repeat regions , insertion sequences , rDNA , etc . ) nor be mapped at all ( i . e . , fragment of sequences not present in the references ) ., Pathogenicity assays with M . pudica and Arabidopsis thaliana ecotype Col-0 were performed according to Deslandes et al . 41 ., Root inoculations used the method of cutting 2 cm from the bottom of Jiffy pot–grown plants , followed by immersion for 5 min in a suspension of bacteria grown overnight and diluted to an OD600 of 0 . 1 in water ., R . solanacearum and derivatives were tested for the HR ability by infiltrating a bacterial culture adjusted to 108 cells/millilitre into tobacco ( cultivar Bottom Special ) leaf parenchyma as described previously 35 ., For M . pudica nodulation assay and cytology , seeds were surface sterilised and planted under sterile conditions using the tube method of Gibson as previously described 13 , ( except tubes contained Fahraeus 42 slant agar and liquid water ) ., For the selection of nodulating evolved clones , 107 bacteria par tube were used as inoculum ., Otherwise , 104 bacteria were routinely inoculated per tube unless specified ., Nitrogen fixation was estimated by visual observation of the vigour and foliage colour of 40/60-d-old plants on at least 20 plants ., For reisolation of nodule bacteria , nodules were surface sterilised 10 min with 2 . 6% sodium hypochlorite , rinsed five times , then crushed and dilutions plated on the appropriate solid medium ., For each M . pudica tube , ex planta number of bacterial generations is estimated at a maximum of 5 , and in planta generation number is calculated using the formula log ( number of bacteria/nodule ) /log2 ., LacZ-tagged infecting bacteria were stained according to the standard procedure ., Briefly , roots were fixed in glutaraldehyde 1 . 5% in K phosphate buffer for 30 min under vacuum condition followed by 1 h at room temperature ., After washing , roots were incubated overnight with the staining solution at 28°C ( 0 . 1 M K phosphate pH 7 . 4 , 2 mM K ferricyanide , 2 mM K ferrocyanide , and 0 . 08% of X-gal in dimethylformamide ) ., Roots were washed and used for microscopic analysis ., To analyse infection of gfp-tagged bacteria , root and nodules were fixed in paraformaldehyde 3 . 7% in phosphate buffered saline ( PBS ) for 30 min under vacuum , then washed and used directly or cut for nodule sections 60-µm thick using a Leica VT1000S vibratome ., Samples were observed by using a fluorescence ( Zeiss Axiophot Fluorescence microscope ) or confocal microscope ( Leica SP2 ) ., For fine histological examination , nodules were fixed in glutaraldehyde ( 2 . 5% in phosphate buffer 0 . 1 M pH 7 . 4 ) , osmium treated , dehydrated in an alcohol series , and embedded in Epon 812 ., Semithin nodule sections were observed by brightfield microscopy after staining in 0 . 1% aqueous toluidine blue solution and observed under a Zeiss Axiophot light microscope ., Ultrathin sections were stained with uranyl acetate and observed with a Hitachi EM600 electron microscope ., For the two-step infections , we proceeded as follows ., M . pudica plants , grown as described above , were first infected with the lacZ-tagged hrcV chimeric strain ., After 9 d of infection , once nodules were formed , a secondary infection was performed by using the gfp-tagged hrcV chimeric strain ., Two weeks after , nodules were fixed in paraformaldehyde 3 . 7% as previously described and used for cytological analysis ., Strains were grown overnight at 28°C in MM supplemented with the appropriate carbon source , vitamins , and tetracycline ., Overnight cultures were then diluted to an OD600 of 0 . 005–0 . 01 in MM with tetracycline ±15 µM final concentration of luteolin and grown a minimum of 16 h until an OD600 of 0 . 7 was reached ., The cultures were then assayed for β-galactosidase activity ( Miller units ) according to Miller , 1972 43 ., The β-galactosidase activities represent an average of quadruplicate samples from two separate experiments ., NFs were produced , purified , and characterized as previously described 15 .

Introduction, Results/Discussion, Materials and Methods

Rhizobia are phylogenetically disparate α- and β-proteobacteria that have achieved the environmentally essential function of fixing atmospheric nitrogen in symbiosis with legumes ., Ample evidence indicates that horizontal transfer of symbiotic plasmids/islands has played a crucial role in rhizobia evolution ., However , adaptive mechanisms that allow the recipient genomes to express symbiotic traits are unknown ., Here , we report on the experimental evolution of a pathogenic Ralstonia solanacearum chimera carrying the symbiotic plasmid of the rhizobium Cupriavidus taiwanensis into Mimosa nodulating and infecting symbionts ., Two types of adaptive mutations in the hrpG-controlled virulence pathway of R . solanacearum were identified that are crucial for the transition from pathogenicity towards mutualism ., Inactivation of the hrcV structural gene of the type III secretion system allowed nodulation and early infection to take place , whereas inactivation of the master virulence regulator hrpG allowed intracellular infection of nodule cells ., Our findings predict that natural selection of adaptive changes in the legume environment following horizontal transfer has been a major driving force in rhizobia evolution and diversification and show the potential of experimental evolution to decipher the mechanisms leading to symbiosis .

Most leguminous plants can form a symbiosis with members of a group of soil bacteria known as rhizobia ., On the roots of their hosts , some rhizobia elicit the formation of specialized organs , called nodules , that they colonize intracellularly and within which they fix nitrogen to the benefit of the plant ., Rhizobia do not form a homogenous taxon but are phylogenetically dispersed bacteria ., How such diversity has emerged is a fascinating , but only partly documented , question ., Although horizontal transfer of symbiotic plasmids or groups of genes has played a major role in the spreading of symbiosis , such gene transfer alone is usually unproductive because genetic or ecological barriers restrict evolution of symbiosis ., Here , we experimentally evolved the usually phytopathogenic bacterium Ralstonia solanacearum , which was carrying a rhizobial symbiotic plasmid into legume-nodulating and -infecting symbionts ., From resequencing the bacterial genomes , we showed that inactivation of a single regulatory gene allowed the transition from pathogenesis to legume symbiosis ., Our findings indicate that following the initial transfer of symbiotic genes , subsequent genome adaptation under selection in the plant has been crucial for the evolution and diversification of rhizobia .

microbiology/plant-biotic interactions, microbiology/microbial evolution and genomics

Following acquisition of a rhizobial symbiotic plasmid, adaptive mutations in the virulence pathway allowed pathogenic Ralstonia solanacearum to evolve into a legume symbiont under plant selection.

journal.ppat.1001013

Epigenetic Analysis of KSHV Latent and Lytic Genomes

Chromatin is a highly dynamic structure of nucleosomes that are composed of DNA wrapped around the core histones ( H2A , H2B , H3 and H4 ) ., Over the past decade , several studies have demonstrated that histones are subject to various posttranslational modifications ( acetylation , methylation , phosphorylation , and ubiquitination ) , which are capable of modulating chromatin structures to thereby influence gene expression 1 ., Hyperacetylation of histones H3 and H4 occurs mainly on promoters and correlates with gene activation , while hypoacetylation is characteristic of repressed genes 2 ., Histone methylation is associated with either activation or repression of genes , depending on which histone lysine residues are mono- , di- or trimethylated ., Various histone methylations are then recognized by specific chromodomain-containing proteins that can function as either transcription factors or as part of large chromatin remodelling/modifying complexes , which eventually determine the activity of target genes 1 ., Histone methylation status fluctuates in response to environmental and developmental conditions ., A number of enzymes that add or remove methylation modifications have been discovered 3 , 4 ., In general , transcriptionally active genes are associated with H3K4me3 and H3K36me3 , whereas trimethylation of H3K9 , H3K27 and H4K20 occurs primarily on repressed genes ., H3K9me3 and H4K20me3 histone modifications are characteristic of pericentric heterochromatin , which is considered to be constitutive heterochromatin 5 , 6 , 7 , 8 , 9 ., On the other hand , H3K27me3 is the marker of highly dynamic and reversible heterochromatin ( facultative heterochromatin ) , and is characteristic of genes that are subject to tissue specific or developmentally regulated expression 10 , 11 , 12 ., Genome-wide analysis of embryonic stem ( ES ) cells revealed that H3K27me3 preferentially localizes on developmental genes , which are repressed in stem cells but are expressed during ES cell differentiation 13 , 14 ., Interestingly , the promoter of large number of these developmental genes are also enriched in activating H3K4me3 suggesting that these genes are silenced but poised for rapid activation in ES cells 15 ., Promoters enriched in both activating ( H3K4me3 ) and repressive ( H3K27me3 ) histone marks , called bivalent promoters , have been associated with rapidly inducible genes in T cells as well 7 ., H3K27me3 is deposited by the evolutionary conserved 600-kDa Polycomb Repressive Complex 2 ( PRC2 ) , which consists of three Polycomb group ( PcG ) proteins ( EZH2 , SUZ12 , EED ) and the histone-binding proteins , RbAp48/46 16 ., The SET domain-containing EZH2 is an H3K27me3 histone methyltransferase , which can be found along entire genomic regions enriched with H3K27me3 in mammalian cells 17 ., H3K27me3 provides a binding platform for PRC1 , a larger Polycomb complex consisting of more than 10 subunits ., In Drosophila , PcG proteins are recruited to their target genes via Polycomb response elements ( PRE ) that can be found in promoters 18 ., It is still unclear how PcG proteins are recruited to their target genes in mammalian cells , but non-coding RNAs and specific DNA sequences similar to PREs have been implicated to be involved in this process ., 16 , 19 , 20 ., Polycomb-mediated gene silencing has been shown to be reversible with H3K27me3 demethylases such as JMJD3 and UTX , which can be recruited to the repressed promoters by transcription activators as has been shown , for instance , in the case of the H3K4me3 methyltransferase complexes 21 , 22 , 23 , 24 ., Viruses replicating in the nucleus are also under the influence of the chromatin during different stages of their life cycles ., Therefore , viruses have evolved various mechanisms to utilize or neutralize the impact of cellular chromatin factors , to ultimately control viral replication and gene expression 25 , 26 , 27 , 28 ., Herpesviruses have a large DNA genome that persists as multicopy circular episomes associated with histones in the nucleus ., Herpesviral infection can lead to two different life cycles: latency and lytic replication ., During latency , the viral episomes are assembled into nucleosomal structures that resemble bulk cellular chromatin ., Regulation of the chromatin structure of the Herpes simplex virus type 1 ( HSV-1 ) genome has been implicated as the underlying cause of the switch between latency and lytic replication as well as being involved in the regulation of lytic gene expression 29 , 30 ., A human tumour virus , called Kaposis sarcoma-associated herpesvirus ( KSHV ) or human herpesvirus 8 ( HHV8 ) , has been consistently identified in Kaposis sarcoma ( KS ) tumours , pleural effusion lymphoma ( PEL ) , and Multicentric Castleman disease 31 , 32 , 33 ., Several studies have indicated that DNA methylation and histone acetylation can play a role in the regulation of KSHV gene expression 34 , 35 ., The immediate early KSHV gene encoded by ORF50 , called RTA ( replication and transcription activator ) , is the master regulatory factor and is sufficient to induce the complete cycle of viral replication 36 , 37 ., The RTA promoter associates with histone deacetylases ( HDACs ) during latency resulting in hypoacetylated histones 34 ., Treatment of latently infected KSHV positive cells with HDAC inhibitors , butyrate or TSA , induces the hyperacetylation of viral chromatin concomitantly with the recruitment of histone acetyltransferases , chromatin remodelling proteins ( Ini1/Snf5 ) and the TRAP/Mediator coactivator complex to the RTA promoter , allowing RTA expression to induce the complete viral gene expression cascade 34 , 38 ., To elucidate the characteristics of the KSHV epigenome , we performed a high-resolution genome-wide analysis to map a set of activating and repressive histone H3 modifications on the entire KSHV genome during latency and reactivation ., Based on their genome-wide profiles , we found that the KSHV genes are associated with a distinctive pattern of active and repressive histone modifications during latency , which ultimately changes upon reactivation ., Importantly , the promoter regions of RTA and several E genes are associated with both H3K4me3 and H3K27me3 marks , suggesting that these promoters have a bivalent chromatin structure that maintains their repression during latency and is also poised for rapid activation upon stimulation ., We also found that while the EZH2 histone methyltransferase is colocalized with H3K27me3 on the entire KSHV latent genome , it rapidly dissociates from the RTA promoter and other IE-E gene-rich genomic regions upon reactivation ., This event ultimately results in reduced level of H3K27me3 , which are concomitant with increasing levels of activating histone marks on the RTA promoter ., Furthermore , treatment of latently KSHV-infected cells with a drug inhibiting the expression of PcG proteins , the small inhibitory RNA-mediated knockdown of EZH2 or the overexpression of H3K27me3 histone demethylases efficiently trigger the lytic reactivation of KSHV ., These data collectively demonstrate that the Polycomb group proteins are involved in the maintenance of KSHV latency by preserving a reversible heterochromatin on the promoter regions of lytic genes such that they are silenced during latency but are poised for rapid activation upon reactivation ., In this study we asked what histone modifications are associated with the KSHV genome during latency and how they change upon reactivation ., For this , we used the well-characterized recombinant KSHV-positive primary effusion lymphoma cell line , TRExBCBL1-RTA , which expresses a Doxycycline ( Dox ) inducible myc/His-tagged RTA incorporated into the cellular genome 39 ., We chose the RTA-mediated reactivation of KSHV instead of chemical inducers such as TPA , TSA or sodium butyrate because these chemicals can globally affect both cellular and viral gene expression , while RTA ensures robust and specific viral reactivation 39 ., Figure S1 showed that Dox treatment ( 1ug/ml ) of the TRExBCBL1-RTA cells for 6 , 12 and 24 hours rapidly induced myc/His-RTA expression , resulting in a gradual induction of the KSHV gene expression cascade ( Figure S1A , B ) 39 , whereas viral DNA replication was apparent only at 24 hpi , which was in correlation with the induction of late gene expressions ( Figure S1B and C ) ., To analyse the changes in the KSHV nucleosome structure upon reactivation , ChIP experiments were performed with a histone H3 specific antibody ., We measured the abundance of specific DNA sequences in the histone H3 immunoprecipitates with qPCR using a set of primer pairs specific for both the promoter and the coding regions of various viral genes including RTA , LANA , K2 , ORF8 , ORF25 , ORF56 and ORF64 ( Figure S2A , B ) ., This revealed comparable levels of H3 association throughout the viral genome during latency ( 0 hpi ) , which did not significantly change on most of the genomic regions at 12 hpi , but dropped sharply at 24 hpi ( Figure S2B ) ., This is in agreement with previous findings showing that disassembly of the viral chromatin is concomitant with viral DNA replication during lytic infection 40 ., In contrast , H3 levels remained constant on cellular promoters during KSHV lytic reactivation , suggesting that H3 dissociation occurs specifically on the viral genome ( Figure S2C ) ., In order to investigate the epigenome of KSHV during latency and lytic reactivation , we tested whether repressive histone modifications associated with lytic genes are responsible for maintaining the repression of lytic gene expression during latency and whether reactivation induces the deposition of activating histone modifications onto the viral genome for lytic gene expression ., To uncover the global distribution of histone modifications on the KSHV chromatin during latency and reactivation , we mapped the genome-wide distribution of activating histone marks acetyl-H3 ( AcH3 ) and H3K4me3 and repressive marks ( H3K9me3 and H3K27me3 ) on the KSHV genome ( Figure 1 and Figure S3 , S4 , S5 , S7 ) ., The ChIP-on-chip experiments were carried out with chromatins prepared from both non-induced ( 0 hpi/latency ) and Dox-induced ( 12 hpi ) TRExBCBL1-RTA cells ., Figure 1 shows the average of two independent ChIP-on-chip biological replicates ( Figure S3 and S4 ) ., To get a high resolution of the localization of histone modifications and proteins of interest on the KSHV genome , we used a 15-bp tiling microarray that contains 8942 overlapping 60 nucleotide-long oligos spanning the entire KSHV genome ., Because changes in H3 occupancy may affect the enrichment of histone modifications on the viral genome , we first investigated the global distribution of H3 on the viral genome during latency and upon reactivation ., The histone H3 ChIP-on-chips revealed that histone H3 enrichment levels were comparable throughout the KSHV genome at 0 hpi and did not significantly change on most parts of the KSHV genome at 12 hpi ( Figure 1 , S3 , S4 ) ., The histone H3 ChIP analysis also showed similar results ( Figure S2B ) ., However , it should be noted that a small viral genomic region between 15 and 30 kb , which contains mostly KSHV unique genes , displayed a detectable decrease of H3 occupancy at 12 hpi ( Figure 1 ) ., In contrast to the uniform distribution of H3 , the different histone modifications displayed distinct patterns and were enriched in specific KSHV genomic regions during latency and reactivation ( Figure 1 ) ., Immunoblot analysis revealed that the expression of H3 as well as the global levels of cellular histone modifications did not change upon KSHV reactivation ( Figure S2 D ) , thus any change of the histone modifications on the KSHV genome is likely to be a consequence of the reactivation-induced change in the KSHV epigenome ., The genome-wide mapping of histone modifications showed that both activating and repressive histone modifications were associated with the KSHV genome during latency ( Figure 1 ) ., While both the activating AcH3 and H3K4me3 marks co-localized on the KSHV genome so did the repressive H3K9me3 and H3K27me3 marks ., However , the activating and repressive histone modifications were mutually exclusive on the bulk of the KSHV genome ( e . g . 30–60 kb and 90–120 kb ) ( Figure 1 ) ., As expected , the latency-associated locus ( 118–128 kb ) where KSHV genes that are constitutively expressed during latency are located , was enriched with the activating H3K4me3 and AcH3 histone modifications but depleted for the repressive H3K9me3 and H3K27me3 ( Figure 1 ) ., Unexpectedly , we also found that several regions of the KSHV genome ( e . g . 1–30 kb and 60–90 kb ) that are not associated with latency-gene expression were also highly enriched in both H3K4me3 and AcH3 histone marks ( Figure 1 ) ., Strikingly , the PcG protein-mediated repressive histone modification , H3K27me3 , was widely distributed throughout the KSHV genome , whereas the H3K9me3 repressive histone modification was restricted mainly to two genomic regions ( 30–60 kb and 95–115 kb ) encoding a number of late genes ( Figure 1 ) ., Interestingly , the activating H3K4me3 and AcH3 histone modifications were absent in these two genomic regions where the repressive H3K9me3 and H3K27me3 histone modifications coexisted , suggesting that KSHV genes in these regions have a strongly repressive heterochromatin structure during latency ( Figure 1 ) ., The RTA-induced initiation of KSHV lytic gene expression program results in the redistribution of histone modifications on viral genome ., We calculated the 12hpi/input ratio to view the changes in histone modifications ( Figure 1 ) ., Since H3 enrichment was comparable between 0 and 12 hpi on most parts of the viral genome and viral DNA replication had yet to occur by 12 hpi , we also hybridized the 12 hpi-ChIPs against the 0 hpi-ChIPs , which allowed us to specifically observe changes in histone modification levels on the KSHV genome upon lytic reactivation ( Figure S7C ) ., Others have also recently applied similar ChIP-on-chip analyses in studies investigating the epigenetic reprogramming of the host genome by the adenoviral protein E1a 41 ., Our ChIP-on-chip analysis showed that the enrichment of the activating histone modifications was elevated the most when it was concomitant with the reduction of repressive histone marks in the genomic regions between 1 and 30 kb containing several early genes and between 68 and 77 kb , which encodes the IE proteins ORF45 , ORF48 , ORF50 ( RTA ) and K8 ( KbZIP ) ( Figure 1 and S7 ) ., These changes in the viral chromatin are indicative of robust transcriptional activation and are consistent with the KSHV gene expression profile in that expression of the lytic genes in these genomic regions is induced in the early phase of reactivation 39 , 42 , 43 ., In contrast , only minor changes in the levels of the AcH3 and H3K4me3 activating marks were detected in the genomic regions that encode large number of late genes ( 30–60 kb and 95–115 kb ) , whereas significant changes of the H3K27me3 repressive mark were observed at 12 hpi ( Figure 1 and S7 ) ., In summary , these results demonstrate that the activating histone modifications of the latent KSHV genome are preferentially enriched in the constitutively active latency-associated genomic locus and in the early-lytic gene-containing genomic regions ., In contrast , the H3K9me3 and H3K27me3 repressive histone marks are primarily enriched in the genomic regions that encode many late genes during latency ( 0 hpi ) , and they remain associated with these regions during the early phase ( 12 hpi ) of reactivation as well ., While only a few genes are expressed during latency , all viral genes are expressed upon lytic reactivation in a temporal and sequential order ., This suggests that distinctive histone modifications may be associated with the different viral promoters to ultimately determine the timing and rate of viral gene expression ., Thus , we attempted to delineate the characteristics of the chromatin structures associated with the regulatory regions of KSHV genes during latency and lytic reactivation ( Figure 2 , S6 , S8 ) ., For this , we aligned the KSHV open reading frames ( ORFs ) relative to their translational start sites ( TSS ) and plotted the signal intensities of probes derived from the ChIP-on-chip analysis across a 2-kb region spanning 1 kb on either side the TSS ( please see Materials and Methods for details ) ., The rationale of this strategy was based on a number of considerations ., ( i ) While the transcriptional start sites have only been identified for a few KSHV genes , they are generally within a few hundred base pairs of their TSS , showing that the TSS can be used as a reference point ., ( ii ) Due to the compact structure of the KSHV genome , the promoters are usually closely localized upstream of the TSS ., ( iii ) The 1 kb downstream region of the TSS was included in the analysis because several KSHV genes have introns close to the TSS at their 5′ ends , which may contain gene regulatory elements ., Based on these factors , the 2-kb sequences around the TSS were considered to be the gene regulatory regions of KSHV genes ., In fact , a similar strategy has also been used to analyse the recruitment of the KSHV transactivators , Rta and K-bZIP , to eighty-three putative KSHV promoters in TRExBCBL1-RTA cells 44 ., Using the signal intensities of the probes derived from the ChIP-on-chip analysis , we performed an average linkage hierarchical clustering within each gene class ( La or latent , IE , E , L ) , which gave us a comprehensive overview of the repressive and activating histone modifications across all the 2-kb TSS regions ( Figure 2 and S8 ) ., Because of the genome-wide distribution of H3K27me3 on the KSHV genome , we performed a ChIP-on-chip assay to determine whether EZH2 , the H3K27me3 histone methyltransferase of the PcG proteins , associates with the KSHV genome ., ( Figure 4A ) ., ChIP-on-chip assays showed that EZH2 almost completely colocalized with H3K27me3 throughout the entire KSHV genome during both latency and reactivation ( Figure 4A , S7 , S10 ) ., Additional ChIP assays showed that EZH2 and SUZ12 ( another subunit of the PcG complex PRC2 ) were found on the RTA and ORF25 promoters enriched with H3K27me3 , but not on the LANA promoter ( Figure 4B ) ., Besides its regulatory role in cellular gene expression , the CBF1 transcription activator has been shown to play an active role in KSHV gene expression as well 45 , 46 ., Since the RTA promoter contains several CBF1 binding sites ( Figure S11 A ) 47 , 48 , we studied the recruitment of CBF1 onto the RTA promoter during reactivation ., The ChIP assay showed that CBF1 was recruited to the RTA promoter , primarily at 1 . 4 kb upstream of the RTA translational start site where three putative CBF1 binding sites are closely located ( Figure 4C and Figure S11 A ) ., Furthermore , not only does this putative CBF-binding region of RTA promoter efficiently binds CBF1 in vitro , but its deletion also resulted in a dramatic decrease of the RTA-mediated autoactivation of its own promoter ( Figure S11 B , C ) ., Additional ChIP experiments revealed that RNAPII is also recruited to the promoter regions of RTA and LANA upon reactivation ( Figure 4C ) ., Recruitments of CBF and RNAPII to the RTA and LANA promoters were specific since they were not recruited to the promoter of the late gene , ORF25 , whose expression was still blocked at 6 and 12 hpi ( Figure 4C and S1B ) ., The increase of RNAPII over the 3-kb upstream region of RTA may be not surprising given that this genomic region also includes the promoters of other lytic genes ( ORFs 45 , 46 , 47 , 48 ) as well as an alternative upstream promoter for RTA 49 ., These results illustrate the dynamic associations of the PcG complex and transcriptional activators with KSHV genome during latency and lytic reactivation ., The dynamic association of EZH2 with KSHV genome suggests that PcG-mediated H3K27me3 histone modification is involved in the repression of lytic gene expression during latency ., To address this issue , HA-tagged JMJD2A , JMJD3 and UTX histone demethylases were expressed in Vero-rKSHV . 219 cells to test if the elimination of histone methylations can trigger KSHV reactivation ( Figure 5A ) ., JMJD2A is an H3K9me3-specific histone demethylase 50 , JMJD3 and UTX are H3K27me3-specific histone demethylases 21 , 51 , and UTXmut is an enzymatically inactive form of UTX that contains a single point mutation ( H1146A ) in the Fe2++ ion binding site 51 ., JMJD3 and UTX have been shown to eradicate the H3K27me3 repressive mark , resulting in the upregulation of PcG-targeted gene expression 21 , 52 ., JMJD2A and JMJD3 or UTX expression detectably suppressed the steady-state levels of H3K9me3 or H3K27me3 , respectively , in transfected Vero cells ( Figure S12B , C ) ., Since Vero-rKSHV . 219 cells express red fluorescent protein ( RFP ) from the KSHV lytic PAN promoter and green fluorescent protein ( GFP ) from the EF-1α promoter , RFP expression has been extensively used as a marker of KSHV lytic reactivation 53 ., Immunofluorescence analysis revealed that JMJD3 and UTX efficiently triggered KSHV reactivation , while JMJD2A and UTXmut did not ( Figure 5A , B ) ., Furthermore , coexpression of JMJD2A and JMJD3 showed no significant synergistic effect on KSHV reactivation ( Figure 5A , B ) ., Finally , JMJD2A , JMJD3 , UTX and UTXmut were expressed at comparable levels ( Figure S12A ) ., These results bespeak the importance of the H3K27me3 histone modification in the maintenance of KSHV latency ., A small molecule , 3-Deazaneplanocin A ( DZNep ) , has been shown to inhibit the expression of the Polycomb repressive complex 2 ( PRC2 ) components ( EZH2 , SUZ12 , and EED ) , resulting in the suppression of H3K27me3 histone methylation and the upregulation of PcG target genes in vivo 54 ., To further test the role of H3K27me3 histone methylation in KSHV latency , we treated KSHV and EBV co-infected primary effusion lymphoma JSC-1 cell line with DZNep , followed by immunoblotting assays ( Figure 5 C and D ) ., DZNep treatment dramatically decreased the EZH2 and SUZ12 and , thereby , H3K27me3 levels , ultimately resulting in the induction of the expression of polycomb-targeted cellular MYT1 gene ( Figure 5E ) ., However , H3K9me3 , histone H3 , and actin levels were not affected under the same conditions ( Figure 5C ) ., We found that DZNep treatment efficiently induced the reactivation of KSHV , but not EBV: KSHV Rta and K8 expressions were detected as early as 2 days after DZNep treatment , while the EBV IE protein Zta was not induced ( Figure 5D ) ., Real time quantitative RT-PCR also showed the induction of other early ( ORF56 ) and late KSHV genes ( ORFs 8 , 25 , 64 ) , suggesting that PRC2 depletion activates the gene expression cascade of KSHV from the repressed latent state ( Figure 5E ) ., Besides the downregulation of EZH2 and SUZ12 , DZNep also induced apoptosis of JSC-1 cells , detected by monitoring the cleavage of the apoptosis marker PARP ( Figure S13 A , B ) ., To exclude the possibility that apoptosis may have influenced KSHV reactivation , we treated JSC-1 with stauorosporine ( STS ) , which also induced apoptosis in JSC-1 as shown by the cleavage of PARP ( Figure S13 C ) ., In contrast to DZNep treatment , STS treatment affected neither the steady state levels of EZH2 and H3K27me3 , nor did it reactivate KSHV from latency ( Figure S13 C , D ) ., This suggests that DZNep-mediated H3K27me3 reduction triggers KSHV reactivation ., This was further supported by the fact that specific shRNA-mediated depletion of EZH2 induced the expression of number of lytic genes ( Figure 5F , G ) ., In summary , the depletion of the PcG proteins in latently infected cells induces the lytic reactivation of KSHV , suggesting that PcG proteins play an important role in the maintenance of KSHV latency ., The genome-wide transcriptional analysis of KSHV gene expression revealed that despite the differences in the features of their promoters , viral genes with similar functions display analogous expression patterns during the lytic replication cycle , implying the existence of a common regulatory mechanism for their gene expression 39 , 42 , 43 , 55 ., In fact , cellular genes with related functions often have common chromatin structures that are associated with specific histone modifications whereby expression of large sets of genes can be coherently coordinated by epigenetic mechanisms 56 ., Our ChIP-on-chip analysis identifies several distinct chromatin domains with different histone modifications on the latent KSHV genome , suggesting that expression of viral genes within each chromatin domain may be co-regulated ( Figure 1 , 2 , S7 , S8 ) ., Specifically , latent genes clustered in the latency-associated genomic locus have H3K4me3/AcH3-rich chromatin domain during latency and reactivation , which is in correlation with the constitutively active transcription of latency-associated genes ., The genomic region encoding the IE genes ORF50 and ORF48 has a bivalent chromatin domain defined by the concomitant presence of the activating H3K4me3 and the repressive H3K27me3 marks during latency , which rapidly changes upon reactivation with increasing AcH3 and H3K4me3 and decreasing H3K27me3 ( Figure 2 , 3A ) ., Importantly , the chromatin bivalency of the RTA promoter ensures the repression of RTA during latency , but also readies it for rapid activation upon reactivation ( Figure 3A , S1 ) ., Bivalent chromatin is characteristic of many inducible cellular genes that are involved in the regulation of development and immune responses such that these genes are repressed yet primed for rapid activation 7 , 15 ., KSHV genomic regions encoding a number of late genes are associated with repressive H3K9me3 and H3K27me3 modifications during both latency and the early phase of lytic reactivation , which is in accordance with the observed silencing of late genes ., Strikingly , the high expression of late genes is observed at the time of the viral DNA replication concomitantly with the disassembly of viral chromatin ( Figure S1 , S2 ) ., These data suggest that viral DNA replication may play a role in the disruption of the repressive heterochromatin associated with viral late genes , which ultimately facilitates late gene expression 40 ., However , a recent study has shown that a portion of replicating herpesviral genomes can be chromatinized even during lytic replication , indicating that viral chromatin may be continuously involved in the regulation of late gene expression 57 ., In addition , the deletion of several MHV68 genes has been shown to result in blocking of late gene expressions without affecting viral DNA replication 58 , 59 , 60 ., This suggests that viral replication may not directly influence the disassembly of the heterochromatin of late genes ., Based on the histone modification patterns associated with the promoter regions of E genes , three distinctive groups could be observed ( Figure 2 , S8 ) ., The chromatin structure of the promoters of Group I genes resembles that of cellular bivalent promoters with the exception that they are also enriched in H3K9me3 during latency ., However , the depletion of H3K9me3 is contemporaneous with the decrease of H3K27me3 upon reactivation , suggesting that both repressive histone marks are replaced by activating histone marks upon reactivation ., The promoters of Group II genes are enriched primarily with activating histone modifications during both latency and reactivation , similar to those of IE genes , whereas the promoters of most Group III genes are mainly associated with repressive histone marks during latency and in the early phase of reactivation ( 12 hpi ) , resembling Group IV late genes ., This indicates that despite the different gene expression profiles during the lytic reactivation cycle , some of the lytic genes show similar histone modification patterns in their promoters , suggesting that other histone modifications may be also associated with these promoter regions to generate the distinct gene expression profiles ., This topic will be actively investigated in the future ., Furthermore , our observation that the TSS regions/promoters of several E genes are enriched with the activating AcH3 and H3K4me3 histone marks but depleted for the repressive histone modifications during latency , suggests that although transcription of these E genes may have been initiated , RNAPII is likely stalled on their promoters ( Figure 2 and 3C ) ., In fact , several of these E genes ( K2 , K5 , K6 , K7 , K11 ) have been shown to be temporally expressed immediately after de novo infection or rapidly express upon lytic reactivation 55 , 61 ., These E genes carry immune modulatory and/or antiapoptotic functions so their rapid expressions seem to be crucial for the virus to escape host immune recognition or attack during the early phase of the lytic life cycle of KSHV ., This suggests that their promoters are primed with activating histone modifications and probably have preassembled RNA polymerase II complexes during latency as also seen with a large number of inducible cellular genes 7 , 62 ., However , this raises the question of how their gene expressions are suppressed during latency despite the presence of an active chromatin structure ., It is intriguing that a large number of the E genes that are enriched primarily with AcH3 and H3K4me3 activating marks are also Rta-inducible , suggesting that the cooperation of Rta with the active chromatin structure may be necessary to activate expression of these E genes ., Furthermore , it is conceivable that the stalled RNAPII on their promoters also requires the recruitment of specific cellular transcription factors such as PTEFb , in order to allow the conversion of RNAPII from a restricted state to an elongation-competent state 62 ., Histone H3 ChIP-on-chip revealed that a small viral genomic region ( between 15 and 30kb ) mostly containing KSHV unique genes displayed a detectable decrease of H3 occupancy at 12hpi ( Figure 1 ) ., Thus , the decrease of the repressive H3K27me3 histone mark within this region upon reactivation may potentially be a consequence of the dissociation of H3 occupancy ., However , the decrease of H3 occupancy in this region does not directly correlate with the changes in histone modifications as H3K27me3 decreases in both 15–20kb and 25–30kb regions at 12 hpi , but while H3K4me3 and AcH3 increase in the 15–20kb region ( ORFs 10 , 11 , 70 , K3 ) they decrease in the 25–30kb region ( ORFs K5 , K6 , K7 , PAN ) ., Thus , changes in enrichment of histone modifications seem to be gene-specific and may not necessarily be due to changes in nucleosome occupancy ., In contrast to the genome-wide repressive role of H3K27me3 , the effect of the heterochromatin histone mark H3K9me3 on viral gene expression seems to be limited: enrichment of the H3K9me3 is restricted to specific genomic regions ( Figure 1 ) and the H3K9me3 histone demethylase JMJD2A does not efficiently induce KSHV lytic replication in Vero cells ( Figure 5A ) ., However , it is possible that JMJD2A expression may induce KSHV reactivation in different cell types or H3K9me3 histone demethylases other than JMJD2A may contribute to KSHV reactivation 50 ., This is in agreement with the Herpes simplex virus ( HSV-1 ) latent genome: while the H3K9me2 , H3K9me3 , and H3K27me3 modifications are detected , all the tested viral promoters are most enriched in H3K27me3 63 ., On the other hand , the inhibition of the H3K9me3 histone demethylase LSD1 has been shown to block the reactivation of HSV-1 from latency 30 , and the enrichment of H3K9me3 and relatively low levels of H3K27me3 were found on the latent genome of the gammaherpesvirus EBV 64 , 65 ., These studies indicate that H3K9me3- and H3K27me3-associated chromatin-based repression mechanisms may be a common feature of herpesvirus gene express

Introduction, Results, Discussion, Materials and Methods

Epigenetic modifications of the herpesviral genome play a key role in the transcriptional control of latent and lytic genes during a productive viral lifecycle ., In this study , we describe for the first time a comprehensive genome-wide ChIP-on-Chip analysis of the chromatin associated with the Kaposis sarcoma-associated herpesvirus ( KSHV ) genome during latency and lytic reactivation ., Depending on the gene expression class , different combinations of activating acetylated H3 ( AcH3 ) and H3K4me3 and repressive H3K9me3 and H3K27me3 histone modifications are associated with the viral latent genome , which changes upon reactivation in a manner that is correlated with their expression ., Specifically , both the activating marks co-localize on the KSHV latent genome , as do the repressive marks ., However , the activating and repressive histone modifications are mutually exclusive of each other on the bulk of the latent KSHV genome ., The genomic region encoding the IE genes ORF50 and ORF48 possesses the features of a bivalent chromatin structure characterized by the concomitant presence of the activating H3K4me3 and the repressive H3K27me3 marks during latency , which rapidly changes upon reactivation with increasing AcH3 and H3K4me3 marks and decreasing H3K27me3 ., Furthermore , EZH2 , the H3K27me3 histone methyltransferase of the Polycomb group proteins ( PcG ) , colocalizes with the H3K27me3 mark on the entire KSHV genome during latency , whereas RTA-mediated reactivation induces EZH2 dissociation from the genomic regions encoding IE and E genes concurrent with decreasing H3K27me3 level and increasing IE/E lytic gene expression ., Moreover , either the inhibition of EZH2 expression by a small molecule inhibitor DZNep and RNAi knockdown , or the expression of H3K27me3-specific histone demethylases apparently induced the KSHV lytic gene expression cascade ., These data indicate that histone modifications associated with the KSHV latent genome are involved in the regulation of latency and ultimately in the control of the temporal and sequential expression of the lytic gene cascade ., In addition , the PcG proteins play a critical role in the control of KSHV latency by maintaining a reversible heterochromatin on the KSHV lytic genes ., Thus , the regulation of the spatial and temporal association of the PcG proteins with the KSHV genome may be crucial for propagating the KSHV lifecycle .

KSHV is a ubiquitous herpesvirus that establishes a life-long persistent infection in humans and is associated with Kaposis sarcoma and several lymphoid malignancies ., During latency , the KSHV genome persists as a multicopy circular DNA assembled into nucleosomal structures ., While viral latency is characterized by restricted viral gene expression , reactivation induces the lytic replication program and the expression of viral genes in defined sequential and temporal order ., Posttranslational modifications of the viral chromatin structure have been implicated to regulate viral gene expressions but the underlying gene regulatory mechanisms are still elusive ., Here , we demonstrate that the latent and lytic chromatins of KSHV are associated with a distinctive pattern of activating and repressive histone modifications whose distribution changes upon reactivation in an organized manner in correlation with the temporally ordered expression of viral lytic genes ., Furthermore , we demonstrate that the evolutionarily conserved Polycomb group proteins , that maintain the repression of genes involved in hematopoiesis , X-chromosome inactivation , cell proliferation and stem cell differentiation , also play a critical role in the regulation of KSHV latency by maintaining a repressive chromatin structure ., Thus , the epigenetic program of KSHV is at the crux of restricting latent gene expression and the orderly expression of lytic genes .

genetics and genomics/epigenetics, virology/persistence and latency, virology/viral replication and gene regulation, virology/viruses and cancer

journal.pbio.1001197

Deficient Induction Response in a Xenopus Nucleocytoplasmic Hybrid

Investigation of the mechanisms generating the characters or phenotypes during development has revealed the importance of the nucleus and its DNA content in directing developmental processes 1–4 ., Nonetheless , the cytoplasm of the egg is responsible for the specification of many early aspects of development , including polarity , as well as cleavage type and developmental timing 3–6 ., In principle , for the nucleus of one species to be compatible with the cytoplasm of the egg of another species , the foreign species nucleus must not interfere beyond a certain threshold with the maternally regulated developmental processes of the cytoplasmic ( egg ) species , while the recipient egg cytoplasm also needs to fully “activate” and support development promoted by the foreign nucleus ., Thus , as a general rule , it is possible to generate viable offspring via interspecies Somatic Cell Nuclear Transfer ( iSCNT ) only if the egg cytoplasm and the donor nucleus come from two very closely related species or from sub-species , which develop in a highly similar manner ., Indeed , if the two species used are sufficiently distant , the resulting embryos rarely progress normally through embryonic development and often arrest at the stage of EGA or soon after 2 , 3 , 7 , 8 ., One of the first scientists who became interested in this field was Baltzer , who revealed the importance of the nucleus in development using androgenetic Triturus cybrids ., Indeed , he observed that when a sperm from one species is combined with the egg cytoplasm of another species , androgenetic development differs from that when the sperm is of the same species as the egg , and in fact leads to severe developmental defects 1 ., He , and others , further recorded differences between the development of reciprocal androgenetic cybrids 9–11 , which could in principle suggest that the basis of the incompatibilities between the respective nuclei and cytoplasms of two given species might not necessarily be reciprocal ., Later , the method of nuclear transfer 12 not only enabled the transplantation of diploid nuclei into enucleated eggs in virtually any species combinations , but also allowed the transfer of nuclei from cybrid embryos back to their own species egg cytoplasm ., Using this technique , Moore ( 1958 ) showed that the nucleocytoplasmic incompatibilities between two Rana species ( R . pipiens and R . sylvatica ) led to irreversible nuclear damage 13 ., Similar conclusions were later attained when back-transfer experiments were performed with the cybrids made from two Xenopus species ( X . laevis and X . tropicalis ) , suggesting that irreversible nuclear damage may be a common effect of nucleocytoplasmic incompatibilities 14 ., Interestingly , cybrid lethality was shown to occur even in a combination ( R . palustris nuclei into R . pipiens cytoplasm ) in which no cytologically detectable chromosome damage was found to occur and back-transferred embryos developed normally 15–17 , suggesting that nuclear damage is not the whole explanation for developmental defects in cybrids ., Also , a few experiments in which pieces of cybrid embryos were grafted onto normal embryos of either parental species suggested that the developmental defects of these embryos were cell autonomous , as contact with normal tissue did not rescue their developmental potentials 14 , 16 ., Finally , a more extreme cybrid combination ( D . pictus nucleus into X . laevis egg cytoplasm ) also generated by iSCNT , arrested before gastrulation , had reduced mRNA synthesis and did not initiate rRNA synthesis 18 ., It , however , remains unclear whether these defects were the primary causes of the arrest , or secondary to other incompatibilities ., Work later performed in fish using iSCNT suggested that major differences in chromosome numbers could be one of the essential factors causing the nucleocytoplasmic incompatibilities 19 , 20 ., Interestingly , chromosome loss was observed in lethal fish hybrids generated by cross-fertilization , while in one such combination , phospho-histone H3 abnormally persisted on the lagging chromosomes during anaphase 21 , 22 ., Consistent with the amphibian work , cybrid lethality can , however , occur without any obvious defects in chromosome segregation , since a fish cybrid combination ( goldfish nucleus into loach egg cytoplasm ) that does not suffer from chromosome elimination is embryonic lethal 20 , 23 ., In the meantime , massive experimentation with iSCNT in mammals has also explored the limits of this technique and provided new insights regarding the potential causes of the developmental arrest in lethal cybrid combinations ., One main conclusion derived from several reports suggested that a major barrier to cybrid development must be manifested at the stage of EGA , since it coincides with the stage of arrest of a majority of lethal mammalian cybrid combinations 7 , 8 ., This hypothesis was recently supported by transcriptional analyses 24–27 ., A second possibility is a potential incompatibility between the maternal mitochondrial genome and that of the foreign species nucleus , leading to defects in mitochondrial function in cybrids 7 ., A reason for this suspicion comes from the fact that higher mutation rates ( compared to that of genomic DNA ) combined with maternal inheritance can lead to rapid divergence in mitochondrial DNA during evolution 28 ., Also , the efficiency of same-species bovine SCNT is increased if the donor and recipient cells have the same mitochondrial haplotype 29 , while ATP levels were reduced in chimpanzee/bovine iSCNT embryos 27 ., Finally , evolutionary distances as little as 8–18 million years lead to fatal defects in oxidative phosphorylation in primate and rodent xenomitochondrial cybrid cell lines 30 , ., Pioneering work has thus established that there are developmental incompatibilities between the nucleus and the cytoplasm of sufficiently distant species ., Yet the rules that determine the compatibility between the maternal cytoplasmic content and that of the nucleus in the context of early development remain poorly defined ., Equally obscure are the precise initial faults in the developmental mechanisms that eventually lead to the arrest in cybrid embryos ., To better understand the nature of the nucleocytoplasmic incompatibilities that exist between relatively distant species , we have analysed here the developmental potentials and defects of reciprocal X . laevis and X . tropicalis hybrids and those of the lethal cybrids formed by the combination of a haploid X . tropicalis sperm nucleus and a X . laevis egg cytoplasm ., In all haploid Anura , the onset of gastrulation is delayed by the time it takes for all the cells to undergo approximately one additional division , until they reach the same nucleocytoplasmic volume ratio as in diploid embryos 34 ., In addition to this developmental retardation , X . laevis haploid embryos are microcephalic , have a shorter axis , and suffer from lordosis and a bulging abdomen ( Figure 1A–B; 35 ) ., Haploids also have a feeble heart , are much less active than diploids , and are subject to oedema , such that no haploid X . laevis has ever reached metamorphosis 34 , 35 ., Consistent with this , lxl embryos showed all of the early phenotypes described above ( Figure 1A–B , Table 1 , Videos S1–S2 ) ., Development of lxl embryos was also briefly investigated and found to be identical to that of lxl embryos ( unpublished data ) ., We further found that the development of txt embryos was comparable to that of lxl or lxl in all respects ( Figure 1C–D , Table 1 ) ., Therefore , both species possess a roughly equal early developmental potential in the androgenetic haploid state , developing into similarly advanced stunted swimming tadpoles with a frequency above 80% ( Figure 1A–D , Table 1 ) ., We then asked whether the addition of a haploid nucleus from one of these species would interfere with haploid development of the other species ., Reciprocal in vitro cross-fertilization between X . laevis and X . tropicalis has previously been reported 36–38 ., Whereas the cross-fertilization of the eggs of X . tropicalis with X . laevis sperm is very inefficient ( ∼3% ) , that of X . laevis eggs with X . tropicalis sperm is comparable to that of X . laevis self-fertilization ( unpublished data; 37 ) ., It has been stated that lxt hybrids are viable and can develop to the adult stage 36 , yet further characterization was lacking ., We have thus analysed the development of the reciprocal hybrids that can be generated from X . laevis and X . tropicalis ., The lxt hybrid embryos develop into swimming tadpoles ( stage 40 ) with a frequency that is comparable to that of lxl or txt control embryos ( Figure 1E , Table 1 , Text S1 , Figure S1 ) , suggesting that the addition of a haploid set of X . tropicalis chromosomes does not interfere with gynogenetic haploid X . laevis development ., Interestingly , the defects of lxt hybrids are highly reminiscent of those seen in X . laevis haploids ( lxl or lxl ) , although they have a markedly decreased severity in the hybrids ( Figure 1A–B , E , Table 1 ) ., Thus , an additional haploid set of X . tropicalis chromosomes is in fact beneficial to X . laevis haploid development since lxt hybrids develop further than X . laevis haploids ( lxl or lxl ) , which never reach metamorphosis ( Figures 1A–E , S1 , Table 1; 34 , 35 ) ., The reciprocal txl hybrid embryos all died as late blastulae or very early gastrulae ( Table 1 ) ., This indicates that an additional haploid set of X . laevis chromosomes is very damaging to the early development of a gynogenetic X . tropicalis haploid ., We have not further characterized this arrest ., The results thus suggest that a haploid X . tropicalis nucleus does not interfere with X . laevis maternal and embryonic development , while a haploid X . laevis nucleus severely interferes with X . tropicalis maternal or embryonic developmental processes ., Since a haploid X . tropicalis nucleus did not interfere with X . laevis haploid development , we next asked whether the cytoplasm of a X . laevis egg can support the normal development promoted by a X . tropicalis nucleus ., Earlier experimentation with iSCNT suggested that the cytoplasm of the X . laevis egg was not capable of reprogramming and/or sustaining normal development promoted by a X . tropicalis neurula/tailbud stage somatic nucleus 14 ., If this nucleocytoplasmic incompatibility between the X . laevis egg cytoplasm and a X . tropicalis somatic nucleus was not due to defects linked to nuclear transfer or reprogramming , the cytoplasm of a X . laevis egg should also be incapable of sustaining the normal development promoted by a X . tropicalis sperm nucleus to the swimming tadpole stage ., We therefore compared the development of lxt cybrids to that of lxl or txt same-species controls ., The lxt cybrids developed relatively normally at first and were indistinguishable from control lxl androgenetic haploids until they reached the beginning of gastrulation and the appearance of the dorsal lip of the blastopore ( stage 10 . 25 ) , approximately 1 h after lxl diploids ( Table 1 , Videos S1–S2 ) ., However , the lxt cybrid embryos subsequently showed developmental retardation and consistently failed to close their blastopore , formed abnormal neurulae , and all died as abnormal , non-swimming postneurulae ( Figure 1F–G , Table 1 , Video S2 ) ., The proportion of embryos reaching a postneurula stage ranged from <10% to >80% depending on male/female combinations ( or egg batches ) ., The most developmentally advanced of these cybrid embryos had a rudimentary sucker , microcephalic head , and pigmented elementary eyes ., A few ( <5% ) also sporadically underwent bursts of rhythmic muscular contractions and/or developed a primitive caudal fin , and very few ( <1% ) also showed posterior axis elongation ( Figure 1G shows the most developed lxt embryo obtained , while Figure 1F shows a more typical example ) ., Exceptionally well-developed lxt cybrid individuals could survive for up to almost a week ., Overall , the terminal phenotype of these lxt cybrid embryos is very similar to those ( diploids ) previously obtained by iSCNT 14 ., One difference , however , is that they are less elongated , which is likely to be the result of the difference in ploidy , since same-species Xenopus haploids are readily characterized by reduced axis elongation ( Figure 1A–D; 34 , 35 ) ., Thus , lxt androgenetic haploid cybrids have a reduced developmental potential compared to same-species androgenetic haploid controls ( lxl or txt ) , demonstrating the existence of a developmental nucleocytoplasmic incompatibility between these two species that is not due to nuclear transfer or reprogramming defects ., Even though the replication of X . tropicalis nuclei in X . laevis cytoplasm may trigger unknown nuclear aberrations 14 , chromosome loss was not observed in the lxt cybrid embryos ( 4/4 lxt embryos had cells in which the expected haploid chromosome complement of X . tropicalis was clearly visible in metaphase squash preparations; unpublished data ) ., Attempts to generate the reciprocal txl androgenetic haploid cybrid were unsuccessful , probably owing to the low efficiency of cross-fertilization in this direction ( Table 1 ) , and thus the developmental potential of a haploid X . laevis nucleus in a X . tropicalis egg cytoplasm remains undefined ., These results thus indicate that even though the presence of a X . tropicalis haploid nucleus does not interfere with ( and even improves ) gynogenetic X . laevis development , the X . laevis egg cytoplasm does not support the development that is promoted by a X . tropicalis nucleus as well as the X . tropicalis egg cytoplasm ., Furthermore , it establishes the onset of gastrulation ( stage 10 ) as the critical stage where the nucleocytoplasmic incompatibility is first manifested ., A major barrier to the development of cybrid embryos is believed to reside at the stage of EGA 7 , 18 , 24–27 ., Suppression of transcription with α-amanitin ( intra-cytoplasmic concentration of 50 µg/ml ) causes X . laevis embryos to arrest prior to gastrulation ( unpublished data; 39 ) ., It is therefore plausible that the components of the X . laevis egg cytoplasm are unable to efficiently activate transcription from the X . tropicalis genome , resulting in the observed gastrulation defects ., To test this , we used quantitative RT-PCR to evaluate the mRNA content of several embryonically transcribed genes in stage 10 . 25 lxt cybrid embryos ., The relative quantity of transcripts for Xbra , Chordin , Gata4 , and Mixer at this stage in lxt cybrids was not significantly different from txt or txt control embryos ( Figure 2A ) ., We therefore conclude that the gastrulation defects of lxt cybrids do not arise from a generalized inefficient EGA ., To exclude the possibility that differences in the splicing or translation machineries between the two species could instead lead to inefficient protein synthesis from these properly concentrated embryonic transcripts following EGA , we investigated the production of Xbra protein in lxt cybrids ., Western blot comparison of stage 11 embryos revealed that the relative concentration of Xbra protein in lxt cybrid embryos was similar to that in control X . laevis embryos ( lxl and lxl ) ( Figure 2B ) ., It may be important to note that Xbra protein concentration is markedly reduced in X . laevis egg-based embryos ( lxl , lxl , lxt ) relative to X . tropicalis egg-based embryos ( txt , txt ) ( Figure 2B–C ) ., This suggests that the concentration of Xbra protein at stage 11 is different in X . laevis and X . tropicalis embryos , and maternally/cytoplasmically regulated in the cybrid ., However , since the level of Xbra protein present in the X . laevis egg-based embryos is similar regardless of whether it is encoded by a X . laevis or X . tropicalis genome , these results suggest that the early gastrulation defects of the cybrid embryos do not result from a generalized deficiency in EGA or protein synthesis ., The last phase of EGA in Xenopus consists of the activation of rDNA transcription and nucleologenesis 40 , 41 ., Nucleologenesis requires factors present in the oocyte nucleolus in mammalian embryos 42 , and was defective in cybrids generated by iSCNT 18 , 26 , 43 ., To verify whether the last phase of EGA is completed in lxt cybrids , we analysed nucleologenesis ( the nucleolus itself results from active rDNA transcription 44 ) in these embryos ., No statistical difference was , however , found regarding nucleoli numbers between the nuclei of the lxt cybrids and control haploids ( lxl or txt ) ( Figure S2A–G ) , indicating that the X . laevis cytoplasm efficiently recognizes the X . tropicalis nucleolar organizer ., Nucleolar integrity in lxt cybrids was further confirmed by the broad intra-nucleolar distribution of fibrillarin 26 , identical to the controls ( Figure S2H–J ) ., Interestingly , lxt hybrid embryos had significantly fewer nuclei with two nucleoli than either diploid controls ( lxl and txt ) ( Figure S2A–G ) , suggesting that one of the nucleolar organizers is partially dominant ., Our results , however , suggest that nucleologenesis , and thereby rRNA synthesis , is successful and that EGA is therefore completed in lxt cybrids; their early gastrulation defects must therefore arise from other incompatibilities ., An incompatibility between the maternal species mitochondrial genome and the foreign species nuclear-encoded mitochondrial genes could lead to deficient energy production and underlie lethality in cybrids 7 , 45 ., Mitochondrial ATP synthesis is indeed required for Xenopus embryos to initiate gastrulation ( 100% of X . laevis or X . tropicalis embryos ( n\u200a=\u200a30 each ) arrested at stage 9 when cultured in 40 µM oligomycin ( unpublished data ) , an inhibitor of mitochondrial ATP synthase 46 ) ., We used a luciferase-based assay to determine the absolute ATP content at various time points during early embryonic development in the diverse kinds of X . laevis egg-based embryos ( lxl , lxl , lxt , and lxt ) ., The average number of ATP molecules per X . laevis egg obtained by this method was 1 . 5 nmoles ( from three different frogs ) , close to the 1 . 6 nmoles for in vitro matured oocytes that was previously measured using chromatography 47 ., Overall , the ATP content in all X . laevis egg-based embryos tested decreased until stage 10 . 25 to about 2/3 of the egg content , and then remained constant or slightly increased until stage 11 . 5 ( Figure 3A ) ., The ATP content curves of the two kinds of diploid embryos ( lxl and lxt ) were very similar to each other , while those of the two kinds of haploid embryos ( lxl and lxt ) appeared slightly different from the diploid curves , which may reflect different energy dynamics between haploid and diploid embryos ., No statistical difference ( p>0 . 05; two-tailed t test ) in ATP content was found between lxt cybrids and control lxl sibling embryos at any time point until stage 11 . 5 ( Figure 3A ) ., Thus , we conclude that the early gastrulation defects in lxt cybrid embryos are not due to reduced ATP levels ., Energy stress in all eukaryotes is detected in a very sensitive manner by the AMP-activated protein kinase ( AMPK ) , which becomes phosphorylated in its activation loop following an increase in the AMP∶ATP ratio 48 ., We have thus used anti-phospho-AMPK antibodies to detect AMPK phosphorylation in various kinds of embryos at stage 11 , well after the onset of the gastrulation defects of lxt cybrids ., The level of AMPK phosphorylation in these cybrids was similar to that of control embryos ( Figure 3B ) ., Therefore , we conclude that the gastrulation defects in lxt cybrid embryos are not due to ATP depletion or energy stress ., It appears unlikely that an incompatibility between the X . laevis mitochondria and the X . tropicalis nucleus that would not affect ATP levels could explain the early gastrulation defects occurring in these cybrids ., To gain insights into the mechanisms responsible for the developmental faults of lxt cybrid embryos , we sought to understand the basis of their early gastrulation defect , namely the failure to close their blastopore and elongate their body axis ( Figure 1 , Video S2 ) ., Blastopore closure and body axis elongation are both highly dependent on efficient convergence and extension of the involuting marginal zone 49 ., To test the efficiency of induction and convergence-extension movements in the gastrulating cybrid embryos , we compared the elongation of stage 10 . 5 dorso-marginal explants from these embryos to that of similar explants from control embryos ., We adopted the following system to score the induction response 50 ., If the explants are not induced , they remain spherical ( no elongation ) ., If induction and efficient convergence-extension occur , the explants elongate such that their length/width ratio becomes greater than two ( well elongated ) ., If the explants are induced but do not undergo efficient convergence-extension , they only partially elongate ( stump ) ., Over 70% of stage 10 . 5 dorso-marginal explants taken from control embryos ( lxl , lxl , txt , or txt ) underwent efficient convergence-extension , while the remaining also elongated , but to a lesser extent ( Figure 4 , Table 2 ) ., In contrast , few ( 14% ) of the explants from lxt cybrid embryos underwent efficient convergence-extension , while most ( 67% ) elongated to a lesser extent and some ( 19% ) did not elongate ( Figure 4 , Table 2 ) ., Therefore , we conclude that the dorso-marginal region of lxt cybrid embryos is defective in induction response and convergence-extension during gastrulation , and this may be responsible , at least in part , for their incapacity to close their blastopore and properly elongate their body axis ., Gastrulation movements and convergence-extension in Xenopus are driven by cells of the mesoderm , which arises at the equatorial region following the perception of a mesoderm-inducing signal that is generated by the vegetal hemisphere ., If the cells of the animal hemisphere are not exposed to this signal , they remain ectodermal and do not elongate 51 , 52 ., Therefore , the reduced elongation response of the cells originating from the animal hemisphere in lxt cybrid embryos could in principle result either from deficient induction signal emission from the vegetal cells , or from a defective response of the animal cells to correct levels of induction signals , or both ., To determine whether the vegetal hemisphere of the lxt cybrids secrete signals capable of inducing efficient convergence-extension in adjacent animal cells , we compared the elongation of naïve lxl animal caps ( stages 8–9 ) that were conjugated to same-stage vegetal hemispheres of the following kinds: lxl , txt , txt , and lxt ., Whereas the vegetal halves of control embryos ( lxl , txt , and txt ) were equally good at inducing lxl animal cap elongation , there was a marked reduction in the proportion of animal caps efficiently elongating following induction by the vegetal hemisphere of lxt cybrids ( Figure 5 , Table 3 ) , suggesting that reduced emission of inductive signals by the vegetal half of the cybrid embryos may contribute to their convergence-extension defects ., Nonetheless , a significant proportion ( 30% ) of the lxl animal caps that were conjugated to lxt cybrid vegetal halves demonstrated efficient elongation , identical to the controls , suggesting that the vegetal cells of lxt cybrids can provide sufficient mesoderm-inducing signals to trigger efficient convergence-extension and elongation of animal cap cells , albeit in a reduced proportion of embryos ( Figure 5 , Table 3 ) ., It seems therefore unlikely that the only problem underlying the gastrulation defects , which occur in 100% of lxt cybrid embryos , is a deficient secretion of mesoderm-inducing signals by their vegetal hemisphere , although this may indeed contribute to the problem in many embryos ., We thus investigated the possibility that the cells of the animal hemisphere in lxt cybrid embryos do not respond properly , even to normal levels of mesoderm-inducing signals ., We compared the response of unspecified animal caps ( stages 8–9 ) isolated from lxt cybrid embryos that were conjugated to diverse kinds of same-stage vegetal hemispheres ( lxl , txt , txt , and lxt ) ., Strikingly , elongation of these animal caps was only marginally ( ∼10%–20% ) improved by their conjugation to any of the different non-cybrid vegetal halves tested ( lxl , txt , txt ) ( Figure 5 , Table 3 ) ., This suggests that the animal cap cells in the majority of the cybrid embryos do not undergo efficient convergence-extension , even if exposed to normal levels of mesoderm-inducing signals , coming from the vegetal hemispheres of either species embryos ., In contrast , the elongation of txt animal caps in this assay was not significantly different from that of lxl ( Table 3 ) , confirming that the poor elongation of lxt animal caps is not solely the result of their ploidy ., Therefore , the reduced elongation response of animal cap cells of lxt cybrid embryos results both from deficient induction signal emission from their vegetal hemisphere and from a defective response of their animal cells , even to a normal level of inductive signals ., Mesoderm specification and animal cap elongation can be induced in vitro in a dish containing nanomolar concentrations of Activin A in a dose-dependent manner 51 , 52 ., In such an assay , animal caps isolated from X . laevis or X . tropicalis diploid embryos both have the same competence to respond to activin in terms of the induction of differentiation and gene expression 53 ., We used this system to further test the induction and elongation efficiency of stage 8 animal cap cells isolated from lxt cybrid embryos ., As expected , a significant proportion of the animal caps isolated from control embryos ( lxl , lxl , txt , and txt ) elongated well in response to activin in a dose-dependent manner ( 5 ng/ml for 20 or 60 min ) , although the elongation was generally less efficient in haploid embryos ( Figure 6 , Table 4 ) ., This was expected since axis elongation in haploid embryos is reduced compared to diploids ( Figure 1; 34 , 35 ) ., A reduced proportion of naïve animal caps isolated from lxt cybrid embryos elongated in response to similar doses of activin in a dose-dependent manner , but strikingly they never underwent efficient convergence-extension ( Figure 6 , Table 4 ) ., This result confirms that the reduced convergence-extension in the cybrid embryos largely results from a deficient response of the animal cap cells , even to normal levels of mesoderm-inducing signals ., If the sensitivity to activin is compromised in lxt cybrid embryos , further increasing the activin induction treatment might be expected to rescue their defects in induction response and convergence-extension ., Increasing the activin treatment , either by quintupling activin concentration or doubling the treatment time , indeed caused a higher proportion of the animal caps to elongate , and a few even underwent efficient convergence-extension ( Figure 6 , Table 4 ) ., These results indicate that the sensitivity to activin is compromised in lxt cybrid embryos ., However , even if the induction treatment is increased to ensure the perception of induction signals ( and an elongation response ) in almost all embryos , the vast majority of these still do not undergo efficient convergence-extension ( Figure 6 , Table 4 ) , suggesting that other incompatibilities manifest themselves by preventing efficient convergence-extension to occur during gastrulation ., We observed that Xbra protein concentration is markedly lower in all X . laevis egg-based embryos ( lxl , lxl , lxt ) compared to X . tropicalis egg-based embryos ( txt , txt ) ( Figure 2B–C ) ., Following the induction of mesodermal cells , one function of Xbra consists of suppressing migratory movements to instead promote convergence-extension 54–56 ., One possibility is therefore that a lower ( X . laevis–like ) concentration of Xbra protein does not suppress cell migration enough to permit convergence-extension in cells with a X . tropicalis genome ., To test this hypothesis , we overexpressed Xbra in lxt cybrid animal caps prior to activin treatment ., As expected , such treatment did not affect the proportion of cybrid animal caps that responded to activin induction by undergoing some degree of elongation , while a few of them underwent efficient convergence-extension ( Table 4 ) ., When combined with prolonged activin exposure , this treatment rescued convergence-extension in 29% of cybrid animal caps ( Figure 6 , Table 4 ) ., These results together suggest that the maternally regulated difference in Xbra protein concentration between the two species is partly responsible for the inefficient convergence-extension in gastrulating lxt cybrids , while reduced mesoderm-inducing signal emission and sensitivity also contributes to this phenotype ., To validate these conclusions , we have attempted convergence-extension rescue in whole cybrid embryos using means expected to upregulate induction and/or Xbra signalling ., One consisted in the injection of Activin A protein into the blastocoel of lxt cybrid blastulae , and the second in widely overexpressing FRL-1 , an EGF-CFC family member that is a limiting co-factor in nodal signalling and mesoderm induction 57 , 58 ., These treatments both significantly improved blastopore closure and embryo elongation ( Figure 7 ) , two processes whose success is highly dependent on efficient convergence-extension ., Widely overexpressing Xbra in whole cybrid embryos also improved elongation ( p\u200a=\u200a4×10−7 ) , but it impaired blastopore closure and the resulting embryos were highly abnormal ( unpublished data ) ., These results further support the hypothesis that the nucleocytoplasmic incompatibilities that lead to inefficient convergence-extension in lxt cybrid embryos result from deficient induction signalling and response , and from inadequate Xbra protein concentration ., It is estimated that X . laevis and X . tropicalis diverged from a common ancestor approximately 50–65 million years ( MY ) ago 59–62 ., In comparison , humans are separated from the common chimpanzee by only approximately 6 MY 30 , while the extant placental mammal lineage evolved over approximately 135 MY 63 , 64 ., Considering previous iSCNT reports of EGA defects in many lethal cybrids 18 , 24–27 , it was somewhat surprising to observe normal activation of key embryonic genes in our amphibian cybrid ., However , the evolutionary separation between the mammalian and amphibian combinations tested in these articles is more considerable , respectively , ranging from ∼65 to ∼100 MY , and ∼235 MY 61 , 65 ., Also , given that in X . laevis and X . tropicalis the expression patterns of transgenes generated with promoters from one species are generally maintained in the other species 66 , and that there is a high level of amino acid identity ( >98% overall ) between the homologous proteins of each species 67 , this result was not entirely unexpected ., Moreover , because interspecies differences in transcription factor binding and gene expression are primarily directed by genetic sequence rather than cellular components 68 , in our case X . laevis transcription factors are expected to bind to and promote transcription of X . tropicalis genes in a X . tropicalis specific manner ., Our data also corroborate a study in a lethal loach–goldfish cybrid in which early embryonic expression of two genes ( ntl and gsc ) took place normally 23 ., Since only a handful of genes were tested in both cases , it remains possible that the embryonic transcription of other genes is a

Introduction, Results, Discussion, Materials and Methods

Incompatibilities between the nucleus and the cytoplasm of sufficiently distant species result in developmental arrest of hybrid and nucleocytoplasmic hybrid ( cybrid ) embryos ., Several hypotheses have been proposed to explain their lethality , including problems in embryonic genome activation ( EGA ) and/or nucleo-mitochondrial interactions ., However , conclusive identification of the causes underlying developmental defects of cybrid embryos is still lacking ., We show here that while over 80% of both Xenopus laevis and Xenopus ( Silurana ) tropicalis same-species androgenetic haploids develop to the swimming tadpole stage , the androgenetic cybrids formed by the combination of X . laevis egg cytoplasm and X . tropicalis sperm nucleus invariably fail to gastrulate properly and never reach the swimming tadpole stage ., In spite of this arrest , these cybrids show quantitatively normal EGA and energy levels at the stage where their initial gastrulation defects are manifested ., The nucleocytoplasmic incompatibility between these two species instead results from a combination of factors , including a reduced emission of induction signal from the vegetal half , a decreased sensitivity of animal cells to induction signals , and differences in a key embryonic protein ( Xbra ) concentration between the two species , together leading to inefficient induction and defective convergence-extension during gastrulation ., Indeed , increased exposure to induction signals and/or Xbra signalling partially rescues the induction response in animal explants and whole cybrid embryos ., Altogether , our study demonstrates that the egg cytoplasm of one species may not support the development promoted by the nucleus of another species , even if this nucleus does not interfere with the cytoplasmic/maternal functions of the egg , while the egg cytoplasm is also capable of activating the genome of that nucleus ., Instead , our results provide evidence that inefficient signalling and differences in the concentrations of key proteins between species lead to developmental defects in cybrids ., Finally , they show that the incompatibilities of cybrids can be corrected by appropriate treatments .

When two species evolve separately for several million years , their respective genomes accumulate many small changes that together are responsible for the differences in their characters ., Some of these affect the way eggs are prepared inside the germline , and/or how embryos develop , such that the egg cytoplasm of a given species can only support development promoted by its own genome or nucleus ., Thus , developmental incompatibility arises between the cytoplasm and the nucleus of distant species during evolution and we dont know its mechanism ., We have studied this phenomenon in an advantageous system using two evolutionarily distant frog species ( Xenopus laevis and Xenopus tropicalis ) ., We found that hybrid frog embryos with X . laevis cytoplasm and X . tropicalis nuclei are always defective in an important process that is necessary to generate morphogenetic cell movements during development ., Through a series of experiments in which we dissect out and/or recombine parts of such hybrid embryos and observe their behaviour in culture , we show that this phenomenon occurs because of malfunctions in the signalling cascade that is responsible for generating these cell movements ., Thus , we postulate that inefficient molecular signalling contributes to the death of such hybrids .

developmental biology, embryology, evolutionary biology, molecular development, biology, morphogenesis, evolutionary developmental biology

Defects in induction signaling and response underlie the nucleocytoplasmic incompatibility between two evolutionarily distant frog species, while specific treatments partially restore this response in explants and whole embryos.

journal.pgen.1000078

Inferring Human Colonization History Using a Copying Model

According to current models , modern humans arose in Africa and spread around the world , with little or no genetic contribution from the hominid populations that they displaced 1 , 2 , 3 ., Genetic diversity decreases progressively with land distance from East Africa 4 providing support for a “serial dilution” model in which diversity was lost progressively in sequential bottlenecks associated with small founder population sizes as new territories were colonized 5 , 6 ., However , the good fit of serial dilution models might principally reflect recent admixture , which will tend to smooth diversity clines ., Numerous questions remain about how many independent bottlenecks occurred as new continents were colonized , the exact land routes involved , and whether there have been genetically important migrations that do not conform to a model of progressive outward expansion 7 , 8 , 3 ., Statistical inference of colonization history represents a considerable challenge ., A reasonably detailed description would include ( 1 ) the times of major population splits , ( 2 ) the effective sizes of each distinct population and/or a list of major bottlenecks and ( 3 ) times of major admixture events , when previously distinct populations met and the contributions of the distinct populations to the new hybrid population ., Even a complex population based history does not fully describe migration patterns , since isolation by distance can also be important ., DNA is passed down through generations in linear segments whose boundaries are determined by meiotic crossovers ., Modeling the segment-by-segment inheritance of genetic material is technically challenging even assuming simple demographic scenarios 9 ., Adding modern and ancient population subdivision makes computations more complex and introduces the problem of choosing amongst a very large number of possible split and merger scenarios ., We take an approach that models the segmental pattern of human inheritance and also allows comparison between numerous distinct historical scenarios ., The approach is predicated on populations arising in an order that can be inferred from the data ., For any given ordering of the populations in the sample , we use the copying-with recombination model of Li and Stephens 10 to reconstruct all of the chromosomes ., Different orderings of the populations can be compared based on the overall likelihood of generating the entire set of chromosomes in the sample ., Since all the data we analyze is from contemporaneous samples , the assumption of an ordering is incorrect if interpreted literally ., However , under a serial dilution model , for example , it is natural to think of populations arising sequentially during radiation from Africa ., Subseqent migrations and admixture have complicated this picture but a sufficient signal of these early events remains that the ordering our approach generates can for the most part be interpreted reasonably easily ., For example , the “Out of Africa” bottleneck has left a signal of greater genetic diversity in Africans , both at the nucleotide 11 and haplotype levels 12 in the great majority of African and non-African populations , whatever their subsequent demographic history ., One of the properties of the Li and Stephens model is that the likelihood of an ordering will generally be higher if the most diverse haplotypes are created first ., Our analysis finds the same strong signal that is evident in the summary statistics of diversity; for the dataset of Conrad et al 12 the likelihood of generating two populations , one of which is African , is always higher if the African population is first ., In addition to the order in which populations were founded , we would also like to learn about patterns of ancestry ., For each new population , a subset of individuals from the previously formed populations is designated as a “donor pool . ”, In the model , each new haploid genome or “haploid” is formed by copying chromosomal segments from the donor pool or from previously created haploids in the same population ( for notational simplicity we assume that every individual consists of two haploids that each contain one of the two copies of the 22 autosomes ) ., The model allows different donor pool combinations to be compared according to the likelihood of generating all the chromosomes in the new population ., The number of individuals from each population in the donor pool with the highest likelihood provides an indication of the relative importance of different ancestral sources ., For convenience , we refer to the donors using the labels of the modern populations they come from , but they in fact represent surrogates for the shared common ancestors of the donor and recipient populations ., The generation of individuals from a single population is illustrated for a hypothetical example in Figure 1 ., We tested our inference method using data simulated under a coalescent model 13 , 14 , with individuals sampled from five populations , labelled A-E , that were generated by sequential bottlenecks ( Figure 2-, ( a ) ) ., Parameters were guided by previous demographic estimates 15 , with the first bottleneck approximately corresponding to the “Out of Africa” event ., In 10 independent realisations of the same scenario ( 5 with simulated recombinational hotspots , 5 without ) , the model correctly inferred both the order in which the populations were founded and which populations gave rise to each new one ( Figure 2-, ( b ) ) and did not infer any additional , spurious sources of ancestry ., We then complicated the model by giving populations D and E ancestry from two sources ( Figure 2-, ( c ) ) ., The model continued to infer the correct ordering for the formation of the populations and correctly identified the single sources for populations B and C and the two sources for population E in every case ., However , in 7 of the 10 simulations , the ancestry of population D was inferred incorrectly , with the model either failing to include population A as an ancestor ( as shown in Figure 2-, ( d ) ) , mistakenly including population B , or both ( Table S1 ) ., We conclude that , at least for relatively simple scenarios , the model provides an accurate indication of historical relationships between populations but does not always correctly identify minority sources of ancestry , in particular when admixture is ancient ., One potential confounding factor in SNP data is ascertainment bias ., The SNPs that are chosen for genotyping are often ascertained based on a limited sample of individual who come from one or a small number of ethnic groups ( typically Europeans ) ., For example , in the data of Conrad et al . , heterozygosity of the SNPs was actually highest in the Middle East , Central and South Asia and Europe , although these populations are known to be less diverse than Africans ., Our method reconstructs haplotypes and therefore we expect it to depend principally on patterns of haplotype sharing and diversity , which a priori should be less sensitive to the ascertainment protocols of individual SNPs ., Indeed , in the data of Conrad et al . , the haplotype diversity is highest in Africans 12 ., In order to test for an effect of ascertainment bias , we performed inference in two extreme ascertainment schemes: one in which we selected SNPs for all populations based only on those that were heterozygous in population C , and one in which we selected SNPs for all populations based only on those that were heterozygous in population E . The former might represent ascertainment based only on European or Middle Eastern populations ., The latter would represent an even more extreme and biased ascertainment , such as ascertaining SNPs using only native Americans ., We used 10 of the simulations described above ( the ones without recombination hotspots ) ., In 9/10 cases , results were not discernably different from those based on using all SNPs ., In the remaining simulation , population B and C were swapped in the inferred ordering under both ascertainment schemes ., We conclude that even extremely biased ascertainment has a modest effect on inference ., Our results might also be confounded by the incomplete nature of the sample and by the many complexities of human population history ., We have performed additional simulations in order to assess how complications to the scenarios shown in Figure 2 would affect inference ., We first evaluated the effect of leaving a population out of the simulated datasets ( population D ) ., For all four simulations ( two as illustrated in Figure 2-a , one with and one without recombination “hotspots , ” and two as illustrated in Figure 2-c , one with and one without recombination “hotspots” ) , population C was chosen as a significant donor population for E . Remaining inference was correct ( i . e . no other spurious donors were detected , and for the simulations illustrated in Figure 2-c , the model picked up the additional contribution from population B . ) This is what is expected: with the appropriate donor population missing , our model chooses as its replacement the population that contributed the majority of genetic material to the missing donor population ., Complex patterns of admixture might considerably complicate inference ., We modified the scenarios shown in Figure 2-a and Figure 2-c by adding recent admixture , either from D to C or from A to C . Examples are shown in Figures 3-a and 3-c ., A genetic contribution from population D to C had little effect on inference in 10 different simulations ( Figure 3-d ) ., These results show that “back admixture” , for example migrations into Africa , will generally not be detectable by our method ., In this simulated example at least , the back admixture did not affect the rest of the inference ., The effect of a recent contribution from population A to population C was more substantial ., In 5/10 cases ( four for the scenario shown in Figure 3-a ) the inferred order of populations B and C were swapped ( Figure 3-b ) ., The swapping of the populations leaves the genetic connections between the populations correct but inferences on which are sources and which are sinks are confused by the multi-layered migrational history ., We used the same approach to infer the order of birth and ancestral sources of the 53 populations in the Human Genome Diversity Panel using the data from 2 , 540 linked SNPs across 32 autosomal regions genotyped by Conrad et al 12 ., The highest likelihood scenario is shown in Figure 4 and Movie S1 ., By visually inspecting these results , we have identified nine phases in the colonization of the world ., This subdivision is subjective and the phases should not be thought of as occurring strictly in chronological order ., For example , East Asia and Europe are peopled almost independently , making their relative position in the ordering nearly arbitrary ., Furthermore , Melanesia has multiple sources that reflect ancient and recent migrations that introduced very distinct genetic material ( see 16 for a review ) ., Its inferred place in the ordering reflects the most recent of these migrations ., Nevertheless , the phases do reflect progressive outward expansion , analogous to that implied by serial dilution models ., 1 . Sub-Saharan Africa ., The first population in the ordering are the San , who are hunter gatherers that live in Southern Africa ., Before the Bantu expansion over the last 3 , 000 years , the ancestors of the San occupied most of Southern Africa , but they have been progressively displaced and currently are restricted to a few pockets 17 ., The San contributed ancestry to the next four populations ( the Biaka Pygmies , Bantu from South Africa and Kenya , and Mbuti Pygmies ) but none subsequent to that ., The Bantu are inferred to have contributed to each subsequent African population ., 2 . North Africa ., The Mozabites are the only African population in the sample from above the Sahara ., In our analysis , they are the 8th and final African population to arise and are also distinctive because they represent the first population that uses less donor individuals ( 46 from the Mandenka , Yoruba , and Kenyan Bantu ) than their predecessor the Mandeka , who used 64 donors from four populations ., We interpret the smaller number of donors as evidence for a bottleneck in the history of the Mozabites , that is not shared by the other African populations in the sample ., The small number of donor populations implies that only a subset of the human populations present at the time of the bottleneck contributed to the Mozabite lineage ., 3 . Central Eurasia ., There is no clear pattern to the order of colonization of central Eurasia , with the initial Central Asian populations ( Makrani , Uygur ) interspersed with those from the Near East ( Bedouin , Palestinians ) and the eastern edge of Europe ( the Adygei ) ., All of these populations have Mozabites as donors , with the first three populations also using Kenyan Bantu ., For these three , all 28 Mozabite individuals were used in generating each of the three populations , making it possible that some of the Bantu chromosomes would have been replaced by additional Mozabites or other North or East Africans if they were present in the sample ., Overall , non-African populations can each trace approximately 3/4 of their ancestry via the Mozabites ( Movie S2 , Table S4 ) ., The total number of donors increases progressively from 39 for the Makrani to 141 for the Adygei ., The high interconnectedness of these populations presumably reflects the absence of region-specific bottlenecks and/or multiple episodes of gene flow between Eurasian populations subsequent to the initial colonization event ( s ) ., 4 . Central Europe ., Aside from the Adygei , the first European populations to arise are the French , Tuscans , and Italians ., These three populations have an average of 260 donors , including those from the Mozabites and several Near Eastern and Central Asian populations ., This is a larger number than for any non-European population in the sample and highlights the diverse sources of European ancestry ., 5 . Pre-Han East Asia ., The first 8 East Asian populations ( Cambodia , Mongolia , Oroqen , Xibo , Yi , Tu , Daur , Naxi ) have 50-84 donors , including all 32 individuals from two central Asian populations , the Uygur and the Hazara ( except the Tu who use 24/32 ) ., This represents an entirely distinct source of ancestry from European populations , who each receive less than 10% of their ancestry via the Uygur and almost none via the Hazara ( Movie S2 , Table S3 ) ., The only other external donors are the Pathan ( contributing 12 chromosomes to Mongolians ) and the Burusho , Sindhi and Mozabites , who contribute 23 , 15 , and 4 donors to the Cambodians respectively ., We interpret the paucity of donors and the consistence of ancestry patterns as evidence for a single East Asian bottleneck ., 6 . The extremities of Europe ., The final four European populations ( the Sardinians , Russians , Orcadians and Basque ) all lie on the extremities of the continent ., As well as having many European donors , these populations also have a large number from the Near East and Central Asia , consistent with Europe absorbing multiple waves of migrants ., The Russians have 375 donors , more than for any other population , including from the Yi , Tu , and Mongolians , indicative of admixture with Far-Eastern populations ., The Basque have 4 Hezhen donors but are otherwise similar to other Europeans ., 7 . The Han expansion ., The Han receive their ancestry exclusively from other East Asian populations ( including the more westerly Xibo ) and represent a principal source of ancestry for several subsequent populations that also have principally East Asian ancestry ( She , Japanese , Dai , Lahu , Han from Northern China , and Miao ) ., 8 . The Americas ., The Colombians are the first Amerind population ., 47% of their ancestry can be traced via the Hazara , which is marginally less than typical East Asian populations such as the Han ( 54% ) or Xibo ( 59% ) ( Movie S2 , Table S3 ) ., However , within the descendents of the putative EastAsia bottleneck , their donor pool is diverse , implying that none of the populations in the sample provides a good proxy for the original group or groups that crossed the Bering straight ., The Colombians also have French donors , which may reflect post-Colombian admixture ., The second American population , the Pima , represents the first North American population ., As well as using all 7 Colombians as donors , it uses 8 Mongolians and 4 Oroquen ., Neither of these populations acted as donors to the Colombians , suggesting distinct colonization events from different sources ., Subsequent American populations did not have any non-Amerind donors , except for the Mayans who have Bantu and Tuscan donors , presumably due to post-Columbian admixture 18 ., 9 . Pacific Islands ., All but two of the East Asian populations that donate to the Colombians also donate to the Melanesians , and the Japanese are again the most numerically important with 20 donors ., However , the Melanesians have several additional sources of ancestry ., These include three populations which are products of the East Asian bottleneck ( Oroquen , Han , and Pima ) , in addition to Central Asian populations ( Burusho and Brahui ) and Russians ., Three Mozabite donors are also estimated , which falls slightly below our conservative threshold for significance ( Methods ) ., In total , the Melanesians trace 38% of their ancestry via the Hazara , which is less than East Asian or Amerind populations and implies independent sources of ancestry ., The Papuans receive ancestry only from Melanesians and Cambodians , suggesting a shared common bottleneck ., One concern for this dataset is that the number of individuals varies widely among populations ( from 6 to 45 ) ., We investigated whether this might have a substantial effect on our results by correlating the number of individuals in each population with both its position in the inferred ordering ( Figure S1 ) and the total number of donors it received ( Figure S2 ) ., Using simple linear regression , no strongly significant correlation was found in either case ( p-value > 0 . 05 ) ., In our inferred scenario , Pima are the first North American population in the ordering and receive ancestry from the first South American population , the Colombians ., The Pima have two additional donor populations , the Oroquen and Mongolians , both of whom reside in Mongolia and neither of which are donors to Colombians ., This result is intruiging because it suggests independent sources for North and South Americans and hence multiple waves of migration into the continent , contradicting the current consensus based on available data 23 ., We tested the robustness of this inference by swapping the two populations in the ordering and re-inferring donors using the same protocol ., The Pima replaced their Colombian donors with a small number of East Asians who were donors to the Colombians ( 4 donors each from Naxi and She ) , but the Mongolians and Oroquen remained majority donors ., This result mirrors what is found in our simulations; if a donor population is missing ( or also present in insufficient numbers in the sample ) then it will typically be replaced by one or more of its own donors ., The Colombians gained the Pima and lost a substantial number of other donor populations , but kept several from populations that did not contribute to the Pima in either ordering ( Daur , Hezhen , Xibo and Burusho ) ., These results are consistent with substantial gene flow between North and South America but also imply that these have not been strong enough to overwhelm a clear signal of independent colonization ., These results also suggest a geographically and historically very plausible scenario: The populations colonizing North East Asia whose members crossed the Bering Strait and whose descendents eventually reached South America were replaced by a population more closely related to modern East Asians ( and specifically modern Mongolians ) ., This population subsequently also crossed the Bering Strait and contributed substantially to the ancestry of North American Amerinds ., This second wave of migration provides an explanation for the relationship between distance from Siberia and genetic similarity to Siberians 23 , which was previously attributed to serial dilution 23 ., It also explains why an analysis of the population structure of the Pima and two South American populations based on genome-wide SNP data , using the admixture model of STRUCTURE 24 , inferred that the South American populations had a single source of ancestry but the Pima had received approximately half of their ancestry from a second , additional source 25 ., Simulation results have shown that the admixture model of STRUCTURE can be surprisingly successful in detecting ancient admixture , even in the absence of source populations , if the number of markers used is sufficiently large 26 ., In our inferred scenario there is little gene flow between East Asian and Europeans and the Yakut is the only East Asian population to have two European donors; the Russians and the Orcadians ., The Russian contribution is not surprising because the Yakut live in North East Russia ., The Orcadian contribution is particularly noteworthy because removing these donors reduces the log-likelihood of generating the Yakut chromosomes by 2 . 5 times more than removing donors from any other population ( Table S2 ) ., The Orcadians are also the only other European population to donate to other East Asians , namely the Han from Northern China and the Hezhen , who are also amongst the most Northerly East Asian populations in the sample ., On this basis we hypothesize that there has been an episode of gene flow from Europe to East Asia ., We tested the robustness of this inference by putting Orcadians last in the ordering ., The Yakut replaced the Orcadians with Sardinians , who are a major donor to the Orcadians ., The Hezhen and the Han from Northern China did not acquire new European donors , consistent with the gene flow from Europe being less quantitatively important to these two populations than to the more Northerly Yakut ., Orcadians did not gain any East Asian donors by being placed last in the ordering , strengthening the inference that the direction of the gene flow was from Europe to East Asia ., Our results provide evidence for two continent-scale bottlenecks , the first affecting non-Africans and the second affecting East Asians , with both groups having a small number of donors from outside the region ., Unfortunately , the limitation of both our method and the sampled populations make it difficult for us to make detailed inferences about the nature of these bottlenecks ., Most of the ancestry of non-Africans comes via the only only North African population in the sample , the Mozabites , who are also the last African population to be formed ., However , their intermediate position might reflect back migration from the Middle East and/or Europe27 , 28 , 29 ., Simulation results suggest that our method is likely to miss this type of back admixture ., Indeed , if Mozabites are allowed to receive ancestry from any populations and not only those that precede them in the ordering , they get approximately 70% from these two regions , consistent with the results of STRUCTURE for the same populations 30 ., In any case , a much better sample of East and North African populations would be required to elucidate the nature of the bottleneck ., A similar problem of interpretation occurs for the East Asian bottleneck ., A majority of the ancestry of East Asians comes via two central East Asian populations , the Uygur and the Hazara ., However these populations could have come to resemble East Asians through back migration ., Indeed , if these populations are placed last in the ordering , then more than 40% of their donors are East Asian ., If donors for the East Asian populations are inferred while excluding the Uygur and the Hazara from the dataset , the first populations have a somewhat larger number of donors from a wider range of Central or West Asian populations ( Brahui , Makrani , Balochi , Sindhi and Adygei ) than shown in Movie S1 , but populations later in the ordering revert to having predominantly East Asian donors , supporting a strong East Asian bottleneck that contrasts with the wide sources of ancestry of Europeans ., The major simplification of our model is to assume that the populations were founded in an order ., Since the DNA samples came from living humans , the ordering does not reflect age , but instead bottlenecks and admixture events that distinguish more recently formed populations from older ones ., Complexities in human history make this ordering somewhat arbitrary ., For example , the Melanesians have been founded by multiple waves of migrations ., Their position late in our ordering reflects the substantial proportion of their ancestry that comes from East Asians ., However they also have other , independent sources of ancestry that reflect migrations that are likely to predate those that gave rise to the modern East Asian populations ., Information on the timing of different waves of migration could potentially be obtained from more extensive DNA sequence datasets by examining the sizes of the blocks of DNA that are inherited from different donor populations ., Recent admixture would result in individuals sharing large contiguous segments from particular donor populations 26 , 31 ., Recent shared ancestry would result in individuals receiving large contiguous segments from particular donor haploids ., A fully realistic history would avoid any ordering of the modern populations ., One potential avenue for extending the current approach to achieve this goal would be to impute chromosomes from “ancestral populations , ” which would both represent populations that existed in the past and also act as efficient donors for the modern haplotypes ., Generation of such populations poses a number of statistical and computational challenges but could potentially allow a chronological , multi-layered history to be inferred ., Accurate reconstruction of historical migrations depends crucially on the use of appropriate samples and any geographical interpretation can be confounded by major population movements ., Further , it should ideally be demonstrated that the results are robust to which parts of the genome are used in analysis ., Further methodological innovation and genome-wide SNP datasets from diverse human populations 25 , 32 should allow unprecedented detail in the reconstruction of the ancestry of extant humans ., We used the 32 autosomal regions in Conrad et al 12 , each of which consisted of approximately 80 biallelic SNPs across 330 kilobases of the genome ., SNP data were collected for a total of 927 individuals sampled from 53 different populations , with sample sizes ranging from 6 to 45 individuals per population ., Data were kindly provided to us as haplotypes , which were phased using fastPHASE 33 on each region as previously described 12 ., Li and Stephens 10 described a likelihood based model that captures the principal features of the genealogical process with recombination while remaining computationally tractable for large datasets ., Under the model , the chromosomes are generated in order , with chromosomes being copied segment-by-segment from those earlier in the ordering ., In our notation , every individual consists of two haploids , each consisting of a single phased haplotype per genotyped region ., The L total SNPs in each haploid are listed one region at a time , in order within each region ., Suppose that we wish to generate a particular haploid h* , using j pre-existing donor haploids h1 , … , hj ., Let ρ represent the crossover recombination rate per unit physical distance across the genome , assumed fixed ., The conditional probability Pr ( h* | h1 , … , hj; ρ ) is structured as a Hidden Markov model , where the hidden state Xl represents the existing haploid from the set h1 , … , hj that haploid h* copies from at each site l = 1 , … , L ., The switches in copied-from haplotype are modelled as a Poisson process with rate ρ/j ., The transition probabilities for X between sites l and l+1 are as follows: ( 1 ) where dl is the physical distance between SNPs l and l+1 ., If l and l+1 are on separate genetic regions , we set dl = ∞ ., The observed state sequence component of the Hidden Markov Chain , the probability of observing a particular allele given the haploid that h* is copying from at a given SNP , allows for “imperfect” copying that depends on a per site mutation parameter : ( 2 ) Here hj , l refers to the allelic type of haploid j at SNP l ., The mutation parameter is fixed , as in 10 , as Wattersons estimate with one expected mutation event per site ,, i . e ., 34 for J total haploids ., To calculate Pr ( h* | h1 , … , hj; ρ ) , a summation is performed over all permuations of the copying process ,, i . e . a summation over all possible x , which can be accomplished efficiently using the forward algorithm ( e . g . 35 ) ., In the analyses presented here , we used an alteration of ( 1 ) above , using the “PAC-B” version described in 10 ., Note that the probability of recombination events ( i . e . switches ) and mutations goes down as the number of haploids j increases ., This mirrors a key property of data generated under the coalescent , that the probability that a segment from an additional chromosome will be identical by descent with a segment from chromosomes 1…j increases with, j . This property also means that different orderings will have different likelihoods that at least in part reflect the demographic history of the individuals in the sample ., For example , if a subset of individuals in the sample have a particularly high level of diversity , then the overall likelihood will generally be higher if these individuals are generated early rather than late in the ordering ., In previous implementations of the Li and Stephens algorithm , it has been assumed that each new haplotype is made using all previous haplotypes ., This leads to the formula for the probability of observing J haploids , conditional on ρ: ( 3 ) where as in 10 ., However , in the context where individuals come from differentiated populations , a higher likelihood may be obtained by using only a subset of the pre-existing individuals as donors ., In order for a donor individual to increase the likelihood of generating h* , there needs to be chromosomal segments , whether large or small , that are more similar to h* than any of the others in the donor pool ., Individuals from populations that are more differentiated from h* than others in the donor pool are likely to contain few such segments ., Further , every individual increases the value of j by 2 , and for each segment that is copied a 1/j term appears in the likelihood , corresponding to choosing amongst the j donor haploids ., Thus the presence of differentiated individuals in the donor pool can decrease the overall likelihood ., Here we are interested in investigating ancestry at the population level ., We therefore make some assumptions about orderings and donors that are justifiable if the individuals within each population share the same demographic history ., In practice , population labels are initially defined based on geographic and ethnic criteria , and the degree of homogeneity within the labelled populations can be assessed on multilocus genetic data 18 ., These assumptions considerably reduce the computational complexity of the problem ., Within each population , haploids are assumed to be generated – and donors are used in generating them – in the order they appear in the input file ., In generating a set of haploids H across K populations , we further assume that: 1 . The K populations are generated in sequence according to

Introduction, Results, Discussion, Materials and Methods, Simulations

Genome-wide scans of genetic variation can potentially provide detailed information on how modern humans colonized the world but require new methods of analysis ., We introduce a statistical approach that uses Single Nucleotide Polymorphism ( SNP ) data to identify sharing of chromosomal segments between populations and uses the pattern of sharing to reconstruct a detailed colonization scenario ., We apply our model to the SNP data for the 53 populations of the Human Genome Diversity Project described in Conrad et al . ( Nature Genetics 38 , 1251-60 , 2006 ) ., Our results are consistent with the consensus view of a single “Out-of-Africa” bottleneck and serial dilution of diversity during global colonization , including a prominent East Asian bottleneck ., They also suggest novel details including: ( 1 ) the most northerly East Asian population in the sample ( Yakut ) has received a significant genetic contribution from the ancestors of the most northerly European one ( Orcadian ) ., ( 2 ) Native South Americans have received ancestry from a source closely related to modern North-East Asians ( Mongolians and Oroquen ) that is distinct from the sources for native North Americans , implying multiple waves of migration into the Americas ., A detailed depiction of the peopling of the world is available in animated form .

Humans like to tell stories ., Amongst the most captivating is the story of the global spread of modern humans from their original homeland in Africa ., Traditionally this has been the preserve of anthropologists , but geneticists are starting to make an important contribution ., However , genetic evidence is typically analyzed in the context of anthropological preconceptions ., For genetics to provide an accurate and detailed history without reference to anthropology , methods are required that translate DNA sequence data into histories ., We introduce a statistical method that has three virtues ., First , it is based on a copying model that incorporates the block-by-block inheritance of DNA from one generation to the next ., This allows it to capture the rich information provided by patterns of DNA sharing across the whole genome ., Second , its parameter space includes an enormous number of possible colonization scenarios , meaning that inferences are correspondingly rich in detail ., Third , the inferred colonization scenario is determined algorithmically ., We have applied this method to data from 53 human populations and find that while the current consensus is broadly supported , some populations have surprising histories ., This scenario can be viewed as a movie , making it transparent where statistical analysis ends and where interpretation begins .

evolutionary biology/human evolution, genetics and genomics

journal.pgen.1006710

A chromosome 5q31.1 locus associates with tuberculin skin test reactivity in HIV-positive individuals from tuberculosis hyper-endemic regions in east Africa

One third of the world’s population has been infected with Mycobacterium tuberculosis ( MTB ) 1 , 2 ., Subsequent tuberculosis disease ( TB ) occurs during the lifespan of about 10% of those infected1–3 ., Tuberculosis is a major cause of morbidity and mortality worldwide , with 1 . 5 million deaths and 9 . 6 million new cases of active disease reported in 20141 ., Tuberculosis is the primary cause of death in people co-infected with the human immunodeficiency virus ( HIV ) , and 400 , 000 of the global TB deaths in 2014 occurred in this patient population 1 , 4 ., The immunosuppression from HIV facilitates progression to active disease directly following infection , or by the reactivation of a latent MTB infection5 , 6 ., While the clinical trajectory of a given MTB infection has many determinants and possible outcomes , infection per se is a necessary prerequisite ., Of note , about 10–20% of people living in areas hyperendemic for MTB , which virtually guarantees repeated exposure , appear to be resistant to infection7–10 ., Historically , MTB infection has been evaluated with a tuberculin skin test ( TST ) measuring the induration caused by a delayed type hypersensitivity reaction to an intradermal injection of MTB purified protein derivative ( PPD ) 11 , 12 ., In endemic areas , induration ≥ 5mm measured between 48 and 72 hours post-injection is indicative of infection ., A study of TST reactivity among siblings demonstrated high heritability , suggesting a possible genetic component to the MTB infection resistance phenotype13 , 14 ., Several studies have capitalized on this finding and identified loci relevant to the MTB infection phenotype ., A family-based linkage analysis of TST response identified SLC6A3 and a region on chromosome 11 ( p14 ) as linked to infection8 ., A full genome microsatellite scan comparing persistent MTB negative patients to those with latent infections identified an association with the SLC11A1 gene , and candidate regions on chromosomes 2 ( q14 , q21-q24 ) and 5 ( p13-q22 ) 15 ., Recently , novel methods for evaluating MTB infection status have been developed ., Interferon-gamma release assays ( IGRAs ) detect the concentration of IFN-γ in response to a mixture of MTB-specific antigens16 , 17 ., The purified protein derivative used in TST has some antigenic overlap with the Bacille Calmette-Guérin ( BCG ) vaccine , although 10 years post-vaccination the confounding effect is minor; approximately 1% of adult subjects inoculated at birth with BCG are TST-false positive 18 ., IGRAs’ antigens have no overlap with the BCG vaccine , and maintain excellent specificity in individuals who had childhood BCG vaccinations 16 , 17 ., However , in people with compromised immune systems and previously exposed to MTB , anergy due to immunodeficiency may prevent detection of a positive TST and/or IGRAs ., Inclusion of negative and positive assay controls allows us to better assess this potential confounder ., We used a genome-wide approach to evaluate common variants for association with TST response in a patient population that hypothetically allows us to identify extreme genetic effects ., Namely , we hypothesized that HIV-positive individuals who live in areas endemic for tuberculosis but who do not get infected , are strongly genetically resistant to MTB ., Using two recently concluded prospective cohorts of tuberculosis disease from Tanzania and Uganda , with available TST and IFN-γ results , we identified a variant on chromosome 5q31 . 1 , near SLC25A48 and IL9 that imparts resistance to MTB infection in immunocompromised individuals ., Sex was significantly associated with TST status in the combined Ugandan and Tanzanian cohorts ( Odds Ratio ( OR ) for males 1 . 91 , 95% confidence interval ( CI ) 1 . 27–2 . 86 , p = 0 . 002; Table 1 ) , but it did not associate when studied in Uganda ( p = 0 . 762; Table, 2 ) or Tanzania ( p = 0 . 349; Table, 3 ) alone ., Age was not significantly associated with TST status in the combined cohort ( p = 0 . 108; Table 1 ) , nor in Uganda ( p = 0 . 384; Table, 2 ) or Tanzania ( p = 0 . 153; Table, 3 ) alone ., Therefore , all analyses below were adjusted for sex , 10 principal components , and cohort of origin when Tanzanian and Ugandan datasets were combined ., In logistic regression analysis adjusted for covariates , we observed a genome-wide significant association between a dominant genetic effect of rs877356 on chromosome 5q31 . 1 and binary TST status in the combined cohort ( OR = 0 . 27 , 95% CI 0 . 17–0 . 42 , p = 1 . 22x10-8 , Table 4 , S1–S3 Figs ) ., The variant had consistent effects in Uganda ( OR = 0 . 17 , 95% CI 0 . 08–0 . 37 , p = 9 . 18x10-6; Table 5; S4 Fig ) and Tanzania ( OR = 0 . 33 , 95% CI 0 . 18–0 . 59 , p = 1 . 81x10-4; Table 6 , S5 Fig ) ., Linear regression analyses of continuous size of TST induration under a dominant genetic model produced similar results ( combined cohort beta = -4 . 14 , 95% CI -5 . 55 to -2 . 74 , p = 1 . 45x10-8; S1 Table ) ., Variant rs877356 met the multiple testing-adjusted threshold for this study ( 3 . 08x10-7 ) and was nearly genome-wide significant in an additive model using binary TST status ( OR = 0 . 33 , 95% CI 0 . 222–0 . 493 , p = 5 . 45x10-8; S2 Table ) , and continuous size of TST induration ( combined cohort beta = -3 . 34 , 95% CI -4 . 53 to -2 . 14 , p = 6 . 95x10-8; S3 Table ) ., This SNP was in Hardy Weinberg equilibrium in Tanzania ( p = 0 . 68 ) and Uganda ( p = 0 . 21 ) ., No other unimputed SNPs were significant at the multiple testing corrected threshold in any of the genetic models tested ( Tables 4–6 , S1–S5 Tables ) ., To evaluate SNPs in the region not included on our genotyping array , we imputed SNPs within 0 . 5 megabases of rs877356 ., One SNP , rs17169187 , in high linkage disequilibrium ( LD ) with rs877356 ( D’ = 1 in both cohorts , r2 = 0 . 99 in Tanzania , 0 . 98 in Uganda ) and 2 , 340 bases away , is the variant with the most significant association to binary TST status using a dominant model ( combined cohort OR = 0 . 26 , 95% CI 0 . 16–0 . 40 , p = 4 . 57x10-9; Fig 1 , S6A Table ) ., The results were consistent with those from linear regression on continuous size of TST induration ( combined cohort beta = -4 . 29 , 95% CI -5 . 69 to -2 . 88 , p = 4 . 58x10-9; S7A Table ) ., This variant is also genome-wide significant in additive modeling of both a binary TST designation ( combined cohort OR = 0 . 320 , 95% CI 0 . 214–0 . 478 , p = 2 . 56x10-8; Fig 1 , S6B Table ) and continuous size of TST induration ( combined cohort beta = -3 . 43 , 95% CI -4 . 62 to -2 . 24 , p = 2 . 84x10-8; S7B Table ) ., Adjustment for CD4 count did not significantly affect our results ( S8 Table ) ., In the Tanzanian cohort , IFN-γ responses to positive control ( PHA ) and negative control ( medium ) antigens did not differ by TST results , but were significantly higher in TST cases for all mycobacterial antigens ( S9A Table ) ., In Uganda , we observed the same trends; however , due to smaller sample sizes , the comparisons were not statistically significant ( S9B Table ) ., Separately , we examined the prevalence of TST-positivity in the entire Ugandan household contact study cohort , and found that although the prevalence of TST+ in HIV+ is significantly lower , it is still very high ( 71% in HIV- versus 62% in HIV+ , p = 0 . 003; S10 Table ) ., Furthermore , the distribution of TST induration examined as a continuous variable did not differ by HIV status ( p = 0 . 06 ) ., In the GWAS analysis , removing either potentially false negative subjects ( n = 16 ) , potentially false positive subjects ( n = 20 ) , or both did not affect the results substantially ( S11–S14 Tables ) ., The variant was also genome-wide significant when we included patients with prior tuberculosis in the analyses ( S15 Table ) ., We found the strongest single variant association using a dominant model of rs877356; therefore , we used dominant coding of the SNP in 2-variant haplotype in the SLC25A48 region while using additive models of all other SNPs ., An rs877356-rs2069885 haplotype had the strongest association in this analysis ( omnibus p = 1 . 59x10-12 in the combined cohort; Table 7 ) ., The haplotypes had similar association in the Ugandan ( p = 2 . 51x10-8; Table 8 ) and Tanzanian cohorts ( p = 1 . 37x10-11; Table 9 ) , with the T-G haplotype frequencies being 0 . 32/0 . 60 and 0 . 20/0 . 45 in TST+/TST- subjects , representing a similar enrichment in both cohorts ( Tables 8 and 9 ) ., The haplotype , C-G , also had a consistent distribution between the cohorts , with a TST+/TST- frequency of 0 . 58/0 . 33 in Uganda and 0 . 68/0 . 48 in Tanzania ( Tables 8 and 9 ) ., The results were consistent in additive modeling of both SNPs ( p = 2 . 59x10-9 in the combined cohort; S16 Table ) ., The haplotype had similar association in the Ugandan ( p = 1 . 03x10-5; S16B Table ) and Tanzanian cohorts ( p = 6 . 35x10-5; S16C Table ) ., In addition , patterns of linkage disequilibrium ( LD ) were strikingly similar across the whole region in both Ugandan and Tanzanian cohorts ( S6 and S7 Figs ) , an unexpected result given the greater variation ( and reduced extent ) of LD among African populations ., Remarkably , in the same cohorts , high similarity in LD structure was previously found near IL12B , encompassing a variant associated with resistance to active TB in HIV+ individuals and displaying signals of strong selection19 ., We also determined whether previously associated or linked loci were significant in our results ., Several regions previously shown to be linked to TST response were nominally significant in our study ( 10−3 > p > 10−4 ) , including ones on chromosomes 2 , 5 and 11 ( S17 Table ) 8 , 15 , 20 ., Chromosome 11p14-15 associated with TST response in our analyses as it did previously21 ., Although our most significant region on chromosome 11 was distal to the linkage peak , the region directly under the peak was almost as significant ( p~10−3 ) ( S18 Table ) ., Another previous association signal , IL-10 , did not show signs of replication in our study ( S17F Table ) ., These results overall support the validity of our study design as most previous regions replicated ., In this study we examined the association of common genetic variants with Mycobacterium tuberculosis infection in HIV+ patients from the extended follow-up of the DarDar vaccine trial in Tanzania and the Household Contact study in Uganda ., By applying the “experiment of nature” strategy outlined in a genetic study of tuberculosis disease with the same cohorts 19 , we hypothesized that these immunosuppressed patients who live in MTB endemic areas but do not get infected have strong innate resistance ., This hypothesis and approach were validated as we identified a novel association between protection from MTB infection and rs877356 with a large effect size ., This variant is 9 , 119 bases upstream of the coding region of SLC25A4822 , a Homo sapiens solute carrier family 25 , member 48 ., SLC25A48 is a mitochondrial carrier of amino acids23 , 24 ., This SNP is also 57 , 662 bases downstream from IL9 , which we think is a particularly compelling candidate ., Both genes have supporting evidence that may implicate them ., With respect to SLC25A48 , there is evidence from GTEX that this SNP is an eQTL for a lncRNA closer to it than IL9 ( http://gtexportal . org/home/eqtls/bySnp ? snpId=rs877356&tissueName=All ) ., In contrast , the involvement of IL9 as the potentially causal gene in our association study was supported by our haplotype analyses ., The rs877356-rs2069885 haplotype had the most significant association in this region ., The SNP , rs2069885 , is 66kb away from rs877356 , and is a missense variant in IL9 ( Threonine ( ACG ) ->Methionine ( ATG ) ) 22 ., While rs2069885 was not significant in univariate analyses ( p = 0 . 091 in the combined cohort for TST as a binary outcome and with an additive model ) , the association of the haplotype was several orders of magnitude more significant than that of rs877356 alone ., Although we cannot at the present distinguish which of these two genes , if either , is the truly associating one , IL9 is an attractive candidate for resistance to MTB infection because of its association with bronchial hyperresponsiveness 25 , which is hereditary and a risk factor for asthma25–27 ., Of note , the prevalence of asthma in East Africa is high , especially in urban settings28 , childhood MTB infection protects from asthma , and an inverse relationship between incidence of active TB and asthma has been reported 29 , 30 ., IL9 was originally described as a T cell and mast cell growth factor , but has since been found to have pleiotropic effects on the immune system31–33 ., IL9 promotes IL4-mediated production of IgE and IgG antibodies34 , 35 , and bronchial hyper-responsiveness is associated with elevated serum IgE levels25 , 36 ., IL9 also promotes proliferation of hematopoietic progenitor cells37 , 38 , and it has specific effects on lungs ., In airway smooth muscle cells , IL9 induces the expression of chemokine CCL11 , thereby inducing eosinophil chemotaxis and allergic reactions , and in airway epithelial cells , IL9 directly induces mucous production and stimulates IL13 , which leads to further airway inflammation and perhaps reduced risk of MTB infection 31 , 32 , 39–41 ., The TST phenotype can be studied both as a binary variable , < versus ≥ 5mm induration , or as a continuous outcome ., Our single-SNP association results were consistent using both outcomes ., Variant rs877356 was genome-wide significant in both logistic and linear regression models in the combined cohort using a dominant genetic model as well as at a multiple testing corrected level in an additive model ., The most significant imputed variant in the region , rs17169187 , was genome-wide significant for both outcomes in additive and dominant modeling ., One possible limiting factor of these conclusions is immune anergy , which is a potential confounder in studies of TST reactivity , especially in an HIV+ context ., TST responses can be < 5mm because a patient has not been infected with MTB , or in case of anergy , is unable to mount a hypersensitivity reaction to PPD even if infected ., However , we believe our results are unlikely to be confounded by anergy for several reasons ., First , if anergy existed , it would result in misclassifying cases as controls , which would decrease power and underestimate effect sizes ., Since we observed significant effects , this was not the case in our data ., Second , we leveraged existing interferon-γ response data in both cohorts to evaluate confounding by immunosuppression ., We removed all patients suspected of immune anergy prior to analysis , and further adjustment for a missing response variable did not affect the association of our variant , demonstrating the robustness of our findings ., Particularly in the Tanzania data , where the reported rate of TST-positivity in HIV-infected is lower than in HIV-uninfected individuals42 , the PHA responses were quite high and did not differ by TST status , demonstrating that individuals do indeed mount immune responses ., Analyses utilizing these immunologic data , where available , showed significant effects for the same SNP , suggesting our results are robust to immunological differences between subjects ., Third , data from the entire household contact study in Uganda indicates that anergy is not an issue in that cohort: the prevalence of TST-positivity in HIV-infected individuals is 62% , compared to 71% in HIV-uninfected individuals and ~34% in HIV-uninfected community controls ( S10 Table and 43 ) ., This high rate of TST-positivity in the HIV-infected subjects is inconsistent with anergy being a major confounder in this population ., Furthermore , since we see similar genetic effects in the Ugandan and Tanzanian cohort , it is unlikely that anergy is a problem in Tanzania and not in Uganda ., Lastly , we replicated loci that had been previously associated with TST in independent HIV- cohorts , further validating our design ., Unfortunately , data on PHA and CD4+ count were unavailable for some of the subjects in this study , so we were unable to fully explore some of these potential explanations ., In summary , the aforementioned sensitivity analyses and other factors make anergy an unlikely cause of the observed association in these data , though we cannot absolutely exclude this possibility ., Future studies should examine this locus as a candidate for association with TST ., As we have previously shown for tuberculosis disease19 , the present study confirms that the choice of an extreme phenotype , HIV+ patients who live in MTB endemic areas but do not get infected , enriches for major , homogeneous genetic effects ., This design permits the use of relatively small sample size even in a genome-wide association study ., Although the small sample size is the biggest weakness in this study , the large and replicated effect size observed in this unique study design and populations allowed us to find significant associations in an apparently relevant region of the genome ., The variant with the most significant association is near IL9 , a gene with a substantial role in airway inflammation , bronchial asthma , and other respiratory infections44 , 45 ., This , along with observational studies of the inverse incidence of asthma and tuberculosis , leads to the conclusion that the same gene whose over-expression plays a significant role in the pathogenesis of asthma , could also prevent MTB infection by the same mechanism ., HIV+ subjects from a cohort in Tanzania and one in Uganda were included in this study ., A complete description of the study cohorts and genetic analysis methods is provided in our previous work 19 ., For participants from the extended follow-up of the DarDar vaccine trial , 5ml of whole blood was drawn upon enrollment , and DNA was extracted the day of the phlebotomy using the Gentra Puregene Blood kit ( QIAGEN ) in accordance with the manufacturer’s recommendations ., For participants of the Household Contact Study , buffy coats were isolated on site and shipped to Dartmouth College for DNA extraction ., The QIAamp DNA Blood Mini Kit ( QIAGEN ) was used to isolate DNA from the buffy coats ., DNA samples were stored at -80°C before genotyping ., DNA quality was evaluated with the 260/280 ratio using a NanoDrop 2000 spectrophotometer at Dartmouth College ( Thermo Scientific ) and an Electrophoresis Quality Score at the University of Miami ., Samples from the DarDar vaccine trial ( n = 304 ) and the Household Contact Study ( n = 263 ) were submitted for genotyping at the Hussman Institute for Human Genomics , Miami , Florida ., A total of 567 samples passed quality control measures and were genotyped using the Illumina Human Core Exome Beadchip ( 542 , 585 SNPs ) ., SNPs with a genotyping call rate < 0 . 95 and a Hardy-Weinberg equilibrium p-value < 1x10-4 were excluded ., Participants with a per individual genotyping call rate < 0 . 95 were excluded ., Concordance of reported and genotypic sex was verified ., In case of relatedness among study participants ( pi-hat > 0 . 20 ) , one individual was randomly removed ., The final study population included 270 participants from the extended follow up of the DarDar vaccine trial and 199 participants from the Household Contact Study ., All quality control analyses were performed in PLINK ( v1 . 07 ) 53 ., Results for the most significant SNP are shown in S8 Fig . Informed consent was obtained from all patients in the extended DarDar follow-up cohort ., The research ethics committee at the Muhimbili University of Health and Allied Sciences and the Committee for the Protection of Human Subjects at Dartmouth College and the Dartmouth-Hitchcock Medical Center approved this study ., Informed consent was obtained from all subjects in the Household Contact study in Kampala , Uganda ., Ethics committees that approved this work were at Muhimbili University of Health and Allied Sciences , Committee for the Protection of Human Subjects at Dartmouth College ( #14606 ) , Uganda Council for Science and Technology , and University Hospitals of Cleveland ( 10-01-25 )

Introduction, Results, Discussion, Material and methods

One in three people has been infected with Mycobacterium tuberculosis ( MTB ) , and the risk for MTB infection in HIV-infected individuals is even higher ., We hypothesized that HIV-positive individuals living in tuberculosis-endemic regions who do not get infected by Mycobacterium tuberculosis are genetically resistant ., Using an “experiment of nature” design that proved successful in our previous work , we performed a genome-wide association study of tuberculin skin test positivity using 469 HIV-positive patients from prospective study cohorts of tuberculosis from Tanzania and Uganda to identify genetic loci associated with MTB infection in the context of HIV-infection ., Among these individuals , 244 tested were tuberculin skin test ( TST ) positive either at enrollment or during the >8 year follow up , while 225 were not ., We identified a genome-wide significant association between a dominant model of rs877356 and binary TST status in the combined cohort ( Odds ratio = 0 . 2671 , p = 1 . 22x10-8 ) ., Association was replicated with similar significance when examining TST induration as a continuous trait ., The variant lies in the 5q31 . 1 region , 57kb downstream from IL9 ., Two-locus analyses of association of variants near rs877356 showed a haplotype comprised of rs877356 and an IL9 missense variant , rs2069885 , had the most significant association ( p = 1 . 59x10-12 ) ., We also replicated previously linked loci on chromosomes 2 , 5 , and 11 ., IL9 is a cytokine produced by mast cells and TH2 cells during inflammatory responses , providing a possible link between airway inflammation and protection from MTB infection ., Our results indicate that studying uninfected , HIV-positive participants with extensive exposure increases the power to detect associations in complex infectious disease .

Approximately one-third of the world’s population has been exposed to Mycobacterium tuberculosis , the bacterium that causes tuberculosis ., A small number of those infected develop active disease; however , there is a substantial portion of exposed people who do not even show evidence of an immunological response ., These people who appear to resist infection , as measured by a negative tuberculin skin test , represent a subpopulation from which we can learn about resistance ., We used a genome-wide approach to study the genetic basis of this resistance in unique cohorts of hypervulnerable , HIV-positive individuals from Uganda and Tanzania , in which exposure was virtually assured ., We identified one locus that was highly significantly associated and conferred more than 70% protection from infection ., The most significant variant , rs8773656 , was near IL9 and SLC25A48 , and a haplotype including this variant and a missense mutation in IL9 was even more significantly associated with negative skin tests ., Although it is impossible based solely on our data to determine the causal variant or genes , IL9 is an attractive candidate as its product has previously been associated with bronchial hyperresponsiveness , thereby providing a possible link between inflammation and protection from Mycobacterium tuberculosis infection .

tuberculin, medicine and health sciences, genetic dominance, population genetics, genetic mapping, population biology, bacteria, genetic polymorphism, actinobacteria, skin tests, genetic loci, haplotypes, diagnostic medicine, mycobacterium tuberculosis, heredity, genetics, biology and life sciences, human genetics, evolutionary biology, organisms

journal.ppat.1005520

Optimal Combinations of Broadly Neutralizing Antibodies for Prevention and Treatment of HIV-1 Clade C Infection

The ability to elicit potent broadly neutralizing antibodies through immunization remains an elusive goal in the development of an effective HIV-1 vaccine 1 ., This has motivated major efforts over the past 6 years to isolate and characterize Env-specific antibodies from HIV-1-infected individuals who exhibit broad and potent serum neutralizing activity 2–4 ., Through technological advances in single cell sorting of antigen-specific memory B cells 5–11 , high-throughput antibody cloning and screening methods , numerous novel monoclonal antibodies have since been isolated , some of which exhibit exceptional neutralization breadth and potency when tested in vitro against large panels of diverse HIV-1 isolates 7 , 9–20 ., Identification of the epitope targets of these bnAbs has dramatically expanded our knowledge regarding sites of common vulnerability on the Env spike 21 ., Major epitope targets include the CD4bs 5 , 11 , 16 , 19 , 22–27 , a glycan-dependent site in variable region 3 ( V3 ) of gp120 9 , 17 , 28–31 , a V1/V2 glycan-dependent quaternary site on the apex of the Env trimer 9 , 10 , 12 , 32–37 , the MPER 15 , 38–41 , and epitopes bridging both gp120 and gp41 13 , 14 , 18 , 42 ., The hope remains that characterization of these epitope targets and efforts to elucidate the pathways of bnAb development in vivo will eventually result in the rational design of novel immunogens and immunization strategies for eliciting such antibodies through vaccination 12 , 16 , 24 , 43–46 ., However , a more immediate potential exists for using bnAbs in clinical settings of passive transfer for the prevention and/or treatment of HIV-1 infection ., In support of preventative modalities , pre-clinical studies in non-human primates ( NHP ) have demonstrated that passive transfer of bnAbs can confer sterilizing protection against high dose mucosal challenges with chimeric simian-human immunodeficiency viruses ( SHIVs ) 23 , 47–53 ., Studies in NHP and humanized mice have further investigated the therapeutic potential of bnAb infusion in the setting of established viral infection , and demonstrated that transfer of single bnAbs can result in a transient decline in plasma viremia , reduction of proviral DNA , and in some cases extended control of viral replication 53–56 ., However , viral rebound generally occurs once the concentration of transferred antibody decays below the therapeutic range , and the emergence of neutralization resistant escape variants is often observed ., Similar observations were recently described in a phase I clinical study evaluating passive infusion of the CD4bs bnAb 3BNC117 in HIV-1 infected individuals 57 ., While escape from antibody monotherapy remains a concern , additional data from animal model studies have shown that therapeutic strategies employing combinations of bnAbs to simultaneously target different epitopes on the Env spike can impede viral rebound and escape , and exert sustained control of viral replication 53–55 ., Thus , for bnAbs to be effectively employed for treatment of HIV-1 infection , combinations of multiple antibodies will likely be required to confront the extraordinary diversity of the virus and its ability to escape from selective immune pressure ., Recent studies of in vitro neutralization have established that combinations of bnAbs targeting distinct epitopes can act in a complementary and additive manner , and exhibit improved neutralization breadth and potency compared to single bnAbs 58–60 ., In the study by Kong et al . , it was shown that the breadth and potency of bnAb combinations could be reliably predicted using an additive model , with consistent patterns of minor non-additive interactions for particular bnAb combinations , either antagonistic or synergistic 60 ., Certain double , triple and quadruple bnAb combinations were found to achieve 89 to 100% coverage when tested against a large diverse multiclade virus panel ., However , due to the complementary nature of the bnAb combinations , in many cases increased breadth was due to only a single bnAb in the mixture exhibiting neutralizing activity against a given virus ., In a clinical setting , such a bnAb combination would in essence be the equivalent of single antibody monotherapy against a substantial fraction of viruses , which would have a greater opportunity for escape ., Thus , for treatment of HIV-1 infection , it may be advantageous to use bnAb combinations that offer the best potential for active coverage of most viruses by two or more antibodies ., For bnAb immunotherapy in the setting of chronic infection , viral clearance is the most desirable outcome , albeit challenging to achieve ., Thus , more complex options are being considered , such as including combinations of the most potent bnAbs together with latency reversing agents ( LRAs ) and standard antiretroviral drug treatment 61–63 ., For such strategies to be beneficial , bnAbs will need to be effective at three levels ., First , they will need to neutralize the diversity of viruses circulating in the population targeted for treatment ., Second , they will need to effectively neutralize the complex within-host quasispecies that develop during chronic HIV-1 infection ., And finally , they should be effective against the full spectrum of expressed forms of Env on any given virion ., It has been observed that some bnAbs exhibit neutralization curves that plateau well below 100% when tested against particular Env pseudoviruses in vitro 10 , 13 , 64 , 65 ., This well-established behavior is surprising given the genetically clonal nature of viruses used in these assays , and could possibly stem from post-translational variation in the glycosylation patterns or alternate variable loop and structural configurations of expressed Env 13 , 65–68 ., It is a concern that such incomplete neutralization may pose a severe limitation for achieving the desired therapeutic efficacy in vivo ., Thus , an ideal immunotherapy candidate antibody combination should maximize the genetic and antigenic spectrum of viruses that are potently neutralized , while minimizing the impact of incomplete neutralization ., A key question that remains is how many bnAbs will be required for long term beneficial effects in a preventative or therapeutic setting , and which combinations of bnAbs will provide the most potent and active coverage for testing in human clinical trials ., Over the past several years , multiple bnAbs for each major epitope have emerged as viable candidates based on extensive in vitro and pre-clinical animal model testing ., Given the tremendous resources required to move even a single candidate bnAb forward into human clinical trials , rational decisions must be made to select single antibodies , bivalent antibodies , or components of bnAb combinations that will theoretically provide the highest potency and coverage against the diversity of circulating HIV-1 ., As bnAb clinical efficacy studies are currently being planned for conduct in southern Africa , coverage and potency of bnAbs against the HIV-1 clade C viruses that dominate the epidemic in that region is of considerable interest ., Here we utilized a newly described panel of 200 acute/early clade C HIV-1 Env pseudoviruses to assess the breadth and potency of 15 of the most promising bnAb candidates targeting four major epitopes of HIV-1 Env ., A mathematical modeling approach was developed that increased the accuracy in predicting neutralization titers of bnAb combinations ., We experimentally validated the improved accuracy of this model , and then used it to predict the behavior of all possible 2 , 3 , and 4 bnAb combinations using data derived from single bnAb testing ., Using these predictions , we compared the performance of a comprehensive spectrum of potential bnAb combinations , and identified those that provide the most optimal potency , breadth , complete neutralization , and active coverage ., A panel of bnAbs targeting HIV-1 Env was used to assess and compare the breadth and potency of neutralization against acute/early clade C Envs ., Fifteen bnAbs were selected that target four distinct epitope regions: the CD4 binding site ( CD4bs: 3BNC117 , VRC01 , VRC07 , VRC07-523 , VRC13 ) 11 , 19 , 23 , 69 , 70 , the V3-glycan supersite ( V3g: 10–1074 , 10-1074V , PGT121 , PGT128 ) 9 , 17 , the V1/V2-glycan site ( V2g: PG9 , PGT145 , PGDM1400 , CAP256-VRC26 . 08 , CAP256-VRC26 . 25 ) 9 , 10 , 12 , 20 , 32 , and the gp41 MPER epitope ( 10E8 ) 15 ., BnAbs were tested against a panel of 200 clade C HIV-1 Env pseudoviruses using the validated luciferase-based TZM-bl assay ., This virus panel consists of viruses isolated from individuals in the acute/early stages of infection from five southern African countries , including South Africa , Tanzania , Malawi , Zambia , and Botswana ., Serial dilutions of individual bnAbs were tested against each virus using a starting concentration that ranged from 10–50 μg/ml , depending on sample availability at the time of testing ., Neutralizing activities were evaluated using potency-breadth curves ( the percentage of viruses neutralized versus an IC50 or IC80 cutoff , Fig 1A and 1B ) , scatter plots ( Fig 1C and 1D ) and heatmaps ( Fig 1E and 1F ) ., The 5 bnAbs targeting the V1/V2-glycan region neutralized between 67–75% of viruses with positive IC50 titers , and the 4 bnAbs targeting V3-glycan neutralized 54–68% ., When positive , these glycan-dependent bnAbs were strikingly potent ., Using the more stringent IC80 measure , median IC80 titers ranged from 0 . 003–1 . 274 μg/ml for V1/V2-glycan and 0 . 073–0 . 203 μg/ml for V3-glycan bnAbs ( Table A in S1 Text ) ., CD4bs bnAbs tended to exhibit greater breadth ( 71–96% at IC50 ) , but were generally less potent than V1/V2-glycan or V3-glycan antibodies ( median IC80 titers 0 . 30–1 . 58 μg/ml ) ., The MPER directed bnAb 10E8 exhibited lower overall potency ( median IC80 3 . 399 μg/ml ) , yet had exceptional IC50 breadth , neutralizing 98% of viruses ., Even the most resistant isolates were sensitive to at least 3 bnAbs , which most often targeted the CD4bs or MPER ., Overall , clear differences in potency and/or breadth were observed among bnAbs of the same class ( defined here as bnAbs that target the same epitope region ) ., Based on IC50 and IC80 titers , best-in-class bnAbs were CAP256-VRC26 . 25 ( V2-glycan ) , 10-1074V ( V3-glycan ) , VRC07-523 ( CD4bs ) , and 10E8 ( MPER ) ., As visualized in heat maps ( Fig 1E and 1F ) , and by hierarchical clustering ( Fig A in S1 Text ) , bnAbs targeting the same epitope region exhibit similar patterns of neutralizing activity , with clear patterns of complementarity between epitope classes ., For example , distinct clusters of viruses were resistant to V1/V2-glycan antibodies but sensitive to V3-glycan antibodies , whereas other virus clusters exhibit the opposite phenotype ., These data illustrate how different combinations of bnAbs targeting distinct epitopes can complement one another for enhanced coverage against clade C viruses ., Because it is not practical to assay all combinations of bnAbs against a large panel of viruses , a new method to accurately predict combination bnAb neutralization efficacy using the available large-scale single bnAb neutralization data was developed to facilitate rational decisions for selection of the best bnAb combinations for clinical testing ., In a previous study by Kong et al . , the additive model worked well in predicting potency of bnAb combinations using experimental data from single bnAbs 60 ., They also found that the experimental bnAb combination data deviated slightly from model predictions ., Most combinations performed slightly better than predicted , while a few combinations that included a V3-glycan bnAb performed slightly worse than predicted ., The additive model derives from an application of equilibrium mass action kinetics to simplified in vitro antibody-virus interactions ( S1 Text ) ., This theoretical treatment assumes that single bnAb neutralization curves follow Hill curves with Hill exponents equal to one , and that antibodies act independently with little possibility of multiple antibodies inhibiting the same virion ., The first assumption of a unit Hill exponent is largely valid for CD4bs and V3-glycan bnAbs , however , bnAbs targeting the V2-glycan and MPER epitopes frequently exhibit Hill exponents of less than 1 65 , 71 , 72 ., To overcome these limitations of the additive model , we developed a new model , the “Bliss-Hill model” ( BH model ) ., This model combines single bnAb Hill curves ( with arbitrary slopes ) within the framework of the Bliss independence model for the binding of multiple species of ligands to a substrate 72 , 73 , and incorporates a correction for multiple ligands independently attaching to the substrate ( S1 Text ) ., We tested the BH model by using experimental data from combination bnAb neutralization assays ., The assays comprised 10 combinations of 2 , 3 and 4 bnAbs ( including 2-bnAb combinations with both antibodies targeting similar epitopes , Fig B in S1 Text ) assayed against a smaller panel of 20 viruses ., The 20 viruses were chosen because they are sensitive to almost all bnAbs tested and comprise a maximized range of IC80 titers for the bnAb combinations ., The BH model proved highly accurate in explaining the clade C panel bnAb combination data ( Fig 2A , R2 = 0 . 9154 , Pearson r = 0 . 9584 ) ., Moreover , the BH predictions were closer to the observed data than the additive model for 9 of the 10 combinations tested ( Fig 2B , p = 0 . 021 using Binomial Test ) , with the only exception being the combination VRC07-523 + 10-1074V ., Thus the BH model offered a significant , though modest in magnitude , improvement in prediction accuracy over the additive model ., We confirmed this by reanalyzing a larger dataset from Kong et al . , and again found the BH model predictions to be highly accurate ( R2 = 0 . 9655 , Pearson r = 0 . 9862 , Fig C in S1 Text ) ., The BH model performed slightly better than the additive model in all cases , and the difference reached high levels of statistical significance for most of the 2 , 3 , and 4 bnAb combinations tested ., This improvement was due to the systematic trend of BH predictions being more potent than the additive model predictions ( Figs D and E in S1 Text ) , and thus closer to the observed titers since additive model predictions were found to be less potent than the observed titers for most combinations 60 ., Nonetheless , for some antibody combinations , experimentally measured IC80 titers still showed minor deviations from the BH model predictions ( Fig 2 , Figs C-G in S1 Text ) ., For a few viruses , the combination IC80 titers were 3-fold higher than the most potent bnAb in the combination ( Fig D in S1 Text ) , which is counter-intuitive since both the additive and BH models predict greater potency for combinations relative to the component bnAbs ., In such cases we find that the very potent neutralization of a virus by an antibody ( particularly CAP256-VRC26 . 25 , Fig D in S1 Text ) is somewhat inhibited by the presence of additional antibodies , albeit still resulting in potent neutralization by the combination ., Models that incorporated additional parameters based on observed deviations could further improve predictions in some cases ( S1 Text , Figs F and G in S1 Text ) , but the magnitude of deviations were small for most viruses ., Furthermore , using deviation modeling with BH model ( Fig H in S1 Text ) or using additive model ( Fig I in S1 Text ) did not affect the conclusions below , as the best combinations selected were robust using either model ., Passive and active immunization strategies that aim to protect against the acquisition of HIV-1 infection would benefit from information regarding how many and which bnAb combinations provide optimal coverage and potency ., An antibody that may have the best characteristics when considered alone may not have the optimal complementarity when considered for combination bnAb regimens ., We predicted the combination scores for all potential 2 , 3 and 4 bnAb combinations using the BH model on single bnAb neutralization data for 15 bnAbs against 200 clade C viruses , thus enabling direct comparisons of bnAb combinations ., For 2 bnAb combinations , only combinations consisting of bnAbs targeting different epitopes were considered , while for 3 and 4 bnAb combinations , multiple bnAbs targeting the same epitope region were also considered ., Predicted potency-breadth curves for all of the 2 , 3 and 4 bnAb combinations ( 1 , 622 combinations total ) are shown in Fig, 3 . The combinations were stratified by the number of bnAbs targeting different epitopes ( referred to as “categories” , e . g . , CD4bs+V2g is a combination of a CD4bs and a V2-glycan bnAb , and V2g ( 2x ) +V3g has two V2-glycan and one V3-glycan bnAbs ) ., Within each category , multiple combinations were possible due to multiple bnAbs targeting the same epitope ., Best-in-category bnAb combinations were identified as those with the lowest geometric mean IC80 values for the 200 viruses ( highlighted in Fig 3 by dark , bold lines ) ., Of note , the area under the IC80 potency-breadth curve is negatively , but linearly , and almost perfectly correlated to the Log10 geometric mean IC80 ., Thus using either measure gives identical results ., The best-in-category combinations were not always clear , as second best combinations were very comparable ( e . g . CAP256-VRC26 . 25 + 10-1074V + PGT128 or PGT121 with geometric mean IC80 of 0 . 007 and 0 . 0071μg/ml , respectively ) ., Comparisons of best-in-category combinations having the same number of bnAbs are shown in Fig, 4 . Best-in-category 2 bnAb combinations had significantly better predicted potency ( geometric mean IC80 range = 0 . 02–0 . 29 μg/ml ) and breadth ( 88 . 5–97 . 5% of viruses with IC80 < 10 μg/ml ) , than single bnAbs ( geometric mean IC80 = 0 . 17–5 . 91 μg/ml and breadth = 44–92 . 5% ) ., The two best-in-category 2 bnAb combinations , CAP256-VRC26 . 25 ( V2-g ) with either 10-1074V ( V3-g ) ( geometric mean IC80 = 0 . 020 μg/ml ) or VRC07-523 ( CD4bs ) ( geometric mean IC80 = 0 . 021 μg/ml ) were significantly better than the other best-in-category 2 bnAb combinations ( p < 0 . 01 and q-value < 0 . 02 ) ( Fig 4A , 4B and 4C ) ., However , it was unclear which of these two combinations was better , because each pairing had different advantages ., While CAP256-VRC26 . 25 and 10-1074V alone are more potent than VRC07-523 when active ( Table A in S1 Text ) , they have more limited breadth , each neutralizing ~60% viruses at IC80 < 10 μg/ml as compared to 92 . 5% for VRC07-523 ., Consistent with this , we found that the combination of CAP256-VRC26 . 25 + 10-1074V missed ~13% of viruses at IC80 < 10 μg/ml , while CAP256-VRC26 . 25 + VRC07-523 missed only ~3% ., Thus , while CAP256-VRC26 . 25 + VRC07-523 was slightly less potent than CAP256-VRC26 . 25 + 10-1074V , it provides ~10% better coverage ., For 3 bnAb combinations , the best breadth and potency was seen with CAP256-VRC26 . 25 + 10-1074V + VRC07-523 ( Fig 4D , 4E and 4F ) ., This combination , which targets 3 separate epitopes , neutralized 99 . 5% viruses ( all but one in the panel ) at IC80 < 10 μg/ml , with a geometric mean IC80 of 0 . 0083 μg/ml ., The superior performance of this combination draws from the complementary neutralization profiles of the most potent panel bnAbs , CAP256-VRC26 . 25 and 10-1074V , combined with the broad and potent profile of VRC07-523 ( Fig 1 ) ., This combination was significantly more potent than most other best-in-category 3bnAb combinations ( p < 0 . 02 , q < 0 . 03 ) ., Replacing VRC07-523 with either PGDM1400 or 10E8 in combinations containing CAP256-VRC26 . 25 + 10-1074V resulted in a small loss of potency and breadth that was not statistically significant ., Overall , 3 bnAb combinations showed improved breadth ( 89 to 99 . 5% at IC80 < 10 μg/ml ) and markedly improved potency ( geometric mean IC80 of 0 . 008–0 . 060 μg/ml ) than 2 bnAb combinations , with 6 out of 7 best-in-category 3 bnAb combinations predicted to have better geometric mean IC80 than the best 2 bnAb combinations ., The two best-in-category 4 bnAb combinations , one targeting 3 epitopes and another targeting 4 epitopes , had comparable potency ( geometric mean IC80 ~ 0 . 007 μg/ml ) and breadth ( 99 . 5% at IC80 < 10 μg/ml ) ( Fig 4G , 4H and 4I ) , and were more potent and broadly active than 4 bnAb combinations targeting only 2 epitopes ( geometric mean IC80 of 0 . 01 to 0 . 05 μg/ml and breadth 92–98 . 5% at IC80 < 10 μg/ml ) ., Thus bnAb combinations targeting three epitopes showed a significant gain in breadth and potency compared to those targeting two , but the further gain in targeting all four major epitopes , for this panel is negligible ., This information is useful to efforts that aim to achieve optimal coverage and potency to protect against the acquisition of infection in passive or active vaccination settings , but does not take into account ease of escape in the setting of passive immunotherapy for active infection ., Combinations of bnAbs are likely to be advantageous in a therapeutic setting not only to maximize potency and breadth but also to minimize the potential for viral escape by targeting multiple epitopes simultaneously 55 ., Thus , we investigated the extent of simultaneous neutralization by two or more bnAbs in the best-in-category bnAb combinations at different activity thresholds ., First we quantified the percent of panel viruses actively neutralized by at least 2 , 3 or 4 bnAbs in all best-in-category 2 , 3 and 4 bnAb combinations at physiologically relevant concentrations ., We used IC80 thresholds of 1 , 5 and 10 μg/ml , which fall in the range of bnAb serum concentrations in HIV-1 infected patients administered a single dose of 1–30 mg/kg of 3BNC117 57 ., For combinations with multiple bnAbs targeting the same epitope class , a modified counting procedure was employed that accounted for overlap in escape-associated mutations ( S1 Text ) ., The percent of viruses neutralized by the best bnAb combinations at different thresholds of activity are shown in Table B in S1 Text ., We modified the potency-breadth curves for best-in-category bnAb combinations to highlight cases where multiple bnAbs in a combination were simultaneously active ( Fig 5 ) ., These curves show cumulative coverage of the 200 panel viruses at a given predicted combination IC80 value limited by counting only those viruses that were simultaneously sensitive to 2 , 3 or 4 bnAbs at single bnAb IC80 < 1 , 5 , or 10 μg/ml ., When the percentage of viruses neutralized by at least 2 bnAbs was considered , the best coverage at our least restrictive threshold within the experimental assay range of IC50 <10 μg/ml was 92 . 5% , 97 . 5% and 100% for 2 , 3 and 4 bnAb combinations , respectively ( Table B in S1 Text , Fig 5 ) ., This coverage decreased , as expected , to 80% , 91% and 95 . 5% , respectively , when a more stringent IC80 <10 μg/ml threshold was used , and continued to decrease until only 44% , 67 . 5% and 73 . 5% coverage was seen , respectively , at our most stringent threshold of IC80 <1 μg/ml ., The percentage of viruses neutralized when requiring at least three bnAbs in the best-in-category 3 and 4 bnAb combinations to be active was of course even lower at each of these thresholds ., Here , the best coverage at the less restrictive threshold of IC50 <10 μg/ml was 66 . 5% and 89% for 3 and 4 bnAb combinations , respectively , and progressively decreased to only 19 . 5% and 26 . 5% coverage at the most stringent IC80 <1 μg/ml threshold ., Poor coverage was seen at all thresholds when all 4 bnAbs in the best-in-category 4 bnAb combinations were required to be active ., Using extrapolated single bnAb neutralization curves ( see “BnAb combinations reduce levels of incomplete neutralization” below ) , we also investigated coverage with multiple active bnAbs using single bnAb IC80 < 50 μg/ml and < 100 μg/ml ( Fig J in S1 Text ) ., These concentrations roughly approximate the 28 day trough plasma concentrations of passively-administered VRC01 and 3BNC117 in human trials 57 , 74 and more closely approximate the range of plasma concentrations that resulted in transient reductions in plasma viremia in patients who received 3BNC117 57 ., We found that the best coverage with 2 bnAbs active at IC80 <50–100 μg/ml was 93–100% for 2 , 3 and 4 bnAb combinations , and with 3 bnAbs active was 68–92 . 5% ( Fig J and Table B in S1 Text ) ., The overall most potent and broad 2 , 3 , and 4 bnAb combinations ( Fig 4 ) , also had best or close to best coverage with multiple bnAbs active ( Fig 5 ) ., However , best-in-category combinations that included the exceptionally broad but less potent 10E8 showed superior coverage with multiple bnAbs active at less restrictive thresholds ., Neutralization curves for some bnAb/virus pairings can show incomplete neutralization of the genetically clonal virus population 65 ., This suggests that a sub-population of virus is resistant to neutralization by the bnAb even at the highest concentrations tested ., Given the importance of carbohydrates for many bnAb epitopes , post-translational glycan heterogeneity resulting from incomplete carbohydrate addition or modification may be an important contributing factor to such resistant sub-populations 68 ., The inability to neutralize all variants would compromise the use of bnAbs for immunotherapy and may also impede the ability of bnAbs to protect against HIV acquisition ., Hence , we investigated the extent of incomplete neutralization of clonal viruses by various bnAb combinations ., We first analyzed neutralization curves for single bnAbs and bnAb combinations that were experimentally measured in the study by Kong et al . 60 ., We could accurately predict the combination maximum percent inhibition ( MPI ) using the Bliss independence model on single bnAb MPI values ( Methods , Fig K in S1 Text , Pearson r = 0 . 9904 , difference between observed and predicted MPI: median = 0 . 1% , 95% CI = 0–4 . 5% ) ., Using this model , we then predicted the MPI values for the 2 , 3 and 4 bnAb combinations composed of the best single bnAbs against the clade C panel ., Experimental MPI values for single bnAbs are shown in Fig 6A ( see S1 Text for discussion on different assay starting concentrations for panel bnAbs ) , and the predicted MPI values for 2 , 3 and 4 bnAb combinations are shown in Fig 6B , 6C and 6D , respectively ., Incomplete neutralization was observed against several viruses for all single bnAbs and was frequent for the V2- and V3-glycan bnAbs CAP256-VRC26 . 25 and 10-1074V , ( 56% and 44% viruses with MPI < 95% , respectively ) ., A lower frequency of incomplete neutralization was observed with VRC07-523 ( 11% viruses with MPI < 95% ) and 10E8 ( 16 . 5% ) ., Encouragingly , the fraction of resistant variants within a single virus preparation was predicted to decrease with increasing number of bnAbs in a combination , indicating that bnAbs tend to be complementary not only in terms of viral sensitivity at the population level , but in terms of the resistant subpopulations of post-translational Env variants ., The 2 bnAb combination with the least fraction of viruses incompletely neutralized was VRC07-523 + 10E8 ( 2% ) , while VRC07-523 + CAP256-VRC26 . 25 , which had one of the best potency and breadth profiles , had 4% viruses with MPI < 95% ., Consistent with the high levels of incomplete neutralization seen with the V2- and V3-glycan bnAbs , a higher extent of incomplete neutralization was predicted for CAP256-VRC26 . 25 + 10-1074V , where MPI <95% was seen for 18% of viruses ., Strikingly , the 3 bnAb combinations had MPI < 95% for only 0 . 5–1% viruses ( n = 1–2 out of 200 ) , and the 4 bnAb combination never had MPI < 95% for any virus ., The analysis of experimentally measured MPI from the Kong et al . study also showed similar patterns ( Fig L in S1 Text ) ., Studies of passive bnAbs in humans aim to achieve plasma concentrations that for periods of time exceed 25 μg/ml , a dose commonly tested in our neutralization assays 60 ., We therefore experimentally tested the extent of incomplete neutralization at concentrations of up to 100–200 μg/ml against a subset of 24 viruses that were selected based on incomplete neutralization at the lower doses tested ( Fig M in S1 Text ) ., Most of these viruses were still incompletely neutralized at the highest concentrations tested ( only 1 out of 24 showed 95% or higher neutralization ) ., We then estimated the best-fit Hill curves using data points below 25 μg/ml ( Methods , S1 Text ) and used these to predict neutralization at the highest concentrations tested for each of these high-concentration assays ., The predictions were quite accurate ( average root mean square error = 6% , Kendall Tau p = 3 . 7 x 10−5 , Fig N in S1 Text ) ., Thus , using this approach , we predicted the MPI at 100 μg/ml for all best-in-class bnAbs ( Fig N in S1 Text ) and their combinations ( Fig O in S1 Text ) for all 200 clade C panel viruses ., As expected , the fraction of viruses with predicted neutralization less than 95% at 100 μg/ml was reduced compared to the values at 25 μg/ml ., Still , we found substantial levels of incomplete neutralization at 100 μg/ml and these results qualitatively recapitulated the above patterns of MPI at 25 μg/ml for single bnAbs and for bnAb combinations ., The metric instantaneous inhibitory potential ( IIP ) measures the log10 reduction in a single round of infection events in the presence of a drug ., This metric correlates with clinical success of antiretroviral drug combinations , and can be used to characterize their efficacy 75 ., Jilek et al . found that IIPave values ( average IIP during the dosing interval , given drug pharmacokinetics ) of 5–8 logs were necessary for successful antiretroviral therapy ., Drug combinations in this range showed a reduction of viral load to <50 RNA copies/ml at 48 weeks in 70% or more of infected individuals ., Applying their approach , we calculated the IIP values for the best-in-class single bnAbs and best bnAb combinations for the clade C panel ., IIP values for single bnAbs were calculated using either the best-fit Hill curves of experimental neutralization data for the best-in-class bnAbs ( Fig 7 , S1 Text ) , or estimated Hill curves using IC50 and IC80 values ( Fig P in S1 Text ) ( with the former expected to yield more accurate predictions since IIP values are critically sensitive to neutralization close to 100% ) ., Using BH model , we calculated the IIP values ( Methods ) for 2 , 3 and 4 bnAb combinations of the best-in-class bnAbs ( Fig 7 ) ., Since IIP values depend on bnAb concentration , and precise doses and pharmacokinetics of bnAbs are still being established , we analyzed IIP at bnAb concentrations of 1 , 10 and 100 μg/ml ., The 1 and 10 μg/ml concentrations are within the experimental assay range , whereas results for the 100 μg/ml dose are estimates obtained by extrapolation ., The best-in-class single bnAbs had median IIPs of 0 . 4–2 . 8 across viruses , depending on the bnAb and concentration , with CD4bs bnAb VRC07-523 giving the highest value , followed by V3-glycan bnAb 10-1074V ( Fig 7 , Fig P in S1 Text ) ., The best-in-category bnAb combinations showed higher median IIP values of 1 . 2–5 . 0 , 2 . 3–6 . 6 , and 3 . 5–8 . 1 for 2 , 3 and 4 bnAb combinations , respectively ., The 2 bnAb combinations with highest IIP values consisted of VRC07-523 with either CAP256-VRC26 . 25 or 10-1074V , depending on the concentration ., The 3 bnAb combinations with the highest IIP values were VRC07-523 + 10-1074V with either CAP256-VRC26 . 25 or 10E8 , with the latter combination having a slightly better median IIP at 100 μg/ml ( median IIP of 6 . 2 and 6 . 6 , respectively ) ., Single bnAbs rarely had IIP > 5 , the level found to be critical for clinical success of antiretroviral drug combinations 75 , while 2 , 3 and 4 bnAb combinations had IIP > 5 for 0–50% , 1 . 5–79% , and 15–92% of viruses , respectively , depending on concentrati

Introduction, Results, Discussion, Materials and Methods

The identification of a new generation of potent broadly neutralizing HIV-1 antibodies ( bnAbs ) has generated substantial interest in their potential use for the prevention and/or treatment of HIV-1 infection ., While combinations of bnAbs targeting distinct epitopes on the viral envelope ( Env ) will likely be required to overcome the extraordinary diversity of HIV-1 , a key outstanding question is which bnAbs , and how many , will be needed to achieve optimal clinical benefit ., We assessed the neutralizing activity of 15 bnAbs targeting four distinct epitopes of Env , including the CD4-binding site ( CD4bs ) , the V1/V2-glycan region , the V3-glycan region , and the gp41 membrane proximal external region ( MPER ) , against a panel of 200 acute/early clade C HIV-1 Env pseudoviruses ., A mathematical model was developed that predicted neutralization by a subset of experimentally evaluated bnAb combinations with high accuracy ., Using this model , we performed a comprehensive and systematic comparison of the predicted neutralizing activity of over 1 , 600 possible double , triple , and quadruple bnAb combinations ., The most promising bnAb combinations were identified based not only on breadth and potency of neutralization , but also other relevant measures , such as the extent of complete neutralization and instantaneous inhibitory potential ( IIP ) ., By this set of criteria , triple and quadruple combinations of bnAbs were identified that were significantly more effective than the best double combinations , and further improved the probability of having multiple bnAbs simultaneously active against a given virus , a requirement that may be critical for countering escape in vivo ., These results provide a rationale for advancing bnAb combinations with the best in vitro predictors of success into clinical trials for both the prevention and treatment of HIV-1 infection .

In recent years , a new generation of monoclonal antibodies has been isolated from HIV-1 infected individuals that exhibit broad and potent neutralizing activity when tested against diverse strains of virus ., There is a high level of interest in the field in determining if these antibodies can be used to prevent or treat HIV-1 infection ., Because HIV-1 is adept at escaping from immune recognition , it is generally thought that combinations of multiple antibodies targeting different sites will be required for efficacy , much the same as seen for conventional antiretroviral drugs ., How many and which antibodies to include in such combinations is not known ., In this study , a new mathematical model was developed and used to accurately predict various measures of neutralizing activity for all possible combinations having a total of 2 , 3 , or 4 of the most promising antibodies ., Through a systematic and comprehensive comparison , we identified optimal combinations of antibodies that best complement one another for enhanced anti-viral activity , and therefore may be most effective for the prevention or treatment of HIV-1 infection ., These results provide important parameters that inform the selection of antibodies to develop for clinical use .

antimicrobials, antiretrovirals, complement system, medicine and health sciences, immune physiology, body fluids, pathology and laboratory medicine, pathogens, drugs, immunology, microbiology, retroviruses, viruses, immunodeficiency viruses, clinical medicine, rna viruses, pharmacology, antibodies, microbial genetics, immunotherapy, immune system proteins, proteins, medical microbiology, hiv, microbial pathogens, hiv-1, blood plasma, hematology, pharmacokinetics, immune system, biochemistry, blood, anatomy, clinical immunology, virology, viral pathogens, physiology, genetics, microbial control, biology and life sciences, antivirals, lentivirus, organisms

journal.pcbi.1001104

Context-Dependent Encoding of Fear and Extinction Memories in a Large-Scale Network Model of the Basal Amygdala

In classical fear conditioning an animal learns to associate an initially neutral stimulus ( the conditioned stimulus , CS ) with an aversive stimulus ( the unconditioned stimulus , US ) after paired exposure to the CS and the US ., Subsequent repeated non-reinforced presentations of the CS alone result in a decline of the conditioned response , a process called fear extinction 1 ., Fear extinction is a highly context-dependent process: the conditioned fear response returns when the animal is exposed to an extinguished CS outside the extinction context 2 , 3 ., Studies over the last decades have identified the amygdaloid complex as a key brain structure involved in both fear conditioning and extinction 4–6 ., In the lateral nucleus of the amygdala ( LA ) , signals carrying information about the CS and the US converge onto the same neurons where they become associated through activity-dependent plasticity mechanisms 7–9 ., The LA can directly or indirectly influence activity in the central nucleus ( CEA ) 10 , the major output nucleus of the amygdala that can trigger fear responses via its projections to the hypothalamus and to the brainstem 11 ., The basal nucleus of the amygdala ( BA ) has been suggested to play an important role in contextual fear conditioning 12 , 13 , cued fear conditioning 14 , fear extinction 15–17 and context-dependent fear renewal 17 ., Recently , two distinct fear and extinction specific neuronal sub-populations in the BA have been identified 17 ., The balance of activity between fear and extinction neurons was correlated with states of high and low fear , respectively ., Moreover , pharmacological inactivation of the BA blocked the acquisition of fear extinction and context-dependent fear renewal , suggesting that BA fear and extinction neurons may underlie the induction of behavioral changes and contribute to the formation of fear and extinction memories ., These findings raise the question of what the potential mechanisms underlying the differential activation of these two neuronal sub-populations are ., Here , we used a modeling approach based on in vivo physiological data to address this specific question and to draw more general conclusions on potential neural mechanisms involved in fear and extinction memories in the BA ., In vivo stimulation of identified fear and extinction neurons revealed that the two neuronal populations receive differential functional input from the hippocampus and from the medial prefrontal cortex ( mPFC ) 17 ., This finding could reflect anatomical specificity of inputs and/or selective functional plasticity of non-specific inputs ., Independently of these two possibilities , in our model , we assume that anatomically and/or functionally distinct inputs from the hippocampus or the mPFC modulate the activity of BA fear and extinction neurons in a context-specific manner ., That is , sub-populations of BA neurons are innervated by hippocampus/mPFC efferents that represent the current context ., In addition , all BA neurons receive inputs from US/CS responsive LA neurons during conditioning and extinction ., Those sub-populations of BA neurons that receive simultaneous LA and context-specific inputs become responsive during conditioning or extinction and , thus , emulate the “fear” and “extinction” neurons reported by Herry et al . 17 ., Activation of BA neurons per se , however , is not sufficient to cause or prevent a behavioral response , but the selective activation of BA neurons conveys important information about the context-CS relation to the CEA ., Although we do not model here the CEA , we stipulate that context-dependent BA activity provides an instructive signal to CEA neurons ., In the CEA , it is likely that conditioning 18 and possibly extinction learning-induced changes act upon this signal in order to activate or suppress a fear response ., If more experimental data , sufficient to constrain the possible parameter space , become available , then our present model of the BA could be extended to study the impact of context-dependent BA activity on learning-induced changes in the CEA as well ., We test the plausibility of context-dependent activation of BA neurons in two different approaches: first , in an abstract firing rate model; second , in a more realistic spiking neuron network ( SNN ) model of the BA ., Based on the results of our model we provide plausible explanations for several experimental observations in fear and extinction learning and make specific , experimentally testable predictions ., The description of the evolution of the firing rates of BA neurons during fear conditioning and extinction reported by 17 provide certain simple , yet important , indications on the underlying dynamics in the BA network: To test the feasibility of the above observations and their inferences in explaining the emergence of fear and extinction neurons in BA , we first studied the dynamics of a mean-field ( or firing rate ) model of the BA ., Subsequently , we constructed a spiking neuron network ( SNN ) model to examine our hypotheses and their implications under more realistic conditions ., The mean-field model of BA consisted of two neuron populations , A and B , described by Wilson-Cowan type rate dynamics 19 ( Fig . 2A ) ., Both populations were identical in their properties ( Eqs . 1–2 ) and received both CS input and non-specific background input ., There is ample experimental evidence that in different contexts , different sets of hippocampal neurons ( e . g . in CA1 ) are active 20–22 ., Thus , to mimic context-specific inputs - either directly from hippocampus or indirectly via the mPFC or other brain structures such as entorhinal cortex - we provided population A with additional input reflecting , and likewise , population B with additional input reflecting ., Populations A and B were mutually interconnected with inhibitory synapses ., The system of differential equations describing the activity of the populations A and B is as follows: ( 1 ) ( 2 ) where ., The evolution of the connection strengths is given by ( 3 ) ( 4 ) Here , represents the connection strength from population ( or external input ) Y to population X , is the time constant governing the dynamics of population X , kX is the maximum firing rate of population X , and rX captures the refractoriness of neurons in X . The transfer function S is a sigmoid function , integrating all inputs to population X in a non-linear fashion and producing a bounded output rate ., The parameters p and θ of the sigmoid function determine the steepness and the position of its maximum slope , respectively ., The term η ( t ) , with zero mean , reflects the stochastic input to the two populations , mimicking the background activity in the BA ., Equations 3 and 4 describe the dynamics of the connection strengths of the CS afferents onto populations A and B respectively ., These weights were increased in an additive way whenever the respective CS and CTX inputs were present simultaneously and remained constant otherwise ., The parameters aA and aB specify the learning rates ( see also Eqs . 6–8 ) ., We simulated fear conditioning and extinction by applying CS input to both populations in the form of short pulses of 50 ms duration each , based on the experimental design used in 17 ., Contextual input was provided continuously ., Note that we did not make any explicit distinction between the unconditioned stimulus ( US ) and conditioned stimulus ( CS ) ., Instead , we assumed that during conditioning , neurons in the LA initially responded to the US and eventually to the CS , while continuing to respond to the CS during extinction 23 ., The output of these LA neurons was then fed downstream to the BA ., In addition , US or CS inputs from the thalamus or the primary sensory cortex may directly target BA neurons 24 ., In our model , we represented those inputs , independently of their origin , as CS-US in the conditioning context and CS in the extinction context ., For the description of the SNN we adopted the good model description practice proposed by 25 , which provides guidelines for a standardized way of describing complex neural networks ., We share the authors belief that such model description facilitates reproducibility and direct comparisons between models ., Within this framework , we organized the description in different subsections , complemented by additional information on the model parameters ., This collected information is presented in an easily accessible , tabular form in the Supplementary Materials ( Table S1 ) ., Our choice to use leaky-integrate-and-fire ( LIF ) neurons was motivated by four major arguments:, ( i ) multiple combinations of sub-cellular parameters can result in the same network state 26;, ( ii ) even simple neuron models such as LIF with minor modifications are sufficient to reproduce complex in vivo spike patterns 27;, ( iii ) realistically-sized large scale networks of LIF neurons can now be simulated with the currently available simulation technology 28; this is hardly possible for similarly large networks built of detailed compartmental models and , finally ,, ( iv ) the extent to which sub-cellular properties of individual neurons influence the global network dynamics is presently not clear ., Most importantly , however , here we are interested in understanding the key network level properties of the BA which play a critical role in the formation of fear and extinction memories ., For this purpose , the LIF neurons , although they are reduced models of a biological cell , provide an adequate level of biophysical realism , sufficient to identify these key network properties ., We modeled the BA as a random recurrent network , consisting of excitatory ( EXC ) and inhibitory ( INH ) neurons 24 , 29 ., A total number of 4000 neurons corresponds roughly to 10% of all neurons in the rat BA 30 ., The schematic diagram of the network is shown in Fig . 2B ., Each connection from a pre- to a post-synaptic neuron had an assigned probability , the value of which depended on the types of pre- and postsynaptic neurons involved ( EXC and INH , respectively ) : , , , and ., Thus , each EXC neuron received on average excitatory and inhibitory connections ., Likewise , each INH neuron received excitatory and inhibitory connections ., Neurons were allowed to form recurrent connections to themselves ., For the simulations shown in the last figure , we systematically varied the connection probability of the recurrent inhibition from 0 . 1 to 1 . 0 ., EXC and INH neurons received inputs encoding information on the CS ., Similarly to the rate model , these inputs represented initial responses of LA neurons to combined CS and US presentations , later only to the CS ., They might also reflect more peripheral , thalamic or cortical responses to CS-US ., A fraction of BA EXC neurons ( 20% , randomly chosen ) received inputs representing CS and ., Similarly , another 20% of BA EXC neurons received inputs representing CS and ., Thus , similar to the rate model , we assumed that BA EXC neurons receive contextual information directly from the HPC ( or entorhinal cortex ) and/or via the mPFC ., Crucially , CS-US and contextual inputs converged onto the same neurons 8 ., Furthermore , EXC and INH neurons received unspecific background inputs ( BKG ) , representing activity originating in other areas , either within or outside the amygdaloid complex ., The BKG inputs accounted for the baseline spiking activity of EXC and INH neurons at <1 Hz and 10–15 Hz , respectively 24 ., The exact temporal and spatial patterns of the spiking inputs to the BA are not known ., Here , we used independent Poisson spike generators with different firing rates to produce the specific inputs ., Contextual and BKG inputs provided a tonic drive to BA neurons ., By contrast , the CS input had a short duration of 50 ms , based on the experimental design used in 17 ., All external inputs formed excitatory synapses onto their target neurons ., Neurons were modeled as leaky-integrate-and-fire ( LIF ) neurons ., The subthreshold dynamics of each LIF neuron were governed by the following equation ( 5 ) A spike was generated whenever the membrane potential crossed a predefined static threshold θ in upgoing direction ., The potential was then reset to a value Ek and clamped for tref ms before the synaptic integration started again ( Table S1F ) ., Neurons made either excitatory or inhibitory connections onto their postsynaptic targets via conductance-based synapses 31–33 ., The synapses of all connections were non-modifiable , except those providing CS and contextual input to EXC neurons ., These latter , plastic synapses were modified according to the following phenomenological rule: ( 6 ) ( 7 ) ( 8 ) Note that three variables were used: the synaptic weight w and the auxiliary variables c and h ., Each time a presynaptic neuron fired , the value of c increased by a fixed amount ., Afterward , this value relaxed towards zero ., Thus , variable c acted as a synaptic tag , encoding the recent activity in the synapse receiving CS input ., Likewise , variable h encoded information about recent activity in neighboring synapses receiving contextual input ., At the offset of each CS presentation , the variables c and h were probed in the synapses of all EXC neurons and the strength of each synapse was modified accordingly ., The synaptic strengths before and after the update are denoted by w− and w+ , respectively ., If CS and contextual inputs at the same neuron coincided within a temporal window of ∼100 ms , then both synapses were strengthened 34 ., By contrast , if only one of the inputs was present , both synapses were weakened ( Eq . 6 ) ., This decrease of synaptic strength was based on studies reporting that synapses in LA , which had been strengthened during fear conditioning , depotentiated after extinction training 35 , 36 ., We assumed a similar mechanism to hold for the BA ., This type of bidirectional plasticity rule implemented in our model is similar to the BCM rule 37 , the “calcium-control hypothesis” 38–40 and the ABS rule 41 , 42 ., Common in all these rules is the specification that the level of postsynaptic Ca2+ determines the direction of plasticity ( for review see 43 ) ., A large increase in Ca2+ causes LTP , whereas a moderate increase results in LTD ., Low levels of Ca2+ do not cause any modification at all ., We essentially incorporated this bidirectional induction of plasticity in our rule using fixed thresholds ( Fig . 3C ) , rather than sliding ones , as is the case e . g . in the BCM rule ., The parameters a1 and a2 denote the learning rates for potentiation and depotentiation of the synapses , respectively ., Ca2+ influx depends on NMDA receptor activation and sufficient postsynaptic depolarization ., The latter can be caused by coincident presynaptic input or by a backpropagating action potential ( BAP ) ., However , in our model , a BAP was not required ., That is , we assumed that if the total presynaptic firing rates were high enough , they could cause sufficient depolarization to unblock NMDA receptors ., This assumption is supported by experimental evidence showing that a BAP is neither necessary nor sufficient for synaptic plasticity 44 , 45 ., Note that this plasticity rule is also compatible with changes induced purely in the presynaptic terminal ., In fact , experimental evidence suggests that presynaptic induction , completely independent of postsynaptic activity , occurs in the LA 46 ., Thus , the plasticity rule implemented in our model incorporates both changes that are dependent on post-synaptic depolarization , but not postsynaptic spiking , and changes that are presynaptic and entirely independent of post-synaptic depolarization or spiking ., Because in our model the presynaptic spiking was caused by CS and contextual inputs , their total activity encoded in the variables c and h , respectively , determined the direction of plasticity ., Thus , both c and h functioned as eligibility traces for synaptic modification 34 , 47 ., They could be interpreted as describing any relatively slow process associated with the effects of Ca2+ , e . g . autophosphorylation of CaMK-II 39 , 48 ., The terms and in the update rule were introduced to provide upper and lower bounds to the synaptic weights , such that they did not increase or decrease indefinitely ., They also controlled the step-size with which synapses were modified: the closer a weight was to wmax ( wmin ) the smaller were its increments ( decrements ) ., The parameter m represented the action of neuromodulators released during fear conditioning and extinction ., It is known that many neuromodulators target the BA 5 , possibly affecting synaptic plasticity in a complex way ., Among the possible candidates are norepinephrine ( NE ) 49–52 , dopamine ( DA ) 53 , 54 and opioids 5 ., Here , however , lacking more detailed experimental data , we cannot be more specific about which exact neuromodulators are involved and how they interact ., Fortunately , this lack of knowledge does not pose a problem for the plasticity rule we propose , because it is general enough to accommodate any combination of neuromodulators that may turn out to be involved in BA fear processing ., The dynamics of the mean-field model were simulated in MATLAB ., The SNN simulations were written in python ( http://www . python . org ) , using the PyNN interface 55 , http://neuralensemble . org/trac/PyNN to the NEST simulation environment 56 , http://www . nest-initiative . org ., Fig . 4 shows the response of the mean-field model , i . e . the firing rate model , of BA during fear conditioning and extinction ., To simulate fear conditioning in , we stimulated the population A five times with CS , US and inputs ( Eqs . 1 , 2 ) ., This resulted in a progressive strengthening of CS synapses onto population A ( ) ( Fig . 4C ) , accompanied by a corresponding increase in the response of population A ( Fig . 4A ) ., To simulate fear extinction training in , we stimulated population A with CS input and population B with CS and input six times to mimic a different context ., Now , in , the synaptic strength of the CS input synapses ( ) onto population B progressively got stronger , whereas remained unchanged ( Fig . 4D ) ., The slow increase in the response of population B resulted in a small decrease in the response of population A , due to the recurrent inhibition ., When the strength of became larger than ( Fig . 4D ) , the activity of population B dominated and , hence , the response of population A was suppressed ( Fig . 4B ) ., The differential activation of two neuronal sub-populations in two different contexts can be interpreted as fear ( population A ) and extinction ( population B ) neurons as observed in 17 ., This is purely a functional characterization of the two sub-populations , which are identical otherwise ., That is , we used exactly the same parameters for both sub-populations and the differential activation results solely from differences in contextual inputs they receive ., Thus , the two populations were not different in terms of their intrinsic properties ., Of course , cases where the two subpopulations do have different properties can be easily accommodated in the model resulting in an enhancement of the differential activation ., To be consistent with 17 , we used the terms fear and extinction neurons to refer to those subpopulations that are active in and respectively ., Note that we did not include any component that imitates behavioral output , i . e . freezing ., Instead , we assume , in agreement with experimental findings 17 , that high activity of fear neurons directly corresponds to a high level of freezing whereas high activity of extinction neurons and low activity of fear neurons corresponds to low levels of freezing ., Although a simple firing rate model was able to account for the dynamic emergence of fear and extinction neurons , such mean-field models have only limited explanatory and predictive power ., For instance , they assume uncorrelated activity in the underlying neuronal populations and , thus , cannot be used to predict any correlations in firing rate or spike timing that may emerge in the network ., In addition , these models cannot be used to predict the spike patterns of individual neurons ., Thus , to understand the dynamics of the BA network beyond average firing rates only , we simulated a biologically realistic large-scale network composed of spiking neurons ., Again , fear conditioning and extinction were simulated by applying five CS-US presentations in and six CS presentations in respectively ., In the two different environments tonic contextual input was provided to EXC neurons ( cf . Models ) ., The results of the simulation are presented in Fig . 5 ., Initially , all EXC neurons spiked at very low firing rates ., Presentations of the CS-US led to a steady increase in the firing rates of one sub-population ( fear neurons ) within the EXC population , which peaked at the end of conditioning ( Figs . 5A , E amber dots ) ., The increase in activity of fear neurons was a direct consequence of the potentiation of CS and contextual inputs onto fear neurons ( Figs . 5 G , I; amber triangles ) ., In , the fear neurons still responded with high firing rates upon the first CS presentation , even though they did not receive contextual inputs ( Figs . 5A , F ) ., With further CS presentations , however , synapses became potentiated ( Eq . 6 , Figs . 5H , J; cyan dots ) , causing a steady increase in the firing rate of the second sub-population of neurons ( extinction neurons ) ( Figs . 5A , F; cyan dots ) ., The increased recurrent inhibition in the network then caused a decrease in the activity of the fear neurons ( Figs . 5A–C , F ) ., At the end of extinction , the population rate of the extinction neurons peaked , whereas the firing rate of the fear neurons had returned to the initial , pre-conditioning values ., The reduction of fear neurons activity was further facilitated by small depotentiation of CS and contextual input synapses onto the fear neurons ( Eq . 6 , Figs . 4H , J; amber triangles ) ., Note that depotentiation of CS synapses onto extinction neurons also occurred during conditioning ( Fig . 5G ) as described by the learning rule ., By contrast , CTX synapses were not decreased during conditioning , because their initial values were close to the lower bound ( w− ) ( Fig . 5I ) ., During conditioning and extinction the baseline firing rates increased as well ( Fig . 5A ) ., This increase was induced by the strengthening of the contextual inputs ( Figs . 5H , I ) , providing an explanation for contextual conditioning ., However , because only a small percentage of neurons exhibited this increase in firing rates , this could make it difficult to measure it experimentally ., This fact reveals a key advantage of network models which allow for simultaneously sampling a large number of neurons ., Based on this , predictions can be inferred which otherwise would not have been possible ., Note that , again , the assignment of BA EXC neurons in fear and extinction sub-populations is purely a functional one ., That is , neurons were characterized post-experiment as fear or extinction cells depending on whether they responded to the CS after conditioning or after extinction training respectively ., In particular , they were not predetermined in terms of their intrinsic properties and the two sub-populations resulted solely from the differences in the contextual inputs they received ., Also , it is important to emphasize that whereas the population rates of fear and extinction neurons increased gradually during conditioning and extinction training respectively , this was not the case for individual neurons ., Instead , they changed their state quite abruptly from non-responding to responding ( Fig . 6A ) ., The further the training advanced , the more neurons started to respond ., Hence , the gradual increase in population rates ( Figs . 5E , F ) reflects the growing recruitment of responding neurons , rather than a gradual increase of single neuron activity itself ( Fig . 6 ) ., The responsive neurons fired maximally two spikes per CS presentation ., The baseline firing rates for the inhibitory population were normally distributed with a mean of 10 Hz , whereas the CS-evoked rates shifted their distribution towards a mean of 20 Hz ., This is consistent with the neuronal firing patterns in vivo reported by 17 ., Although we performed our main simulations using separated contextual inputs to distinct neuronal subpopulations within the BA ( cf . Models ) , this is not a necessary requirement of the model ., In fact , performing simulations with varying amounts of contextual input overlap showed that fear and extinction neurons still existed as distinct populations , even when contextual inputs had an overlap of around 50% ( Fig . S1 ) ., In addition , the simulations revealed the existence of a third sub-populations of neurons ., These were the neurons receiving inputs in both contexts and , thus , were active during both fear conditioning and fear extinction ( so called persistent neurons ) ., Note that , similar to the case of fear and extinction neurons , the characterization of cells as “persistent” is functional and denotes the fact that these neurons were responding to the CS during both conditioning and extinction ., Moreover , these neurons had much stronger CS and CTX synapses , which resulted in higher firing rates ., This observation of the model is consistent with the experimental data 17 , suggesting that conditioning and extinction are not affected by overlapping inputs , unless the overlap is high ( >50% ) ., Following extinction training in , presentations of the CS in the original fear conditioning context ( ) resulted in context-dependent renewal ( ABA renewal ) of conditioned fear responses 2 ., This renewal phenomenon points at two important aspects of possible neural mechanisms underlying fear extinction:, ( i ) extinction is mainly new learning and only partly unlearning of previously acquired fear memories ( 57; see also Discussion ) ,, ( ii ) extinction learning is context-dependent ., We simulated ABA renewal by changing context at the end of extinction ( Fig . 7 ) ., This resulted in a sudden switch of activity between fear and extinction neuronal subpopulations ., That is , although the activity of extinction neurons was high after extinction learning , the contextual switch caused the activation of fear neurons and a significant drop in the extinction neurons activity ., These results are in complete accordance with the experimental findings reported by 17 ., It is important to note that this rapid activity switch is purely a network phenomenon and not an effect of synaptic plasticity , as the change is much too fast for the plasticity mechanisms to act ., We illustrate this point by depicting the average membrane potentials of 100 randomly selected fear and extinction neurons ( Figs . 5D , 7D; amber and cyan traces respectively ) ., It is evident that in either context there was a clear difference between the membrane potentials of the two cell populations , stemming from the fact that one of the populations continuously received a higher excitatory drive due to the additional contextual input ., Switching contexts led to a corresponding instantaneous switch in the assignment of the contextual input and , hence , in opposite shifts in the average membrane potentials of the two sub-populations , which was immediately reflected in corresponding shifts in the firing rates ., We also modeled the case where the renewal context was different from both the conditioning and the extinction context ( ABC renewal ) ., The results of the simulations revealed that if after extinction training the CS was presented in a third , different , fear neurons became rapidly active again and suppressed extinction neurons ( Fig . S2 middle ) ., However , our model also indicated that the absolute response of fear neurons - and thus the magnitude of the fear response- would be weaker than in the ABA case ., The reason is that in CTX synapses had not been strengthened during the conditioning procedure ., This provides an account for the experimentally observed ABC renewal 58 , 59 explaining why ABC renewal may occur in the first place and also why the effect may be weaker compared to ABA renewal ., Moreover , our simulations also suggested that massive extinction ( extinction over-training ) in can abolish ABC renewal , because depotentiation of CS and afferents onto BA neurons yield less excitatory input to these neurons ., Extinction over-training can also impair ABA renewal , although to a lesser extent ( Fig . S2 right ) ., The reason that ABA renewal is more robust and ABC renewal more vulnerable to massive extinction stems from the fact that in the latter case not only CS synapses onto fear neurons are weakened , but also potentiated CTX synapses are entirely missing ., These findings are in agreement with and provide a possible explanation for the experimentally observed effects of massive extinction 60 ., Although we did not focus on extinction of contextual fear , it is important to note that our model also accounts for this specific conditioning phenomenon ., Indeed , the plasticity rule dictates that in the absence of the CS synaptic weights will decay ., That is , CTX synapses , which had been strengthened during conditioning in and encode contextual fear , will depotentiate in the same context if the CS is not present ., This will yield decreased fear neuron activity and , thus , extinction of contextual fear ., Note that within the framework of our model , this form of extinction is truly unlearning and not masking of contextual fear memories ., The experimentally reported connection probabilities from excitatory to inhibitory neurons as well as among inhibitory neurons in the BA are around 0 . 5 61 ., This is a much higher value than the ones we used in our initial simulations ( Figs . 5–7 , Table S1E ) ., To test the effects of such higher connectivity , we performed additional simulations adopting the experimentally reported values for the connection probabilities ., The qualitative behavior of the model did not change ( data not shown ) ., However , a new aspect in the network dynamics emerged ., High frequency oscillations - typically in the gamma range ( 30–80Hz ) - occurred throughout the simulation in both excitatory and inhibitory populations ., These oscillations were present already in the ongoing activity patterns and CS-US presentation enhanced them even further ( Fig . 8A ) ., They resulted from the high shared connectivity and , hence , large amount of shared inputs that caused correlated spiking in the neurons ., The oscillation frequency was determined by synaptic time constants and delays in the network ., Gamma oscillations in networks of excitatory and inhibitory neurons have been reported in many experiments 62–67 and discussed in multiple theoretical studies 68–75 ., Moreover , several studies have reported gamma oscillations in the amygdala under certain conditions , e . g . in anesthetized animals 76 , in slow wave-sleep 77 , in the presence of reward predicting stimuli 78 and in paradigms involving consolidation of emotional memories 79 ., Therefore , there is at least partial experimental and theoretical support for the gamma range oscillations observed here in high connectivity BA network simulations ., Yet , in networks with high mutual connectivity between excitatory and inhibitory neurons and within inhibitory neuron populations such as in the BA , oscillations should be a prevailing feature and should , therefore , be readily identifiable in vivo recordings under all conditions and not only in the special cases mentioned above ., It is , thus , possible that certain mechanisms operate in the BA that could dam

Introduction, Models, Results, Discussion

The basal nucleus of the amygdala ( BA ) is involved in the formation of context-dependent conditioned fear and extinction memories ., To understand the underlying neural mechanisms we developed a large-scale neuron network model of the BA , composed of excitatory and inhibitory leaky-integrate-and-fire neurons ., Excitatory BA neurons received conditioned stimulus ( CS ) -related input from the adjacent lateral nucleus ( LA ) and contextual input from the hippocampus or medial prefrontal cortex ( mPFC ) ., We implemented a plasticity mechanism according to which CS and contextual synapses were potentiated if CS and contextual inputs temporally coincided on the afferents of the excitatory neurons ., Our simulations revealed a differential recruitment of two distinct subpopulations of BA neurons during conditioning and extinction , mimicking the activation of experimentally observed cell populations ., We propose that these two subgroups encode contextual specificity of fear and extinction memories , respectively ., Mutual competition between them , mediated by feedback inhibition and driven by contextual inputs , regulates the activity in the central amygdala ( CEA ) thereby controlling amygdala output and fear behavior ., The model makes multiple testable predictions that may advance our understanding of fear and extinction memories .

The amygdaloid complex is one of the key brain structures involved in fear-related processes ., A typical way to study neural correlates of fear expression ( e . g . freezing response ) in the amygdala is to perform a fear conditioning paradigm , which yields a conditioned fear response ., This response can be reversed by another procedure called fear extinction ., Thanks to the experimental approaches to date we have some understanding about the putative roles of specific subnuclei within the amygdala in the formation of these fear and extinction memories ., Here , we complement the experimental studies by providing a computational model that addresses the question of how fear and extinction memories are encoded in the amygdala , and specifically , in the basal nucleus ( BA ) ., We propose a specific neural mechanism to explain how the BA may integrate information about a salient , conditioned stimulus and the environment , thereby enabling it to switch the state of the animal from low to high fear and vice versa ., We also provide possible explanations for various other behavioral findings , such as the recovery of fear after it had been extinguished ( renewal ) ., Finally , we make specific , experimentally testable predictions that need to be addressed in future work .

neuroscience/behavioral neuroscience, neuroscience/animal cognition, neuroscience/theoretical neuroscience

journal.pgen.1004782

ARTIST: High-Resolution Genome-Wide Assessment of Fitness Using Transposon-Insertion Sequencing

Transposon-insertion sequencing ( TIS ) 1–4 is a powerful approach that enables rapid and comprehensive definition of an organisms genetic requirements for survival under a variety of different conditions ( reviewed in 5 , 6 ) ., In TIS , a high-density transposon insertion library is grown under a condition of interest , and then subjected to high-throughput sequencing to map the transposon insertion site for each mutant in the library ., The number of reads detected from each insertion mutant is proportional to the fitness of that mutant under the selected growth condition ., Thus , strains carrying transposon insertions in loci required for survival will produce few or no reads , while reads from insertions that do not affect growth will be well-represented ., Genomic regions that are dispensable for growth in optimal laboratory conditions ( e . g . , rich media ) but are required for survival in more stringent growth conditions are termed conditionally essential loci , whereas essential loci are thought to be required under all conditions ., Because TIS is limited by sequencing capacity , here we use the terms ‘essential’ and ‘conditionally essential’ to define regions that are consistently underrepresented in reads in a given condition; the terms encompass both loci that are absolutely necessary for growth and those that can be disrupted , but are required for optimal growth ., Identification of such regions , which can include non-coding sequences in addition to open reading frames , can yield considerable insight into the means by which an organism adapts to different environments ., To date , comparative TIS-based studies have been carried out in diverse bacterial species and have defined genes required for survival in the presence of various nutrients and stresses as well as in experimental models of infection ( reviewed in 5 ) ., While recent studies have firmly established the power and value of TIS , there are several shortcomings in current approaches used for TIS analysis that limit the optimal and widespread application of this technique for conditional essentiality screens ., First , read counts between TIS libraries must be normalized to minimize the differences between libraries to be compared; however , current normalization protocols , which generally rely on scaling the frequency of all insertion mutants by a single factor to equalize the total number of reads per library 1 , 7 , do not take into account differences in library complexity that arise from stochastic processes encountered during the experiment such as biologic bottlenecks and sequence sampling error ., Second , current methods for analysis of conditional essentiality are largely limited to annotated genomic features ( e . g . , ORFs , ncRNAs ) , which prevents de novo discovery of novel conditionally essential sequences ., Finally , current TIS analysis methods usually rely on largely ad-hoc cutoffs ( e . g . , a minimum fold change in reads required for analytic consideration ) , and routinely utilize custom computational tools that are inaccessible to most biologists ., Here , we propose approaches to overcome these limitations of current conditional essentiality analysis ., Our solutions include:, 1 ) explicitly modeling changes in sequence abundance between libraries to simulate the systematic differences that can exist between TIS datasets;, 2 ) a novel adaptation of a hidden Markov model , which enables annotation-independent prediction of functional importance across the entire genome; and, 3 ) implementation of this pipeline on a single platform 8 using well-documented tools in order to offer biologists a standardized method of TIS analysis ., Our new analytic approaches are combined in a pipeline termed ARTIST ( Analysis of high-Resolution Transposon-Insertion Sequences Technique ) , which includes both a previously characterized workflow for the definition of essential loci in a single TIS library 9 and a new pipeline for identification of conditionally essential loci ., We validate the ARTIST pipeline by reanalyzing a TIS dataset from a recent study of Mycobacterium tuberculosis requirements for growth in a mouse model of infection 7 ., Furthermore , we demonstrate ARTISTs versatility by carrying out a new analysis of Vibrio cholerae genomic requirements for infection in a rabbit model using a high-density transposon library created here ., In both instances , ARTIST improves the detection of likely conditionally essential genes , while limiting false positive assignments ., We believe the ARTIST pipeline will greatly facilitate and enhance the effectiveness of future TIS studies in a variety of organisms and conditions ., The ARTIST pipeline is Matlab-based 8 and contains two different analysis tools ( Figure 1 ) ., One arm , termed EL-ARTIST ( for Essential Loci analysis ) , defines all loci that are required for growth ( i . e . , regions with few or no associated transposon insertions ) in a TIS library generated under a single growth condition—commonly , standard laboratory conditions ., While the key features of the EL-ARTIST analysis method were previously described 9 , until now this approach was not publicly available as a standalone tool ., The second arm , Con-ARTIST ( for Conditionally essential loci analysis ) is a new tool that compares transposon libraries that have been grown under different conditions , in order to define conditionally essential loci that are only required for survival under a subset of growth conditions ., The Con-ARTIST workflow includes two novel modules that improve upon current TIS analysis methods ., First , simulation-based resampling aids normalization between libraries that have different frequencies of mutants due to stochastic experimental variation ., Second , a hidden Markov model ( HMM ) dissects the genome in an annotation-independent manner , allowing the definition of both annotated and uncharacterized genomic regions according to their contribution towards growth ., In the first step of the Con-ARTIST workflow ( Figure 1 ) , mapped read counts from all transposon insertions are normalized between TIS datasets using simulation-based resampling of the control library ., This creates independently simulated control libraries that reflect how mutant frequencies can change in an experiment simply due to chance events ., For each of these simulated libraries , the number of reads within every annotated genomic feature ( e . g . , ORFs , ncRNAs , etc . ) is compared to that of the same feature in the experimental dataset using a Mann-Whitney U ( MWU ) statistical test ., Non-parametric statistical tests such as the MWU are preferred , as they make no assumptions about the distribution of reads in each dataset and thus may be less sensitive to biases in the experiment ( e . g . , PCR amplification jackpot events ) ., These MWU tests identify annotated regions that contain significantly different numbers of reads in the control versus the experimental library ., When MWU tests are performed on all simulated control datasets , they provide the user with an estimate of how significance values can change due to chance in the experiment ., The results from the MWU analyses can be used directly for hypothesis generation or to train an annotation-independent hidden Markov model ., The HMM is a statistical model that decodes whether genomic regions belong to a particular biological category ( e . g . , required for growth in vivo ) given the fold changes in read counts at every insertion site in the genome ., The HMM output is a map of every potential transposon insertion site in the genome and each sites likelihood of being required or dispensable for growth under the experimental condition tested ., As the HMM is annotation-independent , this allows the user to scan the genome at fine resolution ( i . e . , down to individual insertions ) and discover novel loci that regulate growth , such as upstream regulatory elements in intergenic regions and domain-coding regions within annotated genes ( see Text S1 for more details ) ., Probabilities within annotated loci can also be combined and a general prediction of essentiality reported for every gene or genomic feature ., The final output will be a table of all genomic loci and their predicted biologic states ( e . g . , no change in growth between experimental conditions , required for growth under the control condition , conditionally underrepresented or conditionally overrepresented ) ., All ARTIST scripts and example files ( Dataset S1 ) as well as a comprehensive user manual ( see Text S1 ) are provided in the supplementary materials ., Deriving meaningful results from conditional TIS experiments relies on understanding whether differences in mutant frequencies between libraries arise from selection or chance ., There are two principal processes that can cause stochastic differences in mutant abundance and interfere with downstream analytical accuracy in TIS-based genetic screens: genetic drift and sampling error ., In the context of a population of transposon mutants , genetic drift can be thought of as change in mutant frequencies due to random events , such as population bottlenecks and expansions 10 , 11 ., A stringent bottleneck will markedly alter the complexity of a library independent of the fitness of its constituent mutants ( Figure S1A , B ) while sampling error occurs when low abundance mutants in a mixed population are missed solely due to low sequencing saturation ( Figure S1C , D ) ., Comparative analyses of Himar transposon libraries created in M . tuberculosis ( previously described by Zhang et al . 7 ) and V . cholerae ( constructed for this study ) grown in vitro and in animal hosts provide clear evidence for the existence of host bottlenecks and for stochastic variability in library recovery ( see below ) ., For example , we created a V . cholerae library that was used to inoculate infant rabbits , a model host for the study of cholera 12 ., This library contained transposon insertions in>60% of all possible insertion sites ( i . e . , TA dinucleotides ) , while mutant recovery was highly variable between individual animals , ranging from 4–48% of possible sites disrupted ( Figure S2A , B ) ., In contrast , the libraries recovered from all rabbits collectively contained insertions in 57% of TA sites when a saturating number of reads ( >3×106 ) was sequenced , indicating that not all loss of V . cholerae library complexity in the host was due to selection ., Similarly , the in vitro M . tuberculosis library contained insertions in ∼62% of potential insertion sites ( Figure S2C ) , but libraries recovered from individual infected animals only had insertions in 26–41% of potential sites ( Figure S2D ) ., When a relatively small proportion of the input V . cholerae library was lost during in vivo growth , the number of reads per locus was extremely reproducible among output libraries ( e . g . , rabbits 2 and 4; R\u200a=\u200a0 . 95 ) , whereas inter-animal correlation was less robust when the recovered libraries were less complex , presumably due to stochastic bottleneck-dependent processes ( Figure S2E ) ., Thus , host bottlenecks , which are present in most experimental TIS infection models 10 , 11 , 13 , can present a major challenge for accurately discerning conditionally essential loci , and represent a stringent test of our methods ability to normalize TIS data ., Routinely , TIS datasets with differing library complexity are multiplicatively scaled to the same number of total reads using a single factor 1 , 7 , which presumes that there will be proportional retention of all neutral mutations in the library , when in fact stochastic events can cause these proportions to change markedly ., To address this issue , we used a multinomial distribution to resample reads from the control data and simulate the effect that stochastic processes may exert on the experimental dataset ., This simulation relies on the assumption that the observed frequencies of insertion mutants in the deeply sequenced control library approximate their true proportions in the population , such that we can use the control frequencies to define the probabilities of a multinomial distribution ., Specifically , the multinomial distribution is scaled by a factor derived from the proportional difference in library complexity ( i . e . , number of unique sites disrupted ) between the control and experimental datasets; this difference approximates the extent of genetic drift and sampling error in the experiment ., Next , we use this multinomial distribution to simulate control datasets that have the same number of total reads as the experimental dataset , but have been subjected to a stochastic loss of library complexity that is similar to that experienced by the experimental library ., The simulation is repeated to create independently simulated control libraries , and the variance between these libraries reflects the extent that noise from chance events can influence the validity of downstream statistical tests ., To assess whether simulation-based resampling enables more accurate downstream statistical analysis , we first tested the robustness of multinomial-based normalization when the in vitro grown V . cholerae library was subjected to increasingly severe simulated bottlenecks ., Bottleneck-passaged libraries and the original TIS library were normalized either by multinomial-based resampling or simple multiplicative scaling of reads ., The normalized libraries were then compared against the original in vitro TIS dataset using a Mann-Whitney U statistical test ., In this test , no genes should appear significant since all libraries are derived from the same original source ., In Figure S3A , we found that multinomial-based normalization ( Resampling ) produced dramatically ( 3–5 fold ) fewer false positive gene assignments when compared to multiplicatively scaling ( MS ) at all bottleneck stringencies ., Thus , a multinomial-based normalization approach is more robust at mitigating the effects of population constrictions ( i . e . , bottlenecks ) than the standard approach of multiplicative scaling ., Since we observed lower false positive rates in simulated data , we tested the merits of our multinomial normalization using animal infection data , which derives from a more complex and relevant biological system and thus offers a stringent test of our modeling approach ., We re-analyzed previously published in vitro and mouse-grown M . tuberculosis datasets 7 , and identified genes that were differentially represented in vivo ( p-value<0 . 01 ) by MWU test after multiplicative scaling of libraries to the same total reads ( as was performed by Zhang et al . but without applying their secondary read count threshold for defining significance ) ., We also performed 100 simulations and MWU tests in the Con-ARTIST pipeline using the same data to model the effect of stochastic population changes in the experiment ., Finally , we determined how reproducibly significant ( p-value<0 . 01 ) the read count changes in all genes were across all 100 simulations , and compared these data to the multiplicatively scaled result above ., While multiplicative scaling produced 340 genes with significant p-values , nearly 100 of those genes failed to reach the same level of significance in the majority of our simulations ( Figure 2A , blue shaded area ) , suggesting that modeling stochastic mutant loss due to genetic drift may limit false positive assignments ., Furthermore , only 121 genes were found to have significantly different read abundance ( p<0 . 01 ) in over 90% of the simulation-based statistical tests ( Figure 2A , B; green shaded areas ) ., Importantly , all 121 genes would have also been predicted by multiplicative scaling , suggesting that we have not misidentified previously non-significant genes ., In-depth analyses of two of the strongest examples of irreproducibility—rv3710 and rv3343c ( Figure 2A , red dots ) —illustrate how the discrepancy between genes with low reproducibility in our analyses but significant p-values by multiplicative scaling can arise ., Despite a relative paucity of transposon insertions in rv3710 in the in vitro library , when compared to the mouse-passaged library , which lacks reads in the entire locus , the gene is found to be conditionally essential by MWU testing when the control library is normalized solely by multiplicative scaling ( Figure 2C ) ., However , rare insertions often disappear in resampled controls , which makes the locus appear far less significantly different in the majority of 100 MWU tests ( Figure 2E ) ., Discrepancies between results from the two normalization approaches can also occur for loci that are well represented by insertions in both in vitro and in vivo libraries , as is the case for rv3343c ( Figure 2D ) ., This gene is deemed significantly underrepresented in vivo when using multiplicative scaling , despite a relatively minor difference in reads between conditions ( ∼2 fold ) , likely because the rank sum-based MWU test is prone toward significance with large numbers of datapoints ., Since rv3343c contains over 160 potential insertion sites and is well disrupted , the locus may appear statistically different due to the large number of datapoints on which the MWU test is run ., In contrast , when rv3343c is subjected to simulation-based normalization , the range in reads from the control library becomes narrower , and significant differences are only observed for a small subset of the MWU comparisons ( Figure 2E ) ., The observations with rv3710 and rv3343c indicate that noise introduced by genetic drift can account for apparently significant differences in read abundance for loci that are both under-disrupted or well-disrupted in the control dataset ., Simulation can also enhance the significance value and improve identification of genes that are conditionally overrepresented ., For example , rv3696c , an enriched gene ( i . e . , more reads in vivo than in vitro; Figure S3B ) , has a p-value of ∼6×10−4 in multiplicative scaling-based analyses ( Figure 2A , orange dot ) , while simulation-based normalization yields an average p-value of ∼1 . 2×10−4 , and yields a significant result in all 100 simulation-based MWU tests ( Figure 2E ) ., These observations suggest that modeling the effects of genetic drift and sampling error biases inherent in TIS datasets can enhance the robustness of statistical analyses ., In particular , simulation-based normalization appears to enable detection of reproducibly significant genes while avoiding many likely false positives ., Importantly , we could not compensate for these biases by simply increasing the p-value stringency when using multiplicatively scaled data , as many moderately significant , but highly reproducible genes such as rv3696c would be lost alongside the potential false positives with broad variances in p-values ( Figure S3C ) ., Most current statistical analyses of TIS data 1 , 7 only analyze insertions within annotated genes and therefore yield relatively low genomic resolution while also omitting intergenic regions ., To circumvent these limitations , we incorporated a hidden Markov model-based module into Con-ARTIST , which seeks to predict ‘hidden states’ ( i . e . , regions that belong to particular biological categories ) by analyzing observed ‘emissions’ ( i . e . , reads from transposon insertions ) ., In TIS analysis , the HMM utilizes read counts from each insertion and those of the immediately preceding insertion to assign hidden states , and thus leverages the information inherent in bacterial genome architecture without aggregating insertions or restricting analysis to previously annotated genomic features ., Recently , HMM-based approaches were independently used by us and another group 9 , 14 to analyze TIS data for the identification of genomic regions required for in vitro growth of V . cholerae and M . tuberculosis , respectively ., However , these approaches were limited to analyzing a single TIS dataset , and a new HMM framework is required to assign additional biological categories in the context of comparative TIS studies ., In Con-ARTIST , after simulation-based normalization , we compare the reads and calculate the fold change at every potential insertion site between the in vitro ( control ) simulations and in vivo ( experimental ) dataset ., Next , MWU tests are conducted as described above for all annotated loci , and the results ( i . e . , the genes having been defined as significantly under- and overrepresented in vivo ) are used to train both the emission probabilities of fold changes at individual transposon sites and the transition probabilities between biological states ., Emission and transition probabilities along with the observed fold changes are then used by the Viterbi algorithm to predict—in an annotation independent manner—whether each insertion site in the genome most likely belongs to one of 4 biological categories:, 1 ) sites that are fully dispensable during both in vitro and in vivo growth;, 2 ) regions that are essential in both conditions;, 3 ) regions that are conditionally enriched ( overrepresented ) in the experimental library; or, 4 ) regions that are conditionally essential ( underrepresented ) in vivo ., To demonstrate that our conditional HMM approach ( Con-ARTIST ) robustly assigns biological significance to different loci , we simulated several in vitro libraries of the Zhang et al . M . tuberculosis dataset 7 and then compared each of the simulated libraries against each other using either MWU analysis or Con-ARTIST ( MWU followed by HMM ) ., Because the simulated libraries are derived from the same dataset , a robust analysis method should not detect any significant differences between them ., We used a range of p-value thresholds to define when loci were significantly different in reads , and determined the fraction of insertions that were thus false positively assigned ., As expected from earlier simulations , both MWU and Con-ARTIST performed very well at low p-value cutoffs with virtually no insertions being called as significantly different in reads between simulations ., However , Con-ARTIST had a more stable false positive rate across a wider dynamic range of p-values ( >10% false positives at a p-value cutoff of 0 . 8 ) than the MWU method alone ( >10% false positives at a p-value cutoff of 0 . 5 ) , suggesting that the inclusion of the HMM is more resistant to false positive assignments than the MWU tests that it is trained upon ( Figure 3A ) ., We further characterized the potential for false positives by comparing the in vitro essentiality assignments of each insertion site in M . tuberculosis and V . cholerae when the data are analyzed either by EL-ARTIST or Con-ARTIST ., In vitro grown libraries of each organism were analyzed by EL-ARTIST and the essential and non-essential loci were defined , while Con-ARTIST was run on both the in vitro and in vivo grown libraries to define conditionally essential and enriched regions , in addition to essential and non-essential loci ., Loci that are found to be required for in vitro growth should be highly concordant between EL-ARTIST and Con-ARTIST ., Indeed , the agreement between essentiality assignments in M . tuberculosis and V . cholerae was approximately 91% and 95% , respectively , with little variation in 100 independent tests ( Figure 3B ) , demonstrating that the inclusion of two additional biological categories ( conditionally essential and enriched ) to the Con-ARTIST HMM framework does not impact our ability to accurately define essential loci ., Thus , Con-ARTIST appears robust relative to a HMM previously used for identification of essential loci 9 ., To further assess Con-ARTISTs utility , we compared M . tuberculosis and V . cholerae genes classified by Con-ARTIST as required for optimal in vivo growth to those identified in previously published TIS or microarray-based studies 7 , 11 , 15 , 16 ., For M . tuberculosis , the Con-ARTIST pipeline classified 118 genes ( Table S1 ) as conditionally essential for mouse infection with high likelihood ( all insertion sites within these genes have>90% probability of being conditionally essential ., Most of these genes ( 84 or 71% ) overlap with those identified in previous studies by Zhang et al . 7 or Sassetti et al . 15 ( Figure 4A ) ., This overlap is significantly greater ( p-value<0 . 05 by one-sided Fishers exact test ) than the overlaps for the Sassetti et al . and Zhang et al . datasets , which were 36% and 28% , respectively ., Additionally , in all three mice , the p-values for Con-ARTISTs conditionally essential genes that overlap with those of Zhang et al . had significantly lower standard deviations across the simulation-based MWU tests than did the p-values for genes that were identified only in Zhang et al . ( Figure 4B ) , which includes a much larger set of conditionally essential genes ( 371 ) , the majority of which were not found either by our analysis or the Sassetti et al . microarray study ., Importantly , Con-ARTIST also identified genes known to be critical for M . tuberculosis virulence , such as members of the ESX-1 locus ( rv3865-rv3877 , Figure S4A ) , which encodes a virulence factor secretion system that is critical for pathogenesis and survival in vivo 17 ., Notably , two conditionally essential ( CE ) genes known to be required for ESX-1 function , rv3869 and rv3871 , which encode a translocon subunit 18 and an ATPase 17 , 19 , respectively , were found to be required for infection by Con-ARTIST and Sassetti et al . 15 , but not by Zhang et al . 7 , indicating that Con-ARTIST is more sensitive than MWU tests alone in identifying conditionally essential genes when using the same raw data ., Con-ARTIST also classified 34 genes as conditionally essential that were not identified in previous studies; many of these genes have consistently fewer read counts in vivo than in vitro ( Figure S4B , Table S1 ) , suggesting they may genuinely be important for infection ., In addition to re-analyzing published M . tuberculosis data , we constructed a new high-density transposon library in V . cholerae , and assessed which genomic regions were required for infection in an infant rabbit model of disease that closely mimics human cholera 12 ., This model was recently used in two additional TIS–based studies 11 , 16 , and we compared those results to the output of Con-ARTIST ., Con-ARTIST classified 201 genes as conditionally essential in vivo ( Table S3 ) ; however , this list included genes whose disruption results in mild growth defects in vitro in a previous study 16 ., Because such genes were treated separately by Kamp et al . , we removed them from our output list and that of Fu et al . in order to facilitate accurate comparisons ., Following this filtering step , 104 genes ( Figure 4C ) remained in Con-ARTISTs set of genes predicted ( using probability cutoffs of 85% , 90% , or 95% produced the same results ) to contribute specifically to growth in vivo ( Figure S5A ) ., The majority of the conditionally essential genes ( 72% ) were also identified in at least one of the other two studies; in comparison , the overlaps for Kamp et al . and Fu et al . were 62% and 22% , respectively ., Con-ARTISTs overlap with Kamp et al . is significantly higher ( p-value<0 . 005 by one-sided Fishers exact test ) than the overlap between Kamp et al . and Fu et al . All three studies classified numerous genes known to be critical virulence factors as conditionally essential in vivo ( Table S3 ) , including proteins involved in the production of the type IV pilus , TCP , which mediates cell-to-cell adhesion 20 and is required for human infection 21 ., Universally detected genes also included those encoding enzymes that mediate synthesis of various amino acids , suggesting that the host does not provide a sufficient supply of these nutrients to support V . cholerae growth in the small intestine ( Figure S6 ) ., Con-ARTIST did not identify 19 genes classed as conditionally essential by both previous analyses ( Figure 4C ) ., In our control library , these genes on average were disrupted at <50% of their TA sites , whereas almost all of the 104 conditionally essential genes that were identified by Con-ARTIST contained a significantly higher percentage of insertions ( Figure S5B ) , suggesting that some conditionally essential genes may have been missed by our analysis because of low insertion frequencies in the control library rather than due to computational issues ., We also constructed in-frame deletions for 8 genes that were defined as conditionally essential by Kamp et al . but were not found to be required by Con-ARTIST and tested the ability of these strains to colonize the rabbit host ., None of the deletion strains had any apparent growth defect in vitro ( Figure S5C ) or significant attenuation in the host ( Figure 4D ) ., This result is consistent with our expectation that Con-ARTIST should reduce false positive assignments ., Con-ARTIST defined 29 V . cholerae conditionally essential genes that were not identified in either of the two previous studies ., Many of these genes belong to pathways that have been previously implicated in V . cholerae growth in vivo 11 , 16 , suggesting that the Con-ARTIST classification is correct ., For example , Con-ARTIST significantly ( p-values<0 . 05 by one-sided Fishers exact tests ) defines more genes linked to oxidative phosphorylation and respiration as important for growth in vivo than found by Kamp et al . and Fu et al . ( Figure S5D ) ., We created deletions in 5 conditionally essential candidates that were defined solely by Con-ARTIST , and assessed the ability of these strains to colonize the rabbit ., These mutants grew normally in vitro ( Figure S5C ) , but two of the five mutants , Δvc0432 and Δvc2055 , were significantly attenuated ( p-value≤0 . 01 ) approximately 30-fold attenuated in vivo ( Figure 4D ) ., Thus , Con-ARTIST has the capacity to identify legitimate conditionally essential loci that were not found using other analysis methods , while apparently minimizing false positive calls , suggesting that the analysis pipeline is robust ., Con-ARTIST enables annotation-independent identification of genomic regions of conditional essentiality , thereby facilitating definition of unannotated intergenic features , including non-coding RNAs ( ncRNAs ) and cis-acting regions ., Con-ARTIST consistently classified 51 V . cholerae intergenic loci ( Table S4 ) and 20 M . tuberculosis intergenic loci ( Table S2 ) as conditionally essential or domain conditionally essential ( i . e . , containing both regions of conditional essentiality and regions that do not significantly vary in reads ) in vivo ., Over 90% of the V . cholerae intergenic regions were upstream of genes found to be required for host infection , suggesting they may identify promoters or 5′ UTRs that control the expression of downstream conditionally essential genes ., An illustration of one such region—the intergenic region upstream of vc2635—is shown in Figure 5 ., vc2635 encodes penicillin-binding protein 1A , a cell wall synthesis enzyme that was recently shown to be required for optimal V . cholerae growth in vivo 22 ., Con-ARTIST defines the upstream intergenic region , IG_vc2635 , as domain conditionally essential , and assigns the boundary between non-essential and conditionally essential sequence adjacent to the predicted −35 and −10 promoter sequences ( Figure 5 ) ., Conditionally essential sequence extends uninterrupted from this site into the vc2635 open read frame , which may suggest a polar effect of the transposon ( e . g . , disruption of a 5′ UTR or other regulatory region ) ., Transcriptomic analysis of V . cholerae 23 detected transcripts that overlap well with the predicted conditionally essential region of IG_vc2635 ( Figure 5 ) ., This example highlights Con-ARTISTs utility in defining conditionally essential features within unannotated intergenic regions ., Con-ARTIST can also define sub-genic regions of conditional essentiality , which can provide insight

Introduction, Results and Discussion, Materials and Methods

Transposon-insertion sequencing ( TIS ) is a powerful approach for deciphering genetic requirements for bacterial growth in different conditions , as it enables simultaneous genome-wide analysis of the fitness of thousands of mutants ., However , current methods for comparative analysis of TIS data do not adjust for stochastic experimental variation between datasets and are limited to interrogation of annotated genomic elements ., Here , we present ARTIST , an accessible TIS analysis pipeline for identifying essential regions that are required for growth under optimal conditions as well as conditionally essential loci that participate in survival only under specific conditions ., ARTIST uses simulation-based normalization to model and compensate for experimental noise , and thereby enhances the statistical power in conditional TIS analyses ., ARTIST also employs a novel adaptation of the hidden Markov model to generate statistically robust , high-resolution , annotation-independent maps of fitness-linked loci across the entire genome ., Using ARTIST , we sensitively and comprehensively define Mycobacterium tuberculosis and Vibrio cholerae loci required for host infection while limiting inclusion of false positive loci ., ARTIST is applicable to a broad range of organisms and will facilitate TIS-based dissection of pathways required for microbial growth and survival under a multitude of conditions .

Transposon insertion sequencing ( TIS ) is a powerful method that couples high-density transposon mutagenesis with next-generation sequencing to comprehensively assess the fitness of thousands of transposon mutants across a genome ., TIS is an extremely flexible technique that has been used to define genomic loci required for bacterial growth and survival in a variety of species and in many different growth conditions , including during host infection ., However , there remain several important limitations to current TIS analysis methods ., First , TIS data are not routinely normalized for the impact of experimental variability; second , most analyses are restricted to annotated loci and do not completely exploit the richness of TIS datasets; finally , TIS analysis methods are not easily accessible to most biologists ., Here we present a pipeline—ARTIST—that addresses these issues and will transform TIS-based studies ., We used ARTIST to conduct robust analyses of Mycobacterium tuberculosis and Vibrio cholerae in vivo TIS datasets and comprehensively defined the genetic requirements of these pathogens for host infection ., The ARTIST pipeline will make TIS analysis accessible to many researchers and greatly enhance the rigor of and insights gained from TIS studies in a wide range of microorganisms .

genetics, biology and life sciences, microbiology

journal.pgen.1003266

A Meta-Analysis of Thyroid-Related Traits Reveals Novel Loci and Gender-Specific Differences in the Regulation of Thyroid Function

Through the production of thyroid hormone ( TH ) , the thyroid is essential for normal development , growth and metabolism of virtually all human tissues ., Its critical role in heart , brain , bone , and general metabolism is illustrated by the clinical manifestations of thyroid disease , which affects up to 10% of the population ., Low thyroid function ( i . e . , hypothyroidism ) can lead to weight gain , high cholesterol , cognitive dysfunction , depression , and cold intolerance , whereas hyperthyroidism may result in weight loss , tachycardia , atrial fibrillation , and osteoporosis ., Mild variation in thyroid function , both subclinical and within the normal range , is associated with these TH-related clinical outcomes as well 1–4 ., The thyroid gland secretes predominantly the pro-hormone thyroxine ( T4 ) , which is converted into the active form triiodothyronine ( T3 ) in peripheral tissues ., The production of TH by the thyroid gland is regulated by the hypothalamus-pituitary-thyroid ( HPT ) axis , via a so-called negative feedback loop ., Briefly , low levels of serum TH in hypothyroidism result in an increased release of thyroid stimulating hormone ( TSH ) by the pituitary , under the influence of hypothalamic thyrotropin releasing hormone ( TRH ) 5 ., TSH , a key regulator of thyroid function , stimulates the synthesis and secretion of TH by the thyroid ., When circulating TH levels are high , as in hyperthyroidism , TRH and TSH synthesis and secretion are inhibited ., In healthy ( euthyroid ) individuals , TSH and free T4 ( FT4 ) levels vary over a narrower range than the broad inter-individual variation seen in the general population , suggesting that each person has a unique HPT axis set-point that lies within the population reference range 6 ., Besides environmental factors such as diet , smoking and medication , little is known about the factors that influence this inter-individual variation in TSH and FT4 levels 7–9 ., The heritability of TSH and FT4 has been estimated from twin and family studies at about 65% and 40% , respectively 10–12 ., However , the underlying genetic variants are not fully established , and the contribution of those discovered so far to the overall variance is modest ., Single nucleotide polymorphisms ( SNPs ) in the phosphodiesterase type 8B ( PDE8B ) , upstream of the capping protein ( actin filament ) muscle Z-line , β ( CAPZB ) and , more recently , of the nuclear receptor subfamily 3 , group C , member 2 ( NR3C2 ) and of v-maf musculoaponeurotic fibrosarcoma oncogene homolog ( MAF/LOC440389 ) genes have been implicated in TSH variation by genome-wide association studies ( GWAS ) 13–15 , whereas SNPs in the iodothyronine deiodinase DIO1 have been associated with circulating levels of TH by candidate gene analysis 16–18 ., To identify additional common variants associated with thyroid function , we performed a meta-analysis of genome-wide association data in 26 , 420 euthyroid individuals phenotyped for serum TSH and 17 , 520 for FT4 levels , respectively ., In addition , we also assessed gender-specific effects and correlation with subclinical thyroid dysfunction ., Given the reported clinical differences in thyroid function in males and females 21–23 , we searched for gender-specific loci by whole-genome sex-specific meta-analysis , analyzing males and females separately in each cohort ., Some of the loci detected in the main meta-analysis were seen at genome-wide significance level only in females ( NR3C2 , VEGFA , NRG1 and SASH1 ) or in males ( MAF/LOC440389 , FGF7 , SOX9 , IGFBP5 ) with either the same top SNP or one surrogate , but effect sizes at their variants were significantly gender-specific only at PDE8B , PDE10A and MAF/LOC440389 , considering a false discovery rate of 5% 24 ., In addition , effects at MAF/LOC440389 were significantly different also at the more stringent Bonferroni threshold of 1 . 9×10−3 ( =\u200a0 . 05/26 ) , and close to significance at PDE8B and PDE10A ( Table 3 ) ., At these latter loci , the TSH-elevating alleles showed a stronger impact on trait variability in males compared to females ( Figure 6 ) ., In addition , the gender specific meta-analysis for FT4 , revealed a novel female-specific locus on chromosome 18q22 , and a novel male-specific locus on chromosome 16q12 . 2 , that had not been detected in the main meta-analysis ( Table 3 , Figure 6 and Figure S2 ) ., The female-specific signal ( rs7240777 , P\u200a=\u200a3 . 49×10−8 ) maps in a “gene desert” region , with the nearest genes NETO1 ( neuropilin ( NRP ) and tolloid ( TLL ) -like 1 ) , located , about 550 kb upstream and FBXO15 ( F-box only protein 15 ) 500 kb downstream ( Figure 5D ) ., The male-specific association is located in intron 11 of the LPCAT2 ( lysophosphatidylcholine acyltransferase 2 ) gene , and near CAPNS2 ( calpain , small subunit 2 ) ( rs6499766 , P\u200a=\u200a4 . 63×10−8 ) , a gene which may play a role in spermatogenesis 25 ., The FT4-elevating alleles in the NETO1/FBXO15 and LPCAT2/CAPNS2 were fully gender-specific , i . e . there was no effect in males and in females , respectively ( P>0 . 01 ) ., Overall , the 20 TSH and the 6 FT4 associations account , respectively , for 5 . 64% and 2 . 30% of total trait variance ., To explore overlap between TSH- and FT4-associated loci and their involvement in the HPT-negative feedback loop , we assessed the associations of the top TSH-associated SNPs on FT4 levels , and vice versa ., For the SNPs in or near PDE8B , MAF/LOC440389 , VEGFA , IGFBP5 , NFIA , MIR1179 , MBIP and GLIS3 the TSH-elevating allele appeared to be associated with decreasing FT4 levels ( P<0 . 05 , Table S4 ) ., However , after application of Bonferroni correction ( threshold for FT4 association of TSH SNPs , P\u200a=\u200a2 . 5×10−3 ) , none of these reciprocal associations remained significant ., By contrast , a positive relationship was seen for one of the FT4 associated loci , since the variant at the LHX3 locus was significantly associated with higher levels of both FT4 and TSH ( P\u200a=\u200a5 . 25×10−3 , with Bonferroni threshold 0 . 05/6\u200a=\u200a0 . 008 ) ., As the presence of reciprocal associations between TSH and FT4 regulating SNPs would be expected from physiology , we tested the power of our study to detect such a relationship ., Power calculation for the top SNP at PDE8B , which has the largest effect on TSH levels , revealed that our meta-analysis only has 9% power to detect an association of FT4 at a Bonferroni P\u200a=\u200a2 . 5×10−3 ., We also carried out a bivariate analysis in the SardiNIA study using poly software to estimate specific contributions 26 ., This analysis showed that most of the observed negative feedback correlation is due to environmental factors ( environmental correlation\u200a=\u200a−0 . 130 , genetic correlation\u200a=\u200a−0 . 065 ) ., To assess possible clinical implications , we investigated whether the variants identified in individuals without overt thyroid pathologies ( i . e . , with TSH levels within the normal range and not taking thyroid medication ) were also associated in individuals with abnormal TSH values ( i . e . , outside the reference range ) , who were not included in the initial meta-analysis as potentially affected by thyroid pathology ., Towards this , we first assessed the global impact of TSH- and FT4-associated SNPs on the risk of increased or decreased TSH levels by comparing weighted genotype risk score ( GRS ) quartiles in the individuals with abnormal TSH values that were discarded for the GWAS analyses ., For the TSH-associated SNPs , the odds of increased TSH levels were 6 . 65 times greater in individuals with a GRS in the top quartile compared to individuals in the bottom quartile ( P\u200a=\u200a3 . 43×10−20 ) ( Table 4 , top panel , lower vs upper tail ) ., When we compared subjects with high TSH values with subjects within the normal TSH reference range , subjects with a GRS in the top quartile had odds of an elevated TSH 2 . 37 times greater than for subjects in the bottom quartile ( P\u200a=\u200a1 . 06×10−17 ) ( Table 4 ) ., With regard to low TSH values versus the normal range , the odds ratio was 0 . 26 ( P\u200a=\u200a5 . 43×10−13 ) ( Table 4 , top panel , lower vs normal tail ) ., By contrast , with the FT4-associated SNPs we found no significant associations for any of the tested comparisons ( data not shown ) ., We also assessed the 20 independent TSH SNPs individually in relation to the risk of abnormal TSH levels by case-control meta-analysis in subjects with high ( cases ) versus low ( controls ) TSH values ., This analysis showed that variants at PDE8B , CAPZB , FGF7 , PDE10A , NFIA and ITPK1 loci are significantly associated ( Bonferroni threshold P\u200a=\u200a2 . 5×10−3 ) with abnormal TSH levels ( Table 4 , bottom panel ) ., PDE8B , CAPZB and FGF7 were also strongly associated with the risk of decreased TSH levels in an analysis of individuals with low ( cases ) versus normal range TSH ( controls ) ., In addition , variants at VEGFA were also significantly associated in this comparison ., Finally , when individuals with high TSH values were analyzed versus controls , the NR3C2 locus appeared significantly associated in addition to PDE8B and CAPZB ., Normal thyroid function is particularly important during pregnancy and elevated TSH levels are implicated in a number of adverse outcomes for both mother and offspring ., We therefore assessed whether the TSH lead SNPs were also associated with elevated TSH during pregnancy , when increased TH production is necessary ., We tested 9 of the 20 lead TSH variants ( or their proxies , see Text S1 ) in a cohort of 974 healthy pregnant women at 28 weeks gestation 27 and found , as expected , that mean TSH levels were correlated with the number of TSH-elevating alleles ( P\u200a=\u200a3 . 0×10−12 , Table S5 ) ., Effect size estimates in pregnant women were not significantly different when compared to those of women in the main gender-specific meta-analysis ( heterogeneity P value>0 . 05 ) , suggesting that the effects of the TSH-elevating alleles are no greater during pregnancy ( data not shown ) ., However , there was evidence of association between the number of TSH-raising alleles and subclinical hypothyroidism in pregnancy , both in the whole sample ( OR per weighted allele: 1 . 18 95%CI: 1 . 01 , 1 . 37 , P\u200a=\u200a0 . 04 ) and in TPO antibody-negative women ( 1 . 29 95%CI: 1 . 08 , 1 . 55 , P\u200a=\u200a0 . 006 ) ( Table S6 ) ., We report 26 independent SNPs associated with thyroid function tests in euthyroid subjects , 21 of which represent novel signals ( 16 for TSH and 5 for FT4 ) ., Overall they explain 5 . 64% and 2 . 30% of the variation in TSH and FT4 levels , respectively ., We observed that carriers of multiple TSH-elevating alleles have increased risk of abnormal TSH levels , and also found association between the number of TSH-elevating alleles and subclinical hypothyroidism in pregnancy ., These results are potentially clinically relevant , because abnormal TSH values are the most sensitive diagnostic markers for both overt and subclinical thyroid disease 4 ., The variants identified in the current study , or those in LD with them , may thus contribute to the pathogenesis of thyroid disease ., Of note , we found eight loci significantly associated with abnormal TSH levels ( PDE8B , PDE10 , CAPZB , VEGFA , NR3C2 , FGF7 , NFIA and ITPK1 ) , of which two were specifically associated with either abnormally low ( VEGFA ) or elevated ( NR3C2 ) TSH values , suggesting differential mechanisms for the contribution of these variants to hyper- and hypothyroidism , respectively ., Interestingly , the mineralocorticoid receptor NR3C2 gene has recently been found to be up-regulated in adult-onset hypothyroidism 28 , and PDE8B and CAPZB have been suggestively associated with hypothyroidism by GWAS 29 ., Alternatively , it may be that carriers of these alleles are healthy individuals who may be misdiagnosed as having thyroid disease because their genetically determined TSH concentrations fall outside the population-based reference range ., More research is required to determine which of these interpretations is correct , and the relevance of these variants as markers for thyroid dysfunction or thyroid-related clinical endpoints ., The evidence for gender-specific differences at several TSH and FT4 regulatory loci is intriguing ., They included variants at PDE8B , PDE10A , and MAF/LOC440389 , which showed significantly stronger genetic effects with pituitary-thyroid function in males , and variants at NETO1/FBX015 and LPCAT2/CAPNS2 which seems to have an effect only in females and males , respectively ., Sex differences in the regulation of thyroid function have generally been linked to the influence of sex hormones and autoimmune thyroid disease , resulting in a higher prevalence of thyroid dysfunction in women , without clear understanding of underlying molecular mechanisms 21–23 ., Our study suggests that differential genes and mechanisms are potentially implicated in the regulation of thyroid function in men and women ., Given the impact of thyroid function on several disease outcomes as well as male and female fertility and reproduction , clarifying the underlying associations may provide additional insight for future interventions ., Although it is well known that TSH and FT4 levels are tightly regulated through a negative feedback loop involving the HPT axis , we detected significant overlap between TSH and FT4 signals only at the LHX3 locus , which was primarily associated in our study with FT4 ., The LHX3 allele is associated with an increase of both TSH and FT4 , which is consistent with the essential role of this transcription factor in pituitary development ., Inactivating mutations in LHX3 cause the combined pituitary hormone deficiency-3 syndrome CPHD3 ( MIM#221750 ) 30 , 31 , characterized by low TSH and FT4 levels ., The positive association of the LHX3 variant with both TSH and FT4 suggests an effect of this allele at the level of the HPT-axis , resulting in an increased exposure to thyroid hormone throughout life ., In contrast , although several of the TSH-elevating alleles appeared to be associated with decreasing FT4 levels , none of these reciprocal associations remained significant after Bonferroni correction ., Lack of loci associated in a reciprocal manner with both TSH and FT4 is somewhat puzzling , as their presence would be expected from physiology ., However , these findings are consistent with initial reports by Shields et al . 27 and more recent findings by Gudmundsson et al . 32 ., A power analysis showed that our study – in spite of being one of the larger conducted so far on these traits – is underpowered to detect an inverse relationship between TSH and FT4 variants , considering a Spearman rank correlation of −0 . 130 between these traits 12 ., As a consequence , contrasting studies on smaller sample sizes may also lack power and cannot be considered robust when testing this relationship 33 ., In addition , we estimated that most of the observed negative feedback correlation is due to environmental factors; so it is unlikely that negative feedback is controlled by a genetic locus with large effect ., This observation can rationalize the lack of reciprocal , significant associations detected for both TSH and FT4 in this and other studies , and further supports the crucial role of the HPT-axis in maintaining normal levels of thyroid hormone ., At present the relationship between the associated variants and specific mechanisms involved in regulating TSH and FT4 levels has not been established , but we have identified strong candidates at the majority of the loci by literature-mining approaches , as detailed below and in Table 5 ., Most of the 16 novel loci implicated in the regulation of TSH are highly represented in the thyroid with the exception of PRDM11 , expressed in brain , ABO , in blood , and MIR1179 ., PDE10A encodes a cAMP-stimulated phosphodiesterase , which was previously only suggestively associated with TSH levels and hypothyroidism 13 , 34 , although the tested variants were weakly correlated with our top signal ( r2\u200a=\u200a0 . 55 with rs2983521 and r2\u200a=\u200a0 . 15 with rs9347083 ) ., The presence of linkage at this gene in families reaching accepted clinical criteria of thyroid dysfunction reinforces the observation that variants in this gene may contribute to clinical thyroid disorders 34 ., PDE10A , together with PDE8B and CAPZB , emerged in our study as the strongest currently known genetic determinants of this trait ., Both PDE8B and PDE10A are implicated in cAMP degradation in response to TSH stimulation of thyrocytes ., In addition , the activity of both PDE10A and CAPZB appear modulated by cAMP 35 , 36 ., These three genes most likely act in a pathway that leads to cAMP-dependent thyroid hormone synthesis and release , thus highlighting a critical role of cAMP levels in thyroid function ., For the other TSH-associated loci ( VEGFA , IGFBP5 , SOX9 , NFIA , FGF7 , PRDM11 , MIR1179 , INSR , ABO , ITPK1 , NRG1 , MBIP , SASH1 and GLIS3 ) , hypotheses can be formulated based on the published literature ( see Table 5 ) , but further studies will be necessary to clarify the exact biological mechanisms and the specific genes involved at each locus ., The association of TSH levels with IGFBP5 , INSR and NR3C2 is , however , an indication of a specific role of the growth hormone/insulin-like growth factor ( GH/IGF ) pathway in thyroid function ., Remarkably , expression of IGFBP5 is tightly regulated by cAMP , again underlying the pivotal role of this second messenger in determining net TSH levels 37 ., For FT4 , the DIO1 , FOXE1 and LHX3 identified loci have strong biological support as potential effectors ., While both DIO1 and FOXE1 were previously associated with FT4 levels and hypothyroidism by candidate gene analysis and functional studies 17–19 , 38–41 , association at LHX3 is novel and is consistent with the essential role of this transcription factor in pituitary development ( see above ) 30 , 31 , 42 , 43 ., Consistent with the role of pituitary in growth , this locus has also recently been associated with height in Japanese 44 ., The associations of AADAT , NETO1/FXBO15 and LPCT2/CAPNS2 with FT4 levels are currently less clear ., It may be relevant that AADAT catalyzes the synthesis of kynurenic acid ( KYNA ) from kynurenine ( KYN ) , a pathway that has been associated with the induction in brain of proinflammatory cytokines that are known to activate the hypothalamo-pituitary-adrenal ( HPA ) axis , in turn affecting the HPT axis and thyroid function , including FT4 levels 45–49 ., Additional pathway analyses by MAGENTA 50 , GRAIL 51 , and IPA ( Ingenuity Systems , www . ingenuity . com ) to look for functional enrichment of the genes mapping to the regions associated with TSH , FT4 or both , yielded no novel interactions ., However , IPA highlighted an over-representation of genes implicated in developmental processes ( 11/26 , P\u200a=\u200a6 . 27×10−6–8 . 85×10−3 ) and cancer ( 16/26 loci , P\u200a=\u200a2 . 44×10−6–9 . 30×10−3 ) ., This is consistent with the notion that a normally developed thyroid gland is essential for both proper function and thyroid hormone synthesis , and that defects in any of the essential steps in thyroid development or thyroid hormone synthesis may result in morphologic abnormalities , impaired hormonogenesis and growth dysregulation ., It is also interesting to note that 11 of the 20 TSH signals and 3 of the 6 FT4 signals are connected in a single protein network , underlying the biological interrelationship between genes regulating these traits ( Figure S3 ) ., While our manuscript was in preparation , a GWAS of comparable sample size was published on levels of TSH in the general Icelandic population , which confirmed 15 of our reported loci ( E . Porcu et al . , 2011 , ESHG , abstract ) , and inferred a role for three TSH-lowering variants in thyroid cancer 32 ., Four additional TSH loci identified by Gudmundsson and colleagues were also associated in our sample-set of euthyroid individuals with p<0 . 05 and consistent direction of effects ( VAV3 , NKX2–3 , TPO and FOXA2 ) ., Finally , 2 loci ( SIVA1 , ELK3 ) could not be tested because the corresponding SNPs or any surrogate ( r2>0 . 5 ) were not available in our data set ( Table S7 ) ., Our study shows that most of the loci described in Icelanders are reproducible in other populations of European origin; differences in sample size , phenotype definition ( i . e . , selection of euthyroid subjects vs general population ) and in the genetic map used to detect associations most likely explain non-overlapping genome-wide significant signals ., Among them , the reported signals at SOX9 , ABO , SASH1 , GLIS3 and MIR1179 will need to be confirmed in other studies; but one of them - GLIS3- is a prime candidate , because it is involved in congenital hypothyroidism 52 ., Interestingly , despite the use of variants detected through whole-genome sequencing in Icelanders , the top signals at seven overlapping loci ( PDE8B , PDE10A , CAPZB , MAF/LOC440389 , VEGFA , NR3C2 , IGFBP5 ) were either coincident or in high LD ( r2>0 . 9 ) with those detected in our HapMap-based meta-analysis ., Thus , such variants are likely to be the causative ones ., In conclusion , our study reports the first GWAS meta-analysis ever carried out on FT4 levels , adds to the existing knowledge novel TSH- and FT4-associated loci and reveals genetic factors that differentially affect thyroid function in males and females ., Several detected loci have potential clinical relevance and have been previously implicated both in Mendelian endocrine disorders ( LHX3 MIMM#221750 , FOXE1 MIMM#241850 , PDE8B MIMM#614190 , NR3C2 MIMM#177735 , INSR MIMM#609968 , GLIS3 MIMM#610199 ) and thyroid cancer ( FOXE1 19 , VEGFA 53 , IGFBP5 54 , INSR 55 , NGR1 32 , MBIP 32 , FGF7 56 ) ., Furthermore , the TSH-associated variants were found to contribute to TSH levels outside the reference range ., Overall , our findings add to the developing landscape of the regulation of hypothalamic-pituitary-thyroid axis function and the consequences of genetic variation for hypo- or hyperthyroidism ., All human research was approved by the relevant institutional review boards , and conducted according to the Declaration of Helsinki ., Cohort description , genotyping and statistical methods for individual study cohorts are reported in Text S1 and Table S1 ., We carried out a meta-analysis including up to 26 , 523 , individuals from 18 cohorts for TSH and up to 17 , 520 individuals from 15 cohorts for FT4 ( see Table 1 ) ., FT4 measures were not available for all 21 , 955 individuals with TSH levels of the 15 participating cohorts ., We combined evidence of associations from single GWAS using an inverse variance meta-analysis , where weights are proportional to the squared standard error of the beta estimates , as implemented in METAL 57 ., Prior to GWAS , each study excluded individuals with known thyroid pathologies , taking thyroid medication , who underwent thyroid surgery , and with out-of-range TSH values ( <0 . 4 mIU/L and >4 mIU/L ) , and an inverse normal transformation was applied to each trait ( Table S1 ) ., Age , age-squared , and gender were fitted as covariates , as well as principal components axes or additional variables , as required ( Table S1 ) ., Family-based correction was applied if necessary ( see Table S1 ) ., Uniform quality control filters were applied before meta-analysis , including MAF <0 . 01 , call rate <0 . 9 , HWE P<1×10−6 for genotyped SNPs and low imputation quality ( defined as r2<0 . 3 or info <0 . 4 if MACH 58 or IMPUTE 59 , 60 were used , respectively ) for imputed SNPs ., Genomic control was applied to individual studies if lambda was >1 . 0 ., The overall meta-analysis showed no significant evidence for inflated statistics ( lambda for TSH , FT4 and were 1 . 05 and 1 . 03 respectively ) ., To evaluate for heterogeneity in effect sizes across populations , we used a chi-square test for heterogeneity , implemented in METAL 57 ., The same test was used to evalute heterogeneity related to iodine intake , by comparing effect sizes obtained in a meta-analysis of studies assessing individuals from South Europe ( InChianti , MICROS , Val Borbera , SardiNIA , totaling up to 7 , 488 subjects ) with those estimated in a meta-analysis of studies assessing individuals from North America ( BLSA , CHS , FHS , OOA , totaling up to 5 , 407 subjects ) ., Finally , the main meta-analysis was carried out independently by two analysts who obtained identical results ., To identify independent signals , each study performed GWA analyses for both TSH and FT4 by adding the lead SNPs found in the primary analysis ( 19 for TSH , and 4 for FT4 , see Table 2 ) as additional covariates to the basic model , and removing those from the test data set ., When lead SNPs were not available , the best proxies ( r2>0 . 8 ) were included ., We then performed a meta-analysis on the conditional GWAS results , using the same method and filters as described above ., We used the standard genome-wide significance cutoff ( P<5×10−8 ) to declare a significant secondary association ., To identify sex-specific effects , each study performed GWA analyses for each gender separately , using the same covariates and transformation as in the basic model ( with the exception of gender covariate ) ., We then performed a meta-analysis on association results using the same method and filters described for the primary analysis ., To evaluate sex-specific differences we tested heterogeneity between effect sizes as described above ., False-discovery rates ( FDRs ) on the 26 associated SNPs were calculated with Rs p . adjust ( ) procedure via the method of Benjamini and Hochberg 24 ., The variance explained by the strongest associated SNPs was calculated , for each trait and in each cohort , as the difference of R2 adjusted observed in the full and the basic models , where the full model contains all the independent SNPs in addition to the covariates ., The estimates from each cohort were combined using a weighted average , with weights proportional to the cohort sample size ., To evaluate the impact of the detected variants with clinically relevant TSH levels , we compared the allele frequencies observed in different categories of individuals in a case-control approach ., Specifically , we compared individuals in the upper and lower TSH tails ( individuals with TSH >4 mIU/L and TSH <0 . 4 mIU/L , respectively , whom were excluded for the GWAS analyses ) , as well as individuals in each tail with those in the normal TSH range ., In the first case , individuals in the lower tail were considered controls and those in the upper tail cases ., In the other two cases , we defined individuals in the normal range as controls and individuals on the two tails cases ., To avoid sources of bias , individuals taking thyroid medication and/or with thyroid surgery were excluded ., Only unrelated individuals were selected from the family-based cohort SardiNIA , while GEE correction was applied to the TwinsUK dataset ., Results from single cohorts were then meta-analyzed ., We first assessed the global impact of the 20 TSH- and 6 FT4-associated variants by defining a genotype-risk score ( GRS ) for each individual as the weighted sum of TSH- and FT4-elevating alleles , with weights proportional to the effect estimated in the meta-analysis ., For each comparison , we then calculated quartiles from the global distribution ( cases+controls ) of the genotype score and used quartile 1 as the baseline reference to compare the number of cases and controls in the other quartiles ., In addition , for TSH-associated variants we conducted single SNP comparisons ., GRS quartile and single SNP analyses were performed by each study separately ., Cohort specific results were then meta-analyzed for both the GRS score and single SNP results only if they had at least 50 cases and 50 controls ., Specifically , cohorts included were: CHS , Lifelines , PROSPER , RS , SardiNIA and TwinsUK ., Bivariate analysis was carried out with the software poly 26 in the SardiNIA cohort using the same individuals included in the GWAS and considering the same covariates and transformation for TSH and FT4 levels ., The URLs for data presented herein are as follows: METAL , http://www . sph . umich . edu/cgs/abecasis/metal MACH , http://www . sph . umich . edu/csg/abecasis/MACH/ IMPUTE , https://mathgen . stats . ox . ac . uk/impute/impute . html LocusZoom , http://csg . sph . umich . edu/locuszoom/ HapMap , http://www . hapmap . org Online Mendelian Inheritance in Man ( OMIM ) , http://www . omim . org/

Introduction, Results, Discussion, Methods

Thyroid hormone is essential for normal metabolism and development , and overt abnormalities in thyroid function lead to common endocrine disorders affecting approximately 10% of individuals over their life span ., In addition , even mild alterations in thyroid function are associated with weight changes , atrial fibrillation , osteoporosis , and psychiatric disorders ., To identify novel variants underlying thyroid function , we performed a large meta-analysis of genome-wide association studies for serum levels of the highly heritable thyroid function markers TSH and FT4 , in up to 26 , 420 and 17 , 520 euthyroid subjects , respectively ., Here we report 26 independent associations , including several novel loci for TSH ( PDE10A , VEGFA , IGFBP5 , NFIA , SOX9 , PRDM11 , FGF7 , INSR , ABO , MIR1179 , NRG1 , MBIP , ITPK1 , SASH1 , GLIS3 ) and FT4 ( LHX3 , FOXE1 , AADAT , NETO1/FBXO15 , LPCAT2/CAPNS2 ) ., Notably , only limited overlap was detected between TSH and FT4 associated signals , in spite of the feedback regulation of their circulating levels by the hypothalamic-pituitary-thyroid axis ., Five of the reported loci ( PDE8B , PDE10A , MAF/LOC440389 , NETO1/FBXO15 , and LPCAT2/CAPNS2 ) show strong gender-specific differences , which offer clues for the known sexual dimorphism in thyroid function and related pathologies ., Importantly , the TSH-associated loci contribute not only to variation within the normal range , but also to TSH values outside the reference range , suggesting that they may be involved in thyroid dysfunction ., Overall , our findings explain , respectively , 5 . 64% and 2 . 30% of total TSH and FT4 trait variance , and they improve the current knowledge of the regulation of hypothalamic-pituitary-thyroid axis function and the consequences of genetic variation for hypo- or hyperthyroidism .

Levels of thyroid hormones are tightly regulated by TSH produced in the pituitary , and even mild alterations in their concentrations are strong indicators of thyroid pathologies , which are very common worldwide ., To identify common genetic variants associated with the highly heritable markers of thyroid function , TSH and FT4 , we conducted a meta-analysis of genome-wide association studies in 26 , 420 and 17 , 520 individuals , respectively , of European ancestry with normal thyroid function ., Our analysis identified 26 independent genetic variants regulating these traits , several of which are new , and confirmed previously detected polymorphisms affecting TSH ( within the PDE8B gene and near CAPZB , MAF/LOC440389 , and NR3C2 ) and FT4 ( within DIO1 ) levels ., Gender-specific differences in the genetic effects of several variants for TSH and FT4 levels were identified at several loci , which offer clues to understand the known sexual dimorphism in thyroid function and pathology ., Of particular clinical interest , we show that TSH-associated loci contribute not only to normal variation , but also to TSH values outside reference range , suggesting that they may be involved in thyroid dysfunction ., Overall , our findings add to the developing landscape of the regulation of thyroid homeostasis and the consequences of genetic variation for thyroid related diseases .

genetics, biology, genetics and genomics

journal.pcbi.1005446

Navigating in foldonia: Using accelerated molecular dynamics to explore stability, unfolding and self-healing of the β-solenoid structure formed by a silk-like polypeptide

An increasing number of functional proteins are reported to have β solenoid structure , such as antifreeze protein 1–3 , curli 4 , and carbonic anhydrase enzyme 5 ., Formed by winding the peptide chain in a left-handed or right-handed fashion , a β solenoid structure usually has a repeat unit consisting of 2 , 3 , or 4 β strands that are connected by turns 6 ., Each of these strands form parallel β sheets with their neighboring strands ., As a result , a β solenoid structure usually has two parallel β sheets with the strands facing in different directions ., The interior of β solenoid structures contains apolar amino acid side chains that are tightly packed 6 , sometimes even interdigitated 7 , resulting in a predominately hydrophobic core structure ., Polypeptides in β solenoid structures have been used as building blocks of fibrils via controlled self-assembly in biomaterials applications ., They have been used to create viral capsids of DNA strands8 , supramolecular nanotapes 9 and pH-responsive gels 10 , which find biomedical application as matrices for human cells 11 , Beta solenoids ( for short: beta rolls ) can form fibrillar structures via two different mechanisms: end-to-end assembly where the terminals are covalently attached to each other , creating a very long β solenoid , or sheet-to-sheet assembly where the sheets associate physically resulting in a stack ., Peralta et al . 12 used the former type of self-assembly to generate micron length amyloid fibrils from spruce budworm antifreeze protein , a modified β solenoid protein ., Beun et al . used the latter type of self-assembly to produced fibrils made of stacks of pH-responsive silk-collagen-like triblocks 13 ., According to experimental reports , the dimensions of fibrils consisting of stacks of Bombyx Mori silk-inspired polypeptides with a sequence of ( GAGAGAGX ) n , where A and G stand for alanine and glycine , respectively , X is a polar residue and n is the number of repeating units 13 are consistent with β-solenoid structures stacked on top of each other 14 ., To obtain a better understanding of the detailed structure of these fibrils , we recently investigated the β-solenoid structure formed by ( GAGAGAGQ ) 10 via conventional molecular dynamics ( cMD ) simulations and found that the most probable structure formed by this sequence is a β-roll structure , with all the hydrophobic alanine side chains pointing inward , as shown in Fig 1 7 ., This structure was found to be more stable than a structure reported earlier by Schor et al . where all the hydrophobic alanine side chains pointed outwards 14 ., Now that the ‘ground state’ structure of the building block in the filament has been determined , we wish to learn more about how initially disordered polypeptides fold into the β-roll structure and assemble to form the filament ., Two different mechanisms have been proposed regarding the folding and docking of silk-inspired polypeptides ., The first mechanism , the “template folding” ( TF ) mechanism , was deduced from the temporal evolution of CD spectra when the polypeptide ( GAGAGAGE ) n formed fibrils 13; according to TF the peptide starts to fold into a β roll structure once it attaches to the growing end of a pre-existing filament ., The experimental growth rate of filaments is very low ( one molecule-per-second ) and the fibril formation is irreversible ., The second mechanism , “solution folding” ( SF ) , proposed by Schor et al . 15 , is based on atomistic simulations , also for ( GAGAGAGE ) ; it claims that a polypeptide first folds in solution into a β-roll structure before it docks to the growing end of a fibril via Glu-Glu side chain interactions ., Both mechanisms showed up in replica exchange Monte Carlo simulations carried out by Ni et al . 16 , of the folding pathways of silk-inspired polypeptides with sequence EIAIAIAR12 ( I is isoleucin and R is Arginine ) ., They found that at low temperature the polypeptide folds into a β-roll configuration before docking to another molecule , but at high temperature it folds after docking to another molecule ., It is important to point out here that the folding pathways proposed by Schor et al . 15 and by Ni et al 16 are based on the β-roll structure predicted by Schor et al . 14 , which has all the alanine side chains pointing outwards from the β-roll ., However , as mentioned earlier , our recent study 7 found that the β-roll structure formed by the silk-inspired polypeptide ( GAGAGAGQ ) 10 has a higher probability to have a hydrophobic core than a hydrophobic shell , i . e . all the alanine side chains should point inwards rather than outwards ., Therefore , the folding pathway of the more probable hydrophobic core structure still remains elusive ., We use an enhanced sampling method , accelerated molecular dynamics ( aMD ) , to study the unfolding behavior of a two-molecule β roll stack ., We choose this method because conventional molecular dynamics ( cMD ) simulations of the folding of a peptide into a β roll are too slow ., This is understandable: the experimental time scale , corresponding to the addition of a single molecule to the growing end of a fibrils is thought to take about a second 13 which is orders of magnitude away from time scales reached in MD simulations ., Accelerated molecular dynamics ( aMD ) has been used on many different oligomers and polypeptides , including alanine dipeptide 17 , bovine pancreatic trypsin inhibitor ( BPT1 ) 18 , G-protein coupled receptors 19 , and streptavidin-biotin complex 20 ., One of the advantages of aMD compared to other enhanced sampling methods is that it does not require pre-defined reaction coordinates ., This allows simulations to explore a broader range of hypothetical kinetic pathways than would otherwise be possible ., In aMD simulations of proteins , boost potentials are added to the potential energy; for proteins one can choose to boost either the total potential energy of the system , or the dihedral energy , or both ., The boost potential is only added to potentials below a pre-defined threshold energy; for energies above that level the original shape is retained ., aMD makes barrier crossing between low energy states easier and therefore provides access to regions of conformational space that are unreachable in cMD simulations ., The long term goal of our study is to elucidate the folding mechanism of silk-inspired polypeptide , as well to identify the dominant forces that maintain the β roll configuration ., As there is no unambiguous way to choose a representative starting configuration , we decided to focus on unfolding rather than folding , which should give us clues as to the likeliness of hypothetical folding pathways ., To this end , we simulate a stack of two molecules having sequence ( GAGAGAGQ ) 10 , each folded into a β roll with a hydrophobic core ( Fig 1 ) and in explicit solvent ., aMD is used to study the unfolding behavior of one of the β roll structures at systematically increased values for the threshold ., We first study the system with a stack of two β roll molecules , using the lowest threshold ., Then we simulate a system with a partially-fixed bottom template and a relatively high threshold; this enables us to observe the unfolding behavior of a single molecule on top of the template , and also gives us information about which forces are most important in maintaining the β roll structure ., Finally , we challenge the system with partially-fixed bottom template by a further increase of the threshold to gain a better picture of the intermediate structures that form along the entire unfolding pathway ., The major findings in this paper are the following ., First , we find that in the system without fixed atoms the molecules unfold in a step-wise fashion , and both molecules unfold completely by the end of the simulation ., The unfolding is initiated mainly from the C terminal ., Second , in the stack with a partially-fixed bottom template the top molecule has more difficulty unfolding than the molecules in the stack without any fixed atoms , indicating the importance of the template in stabilizing the folded structure of a β roll molecule in a stack ., Third , there is a hierarchy of hydrogen bond strengths ., Lateral hydrogen bonds formed between β strands within the β sheets in a β roll structure are stronger than the vertical hydrogen bonds within the β turns; H bonds are weaker when they are closer to the sides of the β turns ., The lateral hydrogen bonds within the bottom layer in the top β roll molecule are stronger than those in the top layer , which suggests that the bottom template strengthens the hydrogen bonds within the β sheet that it contacts ., As the aMD threshold on a system with partially-fixed bottom template is further increased , we find that the β roll on top tends to form hydrogen bonds with the bottom template to resist leaving it , revealing a “self-healing” property , which helps explain the toughness of the fibrils formed in the experiment ., We begin by describing our aMD simulation results on the stack of two β roll molecules with sequence , ( GAGAGAGQ ) 10 , without fixed atoms , and at the lowest threshold as characterized by n = 2 in Eq 7 ., ( Recall that n is an integer in Eq 7 that determines the magnitude of the threshold as a multiple of the acceleration factor . ), In Fig 2 , the order parameter of the top molecule , Ω , which measures the departure of the β roll from its ideal structure , is plotted against the simulation time , revealing the striking result that molecules in the β roll structure unfold in a step-wise fashion ., In cMD simulations , Ω for the top molecule remains roughly constant around 62±2 , indicating that the molecule stays in the β roll structure throughout the entire simulation ., In contrast , in aMD it decreases in a step-wise fashion and eventually reaches zero ., Roughly , five different plateaus can be discerned in the plot: 1–28 ns ( Ω = 52 ) , 32–48 ns ( Ω = 38 ) , 52–66 ns ( Ω = 30 ) , 70–74 ns ( Ω = 24 ) , and 78–90 ns ( Ω = 2 ) ., The representative structures corresponding to the different states shown in Fig 2 were calculated via clustering analysis ., The step-wise unfolding of the β roll structure agrees well with single-molecule force spectroscopy ( SMFS ) measurements by Sapra et al . on the unfolding pathways of β-barrel-forming membrane proteins , OmpG 21 ., By mechanically pulling on a single atom at one end of OmpG they found that each β hairpin of the OmpG β barrel unfolded either individually , or cooperatively with an adjacent β hairpin , causing the OmpG protein to unfold in a step-wise fashion ., The unfolding pathway of the top molecule in the stack as revealed by aMD simulation can be described as follows ., The molecule starts from a perfect β roll structure ( see Fig 2 ( A ) with a Ω of ~ 62 and then reaches its first plateau which lasts from 1 ns to around 28 ns ., The representative structure generated from clustering analysis is shown in Fig 2 ( B ) with one strand lifted off from the N terminal ., By 28 ns , another strand from the C terminal starts to come off the β roll structure as shown in Fig 2 ( C ) ., This is a transient state because the order parameter quickly moves to the second plateau that lasts from around 32 ns to 58 ns ., The representative structures that occur during this plateau are shown in Fig 2 ( D ) , 2 ( E ) and 2 ( F ) ., The majority of structures that occur during this plateau have one strand off of the C terminal and two strands off of the N terminal as in Fig 2 ( D ) and 2 ( F ) ., The second strand from the N terminal goes back to the β roll structure for a short period of time within this plateau , from around 41 ns to 44 ns , as shown in Fig 2 ( E ) ., After 52 ns , the order parameter reaches another plateau , with Ω ~ 30 , during which the third strand peels off the β roll from the C terminal as shown in Fig 2 ( G ) ., This strand goes back to the roll structure for a short period of time as shown in Fig 1 ( H ) , and then quickly comes off the β roll together with the fourth strand from the C terminal at around 70 ns , as shown in Fig 2 ( I ) ., After that , there are only five strands left in the β roll structure , which is not enough to maintain the configuration ., Starting at 73 ns , the molecule collapses quickly and becomes a random coil structure ., Some refolding events occur during the unfolding process , mainly when one loose strand goes back to its original neighbor ., For example , as shown in Fig 2 , the second strand comes off the C terminal at stage d , then goes back to the C terminal at stage e , and finally comes off the C terminal again at stage, f . Moreover , the third strand from the C terminal comes off at stage g , then goes back at stage h , and eventually comes off of the C terminal with the fourth strand at stage i ., The β roll molecules thus appear to unfold in an asymmetric fashion , namely mainly from the C terminal , as evidenced by the unfolding pathway just described ., This finding agrees well with a report by Alsteens et al . based on a steered molecular dynamics ( sMD ) simulation study in which a prototypic TpsA protein , FHA 22 unfolds mainly from the C terminal ., In addition , our simulation suggests that a β roll configuration needs to have a nucleus of a certain size to maintain its structure: the sequence ( GAGAGAGQ ) 10 needs to have at least half of its strands , 5 strands , in a β roll structure , in order to maintain the β roll configuration ., With less folded strands , it collapses and forms an amorphous configuration ., A second simulation of the two-molecule stack without fixed atoms having the same aMD boost parameter ( n = 2 ) was performed in order to check for reproducibility ., This second simulation was performed for 200 ns longer than the first simulation as the chain took longer to completely unfold ., Both simulations exhibit step-wise unfolding behaviors as can be seen in Fig 3 , which plots the order parameter , Ω , versus time for Simulations 1 and, 2 . The two simulations go through the same stages as the unwrapping occurs , each stage is outlined in blue in Fig, 3 . This similarity helps to support the reproducibility of our simulations of the unfolding process ., aMD simulations are then performed on systems containing a stack of ( GAGAGAGQ ) 10 β roll molecules with a partially-fixed bottom template at the threshold potential energy with n = 2 in Eq 7 ., By having a partially-fixed bottom template ( as defined above ) , molecules in the stack do not unfold simultaneously and their unfolded strands do not entangle with each other ., Thus we observe the unfolding behavior of just the molecule on top ., Fig 4 shows the final structures in the three different types of simulations that we ran ., The first type of simulation uses the threshold with n = 2 in Eq 7 and does not have any atoms fixed ., As a result , both molecules in the stack in simulation 1 unfold completely after 80 ns; the snapshot in Fig 4 ( A ) is taken at the point at which Ω = 0 in Fig, 3 . A similar completely unfolded state occurs after 140 ns for simulation 2 in Fig, 3 . The second type of simulation is performed on a system that contains a partially-fixed bottom template and uses an intermediate threshold with n = 2 in Eq 7 ., As shown in Fig 4 ( B ) , the final structure of the top molecule in the stack has three unfolded strands: one off at the N terminus , and two off at the C terminus ., The third type of simulation is again for a system with partially-fixed bottom template , and uses the highest threshold with n = 2 . 5 ., Now , five strands unfold from the top β roll molecule , as seen in Fig 4 ( C ) ., This molecule does not unfold completely even after 300 ns of aMD simulations with an increased threshold , in stark contrast to the behavior observed in the system without partially-fixed template , where the chain collapses quickly when there are only 5 strands left in the β roll ., These observations once again underline the importance of having a partially-fixed bottom template to stabilize the top β roll structure ., The unfolding process for the top molecule in the two molecule stack with partially-fixed bottom template and boosts n = 2 . 0 or n = 2 . 5 resembles that of the molecule in the stack with no atoms fixed ., Fig 5 plots the order parameters of the top molecule in the simulations with boost n = 2 . 5 and n = 2 against simulation time ., The top molecule in the simulation with boost n = 2 . 5 ( Fig 5A ) shows that it goes through 5 stages to reach the final configuration , which has 3 strands coming off the N terminal and 2 strands coming off the C terminal ., This stepwise unfolding is similar to the unfolding behavior of the top molecule in the stack without fixed atoms and n = 2 shown in Fig, 2 . The sequence of steps is: one strand comes off the N terminal , one strand comes off the C terminal , the second strand comes off the C terminal , the second strand comes off the N terminal , and finally the third strand comes off the N terminal ., The only difference between the unfolding process for n = 2 . 5 with fixed atoms and n = 2 without fixed atoms is that the strands from N terminal come off earlier when n = 2 . 5 than when n = 2 . The top molecule in the simulation with n = 2 and partially-fixed bottom template also unfolds in a step-wise fashion as shown in Fig 5B ., Therefore , the unfolding behavior seems to be independent of the value of the boost potential ., To identify the dominant forces in the β roll structure , we plot , for the selected hydrogen bonding atom pairs indicated in Fig 6 , hydrogen bond potentials of mean force ( PMF ) versus distance in Figs 7 and, 8 . These are based on the trajectories generated by the aMD simulations with boost potential n = 2 and partially-fixed bottom template ., Note that here we present the unweighted PMF versus the distance of hydrogen bonded pairs of only one of the two simulations-performed using boost potential n = 2 and partially fixed bottom template ( recall that we performed two simulations for each set of parameters as shown in Table 1 in the method section ) ., The unweighted PMF versus the distance of hydrogen bonded pairs of the other simulation is provided in the supporting information in S2 Fig and S3 Fig . Hydrogen bonds in a β roll configuration are categorized as being either lateral or vertical ( see Fig 6 ) ., Lateral hydrogen bonds refer to the ones formed between the neighboring β strands in a single β sheet , or between neighboring β turns , and vertical hydrogen bonds refer to the ones between atoms in the top and bottom of a single β turn , or between atoms in the β turns of top and bottom molecules ., All the unweighted potential of mean force profiles in Fig 7 ( lateral H bonds ) and 8 ( vertical H bonds ) are calculated with three different bin sizes , resulting in three curves for each plot ., These curves match well with each other , indicating that we have enough samples for the calculation ., The unweighted PMFs associated with the hydrogen bonded atom pairs in Figs 7 and 8 have global minima at ~ 1 . 9 angstroms , indicating that these atoms prefer to stay within the hydrogen bonding distance ., This reveals that the original β roll structure , in which all these atoms can form hydrogen bonds , is more stable than the unfolded structure , where only a few H bonds are possible ., The hydrogen bonding strengths are taken to be the values of the PMF at the first peak in the PMF versus distance curves ., The average hydrogen bond strengths in the two simulations with boost potential n = 2 and partially-fixed bottom template are given in Fig, 9 . The figure shows the hydrogen bonding strengths for the lateral hydrogen bonds between β strands ( green ) and between β turns ( pink ) , and the vertical hydrogen bonds within β turns ( blue ) and between the turns in the top and bottom molecules ( yellow ) ., The first 3 columns represent the strengths of the lateral hydrogen bonds between the neighboring β strands in the top layer of the β roll structure as shown in Fig 6 ., The strengths of the lateral hydrogen bonds along the β strands in both the top and bottom layers of the β roll molecule are weaker when the hydrogen bonds are closer to the β turns: e . g . the hydrogen bonds between residues 51–68 and between residues 53–70 have a lower strength than the hydrogen bonds between residues 53–68 ., The strength of the lateral hydrogen bond formed between residues 56–73 , indicated by the height of the 4th bar , is weaker than the lateral hydrogen bonds within the bottom layer but stronger than the lateral hydrogen bonds within the top layer of the top β roll molecule ., Something similar is observed for the hydrogen bonds between neighboring β strands in the bottom layer of the β roll structure; see the 5th , 6th and 7th columns in Fig, 9 . The lateral hydrogen bonds in the lower layer of the β roll structure are clearly stronger than those in the upper layer of the β roll structure ., As seen in Fig 9 , the 5th , 6th and 7th columns representing the hydrogen bonding strength in the bottom layer of the β roll , are higher than the first three columns representing the hydrogen bonding strength in the top layer of the β roll ., This is likely a consequence of the bottom layer in the top molecule being in direct contact with the bottom template ., The effect of stacking on the stability of the roll was investigated in our previous study 7; there we found that stacking helps stabilize the β roll structure by increasing the number of intra-molecular hydrogen bonds in each β roll molecule ., Here we see that in terms of bond energies that conclusion is confirmed ., The vertical hydrogen bonds are usually weaker than the lateral hydrogen bonds ., This can be observed by comparing the heights in Fig 9 of the first 7 columns , which represent the strengths of the lateral hydrogen bonds , with the heights of the last 3 columns , which represent the strengths of the vertical hydrogen bonds ., The heights of the two blue columns in Fig 9 , which represent hydrogen bonds within the β turns , are smaller than those of the first 7 columns , representing the lateral hydrogen bonds ., This signifies that lateral hydrogen bonds play a more important roll than vertical hydrogen bonds in keeping the molecule in a β roll configuration ., The height of the yellow bar , which represents the average strength of the hydrogen bond between the top and bottom molecules ( there is one such bond per strand ) , is slightly lower than that of the first three columns , indicating that the hydrogen bonds between the two molecules are almost as strong as the lateral hydrogen bonds between the β strands in the upper layer of the top molecule ., This suggests that the hydrogen bonds between the two molecules also play a significant role in maintaining the β roll structure of the top molecule ., In the simulation with the highest threshold energy , where n = 2 . 5 in Eq 7 , and a partially-fixed bottom template , a new intermediate structure shows up ., It contains a β hairpin structure and an anti-parallel β sheet formed by strands from the top and bottom molecules ., Fig 10 ( A ) shows the unweighted PMF versus the distance between the hydrogen on GLN ( residue 24 ) and the oxygen on ALA ( residue 26 ) ., Two minima are identified in the plot , a local minimum at short distance, ( a ) and a global minimum further out, ( b ) ., The intermediate structure associated with the local minimum is shown in Fig 10 ( B ) and its side view is shown in Fig 10 ( C ) ., The structure associated with the global minimum is a distorted β roll structure as shown in Fig 10 ( D ) ., The potential well of the intermediate structure is located at ~ 2 angstroms , indicating that a hydrogen bond forms between the hydrogen on GLN ( residue 24 ) and the oxygen on ALA ( residue 26 ) , i . e . , residues 24 , 25 and 26 have formed a three-amino-acid turn ., Strands 3 and 4 form an antiparallel β sheet structure ., Taken together , the turn and the antiparallel structures are essentially a typical β hairpin structure ., Another anti-parallel β sheet is formed between strand 2 in the top molecule and the silver strand in the bottom molecule ., A side view of this structure is seen in Fig 10 ( C ) which shows how strands 2 and 3 traverse the interface between the two molecules ., The reason this structure forms is that the first strand from the N terminal in the bottom template is not fixed and breaks loose ., This provides enough room for the second and third strands of the top molecule to reach down one layer , forming β sheets with the strand in the bottom template ., The anti-parallel β sheet formed by strands from the top and bottom molecules in the intermediate structure is of particular interest to us as this configuration could potentially inhibit the unfolding process ., In a long fibril with many molecules , the molecules in a β roll structure will probably not always stack as perfectly as in our starting configuration , meaning that strands from some molecules could potentially form β sheets with their folded neighbors , thus preventing the unfolding process ., We call this a self-healing ability because it seems to hinder the β roll molecule from completely unwrapping; it might be one reason why the fibrils observed in the experiments are very strong ., Considering the results obtained here with respect to unfolding , we tentatively propose a hypothetical folding pathway; we emphasize that this is highly speculative and should not be considered as a conclusion supported by the simulation data obtained here , but rather as a direction for further studies ., The folding process most likely starts with docking of a disordered silk-like ( GAGAGAGX ) n domain on a pre-folded molecule acting as template ., This consistent with the observation that the template provides stability to the folded roll , and with the experimental fact that secondary structure develops in parallel with fibril growth ., The disordered domain has to remain long enough in the docked state to allow for nucleation of a minimal folded part , e . g . , a 5-stranded β solenoid ., This step has a very low probability and is therefore likely to be rate-determining , accounting for the very low growth rates observed experimentally11 ., Moreover , it also would explain why silk-like domains of higher number of repeating units , which are likely to have longer residence times and a higher nucleation probability , tend to give faster growth 23 ., Once nucleation has occurred , the remainder of the silk-like domain can fold to form the complete β solenoid ., We used accelerated molecular dynamics ( aMD ) simulations to investigate the unfolding of a stack of two β roll molecules , ( GAGAGAGQ ) 10 ., Although much is known of about the structure of the β solenoid , very little is known about the partially folded conformation of the silk-like polypeptide or the details of the folding/unfolding process ., Unfolding simulations can help us understand biological processes and , when well sampled , can provide us with partially-folded structures ., aMD is able to maintain the original shape of the energy landscape and let the molecule sample conformational space fairly naturally ., Our goal was to identify the dominant forces that keep the silk-inspired polypeptide in a β roll configuration , to investigate the unfolding mechanism of silk-inspired polypeptides ., The β roll structure that we use in this study was obtained from our previous investigation of the stable configuration of the β roll using the same sequence ( GAGAGAGA ) 10 ., Unlike the structure proposed by Schor et al . 14 where all the alanine residues pointed out , forming a hydrophobic shell , the structure used in this study possesses a hydrophobic core which we have shown to be more stable than that with a hydrophobic shell 7 ., The size of the boost potential was chosen carefully ., It should not be so small that unfolding is unlikely to occur , as this would essentially be the same as a conventional MD simulation and it should not be too strong , because this might induce an unrealistic unfolding process ., The number of boost potentials added to the original potential , n , was therefore chosen to reveal both the unfolding and the any spontaneous refolding of the polypeptide that occurred during the simulations ., The unfolding process of the molecule on top without any fixed atoms showed rejoining of the strands as well as unfolding ., Moreover , we saw that the unfolding process can be reproduced by additional simulations with the same parameters and even by simulations with different boost potentials ., To justify the convergence of our simulations , the relaxation time of the peptide backbone vectors is estimated from the time autocorrelation function profile ., S1 Fig show a plot of the time correlation function of the out-of-plane vectors ( the vector that is perpendicular to the plane formed by 3 consecutive carbon atoms ) of the polypeptide versus the simulation time ., The time at which this reaches zero gives a measure of the relaxation time of the peptide 7 ., The relaxation time is less than 10 ns for simulations without fixed atoms , indicating that our 100ns simulation is long enough to reach equilibrium ., The 300 ns simulations with partially-fixed bottom template have relaxation times less than 100 ns , which indicates that the molecules in these systems have reached equilibrium ., By comparing the unfolding order parameter , Ω , versus simulation time between cMD and aMD , we found that a molecule in a stack of two β roll molecules unfolds in a step-wise fashion , i . e . one β strand in the β roll molecule at a time , which agrees well with the experimental study on transmembrane β-barrel protein OmpG by Sapra et al . 21 ., We also found that it unfolds mainly from the C terminal , which matches with the simulation study on a prototypic TpsA protein , FHA by Alsteens et ., al 22 ., Through observing the unfolding and spontaneous refolding of single strand in the β roll structure , we get a better idea of the possible intermediates that might occur during the folding process ., Schor et al . 15 hypothesize that the molecule folds into a β roll structure with a hydrophobic shell by itself , then docks onto another preformed β roll molecule , a “roll n’ dock” process ., The bottom template is found to play an important role in stabilizing the β roll structure of the molecule on top ., This was concluded by comparing the final structure in three sets of simulations with systematically increased threshold energies ., At the lowest threshold energy , both molecules unfold and have a random coil structure by the end of the simulation for systems without any fixed atoms ., When the bottom template is partially fixed , the top molecule is unable to unfold completely , even by the end of 300 ns simulations , indicating the significance of the bottom template in stabilizing the molecule on top of it ., We further elucidate how the bottom template stabilizes the top β role molecule by quantifying the strengths of the various intra and inter molecular hydrogen bonds ., The lateral hydrogen bonds in the lower layer of the top molecule are stronger than those in its upper layer , indicating that the bottom template strengthens the hydrogen bonds in the lower layer of the top molecule ., We further confirm the stabilizing effect of the bottom template reported in our previous investigation , in which we concluded that the bottom template induces more intramolecular hydrogen bonds in the top molecule when it docks on to the bottom template7 ., We also found that the lateral hydrogen bonds between the β strands in a β roll configuration become weaker as they get close to the β turns ., This is due to the fact that the β turn structure is more flexible than the β sheet structure in the β roll molecule ., Vertical hydrogen bonds within the β roll structure are considerably weaker than lateral hydrogen bonds , signifying the importance of lateral hydrogen

Introduction, Results, Discussion, Methods

The β roll molecules with sequence ( GAGAGAGQ ) 10 stack via hydrogen bonding to form fibrils which have been themselves been used to make viral capsids of DNA strands , supramolecular nanotapes and pH-responsive gels ., Accelerated molecular dynamics ( aMD ) simulations are used to investigate the unfolding of a stack of two β roll molecules , ( GAGAGAGQ ) 10 , to shed light on the folding mechanism by which silk-inspired polypeptides form fibrils and to identify the dominant forces that keep the silk-inspired polypeptide in a β roll configuration ., Our study shows that a molecule in a stack of two β roll molecules unfolds in a step-wise fashion mainly from the C terminal ., The bottom template is found to play an important role in stabilizing the β roll structure of the molecule on top by strengthening the hydrogen bonds in the layer that it contacts ., Vertical hydrogen bonds within the β roll structure are considerably weaker than lateral hydrogen bonds , signifying the importance of lateral hydrogen bonds in stabilizing the β roll structure ., Finally , an intermediate structure was found containing a β hairpin and an anti-parallel β sheet consisting of strands from the top and bottom molecules , revealing the self-healing ability of the β roll stack .

Silk-inspired repeated sequences , variants of the sequence from Bombyx Mori silk , have been used to make supramolecular nanotapes , pH-responsive gels , and most importantly self-assembled coat for artificial viruses ., Silk-inspired repeated sequences have shown great potential as promising delivery vehicles in targeted delivery of nucleic acids for gene therapy ., However , the mechanisms regarding the folding and docking of silk-inspired polypeptides remain elusive ., Elucidation of the folding and docking mechanism might help us create sequences with desired self-assembly properties for many biomedical applications ., An enhance sampling method , accelerated molecular dynamics ( aMD ) simulation , is used in this study to investigate the unfolding of a stack of two β roll molecules to shed light on the folding mechanism by which silk-inspired polypeptides form fibrils and to identify the dominant forces that keep the silk-inspired polypeptide in a β roll configuration ., A template-unfolding mechanism and a neat step-wise unfolding fashion are found which agree well with experimental observations .

chemical bonding, classical mechanics, molecular dynamics, oxygen, potential energy, thermodynamics, hydrogen bonding, polypeptides, physical chemistry, chemistry, free energy, physics, biochemistry, hydrogen, molecular structure, peptides, biology and life sciences, physical sciences, computational chemistry, chemical physics, chemical elements

journal.pgen.1000147

Linkage Disequilibrium-Based Quality Control for Large-Scale Genetic Studies

Data quality has been implicated as a source of bias and loss of power in both linkage analyses and population-based association studies 1 , 2 , 3 , 4 ., Quality control ( QC ) is thus a critical step in large-scale studies of genetic variation ., While , on average , high-throughput single nucleotide polymorphism ( SNP ) genotyping assays are now very accurate , the errors that remain tend to cluster into a small percentage of “problem” SNPs that exhibit unusually high error rates ., Because most large-scale studies of genetic variation are searching for phenomena that are rare ( e . g . SNPs associated with a phenotype ) , even this small percentage of problem SNPs can cause important practical problems ., To alleviate these problems attempts are made to identify , and usually remove , problem SNPs before proceeding to a full analysis ., However , while for pedigree studies considerable attention has been given to development of methods for detecting genotyping errors 5 , 6 , 1 , 7 , in population genetic studies rather simple QC filters are typically employed ( e . g . removing SNPs with a high proportion of missing data , or showing very extreme deviations from Hardy–Weinberg equilibrium 8; HWE ) ., Here we describe and illustrate how patterns of linkage disequilibrium ( LD ) can be used to improve QC in large-scale population-based studies ., Intuitively , the method exploits the fact that LD among nearby markers provides built-in redundancy , allowing genotypes at a SNP to be called not only from the experimental data at that SNP , but also using data at nearby , correlated , SNPs ., The result is a QC procedure that can not only identify individual SNPs that potentially have high genotyping error rates , but also automatically correct some incorrect genotypes ., We developed an LD-based QC procedure by modifying an existing statistical model for LD among multiple tightly-linked SNP markers 9 to allow for genotyping error ., In brief , this existing statistical model captures patterns of LD in a population by assuming that each sampled haplotype resembles a mosaic of a ( typically small ) number of “base” haplotypes ., The use of a relatively small number of base haplotypes allows the model to capture the limited haplotype diversity over small regions that is typical of many natural populations , while the mosaic assumption allows the model to capture breakdown in LD with genetic distance ., The original version of this model assumed observed genotypes to be error-free ., Here , to allow for , detect , and correct genotyping errors we modify this model by introducing a “genotyping error rate” parameter at each SNP , and develop statistical methods to estimate these SNP-specific error rates from unphased genotype data ( see Methods ) ., In addition to providing an estimated error rate for each SNP , the approach provides for each genotype a probability that it is incorrect , and a probability distribution for the actual correct genotype ., We assessed the utility of LD-based estimates of genotyping error in two ways ., First , we applied the method to ( unfiltered ) genotype data on parent-offspring trios from the International HapMap Project 10 ( see Methods ) , and compared the LD-based error rate estimates with the number of Mendelian Inconsistencies ( MIs ) at each SNP ., Second , we applied the method to genotypes obtained by using the Affymetrix Mapping 500K chip to genotype the HapMap samples , and compared the LD-based error rates with the number of discrepancies between the Affymetrix genotype calls and the calls in the non-redundant filtered HapMap database ( see Methods ) ., In these two comparisons , the number of MIs , and the number of discrepancies , provide some independent indication of the genotyping error rate at each SNP , against which our LD-based error rate estimates can be compared ., Overall the LD-based genotyping error rate estimates were similar in magnitude to estimates based on MIs and discrepancies ., For the unfiltered HapMap data , the LD-based error rate estimate was 0 . 28% for CEU and 0 . 36% for YRI , slightly higher than the total rate of MI-causing genotyping errors ( 0 . 17% for CEU and 0 . 23% for YRI , assuming each trio containing an MI contains a single genotyping error ) , possibly reflecting the fact that not all genotyping errors will cause an MI 11 ., For the Affymetrix data , the LD-based error rate estimates were 0 . 24% for CEU , 0 . 22% for JPT+CHB , and 0 . 44% for YRI , similar to the average discrepancy rates ( 0 . 29% in CEU and JPT+CHB; 0 . 38% in YRI ) ., ( Note that , since up to half of the discrepancies are likely to be due to errors in the HapMap , rather than Affymetrix , data , the LD-based error rate estimates suggest slightly higher error rates than do the discrepancy data . ), More importantly , SNP-specific LD-based error rate estimates were positively correlated with number of MIs or discrepancies ( Figure 1 ) ., In particular , SNPs with a large number of MIs/discrepancies also tended to have high LD-based error rate estimates ., For example , in the Affymetrix data , among SNPs with at least a 10% discrepancy rate , 60% had an elevated LD-based error rate ( >1% ) , whereas among SNPs with 0 discrepancies , only 5 . 7% had a similarly elevated LD-based error rate ., Similarly , in the HapMap data , among SNPs with at least 9 MIs , 91% had an LD error rate >1% , whereas among SNPs with 0 MIs only 2% had LD error rate estimates exceeding this level ., These results demonstrate the potential for patterns of LD to help identify “problem” SNPs with very high error rates ., We attempted to more fully quantify this potential , but these attempts were hindered by the fact that neither MIs nor discrepancies provide a completely satisfactory “gold standard” against which to compare ., For example , MIs are not effective at identifying all genotyping errors , since many errors ( e . g . miscalling homozygous parents as heterozygotes ) do not lead to MIs ., And while a discrepancy between two genotype calls implies an error in at least one of the calls , it does not indicate which of the calls is incorrect ., We therefore undertook a more qualitative assessment , by visually examining higher-level data from the Affymetrix genotyping assay–specifically , plots of normalized intensities for each allele–for SNPs where our LD-based estimates disagreed most strongly with the numbers of discrepancies ., ( These intensity data are not generally available for the HapMap data . ), Among SNPs with large numbers of discrepancies , but low LD error rates , many of the Affymetrix intensity plots show three well-separated clusters with genotypes apparently correctly-called ( Figure 2a ) ., For example , for 50 JPT+CHB SNPs with 9 discrepancies but with LD error rates <1% , we judged , subjectively , that at least 23 showed relatively clean intensity plots , with little or no evidence of typing error ., A natural explanation for this is that the discrepancies are due to errors in the HapMap database , rather than in the Affymetrix calls from which the LD-based error rates are computed ., In contrast , among SNPs with 0 discrepancies but high LD-based error rates , many of the intensity plots failed to show well-separated clusters in the usual places , and several were suggestive of copy number variation ( Figure 2b ) ., Thus , our LD-based method appears , in some of these cases , to be picking up on meaningful problems with the genotype calls , despite the concordance between the Affymetrix calls and those from HapMap , obtained independently from different genotyping centers ., For other SNPs , whose plots did exhibit three well-separated clusters in the expected places , it may be that the high LD-based error rate estimates are simply inaccurate ., However , it is also possible that some of these SNPs are mis-mapped , since this could produce a high estimated LD-error rate ., During PHASE II of the HapMap , 21 , 177 SNPs from PHASE I were identified as having an ambiguous position , or other signatures that suggest unreliability 12 , and although these SNPs were not included in our comparison it is possible that some similar inaccuracies remain ., We list approximately 600 SNPs with high LD error rate estimates but 0 discrepancies in Text S1 ., The above results illustrate the difficulty of assessing the accuracy of our LD-based error rate estimates ., Even though the LD-based estimates sometimes disagree greatly with the duplicate genotyping results , it is unclear in what proportion of cases the LD-based estimates are inaccurate ., The results also highlight the fact that the LD-based estimates can complement , rather than duplicate , other approaches to QC such as multiple rounds of genotyping ., To further examine the extent to which the LD-based approach complements existing QC procedures , we compared LD-based error rate estimates with the results of testing SNPs for deviations from HWE , which is probably the most common current approach to QC in population studies ., We found LD-based error rates and HWE test statistics to be relatively uncorrelated ( Figure 3 ) , although the subset of SNPs with the highest LD-based error rates overlaps moderately with the subset showing the most significant deviations from HWE: among the top 1% of SNPs in each category in the filtered ( respectively unfiltered ) data , 19% ( respectively 42% ) were shared ., The LD-based method has several advantages over HWE for performing QC: in addition to providing quantitative estimates of the error rate at each SNP , the LD-based method also estimates an error probability for each individual genotype , and can attempt to correct genotypes that it deems likely to be incorrect ., To quantify its success at this we examined whether using our method to correct genotypes reduced the number of MIs/discrepancies , and indeed it did ., Correcting HapMap CEU genotype calls reduced the number of MIs by 33% when parents and children were analysed together , ignoring the known relationships , and by 21% when parents and children were analysed separately ., Correcting the Affymetrix 500K calls reduced discrepancies with HapMap by 13% for CEU samples , 8% for YRI and 11% for JPT+CHB ., Furthermore , although the probabilities assigned to corrected genotypes were not completely well-calibrated , the reduction of discrepancies was appreciably greater for those corrections in which our method was most confident ( Figure 4 ) ., One consequence of this is that one could further improve genotyping accuracy , at the expense of a slightly lower call rate , by treating genotype calls for which the assigned probability of error exceeds some threshold as “missing” ., Alternatively , and perhaps preferably , one could take account of these probabilities in downstream analyses , using Bayesian statistical methods 14 to downweight the influence of genotypes in which one was less confident ., The fact that using LD to correct genotypes reduces both the number of MIs and the number of discrepancies suggests that it also reduces the overall genotyping error rate , and we attempted to quantify this reduction ., However , this was again complicated by the fact that neither MIs nor discrepancies provide perfect gold standards against which to compare ., In the case of discrepancies , a naive analysis , assuming that the error rates in the two data sets are equal ( so half the discrepancies are due to errors in the Affymetrix data ) , and that each genotype error creates a discrepancy , would suggest that our method reduced genotyping error rates by 16-26% ., However , we found several examples of SNPs where correcting genotypes with our method increased the number of discrepancies , but where visual examination of intensity plots suggested that the corrected genotype calls were likely correct , or at least more sensible than the original genotype calls ., For example , consider the three SNPs with 0 discrepancies but high estimated LD error rate in Figure 2b ., In all three cases our method makes many genotype corrections , and , strikingly , the genotypes it chooses to correct tend to cluster together in the intensity plots ., Since our method does not take into account the intensity data in selecting which genotypes to correct this strongly suggests that the LD-based method is picking up on genuine anomalies in the underlying genotype calls , and not simply making mistakes in its corrections ., However , despite this , in all three SNPs every corrected genotype increases the number of discrepancies in the data ., Due to this type of effect the reduction in the number of discrepancies achieved by our method may underestimate the actual reduction in errors achieved , perhaps appreciably ., In the case of interpreting the reduction in MIs , there are different problems ., In particular , there are many ways of reducing MIs that would actually increase the number of genotyping errors ., For example , changing every parent at every SNP to be a heterozygote would completely remove all MIs , while presumably increasing the total number of genotype errors ., However , if genotype changes of this type were being made randomly , independent of actual errors , then we would not expect to see an excess of genotype corrections being made in trio-SNP combinations with MIs ., In fact , 37% of corrected genotypes occurred in a trio-SNP combination with an MI , whereas only 0 . 7% of trio-SNP combinations actually exhibit an MI ., This provides strong indirect evidence that these corrections are actually correcting the genotyping error that lead to the MI , rather than simply randomly changing parents to be heterozygotes ., Also , MIs in trio data can be caused by deletions , rather than simple genotyping error 15 , 16 ., Since our method does not explicitly model deletions it is perhaps unsurprising that it tended to correct genotypes less often in trios whose MIs were consistent with a deletion than in other trios: among trios with deletion-consistent MIs , 33% had at least one genotype corrected , compared with 50% among trios with other MIs ., For a practical application of our method , we applied it to the Chinese and Japanese analysis panels ( CHB+JPT ) in the filtered HapMap database ., Because these panels do not include data on trios , the HapMap QC filter based on MIs could not be applied to these individuals , and so the filtered CHB+JPT data may be expected to contain more genotyping errors than the other panels ., Applying the LD-based QC method to all 2 . 4 million polymorphic loci from the autosomal chromosomes of the 90 CHB+JPT individuals , we estimate an LD-based error rate of 0 . 13% and identify approximately 1 , 500 SNPs with an LD-based error rate greater than 15% ( 4 , 300 exceed 10% ) ., Additionally , we provide over 200 , 000 individual genotypes that our method identifies as likely to be incorrect ( specifically , for which the conditional probability of the observed genotype is less than that for a different genotype ) ., We provide a complete list of SNPs and genotypes at lower error rates and probability thresholds in Text S1 ., We have described and illustrated a novel method for using patterns of LD to improve QC in large-scale population studies ., The method complements existing approaches to QC , and can find genotyping problems that other methods , including duplicate genotyping , may miss ., Performance of the method will depend on several factors , including SNP allele frequency , and the amount of LD in the data , which typically increases with SNP density ., The results we present here are based on relatively dense data ( >500k markers genome-wide ) on ( mostly ) common variants ., However , we have also found the method capable of identifying SNPs with high error rates in substantially less dense data ( e . g . the Illumina Human-1 112k bead chip ) ., For whole-genome resequencing data we would expect performance to be even better for the common variants , due to the increased information , although the potential for LD to detect genotyping errors in very rare variants seems likely to be limited ., While , inevitably , not all genotyping errors can be detected from patterns of LD , the use of LD information is essentially free , is practical for large data sets ( in our implementation , application to 1 , 000 individuals typed at 500 , 000 SNPs would require about 270 hours on a single 3 GHz Intel Xeon processor ) , and has the advantage over tests for HWE that it is able to detect , and in many cases correct , individual genotyping errors ., Our method has been implemented in the software package fastPHASE ., Patterns of LD have previously been recognized as an effective way to estimate missing genotypes 17 , 9 , 14 , 18 , and attempting to use LD to detect genotyping errors is , perhaps , a natural next step ., However , there are many possible approaches to implementing this idea in practice ( e . g . a recent paper 19 takes an approach rather different to the one we took here , based on applying the four-gamete test to pairs of SNPs in the data set ) ., Our approach , which is based on introducing error-rate parameters into a statistical model for multi-locus genotype data , has several desirable features , including providing quantitative estimates of error rates , quantitative assessments of the probability that each individual genotype is wrong , and quantitative assessments of the probability of alternative genotypes to those that are called ., Also , our method is “self-training” , in that it does not require a “gold-standard” set of data to establish normal patterns of LD , but rather establishes normal patterns of LD from the ( imperfect and unphased ) genotype data available ., The model for LD that we used here is particularly well-suited to this purpose , because it can be fit efficiently to unphased genotype data , even when allowing for genotyping error ., Not all models for LD enjoy this property ., For example , the PAC model 20 provides a model for LD that is in some ways preferable to the one we used here , but is considerably harder to fit to unphased data ( even without error ) , requiring more sophisticated and computationally-intensive algorithms ., However , we note that in some cases it might be acceptable to treat a particular phased data set ( e . g . the HapMap data ) as an error-free gold standard , and use it to detect errors in other data sets 18: in this case the PAC model would provide a viable alternative to our approach ., Since our primary motivation was to exploit LD to help detect markers with high genotyping error rates , our model allows error rates to vary across SNPs ., In contrast , we have implicitly assumed equal error rates across individuals ., In fact , due to issues such as DNA sample quality , some individuals may have higher error rates than others ., We already estimate a large number of parameters in the model , and therefore have not attempted to relax this assumption here ., However , this would be an interesting , and potentially useful , extension of this work ., In addition to detecting and correcting genotyping errors , our approach also lends itself to several other applications ., In fastPHASE we have implemented two of these: testing for nonrandom missing data patterns , which may be of interest in genetic association studies where differential missingness patterns between groups can lead to spurious associations; and detecting “strand” errors , where the same SNP has been typed on two different platforms , which , perhaps unbeknownst to the investigator , are assaying different strands ., This last application is particularly important for merging results from different studies performed on different platforms ., As described here , our approach works directly with discrete genotype calls , rather than with underlying intensity data used to obtain these calls ., This has the advantage of making it independent of the genotyping platform used to obtain the data , and also making it applicable to data sets , such as the HapMap genotype database , where the intensities are not readily available ., However , our approach could be readily modified to deal directly with the underlying intensity data , explicitly combining LD information with the intensity data to improve genotype calling accuracy 21 ., From a purely statistical perspective one would expect such a one-stage procedure , when properly implemented , to outperform the two-stage procedure we adopt here ., Further , intensity plots for the Affymetrix 500K data used in this study suggest that the benefits of incorporating both types of information could be considerable: it would allow patterns of LD to help identify cluster centers , and guide genotype calls , when the intensity data at a particular SNP are noisy , but downweight their influence at SNPs where intensity data are clean and unambiguous ., Similarly , our approach could be combined with other types of higher-level data , such as assembled reads from whole-genome resequencing technologies ., In these technologies , genotyping accuracy will be greatly influenced by the fold coverage available ., We anticipate that effective use of LD information will reduce the coverage necessary to obtain a given level of genotyping accuracy , hence reducing the cost of future genome-wide studies of population genetic variation ., The comparisons with MIs reported here were all performed by applying our method to unfiltered data from HapMap trios ., Specifically , we used the CEU and YRI data from chromosome 7 ( 4 January , 2007; NCBI build 35 ) , excluding SNPs that failed QC based on pass-rate ( proportion of genotypes not marked as “missing” ) and duplicate sample discrepancies ., For the comparison with HWE we excluded SNPs which failed HapMap QC due to HWE ( p-value <10−4 ) , since , due to the popularity of HWE as a QC measure , SNPs showing extreme deviations from HWE are likely to be excluded from analyses ., Unless otherwise stated , results are from applying our method separately to each sample of 90 individuals , ignoring the known parent-offspring relationships ., This is because , although the method is designed for samples of unrelated individuals , we have found that it is also effective for data sets where individuals are related to one another , and applying it to all 90 individuals facilitates comparisons with MIs , since these are identified using data on all 90 individuals ., In some cases we also report results obtained from applying the method separately to the parents and children ., The comparisons with discrepancies reported here were all obtained by applying our method to data on the unrelated HapMap individuals obtained using the Affymetrix 500k chip ( http://www . affymetrix . com/support/technical/sample_data/500k_hapmap_genotype_data . affx ) ., Specifically , we considered genotype data on the unrelated samples on all 22 autosomes , separately for each of the 3 HapMap analysis panels ., To calculate the discrepancies , we compared the Affymetrix calls with data from the HapMap database ( 13 March , 2007; NCBI build 36 ) ., We excluded from this analysis those SNPs where HapMap calls were obtained from the same Affymetrix chip ., To view the intensities of these SNPs , we obtained the intensities from the HapMap project website ( http://www . hapmap . org/downloads/raw_data/affy500k/ ) ., Before plotting , we standardized each intensity value by subtracting the mean and dividing by the standard deviation of the intensities among all SNPs for the individual corresponding to that value ( separately for each chip , NSP and STY ) ., Note that although this simple standardization strategy appeared to suffice for our purposes , more sophisticated strategies are generally performed by the best genotype calling algorithms ., For a practical application of our method , we applied it to data on the combined CHB+JPT HapMap genotypes from the HapMap database ( forward strand; 13 March , 2007; NCBI build 36 ) ., We provide a complete list of SNPs with estimated LD error rates , as well as individual genotypes where the conditional probability of the observed genotype was less than 0 . 95 ) ., We incorporated a genotyping error component into a previously-described model for multi-locus LD 9 ., To briefly review this model , let denote the observed unphased genotype for individual i ( 1 , … , n ) at marker m ( 1 , … , M ) ., The model in 9 assumes that the genotypes from each individual , along each chromosome , derive from a hidden Markov model ( HMM ) ., Specifically , at each SNP , each observed allele is assumed to derive from one of K haplotype clusters ( states in the HMM ) , each of which has its own cluster-specific allele frequencies ( emission probabilities ) , the set of which is denoted by θ ., Thus , for unphased data , each observed genotype is assumed to derive from 2 ( not necessarily distinct ) clusters ., To model the LD among nearby SNPs , cluster memberships are assumed to change gradually along each haplotype , specifically according to a Markov process whose jump probabilities are controlled by a parameter r; conditional on a jump at m , cluster k ( 1 , … , K ) is chosen with probability αkm ., Since the clusters ( HMM states ) from which each allele is derived are unobserved , the probability of the genotypes for individual i is obtained by summing over all possible values for these latent variables: ( 1 ) where denotes the vector of latent cluster memberships for individual i ., Conditional on the parameters of the model , genotypes from different individuals are assumed to be independent , and so the likelihood is obtained by multiplying together ( 1 ) across individuals ., See 9 for further details , including methods for computing this likelihood efficiently , and for estimating the parameters of this model by maximum likelihood via the EM algorithm ., Here , we modify this model by letting denote the observed unphased genotype for individual i , and introducing further latent variables xim to denote the corresponding true genotype ., We assume that genotypes g are observed , possibly with error , according to some model p ( g | x , ε ) , given below , where ε represents an error rate ( or vector of rates ) ., The term in ( 1 ) is replaced by a sum: ( 2 ) We apply an efficient algorithm for calculation of this likelihood based on Baum-Welch algorithms for HMMs ( Text S1 ) ., To obtain our results , we restricted attention to a particular error model , represented by the transition probability matrix in Table 1 ., We allow ε to vary by SNP marker , so that ε\u200a= ( ε1 , … , εM ) , where ε\u200a= ( 1 , … , M ) is itself a vector of rates ., Conditional on the model parameters , errors are assumed to occur independently across sites and across individuals ., This particular model does not allow for the observation of a homozygote of one allelic type when the true genotype is a homozygote of the other type , since we expect this type of error to be relatively rare with current genotyping technologies ., However , we did briefly explore various error models , including those which do allow this type of error ( Text S1 ) ., For ( α , θ , r ) , we attempt to obtain maximum likelihood ( ML ) estimates via an EM algorithm ( Text S1 ) ., We fixed the number of clusters ( K ) to be 12 for the analysis of HapMap data ., This choice was based on cross-validation results ( for imputing missing genotypes ) over a range of convenient possibilities of K . We also considered smaller values ( Table 1 in Text S1 ) ., For ε we found that obtaining maximum likelihood estimates was not the best approach ., Note that genotyping assays are , for most SNPs , very accurate , and so , a priori , values of ε are expected to be near 0 ., Because maximum likelihood estimation does not take this prior information into account , it tended to produce too many non-zero estimates of ε ., To alleviate this problem we took the approach of putting a prior distribution on ε , with a mode at 0 , and estimating ε using the maximum a posteriori ( MAP ) estimates ., To facilitate computation we chose priors that were Beta ( a , b ) for the homozygote error rates ε0 and ε2 , and Dirichlet ( a , b , a ) for the heterozygous error rates ( ε0 , 1 , –ε10 , –ε12 , ε12 ) ., With these priors it is straightforward to obtain the MAP estimates using the EM algorithm ., We compared results across three different values of ( a , b ) = ( 1 , 1 ) , ( 0 . 9 , 2 ) and ( 0 . 9 , 2 ) ; the first of these corresponds to a uniform prior , and so the MAP estimates are the maximum likelihood estimates; the second and third produce increasingly strong shrinkage of estimated error rates towards 0 ., Although these comparisons are far from comprehensive , the results ( Table, 1 ) suggested that ( a , b ) = ( 0 . 9 , 2 ) provides a useful tradeoff between shrinking ε towards 0 and still identifying SNPs with high values of ε ., In contrast , ( a , b ) = ( 0 . 9 , 2 ) seemed to shrink error rate estimates too much towards 0 , resulting in very few genotypes being corrected; and , as noted above , the maximum likelihood estimates ( ( a , b ) = ( 1 , 1 ) ) tended to produce too many non-zero estimates of ε , and as a result corrected too many genotypes ( actually increasing the number of discrepancies between HapMap and Affymetrix calls ) ., We calculate an LD-based SNP-specific expected number of genotype errors by summing the conditional probabilities of incorrect genotype calls across all individuals at a particular SNP m as follows: ( 3 ) where and are estimates from the EM algorithm ., Reported SNP-specific LD-based genotyping error rates are obtained by forming the ratio of this sum ( 3 ) to the number of observed ( nonmissing ) genotypes at SNP m ., Reported overall LD-based genotyping error rates are obtained by summing both the numerator and denominator of this ratio across SNPs , and forming the ratio of these sums ., Conditional probabilities of individual genotypes are used to impute corrected genotype calls ., Specifically , a genotype for individual i at marker m may be corrected iffor an alternate genotype a≠gim and some probability threshold c ., To obtain our results we set c equal to 0 . 5 .

Introduction, Results, Discussion, Methods

Quality control ( QC ) is a critical step in large-scale studies of genetic variation ., While , on average , high-throughput single nucleotide polymorphism ( SNP ) genotyping assays are now very accurate , the errors that remain tend to cluster into a small percentage of “problem” SNPs , which exhibit unusually high error rates ., Because most large-scale studies of genetic variation are searching for phenomena that are rare ( e . g . , SNPs associated with a phenotype ) , even this small percentage of problem SNPs can cause important practical problems ., Here we describe and illustrate how patterns of linkage disequilibrium ( LD ) can be used to improve QC in large-scale , population-based studies ., This approach has the advantage over existing filters ( e . g . , HWE or call rate ) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors ., Applying this LD-based QC procedure to data from The International HapMap Project , we identify over 1 , 500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes ., Our method is implemented in the software package fastPHASE , available from the Stephens Lab website ( http://stephenslab . uchicago . edu/software . html ) .

In large-scale studies of population genetic data , particularly genome-wide association studies , considerable effort may be spent on quality control ( QC ) to ensure genotype data are accurate ., Typically , QC steps are applied independently to individual marker loci , with data from suspicious loci being excluded from subsequent analyses ., Here we present a new QC tool , which exploits the fact that correlation of alleles among nearby genetic loci ( linkage disequilibrium; LD ) provides a certain amount of redundancy in genotype information , and that high rates of genotyping error at a marker may leave their trace in unusual patterns of LD ., The method, ( a ) aids in the detection of SNP loci with possibly elevated levels of genotyping error , and, ( b ) in some cases allows for the correction of erroneous genotype calls , thereby salvaging some of the genotype data from the QC filtering process ., We confirm on data from real populations that SNPs identified by this approach do show evidence for containing actual genotyping errors , and we also examine genotype intensity plots to confirm that many individual genotypes corrected by the method do appear to be called in error ., More generally , these results demonstrate the potential utility of incorporating LD information into algorithms for processing and analyzing population genotype data .

mathematics/statistics, genetics and genomics/population genetics

journal.ppat.1000958

Protein Expression Redirects Vesicular Stomatitis Virus RNA Synthesis to Cytoplasmic Inclusions

RNA viruses that replicate within the cytoplasm often form specialized structures that are the sites of RNA replication 1 ., For positive-strand RNA viruses , replication occurs on cellular membranes , including those of the endoplasmic reticulum , secretory pathway , mitochondria and other organelles 2–6 ., Experiments with poliovirus and with flock house virus ( FHV ) have provided compelling evidence that the viral RNA and the non-structural proteins required for RNA replication are localized to such sites ., For FHV , electron microscopy and tomographic reconstructions of spherule-like structures invaginated from mitochondrial membranes confirm that they contain the viral replication machinery 6 ., Double-strand RNA viruses form phase-dense inclusions or “viral factories” to which transcription competent viral cores and the machinery required for RNA synthesis are localized 7 ., In contrast to the structures formed by positive-strand RNA viruses , the double-strand RNA virus factories are not membrane bound 8–10 ., The formation of such specialized replication compartments is thought to concentrate the viral machinery necessary for RNA synthesis and thereby favor catalysis ., Compartmentalization of the replication machinery might also shield the viral RNA from detection by cytosolic innate immune sensors ., In contrast to the evidence for the role of specialized replication compartments for positive- and double-stranded RNA viruses , the exact site of RNA synthesis for non-segmented negative-strand ( NNS ) RNA viruses is less well characterized ., Vesicular stomatitis virus ( VSV ) , a prototype of the NNS RNA viruses , has provided many mechanistic insights into RNA synthesis for NNS RNA viruses 11 ., To initiate infection , VSV delivers a transcription competent ribonucleoprotein ( RNP ) core into the cell 12 ., This core comprises the negative-sense genomic RNA completely encapsidated by the viral nucleocapsid protein ( N ) and associated with the viral RNA dependent RNA polymerase 13 ., The viral components of the polymerase are a 241 kDa large protein ( L ) and a 29 kDa accessory phosphoprotein ( P ) 14 ., The L protein possesses all the catalytic activities required for RNA synthesis 15 , including the various steps of mRNA cap addition 16–24 and polyadenylation 25 , and the P protein serves to bridge interactions between L and the N-RNA template 26 ., An L-P complex transcribes the N-RNA template into a series of mRNAs in a start-stop mode of sequential transcription 27 , 28 ., The polymerase also replicates the genomic RNA to yield progeny antigenomes and genomes ., Replication differs to transcription in that it depends upon ongoing protein synthesis to provide the N protein necessary to encapsidate the nascent RNA 29 ., Cis-acting signals required for RNA replication and for each step of mRNA synthesis , including cap addition and polyadenylation have been defined ( reviewed in 11 ) , and the enzymatic activities mapped at the single amino acid level within L . The site ( s ) within the cytoplasm at which VSV RNA synthesis occurs and the cellular requirements for RNA synthesis remain uncertain ., For rabies virus , a related member of the Rhabdoviridae , pathologic specimens of infected neuronal cells identified inclusion-like structures termed Negri bodies that contain viral nucleocapsids ., This led to the suggestion that such inclusions might be sites of RNA synthesis ., Subsequent studies showed that Negri body-like inclusions appear to be bona fide sites of RNA synthesis as they contain the viral N , P and L proteins necessary for RNA synthesis as well as the mRNA products of transcription 30 , 31 ., That the inclusions may be active sites of synthesis rather than storage compartments was indicated by immune fluorescence ( IF ) microscopy using an antibody to bromodeoxyuridine which detected inclusions following transfection of cells with bromo UTP ( BrUTP ) 30 ., This suggests that the rabies polymerase incorporated BrUTP into RNA that was actively synthesized at the inclusion-like structures ., In contrast to those observations for rabies virus , for VSV it was suggested that RNA synthesis occurs throughout the cytoplasm 32 ., This conclusion was also based on incorporation of BrUTP into RNA 32 ., For VSV , the presence of BrUTP labeled RNA throughout the cytoplasm could , however , reflect synthesis of RNA at specific sites followed by a subsequent distribution throughout the cytoplasm ., The relationship between inclusions and viral RNA synthesis remains therefore , uncertain ., In addition , although experiments performed with rabies and VSV indicate that the viral polymerase can incorporate BrUTP into viral RNA , direct biochemical evidence for this is lacking ., In the present study , working with VSV , we further probed the relationship between inclusion formation and RNA synthesis ., To do this , we used recombinant viruses in which P was fused to eGFP 33 or mRFP ., We show that the P protein together with the N and L proteins are localized to inclusion-like structures in infected cells ., By direct biochemical analysis of the products of RNA synthesis , we demonstrate that L incorporates BrUTP into viral mRNA in vitro as well as in cells ., Imaging the location of the viral RNA synthesis machinery and the viral RNA in infected cells by fluorescent microscopy revealed that the infecting RNP can synthesize mRNA throughout the cytoplasm ., Following protein synthesis , however , viral RNA synthesis appears to be restricted to inclusions ., The viral mRNAs are subsequently transported away from those inclusions in a microtubule-dependent manner to facilitate translation ., Our experiments show that VSV does not require a specialized site for RNA synthesis , but the viral RNA synthesis machinery is redirected to inclusions following protein synthesis ., Recombinant VSV expressing eGFP fused to P was previously described 33 ., We generated a similar recombinant virus in which eGFP was replaced by monomeric RFP using the same strategy except oligonucleotide primers 5′-GAAAAAAACTAACAGATATCATGGCCTCCTCCGAGGACG-3′ and 5′-CTTTTGTGAGATTATCGGCGCCGGTGGAGTGGC-3′ were used to amplify the mRFP gene from pRFP-N1 ( Clontech , Mountain View , CA ) ., Recombinant virus was recovered as described previously 34 ., Amino acids 1594–2109 of VSV L were expressed in Spodoptera frugiperda ( Sf21 ) cells from a recombinant baculovirus generated by cloning the relevant portions of the L gene under the control of the polyhedrin promoter using pFASTBAC-DUAL ( Invitrogen , Carlsbad , CA ) ., An N-terminal hexa-histidine tag was introduced to facilitate L protein purification ., The L protein fragment was purified by affinity chromatography on Ni-nitrilotriacetic acid-agarose ( Qiagen , Valencia , CA ) followed by MonoQ then MonoS ion exchange chromatography ( GE Healthcare , UK ) ., A polyclonal antiserum was obtained following immunization of a single rabbit with purified protein ( Covance , Princeton , NJ ) ., The rabbit antiserum detects full-length VSV L in infected cell lysates by Western blot ( data not shown ) ., Imaging experiments were performed in BSR-T7 , CV-1 or Vero cells ., Cells were fixed with 2% paraformaldehyde for 15 min , washed twice with phosphate buffered saline ( PBS ) ( 137 mM NaCl , 2 . 7 mM KCl , 100 mM Na2HPO4 , 2mM KH2PO4 ) and treated with ice-cold 100% methanol for 3 min ., Cells were rinsed twice with PBS , incubated in PBSAT ( 1× PBS , 0 . 1% Triton ×100 , 1% BSA ) , followed by PBSA ( 1× PBS , 1% BSA ) each for 10 minutes ., For RNA detection , we used a monoclonal antibody against bromodeoxyuridine conjugated to Alexa Fluor-488 ( Invitrogen ) at a 1∶50 dilution in PBSAT ( 1× PBS , 0 . 05% Triton ×100 , 1% BSA ) ., Cells were incubated for 1 hour at RT or 16h at 4°C , prior to detection of immune complexes using a 1∶2000 dilution of a secondary anti-mouse antibody conjugated to Alexa Fluor-488 ( Invitrogen ) ., VSV N and M proteins were detected using monoclonal antibodies 10G4 and 23H12 35 , respectively , which were kindly provided by Dr . Douglas Lyles ( Wake Forest University ) , followed by a 1∶750 dilution of a secondary anti-mouse antibody conjugated to DyeLight 549 ( Jackson ImmunoResearch Laboratories , West Grove , PA ) or Alexa Fluor-488 ( Invitrogen ) ., For detection of L , we used the rabbit polyclonal antiserum at a 1∶1000 dilution followed by an anti-rabbit secondary antibody conjugated to DyeLight-649 ( 1∶750 ) ( Jackson ImmunoResearch ) ., Cellular α-tubulin was detected using a 1∶200 dilution of the monoclonal DM1A antibody ( Sigma , St Louis , MO ) and visualized with Alexa Fluor-594 conjugated secondary antibody ( Invitrogen ) at a 1∶500 dilution ., Calnexin was detected using a 1∶250 dilution of a mouse anti-calnexin antibody ( BD Transduction Laboratories , Franklin Lakes , NJ ) ., GM130 was detected using a 1∶100 dilution of a mouse anti-GM130 antibody ( BD Transduction Laboratories ) ., Early endosomal antigen 1 ( EEA1 ) was detected using a 1∶500 dilution of a mouse anti-EEA1 antibody ( BD Transduction Laboratories ) ., Secondary labeling was performed using 1∶750 dilutions of an anti-mouse antibody conjugated to DyeLight-549 ( Jackson ImmunoResearch ) ., Lysosomes and mitochondria were detected by LysoTracker and MitoTracker dyes ( Invitrogen ) used according to the manufacturers instructions ., Wide-field images were acquired using a Zeiss Axioplan 2 inverted fluorescence microscope ( Carl Zeiss MicroImaging , Germany ) equipped with a 63× ( NA 1 . 4 ) objective ., Samples were excited with a Xenon lamp , and filtered emission photons were collected with a Hamamatsu Orca-HR ( C4742-94 ) camera ( Hamamatsu , Bridgewater , NJ ) ., Confocal images were acquired using a Zeiss observer Z1 microscope ( Carl Zeiss MicroImaging ) fitted with a confocal spinning disk unit ( Yokogawa Electric Corporation , Atlanta , GA ) and a 63× ( NA 1 . 4 ) objective ., Excitation wavelengths were 473 nm for Alexa Fluor-488 , 561 nm for Alexa Fluor-594 or DyeLight-549 and 660 nm for DyeLight-649 ., For 3-D acquisitions , images were captured at intervals of 0 . 26 µm ., The X , Y , Z positions of the stage were controlled using a PZ-2000 automated stage ( Applied Scientific Instrumentation , Eugene , OR ) ., Microscope hardware was controlled with Slidebook 4 . 2 Software ( Intelligent Imaging Innovations , Denver , CO ) ., Vero cells were infected with VSV at an MOI of 3 and fixed 6 hpi with 2 . 5% glutaraldehyde ( Electron Microscopy Sciences , Hatfield , PA ) to preserve membrane integrity and 2% paraformaldehyde ( Sigma ) in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) ( Sigma ) for 1h ., The cells were then postfixed for 30 min in 1% osmium tetroxide ( OsO4 ) /1 . 5% potassiumferrocyanide ( KFeCN6 ) ( Electron Microscopy Sciences ) , washed 3 times in H2O and incubated in 1% aqueous uranyl acetate ( Sigma ) ., This was followed by 2 washes in H2O and subsequent dehydration in grades of alcohol for 5 min each ( 50% , 70% , 95% , 2× 100% ) ., For immunogold EM , infected cells were fixed 6 hpi with 2% paraformaldehyde ( Sigma ) and labeled with primary antibodies against viral L ( 1∶100 dilution ) and N ( 1∶50 dilution ) as above ., To detect P , we infected cells instead with VSV-eGFP-P and visualized the location of P with a rabbit anti-GFP antibody ( 1∶50 dilution ) ( Sigma ) ., Secondary labeling was performed with anti-rabbit or anti-mouse nanogold-1 . 4 nm ( 1∶50 dilution ) in 1% BSA for 1 h at RT ., Samples were washed 5× in 1× PBS/1% BSA for 1h and postfixed in 1% glutaraldehyde ( Electron Microscopy Sciences ) in 1× PBS for 10 min ., Cells were then washed 3 times for 5 min in PBS , followed by 2 washes for 5 min in deionized water and 1 wash for 5 min in 0 . 02 M citrate buffer ., The 1 . 4 nm gold particles were silver enhanced ( giving ∼15–40 nm particles ) by incubating the samples for 4 min in freshly mixed developer using the HQ Silver Enhancement kit ( Nanoprobes , Yaphank , NY ) and rinsed 3 times in deionized water for 1 min ., Cells were treated with 0 . 5% osmium tetroxide before dehydration ., For embedding , unlabeled and immunogold labeled cells were removed from dishes using propyleneoxide ( Sigma ) , pelleted at 3000 rpm for 3 min and infiltrated for 2 h in an equal mixture of propyleneoxide and TAAB Epon ( Marivac Canada Inc . , St . Laurent , Canada ) ., The samples were subsequently embedded in TAAB Epon and polymerized at 60 degrees C for 48 h ., Ultrathin sections ( about 60nm ) were cut on a Reichert Ultracut-S microtome , picked up on to copper grids stained with lead citrate and examined in a TecnaiG2 Spirit BioTWIN ., Images were recorded with an AMT 2k CCD camera ., Viral RNAs were transcribed in vitro as previously described 36 with minor modifications 37 ., Detergent activated , purified recombinant VSV ( rVSV ) ( 10 µg ) was incubated in the presence of nucleoside triphosphates ( 1 mM ATP and 0 . 5 mM each of CTP , GTP and UTP ) ., Where indicated , reactions were supplemented with 0 . 1–1 mM 5-bromouridine 5′-triphosphate sodium salt ( BrUTP ) ( Sigma ) , fluorescein-12-UTP , -GTP , -ATP , Alexa Fluor-488-UTP ( Invitrogen ) , Cy3-17-UTP ( General Electric Life Sciences , UK ) or 15 µCi of α-32P-GTP ( Perkin Elmer , Waltham , MA ) ., As a control , transcripts were also synthesized by T7 RNA polymerase ( New England Biolabs , Beverly MA ) using the previously described VSV expression plasmid pN 38 ., Approximately 30 , 000 BSR-T7 cells grown on cover slips in 24 well plates were infected with VSV at the specified MOI ( 3–500 ) ., At the indicated times post infection , cells were depleted of uridine by low glucose DMEM ( Invitrogen ) supplemented with 20 mM glucosamine ( Sigma ) , and transfected 1h later with 5 mM BrUTP in 250 µl of DMEM supplemented with 6 µl of lipofectamine 2000 ( Invitrogen ) ., In some experiments , cells were treated 15–40 minutes prior to BrUTP labeling with a variety of chemical inhibitors ( Sigma ) ., Specifically , we used 10 µg ml−1 actinomycin D ( ActD ) to inhibit cellular transcription , 100 mM nocodazole ( Noc ) to disrupt microtubules or 10 µg ml−1 puromycin ( Pur ) to inhibit protein synthesis ., For pulse-chase analyses , the cell culture medium was supplemented with 50 mM uridine ( Sigma ) throughout the chase period ., In some experiments , RNAs were simultaneously metabolically labeled by the incorporation of 33 µCi ml−1 3H-uridine ( Perkin Elmer ) from 4–9 hpi ., The products of in vitro synthesis reactions were purified using an RNeasy kit ( Qiagen ) ., For cellular RNA analysis , cytoplasmic extracts were prepared and RNA was purified by phenol-chloroform extraction as described previously 38 ., Where indicated , RNAs were immune precipitated by incubation with a monoclonal antibody raised against bromodeoxyuridine ( Roche Diagnostics , Indianapolis , IN ) ., Immune precipitations were performed in Rose lysis buffer ( 1% Nonidet P40 , 66 mM EDTA , 10 mM Tris-HCl pH 7 . 4 ) and the immune complexes collected using protein G magnetic beads ( NEB ) ., RNA was analyzed by electrophoresis on agarose-urea gels 39 and detected using a Typhoon 9400 PhosphoImager ( GE Healthcare ) ., At the indicated times post infection , cells were starved of L-methionine and L-cysteine for 1h in the presence of 10 µg ml−1 ActD ., Where indicated , cells were exposed to 10 µg ml−1 Pur for 1 h or 100 mM Noc during the last 15 min of starvation ., Proteins were labeled by addition of 17 . 5 µCi 35SEasyTag express ( Perkin Elmer ) in DMEM lacking L-methionine and L-cysteine ( Invitrogen ) ., Where indicated , nocodazole was washed out to permit repolymerization of microtubules ., Total cytoplasmic proteins were analyzed by 10% SDS-PAGE and detected by phosphoimage analysis ., Quantitative analyses were performed using ImageQuant Software ( GE healthcare ) ., Previously we described a recombinant VSV in which eGFP was fused to the N terminus of P 33 ., In cells infected with this virus , we observed that the eGFP-P protein localized to discrete inclusions that were heterogeneous in size and shape ( Figure 1A ) ., This was not simply a consequence of protein overexpression , as we observed that eGFP-P was distributed throughout the cell when expressed alone from a plasmid ( Figure 1B ) ., The eGFP-P inclusions are visually similar to inclusions observed in rabies virus infected cells that were shown to be sites of RNA synthesis 30 ., Consistent with the experiments with rabies virus , the VSV N and L proteins also colocalize with P at inclusions ( Figure 1C and D respectively ) ., The kinetics of VSV replication are very rapid in cell culture with yields of virus increasing by >2 log by 4 hour post inoculation ., We therefore monitored the kinetics of inclusion formation in cells over time ., To do this , we infected cells with rVSV at a multiplicity of infection ( MOI ) of 5 and monitored the location of the N and L proteins by IF microscopy ., Multiple foci of N were detected as early as 2 hours post infection ( hpi ) , with characteristic inclusion-like structures being visualized by 4 h ( Figure 1E ) ., As infection progressed the size of the inclusions appeared to increase ( Figure 1E ) ., In contrast to the viral proteins required for replication , the matrix ( M ) protein was neither enriched nor excluded from these structures ( Figure 1F ) ., To examine the cellular location of the inclusion-like structures , we performed electron microscopy of cells infected with VSV ., As previously 40 , viral inclusion bodies ( VIB ) were detected in the cytoplasm of the cell ( Figure 1G–J ) ., These inclusions do not appear to be associated with a cellular membrane or specific organelle ( Figure 1G ) ., Consistent with this , we did not detect colocalization of the inclusions with markers for the endoplasmic reticulum , Golgi , endosomes , lysosomes , and mitochondria ( Figure S1 ) ., Rather , the inclusions contain the viral N , P and L proteins which were readily detected by immunogold electron microscopy ( Figure 1H–J ) ., These observations confirm that like rabies virus , the VSV replication machinery is found in discrete viral derived inclusion-like structures in infected cells ., To visualize de novo synthesis of viral RNA , we tested the ability of purified VSV L protein to incorporate fluorescent nucleotides in vitro ., Viral RNA synthesis was inhibited in reactions containing fluorescein-12-UTP , -GTP or -ATP , Alexa Fluor-488-UTP or Cy3-17-UTP or the RNA products were not fluorescent ( data not shown ) ., This result indicates that L cannot incorporate nucleotides that contain such large modifications ., To test whether nucleotides with smaller modifications can be incorporated into viral RNA , we supplemented in vitro transcription reactions performed in the presence of 32P-GTP with 5-BrUTP , and monitored the products of RNA synthesis by electrophoresis on acid-agarose gels ( Figure 2A ) ., As the concentration of BrUTP in the reaction increased from 0–1 mM , the overall yield of RNA decreased and the transcripts migrated with a slightly faster mobility ., The altered mobility of the RNA suggests that L incorporates BrUTP into the mRNA as a similar mobility shift is observed for transcripts synthesized by T7 RNA polymerase ( Figure 2A ) ., The presence of BrUTP in the viral transcripts was confirmed by their selective immune precipitation with an antibody directed against bromodeoxyuridine , which failed to precipitate unmodified RNA ( Figure 2B ) ., The BrUTP labeled mRNAs were also retained by oligo dT chromatography , which demonstrates that the mRNAs are full-length and contain polyadenylate ( Figure 2C ) ., The agarose-urea gels separate products based upon their molecular weight as well as charge 39 , which likely accounts for the observed mobility shift ., To examine whether BrUTP is similarly incorporated into viral RNA in cells , we transfected 5 mM BrUTP into BSR-T7 cells that were infected 6 hours earlier with VSV ., Infected cells were subsequently exposed to 3H-uridine in the presence of ActD to permit the labeling of viral RNA , and the total cellular RNA was extracted , purified and BrUTP incorporation determined by immune precipitation prior to electrophoresis on acid-agarose gels ., Consistent with the incorporation of BrUTP by the VSV polymerase in vitro , viral mRNAs were immune precipitated from cells that were transfected with BrUTP , but not from cells that lacked BrUTP ( Figure 2D ) ., This set of experiments demonstrates that VSV L incorporates 5-BrUTP into viral mRNA in vitro and in infected cells ., To visualize the cellular localization of viral RNA , we infected BSR-T7 cells with rVSV-RFP-P , and 5 hours later treated the cells with ActD to inhibit cellular transcription and glucosamine to deplete the intracellular pool of uridine 41 , 42 ., Following a 1 hour incubation , the RNA was labeled by incorporation of BrUTP for 1 hour and was subsequently visualized by IF microscopy ., In infected cells - as evidenced by the RFP-P inclusions - we found BrUTP labeled RNA distributed throughout the cytoplasm ( Figure 3A , row 1 , arrows ) ., No BrUTP labeled RNA was detected in uninfected cells ( Figure 3A , rows 1 and 2 ) ., As expected , in the absence of ActD we observed BrUTP labeled cellular RNA , which was predominantly localized to the nucleus ( Figure 3A , row 3 , arrowheads ) , and no RNA was visualized in cells that did not receive BrUTP ( Figure 3A , row 4 ) ., This result shows that VSV RNA is localized throughout the cytoplasm in infected cells ., We could not discriminate , however , whether viral RNA was synthesized throughout the cytoplasm , or at the RFP-P inclusions followed by subsequent movement ., Consistent with this latter idea , we detected BrUTP labeled RNA in close proximity to inclusions as well as throughout the cytoplasm when BrUTP incorporation was allowed to proceed for only 30 minutes prior to fixation ( Figure 3B , arrows ) ., Movement of viral RNA from inclusions may occur via a passive or an active transport mechanism ., The process of active transport should be dependent upon the presence of an intact cytoskeletal network ., To examine whether the distribution of viral RNA is microtubule-dependent , we monitored RNA localization in VSV infected cells following chemical depolymerization of microtubules ( MTs ) with nocodazole ., Under those conditions viral RNA was confined to specific regions of the cytoplasm ( Figure 4A ) ., In VSV-RFP-P infected cells , we observed the viral RNA surrounding the RFP-P inclusions in discrete quanta following a 40-minute pulse of BrUTP ( Figure 4B , lower panel ) ., These images suggest that viral RNA synthesis occurs at the inclusions , and that viral RNA is transported away from the inclusions in a MT-dependent manner ., To confirm that viral RNA was transported away from inclusions , we performed a pulse-chase analysis ., To do this , we first depleted intracellular pools of uridine with glucosamine ( +Gluc ) 41 , 42 , labeled the RNA by incorporation of BrUTP and then subsequently “chased” with a 10-fold excess of unlabeled uridine ( see schematic in Figure 5A ) ., When nocodazole was absent during the indicated chase period , the viral RNA granules were found a range of distances away from the inclusions rather than closely surrounding them ( Figure 5B ) ., This observation confirmed that the RNA was transported away from the inclusions in a microtubule-dependent manner and suggests that this is an active process ., Consistent with this notion , RNA granules were observed along and in close proximity to microtubules ( Figure 5C , enlarged inset , arrows ) ., These RNA localization experiments reveal that VSV RNA is synthesized at inclusions in infected cells and that the viral RNA is transported away from those inclusions in a microtubule-dependent manner to become distributed throughout the cytoplasm ., The viral protein requirements for RNA synthesis are N , P and L . To determine whether inclusions containing N , P and L are active sites of RNA synthesis , we infected cells with either rVSV or rVSV-RFP-P and visualized RNA and protein using confocal microscopy ., In these experiments , we restricted RNA to its site of synthesis by treating cells with nocodazole prior to transfection of BrUTP ., The viral RNA was observed as granular structures around inclusions that were visualized by RFP-P expression or following staining with antibodies against N or L ( Figure 6A ) ., All visible inclusions are decorated with viral RNA suggesting that they are each sites of RNA synthesis ( Figure 6A and Videos S1 , S2 , S3 , S4 , S5 and S6 ) ., Triple wavelength imaging of the RFP-P , L and the BrUTP RNA confirmed that the viral proteins colocalize and that viral RNA is present at the inclusions ( Figure 6B ) ., This experiment demonstrates that the viral protein requirements for RNA synthesis are localized to inclusions in infected cells , and that those inclusions are sites of RNA synthesis ., Although the N , P and L proteins colocalize to inclusions , the RNA surrounds , but appears to be excluded from , the inclusions ., Whether this reflects synthesis of the RNA at specific sites on the surface of the inclusion or a limitation of detection of the RNA within the inclusion is uncertain ., The RNA decorating the inclusion also colocalized with N protein , but not the P or L protein ., This colocalization with N , may reflect the previously reported association of viral mRNA with N protein 43 , and/or may represent the N encapsidated viral genomes ., The above experiments show that viral RNA is synthesized at , and actively transported away from inclusions ., To establish infection however , the input RNP must synthesize mRNA presumably in the absence of such inclusions ., To determine where such primary transcription occurs , we infected BSR-T7 cells with rVSV-RFP-P at an MOI of 500 in the presence of the protein synthesis inhibitor puromycin and monitored RNA synthesis by BrUTP incorporation ., Genome replication requires the ongoing synthesis of N protein 29 , so treatment of cells with puromycin results exclusively in mRNA synthesis ., Under those conditions , viral mRNA was distributed throughout the cytoplasm even when active transport on microtubules was abolished by treatment with nocodazole ( Figure 7A ) ., This observation suggests that protein synthesis is required for inclusion formation at which subsequent RNA synthesis occurs , and demonstrates that the viral mRNAs are not simply restricted to specific cytoplasmic sites by disruption of the MT network ., By infecting cells with rVSV and detecting the input RNPs and primary transcripts we also show that they are distributed throughout the cytoplasm at distinct locations ( Figure S2 ) ., This distribution of mRNA throughout the cytoplasm is not simply a consequence of inhibiting protein synthesis , as treatment of cells with puromycin at 7 hpi results in the typical distribution of mRNA around inclusions ( Figure 7B ) ., By metabolic labeling of viral RNA , we confirmed that puromycin inhibits genome replication ( Figure 7C ) ., In contrast , nocodazole treatment is relatively inert with regard viral RNA synthesis ( Figure 7D ) ., Taken together these data show that primary viral transcription occurs throughout the cytoplasm , and that protein synthesis is required to establish an inclusion at which subsequent RNA synthesis takes place ., Since genome replication is inhibited in the presence of the protein synthesis inhibitor puromycin , the experiment also directly demonstrates that the inclusions are sites of secondary mRNA synthesis ., To determine whether the transport of the viral mRNA was biologically important , we evaluated the effect of nocodazole treatment on the rate of viral protein synthesis by metabolic labeling ., Following short-term nocodazole treatment , the rate of viral protein synthesis was diminished by 40% compared to that in untreated cells ( Figure 8A and C ) ., In contrast , the rate of total cellular translation was unaffected by nocodazole treatment ( Figure 8A and C ) ., This suggests that a MT-dependent transport process is specifically required for efficient viral protein synthesis ., To correlate those effects on protein synthesis with transport of the VSV mRNA , we performed an experiment in which we washed out nocodazole and metabolically labeled cells with 35S-methionine ( Figure 8B and D ) or monitored the location of the BrUTP RNA ( Figure 8E ) ., Translation of viral mRNA was rapidly restored ( within 15 minutes ) of nocodazole wash-out ( Figure 8B and D ) , and this restoration of protein synthesis capability was congruent with the transport of the mRNA away from the inclusion ( Figure 8E ) ., While full assembly of the microtubule network takes longer than the time period of our labeling experiment , the repolymerization of microtubules is visible within 5–10 minutes of nocodazole wash-out in many fibroblast cells , including Vero and baby hamster kidney cells used here 44 , 45 ., This experiment therefore correlates the effects of nocodazole treatment on protein synthesis with the physical location of the mRNA and provides evidence that the transport of the viral mRNA away from inclusions is required to maintain a high rate of protein synthesis ., Detection of the first RNA synthetic events even following high multiplicity infection is challenging to visualize ., All studies to date have reported on the presence of sites of RNA synthesis once replication has been established ., Here we took advantage of the intrinsic properties of VSV with regard the ability to infect cells at high MOI , and inhibit all RNA synthesis other than that directed by the input genomic RNP complex ., By performing infections in the presence of protein synthesis inhibitor puromycin , we show that primary viral mRNA synthesis occurs throughout the cytoplasm ( Figure 7 ) ., The distribution of the primary mRNAs appears unaffected by nocodazole treatment of cells ( Figure 7A ) , consistent with the idea that the infecting RNP can synthesize RNA anywhere within the cytoplasm and that a specialized site is not required to compartmentalize the RNA synthesis machinery ., In contrast , once viral protein synthesis occurs , RNA synthesis appears to be predominantly localized to inclusions ., Although our experiments demonstrate that protein synthesis is essential for the formation of the inclusion , we cannot be certain whether this reflects a requirement for viral protein synthesis alone , or whether cellular protein synthesis might also be required ., The requirement for viral protein synthesis raises the possibility that the formation of such inclusions may reflect an ability of the host cell to detect the “foreign” viral proteins , which triggers a response that results in the viral replication machinery being corralled into an inclusion-like structure ., Such an idea is also compatible with the observation that inclusions are not observed until viral replication is established ( Figure 1E ) ., Conversely , viral proteins might be specifically targeted to such inclusions to promote RNA synthesis and/or assembly of progeny RNPs ., While the input RNP can synthesize mRNA in the absence of inclusions ( Figure 7 and S2 ) , the picture for genome synthesis is not certain ., The kinetics with which inclusions are detected in cells ( Figure 1E ) suggests that genome replication might occur in the absence of inclusion formation ., We cannot , however , eliminate the possibility that smaller inclusions that are not readily visualized by our microscopy approaches are present prior to genome replication ., Once inclusions are formed , they become the major sites of RNA synthesis ., Although we did not formally demonstrate that RNA replication itself occurs at the inclusion , viral genomes must be present at the inclusion to provide the template for mRNA synthesis ., The simplest interpretation of the data is that replication as well as transcription occurs at the inclusions ., Experiments with th

Introduction, Materials and Methods, Results, Discussion

Positive-strand and double-strand RNA viruses typically compartmentalize their replication machinery in infected cells ., This is thought to shield viral RNA from detection by innate immune sensors and favor RNA synthesis ., The picture for the non-segmented negative-strand ( NNS ) RNA viruses , however , is less clear ., Working with vesicular stomatitis virus ( VSV ) , a prototype of the NNS RNA viruses , we examined the location of the viral replication machinery and RNA synthesis in cells ., By short-term labeling of viral RNA with 5′-bromouridine 5′-triphosphate ( BrUTP ) , we demonstrate that primary mRNA synthesis occurs throughout the host cell cytoplasm ., Protein synthesis results in the formation of inclusions that contain the viral RNA synthesis machinery and become the predominant sites of mRNA synthesis in the cell ., Disruption of the microtubule network by treatment of cells with nocodazole leads to the accumulation of viral mRNA in discrete structures that decorate the surface of the inclusions ., By pulse-chase analysis of the mRNA , we find that viral transcripts synthesized at the inclusions are transported away from the inclusions in a microtubule-dependent manner ., Metabolic labeling of viral proteins revealed that inhibiting this transport step diminished the rate of translation ., Collectively those data suggest that microtubule-dependent transport of viral mRNAs from inclusions facilitates their translation ., Our experiments also show that during a VSV infection , protein synthesis is required to redirect viral RNA synthesis to intracytoplasmic inclusions ., As viral RNA synthesis is initially unrestricted , we speculate that its subsequent confinement to inclusions might reflect a cellular response to infection .

Positive-strand and double-strand RNA viruses compartmentalize their replication machinery in infected cells ., This compartmentalization is thought to favor the catalysis of RNA synthesis , and sequester viral RNA molecules from detection by innate immune sensors ., For the negative-strand RNA viruses that replicate in the cytoplasm , the site of RNA synthesis is less clear ., Here , using a prototype non-segmented negative-strand ( NNS ) RNA virus , vesicular stomatitis virus ( VSV ) , we investigated whether viral derived inclusions are sites of RNA synthesis in infected cells ., Our work shows that prior to viral protein synthesis the invading viral cores synthesize mRNA throughout the host cell cytoplasm ., Viral protein expression leads to the formation of intracytoplasmic inclusions that contain the viral machinery necessary for RNA synthesis and become the predominant sites of transcription ., The newly synthesized viral mRNAs escape the inclusions by transport along microtubules and this facilitates their translation ., Our work demonstrates that in contrast to the positive-strand and double-strand RNA viruses , VSV does not require the establishment of specialized compartments in the cytoplasm of the cell for RNA synthesis ., Our findings suggest that the confinement of RNA synthesis to inclusions once infection is established may reflect a host response to infection .

virology/viral replication and gene regulation, virology

journal.pntd.0003682

The Distribution of Ocular Chlamydia Prevalence across Tanzanian Communities Where Trachoma Is Declining

Epidemic models hypothesize that the prevalence of infection across communities where an infectious disease is disappearing should approach an exponential distribution ., Simulations of mass treatments and decreasing transmission support this ., 1–3 However , these epidemic models typically assume similar transmission parameters across communities , while observational studies suggest transmission heterogeneity even amongst neighboring communities . 4, If this hypothesis is consistent with field data , public health stakeholders would benefit by having the ability to forecast prevalence and learn whether a disease was on its way to elimination ., Trachoma programs offer an opportunity to test these models ., Repeated ocular infection with Chlamydia trachomatis can result in irreversible blindness ., Trachoma has been targeted by The World Health Organization ( WHO ) for elimination as a public health concern by the year 2020 ., Efforts rely on a multifaceted approach of mass antibiotic distributions to clear infection and hygiene improvements such as promoting facial cleanliness and latrine construction to reduce transmission ., Whether due to intervention or secular trend , trachoma is clearly disappearing from many areas ., 5–8 A recent study suggested that the prevalence of infection across 24 communities in two separate regions of Ethiopia approached a geometric distribution , the discrete analog of the exponential ., Longitudinal evidence confirmed trachoma was indeed disappearing in each of these two areas ., 9 Here , we examine a far larger data set from a recent cross-sectional survey in Tanzania to determine the distribution of infection across communities that have received multiple rounds of mass antibiotics and where the prevalence of clinical signs of trachoma was known to be decreasing ., We test the hypothesis that the distribution of Tanzanian prevalence data is exponential ., The study was carried out in accordance with the Declaration of Helsinki ., Verbal consent was obtained from the local chiefs of each community before randomization ., Verbal informed consent from each child participant’s guardian was obtained prior to the examination ., This consent process was appropriate given the high rates of illiteracy in the study area and was approved by all institutional review boards ., The exponential distribution had the lowest ( best ) AICc ., Note those distributions which include the exponential as a special or limiting case will always achieve a likelihood of having observed the data at least as high as the exponential ., However , while the beta , Gumbel , normal , gamma , Weibull , generalized gamma distributions all had slightly better log likelihoods ( slightly better fits ) , these distributions all contained additional parameters and therefore had higher ( worse ) AICc results ., The sensitivity analysis yielded the same results as the main analysis , i . e . removing the 0 prevalence villages in the Iramba district had no effect and the exponential distribution gave the most parsimonious fit by AICc ., Results from the main analysis are summarized in Table, 1 . The fit of the exponential distribution to the data is shown in Fig . 1 along with the fit of those distributions which include the exponential as a special or limiting case ., The Cauchy , log-normal , chi , and chi-squared distributions do not include the exponential as a special or limiting case ., These distributions gave far worse log likelihoods and AICc than the exponential ., The fit of these distributions to the data is shown alongside the exponential in Fig ., 2 . 95% confidence intervals of the shape parameter estimates for the beta , gamma , Weibull , and generalized gamma distributions included 1 , consistent with the special case of an exponential distribution ( Table 2 ) ., The confidence interval for the location parameter of the truncated normal and Gumbel distributions included negative values , which again is consistent with the exponential ., The mixture exponential distribution trivially reduced to a single exponential distribution as the proportion parameter estimate was 0 . 99 and the confidence interval included, 1 . With goodness-of-fit testing , we were unable to reject the hypothesis that the observed data came from an exponential distribution ( p = 0 . 30 ) ., We found no evidence of spatial autocorrelation ., Moran’s I using an inverse weight matrix was- . 09 ( p = 0 . 34 ) and Moran’s I using binary weight matrix was -0 . 02 ( p = 0 . 85 ) ., Here we show that chlamydial prevalence data from Tanzania are consistent with an exponential distribution ., A dedicated control program had reduced the prevalence of clinical signs of trachoma 5-fold over 10 years in these Tanzanian communities ., Of all distributions tested , the exponential had the most parsimonious fit to the data ., Furthermore , the 95% confidence interval for the shape parameter estimate of each of the multi-parameter distributions included the special or limiting case of the exponential ., Lastly , goodness-of-fit testing was unable to reject the hypothesis that the observed prevalence data came from an exponential distribution ., The Suceptible-Infected-Suceptible ( SIS ) epidemic model is used to study the transmission dynamics of pathogens , such as C . trachomatis , which can repeatedly infect individuals ., In its simplest form , this model divides the population into two compartments: those who are susceptible to a disease and those who are infected ., Members of the population flow between compartments at rates that reflect how transmissible the disease is and how quickly one recovers from infection ., The model assumes similar transmission conditions across communities and it is not obvious the prevalence distribution predicted by the SIS model would be observed with heterogeneous communities . 16 , 17, While a smaller study found a prevalence distribution in Ethiopian communities consistent with the SIS model , there is no reason to believe the findings would apply to this far larger Tanzanian survey . 9, One explanation may be that if systems tend towards states of maximum entropy over time , an exponential distribution would not be unexpected; it has the maximum entropy amongst all continuous distributions with finite mean and non-negative values ., 18–20 Furthermore , infection in this cross-sectional survey was a rare event ., Individual factors which normally lead to heterogeneity in transmission parameters contribute less and less as outcomes become more rare . 21, Our study has several limitations ., Models imply an exponential distribution of infection prevalence when infection is disappearing , however we only had evidence that the clinical signs of trachoma were disappearing ., Because the clinical signs ( trachomatous inflammation of the tarsal conjunctiva ) are considered lagging indicators of infection disappears , we assumed infection must have been disappearing as well . 22, It must be noted though that while the prevalence of clinical signs of trachoma is decreasing in these areas of Tanzania from the baseline survey to this 2007–2008 survey , this 2007–2008 survey was not powered to provide district-level estimates ., Furthermore , we chose to fit the prevalence data to continuous as opposed to discrete distributions because communities varied in population size ., Alternatively , we could have scaled discrete distributions by the mean prevalence , as done previously . 9, Instead , we assumed that reported prevalences were a sample from a binomial distribution , given a true unobserved continuous prevalence ., It is possible the prevalence data came from two different exponential distributions ., To explore this , we tested a mixture exponential distribution and found that it reduced to a single exponential ., Our goodness-of-fit testing assumed independence between samples ., To explore this , we performed a Moran’s-I calculation ., Though our Moran’s I calculation suggested there was not statistically significant geographical clustering of infection prevalences , this statistic is not perfect and there may still be some clustering ., Note that if the observed data were strongly autocorrelated and we had not taken this correlation into account , then our parameter estimates would have had less precision and the exponential would have been more difficult to reject ., Thus our analysis was conservative ., Our findings have several implications for trachoma control programs ., An exponential distribution has a relatively heavy tail compared to a Gaussian distribution and outliers are not uncommon ., Therefore we expect occasional high-prevalence communities and such communities do not necessarily suggest transmission hot spots or a failure of control efforts ., In fact , models predict infection will disappear from the tail of the distribution as outliers regress to the mean , even if transmission conditions remain the same . 3 , 23, Reports from Nepal , Tanzania , and the Gambia have noted that infection tends to disappear in high-prevalence villages in otherwise hypo-endemic areas ., 24–27 Assessing whether trachoma control programs are on-track to eliminate infection can be difficult for public health stakeholders ., Large-scale longitudinal surveys of community-wide infection prevalence are costly and resource-intensive to perform ., A single cross-sectional survey , on the other hand , is much more feasible ., If such a survey reveals the distribution of infection prevalence is approximated by the exponential , control programs could benefit knowing disease is on its way to elimination if transmission conditions remain the same ., Further studies are needed to determine whether these findings also apply to clinical activity , the current surrogate for infection used by trachoma programs .

Introduction, Methods, Results, Discussion

Mathematical models predict an exponential distribution of infection prevalence across communities where a disease is disappearing ., Trachoma control programs offer an opportunity to test this hypothesis , as the World Health Organization has targeted trachoma for elimination as a public health concern by the year 2020 ., Local programs may benefit if a single survey could reveal whether infection was headed towards elimination ., Using data from a previously-published 2009 survey , we test the hypothesis that Chlamydia trachomatis prevalence across 75 Tanzanian communities where trachoma had been documented to be disappearing is exponentially distributed ., We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation ., Model selection by Akaike Information Criteria ( AICc ) suggested the exponential distribution had the most parsimonious fit to the data ., Those distributions which do not include the exponential as a special or limiting case had much lower likelihoods of fitting the observed data ., 95% confidence intervals for shape parameter estimates of those distributions which do include the exponential as a special or limiting case were consistent with the exponential ., Lastly , goodness-of-fit testing was unable to reject the hypothesis that the prevalence data came from an exponential distribution ., Models correctly predict that infection prevalence across communities where a disease is disappearing is best described by an exponential distribution ., In Tanzanian communities where local control efforts had reduced the clinical signs of trachoma by 80% over 10 years , an exponential distribution gave the best fit to prevalence data ., An exponential distribution has a relatively heavy tail , thus occasional high-prevalence communities are to be expected even when infection is disappearing ., A single cross-sectional survey may be able to reveal whether elimination efforts are on-track .

Trachoma is the leading infectious cause of blindness and the World Health Organization plans to eliminate it as a public health concern worldwide by the year 2020 ., It can be difficult for local trachoma programs to assess whether disease is headed towards elimination in their area ., Mathematical infectious disease models describe that when a disease disappears , its prevalence across communities in that area form an exponential distribution ., However , this theorem has never been tested with field data ., In this study , we take trachoma prevalence data from Tanzania , in an area where trachoma was known to be disappearing , and find that the prevalence forms an exponential distribution ., The implications of this study could be applied to other infectious diseases to provide evidence that prevalence is headed towards elimination .

journal.ppat.1004339

TLR2 Signaling Decreases Transmission of Streptococcus pneumoniae by Limiting Bacterial Shedding in an Infant Mouse Influenza A Co-infection Model

The bacterial pathogen Streptococcus pneumoniae ( the pneumococcus ) robustly colonizes the upper respiratory tract of humans and is commonly carried asymptomatically ., Colonization rates are highest in early childhood , where they can exceed 80% 1; in crowded environments such as daycare centers 2; and when viral respiratory infections are prevalent 3 ., From its niche in the nasopharynx , the bacterium can invade other host sites , and as a result the pneumococcus is a leading cause of otitis media , pneumonia , and septicemia ., Importantly , colonization of the nasopharynx is the reservoir for pneumococcal disease 4–7 and transmission between hosts 8 , 9 ., While pneumococcal colonization and disease have been well-studied using animal models ( for review see 10 , 11 ) , transmission of the bacterium remains poorly understood ., In humans , close contact is required for transmission , which is thought to occur via respiratory secretions , but the specific host and bacterial factors contributing to this process have not been elucidated 12 , 13 ., Recently , an infant mouse model of pneumococcal transmission has been described 14 ., In this model , ‘index’ mice are given Streptococcus pneumoniae intranasally and co-housed with uninoculated ‘contact’ littermates ., Subsequently , all pups are inoculated with influenza A virus and , following an exposure period , transmission from index to contact mice is assessed by enumerating bacteria in the nasopharynx ., Using this model , this group demonstrated that both increasing the bacterial titer in the index mice and inducing inflammation in the contact mice led to more efficient transmission 15 ., Given the timing of these experiments , these studies suggest a role for the innate immune response in the setting of co-infection in transmission of the pneumococcus from host to host ., The host immune response to influenza has been extensively reviewed 16 , 17 and is briefly summarized here to highlight key elements ., After passing through the mucus layer lining the respiratory tract , influenza A virus is recognized by several pattern recognition receptors ( PRRs ) expressed by epithelial cells , including the Toll-like receptors ( TLRs ) , nucleotide oligomerization domain-like receptors ( NLRs ) , and retinoic acid-inducible gene-I receptors ( RIG-I ) ., Engagement of these receptors induces a signaling cascade that culminates in expression of interferons ( IFNs ) and pro-inflammatory cytokines and chemokines , resulting in recruitment and activation of immune cells , such as neutrophils and macrophages ., A recent study has shown that influenza infection also leads to increased mucin expression by epithelial cells in the respiratory tract 18 ., TLR2 has been implicated in promoting clearance of the pneumococcus 19 , 20 , and stimulation of this receptor has been shown to protect against influenza infection 21 ., Additionally , recent work has shown that signaling through TLR2 can induce anti-viral responses after stimulation by viral and synthetic ligands ., 22 , 23 Thus , we hypothesized that mice deficient for TLR2 would display altered transmission of the bacterium in this model ., We found that mice deficient in TLR2 show heightened acute inflammation in index mice and enhanced transmission due to an increase in number of bacteria shed via nasal secretions ., This model recapitulates many facets of human-to-human transmission of pneumococcal carriage , such as concurrent viral infection and close contact between infants/children , and these findings provide insight into how innate immune responses to infection promote the spread of pathogens from host to host ., Our adaptations to the previously reported infant mouse model of pneumococcal transmission are outlined in the schematic in Figure 1A 14 ., In these experiments , four-day-old pups were intranasally inoculated with pneumococci ( index mice only ) and on day 8 all mice in the litter ( both index and contacts ) were intranasally inoculated with Influenza A/HKx31 ( mouse-adapted H3N2 ) virus ., On day 14 , the pups were sacrificed and bacterial loads were enumerated in nasal lavages ., In wildtype mice , acquisition of colonization was detected in approximately half ( 47% ) of the contact pups ( Figure 1B , flu ) ., In contrast , without influenza ( PBS administered at day 8 ) , none of the contact pups acquired S . pneumoniae ( Figure 1B , mock ) ., Moreover , transmission was not simply due to increased bacterial load in the index mice , as there was no significant difference between the mock or flu index groups ., This phenotype makes this model particularly useful for probing the factors that limit and promote bacterial transmission because transmission can be either increased or decreased by experimental manipulations ., As this infection model utilizes a six-day exposure period , it is possible that contact mice infected early in that window could then go on to spread the bacteria to other contacts ., In order to test this possibility , we repeated the initial experiment but gave S . pneumoniae to the index ( “inoculated contact” ) on day 9 instead of day 4 , to simulate acquisition from an index mouse ., These inoculated contacts were able to spread infection effectively , with four out of five “uninoculated contact” mice acquiring colonization ( Figure S1 ) ., The observed transmission among contact mice makes it unlikely that the ratio of index to contact mice was a significant factor in overall rates of transmission ., We then repeated the transmission experiment using tlr2−/− mice ., All tlr2−/− contact mice acquired pneumococcal colonization and were colonized at high levels ( Figure 2A ) ., As TLR2 deficiency led to increased transmission , we reasoned that stimulation of TLR2 could limit bacterial spread ., To test this , we performed a transmission experiment with wildtype mice and intranasally administered the TLR2 agonist Pam3Cys three times over the course of the exposure period ( days 8 , 10 , and 12 ) ., As depicted in Figure 2B , transmission was significantly less efficient in treated animals than in control litters , with only one mouse out of sixteen acquiring colonization ., We hypothesized that the increase in transmission efficiency seen in tlr2−/− animals could be due to either increased spread by the index mice or increased susceptibility in the contact mice ., To address this question , we assessed whether the transmission phenotype was dependent on the index or contact mice in a mixed litter experiment ., Age-matched litters of wildtype and tlr2−/− mice were inoculated with S . pneumoniae as described above , but the index mice from each litter were switched , such that tlr2−/− index were co-housed with wildtype contacts and wildtype index were co-housed with tlr2−/− contacts ., After the six-day exposure period , only 39% of the tlr2−/− contacts housed with wildtype index mice had detectable levels of colonization ( Figure 2C ) ., This was not significantly different from the wildtype contact group in the previous experiments , but was significantly different from the tlr2−/− contact group ( p\u200a=\u200a0 . 0014 ) ., On the contrary , 89% of wildtype contacts housed with tlr2−/− index mice became infected ( Figure 2C ) ., This was not significantly different from the tlr2−/− contact group , but was significantly different from the wildtype contact group ( p\u200a=\u200a0 . 0013 ) ., These results indicate that the increased transmission phenotype seen in the tlr2−/− mice is linked to the index mouse , which we postulated was due to increased spread of the bacterium from these mice ., However , this effect was not due to an increased bacterial load in the tlr2−/− mice compared to the wildtype ., We thus concluded that TLR2 deficiency likely did not cause an increase in susceptibility to bacterial acquisition in this model ., We next assessed the innate immune response to infection with influenza and pneumococcus , both separately and in the context of co-infection , in our infant mouse model ., To test this , we infected wildtype mice as index pups according to the schematic in Figure 1A , giving PBS doses when appropriate for single or mock-infected groups ., Nasal lavage was performed on day 14 , and a sample of lavage fluid was stained with antibodies against a panel of immune cells ( Ly6G , CD11b , F/480 and CD4 ) ., While no significant influx of macrophages or T cells was seen ( data not shown ) , we noticed significant differences in neutrophil populations , as seen in Figure 3A ., Neutrophils ( Ly6G+ and CD11b+ events ) comprised very little of the total cell count for both mock-infected mice and those given S . pneumoniae alone ., However , when influenza was present , both in single infection and in co-infection with the pneumococcus , we observed a significant neutrophil influx , comprising 62 . 6% and 74 . 7% of the total cell infiltrate , respectively ( Figure 3A ) ., We also visualized neutrophils and bacteria in the lavage fluid via immunofluorescence microscopy ., The representative image in Figure 3B shows multiple intact pneumococci associated with a cluster of neutrophils ., These clusters of neutrophils were not seen in mice that were mock infected or infected with the pneumococcus alone ( data not shown ) ., Taking these results together , we posit that influenza infection sparks neutrophil influx into the nasopharynx , and that this acute inflammatory response is ineffective at clearing the bacteria , but facilitates the spread of bacteria amongst littermates ., Based on the observation that TLR2 deficiency led to increased transmission , we hypothesized that the innate immune response to co-infection with the pneumococcus and influenza differed between these two groups ., When we analyzed the neutrophil content of lavage fluid from co-infected tlr2−/− mice , we found neutrophils to make up a significantly higher percentage of the total cells than in wildtype mice ( mean 86 . 1% , Figure 4A ) ., This was not due to a difference in response to the pneumococcus , as these percentages were not different between wildtype and tlr2−/− infected with S . pneumoniae alone ( Figure 4A ) ., As this result suggested that there was more inflammation in co-infected tlr2−/− mice , we also compared mucus production by analyzing relative expression levels of Muc5ac , the primary secreted mucin of the nasopharynx , by qRT-PCR 24 ., We found that in co-infected tlr2−/− mice , Muc5ac transcript levels were on average 2 . 8-fold higher than in co-infected wildtype mice ( Figure 4B ) ., Recent work has shown that TLR2-dependent signaling can induce anti-viral responses in the form of type I IFN production 22 , 23 ., Considering these findings , we hypothesized that tlr2−/− mice display a weakened anti-viral response , rendering them more susceptible to influenza infection leading to heightened inflammation ., To assess the anti-viral responses produced by the mice used in our model , we examined IFNα expression levels by qRT-PCR at an early time point after influenza inoculation ( 3 days ) and found that co-infected tlr2−/− mice showed a 2 . 6-fold reduction in IFNα transcript compared to wildtype ( Figure 4C ) ., Additionally , levels of viral RNA were 5 . 4-fold higher in tlr2−/− mice than wildtype , suggesting that the increased inflammation observed could be due to increased viral titers in the tlr2−/− host ( Figure 4D ) ., This increase in viral levels was not dependent on the presence of pneumococcus , as evidenced by the ∼3-fold increase in viral RNA in tlr2−/− mice infected with influenza alone ., Thus , increased inflammation appears to be associated with increased transmission of S . pneumoniae by infected hosts ., Supporting this , mice treated with the TLR2 agonist Pam3Cys displayed both lower viral titers ( Figure 4E ) and subsequently , less of an inflammatory response , with fewer total cells in the nasopharyngeal infiltrate ( Figure 4F ) ., Taken together , our data have suggested that inflammation promotes bacterial spread , and thus we hypothesized that increases in inflammation and mucus production lead to increased bacterial shedding in the form of nasal secretions ., In order to determine the number of bacteria being shed in this manner , we adapted a method in which the nose of the mouse is gently pressed onto a nutrient agar plate and exhaled bacteria are then quantified 25 ., We observed that in both wildtype and tlr2−/− co-infected mice , detectable levels of bacteria were shed throughout the experiment , starting on day 10 ( Figure 5A , B ) ., When bacterial counts from days 10–14 are compared between groups , the tlr2−/− mice shed significantly more bacteria then the wildtype group over the course of the exposure window ( p<0 . 0001 ) ., As TLR2 deficiency led to increased shedding , we reasoned that stimulation of this receptor could limit shedding , and thus we repeated the wildtype shedding experiment , including three intranasal administrations of Pam3Cys over the exposure period ., The treated animals shed significantly fewer bacteria than the control litter ( Figure 5E ) , demonstrating that TLR2 stimulation can limit shedding ., Bacterial shedding was dependent on influenza co-infection for both groups , as mice infected with S . pneumoniae alone did not shed appreciable amounts of bacteria at any point in the experiment ( Figure 5C , D ) ., To determine if the numbers of bacteria shed were sufficient to infect contact mice , we intranasally administered 500 CFU of S . pneumoniae ( ∼ID50 for adult mice ) to 7-day-old pups and assessed colonization levels the following day ., We found that this was a sufficient dose to establish colonization with a robust increase in bacterial density over the inoculum size in the infant mouse nasopharynx , with 100% of pups colonized ., Most adult mice , in contrast , were not consistently colonized by this low dose and those that became colonized had bacterial numbers below that of the inoculum – a result that correlated with the lack of pneumococcal transmission observed among adult mice ( Figure 5F ) ., In co-infected infant pups , from days 10–14 , approximately 14% of wildtype mice regularly shed >500 colonies , while 43% of tlr2−/− mice consistently shed at this level or higher , corresponding to the rates of transmission in these two groups ., In contrast , none of the mice given PBS instead of influenza shed above this level ., These experiments suggest that TLR2-dependent inflammation induced by influenza infection promotes shedding of S . pneumoniae through nasal secretions , and the contact between infected and uninfected infant mice is sufficient to mediate bacterial transmission from host to host ., This study aimed to expand existing knowledge of the transmission of respiratory bacterial pathogens by specifically analyzing spread of Streptococcus pneumoniae in an infant mouse model ., Transmission of bacterial pathogens is critical to their success but has long been a black box in the study of pathogenesis due to a lack of tractable animal models ., An infant mouse model utilizing influenza co-infection has been recently introduced 14 , 15 ., These studies have established a preliminary link between inflammation induced by infection and spread of the bacterium ., Here , we sought to identify specific host factors that contribute to this process and to gain further insight into the mechanisms responsible for transmission in this model ., Our results demonstrate a role for the innate immune receptor TLR2 in transmission of the pneumococcus in a flu-dependent manner ., The findings in our report are consistent with previous studies in adult mice , which suggested that TLR2 stimulation by either commensal bacteria 26 or a synthetic agonist 21 is protective against flu infection ., Previous in vitro studies have also demonstrated a role for TLR2-mediated signaling in induction of type I IFNs 22 , 23 ., Our work adds in vivo data to this model , demonstrating that expression of IFN α , a key component of the anti-viral response , was diminished in tlr2−/− mice compared to wildtype mice ., These findings solidify a link between TLR2 activity and type 1 IFN expression ., Importantly , influenza co-infection was required for transmission to occur; this was true for both wildtype and tlr2−/− experimental groups ., Both infection with influenza alone and co-infection resulted in a significant inflammatory influx to the nasopharynx , with the largest proportion of the cellular infiltrate comprised of neutrophils ., In tlr2−/− mice , this response was even more pronounced , with a significantly higher percentage of neutrophils present than in the wildtype samples , while we did not observe any differences in macrophages or other cell types recruited ., The host response to increased viral titers also correlated with higher expression of the mucin Muc5ac ., We hypothesize that these higher levels of virus stimulate an exaggerated acute inflammatory response that drives an increase in nasal secretions , consisting of mucus and inflammatory cells , providing an exit vehicle for the pneumococcus ., As the presence of pneumococcus adds relatively little to inflammation , our findings suggest that it is primarily the host response to the virus that provides this vehicle for shedding , with the bacterium acting as a passenger ., The pneumococcus can thus take advantage of the heightened inflammatory state present in co-infected index animals ., The observation that there is very little inflammation seen in the context of a pneumococcal single infection explains why transmission is not detected when the animals are not given influenza ., The few neutrophils that are recruited to the airway lumen in response to pneumococcal colonization have a limited ability to take up the organism in the absence of specific antibody , and depletion of neutrophils has no effect on bacterial clearance in adult singly infected mice 27 ., Additionally , the neutrophil influx observed in influenza-infected lavages did not appear to be effective at lysing the bacterium , as indicated by the predominance of intact bacterial cells in immunofluorescence images ., Thus , the secretions resulting from robust inflammation are required for transmission , but this inflammatory response must also be ineffective at killing the bacterium ., We also demonstrate here that the inflammation induced by influenza infection promotes bacterial shedding from index mice at or above a level sufficient to infect uninoculated contact mice ., Transmission could be a consequence of bacterial shedding above this threshold level ., We conclude that the increased inflammation in tlr2−/− mice is due mostly to diminished sensing of the virus and inability to control viral infection ., Another consideration is that while tlr2−/− genotype itself does not bias the composition of the host microbiota 28 , it likely causes differences in sensing of the flora ., Although previous studies have shown that the microbiota can affect the immune response to influenza , the contribution of the colonizing pneumococci to the inflammatory response was small in comparison to influenza ., This was the case even though pneumococcal colonization itself stimulates TLR2 signaling 19 ., Thus , it appears that the increased viral load observed in tlr2−/− mice was sufficient to lead to a heightened inflammatory response through sensing by other viral PRRs resulting in more copious purulent mucus secretions ., These secretions carry live bacteria , which are then shed in increased numbers ., As the nursing mother piles infant mice in close proximity to one another , there are thus ample opportunities for the bacteria to spread from one host to another ., These studies also help to explain the transmission differences between adult and infant mice , namely that transmission has not been observed in similarly treated adults because of the higher inoculum required to establish robust colonization in adults ., This is the first study to implicate a specific host factor in transmission of the bacterial pathogen S . pneumoniae ., While stimulation of TLR2 limits transmission , approximately half of wildtype contact mice acquire the bacterium , indicating that other components of the innate immune system must contribute to the inflammation and shedding necessary for this process ., For instance , stimulation of other pattern recognition receptors that respond to influenza , such as TLR3 , TLR7 , and RIG-I , could affect pneumococcal transmission , as shown for TLR2 ., Signaling downstream of other viral PRRs has yet to be fully explored in the context of transmission ., The model detailed here thus shows much promise for investigating these additional microbial and host factors to determine the complete mechanism behind bacterial shedding and its consequences for host-to-host transmission ., This study was conducted according to the guidelines outlined by National Science Foundation Animal Welfare Requirements and the Public Health Service Policy on the Humane Care and Use of Laboratory Animals ., The protocol was approved by the Institutional Animal Care and Use Committee , University of Pennsylvania Animal Welfare Assurance Number A3079-01 , protocol number 803231 ., S . pneumoniae strains were grown statically in tryptic soy broth ( BD , Franklin Lakes , NJ ) at 37°C in a water bath ., All studies described here utilized strain P1121 , a serotype 23F isolate that has been previously used for human carriage studies 29 ., Bacteria were stored in 20% glycerol at −80°C ., Influenza A/HKx31 ( H3N2 ) was grown in the allantoic fluid of 10-day embryonated chicken eggs ( B&E Eggs ) and stored at −80°C ., Viral concentrations for infection were determined by titration in Madin-Darby Canine Kidney cells , as described previously 30 ., All experiments using animals were approved by the Institutional Animal Care and Use Committee of the University of Pennsylvania , and mice were housed in accordance with IACUC protocols ., Wildtype C57BL/6 mice were originally obtained from The Jackson Laboratory ( Bar Harbor , ME ) ., The tlr2−/− mice have been described previously 19 ., All mice were bred and maintained in a conventional animal facility , and both male and female pups were used ., Mice were bred under specific pathogen-free conditions at the University of Pennsylvania ., Four days after pups were born , 1–2 index mice ( such that ratio of index∶contact was always 1∶3 to 1∶4 ) were randomly selected from each litter and inoculated with 2000 CFU of S . pneumoniae suspended in 3 µl PBS intranasally ., Inoculation was performed atraumatically with a blunt pipette tip without anesthesia , and index pups were returned to the litter ., When pups were 8 days old , all infants were given 2×102–2×104 TCID50 Influenza A/HKx31 in 3 µl PBS intranasally ., This H3N2 isolate was chosen because it replicates well in the mouse upper respiratory tract without causing disease 30 ., For mock infections , sterile PBS was given ., When indicated , pups were treated with the TLR2 agonist Pam3Cys on days 8 , 10 , and 12 ., Pam3Cys ( Invivogen ) was resuspended to a concentration of 2 mg/ml in sterile water , and 10 µg doses were given intranasally ., On day 14 , all pups were euthanized by CO2 asphyxiation ., To quantify bacteria , the trachea was exposed , cannulated , and flushed with 200 µl sterile PBS ., PBS lavages were serially diluted and plated on tryptic soy agar containing neomycin ( 20 µg/ml ) to minimize the growth of contaminants ., To obtain RNA from the epithelium , a second lavage was performed with 600 µl of RLT lysis buffer ( QIAGEN ) and stored at −80°C until needed ., Nasal lavage samples ( 100 µl per mouse ) were stained with the following fluorescent antibodies: CD4-FITC , Ly6G-PE , CD11b-perCP and F4/80-APC ( eBioscience ) after blocking with FC Block at 4°C ., Cells were then fixed with 1% paraformaldehyde and assayed using a BD FACSCalibur the following day ., Data were gathered using CellQuest Pro software ( BD ) , analyzed using FlowJo software ( TreeStar ) , and graphed with Prism 5 ( GraphPad ) ., Undiluted nasal lavage fluid was spotted onto glass microscope slides and allowed to air-dry , and then fixed and stained essentially as described 19 , using rabbit serum against type 23F pneumococcus ( 1∶5000 ) and rat anti-mouse α-Ly6B ( 1∶100 , AbD serotec ) primary antibodies with anti-rabbit-Cy3 and anti-rat-FITC conjugated secondary antibodies ( both 1∶600 ) , respectively , along with DAPI staining ., Immunofluorescence images were collected using a Nikon Eclipse E600 ( Nikon Instruments Inc . ) equipped with a liquid crystal ( Micro*Color RGB-MS-C; CRi Inc . ) and a charge-coupled device digital camera ., RNA was isolated from the epithelium lining the mouse nasopharynx following lavage with 600 µl RLT lysis Buffer using an RNeasy Mini Kit ( QIAGEN ) according to the manufacturers instructions ., For all experiments except IFNα analysis , lavages were collected at day 14 ., IFNα levels were measured in lavage collected on day 11 ., cDNA was generated from each sample using a high-capacity reverse transcription kit ( Applied Biosystems ) ., Approximately 10 ng cDNA was used as a template in reactions with forward and reverse primers ( 0 . 5 µM ) and SYBR Green ( Applied Biosystems ) , according to the manufacturers instructions ., Reactions were carried out using the StepOnePlus Real-Time PCR system , and fold changes were calculated using the ΔΔCT method ( Applied Biosystems ) ., GAPDH was used as an endogenous control ., The following primers were used in reactions: influenza nucleoprotein – F 5′-CAGCCTAATCAGACCAAATG-3′ , R 5′-TACCTGCTTCTCAGTTCAAG-3′; MUC5AC – F 5′-CCATGCAGAGTCCTCAGAACAA-3′ , R 5′-TTACTGGAAAGGCCCAAGCA-3′; GAPDH – F 5′-TGTGTCCGTCGTGGATCTGA-3′ , R 5′-CCTGCTTCACCACCTTCTTGAT-3′ , IFNα – F 5′-TCTGATGCAGCAGGTGGG-3′ , R 5′-AGGGCTCTCCAGACTTCTGCTCTG-3′ ., Infant mice were infected as “index mice” as described above for the transmission model ., From day 8 to day 14 , daily sampling was performed for each mouse , in which the nose of the mouse was gently pressed onto tryptic soy agar containing neomycin ( 20 µg/ml ) 10 times to obtain a representative sample ., The mouse was then returned to the cage , and exhaled bacteria were spread across the surface of the plate with a polyester-tipped swab ., Plates were grown overnight at 37°C with 5% CO2 and colonies were enumerated the following day ., Six to eight-week-old ( adult ) and seven-day-old ( infant ) unanesthetized wildtype C57BL/6 mice were intranasally inoculated with 500 CFU of S . pneumoniae P1121 ., Twenty-four hours later , mice were sacrificed by CO2 asphyxiation , and tracheas were exposed and cannulated , then flushed with 200 µl PBS ., Lavage fluid was collected from the nares , serially diluted , and plated on tryptic soy agar ., Colonies were enumerated after overnight incubation at 37°C with 5% CO2 .

Introduction, Results, Discussion, Materials and Methods

While the importance of transmission of pathogens is widely accepted , there is currently little mechanistic understanding of this process ., Nasal carriage of Streptococcus pneumoniae ( the pneumococcus ) is common in humans , especially in early childhood , and is a prerequisite for the development of disease and transmission among hosts ., In this study , we adapted an infant mouse model to elucidate host determinants of transmission of S . pneumoniae from inoculated index mice to uninfected contact mice ., In the context of co-infection with influenza A virus , the pneumococcus was transmitted among wildtype littermates , with approximately half of the contact mice acquiring colonization ., Mice deficient for TLR2 were colonized to a similar density but transmitted S . pneumoniae more efficiently ( 100% transmission ) than wildtype animals and showed decreased expression of interferon α and higher viral titers ., The greater viral burden in tlr2−/− mice correlated with heightened inflammation , and was responsible for an increase in bacterial shedding from the mouse nose ., The role of TLR2 signaling was confirmed by intranasal treatment of wildtype mice with the agonist Pam3Cys , which decreased inflammation and reduced bacterial shedding and transmission ., Taken together , these results suggest that the innate immune response to influenza virus promotes bacterial shedding , allowing the bacteria to transit from host to host ., These findings provide insight into the role of host factors in the increased pneumococcal carriage rates seen during flu season and contribute to our overall understanding of pathogen transmission .

In this study , we sought to identify factors contributing to the transmission of the bacterial pathogen Streptococcus pneumoniae ( the pneumococcus ) , a major cause of otitis media , pneumonia , and septicemia ., Often found as a co-infection with other bacterial and viral pathogens , the pneumococcus is commonly carried by young children and is spread by close human contact , most likely through large droplet respiratory secretions ., The specific determinants of bacterial transmission , however , have not been identified ., This report details our use of an infant mouse model of transmission , which includes influenza A co-infection , to elucidate the mechanism of host-to-host transmission ., We found that the inflammatory response to influenza , which is aggravated in the context of weakened host defense , promotes transmission by inducing bacterial shedding from the mouse nose ., These results show how a bacterial pathogen exploits the host immune response to spread from one host to the next .

bacteriology, gram positive bacteria, bacterial diseases, infectious diseases, innate immune system, medicine and health sciences, pathology and laboratory medicine, immunity, medical microbiology, host-pathogen interactions, streptococcus, microbial pathogens, biology and life sciences, immunology, microbiology, pathogenesis, bacterial pathogens, immune system

journal.pntd.0004428

Modeling Relapsing Disease Dynamics in a Host-Vector Community

An important development in the study of infectious diseases is the application of mathematical models to understand the interplay between various factors that determine epidemiological processes ., Many systems show a rich variety of dynamics that arise from nonlinear interactions ( due to the mixing of different infectious populations ) or temporal forcing ( caused by changes in the average contact rate ) 1 ., Vector-borne diseases are additionally complex with interactions between multiple host and vector species 2–4 ., Compartmental models , such as susceptible , infectious , and removed models ( SIR ) 5 , have been applied to many disease systems in an effort to examine system dynamics ., In these epidemic models , susceptible individuals pass into the infective class , from which they transition to the removed class ., For some diseases , recovered individuals may relapse with a reactivation of infection and revert back to an infective class ., An example of such a system is found in van den Driessche et al . 6 , which included a relapsing rate between the susceptible and the same infected compartment ., Adding additional infected compartments simulates disease systems in which there is a relapsing component , leading to a prolonged infectious period , presumed to be important to disease persistence ., To our knowledge , the addition of a relapsing component has not been applied to a host-vector system ., Noteworthy vector-borne relapsing diseases include tick-borne relapsing fever ( TBRF ) and malaria ., An advantage of these types of models is the ability to vary parameters , while monitoring the overall effect on the disease system , allowing for the exploration of characteristics of the system that may not be well understood ., Tick-borne relapsing fever ( TBRF ) is a cryptic disease that still causes significant morbidity and mortality worldwide , especially in African countries 7–10 ., TBRF is a vector-borne zoonotic disease endemic to central Asia , Africa , and the Americas 11 , and is caused by infection with Borrelia spirochetes ., All but one species of relapsing fever spirochetes are vectored by soft ticks ( Ornithodoros spp . ) 12 ., Relapsing fever is characterized by recurring febrile episodes and generalized symptoms including headache , chills , myalgia , nausea , and vomiting13 ., There is a rapid onset of disease symptoms , with a febrile episode lasting 3–6 days , after which symptoms subside , only to return in 7–10 days ., Symptoms are associated with large number of spirochetes present in the bloodstream ( spirochetemia ) , and subside when the host generates an antibody response against the variable major proteins ( Vmps ) ., The Vmps are involved with antigenic variation , and relapsing fever Borrelia produce a new variant during infection , subsequently attaining high densities 14 , 15 ., Little is known regarding the number of relapses in natural hosts , but studies have shown a range from 1 to 5 in experimentally infected animals 16 ., In humans , there is an average relapse rate of three febrile episodes without treatment , but up to 13 relapses have been observed 17 ., Ornithodoros spp ., ticks are long-lived , fast feeding vectors that are known to live > 10 years , and have been shown to survive for up to five years without feeding 18 ., Ornithodoros ticks are nidicolous ticks that rarely leave the confines of the host nest or burrow and are able to obtain a blood meal and detach from the host in < 90 min ., Additionally , soft ticks only obtain a blood meal about once every 3 months; even when presented with the opportunity to feed daily ., Ornithodoros ticks require several months between feedings and can survive years between feeding ., The longevity of these ticks means that they outlive their rodent hosts , affording the potential to infect several cohorts of rodents over the course of the tick lifespan ., Once infected ticks remain infected and infectious for the duration of their lifespan ., Here , we model TBRF caused by infection with B . hermsii and vectored by O . hermsi ., We parameterize the model with field-derived values from hosts on Wild Horse Island in Montana and a single genomic group I ( GGI ) strain of B . hermsii ., The overall goal of this study was to develop a SIR model using TBRF dynamics to describe a host-vector system with a relapsing class of host individuals ., First , using specific information from a TBRF system located on Wild Horse Island , MT , a model for the dynamics of a single host-vector interaction was developed ., For models with increasing numbers of relapses and multiple hosts , equilibrium analysis was performed and R0 was generalized ., Parameter values were considered in the model to provide theoretical criteria for population stability and to determine the parameters that would result in elimination of the disease ., Finally , single and coupled host-vector systems were explored , focusing on the addition of less competent hosts and the number of relapses needed in order to maintain an endemic equilibrium ., We use the model to ask several important biological questions pertaining to the TBRF system determining effect adding relapsing classes has on pathogen persistence and the effect of multiple host species with varying competency for acquiring and transmitting B . hermsii ., We sought to develop a model based on disease dynamics on Wild Horse Island ( WHI ) , Flathead Lake , Lake County , MT . WHI is the largest island ( ~2100 acres ) on Flathead Lake and like other islands on the lake has a limited diversity of rodent host species ., WHI is almost exclusively inhabited by deer mice ( Peromyscus maniculatus ) and pine squirrels ( Tamiasciurus hudsonicus ) as the terrestrial rodents and provided an important opportunity to develop and parameterize a model including only two hosts ., Although there are two genomic groups ( GGI and GGII ) of B . hermsii present on WHI , we parameterize the model using estimates for only GGI B . hermsii , as host competency experiments have primarily been performed with GGI B . hermsii 16 ., A key assumption for host-vector disease modeling is the definition of the transmission term , which represents the contact between hosts and vectors ., The formulation of the transmission term affects the reproduction number , R0 , which is a central predictor of disease systems 19 ., For host-vector disease models , the transmission term includes the vector biting rate ., This rate controls the pathogen transmission both from the vector-to-host and from the host-to-vector ., The TBRF model follows frequency-dependent transmission assumptions through the biting rate , since a blood meal is only required approximately once every three months regardless of the host population density ., Following this framework , hosts would likely experience an increasing number of bites as the vector population increased ., Given a mathematical model for disease spread , R0 is an essential summary parameter ., It is defined as the average number of secondary infections produced when one infected individual is introduced into a completely susceptible host population 20 ., When R0 < 1 , the disease free equilibrium ( DFE ) at which the population remains in the absence of disease is locally asymptotically stable ., However , if R0 > 1 , then the DFE is unstable and invasion is always possible ( see 21 ) and a new endemic equilibrium ( EE ) exists ., For this study , R0 was extracted following the methodology developed in van den Driessche et al . 22 ( see also 23 , 24 ) for general compartmental disease models , which can be extended to more complicated host-vector disease systems 25 , 26 ., Specific parameter values for this system have not yet been determined , but can be estimated from similar studies and from data collected on O . hermsi from laboratory experiments ., The units of the rates are individuals per month ., Table 1 summarizes the notation for all system parameters and variables ., See Table 2 for specific model values used in all of the host-vector models ., Note that parameters denoted with additional subscripts of ps and dm refers to values specific to the pine squirrel and deer mouse host-vector systems , respectively ., The birth rates for host and vector are each set to a constant value ( β and βv , respectively ) and the compartmental death rates ( for host and vector ) are identical and set equal to birth rate ., Then the death rates must be, μs=μi1=⋯=μij=μr=βj+2, ( 1 ), and, μsv=μiv=βv2 ., ( 2 ), The growth rate of pine squirrels ( βps = 0 . 33 individuals per month ) is an average of the rates found in the literature , i . e . , four individuals per litter at 1 litter per year 27 ., The growth rate of deer mice is also taken from average estimates from the literature; we estimate growth rate based on an average of three litters per year and four young per litter , ( βdm = 1 individual per month ) 28 ., The death rates are determined from Eq ( 1 ) , which depends on the number of relapses in the system ., For example , for a pine squirrel host-vector system with one relapse , all death rates would be 0 . 0825 ., Life history dynamics of O . hermsi are not well documented and virtually nothing is known about the reproductive behavior and survival of these ticks in nature ., Conservative estimates from the laboratory show that soft-bodied ticks lay on average five clutches over their approximately 10 year lifespan with roughly 50 eggs per clutch 29 ( T . Schwan personal communication ) ., Thus , the vector birth rate is βv = 2 . 08 individuals per month ., Following Eq ( 2 ) , we get death rates of μsv = μiv = 1 . 04 for the vector compartments ., The rate at which an individual transitions among infected compartments and to the removed compartment is fixed and is assumed to be the same for all compartments ., As more infected compartments are added to the system , the corresponding constant rates are γ = α = α1 = … = αj-1 , for j infected compartments ., Field parameter estimates have not yet been made for these transition rates ( i . e . , relapse and recovery rates ) ., Laboratory results from three pine squirrels indicate a transition rate of approximately 4 . 35 individuals per month for a single compartment ( Burgdorfer and Mavros 1970 ) ., Then γ = α = α1 = … = αj-1 = 4 . 35 ., Ticks are assumed to bite a host once every three months ( i . e . , f = 0 . 33 ) ., Competency values are between 0 and 1 and thus modify the transmission rate of the infection by multiplying the biting rate ., Burgdorfer and Mavros 16 observed a high competency in pine squirrels successfully infecting 3/3 animals by tick bite or injecting them with triturated ticks ., Using the same methods , they challenged deer mice with B . hermsii and were unsuccessful in establishing infection ., Thus , we used competency values cv = 0 . 95 for the probability of transmission for vectors , cps = 0 . 90 for pine squirrels , and cdm = 0 . 10 for deer mice ., The carrying capacity for the pine squirrel and deer mouse system is determined specifically for WHI ., On WHI there are approximately 425 ha of suitable habitat for pine squirrels with up to a maximum of 2 individuals per suitable habitat patch and approximately 850 ha of suitable deer mouse habitat with a conservative estimate of just less than 12 mice per ha 28 ., Thus , the total number of pine squirrels ( Nps ) is estimated at 850 and total number of deer mice ( Ndm ) is estimated at 10 , 000 ., The soft bodied tick population ( Nv ) is virtually unknown , however , we assume that they are limited to the nests of their hosts ., Initial field collections have found as many as 14 ticks in one nest on the island 30; other collection efforts show > 300 ticks can be collected from a single nest or snag 31 ., Because the estimates of ticks per nest vary largely between our limited collection on WHI and the literature we chose a conservative number of ticks ., We estimate that each squirrel has less than one nest ( because of juveniles in the system ) , and each nest is inhabited by 14 ticks ., We found no ticks in nest material collected from deer mice , however , nest material collected during the human outbreak in 2002 yielded 14 O . hermsi; the carcasses of two deer mice were found nearby and American Robins ( Turdus migratorius ) had been nesting there 30 ., Thus it is nearly impossible to estimate the average number of ticks in a deer mouse nest , or if in fact they are coming in contact with ticks while visiting other nests ., We used an estimate of 20 , 000 total ticks on the island split equally among host systems ., We chose a conservative estimate of 1% of all ticks are infected , as none of 12 of 14 field collected ticks were found to be infected 30 ., Thus , we used Sv ( 0 ) = 9 , 900 ticks for the single host-vector system and Sv ( 0 ) = 19 , 800 ticks for the coupled host-vector system ., A model for the dynamics of TBRF in a single host-vector system is considered ( see Fig 1A ) ., The following assumptions are used to establish a model that is appropriate for the WHI TBRF system for the host pine squirrel and soft tick vector , O . hermsi ., ( 1 ) The only sources of infection occur between the bite of an infective vector and susceptible host and between a bite of a susceptible vector and infective host ( i . e . there are no horizontal or vertical transmission events ) ., ( 2 ) The vector becomes infected and infectious for life immediately upon biting an infectious host ., ( 3 ) The transmission terms are frequency-dependent through the biting rate , f ., ( 4 ) The hosts relapse to different infected compartments ( i . e . different serotypes within the hosts caused by antigenic variation ) at rate α and recover from the disease at rate γ ., ( 5 ) Though mortality rates are noted to differ for each compartment , we assume a constant total population for both hosts and vectors ( N and Nv , respectively ) ., Thus , recruitment ( or birth ) and the sum of the removal ( or death ) rates from each compartment must be equal ( Eqs 1 and 2 ) ., The generalized system for the infection dynamics in a single host-vector system with j—1 relapsing rates for j = 1 infected compartments describes the number of susceptible hosts S ( t ) , infectious hosts Ik ( t ) , removed hosts R ( t ) , susceptible vectors Sv ( t ) , and infected vectors Iv ( t ) , where the total host population is, N=S+∑k=1jIk+R, and the total vector population is Nv = Sv + Iv ( see Fig 1A for a compartmental diagram and Table 1 for parameter definitions ) ., The equations are Host equations:, S•=βS−fcvIvSN−μsSI•1=fcvIvSN−α1I1−μi1I1I•2=α1I1−α2I2−μi2I2 . ., . I•j−1=αj−2Ij−2−αj−1Ij−1−μi ( j−1 ) Ij−1I•j=αj−1Ij−1−γjIj−μijIjR•j=γjIj−μrR ., ( 3 ), Vector equations:, S•v=βvSv−fcSvN∑i=1jIi−μsvSvI•v=fcSvN∑i=1jIi−μivIv ., ( 4 ), To evaluate the invasiveness of the disease in this system , we extracted R0 following the techniques developed by van den Driessche and Watmough 22 by sequentially adding infected compartments ( see S1 for equilibrium analysis and derivations ) ., The form of R0 was then inferred for j—1 relapsing rates between j infected compartments as, R0=fccvμivSv ( 0 ) N ( 0 ) 1α1+μi11+α1α2+μi2⋯1+αj−1γ+μij ., ( 5 ), R0 is directly proportional to the biting rate ( f ) , competency values ( c and cv ) , and the ratio of initial vectors to initial hosts, ( Sv ( 0 ) N ( 0 ) ), and inversely proportional to the vector death rate ( μiv ) and the rate that moves individuals out of the infected compartments ( α α1 , … . , αj-1 , μi1 , … , μij , and γ ) ., In addition , a pattern emerges as more infected compartments are added: a nesting sequence of terms that increase the value of R0 and potentially contribute to a change in stability of the DFE ., To illustrate this concept , we used the pine squirrel host parameters ( Table 2 ) for increasing number of infected compartments and plotted R0 ., R0 crosses 1 at between j = 4 and j = 5 infected compartments ( i . e . , four relapses; Fig 2 ) ., Here , the single host-vector model is expanded to include two hosts , namely pine squirrels and deer mice ., Fig 1B is a compartmental diagram for the two systems with no relapses ., The first host-vector system ( Sps , I1 , ps , Rrs , Sv , ps , Iv , ps ) is coupled with the second system ( Sdm , I1 , dm , Rdm , Sv , dm , Iv , dm ) through ticks biting either host species , with parameter f , and is further controlled by competency values of either the ticks ( cv ) or hosts ( cps or cdm for pine squirrel and deer mice , respectively ) ., Transmission occurs through three mechanisms: 1 ) fcv , which is the biting rate modified by the tick competency through which an infected tick bites a host from each system , 2 ) fcps , which is the biting rate modified by the pine squirrel competency in that a susceptible tick bites an infected pine squirrel , and 3 ) fcdm , which is the biting rate modified by the deer mouse competency , such that a susceptible tick bites an infected deer mouse ., The parameters remain as in the single host vector system , denoted with additional subscripts to represent the respective host-vector system ( either ps or dm ) , and are explained in Tables 1–2 ., The generalized system for the infection dynamics in a coupled host-vector system with j—1 relapsing rates for j = 1 infected compartments describes the pine squirrel system with the number of susceptible hosts Sps ( t ) , infectious hosts Ik , ps ( t ) , and removed hosts Rps ( t ) ., The total pine squirrel host population is, Nps=Sps+∑k=1jIk , ps+Rps ., Likewise , the deer mouse host system consists of susceptible hosts Sdm ( t ) , infectious hosts Ik , dm ( t ) , and removed hosts Rdm ( t ) with a total deer mouse host population of, Ndm=Sdm+∑k=1jIk , dm+Rdm ., The vector compartments are susceptible vectors Sv ( t ) , infected vectors Iv ( t ) and a total vector population of Nv = Sv + Iv ., The equations are Pine squirrel host system:, S•ps=βSps−fcvIvSpsNps−μs , psSpsI•1 , ps=fcvIvSpsNps−α1 , psI1 , ps−μi1 , psI1 , psI•2 , ps=α1 , psI1 , ps−α2 , psI2 , ps−μi2 , psI2 , ps⋮I•j−1 , ps=αj−2 , psIj−2 , ps−αj−1 , psIj−1 , ps−μi ( j−1 ) , psIj−1 , psI•j , ps=αj−1 , psIj−1 , ps−γj , psIj , ps−μij , psIj , psR•j , ps=γj , psIj , ps−μr , psRps ., ( 6 ), Deer mouse host system:, S•dm=βSdm−fcvIvSdmNdm−μs , dmSdmI•1 , dm=fcvIvSdmNdm−α1 , dmI1 , dm−μi1 , dmI1 , dmI•2 , dm=α1 , dmI1 , dm−α2 , dmI2 , dm−μi2 , dmI2 , dm⋮I•j−1 , dm=αj−2 , dmIj−2 , dm−αj−1 , dmIj−1 , dm−μi ( j−1 ) , dmIj−1 , dmI•j , dm=αj−1 , dmIj−1 , dm−γj , dmIj , dm−μij , dmIj , dmR•j , dm=γj , dmIj , dm−μr , dmRdm ., ( 7 ), Coupled vector system:, S•v=βvSv−fcpsSvNps∑i=1jIi , ps−fcdmSvNdm∑i=1jIi , dm−μsvSvI•v=fcpsSvNps∑i=1jIi , ps+fcdmSvNdm∑i=1jIi , dm−μivIv ., ( 8 ), As with the single host-vector system , we performed equilibrium analysis ( S2 ) and the form of R0 was inferred for j—1 relapsing rates between j infected compartments ., Where, PS=cpsNps ( 0 ) 1 ( ∝1 , ps+μi1 , ps ) 1+∝1 , psα2 , ps+μi2 , ps⋯1+∝j−1 , psγ+μij , ps⋯andDM=cdmNdm ( 0 ) 1 ( ∝1 , dm+μi1 , dm ) 1+∝1 , dmα2 , dm+μi2 , dm⋯1+∝j−1 , dmγ+μij , dm⋯ ., ( 10 ), From the coupled host-vector system it is apparent that R0 has the additional dependency for both the host competency values ( cps and cdm ) ., Since competency values are probabilities between 0 and 1 , then they will always decrease the value of R0 as they decrease ., Like the single host-vector system , a pattern emerges as more infected compartments are added to each host system ( Eqs 9 and 10 ) : a nested sequence of terms that increase the value of R0 and potentially contribute to a change in stability of the DFE ., To compare the results of the number of relapses needed for R0 > 1 in the coupled host-vector system with the single host-vector , we added an incompetent deer mouse host system ( cdm = 0 . 2 ) and increased the number of relapses in a pine squirrel host system until R0 reached 1 ., R0 crosses 1 at between j = 7 and j = 8 infected compartments ( seven relapses; Fig 3 ) ., Incorporating a relapsing component into a host-vector SIR modeling framework represents a step towards a better understanding and representation of complex disease systems ., We investigated the disease dynamics of TBRF and used the model to better understand the underlying dynamics and interactions among spirochetes , rodent hosts , and tick vectors that contribute to pathogen persistence ., Disease models were presented that describes ( 1 ) a single host-vector system with a single relapsing class of host individuals , and generalized to j-1 relapsing host classes and ( 2 ) a coupled host-vector model generalized as above to j -1 relapsing host classes ., Analytical techniques allowed for the generalization of R0 with increasing numbers of relapses , and parameters were identified that affect the elimination or persistence of the pathogen ( e . g . , biting rates , competency values , and population numbers ) ., In the single host-vector system , R0 is directly proportional to the biting rate ( f ) , competency values ( c and cv ) , and the ratio of initial vectors to initial hosts, ( Sv ( 0 ) N ( 0 ) ) ., An inverse relationship exists between R0 and the vector death rate ( μiv ) and the rate that moves individuals out of the infected compartments ( α1 , … . , αj-1 , μi1 , … , μij , and γ ) ., When additional relapsing classes are added to the system , R0 always increases because of the addition of a nested sequence of terms that is always > 1 ( Eq 5 ) ., The coupled host-vector system has similar dependencies with additional interesting dynamics that may be very important to understanding pathogen persistence and host diversity ., Coupling of the system with hosts of lower competencies will always reduce R0 ( Eqs 9 and 10 ) ., As the number of incompetent hosts available as blood meals for infected ticks increases , an effect comparable to the dilution effect occurs and R0 always decreases , leading to DFE ., The dilution effect states that in the presence of a second , less competent species , competent host-vector encounters leading to transmission events may be replaced by incompetent host-vector encounters that do not end in a pathogen transmission event , thus decreasing R0 3 , 4 ., The model presented here addresses the presence of multiple hosts with varying competencies and a single pathogen , however , the model can be extended to address not only differences in host species diversity but also the presence of > 1 pathogen strain ., The genetics of B . hermsii have been well characterized and isolates have been shown to fall into two distinct genomic groups , referred to as genomic group I and II ( GGI and GGII ) 32 , 33 ., The presence of both genomic groups of B . hermsii has been documented on WHI , while only GGII B . hermsii has been found to date on the mainland around Flathead Lake where host species diversity is greater than that of the WHI ., Field investigations of rodents on WHI confirmed infection in a single deer mouse ( Peromyscus maniculatus ) infected with GGII B . hermsii ( Johnson et al . In . Prep . ) ., This prompted a laboratory experiment in which we infected deer mice with both GGI and GGII B . hermsii and monitored them for infection ., We challenged deer mice with infection via needle inoculation and infectious tick bite and observed that deer mice show no susceptibility to GGI but are highly susceptible to GGII spirochetes ( Johnson et al . In . Prep . ) ., These findings were in contrast with Burgdorfer and Mavros 16 who were unable to establish infection in deer mice , however , they used infected ticks from a TBRF outbreak near Spokane , WA , U . S . A . , which resulted in isolation of GGI B . hermsii ., The coupled system presented here could be used to examine the effects of not only host species with varying competencies , but also diverse host communities in the presence of B . hermsii GGI and GGII ., The presence of both genomic groups simultaneously may result in a dampening of the dilution effect if GGII is able to infect a diverse array of host species even though GGI is more species limited ., Rodent trapping and tick collection on WHI showed one squirrel and one tick infected with GGI and three squirrels infected with GGII ., On WHI , 95% of all pine squirrels captured were seropositive for relapsing fever spirochetes while only 4% of deer mice possessed antibodies ( Johnson et al . In Prep . ) ., All infected individuals at mainland sites with diverse host species were infected with GGII spirochetes ( Johnson et al . In Prep . ) ., Although there are limitations to the model presented here , the model is an important first step in understanding a relapsing host-vector disease system ., All known complexities of the system were not addressed at this time , including incorporation of GGII strains of B . hermsii which can infect deer mice and possibly a wide range of other potential hosts ( Johnson et al . In Prep . ) ., Although there is conflicting evidence at the rate which transovarial transmission of B . hermsii occurs in O . hermsi , we can see from the R0 calculation that Iv does not appear in the equation and therefore will have little impact on disease persistence in the presence of hosts ., However , the existence of transovarial transmission may provide insight into the implication of O . hermsi serving as the reservoir for B . hermsii , i . e . , the ability to maintain infectious ticks in a prolonged absence of competent hosts and/or hosts in general ., Additionally , the model could be used to explore drivers in the host and vector communities and prevention/intervention strategies may be explored to identify the effectiveness of host control versus vector control ., Further , this may provide insight into human protective measures and the effectiveness of control strategies such as host vaccination; simulations could be run to assess the efficacy of control programs such as vaccination regimes and vector control ., Ecological factors including biotic and abiotic interactions may play a primary role in the emergence and persistence of infectious diseases 34–39 ., Understanding the complete epidemiology of a disease is crucial to advancing the ability to predict and control outbreaks in human and wildlife populations , however , this is rarely an attainable goal ., Sonenshine 40 outlines the sequence of steps typically undertaken when attempting to understand the epidemiology of a given system ., The pathway typically begins with the identification of a clinical syndrome , followed by discovery of the causative disease agent , and then the identification of the source of the agent in nature ., The final step includes investigating the often complex biology and ecology of the hosts and/or vectors involved ., Given the difficulty frequently encountered when attempting to study a disease in nature , the last step is often the most difficult ., The application of advanced modeling techniques to poorly understood systems is often the only way to begin to understand the drivers of these systems ., The ecological dynamics of relapsing fever systems around the world are poorly understood ., Here we use a North American system of relapsing fever caused by B . hermsii; however , information gathered from this modeling exercise can be applied to TBRF systems around the world ., TBRF remains a major public health threat in Africa 41 ., In addition to other TBRF systems , the ideas presented here may provide the groundwork for relapsing components to be included in other disease systems with greater public health implications such as malaria .

Introduction, Methods, Results, Discussion

Vector-borne diseases represent a threat to human and wildlife populations and mathematical models provide a means to understand and control epidemics involved in complex host-vector systems ., The disease model studied here is a host-vector system with a relapsing class of host individuals , used to investigate tick-borne relapsing fever ( TBRF ) ., Equilibrium analysis is performed for models with increasing numbers of relapses and multiple hosts and the disease reproduction number , R0 , is generalized to establish relationships with parameters that would result in the elimination of the disease ., We show that host relapses in a single competent host-vector system is needed to maintain an endemic state ., We show that the addition of an incompetent second host with no relapses increases the number of relapses needed for maintaining the pathogen in the first competent host system ., Further , coupling of the system with hosts of differing competencies will always reduce R0 , making it more difficult for the system to reach an endemic state .

An important development in the study of infectious diseases is the application of mathematical models to understand the interplay between various factors that determine epidemiological processes ., Vector-borne diseases are additionally complex with interactions between multiple host and vector species ., Understanding the transmission dynamics of vector-borne diseases is an important step towards controlling outbreaks and mitigating human infection risk ., Identifying the biotic and abiotic interactions and mechanisms that may contribute to disease emergence , establishment and persistence is necessary for assessing current and future disease risk , as well as developing effective control strategies ., Tick-borne relapsing fever ( TBRF ) is found around the world and is caused by several species of Borrelia spirochetes , which are vectored by soft ticks of the genus Ornithodoros ., TBRF is a cryptic disease that still causes significant morbidity and mortality , especially in some African countries ., Here , we develop and adapt a compartmentalized mathematical model ( SIR ) with a relapsing component to investigate the dynamics of TBRF .

death rates, invertebrates, medicine and health sciences, deer, ixodes, pathology and laboratory medicine, ruminants, infectious disease epidemiology, demography, vector-borne diseases, vertebrates, animals, mammals, population biology, ticks, infectious disease control, infectious diseases, epidemiology, pathogenesis, disease vectors, arthropoda, people and places, population metrics, rodents, arachnida, squirrels, host-pathogen interactions, biology and life sciences, organisms

journal.pgen.1004816

The Evolution of Fungal Metabolic Pathways

As one of the primary decomposers of organic material in nature , fungal species catabolize a wide diversity of substrates 1 , including cellulose and lignin , the two most abundant biopolymers on earth 2 ., Fungi are also superb chemical engineers , capable of synthesizing a wide variety of metabolites , including amino acids , small peptides , pigments and other natural products with potent toxic activities , such as antibiotics and mycotoxins 3–6 ., Fungal metabolites have historically been divided into primary , that is metabolites essential for growth and reproduction , and secondary , which include ecologically important metabolites not essential to cellular life 7 , 8 ., However , this distinction is arbitrary when applied to metabolic pathways rather than their products not only because the essentiality of a given pathway is species-specific 9 but also because the pathways that generate primary and secondary metabolites are not mutually exclusive 10 , 11 ., Perhaps more informatively , pathways can be divided into those shared by most organisms , which can be considered as belonging to general metabolism , and those specialized pathways that have evolved in response to the specific ecologies of certain lineages and , as a result , are more narrowly taxonomically distributed ., An intriguing feature of specialized metabolic pathways in fungi is that constituent genes are often physically linked on chromosomes forming what are known as gene clusters 12 , 13 ., Fungal metabolic gene clusters are distinct from the developmental gene clusters typically found in animal genomes , such as the Hox gene clusters; whereas animal gene clusters are composed of tandemly duplicated genes 14 , 15 , fungal metabolic gene clusters comprise genes that are evolutionarily unrelated ., Fungal metabolic gene clusters participate in diverse activities including nitrogen 16 , 17 , carbohydrate 18 , amino acid 19 , and vitamin 12 metabolism as well as in xenobiotic catabolism 11 , 20 and the biosynthesis of secondary metabolites e . g . , 21–28 ., Although this extraordinary metabolic diversity , whether in the form of clustered or non-clustered pathways , is integral to the entire spectrum of fungal ecological strategies ( e . g . , saprotrophic , pathogenic and symbiotic ) , we still know little about the evolutionary processes involved in its generation ., Gene duplication ( GD ) , a major source of gene innovation , is often implicated in the evolution of fungal metabolism e . g . , 29–31 , especially in the context of whole genome duplication ( WGD ) 32–34 and gene family expansion 35 , 36 ., Notable examples include the GD of enzymes involved in organic decay 30 , starch catabolism 37 , degradation of host tissues 31 , 38 , 39 and toxin production 36 ., Repeated rounds of GD , followed by divergence and differential gene loss , have also been invoked to explain the evolution of the gene clusters that generate the diverse alkaloids produced by plant symbiotic fungi 4 ., A second key source of metabolic gene innovation in fungi is horizontal gene transfer ( HGT ) 40–44; significant cases include the transfer of genes involved in xenobiotic catabolism 45 , 46 , toxin production 45 , 47 , degradation of plant cell walls 48 , 49 , and wine fermentation 50 ., More recently , HGT has been shown to be responsible for the transfer of entire metabolic gene clusters between unrelated fungi 11 , 51–58 ., Although both GD and HGT have been extensively studied in fungal genomes , how these two major sources of gene innovation have interacted with clustered and non-clustered metabolic pathways and sculpted their evolution is largely unknown ., To address this question , we analyzed 247 , 202 enzyme-encoding genes from 208 diverse fungal genomes whose protein products participate in hundreds of metabolic reactions ., We found that both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts ., On average , 90 . 0% of clustered metabolic genes underwent GD and 4 . 8% underwent HGT , whereas 88 . 1% and 2 . 9% of non-clustered metabolic genes experienced GD and HGT , respectively ., Remarkably , some genera appear to have undergone a larger number of HGT events than entire subphyla ., While the effect of GD was largely stable across metabolic categories , HGT varied extensively ., These results suggest that GD is the dominant and stable process underlying fungal metabolic diversity , whereas HGTs impact is more pronounced in specific lineages and metabolic categories ., The disproportionate effect of GD and HGT on clustered genes renders metabolic gene clusters into hotspots of metabolic innovation and diversification in fungi ., Analysis of 208 fungal genomes identified 247 , 202 Enzyme Commission ( EC ) -annotated metabolic genes ( ECgenes for short ) , which encoded proteins catalyzing 875 distinct enzymatic reactions in 130 metabolic pathways ( Figure 1; Table S1; Table S2 ) ., The percentage of the fungal proteome dedicated to metabolism was 15 . 4% in Saccharomycotina , 12 . 6% in Pezizomycotina and 8 . 9% in Agaricomycetes ( Table S3; Figure S1 ) ., Examination of fungal metabolism for the presence of metabolic gene clusters revealed that 3 . 0% ( 7 , 409 ) of ECgenes belonged to 3 , 408 distinct gene clusters , with the average genome containing 16 . 7 metabolic gene clusters and 36 . 3 clustered ECgenes ( Table S3 ) ., The percentage of clustered ECgenes was highly variable across the major lineages , being more than two-fold greater in the two Ascomycota lineages , namely Pezizomycotina ( 3 . 6% of ECgenes ) and Saccharomycotina ( 3 . 7% ) , than in Agaricomycetes ( 1 . 6% ) ( Figure 1 , Table S3 ) ., For example , the plant pathogen Fusarium solani species complex species 11 ( a . k . a . , Nectria haematococca , Sordariomycetes ) had 152 clustered ECgenes ( representing 6 . 2% of its ECgenes ) , the most of any genome analyzed , the yeast Torulaspora delbrueckii ( Saccharomycotina ) had 59 clustered ECgenes ( 7 . 3% ) , whereas the ectomycorrhizal fungus Laccaria bicolor ( Agaricomycetes ) had only 14 clustered ECgenes ( 1 . 1% ) ., To test whether clustering was variable across fungal metabolism , we used the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) metabolism hierarchy 10 to assign all ECgenes to 12 overlapping , higher-order metabolic categories ( carbohydrate , energy , lipid , nucleotide , amino acid , glycan , cofactor/vitamin , terpenoid/polyketide , other secondary metabolite , xenobiotics , biosynthesis of secondary metabolites , and microbial metabolism in diverse environments ) ., We found that the proportion of clustered ECgenes varied significantly across metabolic categories ( Figure 2 , Table S4 ) ., For example , clustered ECgenes from all lineages were significantly overrepresented in the KEGG categories carbohydrate and terpenoid/polyketide and underrepresented in the glycan category ., In addition , the proportion of clustered ECgenes in a given category often varied significantly between lineages ., For example , clustered ECgenes in the nucleotide and xenobiotic categories were only significantly overrepresented in Saccharomycotina and Agaricomycetes; clustered ECgenes in the same categories were underrepresented in Pezizomycotina ( Figure 2 ) ., Similarly , clustered ECgenes in the amino acid and lipid categories were underrepresented in Saccharomycotina , whereas clustered ECgenes in these same categories were overrepresented in Pezizomycotina and Agaricomycetes ( Figure 2 ) ., To evaluate the impact of GD and HGT on fungal metabolism , we inferred GD and HGT events by reconciling the gene tree of each ECgene to the fungal species phylogeny 59–61 ., Specifically , we assigned costs to GD , HGT , gene loss , and incomplete lineage sorting ( ILS ) and determined the most parsimonious combination of these four events to explain the ECgene tree topology given the consensus species phylogeny ., Therefore , HGT events were inferred only when an ECgene tree topology was contradictory to the species phylogeny and could not be more parsimoniously reconciled using a combination of differential GD and gene loss ., We evaluated multiple HGT costs and ultimately implemented a cost four times greater than the GD cost because it was the lowest HGT cost that recovered three published cases of HGT without any additional ( e . g . , potentially spurious ) cases of HGT in the corresponding ECs ( Table S5 ) ., On average , 88 . 7% of ECgenes per genome were inferred to have undergone one or more GD events ( Table S3 ) ., This percentage was lower in early diverging lineages; this was the case for both taxa with typical gene densities ( e . g . , Chytridiomycetes ) as well as for the extremely reduced microsporidians , which displayed the lowest percentages of duplicated metabolic genes ( 49 . 0% and 49 . 5% of ECgenes in E . cuniculi and E . intestinalis , respectively ) ., While the low percentages of GD in microsporidians are likely explained by genome streamlining , the low percentages observed in other early diverging lineages are harder to explain , although we note that their current sparse representation in the set of sequenced fungal genomes increases the uncertainty associated with estimating GD and HGT ., In contrast , 93 . 7% of ECgenes underwent GD in the Agaricomycetes ( Figure 1 ) , with the button mushroom , Agaricus bisporus , having 97 . 0% of its ECgenes affected by GD ( 704 to 722 ECgenes depending on the strain ) ., GD percentage was also high in the Saccharomycotina ( 91 . 4%; Figure 1 ) , including in species belonging to the Saccharomyces sensu stricto group , where the average increased to 95 . 3% , most likely as a consequence of an ancient whole genome duplication 33 , 62 ., Our analysis also identified that on average 2 . 8% of ECgenes per genome had undergone one or more HGT events ( Table S3 ) , which could be traced back to 823 unique HGT events ., The Pezizomycotina showed the highest percentage of HGT of all the major lineages , with an average 4 . 1% of ECgenes transferred per genome , and Saccharomycotina the lowest , with an average 1 . 8% of ECgenes transferred ( Table S3; Figure 1 ) ., Remarkably , some Pezizomycotina genera showed nearly as many or more HGT events than the entire Saccharomycotina subphylum ( Figure 3; Figure S2 ) ., For example , we identified 111 HGT events since the last common ancestor of the 15 Aspergillus species , the largest for any genus included in our analysis , but only 60 HGT events since the last common ancestor of the 48 Saccharomycotina genomes ., Notwithstanding the fact that genome coverage and age are not the same across fungal genera , several other Pezizomycotina genera showed an abundance of HGT events including Cochliobolus ( 53 HGTs; 8 genomes ) , Fusarium ( 52 HGTs; 4 genomes ) , and Trichoderma ( 50 HGTs; 6 genomes ) ., Within the Agaricomycetes , the highest concentration of HGT events was observed in the two Agaricus bisporus genomes ( 23 HGTs ) ., Examination of the degree to which GD and HGT have differentially impacted clustered and non-clustered metabolic genes revealed significant differences ( Figure 4; Table S6 ) ., On average , 90 . 0% of clustered ECgenes and 88 . 1% of non-clustered ECgenes underwent GD ( P\u200a=\u200a4 . 58×10−4 ) ., Similarly , 4 . 8% of clustered ECgenes underwent HGT compared to 2 . 9% of non-clustered ECgenes ( P\u200a=\u200a4 . 02×10−12 ) ., Examination of the impact of GD and HGT in the three major lineages shows that only in the Pezizomycotina was the percentage of GD and HGT significantly higher for clustered ECgenes than for non-clustered ECgenes ( GD: 93 . 3% for clustered ECgenes versus 89 . 5% for non-clustered , P\u200a=\u200a1 . 74×10−11; HGT: 6 . 6% for clustered ECgenes versus 4 . 0% for non-clustered , P\u200a=\u200a2 . 77×10−10 ) , suggesting that the trend is largely driven by Pezizomycotina ., In fact , in both Saccharomycotina and Agaricomycetes GD was more common in non-clustered ECgenes than in clustered ECgenes ( P\u200a=\u200a0 . 02 and P\u200a=\u200a0 . 01 , respectively; Figure 4 ) ., HGT was more common in Saccharomycotina non-clustered ECgenes than in clustered ones , whereas in Agaricomycetes a higher incidence of HGT events was observed in clustered ECgenes , although neither of these associations was statistically significant ( P\u200a=\u200a0 . 54 and P\u200a=\u200a0 . 16 , respectively; Table S6 ) ., To test whether GD and HGT prevalence varied across fungal metabolism , we examined the rates of the two processes in each of the 12 KEGG metabolic categories across our three major lineages ., We found that the effect of GD was generally consistent across metabolic categories , with 9/12 categories showing the same pattern of under/overrepresentation of duplicated ECgenes across the three lineages ( Figure 2 , Table S4 ) ., Specifically , the categories carbohydrate , glycan , and biosynthesis of secondary metabolites were overrepresented , the categories lipid , nucleotide , cofactor/vitamin , other secondary metabolites , and xenobiotics were underrepresented , whereas energy was not differentially represented in duplicated and non-duplicated ECgenes in all three lineages ., Unlike GD , HGT differentially affected metabolic categories in a lineage-specific fashion , with 10/12 categories differing in the pattern of under/overrepresentation of duplicated ECgenes across lineages ( Figure 2 , Table S4 ) ., For example , ECgenes in biosynthesis of secondary metabolites were overrepresented for HGT events in Pezizomycotina and Saccharomycotina , but not in Agaricomycetes ., In contrast , ECgenes were overrepresented for HGT in lipid and terpenoid/polyketide in Agaricomycetes but underrepresented in the Pezizomycotina ., Only 2 categories , amino acid and microbial metabolism in diverse environments , were overrepresented in transferred ECgenes across all three lineages ., On average 88 . 7% of fungal ECgenes retain the signature of one or more GD events in their ancestry compared to only 2 . 8% for HGT ( Table S3 ) ., Even though these percentages are not directly comparable because reconciliation of ECgene histories with the species phylogeny requires that costs are assigned for every inferred GD or HGT event 60 , our finding that nearly nine out of every ten metabolic genes have undergone GD suggests that this is the dominant source of gene innovation underlying fungal metabolism ., These results are consistent with the hypothesis that specialized metabolic pathways evolve via GD from general metabolic precursors ., Support for this hypothesis has come from phylogenetic analysis of single gene families 63 , 64 such as the polykeytide synthases , which share a common evolutionary origin with the fatty acid synthases of general metabolism 65 ., Further diversification of genes involved in specialized pathways may occur through additional duplication , functional divergence and differential loss in response to variable ecological pressures as has been proposed for polyketide , nonribosomal peptide and alkaloid biosynthesis genes 4 , 66–68 ., Our analysis showed that certain lineages in the Pezizomycotina and Agaricomycetes have increased HGT rates ., Interestingly , bacteria-to-fungi HGT events are also elevated within Pezizomycotina , particularly in Fusarium and Aspergillus genomes 43 ., HGT of entire chromosomes has been reported in Fusarium 69 , 70 , a genus in our analysis , which in addition to Aspergillus , Cochliobolus and Magnaporthe , appears not only receptive to HGT but also includes highly virulent plant and animal pathogens , ecological lifestyles associated with many known cases of HGT 11 , 45 , 47 , 51 , 69–71 ., Similarly , mycoparasitism in the genus Trichoderma may also provide ecological opportunities for fungal-to-fungal HGT ., GD alone or in combination with HGT affected nearly every reaction in fungal metabolism ( 727 , 95 . 7% of ECs that passed the phylogenomic analysis; Figure 5 ) ., The effect of both GD and HGT varied between metabolic categories , suggesting that some pathways may tolerate the introduction of new genes better than others ., One possible explanation for this variation is that the metabolic networks associated with the different functional categories have different degrees of connectivity ., Genes whose products make up large protein complexes or that have many interacting partners exhibit less variation in copy number 35 , perhaps because unbalanced increases in gene dosage can lead to malformed protein complexes and a buildup of toxic intermediates in metabolic pathways 72–74 , and might be less likely to undergo GD 75 , 76 as well as HGT 77 ., In addition to gene dosage effects , deleterious interactions between native and horizontally acquired proteins that function as parts of multi-protein complexes , and as a consequence have distinct co-evolutionary histories , are likely also important barriers to HGT 77 , 78 ., Another possible explanation is that the source of the variation of GD and HGT lies in the differing functions encoded by these metabolic categories ., Gene innovation is often correlated with molecular function , with informational genes such as those involved in DNA replication , transcription and translation duplicated and transferred less often than metabolic genes 35 , 76 , 78 ., Within metabolism , one might expect that widely distributed pathways involved in universal metabolic functions , such as oxidative phosphorylation and the citric acid cycle , are more likely to be functionally constrained and , as a consequence , less likely to tolerate GD or HGT of their constituent genes ., In contrast , GD and HGT might be more advantageous for specialized metabolic pathways that are under strong selection in fluctuating environments 11 ., 33 EC reactions are associated with 332 ECgenes that are never duplicated or transferred in our analysis; 31 of these 33 reactions ( 93 . 9% ) are also never clustered ( Table S7a ) ., For the majority of these ECs , the reason for the apparent lack of GD or HGT is because they are represented by only a few ECgenes in our analysis; therefore , their ECgene trees consist of few taxa with topologies in agreement with the consensus species phylogeny ., For other EC reactions in this set , strong selection pressure to maintain a single , native gene copy could explain the lack of GD and HGT ., Only three genes annotated with EC reaction numbers and which were never duplicated or transferred in our analysis were present in the Saccharomyces cerevisiae genome ( YNL219C 2 . 4 . 1 . 259 , YBR003W 2 . 5 . 1 . 83 , and YPR184W 3 . 2 . 1 . 33 ) ., When examined against the yeast phenotype and interaction data from the Saccharomyces Genome Database ( http://www . yeastgenome . org ) , these three genes displayed a variety of phenotypes and all their null mutants were viable ( Table S7b ) ., Interestingly , overexpression of two of the ECgenes ( YNL219C 2 . 4 . 1 . 259 and YBR003W 2 . 5 . 1 . 83 ) resulted in reduced rate of vegetative growth in S . cerevisiae ( Table S7b ) , suggesting that the acquisition of additional gene copies through GD or HGT could be disadvantageous ., Furthermore , one S . cerevisiae ECgene , a glycosyltransferase ( YNL219C 2 . 4 . 1 . 259 ) involved in the biosynthesis of asparagine-linked glycans , has a very complex interaction network of 315 described physical and genetic interactions ( Table S6a ) , which could serve as an additional barrier to GD and HGT ., 3 . 0% of fungal genes examined in our study lie within gene clusters ., This is likely a conservative estimate because ECgene annotation is better for general rather than specialized metabolism ., Although our analysis includes many specialized pathways ( Table S2 ) , such as biotin production ( KEGG map00780 ) , nitrate assimilation ( map00910 ) and terpenoid backbone biosynthesis ( map00900 ) , and the fraction of enzymatic reactions encoded by clustered ECgenes is extensive ( 441 reactions , 50 . 4% of ECs; Figure 5 ) , lineage-specific genes involved in specialized metabolic pathways are less likely to be included ., In addition , fungal metabolic gene clusters are often identified through the presence of one or more conserved synthesis genes ( e . g . , genes encoding polyketide synthase or nonribosomal peptide synthase enzymes ) ; proper demarcation of associated genes encoding modifying enzymes ( e . g . , oxidases and transferases ) is challenging because they often lack functional annotation and are lineage-specific , leading to underestimates of gene cluster size ., Gene clustering in fungi is positively associated with both GD and HGT , but this pattern appears to be driven by Pezizomycotina ECgenes ( Figure 4 ) ., Saccharomycotina ECgenes cluster more often than the global fungal average but are less often affected by HGT , whereas Agaricomycetes display the opposite trend; they experience more HGT but less gene clustering ( Figure S3 ) ., GD affects nearly all ECgenes , and this large sample size undoubtedly contributes to the statistical significance of its association with gene clustering , even though the fold increase in the percentage of GD events observed in clustered versus non-clustered ECgenes is only 1 . 02 ., In contrast , the effect of HGT on clustered genes is 1 . 66 fold greater than its effect on non-clustered genes ., The uniqueness and wide distribution of fungal metabolic gene clusters has given rise to many models that attempt to explain their formation and maintenance 53 , 79–83 ., For example , the selfish gene cluster model proposes that HGT allows gene clusters to avoid being lost by facilitating colonization of new genomes 84 , 85 ., Although several instances of HGT of fungal gene clusters have been discovered in recent years 11 , 51–58 , clustered pathways are also more likely to be lost than non-clustered ones 53 ., The small percentage of clustered genes affected by HGT in our analysis ( 4 . 8% ) , albeit larger than the background percentage of transferred un-clustered genes ( 2 . 9% ) , suggests that selfishness is unlikely to be the predominant mechanism driving gene cluster formation and maintenance in fungi ., Nevertheless , the association between metabolic gene clusters and GD/HGT suggests that gene clustering can facilitate the duplication and transfer of entire metabolic pathways ., This is consistent with the view that the barriers to gene innovation acting on gene clusters may be lower than those acting on single genes because the latter undergo GD or HGT in the absence of their functional partners ., A custom enzyme classification pipeline assigned EC numbers to protein-coding genes from the genomes of 208 fungi and 9 stramenopiles ( five oomycetes and four algal relatives ) , which were included in this analysis because of published reports of HGT between oomycetes and fungi 44 ., Each gene was queried against a database of KEGG orthology ( KO ) -annotated proteins from 53 KEGG Organisms ( Table S8 ) using ublast ( http://drive5 . com/usearch ) with an accel setting of 0 . 7 and minimum identity cutoff of 0 . 3 ., A KO term was assigned to the query for ublast hits with greater than 80% sequence identity and no more than 10% difference in length ., In cases where highly similar matches were not recovered , KO terms were assigned to query sequences with respect to the ublast hits showing the lowest e-values; all ublast hits that followed the first e-value increase of 10−50 or greater were excluded ., EC numbers were assigned according to KO term ( http://www . genome . jp/kegg-bin/get_htext ? ko00001 . keg ) ., Fungal proteomes were screened for metabolic gene clusters as described 81 ., Briefly , two ECgenes were considered clustered if they were separated by no more than 6 intervening genes according to published annotation and their EC numbers were nearest neighbors in one or more KEGG pathways ., Gene clusters were inferred by joining overlapping metabolic gene pair ranges that were separated by no more than 6 intervening genes; the cutoff of 6 intervening genes was determined empirically with reference to previous analyses of both primary 52 , 53 and secondary 54 metabolism clusters ., We constructed a draft fungal species phylogeny using protein sequences of the widely used DNA-directed RNA polymerase II subunit RPB2 marker , which were aligned with mafft using the E-INS-i strategy 86 ., The resulting alignment was trimmed with trimal using the automated1 strategy 87 , and the topology was inferred using maximum likelihood ( ML ) as implemented in raxml version 7 . 2 . 8 88 using a PROTGAMMALGF substitution model and rapid bootstrapping ( 100 replications ) ., Branches with bootstrap support less than 50 were collapsed using the Consense module in the phylip program 89 ., The final bifurcating and consensus ( multifurcating ) species phylogenies ( File S1 ) were constructed by making targeted corrections to the RPB2 topology based on published literature ( Table S9 ) ., ECgene trees were constructed using a custom phylogenomic pipeline ( Figure S4 ) ., Guide trees were first constructed for each ECgene family with mafft using the scores of pairwise global alignments 86 and rooted with the notung rooting optimization algorithm using event parsimony ., This distance-based guide tree and the consensus species phylogeny were used to delineate groups of homologs by aiming to maximize taxonomic diversity while minimizing the number of paralogs in each gene tree ., The ECgene sequences from each one of these groups of homologs were then extracted in FASTA format for phylogenomic analysis ., FASTA files of ECgenes with less than 4 or more than 1000 sequences were excluded ., Sequences were aligned in mafft using the auto strategy selection 86 ., Alignments were trimmed in trimal using the automated1 trimming strategy 87 , and trimmed alignments shorter than 150 amino acid residues were discarded ., Phylogenetic trees were constructed using fasttree 90 with a WAG+CAT amino acid model of substitution , 1000 resamples , four rounds of minimum-evolution subtree-prune-regraft moves ( -spr 4 ) , and the more exhaustive ML nearest-neighbor interchange option enabled ( -mlacc 2 –slownni ) ., Gene tree-species phylogeny reconciliation was performed in notung using its duplication , transfer , loss and ILS aware parsimony-based algorithm 59–61 , 91 ., Ambiguity in the fungal species phylogeny and low branch support in ECgene trees were handled through a multi-step approach ., First , ECgene tree branches with less than 0 . 90 SH-like local support were collapsed using treecollapsercl v4 ( http://emmahodcroft . com/TreeCollapseCL . html ) ., This collapsed ECgene tree was rooted and its polytomies resolved against the bifurcating species phylogeny ., This resolved ECgene tree was then reconciled to the multifurcating , consensus species phylogeny using a duplication cost of 1 . 5 , loss cost of 1 and ILS cost of 0 ., Transfer costs of 2 , 4 , 6 , 8 , 10 and 12 as well as the option to prune taxa not present in the gene tree from the species phylogeny were evaluated ., A transfer cost of 6 with the prune option enabled best recovered published cases of HGT between fungi ( Table S5 ) ., Percent GD and HGT were expressed over the 152 , 835 fungal ECgenes that passed this reconciliation pipeline ., Because a single ancestral HGT event could be recorded in multiple ECgene trees , we defined unique HGT events as all cases where ECgenes assigned to the same EC number were inferred to have undergone HGT to/from the same recipient/donor nodes in the species phylogeny ., Fishers exact tests were performed using the R function fisher . test with a two-sided alternative hypothesis 92 ., P values were adjusted for multiple comparisons using the R function p . adjust with the Benjamini & Hochberg ( BH ) method 93 ., Box-and-whisker plots were created using the R plotting system ggplot2 94 .

Introduction, Results, Discussion, Materials and Methods

Fungi contain a remarkable range of metabolic pathways , sometimes encoded by gene clusters , enabling them to digest most organic matter and synthesize an array of potent small molecules ., Although metabolism is fundamental to the fungal lifestyle , we still know little about how major evolutionary processes , such as gene duplication ( GD ) and horizontal gene transfer ( HGT ) , have interacted with clustered and non-clustered fungal metabolic pathways to give rise to this metabolic versatility ., We examined the synteny and evolutionary history of 247 , 202 fungal genes encoding enzymes that catalyze 875 distinct metabolic reactions from 130 pathways in 208 diverse genomes ., We found that gene clustering varied greatly with respect to metabolic category and lineage; for example , clustered genes in Saccharomycotina yeasts were overrepresented in nucleotide metabolism , whereas clustered genes in Pezizomycotina were more common in lipid and amino acid metabolism ., The effects of both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts and were differentially distributed across fungal lineages; specifically , GD , which was an order of magnitude more abundant than HGT , was most frequently observed in Agaricomycetes , whereas HGT was much more prevalent in Pezizomycotina ., The effect of HGT in some Pezizomycotina was particularly strong; for example , we identified 111 HGT events associated with the 15 Aspergillus genomes , which sharply contrasts with the 60 HGT events detected for the 48 genomes from the entire Saccharomycotina subphylum ., Finally , the impact of GD within a metabolic category was typically consistent across all fungal lineages , whereas the impact of HGT was variable ., These results indicate that GD is the dominant process underlying fungal metabolic diversity , whereas HGT is episodic and acts in a category- or lineage-specific manner ., Both processes have a greater impact on clustered genes , suggesting that metabolic gene clusters represent hotspots for the generation of fungal metabolic diversity .

Fungi are important primary decomposers of organic material as well as amazing chemical engineers , synthesizing a wide variety of natural products , some with potent toxic activities , including antibiotics and mycotoxins ., In fungal genomes , the genes involved in these metabolic pathways can be physically linked on chromosomes , forming gene clusters ., This extraordinary metabolic diversity is integral to the variety of ecological strategies that fungi employ , but we still know little about the evolutionary processes involved in its generation ., To address this question , we analyzed 247 , 202 enzyme-encoding genes participating in hundreds of metabolic reactions from 208 diverse fungal genomes to examine how two major sources of gene innovation , namely gene duplication and horizontal gene transfer , have contributed to the evolution of clustered and non-clustered metabolic pathways ., We discovered that gene duplication is the dominant and consistent driver of metabolic innovation across fungal lineages and metabolic categories; in contrast , horizontal gene transfer appears highly variable both across organisms and functions ., The effects of both gene duplication and horizontal gene transfer were more pronounced in clustered genes than in their non-clustered counterparts suggesting that metabolic gene clusters are hotspots for the generation of fungal metabolic diversity .

biochemistry, secondary metabolism, fungi, biology and life sciences, molecular evolution, evolutionary biology, metabolism, organisms

journal.pcbi.1003886

Estimating HIV-1 Fitness Characteristics from Cross-Sectional Genotype Data

The emergence of drug resistant mutants remains a major obstacle to long-term treatment success of highly active antiretroviral therapy ( HAART ) against HIV-1 1 , 2 ., Mathematical models of in vivo viral infection dynamics have provided critical insights into HIV-1 disease and therapy by disentangling viral and target cell dynamics 3 , 4 , quantifying drug class specific effects on viral load decay 5 , 6 and elucidating general principles of antiretroviral therapy 7 , 8 ., Their utility in studying the emergence of drug-specific mutations and resistance , however , is limited by the availability of realistic mutation landscapes ., Existing approaches typically use mutation schemes that are unspecific for the drug or coarse-grained 9–11 ., On the other hand , statistical models of mutational pathways have been used to understand the evolution of drug-resistance in vivo , for example , by estimating evolutionary landscapes of viral mutations based on in vivo data 12–15 , establishing genotype–phenotype maps 16 and predicting individual treatment outcomes 17 , 18 ., These approaches , however , do not integrate details of the viral infection dynamics and the specific actions of different drug classes ., In viral mutational landscapes , the path to resistant mutants that fixate and eventually cause therapy failure typically consists of several intermediate mutants ., Understanding the accumulation of mutations and associated genotypic and phenotypic changes is critical for prediction of treatment failure and selection of optimal patient-specific treatments 19 ., Additionally , it has been observed that models incorporating quasispecies distributions of HIV-1 mutants can lead to a different qualitative behaviour than what would be expected from simplified mutation models 20 ., In a drug-free environment , a viral mutant genotype usually incurs a loss in fitness 21 , which is offset by resistance effects in the presence of the drug ., This loss in fitness , quantified in terms of a fitness cost , is an important parameter dictating the appearance of mutants and hence affecting viral suppression and therapeutic success 22 ., Although fitness landscapes of viruses have been studied for a long time 23–25 , the paucity and quality of experimental data have always been major limitations 26 ., Experimental investigations on viral fitness rely on techniques such as growth competition assays , parallel infection methods , and other replication measurement assays in in vitro settings 27 ., Replication capacities are typical readouts of such assays and they are considered to be measures of viral fitness 28 ., However , there have been controversies over appropriate quantification of viral fitnesses and the clinical relevance of such in vitro fitness measures ( see 29 for a review ) ., Statistical techniques have been developed and used to estimate relative fitness of viral mutants from longitudinal in vitro data 30 , 31 ., Attempts to estimate fitness parameters in an in vivo setting 32 , 33 have relied on detailed time course measurements of different mutant strains , which is a severe limitation in the most common situation of sparse data collected during routine clinical diagnostics ., The objective of this article is to enable estimation of in vivo fitness parameters from common cross-sectional clinical data by combining and linking statistical methods designed for cross-sectional data with a mechanistic model of virus dynamics , which explicitly accounts for viral fitness ., This integration is achieved by, ( i ) learning drug-specific mutational pathways from cross-sectional in vivo data and modelling viral infection dynamics on these genotype lattices , and, ( ii ) by coupling the resistance factor , an abundant and accessible in vitro measure of drug resistance , to drug efficacy and rate constants of the virus-host dynamics in vivo ., This approach allows to leverage sparse clinical data for the estimation of in vivo fitness characteristics , which is a first step towards analyzing and ultimately predicting clinical outcomes of drug combinations and assessing causes of therapy failure ., Specifically , our statistical approach to estimate mutational pathways from in vivo data is based on continuous-time conjunctive Bayesian networks 16 ., The viral infection is described based on an established and validated viral dynamics model 6 , 34 that explicitly allows for the incorporation of the action of all approved drug classes ., Viral resistance is included via drug-specific resistance factors estimated from in vitro data by isotonic regression models ., The integration is finally achieved by a quantity common to both approaches , the estimated/predicted waiting time for different mutations ., There has been great interest in performing simulations of antiretroviral therapy to assess treatment outcome ., Recent studies 35 have shown how even monotherapy simulations using simple viral dynamics models can yield valuable insights on treatment failure and answer clinically relevant questions pertaining to combination therapy ., However , implementing multiple-drug therapy with realistic mutational pathways remains a limitation in this regard ., Our modelling approach extends naturally to multiple drugs and is a step towards using sparse clinical data effectively to simulate treatment regimens ., We estimate fitness characteristics of mutants for two antiretroviral drugs from two different major drug classes: zidovudine ( ZDV ) , a nucleoside reverse transcriptase inhibitor , and indinavir ( IDV ) , a protease inhibitor ., Then , we characterize the interplay between fitness costs and resistances in mutant selection during therapy by computing selective advantages of different mutant genotypes ., Finally , we illustrate how our model extends to multiple drug therapy by simulating a dual therapy with ZDV and IDV and examine reasons for virological failure in such a setting ., We used a dataset obtained from the Stanford HIV Drug Resistance Database described in 36 to estimate possible mutational pathways and phenotypic resistance levels under the selective pressure of ZDV ., This dataset consists of 1392 observations of HIV reverse transcriptase ( RT ) genotypes and associated measurements of phenotypic resistance to ZDV ., Phenotypic resistance levels are defined as the logarithm of the fold-change in virus susceptibility to the drug in comparison to the wild type ., We focussed on the key thymidine-analog mutations ( TAMs ) that arise under ZDV monotherapy: 41L , 67N , 70R , 210W , 215Y and 219Q , where , for instance , 41L denotes the presence of leucine ( L ) at position 41 of the HIV RT ., The genotypes considered were classified into those that exclusively contain mutations from the well-studied TAM-1 ( 41L , 215Y , 210W ) or TAM-2 ( 67N , 70R , 219Q ) pathways 37 , 38 , and two mixed mutant genotypes that contain mutations from both pathways ( with cross-TAM profiles ) ., Mixed mutants are observed to generally occur with a lower frequency 39 ., Using this dataset , the resistance factors of the genotypes and the partially ordered set ( poset ) of resistance mutations were estimated using isotonic conjunctive Bayesian network ( I-CBN ) models ., In conjunctive Bayesian networks , a partial order is used to encode dependencies among mutations ., A genotype is formally defined as a subset of mutations ., The set of genotypes compatible with the order constraints of the poset is called the genotype lattice ( Figure 1 ) ., In the I-CBN model , isotonic regression is used to associate to each genotype a phenotypic drug resistance level in such a way that resistance levels are non-decreasing along any mutational pathway in the genotype lattice ., Next , we used continuous time conjunctive Bayesian networks ( CT-CBN ) to estimate the rate at which each mutation establishes in the viral population ., The fixation times of mutations are assumed to follow independent exponential distributions ., The waiting process for a mutation begins only when all its parent mutations in the poset have been established ., The data needed for the estimation of this model is a list of genotypes ., In the CT-CBN model , genotypes are assumed to be observed after an unknown sampling time ., The sampling times are themselves assumed to be random and exponentially distributed ., Since we do not know explicitly the time points at which the mutations have occurred , we used the censored CT-CBN model to estimate the fixation rates 40 ( See Methods for details ) ., We used a drug efficacy of on the wild type ( corresponding to a drug concentration of 3 times the IC50 ) to illustrate our results ., This value was chosen to match average nadir values in viral load after ZDV monotherapy ( a drop of 1 log unit from baseline , within 7–10 days of therapy and a nadir at 3 weeks ) 41 ., The estimated fitness costs and selective advantages ( Table 1 and Figure 2 ) were in excellent agreement with established knowledge from several in vitro assays and some in vivo observations ., The agreement holds true for general reported ranges of fitness costs ( ) 33 , 42–44 as well as for statements concerning specific mutations ., For example , it is well known that the addition of the 210W mutation into a {41L , 215Y} backbone has opposing effects on fitness depending on the presence or absence of ZDV ., In the absence of ZDV , the triple mutant {41L , 210W , 215Y} has been observed to be less fit than {41L , 215Y} , while the introduction of ZDV causes a reversal , i . e . , the triple mutant becomes fitter than the double mutant 45 , 46 ., This was well-reflected in our estimates ( Table 1 ) ., We observed that this reversal in fitness upon adding ZDV , was because of the higher fitness cost of the triple mutant being more than offset by the resistance acquired in the presence of drug ., This can be seen by comparing the selective advantages and fitness costs for the corresponding double and triple mutants in the TAM-1 pathway ( Figure 2 ) ., TAM-1 mutations are known to occur at almost double the frequency of TAM-2 mutations 47 ., This difference was reflected in our results by mutants containing TAM-1 mutations having lower fitness costs than those containing TAM-2 mutations ., Additionally , we also observed that the selective advantages of TAM-1 mutants is higher , on average , than their TAM-2 counterparts ., This is also in concordance with observations that TAM-2 mutations accumulate only after much longer durations of monotherapy with ZDV 39 ., The presence of 41L together with 215Y is a strong predictor of virological failure in patients on ZDV monotherapy 48 ., We estimated a low fitness cost for this TAM-1 double mutant and also observed the presence of these two mutations in mutant genotypes contributing to therapy failure ., Our estimated fitness costs were also supported by other in vitro investigations on the order of fitness values , such as the TAM-1 triple mutant {41L , 210W , 215Y} being fitter than its TAM-2 counterpart 43 and the TAM-2 double mutant {67N , 70R} being less fit than the single mutant {67N} 49 ., Notably , we concurred with the observation in 49 that the occurrence of 70R in a 67N or {67N , 219Q} backbone has a significant cost ., We further studied parameter identifiability by considering an ensemble of fits ., All fits with a root mean squared deviation ( RMSD ) of between the normalized statistical and mechanistic waiting times , were treated as equally valid ( see Methods ) ., Across all such valid fits , we observed a strong and statistically significant Spearman rank correlation of the estimated fitness costs ( , p\u200a=\u200a0 . 020 ) and selective advantages ( , p\u200a=\u200a0 . 017 ) ., This indicated that our predictions on the ranking of fitness costs and selective advantages of the different mutant genotypes were strongly conserved ., Additionally , in about 90% of the valid fits , we found that the average fitness cost of TAM-1 mutants was less than that of TAM-2 mutants , while in approximately of valid fits , the deleterious effect of 210W inserted in a {41L , 215Y} backbone in the absence of ZDV was preserved ., Similarly , we examined the validity of each of our conclusions and found that they were all well-conserved across the valid fits ( see Supplementary Table S3 and Supplementary Figure S1 in Supporting Information for details ) ., Moreover , since the parameters of the virus dynamics model are subject to uncertainties , we examined the validity of our predictions under uncertainty of all the viral turn-over parameters , considering perturbation of up to ., Our model predictions remained robust even under these parameter perturbations ( see Supplementary Text S1 , Supplementary Figure S2 in Supporting Information for details ) ., Additionally , we also tested the impact of variability of RFs estimated in the first-stage , on the estimation of fitness costs ., Again , the order in the estimated fitness costs remained preserved ( see Supplementary Text S1 and Supplementary Figure S6 ) ., In addition to estimated fitness characteristics , the viral load time courses predicted by our model gave insights into the dynamics of different mutations ., In the TAM-2 pathway , we observed a transient disappearance of mutation 70R before its eventual fixation , as was reported earlier 50 ., This phenomenon is attributed to the competition between TAM-1 and TAM-2 mutations: the mutation 70R appears initially and is then outcompeted by 215Y ., 70R later fixates in the population after being associated with 67N and other TAM-1 mutations ( Figure 3 ) ., We also concurred with studies 11 attributing the initial rebound after ZDV monotherapy to insufficient suppression of the wild type , rather than the early selection of mutations ., We again used the Stanford HIV Drug Resistance Database 36 to estimate the poset ( Figure 4A ) and genotype lattice ( Figure 4B ) of mutations associated with resistance to indinavir ( IDV ) , a protease inhibitor , and corresponding resistance factors of IDV mutants ., As for ZDV , the poset , the genotype lattice , and the rate of fixation for each mutation were determined by the I-CBN and CT-CBN models ., The dataset for IDV consists of 2170 observations of HIV reverse transcriptase ( RT ) genotypes and their paired measurements of phenotypic resistance to IDV ., We focussed on the five mutations 46I , 54V , 71V , 82A , and 90M ., Four of these ( 46I , 54V , 82A and 90M ) are among the most frequent primary ( major ) mutations reported in the Stanford HIV Drug Resistance Database under IDV therapy 36 ., We chose 71V to represent a common secondary ( minor ) mutation to study possible compensatory fitness effects ., We used a drug efficacy on the wild type to illustrate our results ., This value was chosen to match average nadir values in viral load after IDV monotherapy ( a drop of 1–1 . 5 log units within 3–4 weeks of therapy ) 51 , 52 ., The estimated fitness costs , resistance factors and selective advantages ( Table 2 and Figure 5 ) agreed well with reported experimental findings ., In general , we observed that early mutations have a high fitness cost , while the accumulation of further mutations succeeded in compensating almost entirely for this loss in fitness ( Figure 5A ) ., This is in agreement with clinical observations that mutations selected early during therapy with protease inhibitors cause impaired protease function and that subsequent accumulation of mutations compensates for this fitness cost 27 , 53 ., A striking behaviour that we observed was the presence of staircases in the fitness landscape , which has also been described earlier 54 ., We observed a monotonic increase of the average selective advantages of the mutants with increasing number of mutations ( Figure 5C ) ., This observation provides additional reasoning for the accumulation of mutations during IDV therapy ., Notably , the high fitness costs for the double and triple mutants ( Figure 5A ) were not sufficient to deter their occurrence , as the fitness costs were well-offset by resistance ( Figure 5B ) , which facilitated further climbing of the fitness landscape by accumulating mutations ( Figure 5C ) ., In addition to these general fitness trends , specific characteristics of particular mutations were also in line with prior findings ., The minor mutation 71V is known to play a compensatory role 55 ., In our estimates , this was observed by a partial recovery in fitness of the triple mutant {46I , 71V , 90M} compared to the double mutant {46I , 90M} from 0 . 66 to 0 . 44 ( Table 2 ) ., In the presence of IDV , the addition of 54V to a {71V , 82A} backbone is known to not confer a significant advantage 56 , and this was reflected by a ratio of approximately 1 . 3 for the selective advantages of this pair of mutants ., Furthermore , as in 57 , we noted a higher fitness cost for the single mutant 71V as compared to 90M ., Nijhuis et al . 55 observed the persistence of protease resistant mutants for long periods of time even after the cessation of therapy ., They argued that the reversal of the underlying mutations might not be feasible due to lower replication capacities of intermediate mutants upon reversion ., Our results support this hypothesis by showing that the most resistant strain that develops after therapy failure is very unlikely to reverse back in the mutational landscape , owing to a fitness barrier encountered in its reversion to the wild type ( Figure 5A ) ., As with ZDV , we studied parameter identifiability by considering an ensemble of fits with an RMSD between the statistical and mechanistic waiting times ., There was a statistically significant Spearman rank correlation ( , p\u200a=\u200a0 . 014 ) between the best estimate of fitnesses and all other valid fits ., We found the average fitness estimates ( Figure 5C ) to be very strongly conserved ( , ) ., We also examined each of the results above and observed consistency across the valid fits ( see Supplementary Table S3 and Supplementary Figure S1 in Supporting Information for details ) ., For example , in 77% of the valid fits , 71V was observed to play a compensatory role by lowering fitness costs , while in 65% of fits , the single mutant 90M was fitter than 71V ., There is great interest in using viral dynamics models to study antiretroviral treatment to assess therapy outcomes and simulate clinical trials 35 ., Our model extends naturally to multiple-drug therapy ., To illustrate this , we performed simulations of a dual antiretroviral therapy with zidovudine ( ZDV ) and indinavir ( IDV ) ., We used the posets of mutations for ZDV and IDV that we have estimated earlier ( Figure 1A and Figure 4A , respectively ) , together with the resistance factors and fitness costs of the different mutant genotypes ( see Methods for details ) ., Our goal was to assess treatment outcomes and reasons for failure of the dual regimen ., To this end , we used a range of values for ZDV and IDV to account for differential drug effects and adherence patterns , and studied the treatment outcome by monitoring the total viral load ., Our simulations enabled us to predict the dominant mutant genotypes at the point of failure ( we defined failure at the point when the viral load crossed a threshold of 500 copies/ml ) ., Based on this , we classified failure as being due to wild type , mutations resistant to ZDV , mutations resistant to IDV or mutations resistant to both drugs used ., We observed that there are different regimes of the individual drug efficacies ( and ) that result in varying causes of failure ( Figure 6A ) ., With =\u200a0 . 75 and =\u200a0 . 90 , for example , we observed virological failure after 3 months ( Figure 6B ) due to mutations resistant to both drugs ., In this case , the wild type is sufficiently suppressed and declines during the treatment period ., However , we identified regions in the - plane , where treatment failure occurred due to insufficient suppression of the wild type ., We classified the treatment as having failed owing to the wild type , if the wild type was the dominant genotype at the point of virological failure ., We note that the wild type would eventually be out-competed by resistant mutants in all situations ., Such situations of failure with the wild type could indicate insufficient drug pharmacokinetics , a low drug efficacy or poor adherence ., This would have implications in designing a salvage therapy regimen , subsequent to failure ., Additionally , we also observed that there are combinations of ( ) , for which failure occurs due to mutations to one of the two drugs ( Figure 6A ) ., Our model , thus , enabled prediction of viral evolution under a multiple-drug treatment scenario ., We noted that for a certain of one drug , predicting the treatment outcome predicted from monotherapy simulations might lead to qualitatively different results , as opposed to using a model with multi-drug therapy ., For example , the value of below which failure with wild type is detected was different in ZDV monotherapy simulations ., This reiterates the value of implementing multi-drug treatment regimens in in silico simulations ., We further emphasize that in order to simulate a certain combination , our approach needs only clinical data from treatment regimens in which the individual drugs are a part ., For instance , in the current example of dual therapy with ZDV and IDV , the estimation of resistance factors and fitness characteristics relied on sparse cross-sectional clinical data from treatment regimens that included ZDV and/or IDV ( not necessarily both ) ., Subsequent to this , we were able to simulate the dual therapy and assess genotypic reasons of therapy failure ., We have presented an HIV-1 infection dynamics model with statistically learned drug-specific in vivo mutational landscapes ., Our approach relies on typical and frequently available clinical data , which consists of left-censored observations , as opposed to extensive time course measurements of different mutant genotypes , that are generally scarce ., Mutations are detected through their appearance in specific mutant genotypes and our model describes how their dynamics is coupled ., Although there is considerable debate on appropriate measures of fitness and clinically relevant quantifiers 29 , evidence exists for the influence of fitness on transmission efficiency 58 , plasma viral load 59 and treatment interruption outcomes 60 ., Assessing fitness characteristics has also been shown to have in vivo clinical relevance as correlations between fitness measures and standard treatment outcome markers , such as viral load , 61 have been demonstrated ., We estimated fitness characteristics of drug resistant HIV-1 mutants and illustrated our results for ZDV , a nucleoside reverse transcriptase inhibitor , and IDV , a protease inhibitor ., Our estimated fitness costs and selective advantages showed excellent agreement with experimental knowledge ., So far , mechanistic modelling of HIV infection has mainly used fixed fitness costs with the infection dynamics being examined for different values of these fitness costs 6 , 9 ., However , it is well known that fitness landscapes of HIV-1 are highly rugged 26 ., Earlier work characterizing in vivo fitness characteristics has relied on detailed viral load measurements of different mutant strains , which is rarely possible in realistic clinical situations ., Our approach utilized statistical learning methods to estimate mutational landscapes from clinical data that were then incorporated into a mechanistic viral dynamics model ., Our results are also in agreement with an earlier study 11 where a mechanistic modelling approach was used to fit drug efficacy and fitness parameters to clinically observed mutant data under zidovudine and lamivudine therapy ., As observed in this study , we also noted that the lack of adequate suppression of the wild type strain contributes significantly to the initial rebound in the viral load ., In this scenario , the wild type strain initially rebounds leading to virological failure and then later declines after being out-competed by the mutants ., In comparison to 11 , our model is a more detailed version in the mechanistic sense and further accounts for more realistic mutation pathways ., For the protease inhibitor IDV , our results clearly demonstrated the incentive for the accumulation of mutations by HIV-1 in spite of significant losses in fitness incurred by the first few mutations ., Interestingly , the high fitness costs of double and triple mutants do not deter their occurrence , and their appearance paves the way for later mutants with higher fitness 53 ., In line with earlier studies 55 , we observed a fitness barrier that prevents reversion to wild type upon cessation of therapy ., The simulation of antiretroviral treatments has generated interest , particularly in the context of assessing therapy failure and in explaining puzzling clinical observations ., For example , clinical trials have shown 62 , 63 that some protease-inhibitor containing regimens fail without mutations being detected in the protease region of the HIV genome ., Bloomenfeld et al . 35 used a simple viral dynamics model and information on mutations and drug pharmacokinetics from literature to simulate monotherapies and deduced that the short time spent by protease-resistant mutants in the mutant-selection window was responsible for lack of selection of mutations in the protease regimens ., However , the inclusion of only single point mutants is a limitation in modelling long-term treatment and in ascertaining the impact of a certain failed regimen on potential salvage therapies ., Our model includes different target cells ( T-cells and macrophages ) , a latent reservoir , multiple major drug-resistance mutations and extends to combination therapies , and hence represents a first step in using viral dynamics models informed by mutations and resistance through statistical learning from clinical data , to assess and understand the impact of a failed regimen ., The long-lived infected macrophages and latently infected cells in the virus dynamics model contribute to different later stages of viral decay and their impact would be significant in the analysis of multiple-drug regimens ., The presented viral infection dynamics model incorporating drug-specific in vivo mutation landscapes aimed at capturing the complex competition dynamics between the different mutant strains ., It was based on a simplified representation of drug pharmacokinetics ( PK ) and effect ., If detailed data on drug PK and patient-specific viral load dynamics and baseline characteristics are available , a population-pharmacokinetic/pharmacodynamic analysis would be the appropriate approach to account for inter-individual variations 64 ., In the absence of such detailed data , we assessed the impact of time-varying drug concentrations on our model predictions by integrating a simple two-compartment PK model of ZDV ., The mechanistic predicted waiting times retained a high and significant correlation with the average statistical waiting times ( details in Supplementary Text S1 , Supplementary Table S4 and Supplementary Figure S3 ) ., Hence , our simplifying assumption of a constant drug concentration and effect seems reasonable and is in line with most prior analyses 65 ., Further , while deterministic simulations represent the average dynamical behaviour of the system , stochastic effects need to be incorporated using numerical hybrid algorithms to explain the variability in clinical data ., We performed an initial analysis by using such a hybrid deterministic-stochastic algorithm 66 , that switches from deterministic to stochastic regime below a certain threshold ( separately for each reaction ) ., While , we observed a delay in the appearance of certain mutations in agreement with previous observations 67 , the model predictions remained robust with regard to the order of appearance of mutations and the predicted waiting times continued to be significantly correlated with the statistical waiting times used to fit the model ( details in Supplementary Text S1 , Supplementary Table S6 and Supplementary Figure S5 ) ., There are several mechanisms of resistance in HIV-1 infection ., In addition to the mechanisms included in the two-stage virus dynamics model , features such as the compensatory Gag mutations 68 and other compensatory mechanisms adopted by HIV-1 have also been described , including frame-shifts in the Gag region that increase viral protease expression levels 69 ., These effects can be integrated by including information on the Gag region into the mutational scheme and this extended model may then partially account for higher observed fitness levels of some mutants ., In summary , we have presented a new approach to model HIV-1 infection dynamics that incorporates drug-specific in vivo mutational landscapes and allows for the estimation of mutant fitness characteristics ., Importantly , it relies only on cross-sectional clinical data and , as demonstrated , extends naturally to combination therapies ., We believe that it is a promising approach to analyze treatment outcomes with drug combinations or to study optimal switching strategies ., The viral infection cycle was described by the two-stage model presented in 34; see Figure 7 for a graphical representation and description , Supplementary Text S1 for the corresponding system of ordinary differential equations ( ODEs ) and Supplementary Table S1 for the parameters used ., The model allows for integrating drug-specific mutation schemes and the actions of all approved antiretroviral drug classes including reverse transcriptase inhibitors ( RTIs ) , protease inhibitors ( PIs ) and integrase inhibitors ( InIs ) ., Mutations in the viral genome occur during the process of reverse transcription , but manifest themselves only after the viral DNA has been integrated into the host genome ., Hence , mutations were modelled to occur between early infected cells ( first stage ) and late infected cells ( second stage ) ., We considered only mutation events between genotypes differing by a single amino acid site , owing to the fact that , though multiple amino acid changes are simultaneously possible , the probability of such events is very low ., The probability of a mutation that changes the genotype from to was assumed to be for a single underlying base-pair mutation and for a double underlying base-pair mutation , where denoted the probability of mutation per base-pair per cycle of replication ( see Supplementary Text S1 for details , in particular regarding the choice of ) ., Since mutations are primarily a result of error-prone reverse transcription 70 , forward and backward mutations were considered ., The average error rate in viral reverse transcription is about mutations per nucleotide per cycle of replication; and two-thirds of these mutations are known to be base-pair substitutions 70 ., In agreement with 11 , we used the following nucleotide-specific mutation rates: for a G A nucleotide change , we set a mutation rate of ( because about half of the base-pair mutations are of this type 70 ) , for mutations involving nucleotide changes A G , we used a lower rate of , and for transversion and other mutations ( involving a change from a purine to a pyrimidine or vice-versa ) , we set ., As with infectious viruses , we also included different mutant strains of non-infectious viruses , since viral detection assays do not distinguish between infectious and non-infectious viral particles ., The effect of an antiretroviral drug on a viral genotype was modelled by a fractional reduction of the targeted process , characterized by the drug efficacy parameter ( 1 ) where denotes the drug concentration at which the fractional reduction is 50% ., The subscript indicates that the -value and thus the drug efficacy was assumed to be genotype dependent ( see below ) .,

Introduction, Results, Discussion, Methods

Despite the success of highly active antiretroviral therapy ( HAART ) in the management of human immunodeficiency virus ( HIV ) -1 infection , virological failure due to drug resistance development remains a major challenge ., Resistant mutants display reduced drug susceptibilities , but in the absence of drug , they generally have a lower fitness than the wild type , owing to a mutation-incurred cost ., The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success ., Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance ., Here , we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically ., We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs , the reverse transcriptase inhibitor zidovudine ( ZDV ) and the protease inhibitor indinavir ( IDV ) ., Well-known features of HIV-1 fitness landscapes are recovered , both in the absence and presence of drugs ., We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants ., Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure ., The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure .

Mutations conferring drug resistance represent major threats to the therapeutic success of highly active antiretroviral therapy ( HAART ) against human immunodeficiency virus ( HIV ) -1 infection ., Viral mutants differ in their fitness and assessing viral fitness is a challenging task ., In this article , we estimate drug-specific mutational pathways by learning from clinical data using statistical techniques and incorporate these into mathematical models of in vivo viral infection dynamics ., This approach enables us to estimate mutant fitness characteristics ., We illustrate our method by predicting fitness characteristics of mutant genotypes for two different antiretroviral therapies with the drugs zidovudine and indinavir ., We recover several established features of mutant fitnesses and quantify fitness characteristics both in the absence and presence of drugs ., Our model extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure ., Additionally , our modelling approach relies only on cross-sectional clinical data ., We believe that such an approach is a highly valuable tool in assisting the choice of salvage therapies after treatment failure .

evolutionary biology, systems biology, biology and life sciences, computational biology

journal.pgen.1000508

Genome-Wide Association Scan Meta-Analysis Identifies Three Loci Influencing Adiposity and Fat Distribution

The accumulation of abnormal amounts of intra-abdominal fat ( central adiposity ) is associated with serious adverse metabolic and cardiovascular outcomes , including type 2 diabetes ( T2D ) and atherosclerotic heart disease 1 ., Indeed , because the medical consequences of increasing fat mass are disproportionately attributable to the extent of central adiposity , measures of overall adiposity , such as body mass index ( BMI ) , fail to capture all of this risk 2 , 3 ., Measures of central and overall adiposity are highly correlated ( BMI has r2∼0 . 9 with waist circumference WC and ∼0 . 6 with waist-hip ratio WHR , Table S1 ) ., WC and WHR are correlated with more precise measures of intra-abdominal fat measured by MRI in obese women ( r2∼0 . 6 and 0 . 5 , respectively ) 4 ., Several lines of evidence indicate that individual variability in patterns of fat distribution involves local , depot-specific processes , which are independent of the predominantly neuronal mechanisms that control overall energy balance ., First , anthropometric measures of central adiposity are highly heritable 5 and , after correcting for BMI , heritability estimates remain high ( ∼60% for WC and ∼45% for WHR ) 6 ., Second , there are substantial gender-specific differences in fat distribution , and these appear to reflect genetic influences 7 ., Third , uncommon monogenic syndromes ( the partial lipodystrophies ) demonstrate that DNA variants can have dramatic effects on the development and/or maintenance of specific regional fat-depots 8 ., Efforts to identify common and rare variants influencing BMI and risk of obesity have emphasized the key role of neuronal ( hypothalamic ) regulation of overall adiposity 9–17 but provided few clues to processes that are specifically responsible for individual variation in central obesity and fat distribution ., Definition of the mechanisms involved in the regulation of fat distribution in general , and visceral fat mass in particular , is therefore key to understanding obesity and its accompanying morbidity and mortality ., Given the challenges associated with the pharmacological manipulation of hypothalamic processes , the identification of pathways influencing abdominal fat accumulation would also present novel opportunities for therapeutic development ., With this in mind , we set out to identify genetic loci influencing anthropometric measures of central obesity and fat distribution , namely , WC and WHR ., Our meta-analysis of 16 genome wide association studies ( GWAS ) , followed by large-scale replication testing , generating a combined sample of up to 118 , 691 individuals of European origin , has identified three loci associated with these critical biomedical traits ., The stage 1 meta-analysis combined data from 16 GWAS scans ( N\u200a=\u200a38 , 580 , all of European ancestry ) informative for anthropometric phenotypes ( Table S3 ) ., We selected two complementary but related measures of central adiposity for analysis: waist circumference ( WC ) and waist-hip ratio ( WHR ) ( Table S4 ) ., In total , 2 , 573 , 738 directly typed or imputed SNPs were tested for association using regression analysis under an additive model ( see Table S5 for details ) ., We conducted a weighted Z-score meta-analysis combining gender- and sample-specific association P-values gathered from each contributing study ., We also performed an inverse-variance meta-analysis using regression results ( β-estimates and standard errors ) after applying uniform analysis procedures across all contributing samples ., The results of the two meta-analyses were highly-congruent ., Here , we report association P-values based on the former , as it was the first-completed and was used to select SNPs for follow-up genotyping ., Reported effect-size estimates derive from the latter ( see Methods for further details ) ., The individual studies as well as the results from the overall meta-analysis were corrected for residual inflation of the test statistic using genomic control methods 18 ., The overall genomic control lambda ( λGC ) in the mixed-gender analysis were λGC_WC\u200a=\u200a1 . 09 ( λGC_WC_1000\u200a=\u200a1 . 003 standardised to a sample size of 1000 ) and λGC_WHR\u200a=\u200a1 . 07 ( λGC_WC_1000\u200a=\u200a1 . 002 ) ( see Text S1 ) 19 ., From these data , we identified a set of 76 SNPs ( one per independent region of association , based on an arbitrary follow-up P-value threshold of 10−5 in preliminary pre-GC corrected analyses ) that showed evidence of association to one or both of the traits ( Figure 2 ) ., As might have been expected given the strong correlations between measures of central adiposity and BMI , the most significant associations for WC and WHR were observed for SNPs mapping near FTO ( rs1421085 , WC , P\u200a=\u200a3 . 7×10−20 ) and MC4R ( rs17700144 , WC , P\u200a=\u200a6 . 2×10−11 ) ., These two markers are highly correlated ( r2>0 . 8 ) with markers that represent two of the strongest signals for overall adiposity ( Table S10 ) 9–10 , 12–14 , 16–17 , 20 ., From this initial set of 76 WC- and/or WHR- associated signals , we sought to enrich for variants with specific impacts on central adiposity , by identifying a subset of 23 SNPs for which there was greatest evidence for a disproportionate effect on central adiposity , as opposed to overall adiposity or height ., These 23 variants all had strong ( i . e . P≤10−5 ) associations with WC and/or WHR while displaying only weak evidence of an association with overall adiposity ( BMI , P>0 . 01 ) or adult height ( P≥0 . 005 ) in the stage 1 GWAS meta-analysis data ( Table S2 ) ., We also included three variants for reasons of biological candidacy , even though they did not precisely meet all P-value threshold criteria ( see Table S2 ) ., Given the stage 1 sample size of 38 , 580 , the follow-up P-value threshold of 10−5 provides 80% power to detect a per-allele beta of 0 . 045 ( equivalent , for example , to a per-allele effect on WC of approximately 0 . 5 cm ) , given an additive model and MAF of 20% ., For these 26 SNPs , we obtained in silico follow-up data from another 8 studies with GWAS data ( Stage 2a: maximum N\u200a=\u200a13 , 830 individuals , all European-ancestry ) , and performed de novo genotyping in subjects from 20 additional studies ( Stage 2b: maximum N\u200a=\u200a56 , 859 , all European-ancestry ) ( Table S3 ) ., Follow-up analyses were restricted to the precise phenotype ( s ) ( WC and/or WHR ) for which the SNP had been selected in stage 1 making a total of 30 SNP-phenotype combinations ( Tables S2 and S6 ) ., After combining gender- and study-specific measures of association across all studies ( maximum possible N\u200a=\u200a109 , 269: Tables S2 and S3 ) , we identified three signals reaching genome-wide levels of significance in the joint analysis of stage 1 and stage 2 data ( P<5×10−8 , Table 1 , Figure 3 ) ., In all three instances , the association was observed with WC ., The first ( rs987237 , chromosome 6p12: P\u200a=\u200a4 . 5×10−9 ) maps near TFAP2B , which encodes transcription factor activating enhancer-binding protein 2 beta ., The second ( rs7826222 , chromosome 8p23 . 1: P\u200a=\u200a1 . 2×10−8 ) resides near MSRA , encoding methionine sulfoxide reductase A , whilst the third ( rs6429082 , chromosome 1q42 . 3: P\u200a=\u200a2 . 6×10−8 ) is located within the TBCE ( tubulin folding cofactor E ) gene region ., As a final stage of confirmation , we analysed genotype data for rs987237 , rs7826222 and rs6429082 made available to us by the CHARGE consortium , whose members had recently completed a GWAS meta-analysis of WC in 31 , 375 individuals ( of which up to 6 , 702 individuals were overlapping with samples from our stage 2 and were removed before the joint meta-analysis ) ., At TFAP2B , CHARGE analyses revealed directionally-consistent association with WC ( rs987237 , N\u200a=\u200a31 , 372 , P\u200a=\u200a3 . 6×10−4 ) resulting in a combined P-value of 1 . 9×10−11 ( N\u200a=\u200a118 , 691 ) ., At MSRA , genotypes for rs7826222 could only be imputed in a subset ( N\u200a=\u200a8 , 097 ) of CHARGE samples ( this reflects SNP nomenclature issues rather than data quality – see Text S1 ) ., Nonetheless , the effect in CHARGE was directionally consistent ( P\u200a=\u200a0 . 28 ) , and in the overall results ( N\u200a=\u200a80 , 210 ) for this SNP , the evidence for association with WC was improved ( P\u200a=\u200a8 . 9×10−9 ) ( Table 1 ) ., In contrast , rs6429082 in TBCE showed no evidence of association with WC in the full CHARGE data set ( N\u200a=\u200a31 , 373 , P\u200a=\u200a0 . 12 ) ., Since analysis of the combined data set no longer reached genome-wide significance ( P\u200a=\u200a2 . 9×10−7 ) , further studies will be required to establish the status of this signal ., For the TFAP2B and MRSA loci , there was no evidence of heterogeneity of effect size across the various sample sets , and no evidence that the inclusion of diabetes or coronary artery disease case samples had any impact on the associations ( Table S2 ) ., Given the clear gender dimorphism of central obesity , and evidence that some genetic effects on fat distribution may be gender-specific 7 , we reanalysed the stage 1 GWAS data , looking for effects restricted to males or females only ., These analyses revealed a further locus of interest ., SNPs , including rs2605100 , within a gene desert on chromosome 1q41 ( 138 kb from ZC3H11B and 259 kb from LYPLAL1 , encoding lysophospholipase-like protein, 1 ) had shown modest evidence for association with WHR in our primary ( both genders included ) analysis ( P\u200a=\u200a3 . 6×10−6 ) ( Table S2 ) ., However , in gender-specific analyses , this association was clearly restricted to females ( P\u200a=\u200a1 . 3×10−8; males: P\u200a=\u200a0 . 50 ) ., When stage 1 and stage 2 data were combined , the female-only signal remained highly-significant ( P\u200a=\u200a2 . 6×10−8 ) ( Table, 1 ) with evidence of effect-size heterogeneity between genders ( P\u200a=\u200a1 . 1×10−3 ) ., As the CHARGE GWAS analyses were restricted to WC , we were unable to follow-up the LYPLAL1 signal in these data ., We had designed this study to be complementary to equivalent analyses of overall adiposity ( as measured by BMI ) conducted on many of the same samples 10 , 12–14 , 16 , 17 ., By focusing on widely-available anthropometric proxies of central adiposity , and targeting replication to those signals which , in the GWAS data , had the most compelling evidence for disproportionate effects on central adiposity , our aim had been to enrich for variants influencing regional rather than overall obesity , and thereby overcome the very strong correlations between these measures ., We were interested therefore in establishing the extent to which the confirmed , genome-wide associations identified at/near TFAP2B , MSRA and LYPLAL1 were indeed specific for central fat accumulation as opposed to being driven by other highly-correlated anthropometric traits ( most notably overall adiposity as measured by BMI ) ., To evaluate this , we used data from the stage 2 replication samples , from which we can expect to obtain less biased estimates of the relative effects across anthropometric phenotypes ., In the case of TFAP2B , these stage 2 data indicated that , notwithstanding the evidence for discordant effects in the stage 1 data ( which led to its selection for follow up ) , rs987237 showed strong associations with overall adiposity ( P\u200a=\u200a7 . 0×10−12 for BMI in stage 2 alone ) ., The association with WC remained only nominally significant in stage 2 ( P\u200a=\u200a0 . 02 ) after adjustment for BMI ., The TFAP2B rs987237 G allele was weakly associated with overall fat mass ( 0 . 15% difference per-allele P\u200a=\u200a0 . 02 in 29 , 316 individuals with bioimpedance data; 0 . 25% difference per-allele P\u200a=\u200a0 . 02 in 13 , 039 additional individuals with dual energy X-ray absorptiometry ( DXA ) measures: Table S7 ) ., In the 7 , 346 individuals for which we had DXA information on fat distribution , there was no apparent association with percent central fat mass ( P\u200a=\u200a0 . 98 ) , although this analysis is underpowered ., These data suggest that the chromosome 6p12 signal exerts its predominant effect on fat accumulation at multiple sites , a finding consistent with the known biology of TFAP2B , which is the most obvious candidate gene in the locus ., TFAP2B encodes a transcription factor preferentially expressed in adipose tissue , and over-expression of the transcript in 3T3L1-adipocytes leads to insulin sensitivity via enhanced glucose transport and increased lipid accumulation 21 , 22 ., Over-expression of TFAP2B also down-regulates expression of the insulin-sensitizing hormone adiponectin by direct transcriptional repression 23 ., Genetic variants within TFAP2B have recently been reported to correlate positively with TFAP2B transcript levels in adipose tissue 24 ., Thus , TFAP2B can be added to the growing list of loci influencing overall adiposity 10 , 14 , 16 , 17 ., However , in contrast to most of the variants previously implicated in monogenic or multifactorial forms of obesity , which exert their effects on overall adiposity at the hypothalamic level 10 , 12–14 , 16–17 , TFAP2B may be involved in global adipocyte response to positive energy balance ., In contrast , the signal on chromosome 1q41 ( near LYPLAL1 ) showed relatively modest associations with overall obesity ( stage 2 , women only , P\u200a=\u200a1 . 9×10−4 for BMI ) and WC ( P\u200a=\u200a0 . 01 ) ., Crucially , the strength of the association with WHR was substantially greater after adjustment for BMI ( stage 2 , women only , P\u200a=\u200a4 . 3×10−6 ) ., In the limited subset of women ( N\u200a=\u200a7 , 228 ) for whom direct measures of hip circumference ( HC ) could be retrieved , and in whom there was a proportionate signal for WHR ( P\u200a=\u200a5 . 2×10−4 ) , we found no association with HC ( P\u200a=\u200a0 . 7 ) and a directionally consistent trend of association to WC ( P\u200a=\u200a0 . 06 ) ., Whilst these data would suggest that the LYPLAL1 signal does indeed have a specific effect on fat distribution , our own DXA data on regional fat distribution are non-contributory ( N\u200a=\u200a5 , 455 ) ( Table S7 ) , and large-scale clinical imaging studies will be required to explore this further ., The obvious candidate within this locus ( although it lies ∼259 kb downstream of the most strongly-associated variant ) is LYPLAL1 ., This gene encodes a lysophospholipase-like 1 protein thought to act as a triglyceride lipase and reported to be up-regulated in subcutaneous adipose tissue of obese subjects 25 ., Biological connections between the MSRA locus and adiposity phenotypes are unclear at this stage ., The signal near MSRA showed only weak association with overall adiposity ( P\u200a=\u200a2 . 2×10−3 for BMI in stage 2 ) , but the strong association with WC in stage 2 samples became non-significant after BMI-adjustment ( P\u200a=\u200a0 . 11 ) ., The main proposed function of MSRA is to repair oxidative damage to proteins by enzymatic reduction of methionine sulfoxide ., An alternative candidate in the vicinity is TNKS , which encodes a TRF1-interacting ankyrin-related ADP-ribose polymerase ( tankyrase ) ., Tankyrase is a peripheral membrane protein known to interact with insulin-responsive aminopeptidase ( IRAP ) in GLUT4 vesicles in adipocytes 26 , 27 ., Thus TNKS has a putative role in insulin-regulated glucose disposal into fat and other tissues ., We estimated the variance in these traits attributable to the loci discovered using data from the KORA-S4 sample ( the largest population-based sample within stage 2 ) ., The explained variance of WC was estimated to be 0 . 05% for rs987237 ( TFAP2B ) and 0 . 04% for rs7826222 ( MSRA ) ., This corresponds to absolute WC effect sizes of 0 . 49 and 0 . 43 cm respectively ( as estimated across all population based samples in stage 2 ) ., The SNP near LYPLAL1 ( rs2605100 ) explains 0 . 02% of the WHR variance in women ( absolute effect size on WHR of 0 . 0014 ) ., The accumulation of central adiposity has serious adverse health consequences including hyperlipidemia and increased risks of T2D ., We examined the relationships between adiposity-related SNPs and these clinical phenotypes using available GWAS meta-analysis data ( Text S1 ) ., We found an association between the WHR-increasing G-allele of rs2605100 ( LYPLAL1 ) and increased fasting triglycerides ( P\u200a=\u200a3 . 9×10−4; Table S8 ) in data from a recent GWAS meta-analysis of 14 , 343 European samples 28 ., This is further supported by a parallel GWAS meta-analysis effort in 19 , 840 samples where the G allele is similarly associated with increased triglycerides ( P\u200a=\u200a0 . 02 ) 29 ., Using T2D case-control data from the DIAGRAM consortium 30 , we found directionally-consistent , though only weak , associations with T2D-risk , most obviously at TFAP2B ( P\u200a=\u200a0 . 09; Table S9 ) ., An association between other non-HapMap TFAP2B variants and T2D has previously been reported in Japanese samples 21 ., These T2D-associated variants show modest linkage disequilibrium to our WC associated SNP in UK samples ( IVS1774_G/T and rs987237 , r2\u200a=\u200a0 . 42; intron_1+2093_ ( A/C ) and rs987237 , r2\u200a=\u200a0 . 67 ) ., Thus , we see some evidence that the variants identified have anticipated effects on downstream phenotypes , although , as recently demonstrated for FTO ( which has more marked effects on adiposity than the signals described here ) , analyses of this type have only limited power even in extremely large data sets 31 ., In summary , by focusing on anthropometric measures of central obesity , we have identified three loci strongly implicated in the regulation of human adiposity and fat distribution ., The extent of phenotypic variation explained by these variants is small ., However , the variant or variants at each locus which are directly involved in influencing these traits are yet to be identified , and these may have more substantial effects ., Even if this is not the case , effect size has very little bearing on the biological pertinence of these findings nor the potential impact of perturbing these pathways through therapeutic modification ., Although determination of the influence of these signals on the development and maintenance of specific fat depots will require analyses that relate genetic variation to detailed imaging data in large numbers of subjects , the loci identified appear to highlight a variety of novel mechanisms involved in the regulation of adiposity ., At this stage , it is unclear to what extent these same loci influence fat distribution in other ethnic groups , such as South Asians , in which patterns of fat distribution , and the relationships between fat distribution and metabolic disturbance , differ from those in Europeans ., The data are consistent with a model whereby fat mass and distribution are determined through the concerted action of processes acting at the level of both the hypothalamus and peripheral fat depots ., Our study began with a genome-wide screen for discovery of loci potentially associated with two different anthropometric measures of central adiposity: waist circumference ( WC ) and waist-hip-ratio ( WHR ) 1 ., For each of the traits we combined the summary statistics of 16 genome-wide association studies ( GWAS ) in meta-analyses with 38 , 580 ( WC ) and 37 , 670 ( WHR ) individuals , respectively ( stage 1 ) ., These studies included nine population-based cohorts , four case cohorts ( three for T2D and one for Hypertension ) , and three control cohorts ( two originally paired with T2D and one with Breast Cancer ) ( Table S3 ) ., Following the discovery GWA meta-analysis , follow-up of our top association signals involved:, ( a ) addition of data for markers of interest from studies with pre-existing “in-silico” GWA results ( stage 2a; eight cohorts , maximum 13 , 830 individuals ) and, ( b ) “de novo” genotyping ( stage 2b; 20 cohorts , maximum 56 , 859 individuals ) giving a total of 70 , 689 ( WC ) or 61 , 612 ( WHR ) follow-up samples ( collectively referred to as stage 2 ) ., In addition , genome wide signals for WC identified after stage 2 were confirmed using data with The Cohorts for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium , whose meta-analysis included eight studies totaling 31 , 375 individuals ., All samples included in these analyses were of European ancestry ., We also undertook gender specific analysis of the stage 1 GWAS ., An overview of the study design and results is given in Figure 1 ., Further , our genome wide signals for WC identified after stage 2 were confirmed using data from the “Cohorts for Heart and Aging Research in Genomic Epidemiology” ( CHARGE ) consortium , which members had performed a GWAS meta-analysis of 31 , 375 samples for WC ( Table 1 ) .

Introduction, Results/Discussion, Methods

To identify genetic loci influencing central obesity and fat distribution , we performed a meta-analysis of 16 genome-wide association studies ( GWAS , N\u200a=\u200a38 , 580 ) informative for adult waist circumference ( WC ) and waist–hip ratio ( WHR ) ., We selected 26 SNPs for follow-up , for which the evidence of association with measures of central adiposity ( WC and/or WHR ) was strong and disproportionate to that for overall adiposity or height ., Follow-up studies in a maximum of 70 , 689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B ( WC , P\u200a=\u200a1 . 9×10−11 ) and MSRA ( WC , P\u200a=\u200a8 . 9×10−9 ) ., A third locus , near LYPLAL1 , was associated with WHR in women only ( P\u200a=\u200a2 . 6×10−8 ) ., The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass , whereas LYPLAL1 displays a strong female-only association with fat distribution ., By focusing on anthropometric measures of central obesity and fat distribution , we have identified three loci implicated in the regulation of human adiposity .

Here , we describe a meta-analysis of genome-wide association data from 38 , 580 individuals , followed by large-scale replication ( in up to 70 , 689 individuals ) designed to uncover variants influencing anthropometric measures of central obesity and fat distribution , namely waist circumference ( WC ) and waist–hip ratio ( WHR ) ., This work complements parallel efforts that have been successful in defining variants impacting overall adiposity and focuses on the visceral fat accumulation which has particularly strong relationships to metabolic and cardiovascular disease ., Our analyses have identified two loci ( TFAP2B and MSRA ) associated with WC , and a further locus , near LYPLAL1 , which shows gender-specific relationships with WHR ( all to levels of genome-wide significance ) ., These loci vary in the strength of their associations with overall adiposity , and LYPLAL1 in particular appears to have a specific effect on patterns of fat distribution ., All in all , these three loci provide novel insights into human physiology and the development of obesity .

genetics and genomics/complex traits, diabetes and endocrinology/obesity, diabetes and endocrinology, genetics and genomics, diabetes and endocrinology/type 2 diabetes

journal.ppat.1000178

The Core and Accessory Genomes of Burkholderia pseudomallei: Implications for Human Melioidosis

Melioidosis is a potentially fatal infectious disease of humans and animals caused by the Gram-negative bacterium Burkholderia pseudomallei ( Bp ) 1 ., An environmental saphrophyte found in South East Asia , Bp infections in endemic areas may be responsible for up to 20% of deaths due to septicemia 2 , 3 , and Bp has been designated a Category B biothreat agent 4 ., A wide spectrum of disease symptoms are associated with melioidosis often leading to late diagnosis and treatment 5 ., Commonly presenting as an acute septicemic illness , chronic Bp infection is also well recognized which can be confused with TB or malignancy 6 ., Besides humans , Bp has a broad host range and can infect nematodes , amoebae , dolphins , birds , swine , sheep , and gorillas 7–11 ., Bp can also be isolated from diverse environmental sources such as soil , water , and air 12–17 ., Identifying the molecular factors responsible for this tremendous ecologic flexibility may improve our understanding of microbial survival and adaptation , and suggest novel diagnostic and treatment strategies for melioidosis ., The phenotypic versatility of Bp is likely to be underpinned by the presence of a highly dynamic genome ., For example , lateral gene transfer events may cause large-scale variations in genome content 18 ., The portion of the genome that is variably present between individual strains is often termed the “accessory genome” , to distinguish these genes from genes common to all strains in a population and involved in essential functions ( the “core” genome ) ., In several microbial species , accessory genes have been shown to play key roles in host adaptation and , in the case of Bp , the accessory genome may contribute to virulence and antibiotic resistance 19 ., Interestingly , previous studies indicate that in Bp , gene loss , as well as gene acquisition events , can both cause phenotypic shifts towards virulence ., For example , comparisons between Bp and B . thailandensis , an avirulent closely related species , have shown that an important evolutionary step in the development of Bp pathogenicity was the loss of an anti-virulence arabinose assimilation cluster 20 , 21 ., Such findings thus raise a compelling need to accurately define the core and accessory genomes of Bp ., In other γ proteobacteria genera ( E . coli , Pseudomonas , Vibrio ) , the accessory genome can encompass up to 20% of all genomic content , and similar percentages may also hold for Burkholderia spp ., 22–25 ., However , to date , comprehensive qualitative and quantitative studies of the core and accessory genome in Bp have not been carried out , and the full extent to which gene content differences contribute to virulence in Bp is still unclear ., While some previous studies have attempted to explore these issues , they have not incorporated data from the whole genome 19 , 26–28 , or have used only a very small sample of strains 29 , 30 ., In this study , we performed a detailed array-based comparative genomic hybridization ( aCGH ) analysis of close to 100 clinical , animal and environmental Bp isolates from South East Asia ., To our knowledge , this is the first time a whole genome comparative study has been applied to such a large Bp strain cohort ., We found that 86% of the reference Bp K96243 genome was present in all the strains , while the remaining 14% was variably present across the strain panel ., Surprisingly , isolates associated with human melioidosis exhibited a tendency to harbor certain GIs compared to isolates from either animal or environmental sources , suggesting that genes on these mobile elements might facilitate colonization of the human host ., Taken collectively , our results support the notion that the Bp accessory genome may play a central role in adaptation and virulence ., Besides providing important evidence concerning genes likely involved in Burkholderia pathogenesis , this study also raises the possibility of targeting molecular diagnostics to specific Bp accessory regions for monitoring the presence of human-virulent variants in the environment ., Using a previously validated Bp K96243 DNA microarray 30 , 31 , we generated aCGH profiles for ninety-four Bp strains isolated from human patients , animals , and environmental soils in Singapore , Malaysia or Thailand ( Table S1 ) ., We applied a Gaussian Mixture Model ( GMM ) to the aCGH data and identified 750 out of 5369 genes ( 14% ) as being variably present across the strain panel ( see Methods and Figure S1 ) ., The variability of the 750 genes was experimentally validated by several independent methods , including bioinformatic comparisons to previously-known variable genes , comparisons against publicly available genome sequences , and experimental confirmation by targeted PCR assays ( Figure S2 and Table S2 ) ., 86% of the Bp K96243 genes ( 4619 ) were found in all strains , representing the Bp core genome ( Figure 1 ) ., Using pathway analysis , we found that the core genes were significantly over-represented in several functions necessary for basic bacterial growth and survival , including amino acid metabolism ( 1 . 52×10−3 ) , inorganic ion transport ( 3 . 96×10−3 ) , nucleotide metabolism ( 1 . 52×10−2 ) and protein translation ( 7×10−3 ) ( Table 1 ) ., The core genes were also significantly enriched in genes conserved in other Burkholderia species ( Bp , B . mallei , B . thailandensis and B . cepacia ) ( p\u200a=\u200a8 . 68×10−11 ) ( Text S1 and Table S3 ) ) , suggesting that a significant proportion of these Bp core genes may represent core genes in other related species as well 32 ., Besides these basic housekeeping functions , the Bp core genes were also significantly enriched in commonly encountered virulence-related genes such as secretion proteins , capsular polysaccharides , exoproteins , adhesins , fimbriae and pili ( p\u200a=\u200a1 . 8×10−3 ) ( Table 1 ) ., For example , three Bp-specific fimbrial gene clusters ( BPSL1626-1629 , BPSL1799-1801 , BPSS0120-0123 ) were found in all strains ., This finding suggests that most , if not all , Bp isolates are likely to possess a common ‘virulence machinery’ ., Notably , many of these conventional virulence genes are also found in other related species such as B . thailandnesis that although non-infectious to mammals can kill other species such as nematodes 20 , 33 ., This is consistent with the possibility that Bp might have descended from a pathogenic ancestor with a non-mammalian host ., 14% of the Bp K96243 genome was variable across the strain panel , representing the Bp accessory genome ., Since our analysis is confined to genetic elements present in the reference K96243 genome , the extent of genomic variability reported here should be regarded as a lower limit ., The 750 variable genes were equally distributed between both Chromosome 1 and Chromosome 2 after normalizing for chromosome size differences ., The accessory genes were significantly enriched in paralogous genes ( p\u200a=\u200a2×10−7 ) and genes encoding hypothetical proteins ( p\u200a=\u200a3×10−4 ) ( Table 1 ) ., Approximately one-third ( 30 . 8% ) of the accessory genes were localized to a series of previously identified “genomic islands” ( GIs ) in the K96243 genome 34 ., GIs are regions bearing unusual sequence hallmarks , such as atypical GC content and/or dinucleotide frequencies , and are likely to have been recently acquired by lateral gene transfer ., Of sixteen GIs in the K96243 genome , fourteen GIs were represented by accessory genes ., In contrast , two GIs ( 7 and 14 ) were found in all strains , suggesting that GIs 7 and 14 should be regarded as part of the Bp core genome ., Besides the GIs , we also identified several novel regions of at least three contiguous probes that were absent in at least three strains ., Henceforth referring to these regions as ‘indels’ , we identified eight indels on chromosome 1 , and twelve on chromosome 2 ( Table 2 ) ., We experimentally validated two of these indels using PCR assays ( Figure S3 ) ., The indels ranged in size from 1 . 3 to 7 . 5 kb , and were absent in 12 . 9% to 45 . 2% of strains ( Figure 2 ) ., Three indels ( n1 , n4 and n11 ) were associated with atypical GC content ( 53 . 7–58 . 6% , compared to 68% for the Bp genome ) , and four ( n2 , n9 , n11 and n16 ) carried genes characteristic of mobile genetic elements such as integrases , transposases and bacteriophage-related genes , consistent with lateral transfer ., These indels may therefore share similar dynamics to the larger genomic islands , and may be considered as genomic “islets” ., In other species , analogous islets which are typically <10 kb long , have been shown to play a role in virulence ( e . g . the sifA islet in S . typhimurium ) 35 ., Of note , n16 and n18 were flanked at both their 5′and 3′ends by tandem repeat sequences , while n4 , n6 , n8 and n19 possessed sequence repeats at either their 5′ or 3′ ends ., In some cases , the islets in the Bp genomes may actually form part of the larger GIs ., For example , n2 ( BPSL0741-BPSL0744 ) was located at the 5′ boundary of GI 4 ( BPSL0745-BPSL0772 ) , while n11 ( BPSS0395-BPSS0397 ) was located immediately 3′ to GI 13 ( BPSS0378-BPSS0391A ) ., Three indel regions ( n6 , n12 and n19 ) contained genes associated with LPS metabolism ., Lipolysaccharides ( LPS ) are macromolecular components on the outer membranes of Gram-negative bacteria composed of lipid A , core oligosaccharide , and O-antigen polysaccharides 36 ., LPS molecules are commonly immunogenic and have been previously implicated in virulence for numerous microbes 37 , 38 ., Region n6 ( BPSL2666-BPSL2668 ) contains a phosphoglucomutase ( BPSL2666 ) , a lipopolysaccharide LPS biosynthesis protein ( BPSL2667 ) and a glycosyltransferase ( BPSL2668 ) , and was located four genes away from a larger LPS biosynthesis cluster ( BPSL2672-BPSL2688 ) ., Both regions n12 ( BPSS0427 - BPSS0429 ) and n19 ( BPSS2245-BPSS2255 ) contained two O-antigen related genes , including O-acetyltransferase and glycosyltransferase ., While n12 corresponds to a previously identified type III O-PS polysaccharide gene cluster 39 , the contribution of n19 genes to Bp LPS biology is currently unknown ., The identification of three physically unlinked indels related to LPS metabolism provides a mechanism by which high levels of LPS diversity may be maintained in the Bp population 40 ., To explore if differences in accessory genome content might be associated with host adaptation or the propensity to cause disease , we applied unsupervised clustering to cluster the strains using the entire set of 750 accessory genes ( “accessory genome clustering” , AGC ) ., We identified three large AGC clusters each containing 27 to 42 strains , with each cluster containing at least 4–6 sub-branches ( Figure 3 ) ., Most strikingly , the majority of human clinical isolates ( 73 . 1% ) fell into one AGC cluster ( Clade C ) , another cluster contained 73 . 7% of the animal isolates ( Clade A ) , and a third cluster contained 45% of the environmental isolates ( Clade E ) ., Similar results were obtained when the clustering was repeated using either Chromosome 1 or Chromosome 2 accessory genes ( Figure S4 ) ., The over-representation of human clinical isolates in the C clade was highly significant ( P\u200a=\u200a2 . 001×10−14 , Fishers exact test ) , and of the remaining 13 clinical isolates nine segregated within the E clade and four in the A clade ., This clustering pattern is unlikely to represent differences in geographical distribution , since the majority of the clinical ( 65% ) , animal ( 89% ) and environmental isolates ( 80% ) were isolated in Singapore within a ∼700 km2 region or from nearby islands ., Furthermore , clinical isolates from Thailand clustered with the other clinical isolates , despite being geographically remote ., This analysis therefore suggests that strains associated with human melioidosis may possess an accessory genome distinct from most animal and environmental strains ., We also note that all three clades contained environmental isolates , which is consistent with the view that the environment represents a diverse reservoir from which human and animal adapted strains emerge ., We then performed a supervised analysis to identify which of the 750 accessory genes were significantly different between the C and A/E clades ., Of the 750 genes , 218 genes were commonly present in isolates in the C clade but absent from strains in the other two clusters ( Figure 4A ) ., Strikingly , we found that almost all of these 218 genes ( 85% ) were localized to the GIs , with all fourteen GIs being represented ., This figure ( 85% ) is significantly higher than the 31% of all accessory genes located on GIs , raising the possibility that GIs may play an important role in determining ecological niche and host adaptation ., Is there any direct evidence that genes encoded on GIs , and which define the C clade , might play an important role in the biology or pathogenicity of Bp ?, Unfortunately , almost 35% of the GI genes encode ‘hypothetical’ proteins ( Table S4 ) , meaning that their function is unknown ., For those genes specific to the C clade where functions could be assigned , several broad functional classes were represented ., For example , GI8 contains several genes spermidine/putrescine transport genes ( potB , potC , potG ) , which have been associated with biofilm formation and the regulation of Type III secretion genes 41 , 42 ., Type I restriction-modification enzymes are found on GI5 and GI10 , and a glutathione S-transferase gene ( BPSS2048 ) on GI16 may impart resistance to oxidative stress ., Also supporting their potential role in Bp biology , several GI genes exhibited distinct and complex gene expression patterns during Bp growth ( Text S2 ) ., However , the role of such genes in pathogenesis remains speculative ., In order to explore this further , we generated an experimentally mutated strain ( ATS2053 ) disrupted in BPSS2053 , a GI 16 gene encoding a hemagglutinin-related protein , and determined the adherence of the mutant strain to human buccal epithelial cells ., A highly significant reduction in the adherence to buccal epithelial cells was noted between the 1026b clinical isolate and the isogenic ATS2053 mutant strain ( mean adherence: 1026b - 16 . 3±3 . 2 vs ATS 2053 - 4 . 4±1 . 7 , p<0 . 001 , Students t test ) ., This finding provides evidence pointing both to the biological relevance of GI genes , but more specifically to a role of these genes in virulence ., Finally , we examined the concordance between strain clusters defined on the basis of accessory gene content and the phylogenetic signal within the Bp core genome ., We characterised 45 representative isolates by Multilocus Sequence Typing ( MLST ) , a typing scheme that indexes variation at seven core housekeeping genes 43 ., Using the previously published Bp scheme 44 , we resolved the 45 isolates into 9 sequence types ( ST 46 , 51 , 54 , 84 , 169 , 289 , 414 , 422 and 423 ) ., Seven of these STs ( ST51 , 54 , 84 , 46 , 169 , 289 , 414 ) have been previously observed in Malaysia , Thailand , and Singapore and two ( ST422 and 423 ) are specific to Singapore 44 , 45 ., Previous analyses of MLST for Bp have highlighted the difficulties in building robust phylogenetic trees for this species , owing to a paucity of informative sites in the concatenated data and frequent homologous recombination 46 ., We thus favored a categorical approach to comparing the AGC and MLST data by examining the distribution of sequence types across the three clades defined by the AGC data ( Table 3 ) ., This analysis revealed that the STs are not randomly distributed between the three clusters , indicating some consistency between the MLST and AGC datasets ., Most strikingly , of the 20 ST51 isolates , 17 clustered within the animal-associated clade ( A ) , three within the clinical C clade , and none in the environmental E clade ., Of the other STs where at least 4 isolates were observed , all four ST422 isolates corresponded to the C clade , and all four ST84 isolates clustered within the E clade ., Finally , of the nine ST423 isolates , five clustered within the C clade and four in the E clade ., These data suggest that the animal-associated clade is likely to correspond to a single clone ( ST51 ) and provides some evidence for concordance between STs 422 and 84 with the AGC data , although the evidence in these latter cases is equivocal due to the small number of strains ., In contrast , the “split” of the ST423 isolates between the clinical and environmental clades , and the 3 ST51 isolates belonging to the clinical clade , represent clear discrepancies between the two datasets ., Possible explanations for these discrepancies may represent convergence of either the MLST or the AGC data , as discussed below ., In this report , we present a comprehensive aCGH analysis for a large series of natural Bp isolates ., We found that the accessory ( variably present ) portion of the Bp genome corresponds to ∼14% of the whole genome content , which is broadly similar to other γ-proteobacteria ., Since this approach is limited to the detection of elements present in the Bp K96243 genome , and novel elements in query genomes are not detected , this estimated fraction of the accessory genome should be regarded as a lower bound ., In the only published study of a Bp genome sequence to date , Holden et al ( 2004 ) computationally identified 16 GIs comprising 6% of the K96243 genome 34 , and our data confirm that most of these islands are indeed highly variable between strains ., However , two GIs ( 7 and 14 ) were found in all strains and should thus be regarded as part of the Bp core genome ., Furthermore , our data also revealed the variable presence of several other small genomic islets/indels across the two chromosomes , which might contribute to the phenotypic diversity of Bp ., Notably , we observed that several indels ( n6 , n12 and n19 ) were related to LPS biology ., Currently , the exact contribution of LPS to Bp virulence is unclear ., For example , DeShazer et al ( 1998 ) showed that Bp type II O-PS is essential for serum resistance and virulence 47 , and mice pre-immunized with Bp LPS displayed enhanced survival to a subsequent challenge 48 ., In contrast , other groups have reported that Bp LPS exhibits a reduced ability to activate immune cells compared to E . coli LPS , suggesting that LPS might play only a minimal role in Bp virulence ., It is possible that these conflicting results might reflect heterogeneity in LPS pathways resulting from the variable presence of these indels , and represent an important mechanism for host adaptation ., Interestingly , while it was recently shown that type III O-PS mutants ( indel n12 ) do not appear to exhibit significant virulence attenuation in mouse infection assays 39 , we have found in preliminary work that Bp strains lacking the indel n19 LPS cluster generally exhibited lower levels of virulence compared to strains where this cluster was present ( SSH , data not shown ) ., In the AGC tree , n19 was absent both from three strains segregating as a single branch in the A clade , and from 5 strains in the C clade that segregated across multiple branches ., This suggests that n19 may have been recurrently lost in different Bp lineages ., Further experiments are clearly required to understand the role of these LPS clusters in Bp virulence ., We also found that the Bp strains could be clustered into distinct clades based on both the presence and absence of specific accessory genes ., Of primary interest , strains belonging to the C clade of clinical isolates were largely defined by the presence of 218 genes , of which 85% are localized to the GIs ., These findings provide evidence for a distinct repertoire of Bp genes that may cause a predisposition to human disease and that these genes tend to be located on GIs ., Although many of the genes encoded on the GIs are of unknown function , we present experimental evidence that a strain mutated in one of these genes exhibited decreased adherence to human buccal endothelial cells , supporting a role in virulence potential ., We also observed coordinated growth-associated expression of several GI genes , which is also consistent with the view that they play an important biological role ., What might this biological role be ?, At present , we consider it most likely that this “virulent” combination of genes has likely emerged for reasons other than to cause human disease , particularly since cases of human ( or animal ) infection are relatively rare compared to the density of Bp in the soil ., In contrast to bacteria which are obligately associated with eukaryotic hosts , soil bacteria such as Bp commonly face extreme and unpredictable biotic and abiotic challenges including extreme temperature shifts , solar radiation , variable humidity , competition for nutrients , and the requirement to survive ingestion by predatory protozoa , nematodes , the production of bacteriocides from other bacteria and phage infection ., It thus seems entirely plausible that genes facilitating survival against these environmental challenges might have also indirectly enhanced the microbes ability to colonize and “accidently” infect a human host , particularly when the host is immunocompromised 49 ., Another possibility that might explain the enrichment of GIs in the clinical isolates is that Bp is undergoing cryptic cycling through normal human hosts ( as opposed to the immunodeficient host ) , and that these GIs are selected during this host-pathogen interaction ., In melioidosis-endemic NE Thailand , the majority of healthy individuals have antibodies to Bp by the age of 4 years , indicating a constant exposure to the bacterium that may occur by inoculation , inhalation or ingestion 50 ., Within these normal hosts , Bp is likely to spend a period of time being exposed to the effects of the host immune response , after which the microbe may experience bacterial death , persistence , or expulsion from the host in a viable state and subsequent return to the environment ., This latter process might occur through skin desquamation or urine and stool , since human excrement commonly finds its way back to the environment ., Such cryptic cycling of Bp through the normal human host population could also lead to the selection of factors that promote survival in vivo ., However , as we consider the human host to be a relatively minor component of Bp ecology , we argue that this scenario is , on balance , less likely ., The availability of both MLST and aCGH data for a representative sub-sample of isolates also provided us the opportunity to compare clade distributions defined either by accessory genome content or allelic variation in the core genome ., We found that the animal associated strains largely corresponded to a single MLST clone ( ST51 ) ., These isolates were assembled from three distinct sources: the Singapore zoo , the University of Malaya and a pig abbatoir in Singapore ., The soil isolates corresponding to ST51 ( which also clustered in the A clade ) were not isolated from soil samples in proximity to the animal ST51 isolates , which suggests that this genotype is also present in the environment ., The homogeneity of these isolates is therefore striking and cannot be explained simply by sampling bias ., The consistency between the microarray and MLST data strongly suggest that this clade is monophyletic , and that the strains harbour similar gene repertoires by virtue of common descent ., In contrast , we also observed clear discrepancies between the MLST and aCGH clades ., For example , three ST51 isolates clustered within the clinical aCGH clade , and ST423 was split between the clinical and environmental aCGH clades ., There are three possibilities to explain these discrepencies:, i ) The MLST data represents the ancestral state which is inherited by descent into two AGC-defined clades - this is unlikely for the animal cluster as the vast majority of isolates are ST51 , but might conceivably explain the ST423 split between the clinical and environmental clades ., ii ) Convergence of the MLST alleles - this would imply that isolates with the same ST are not identical by descent but happen to share the same combination of alleles ., The presence of a few very common alleles for each gene , combined with high rates of recombination in Bp make this possibility more likely ., iii ) Independent convergence of gene content to one of the three clusters ., Unless large numbers of genes can be transferred in single events , this possibility seems less parsimonious than, ( ii ) ., More data are required to examine which of these hypotheses is most likely ., In summary , our study provides direct experimental confirmation that the Bp genome is highly plastic , and that gene acquisition and deletion are major drivers of this variability ., This variability is far from random , and is functionally biased towards genes involved in mobile elements , hypothetical and paralogous genes , and LPS biosynthesis ., Furthermore , genes on mobile elements may predispose individual strains , either directly or indirectly , towards causing human disease ., We believe this latter result is significant in that most Bp research to date has focused on virulence components in the Bp core genome rather than genes on mobile elements ., We conclude by noting that most of the Bp genome sequences currently available have been obtained from human clinical isolates ., Given our results , it might be highly informative to subject a panel of animal and environmental Bp isolates to similar detailed genome analysis as well ., Ninety-four Bp isolates were used in this study ., These include:, a ) the K96243 reference strain ,, b ) 52 clinical isolates from melioidosis patients between 1996 and 2005 ,, c ) 19 animal isolates from various species ( eg monkeys , pigs , birds , and dogs ) diagnosed with melioidosis between 1996 and 2000 ,, d ) 20 soil isolates from 1994 to 2003 , and, e ) two type strains ( ATCC23343 and ATCC15682 ) ., All strains were isolated in Singapore , neighboring islands , or surrounding countries ( Malaysia , Thailand ) ., The isolates were sampled from a diversity of locations and not a single site , supporting their unbiased nature ( Aw Lay Tin and Joseph Tong , personal communication ) ., Further strain information is provided in Table S1 ., Strains were cultured on Tryptone Soy Agar ( TSA ) ( Difco Laboratories , Detroit , Michigan ) at 37°C , and genomic DNA extracted using a genomic DNA purification kit ( Qiagen ) ., The Bp DNA microarray has been previously described 29–31 and comprises approximately 16 , 000 PCR-amplified array probes representing all 5742 predicted genes in the K96243 genome printed in duplicate ., Test genomic DNA ( 2 µg ) was fluorescently labeled with Cy3-dCTP ( Amersham Pharmacia Biotech ) using nick-translation and co-hybridized to the array with an equal quantity of Cy5-dCTP ( Amersham Pharmacia Biotech ) labeled reference K96243 DNA ., The absence of significant dye-bias artifacts was confirmed by analyzing reciprocal dye-swap hybridizations for 10 isolates data not shown , also see ref 29 ., Raw fluorescence data was acquired using an Axon scanner with GENEPIX v4 . 0 software ( Axon Instruments , Redwood City , CA ) ., Individual arrays were internally normalized between the Cy3 and Cy5 channels by LOWESS normalization , and the entire dataset was cross-normalized by median-scaling each array to the same Cy3/Cy5 ratio ., To filter the microarray data , we eliminated probes exhibiting a missing value score across >40% of samples ( indicating that they were not reliably measured ) , and probes whose genomic loci were redundant with other probes ., This data filtering procedure generated a final high-quality data set of 5369 non-redundant probes ., The entire microarray data set is available at the Gene Expression Omnibus database under accession number GSE9491 ., A Gaussian mixture model ( GMM ) 51 was used to identify accessory and core genes in the data set ., In concept , a GMM fits a test signal distribution ( such as microarray data ) to either a single or double gaussian curve , and the likelihood that the distribution corresponds to a single curve is computed ., The GMM was applied in two stages ., First , p-values were computed using the aCGH profiles of each individual array spot , following a chi-square distribution with 3 degrees of freedom under the null hypothesis that the data distribution of the spot follows a 1-gaussian distribution ., Second , since each probe was spotted twice on the array , we obtained composite p-values of each array probe using Inverse Chi-square Meta-Analysis 52 , squaring the p-values of both spots belonging to the same probe ., This latter statistic follows a chi-square distribution with 4 degrees of freedom ., All p-values were corrected for multiple-hypothesis testing according to the Benjamini-Hocheberg procedure 53 ., A cut-off of p≤1 . 83E-08 was selected to define the top 750 most highly variable probes , representing the accessory genome ., All protein coding sequences in the Bp K96243 genome were queried by BLASTP against the Cluster of Orthologous group ( COGs ) database , a public bioinformatic database that groups protein sequences on the basis of phylogenetic similarity to various cellular functions , such as protein translation , DNA replication and transcription , nuclear structure and defense mechanisms ( accessible at http://www . ncbi . nlm . nih . gov/COG/new/ ) ., Matches were defined as database hits with an e-value threshold of <10−6 ., Based on the COG assignments , the K96243 proteins were assigned to functional categories ., Fishers exact tests were used to identify significantly overrepresented COG categories in either the core or accessory genes ., To identify conserved genes ( metagenes ) across four Burkholderia species , we queried the 3460 Chr 1 and 2395 Chr 2 ORFs in the Bp K96243 genome against the B . cenocepacia ( Bc ) , B . mallei ( Bm ) , and B . thailandensis ( Bt ) genomes using tblastn 32 ( Text S1 ) ., To minimize the number of ambiguous predictions including ORFs with matches to multiple genomic locations , we constrained the resulting matches to have I ) a minimum length of 50 amino acids , II ) a minimal e-value cut-off of 1e-6 and III ) a minimum percent identity of 50% ., Homology assignments returned 2675 genes and were validated by a reciprocal blast assay resulting in 2590 genes ., Control analyses using either Bc , Bm or Bt as starting reference genomes yielded similar metagene sets ( data not shown ) ., Paralogous genes were identified using the CD-HIT program 54 as genes with >60% identity to one another , following established studies 55 , 56 ., Tandem repeat regions in the K96243 genome were identified using the Tandem Repeats Finder program 57 ., Phylogenetic trees based on aCGH profiles were constructed using MultiExperiment Viewer ( MeV ) version 4 ( http://www . tm4 . org/mev . html ) using an average linkage clustering algorithm with a Euclidean distance metric ., Support trees were based on 1000 bootstrap samples ., Neighbor-joining trees based on MLST sequence data were constructed by MEGA ver . 2 . 1 software using the Kimura-2-parameter method of distance estimation ., eBURST v3 ( http://eburst . mlst . net ) was used to demonstrate relationships between closely related STs ( those differing at only a single locus ) 58 , 59 , with the tree files visualized using PhyloDraw 60 ., The BPSS2053 ( fhaB ) gene was disrupted in strain DD503 , an isogenic derivative of wild-type 1026b ., In DD503 , the amr locus , encoding a multidrug efflux system , has been experimentally deleted 61 ., The increased antibiotic susceptibility of DD503 makes it a useful strain for allelic exchange experiments as it allows the use of currently available allelic exchange vectors ., There is no significant difference in virulence between the1026b pa

Introduction, Results, Discussion, Methods

Natural isolates of Burkholderia pseudomallei ( Bp ) , the causative agent of melioidosis , can exhibit significant ecological flexibility that is likely reflective of a dynamic genome ., Using whole-genome Bp microarrays , we examined patterns of gene presence and absence across 94 South East Asian strains isolated from a variety of clinical , environmental , or animal sources ., 86% of the Bp K96243 reference genome was common to all the strains representing the Bp “core genome” , comprising genes largely involved in essential functions ( eg amino acid metabolism , protein translation ) ., In contrast , 14% of the K96243 genome was variably present across the isolates ., This Bp accessory genome encompassed multiple genomic islands ( GIs ) , paralogous genes , and insertions/deletions , including three distinct lipopolysaccharide ( LPS ) -related gene clusters ., Strikingly , strains recovered from cases of human melioidosis clustered on a tree based on accessory gene content , and were significantly more likely to harbor certain GIs compared to animal and environmental isolates ., Consistent with the inference that the GIs may contribute to pathogenesis , experimental mutation of BPSS2053 , a GI gene , reduced microbial adherence to human epithelial cells ., Our results suggest that the Bp accessory genome is likely to play an important role in microbial adaptation and virulence .

Melioidosis is a serious infectious disease of humans caused by Burkholderia pseudomallei , a soil bacterium endemic to many areas in South East Asia ., Besides humans , B . pseudomallei is also capable of infecting many other species and can be isolated from diverse environmental sources including soil , water , and air ., In this study , we used DNA microarrays to probe the stability of the B . pseudomallei genome in a large panel of clinical , animal , and environmental strains ., We found that evidence of a highly dynamic B . pseudomallei genome , with up to 14% being variably present across different strains ., Surprisingly , strains recovered from human patients were significantly associated with the presence of “genomic islands” , corresponding to regions of DNA directly acquired from other microorganisms ., Genes on these genomic islands may thus play an important role in the pathogenesis of human melioidosis .

microbiology/environmental microbiology, microbiology/microbial evolution and genomics

journal.pgen.1002433

Genome-Wide Assessment of AU-Rich Elements by the AREScore Algorithm

Gene expression is extensively regulated at both transcriptional and posttranscriptional levels ., In the cytoplasm , numerous mechanisms act on mRNAs to ensure their proper localization , translation and stability 1 ., Together with the rate of transcription , the lifespan of an mRNA is a key determinant of the level at which any given gene is expressed ., Half-lives differ widely between transcripts , ranging in human cells from 5 minutes to >10 hours 2 , 3 ., In yeast , mRNAs are degraded more rapidly and their half-lives range from 3 to >90 minutes 4 ., AU-rich elements ( AREs ) are well-characterized cis-acting regulatory sequences that strongly accelerate the degradation of mammalian mRNAs ., AREs were initially discovered in 3′ untranslated regions ( UTRs ) of short-lived transcripts encoding cytokines 5 , 6 , and since have been proposed to reside in 5–8% of all transcripts 7 ., However , the frequency of functional AREs in a given cell type is certainly lower because genome-wide measurements of mRNA decay rates showed that the presence of AU-rich sequences correlates only to a limited extent with rapid mRNA decay: In primary human T-cells , only about 25% of mRNAs with AU-rich sequences were found to decay rapidly 2 , and in the hepatocellular carcinoma cell line HepG2 this proportion was only 10–15% 8 ., Although there is no strict consensus sequence for AREs , the following key motifs have been identified: AUUUA pentamers that frequently occur in multiple copies , which may overlap or localize in close proximity 6 , 9; a related nonameric motif UUAUUUAUU or UUAUUUA ( U/A ) ( U/A ) , which is strongly linked to rapid mRNA decay 10 , 11 , 12 , 13; and a generally U-rich or AU-rich context required for maximum efficiency of either pentamers or nonamers 9 ., AREs can be distinguished according to different deadenylation kinetics , which gave rise to a widely used classification published by Shyu et al . 14 ., Class I AREs ( e . g . c-myc , c-fos ) contain a few scattered pentamers within a larger U- or AU-rich context , and mediate synchronous deadenylation indicative of a distributive exoribonuclease ., Class II AREs ( e . g . GM-CSF , IL-3 and TNFα ) have a cluster of 4–7 partially overlapping pentamers within a U-rich context , and mediate asynchronous deadenylation indicative of a processive exoribonuclease ., Class III AREs ( e . g . , c-jun ) lack pentamers and have been less well characterized ., Khabar et al . proposed an alternative classification of AREs into five groups based on the number of overlapping AUUUA pantamers 15 ., This classification has been used to mine databases for the occurrence of ARE-regulated genes 7 , yet the functional implication of this classification has not been thoroughly tested ., ARE-mediated mRNA decay ( AMD ) depends on specific RNA-binding proteins ( BPs ) that recognize AREs and target the mRNA for rapid degradation 16 ., The Tis11 zinc finger proteins are ARE-BPs with a major role in AMD ., The mammalian Tis11 family comprises TTP 17 , BRF1 18 and BRF2 19 , all of which are potent inducers of mRNA degradation ., These proteins share a highly conserved tandem C3H zinc finger domain required for RNA binding ., TTP is the best characterized member of this family and acts as a suppressor of inflammation in mice by controlling the expression of tumor necrosis factor-α ( TNFα ) 17 ., Further studies showed that TTP causes the degradation of many additional mRNAs related to the immune response ( reviewed in 20 ) ., TTP induces the degradation of its target mRNAs by recruiting the components of the general RNA degradation machinery such as the exosome 21 , the decapping complex 22 and the Ccr4-Caf1-Not deadenylation complex 23 ., Moreover , TTP is regulated through phosphorylation by the p38-MAPK – MK2 kinase cascade ., Direct phosphorylation by MK2 causes binding of 14-3-3 adaptor proteins and decreases the activity of TTP 24 , 25 by interfering with the ability of TTP to recruit the Ccr4-Caf1-Not deadenylation complex 26 , 27 ., In turn , the protein phosphatase 2A dephosphorylates TTP and thereby activates AMD 28 ., Very little is known about AMD in Drosophila melanogaster ., So far , only the mRNAs encoding CecA1 and bnl were shown to contain a functional ARE 29 , 30 , 31 ., The CecA1 ARE binds to Tis11 , the homologue of TTP in D . melanogaster , which in turn promotes rapid degradation of CecA1 mRNA by enhancing deadenylation 29 ., Interestingly , expression of mammalian TTP could compensate for the knock down of Tis11 in Drosophila cells 30 , suggesting evolutionary conservation ., While the regulation of CecA1 mRNA degradation has been well characterized , there is no experimental study addressing more generally the role of AMD in D . melanogaster ., Here we report the development of AREScore , a software application by which mRNAs can be assessed for the presence of AREs ., After validating the AREScore using half-life measurements of human and mouse mRNAs , the transcriptome-wide AREScore distribution was analyzed across 14 metazoan species ., The AREScore was then applied to the analysis of AMD in Drosophila SL2 cells ., By combining biochemical and bioinformatic approaches , we provide evidence for a specific set of mRNAs regulated by Tis11 , and for the broader role of AREs in controlling mRNA degradation in D . melanogaster ., With the aim to identify genes containing AREs in any given set of sequences , we developed an algorithm termed AREScore , schematically depicted in Figure 1A ., Its purpose is to provide a numerical measure of the potential strength of an ARE , and assess the occurrence of AREs on a transcriptome-wide level ., The AREScore is based on quantifying three typical features of AREs: the number of AUUUA pentamers , the proximity between pentamers , and the presence of a region with high AU content surrounding AUUUA pentamers ., The UUAUUUA ( U/A ) ( U/A ) nonamer was not counted as a separate parameter because it largely corresponds to two overlapping AUUUA pentamers or a pentamer within a region of high AU content ., The algorithm first counts AUUUA pentamers and attributes a fixed value of 1 for each pentamer to generate a basal score ., It then calculates the distance between neighboring pentamers , and adds a value to the basal score if pentamers are close to each other ., Likewise , a value is added if pentamers are located within a region of high AU content , herein termed an AU-block ., To increase the flexibility of AREScore , users can change the values that are added to the basal score , and alter the settings that define an AU-block ., Thereby users can adapt the algorithm to their needs and particular questions ., A web-based version of AREScore is available at http://arescore . dkfz . de/arescore . pl ., To validate the algorithm , we calculated the AREScore for every human mRNA in the RefSeq database with a 3′UTR length ≥10 nucleotides ( nt ) , whereby many falsely annotated 3′UTRs could be excluded from the analysis ., In Figure 1B , the AREScore was then compared to previously measured mRNA half-lives in human DG75 B-cells 32 ., The AREScore shows a slight , but statistically highly significant , negative correlation with mRNA half-life ( Spearman rank correlation coefficient RS\u200a=\u200a−0 . 155 , p<0 . 0001 ) ., The correlation was more apparent when mRNAs were classified into groups with similar AREScores and the average half-life was plotted for each group ( Figure 1C ) ., We then used Receiver Operating Characteristic ( ROC ) analysis to assess the predictive power of the AREScore in this dataset ( Figure 1D ) ., Every possible AREScore value was tested for its ability to discriminate the 10% most short-lived mRNAs from the 10% most long-lived ones ., For instance , mRNAs with an AREScore ≥3 . 9 make up 53% of the short-lived mRNAs ( true positive rate ) , but only 14% of the long-lived mRNAs ( false positive rate ) ., By plotting true positive rate against false positive rate for every possible AREScore , the ROC curve is obtained ., The area under this curve ( AUC ) corresponds to the probability that a random short-lived mRNA has a higher AREScore than a random long-lived mRNA ., With a value of 0 . 75 , the AUC is well above that of a random predictor ( AUC\u200a=\u200a0 . 5 ) ., In a similar manner , we compared the AREScore of mouse mRNAs with half-lives measured previously in mouse NIH3T3 fibroblasts 33 ., This analysis again showed a weak but highly significant negative correlation between AREScore and mRNA half-life ( Figure 1E , RS\u200a=\u200a−0 . 147 , p<0 . 0001 , and Figure 1F ) ., With an AUC of 0 . 73 , the ROC curve of the mouse dataset ( Figure 1G ) is very similar to the curve of the human dataset ( Figure 1D ) ., Taken together , the comparison of AREScores with genome-wide measurements of mRNA half-lives showed that mRNAs with a high AREScore are more likely to be short-lived , both in human and mouse cell lines ., To further validate the AREScore , we analyzed a set of transcripts that we had previously identified as TTP-associated mRNAs in mouse macrophages using RNA-immunoprecipitation 13 ., Figure 1H shows that the AREScore is very high among the 135 TTP-associated mRNAs ( median 7 . 8 ) compared to the entire mouse transcriptome ( median 1 . 3 ) or a more stringent control set of randomly chosen , concatenated 3′UTR sequences ( median 4 . 65 ) whose lengths were matched to the lengths of the TTP-associated 3′UTRs ., To test whether the AREScore distribution of the TTP-associated mRNAs was statistically different from the controls , we compared the frequency of mRNAs with an AREScore <4 and ≥4 in 2×2 contingency tables ( Tables S1 and S2 ) ., P-values were calculated either by χ2-test or Fishers exact test , and found to be <0 . 0001 for both comparisons ., Thus , the AREScores of the TTP-associated mRNAs were significantly higher than the AREScores of both control groups ., This confirmed that the AREScore is a useful tool to identify ARE-containing mRNAs ., Having the AREScore at hand as a numerical tool to estimate the abundance and strength of AREs in any given genome , we calculated the AREScore of all annotated transcripts with a 3′UTR length ≥10 nt for Homo sapiens and four important metazoan model organisms , Caenorhabditis elegans , D . melanogaster , Danio rerio , and Mus musculus ., The analysis shows that in all five species , the vast majority of mRNAs have a score below 4 ( Figure 2A ) ., Differences became apparent when frequencies were plotted on a logarithmic scale ( Figure 2B ) ., The highest AREScore is 17 . 4 in C . elegans and 34 . 3 in D . melanogaster , whereas in the two mammalian species AREScores go beyond 60 ., These differences are also visible in the plot of cumulative frequencies ( Figure 2C ) , which shows the highest prevalence of low AREScores in C . elegans and of high AREScores in H . sapiens ., It was interesting to note that the 3′UTR length follows a similar pattern ( Figure 2D ) , with C . elegans having the by far shortest 3′UTRs ( median: 140 nt ) , followed by D . melanogaster ( 207 nt ) and D . rerio ( 402 nt ) , and considerably longer 3′UTRs in the two mammalian species ( 704 nt in mouse , 804 in man ) ., This analysis shows that mRNAs with high AREScores as well as long 3′UTRs are more abundant in the two mammalian species , which likely reflects the need for additional elements regulating gene expression ., To test whether AREs are truly enriched in any of the transcriptomes we analyzed , we compared the AREScore distribution in different species with sets of randomized sequences that have identical A/T/G/C contents and length distributions ( Figure 3 and Figure S1 ) ., This comparison revealed that mRNAs with high AREScores ( ≥10 ) are overrepresented in the transcriptome of H . sapiens ( Figure 3A ) ., In D . melanogaster , the enrichment already starts at an AREScore of 4 ( Figure 3B ) , whereas there is no enrichment of mRNAs with higher AREScores in C . elegans ( Figure 3C ) ., We then expanded this analysis to the transcriptomes of 11 additional species , covering most of the major branches of metazoan evolution ( Figure S1 ) ., Only for Annelida and Crustacea , no properly annotated transcriptomes were available ., In the 14 species analyzed , the frequency of mRNAs with an AREScore ≥10 was compared to the frequency of AREScores ≥10 in sets of randomized control sequences ( Figure 3D ) ., mRNAs with an AREScore ≥10 were found to be overrepresented in the transcriptomes of H . sapiens , M musculus , Gallus gallus ( chicken ) , Danio rerio ( zebrafish ) and D . melanogaster ., This is reflected by a positive Φ coefficient , a measure for how strongly AREScores ≥10 are associated with the actual transcriptome as compared to the randomized control ., In all these cases , the difference was significant as determined by χ2-test ., mRNAs with an AREScore ≥10 were also more abundant in Ixodes scapularis ( deer tick ) , although in this case the difference was statistically not significant ., In the 8 other species analyzed , mRNAs with an AREScore ≥10 were less abundant than in the randomized control sequences ., Thus , our analysis suggested that AREs were selected for during the evolution of several vertebrate species ( Xenopus laevis being the exception ) as well as D . melanogaster ., Given that we found AREs to be overrepresented in the D . melanogaster transcriptome ( Figure 3B ) and that little is known about the general importance of AMD in this organism , we decided to experimentally address the functional relevance of the AREScore in Drosophila ., We first established an assay to measure AMD in D . melanogaster SL2 cells by generating firefly luciferase ( FL ) reporter genes containing the ARE of mouse interleukin ( IL ) -3 in the 3′UTR ( Figure S2A , IL3 ARE sequence depicted in Figure S3 ) ., Expression of the FL reporter gene was found to be strongly suppressed by the IL3 ARE in SL2 cells , both at the protein ( luciferase activity ) and mRNA level , and suppression was due to accelerated degradation of the reporter mRNA ( Figure S2B–S2D ) ., We then tested several factors for their involvement in Drosophila AMD by knocking down their expression using dsRNAs ., Whereas knock down ( kd ) of Tis11 and Not1 , a core protein of the cytoplasmic Ccr4-Caf1-Not deadenylation complex , caused elevated expression of the ARE-reporter , other proteins such as Rox8 , AGO1 , AGO2 , LSm1 and pcm did not affect reporter gene expression ( Figure 4A ) ., Since Drosphila Not1 is important for mRNA deadenylation in general 34 , 35 , we focused on Tis11 as an ARE-specific RNA binding protein ., Our goal was to examine the AREScore of mRNAs regulated by Tis11 ., We first confirmed that the dsRNA against Tis11 potently suppressed the expression of Tis11 mRNA ( Figure 4B ) , and that Tis11 kd stabilizes the FL-mIL3-ARE reporter mRNA ( Figure 4C ) ., To identify Drosophila mRNAs regulated by Tis11 , we determined the mRNA expression profile in SL2 cells after knocking down Tis11 or , as a control , GFP ., Since direct targets of Tis11 are expected to show higher expression levels after Tis11 kd , we concentrated on the 53 mRNAs that we found to be upregulated by a factor of at least 1 . 41 ( 0 . 5 log2-transformed ) after Tis11 kd in the microarray analysis ( Figure 5A ) ., 20 out of these mRNAs were chosen for confirmation by qPCR , and for 18 of them we could verify that Tis11 kd causes a an increase in expression of minimally 1 . 41-fold ( Figure S4 ) , indicating that our microarray dataset was reliable ., The Vir1 mRNA , which was strongly upregulated by Tis11 kd ( Figure S4 ) , has an AREScore of 5 . 6 and a readily detectable ARE ( Figure S3 ) ., Indeed , an FL reporter mRNA containing the ARE of Drosophila Vir1 was stabilized by kd of Tis11 ( Figure 4C ) ., Out of the 53 mRNAs sensitive to Tis11 kd , we then determined the AREScore for those 49 transcripts whose annotated 3′UTR length is ≥10 nt ., In comparison to the AREScore distribution of the entire D . melanogaster transcriptome , the Tis11-sensitive mRNAs showed an increased abundance of AREScores ≥4 ( Figure 5B ) ., By χ2-test , this increase was statistically significant with a p-value of 0 . 0011 ( Table S3 ) , suggesting that target mRNAs of Drosophila Tis11 share characteristics with mammalian AREs ., After applying the AREScore to the subgroup of Tis11-sensitive mRNAs , we wanted to assess the importance of AREs in regulating Drosophila gene expression more generally ., If AREs are wide-spread elements that promote mRNA degradation but do not affect transcription , a first prediction is that , on average , mRNAs with a high AREScore should be expressed at lower levels ., A second prediction is that these mRNAs should have shorter half-lives ., We tested the first prediction by comparing the expression levels of 6657 mRNAs , derived from our microarray analysis in SL2 cells , with their AREScores ( Figure 6A ) ., Indeed , we observed a tendency for mRNAs with high AREScores to be expressed at lower levels ., We further grouped the mRNAs into 9 categories according to their AREScore , and compared the average expression levels of each group to the overall average ( Figure 6B ) ., The two groups with very high AREScores ≥12 showed average expression levels that were more than 3-fold ( 1 . 6 log2-transformed ) below the overall average , and the reduction in expression was already significant above an AREScore of 8 ., We then compared the 3′UTR lengths with the AREScores of all 6657 mRNAs ( Figure 6C ) ., As expected , we found a very strong correlation between these two parameters ( RS\u200a=\u200a0 . 57 , p<0 . 0001 ) ., Thus , it was important to assess whether the 3′UTR length on its own had an influence on mRNA expression levels ( Figure 6D and 6E ) ., Two opposing correlations were apparent: mRNAs with very short 3′UTRs <100 nt , and mRNA with long 3′UTRs ≥1000 nt were expressed at significantly reduced levels , whereas mRNAs with 3′UTRs of intermediate length ( 100–999 nt ) showed the highest expression levels ., In fact , the 3′UTR length appeared to have a stronger influence on the expression level than the AREScore , as mRNAs with 3′UTRs ≥2000 nt were expressed more than 5-fold ( 2 . 4 log2-transformed ) below the overall average ., To examine whether the predictive power of the AREScore is independent of 3′UTR length , we chose to analyze a subgroup of 1781 mRNAs with 3′UTRs between 200 and 499 nt ( pink bars in Figure 6E ) ., In this group , the length of the 3′UTR per se does not negatively correlate with mRNA levels ( Figure 6F ) , whereas mRNAs with higher AREScores do show a trend towards reduced expression levels ( Figure 6G ) ., Indeed , the 35 mRNAs that have an AREScore ≥8 within this group had a more than 3-fold ( 1 . 6 log2-transformed ) reduced average expression level compared to the 1746 mRNAs that have an AREScore between 0 and 7 . 99 ( Figure 6H ) , and this difference was highly significant ( p<0 . 0005 ) ., In contrast , the average expression level of the 35 mRNAs with the longest 3′UTRs was only 1 . 4-fold ( 0 . 5 log2-transformed ) below the expression level of the remaining 1749 mRNAs with the shorter 3′UTRs ., From this comparison we concluded that a high AREScore correlates with lower mRNA expression levels independently of the 3′UTR length ., Finally , we tested the second prediction that mRNAs with higher AREScores should undergo more rapid decay ., To this end , we measured the half-lives of 26 mRNAs with high accuracy by qPCR ( Table 1 , Figure S5 ) ., 12 mRNAs were chosen from the group of Tis-11 sensitive mRNAs , and 14 from the large pool of mRNAs that are not affected by Tis11 kd ., To cover the entire range , 5 mRNAs had a high AREScore ≥8 , 8 mRNAs had a medium AREScore between 4 and 7 . 99 , and 13 had a low AREScore <4 ., In Figure 7A , we plotted the half-lives of these mRNAs against the AREScore ., The most striking observation was that 9 out of 10 mRNAs with an AREScore of 0 degraded very slowly with half-lives >240 minutes ., On the other side , the two mRNAs with the highest AREScore ( CG115435 from the group of Tis11-sensitive mRNAs and Reck and from the control group ) also had the shortest half-lives ., In our analysis of 26 mRNAs , the Spearmans rank correlation coefficient RS between the two parameters equals −0 . 73 , and this correlation was highly significant ( p<0 . 001 ) ., We also compared the half-lives of these 26 mRNAs with their 3′UTR length ( Figure 7B ) , and found a weaker correlation ( RS\u200a=\u200a−0 . 61 , p<0 . 001 ) ., ROC analysis was then applied to test the ability of both AREScore and 3′UTR length to discriminate labile mRNAs with half-lives <140 minutes from stable mRNAs with half-lives >240 minutes ( Figure 7C ) ., The AREScore performed extremely well in this test with an AUC of 0 . 95 , better than 3′UTR length with an AUC of 0 . 87 ., Clearly , the AREScore identifies short-lived mRNAs in D . melanogaster , showing that AREs are general regulatory elements in this organism ., In this report , we developed AREScore as an algorithm to identify AREs and provide a measure for their potential strength ( Figure 1 ) ., The AREScore was validated using genome-wide mRNA half-life measurements in human DG75 B-cells 32 and mouse NIH3T3 fibroblast 33 ., Although the correlation between AREScore and mRNA half-life was weak ( RS\u200a=\u200a−0 . 155 and −0 . 147 in the two data sets , respectively ) , it was statistically highly significant ., To our knowledge , this is the best correlation observed so far between any parameter and mRNA half-lives on a genome-wide scale ., The potential of the AREScore could be further demonstrated with a set of TTP-associated mRNAs that we had previously identified by RNA-IP in mouse macrophages 13 ., AREScores were much higher in this set of mRNAs than in the two control groups ( Figure 1H ) ., Among the Tis11-sensitive mRNAs that we identified in Drosophila SL2 cells , we also observed an increased frequency of mRNAs with higher AREScores ( Figure 5 ) , suggesting that Drosophila Tis11 recognizes AREs with sequence features similar to mammalian AREs ., Khabar et al . used bioinformatic tools to generate the ARE-database ( ARED ) , a comprehensive list of potential AREs in the human , mouse and Drosophila genome 7 , 31 , 36 ., The principle behind ARED is that it classifies AREs according to the number and density of AUUUA pentamers and surrounding AU-rich sequences , which correlates , to some degree , with the potential strength of the ARE ., In contrast to ARED , the purpose of AREScore is not to make categories , but rather generate a single score that provides a measure for the likelihood and potential strength of an ARE ., It is important to emphasize that in the absence of experimental validation , neither ARED nor AREScore is able to predict with absolute certainty whether a given mRNA contains a functional ARE ., For the AREScore , the false positive rate was visualized by ROC analysis , whereby the AREScore is tested for its ability to discriminate between the 10% most short-lived and the 10% most long-lived mRNAs ( Figure 1D and 1G ) ., For ARED , the false positive rate is not known ., An advantage of AREScore is that it can be applied easily to any genome or set of sequences ., Thus , we were able to compute the AREScore distribution for the transcriptomes of 14 species representing all but two of the major branches of metazoan evolution ( Figure 2 and Figure S1 ) ., The analysis showed that mRNAs with high AREScores are most abundant in man and mouse , the two mammalian species analyzed ., Comparison to randomized control sequences revealed that mRNAs with high AREScores ( ≥10 ) are overrepesented in man , mouse , chicken , zebrafish and the fruit fly ( Figure 3 ) ., This suggests that AREs were under positive selection pressure during the evolution of these organisms ., On the other hand , high AREScore mRNAs are underepresented in the sponge A . queenslandica , the freshwater cnidarian H . magnipapillata , the mollusc A . californica and the nematode C . elegans , suggesting that AREs did not expand in the genomes of metazoans with simpler body plans ., Alternatively , the element corresponding to the ARE might have different sequence features in these organisms ., Given that very little is known about AREs in D . melanogaster , we then made use of the AREScore to address the role of AMD in Drosophila SL2 cells ., Using an FL-based reporter assay , we first tested several factors and found that knocking down Tis11 or Not1 caused inhibition of AMD , whereas the kd of Rox8 , AGO1 , AGO2 , LSm1 or pcm had no effect ( Figure 4 ) ., The requirement of Tis11 for AMD is in good agreement with the well documented role of TTP in mammalian AMD 37 as well as previous reports demonstrating that Tis11 participates in AMD in Drosophila cells 29 , 30 , 31 , 38 ., The requirement for Not1 may be linked to our recent finding that mammalian TTP recruits the Caf1 deadenylase through its association with Not1 23 ., Not1 is the scaffold protein of the Ccr4-Caf1-Not deadenylase complex that plays a key role in cytoplasmic mRNA turnover ., In Drosophila , Not1 was shown to be important for bulk mRNA deadenylation and , more specifically , for the rapid deadenylation of Hsp70 mRNA 34 , 35 ., A previous report had suggested that AGO1 and AGO2 are required for the rapid degradation of a reporter mRNA containing the ARE of mammalian TNFα in Drosophila S2 cells 38 ., In our assay , kd of the argonaute proteins AGO1 and AGO2 did not affect expression of the reporter gene containing the ARE of mouse IL-3 ( Figure 4A ) , indicating that AGO proteins are not generally required for AMD ., As potential substrates of AMD , we then identified 53 mRNAs whose expression was elevated after kd of Tis11 ( Figure 5A ) ., The AREScore of these Tis11-sensitive mRNAs was found to be higher in comparison to the distribution in the entire D . melanogaster transcriptome ( Figure 5B ) , and the difference was statistically significant for mRNAs with AREScores ≥4 ( Table S3 ) ., CecA1 mRNA , previously identified as a target of Tis11 29 , 30 , 31 , did not come up as Tis11-sensitive simply because this mRNA is not represented on the Affymetrix Drosophila Genome 2 . 0 array that we used for our study ., We then compared the expression levels of 6657 mRNAs in SL2 cells with their AREScore ( Figure 6A–6E ) , and observed that mRNAs with high AREScores have reduced expression levels ., However , this effect may be indirect because the AREScore strongly correlates with 3′UTR length ., Indeed , when grouping mRNAs according to their 3′UTR length , we again observed that mRNAs with long 3′UTRs have lower expression levels ., The impact of 3′UTR length was in fact stronger than the impact of the AREScore ., Long 3′UTRs are likely to correlate with low expression levels through the presence of different suppressive elements including AREs and miRNA-binding sites ., Moreover , the distance between the stop codon and the poly ( A ) tail is a determinant of nonsense-mediated mRNA decay 39 and may thereby as well contribute to mRNA suppression ., We also noted that mRNAs with very short 3′UTRs <100 nt are expressed below the overall average ., A possible explanation is that very short 3′UTRs might lack stabilizing elements , although there is little experimental evidence that such elements are abundant ., To examine the impact of the AREScore independently of its correlation with 3′UTR length , we chose a group of mRNAs with intermediate 3′UTRs ( Figure 6F–6H ) ., Within this group we could observe that mRNAs with an AREScore ≥8 had a more than 3-fold reduced average expression level compared to the mRNAs with AREScores <8 ., Since 3′UTR length had a much smaller effect on mRNA levels in this group , we concluded that the AREScore is an independent parameter that correlates with suppressed mRNA levels ., Given the multitude of factors that affect mRNA stability and transcription rates , it is remarkable that the AREScore alone has a detectable influence on mRNA expression levels ., Finally , we measured the decay rates of 26 mRNAs in Drosophila SL2 cells ( Table 1 ) ., Indeed , we observed a very strong , negative correlation between mRNA half-life and the AREScore ( RS\u200a=\u200a−0 . 73 , Figure 7 ) , which was higher than the correlation with 3′UTR length ( RS\u200a=\u200a−0 . 61 , ) ., Since we measured mRNA half-lives both in control GFP and Tis11 kd cells , we could also identify three mRNAs that are significantly stabilized by the absence of Tis11 ., These mRNAs encode for peroxidasin ( Pxn ) , CG15435 , a C2H2 zinc finger protein of unknown function , and CG7115 , a protein phosphatase of the PP2C family ., Taken together , our analysis provides compelling evidence that AREs are functional regulatory elements in D . melanogaster cells whose suppressive effect can be detected on a transcriptome-wide level ., Interestingly , we found two short-lived mRNAs with a high AREScore ( Reck and CG32512 ) in our control group of mRNAs that are not sensitive to Tis11 kd ., This indicates that in addition to Tis11 , other proteins also participate in regulating AMD ., It is clear that we have only begun to understand the posttranscriptional regulatory network that controls gene expression through mRNA turnover in D . melanogaster ., Plasmid pRp128-RL ( p2933 ) 40 contains the Drosophila RNA polymerase III 128 kDa subunit promoter to drive RL expression , and was kindly provided by Michael Boutros ( German Cancer Research Center , Heidelberg ) ., For pRp128-FL ( p2934 ) , pRp128-RL was digested with SpeI/NheI and religated to remove part of the polylinker ., In the resulting construct , the RL-containing HindIII/XbaI fragment was replaced with the FL-containing HindIII/XbaI insert from pGL3-Basic ( Promega ) ., For pRp128-FL-mIL3-ARE ( p2935 ) , the mouse IL-3 ARE sequence ( NM_010556 . 4 , nt 680–744 ) was amplified by PCR using primers G1090/G1091 ( Table S4 ) and inserted into the XbaI site of pRp128-FL ., The control vector pRp128-FL-mIL3-INV ( p2936 ) was constructed in the same way with the IL-3 ARE inserted in the opposite orientation ., To generate pAc5-FL-mIL3-ARE ( p2937 ) , a 3 . 8 kb FL-containing HindIII ( blunt ) –BglII fragment was excised from plasmid pRp128-mIL3-ARE and ligated to KpnI ( blunt ) - BglII fragment ( 2 . 4 kb ) with Ac5 promoter obtained by digestion of pAc5 . 1b-EGFP-dmDcp1 ( p2450 ) ( kindly provided by Elisa Izaurralde , Max Planck Institute for Developmental Biology , Tübingen , Germany ) ., For pAc5-FL-Vir1-ARE ( p2938 ) , the D . melanogaster Vir1 3′UTR ( NM_165011 . 2 , nt 1521–1830 ) was first amplified by RT-PCR using primers G1673/G1674 ., An ARE-containing 191 nt long fragment ( NM_165011 . 2 , nt 1640–1830 ) was re-amplified by PCR using XbaI site-containing primers G1681/G1679 and inserted into the XbaI site of pRp128-FL to generate pRp128-FL-Vir1-ARE ., Finally , the Ac5 promoter was excised as a SapI–BglI fragment from pAc5-FL-mIL3-ARE and cloned into the SapI/BglI sites of pRp128-FL-Vir1-ARE , thereby replacing the pRp128 promoter ., pAc5-FL ( p2939 ) was generated in a similar manner by cloning the SapI–BglI fragment from pAc5-FL-mIL3-ARE into the SapI/BglI sites of pRp128-FL ., Drosophila SL2 cells were cultivated at 26°C under atmospheric CO2 in Schneiders Drosophila Medium ( Invitrogen-Gibco , Cat . No . 11720-034 ) supplemented with 10% foetal bovine serum ( Biochrome Superior FBS , Cat . No . S0615 ) , 50 U/ml penicillin and 0 . 05 mg/ml streptomycin ( both Pan Biotech ) ., All DNA transfections were performed using Effectene reagent ( Qiagen , Cat . No . 301425 ) according to the manufacturers instructions ., When combined with RNAi , cells were first treated with dsRNA for two days , followed by DNA transfection for two additional days ., For luciferase assays , 10 . 000 cells were seeded per well of a 384-well plate ( Greiner ) , treated with 250 ng of dsRNA and transfected with 7 ng o

Introduction, Results, Discussion, Methods

In mammalian cells , AU-rich elements ( AREs ) are well known regulatory sequences located in the 3′ untranslated region ( UTR ) of many short-lived mRNAs ., AREs cause mRNAs to be degraded rapidly and thereby suppress gene expression at the posttranscriptional level ., Based on the number of AUUUA pentamers , their proximity , and surrounding AU-rich regions , we generated an algorithm termed AREScore that identifies AREs and provides a numerical assessment of their strength ., By analyzing the AREScore distribution in the transcriptomes of 14 metazoan species , we provide evidence that AREs were selected for in several vertebrates and Drosophila melanogaster ., We then measured mRNA expression levels genome-wide to address the importance of AREs in SL2 cells derived from D . melanogaster hemocytes ., Tis11 , a zinc finger RNA–binding protein homologous to mammalian tristetraprolin , was found to target ARE–containing reporter mRNAs for rapid degradation in SL2 cells ., Drosophila mRNAs whose expression is elevated upon knock down of Tis11 were found to have higher AREScores ., Moreover high AREScores correlate with reduced mRNA expression levels on a genome-wide scale ., The precise measurement of degradation rates for 26 Drosophila mRNAs revealed that the AREScore is a very good predictor of short-lived mRNAs ., Taken together , this study introduces AREScore as a simple tool to identify ARE–containing mRNAs and provides compelling evidence that AREs are widespread regulatory elements in Drosophila .

Many genes are regulated at the posttranscriptional level by factors that influence the stability of the messenger RNA ., In mammals , AU-rich elements are known to cause rapid degradation of messenger RNAs and thereby suppress gene expression ., In order to identify such elements on a genome-wide scale , we developed a bioinformatic tool with which we can score messenger RNAs for the presence of AU-rich elements ., Using the AREScore algorithm , we observe that AU-rich elements correlate with reduced messenger RNA stability and expression levels ., We then used the AREScore to compare the transcriptomes of 14 metazoan species and found that messenger RNAs with high AREScores are enriched in several vertebrates and the fruit fly Drosophila melanogaster ., We identified messenger RNAs whose levels are regulated by the Drosophila Tis11 protein , which binds to AU-rich elements ., Our study introduces the AREScore as a means to globally assess AU-rich elements and predict short-lived messenger RNAs ., Furthermore , it demonstrates the regulatory role of AU-rich elements in suppressing gene expression by accelerating messenger RNA degradation in D . melanogaster cells .

gene regulation, rna stability, eukaryotic cells, genome analysis tools, molecular genetics, gene expression, biology, molecular biology, transcriptomes, biochemistry, rna, cell biology, nucleic acids, genetic screens, genetics, cellular types, genomics, molecular cell biology, computational biology, genetics and genomics

journal.pntd.0007182

A systematic review of scabies transmission models and data to evaluate the cost-effectiveness of scabies interventions

Scabies is a common dermatological conditions 1 , affecting more than 130 million people at any time 2 ., It is a neglected disease caused by the mite Sarcoptes Scabiei 3 ., Scabies often results in severe itching , and in some patients , including those with compromised immunity , it may progress to “crusted scabies” ( CS ) ., The fissures associated with scabies provide a portal of entry for bacteria , potentially resulting in secondary infections , sepsis , indirect effects on renal and cardiovascular function , and death due to complications 4 ., Secondary bacterial superinfections are uncommon in Western countries 5 ., Worldwide , scabies is responsible for 0 . 07% of the total burden of disease 6 ., Compared to its disease burden , scabies research is severely underfunded 7 , 8 , even though it imposes major costs on healthcare systems 2 ., Various countries and organisations have identified scabies control as a public health priority and the World Health Organisation Strategic and Technical Advisory Group for Neglected Tropical Diseases recently recommended that scabies be included in the Neglected Tropical Disease profile in category A 2 , 9–11 ., Elimination of scabies is difficult , as cured patients often get re-infected ., Treatment strategies range from treating individuals and their contacts , to mass drug administration ( MDA ) strategies 12–19 , which involves treating whole communities at once ., Drugs include oral ivermectin as well as a range of topical treatment options ., It is unknown which ( combination of ) treatment strategies results in the best health outcomes against the lowest costs , and to what extent this differs between communities ., Health-economic modelling may help answer such questions ., To determine the cost-effectiveness of interventions against scabies , it is crucial to take into account it’s infectious nature since the extent to which interventions impact transmission will ( to a large part ) determine their cost-effectiveness ., While such disease transmission approaches have successfully been applied to guide interventions against other infectious diseases like Ebola and influenza 20 , few attempts have been made to use modelling to aid decision-making about scabies intervention strategies ., Scabies interventions include efforts aimed at access and coordination of services , scabies detection , primary care , acute care , specialised care , social work , follow-up , or combinations of the above ., Given the multifaceted nature of interventions required to combat scabies , health-economic evaluation requires a comprehensive modelling approach ., For this article , a systematic review of existing scabies models was conducted to inform the development of a proposed modelling framework which can be adjusted to different situations/communities ., The proposed modelling framework aims to determine long-term effects of alternative interventions on the incidence , prevalence , quality of life ( QoL ) , resource use and costs associated with scabies and CS ., It can be used as aid for creating a scabies transmission model , the details of which will be determined by the context ( population ) and the question being addressed ., For models to be of use for decision-makers , a range of clinical and economic inputs is required ., However , up to now , no systematic overview of evidence-based information on these inputs , including evidence on the biology of scabies , patient QoL and resource use has been published ., This systematic literature review fills this gap by providing an overview of published information that can be used to inform scabies modelling in human populations , and improve decision-making about scabies interventions in affected communities ., After discussing the search strategy , this paper will first discuss characteristics of published scabies models and a proposed , comprehensive scabies modelling framework ., Secondly , the paper will discuss potential model inputs , consecutively: the life cycle of scabies mites , patient QoL , and resource use associated with scabies and CS ., In order to inform our proposed modelling framework design , a systematic literature search was performed on 26 and 27 July 2017 , searching the databases PubMed , Medline , Embase , CINAHL , and the Cochrane Library ., Search terms related to the disease ( scabies OR sarcoptes ) were combined with search terms identifying the type of information required ( model OR “modeling” OR “modelling” OR transmission OR utility OR “quality of life” OR “economics” OR “economic” OR “cost-effectiveness” OR “cost-utility” OR “cost” OR “cost-of-illness” OR “cost-consequence” OR “cost-consequences” OR “efficacy” OR “effectiveness” or “impact” ) ., Articles were limited to humans only , had to be published in English and after the year 2000 ., Studies from before the year 2000 were only included if they were cited in a more recent source ( post 2000 ) , confirming their continued relevance ., Studies that considered scabies but did not present any models or model inputs on scabies biology , QoL or resource use were excluded ., Articles were also excluded when they presented a case study of a single patient or an outbreak of scabies in a single institution , and if they simply provided a discussion of scabies guidelines or protocols in a particular country or institution ., Those studies that mentioned scabies as one of a number of diseases/indications/causes/comorbidities were also excluded ., Reference lists of reviews were used to identify any papers missed through the search strategy ., Data extractions were performed by the primary author , per topic area: ( 1 ) models , ( 2 ) biology of scabies , ( 3 ) patient QoL and ( 4 ) resource use ., Data items were not predefined , meaning that all data on the various topic areas ( e . g . QoL ) was included and presented , independent of reported outcome measures ( e . g . which type of QoL questionnaire was used ) ., After the data was extracted per topic area , it was evaluated which data per topic addressed simple scabies , CS , and which were based on populations including both simple scabies and CS patients ., Results were not meta-analysed ., The validity and reliability of disease models and inputs is highly dependent on the research question they try to answer , the population of interest , and how the models are informed/how the inputs are used ., Therefore , this review did not include a quantitative risk of bias assessment , but a qualitative description of the limitations of the various models and input parameters ., All scabies models were evaluated on their main characteristics , and strengths and limitations were identified ., Strengths of the various models were combined to design a new , proposed modelling framework ., Relevant data from papers on the biology of scabies , patient QoL and resource use was extracted and described ., A review protocol has not been published ., A completed PRISMA checklist is provided as supplementary material ., Four scabies models were identified from the literature ( Table 1 ) ., One model was a Markov decision tree , two were compartmental models , and one was an agent-based , network-dependent Monte Carlo model ( see Table 2 for a description of these model types ) ., None of the models identified specifically addressed CS ., Bachewar et al . 21 published the Markov decision tree as part of a randomised clinical trial comparing three alternative treatment regimens ., As opposed to the other models , this model does not consider scabies epidemiology , transmission or population dynamics ., A Markov decision tree is provided , which is used to calculate the cost-effectiveness of alternative treatments , using efficacy data from the trial ., The model serves as a mechanism to calculate and compare costs for a range of interventions , without considering the biology or transmission of scabies ., This limits its use compared to the other identified models ., Gilmore 22 published an agent-based Monte Carlo model , using a variety of small-world network architectures to gain insight into scabies dynamics and the effect of alternative treatment strategies ., This study focused on childhood scabies and found that in the absence of an effective vaccine , and with scabies continually imported to communities from non-local contacts , eradication is impossible and open-ended treatment regimens are required ., A crucial advantage of Gilmore’s model is that it allows for non-random mixing patterns 25 , since it is likely that the contacts between individuals that result in scabies infestation , do not occur at random 26 ., Mixing patterns are a characteristic of a network ( e . g . community ) referring to the extent to which nodes ( e . g . people ) connect ( e . g . are in close enough contact to result in infection ) ., Bhunu et al . 23 published a deterministic , compartmental model , using Descartes’ rule of signs and numerical simulations to show endemic equilibria and determine whether the current treatment regime is sufficient to control scabies infection , or whether a vaccine is required ., Assumed values for key model parameters were not substantiated , and it is not clear what the model is calibrated to ., This means it is impossible to evaluate the reliability of the model and any of its results ., The model focussed on predicting the potential impact scabies vaccination might have in case a vaccine would become available ., It should likely be viewed as a theoretical exercise rather than one that provides actual insights into scabies epidemiology or into the effectiveness of any available intervention ., Lydeamore et al . 24 recently published another compartmental model to explore the impact of alternative MDA treatment strategies ., As opposed to the other models , this model aimed to capture the mite’s life cycle in relation to the host ., The authors considered this critical , as the parasite’s life state ( e . g . eggs versus living mites ) can interact critically with treatment success or failure ., This is a valuable model characteristic when studying the effectiveness of different treatment types ( e . g . , ovicidal versus non-ovicidal ) ., In contrast to the model by Gilmore at al . , homogeneous mixing is assumed ., None of the models included QoL ., An advantage of including QoL is that it can be used to quantify the impact of a wide range of conditions and that ( under certain requirements ) it can be multiplied with duration of life to obtain QALYs ( quality-adjusted life years ) ., By measuring the impact of interventions on QALYs , outcomes can be compared not only between different ( types of ) interventions but also across different disease areas ., This is needed to inform decision-making , particularly when funds need to be distributed over interventions/programs in a range of different areas ., In models , QoL can be used as outcome measure , for example by weighting health states ( e . g . “CS grade 1” ) by their associated utility value ., Furthermore , disutilities can be attached to complications as well as treatment-related adverse events ., The same is true for costs , which can be attached to the various health states and events in cost-effectiveness models ., Only the model by Bachewar et al . included a cost-effectiveness analysis , but it did not take into account transmission dynamics ., Combining the strengths of the abovementioned models , Fig 2 provides a proposed modelling framework to inform cost-effectiveness analyses ., This framework can be used as aid for creating a scabies transmission model , the details of which will be determined by the context ( population ) and the question being addressed ., Like the model by Gilmore ( 2011 ) , it allows for modelling networks and mixing patterns ., This is a valuable attribute in case the model will be used for evaluating the cost-effectiveness of interventions which aim , for example , to prevent reinfection through household level interventions ( e . g . ensuring treated CS patients return to scabies-free homes ) , or community interventions to reduce the prevalence of scabies ., Mixing patterns can be based on assumptions or , preferably , appropriate data collection ., For example , to inform scabies transmission modelling in indigenous communities in Australia , information is currently being collected on the number of infections and reinfections in the various communities , living conditions ( e . g . number of persons per household ) , and the extent to which scabies-free zones are being established when CS patients return from the hospital ., Network size and structure will be dependent on the type of community that is being modelled , including age structure since children tend to have a higher probability of acquiring scabies 27 ., For more information on challenges when modelling contact networks , see Eames et al . 2015 28 ., The proposed modelling framework also aims to capture the biology and natural history of scabies transmission in humans , in particular the life-cycle of the mites as this can impact treatment success rates 24 ., Depending on the modelling question at hand , it may be possible to simplify the proposed modelling framework or use other types of modelling methods ., For example , when the difference between ovicidal versus non-ovicidal treatments is irrelevant to the question at hand , it may not be needed to track life-cycle stages of mites in individual patients ., As a rule , simple models should be preferred over complex ones when the decision problem allows , since they are easier to understand , less prone to inaccuracies , and quicker to develop and run 29 ., On the other hand , oversimplification may result in unreliable or invalid results when relevant risk-factors or dependencies between modelled states or agents are not taken into account ., While it may be possible to simplify the model for some questions , others might require additional health states , for example to allow modelling of long-term complications ( e . g . chronic renal failure ) and their effects ., For more information on different types of modelling methods , see Siettos and Russo 2013 30 ., Within the proposed modelling framework , model time should be counted in days , to account for processes like infection , the scabies life-cycle , and treatment effects ., Other processes may take substantially longer , such as processes related to certain complications , and impacts on life expectancy ., For most modelling questions , a life time horizon will be sufficient ., None of the identified models explicitly included CS ., This is a shortcoming , since CS is associated with high infectivity , morbidity and mortality compared to simple scabies , and has often been overlooked in scabies program design ., The proposed modelling framework incorporates a probability of moving from simple scabies to CS , which can be dependent on ( amongst other factors ) immune status of the patient ., While other infectious disease models often include some notion of seasonality , this has not been the case for scabies models ., Although scabies mites show increased mite movement and increased transmission in a warm environment , a study in Malawi found that scabies was more prevalent during the cold , dry season , possibly due to close interpersonal contact in crowded indoor environments 31 ., Other studies , however , show no obvious seasonal variation at all 31–33 ., In the absence of evidence to the contrary , the proposed modelling framework does not accommodate seasonality ., The following sections of this paper discuss input parameters that can be used to inform the suggested modelling framework , or other newly developed scabies models ., Following Lydeamore et al . 24 , it is crucial to account for the life cycle of the scabies mites to model treatment effectiveness , especially when aiming to discriminate between ovicidal and non-ovicidal treatments ., The literature review identified 14 articles presenting information on the life cycle of scabies mites ( see Table 3 ) ., Since there is often a lack of Sarcoptes scabiei var ., hominis mites , many studies have relied on animal strains of scabies mites and a host animal model such as rabbits or pigs 34 ., The studies obtained through the search strategy did not provide any other information specifically on the life cycle of Sarcoptes scabiei var ., hominis apart from “survival away from host” ., However , based on direct comparisons , Sarcoptes scabiei var ., canis seems to be a suitable model for sarcoptes scabiei var ., hominis 34 , 35 ., The literature review identified 3 QoL studies performed in scabies patients: 1 from China ( = 96 ) , 1 from Brazil ( n = 105 ) and 1 from India ( n = 102 ) ., None of these studies addressed the QoL of CS patients compared to the QoL associated with simple scabies , and two of the studies 48 , 49 excluded CS patients ., Jin-Gang et al . 48 used the Dermatology Life Quality Index ( DLQI ) , and Worth 50 and Nair 49 used a modified version of that same questionnaire ., Modifications made by Worth et al . included:, 1 ) adapting the language to local culture and attitudes;, 2 ) modifying questions to increase relevance for persons living in an urban slum in the tropics; and, 3 ) changing questions that were not applicable in children ., Nair et al . used the modified version from Worth et al . , with slight modifications as per the requirements of the Indian population ., Table 4 shows QoL results from the three studies , as per category of effect from scabies on QoL ., Note that categorisation was based on classifiers described in the studies ( e . g . “small effect” ) , not the modified DLQI item scores , since the questionnaires differed slightly between studies ., Jin-Gang et al . reported a mean DLQI score of 10 . 09 ( sd 5 . 96 ) , with most QoL impact of scabies due to symptoms , embarrassment , work or study and sexual difficulties ., Most common categories of impairment according to Worth et al . were feelings of shame ( 77 . 2% in adults , 46 . 6% in children ) , the need to dress differently ( 35 . 1% in adults , 29 . 3% in children ) , restriction on leisure activities ( 24 . 6% in adults , 36 . 8% in children ) , stigmatisation at work/school ( 21 . 1% in adults , 25 . 0% in children ) , social exclusion ( 24 . 6% in adults , 17 . 9% in children ) , teasing ( 26 . 3% in children ) , and problems with sexual partners ( 10 . 9% in adults ) ., Women/girls perceived more restrictions than men/boys ., A review of studies using the Children’s Dermatology Life Quality Index ( CDLQI ) questionnaire to measure QoL in skin conditions 51 found an overall estimated CDLQI score of 9 . 2 ( 95%CI: 0 . 0–20 . 3 ) associated with scabies ., The review identified two studies that were not identified in our literature review: Balci et al . 52 and Lewis-Jones & Finlay 53 ., Both included children with a wide range of skin diseases , including only few scabies patients ( n = 9 and n = 6 , respectively ) ., Olsen et al . commented that while scabies might have a large effect on QoL at the time of completing the questionnaire , this may only be over a short time as it is curable ., While also curable , the disutility of CS may be more substantial , given the severity of associated symptoms and complications ., While simple scabies is relatively straightforward to treat , patients may not seek care , may wait a long time before doing so , and may be misdiagnosed ., In Cameroon , Kouotou et al . 54 found that it takes 4 to 720 days between the onset of symptoms and the first consultation with a dermatologist , with a mean of 77 . 1 days ( sd 63 . 7 ) ., At the first consultation with a dermatologist , 74 . 9% had already tried previous treatment , such as antibiotics , antifungals , antihistamines or plant-based medicines ., Based on claims data for the employer-sponsored privately-insured population in the United States , treating one episode of scabies costs on average 95 USD 55 ., When selecting on episodes for which drug treatment was claimed , costs were 163 USD per episode ., Given the incidence of scabies , this results in an overall annual economic burden of 10 . 4 million USD for treating scabies in this population , most of which ( 3 . 7 million USD ) is for children ≤15 years ., Costs between alternative treatment options differ substantially ., For a cost-benefit analysis from a US perspective , we refer to Elgart 56 ., In some populations , scabies-associated health resource use is substantial ., In five remote communities of Northern Australia , only a few aboriginal children ( 16% ) manage to reach their first birthday without having at least one documented episode of scabies and/or skin sores 57 ., Here , the median number of presentations per child under 12 months due to scabies is 3 ( IQR 1 , 5 ) ., Of these children , 70 . 5% present more than once , and the average age of first presentation is 4 months ( IQR 2–7 ) ., In another Australian study , Whitehall et al . 32 found that the mean duration of hospital admissions for children with scabies is 4 . 5 days ., Scabies comprised 4 . 2% of the total number of admissions for all reasons , and 8 . 3% of all bed days ., The minimum cost per admission was 9 , 584 . 07 AUD ., In Australia , the estimated annual cost associated with the management of pediatric scabies and pyoderma per patient was 10 , 000 AUD in 2013 58 ., Resource use may differ substantially between locations/communities , depending on the healthcare system , funding , remoteness , and cultural differences , amongst other factors ., Local data collection will generally be required to inform model inputs ., Resource use or costs for treating CS have not been published ., A cost-of-illness study from an Australian perspective is currently being performed by ( part of ) the authors of this paper ., Note that the cost-effectiveness of interventions to prevent CS may be substantially impacted by their ability to prevent long-term complications ( e . g . rheumatic fever and chronic valvular heart disease ) which may increase costs and decrease patient life expectancy and quality of life ., Based on a systematic literature review , this paper discusses published models and proposes a new , comprehensive modelling framework to develop cost-effectiveness analyses of treatments for scabies ., Models should be informed by population , disease and treatment characteristics , which may differ between communities ., Available information on required model inputs was systematically reviewed ., Prior to this review , the literature lacked a good account of these inputs , including the life cycle of scabies mites , patient QoL , and resource use ., This review resolves this problem and should be supplemented by locally specific data collections and expert opinion where required ., There is a lack of reliable , comprehensive information about scabies biology and the impacts this disease has on patients and society ., This may be due to the limited amount of resources directed towards scabies research 7 , 8 , and its tendency to affect resource-poor populations ., Given the efficacy of available treatments and the relatively low costs of these treatments ( although still prohibitively expensive in some low-income settings ) , current high prevalence rates of scabies are unacceptable ., Interventions should aim to reduce scabies incidence in a sustainable , cost-effective manner ., In doing so , it may be worth focusing additional efforts on identifying and treating patients with CS , who can be “core transmitters” of the disease , while experiencing high morbidity and mortality rates 38 ., The importance of targeting CS patients has often been overlooked in program design for simple scabies ., Scabies elimination efforts should be prioritised for communities that are worst affected , and with sustained intervention , this is a realistic goal 59 ., Given that many of these communities are resource-poor , cost-effective use of resources is crucial and can be informed by health-economic modelling , taking into account community-specific resource constraints and expected budget impact of proposed interventions ., Furthermore , careful data collection ( for example , aided by making scabies a notifiable disease ) may help guide funds to where they are most needed ., While the current article provides a comprehensive overview of key issues and a proposed modelling framework to aid future scabies modelling work , it is only a first step in this direction ., Researchers and policy makers are encouraged to use and adjust this modelling framework to develop an economic evaluation predicting the ( cost ) -effectiveness of interventions against scabies in their population ( s ) of interest ., Any input on the proposed modelling framework by external parties is welcomed ., As with all health economic models , model transparency and validation of the results is critical to its success and potential impact ., The current modelling framework has not been validated and should only be used as an aid for model development ., By using the current review and proposed modelling framework to substantiate the modelling approach and select appropriate inputs , transparency can be improved ., Proper validation involves face validity , verification of internal validity , external validity , and predictive validity ., Health economists and modellers working in the field of scabies are referred to the ISPOR report on model transparency and validation for recommendations on how to appropriately validate and report on their model and results in a population of interest 60 ., For data inputs that are uncertain , real-world data collection may be crucial to ensure reliability of modelling outcomes ., Furthermore , the impact of uncertain model inputs can be tested by using sensitivity analyses to determine how variation in modelling inputs impacts the results , both deterministically and probabilistically ., Given the identified knowledge gaps , it is important to perform extensive sensitivity analysis in any scabies model that will be developed ., Meanwhile , grant bodies are encouraged to invest in scabies research to address the knowledge gaps identified in this review regarding the biology , QoL and cost impact of simple scabies and CS ., As far as the authors are aware , transmission modelling has seldom been used to answer questions on scabies interventions ., One reason for this may be the lack of readily available information to inform modelling work , which this review aims to ( at least partially ) address ., Another reason may be unfamiliarity or scepticism on the side of authorities and funding bodies with respect to the value of theoretical results obtained from modelling ., Health economists and other scientists can best illustrate the value of modelling by using evidence-based , validated approaches to tackle relevant , real-world questions which can directly inform clinical or governmental decision-making .

Introduction, Methods, Results, Discussion

Scabies is a common dermatological condition , affecting more than 130 million people at any time ., To evaluate and/or predict the effectiveness and cost-effectiveness of scabies interventions , disease transmission modelling can be used ., To review published scabies models and data to inform the design of a comprehensive scabies transmission modelling framework to evaluate the cost-effectiveness of scabies interventions ., Systematic literature search in PubMed , Medline , Embase , CINAHL , and the Cochrane Library identified scabies studies published since the year 2000 ., Selected papers included modelling studies and studies on the life cycle of scabies mites , patient quality of life and resource use ., Reference lists of reviews were used to identify any papers missed through the search strategy ., Strengths and limitations of identified scabies models were evaluated and used to design a modelling framework ., Potential model inputs were identified and discussed ., Four scabies models were published: a Markov decision tree , two compartmental models , and an agent-based , network-dependent Monte Carlo model ., None of the models specifically addressed crusted scabies , which is associated with high morbidity , mortality , and increased transmission ., There is a lack of reliable , comprehensive information about scabies biology and the impact this disease has on patients and society ., Clinicians and health economists working in the field of scabies are encouraged to use the current review to inform disease transmission modelling and economic evaluations on interventions against scabies .

Scabies is a neglected tropical disease affecting more than 130 million people , with major costs on health care systems worldwide ., While effective treatments exist , it is unknown which treatment strategies result in the best outcomes against the lowest costs , and to what extent this differs between communities ., Health economic modelling can help answer these questions , but has rarely been used in this disease area ., This review discusses all available scabies transmission models ( n = 4 ) , and uses them to create a new , comprehensive modelling framework ., This framework can be used as aid for creating a scabies transmission model , the details of which will be determined by the context ( population ) and the question being addressed ., The current paper also reviews the data that is needed to inform scabies modelling: on scabies biology , quality of life and resource use ., Unfortunately , available data is limited and particularly data on crusted scabies ( associated with high morbidity and mortality rates ) is rare ., With this review , we hope to assist researchers and policy makers to predict and/or evaluate the cost-effectiveness of interventions against scabies in their population ( s ) of interest ., To tackle scabies , it is key to use effective treatment strategies in a cost-effective and sustainable way ., The models and data described in this review , may help researchers , clinicians and funding bodies to facilitate this .

invertebrates, medicine and health sciences, cost-effectiveness analysis, engineering and technology, economic analysis, tropical diseases, social sciences, parasitic diseases, animals, health care, vaccines, decision analysis, developmental biology, ectoparasitic infections, management engineering, sexually transmitted diseases, neglected tropical diseases, infectious disease control, research and analysis methods, infectious diseases, decision trees, health economics, scabies, life cycles, mites, economics, arthropoda, eukaryota, quality of life, biology and life sciences, organisms

journal.pgen.1004920

Century-scale Methylome Stability in a Recently Diverged Arabidopsis thaliana Lineage

Differences in DNA methylation and other epigenetic marks between individuals can be due to genetic variation , stochastic events or environmental factors ., Epigenetic marks such as DNA methylation are dynamic; they can be turned over during mitosis and meiosis or altered by chromatin remodeling or upon gene silencing caused by RNA-directed DNA methylation ( RdDM ) ., Moreover , changes in DNA sequence or structure caused by , for instance , transposable element ( TE ) insertion , can induce secondary epigenetic effects at the concerned locus 1 , 2 , or , via processes such as RdDM , even at distant loci 3–5 ., The high degree of sequence variation , including insertions/deletions ( indels ) , copy number variants ( CNVs ) and rearrangements among natural accessions in A . thaliana provides ample opportunities for linked epigenetic variation , and the genomes of A . thaliana accessions from around the globe are rife with differentially methylated regions ( DMRs ) 6–10 , but it remains unclear how many of these cannot be explained by closely linked genetic mutations and thus are pure epimutations 11 that occur in the absence of any genetic differences ., The seemingly spontaneous occurrence of heritable DNA methylation differences has been documented for wild-type A . thaliana isogenic lines grown for several years in a stable greenhouse environment 12 , 13 ., Truly spontaneous switches in methylation state are most likely the consequence of incorrect replication or erroneous establishment of the methylation pattern during DNA replication 14–16 ., A potential amplifier of stochastic noise is the complex and diverse population of small RNAs that are at the core of RdDM 17 and that serve as epigenetic memory between generations ., The exact composition of small RNAs at silenced loci can vary considerably between individuals 13 , and stochastic inter-individual variation has been invoked to explain differences in remethylation , either after development-dependent or induced demethylation of the genome 18 , 19 ., Such epigenetic variants can contribute to phenotypic variation within species , and epigenetic variation in otherwise isogenic individuals has been shown to affect ecologically relevant phenotypes in A . thaliana 20–22 ., In addition to these spontaneous epigenetic changes , the environment can induce demethylation or de novo methylation in plants , for example after pathogen attack 23 ., Recently , it has been proposed that repeated exposure to specific environmental conditions can lead to epigenetic differences that can also be transmitted across generations , constituting a form of ecological memory 24–27 ., The responsiveness of the epigenome to external stimuli and its putative memory effect have moved it also into the focus of attention for epidemiological and chronic disease studies in animals 28 , 29 ., How the rate of trans-generational reversion among induced epivariants with phenotypic effects compares to the strength of natural selection , which in turn determines whether natural selection can affect the population frequency of epivariants , is largely unknown 30–33 ., To assess whether a variable and fluctuating environment is likely to have long-lasting effects in the absence of large-scale genetic variation , we have analyzed a lineage of recently diverged A . thaliana accessions collected across North America ., Using a new technique for the identification of differential methylation , we found that in a population of thirteen accessions originating from eight different locations and diverged for more than one hundred generations , only 3% of the genome had undergone a change in methylation state ., Notably , epimutations at the DNA methylation level did not accumulate at higher rates in the wild as they did in a benign greenhouse environment ., Using genetic mutations as a timer , we demonstrate that accumulation of methylation differences was non-linear , corroborating our previous hypothesis that shifts in methylation states are generally only partially stable , and that reversions to the initial state are frequent 12 , 34 ., Many methylation variants that segregated in the natural North American lineage could also be detected in the greenhouse-grown population , indicating that similar forces determined spontaneous methylation variation , independently of environment and genetic background ., Population structure could be inferred from differences in methylation states , and the pairwise degree of methylation polymorphism was linked to the degree of genetic distance ., Together , these results suggest that the environment makes only a small contribution to durable , trans-generationally inherited epigenetic variation at the whole-genome scale ., Previous studies of isogenic mutation accumulation ( MA ) lines raised in uniform greenhouse conditions identified many apparently spontaneously occurring pure epimutations 12 , 13 ., To determine whether variable and fluctuating environments in the absence of large-scale genetic variation substantially alter the genome-wide DNA methylation landscape over the long term , we analyzed a lineage of recently diverged A . thaliana accessions collected across North America ., Different from the native range of the species in Eurasia , where nearly isogenic individuals are generally only found at single sites , about half of all North American individuals appear to be identical when genotyped at 139 genome-wide markers 35 ., We selected 13 individuals of this lineage , called haplogroup-1 ( HPG1 ) , from locations in Michigan , Illinois and on Long Island , including pairs from four sites ( Fig . 1A , S1 Table ) ., Seeds of the accessions had been originally collected between 2002 and 2006 during the spring season , from plants at the end of their life cycle ., Because rapid flowering in the greenhouse was dependent on an extended cold treatment , or vernalization , we conclude that the parental plants had germinated in autumn of the previous year and overwintered as rosettes ., Climate data from the nearest respective weather station confirmed that precipitation and temperature regimes had varied considerably between sites in the growing season preceding collection ( S1-S2 Fig . ) ., Whole-genome sequencing of pools of eight to ten siblings from each accession identified a shared set of 670 , 979 single nucleotide polymorphisms ( SNPs ) and 170 , 998 structural variants ( SVs ) relative to the Col-0 reference genome , which were then used to build a HPG1 pseudo reference genome ( SOM: Genome analysis of HPG1 individuals; S2-S3 Table; S3 Fig . ) ., Only 1 , 354 SNPs and 521 SVs segregated in this population ( S4 Table , S4-S5 Fig . ) , confirming that the 13 strains were indeed closely related ., Segregating SNPs were noticeably more strongly biased towards GC→AT transitions than shared SNPs , especially in TEs , although the bias was not as extreme as in the greenhouse-grown MA lines ( Fig . 1B ) 36 ., A phylogenetic network and STRUCTURE analysis based on the segregating polymorphisms reflected the geographic origin of the accessions ( Fig . 1A , C; S6 Fig . ) ., Three of the pairs of accessions from the same site were closely related , and were responsible for many alleles with a frequency of 2 in the sampled population ( Fig . 1D ) ., If the spontaneous genetic mutation rate is similar to that seen in the greenhouse 36 , the HPG1 accessions would be 15 to 384 generations separated from each other ., With a generation time of one year , their most recent common ancestor would have lived about two centuries ago , which is consistent with A . thaliana having been introduced to North America during colonization by European settlers 37 ., This is also in line with the fact that in several US herbarium collections , A . thaliana specimens from the mid-19th century can be found , among these specimens from the Eastern Seaboard and the Upper Midwest ., We conclude that the HPG1 accessions constitute a near-isogenic population that should be ideal for the study of heritable epigenetic variants that arise in the absence of large-scale genetic change under natural growth conditions ., Because we observed only a weak positive correlation between genetic distance and phenotypic differences in the greenhouse ( S7 Fig . ) , we also infer that life history differences on their own should have little effect on the epigenetic landscape ., To assess the long-term heritable fraction of DNA methylation polymorphisms in the HPG1 lineage , we grew plants under controlled conditions for two generations after collection at the natural sites , before performing whole methylome bisulfite sequencing on two pools of 8–10 individuals per accession ( S5 Table ) ., We sequenced pools to reduce inter-individual methylation variation and fluctuations in methylation rate caused by stochastic coverage or read sampling bias ., After mapping reads to the HPG1 pseudo reference genome , we first investigated epigenetic variation at the single-cytosine level ., There were 535 , 483 unique differentially methylated positions ( DMPs ) , with an average of 147 , 975 DMPs between any pair of accessions ( SD = 23 , 745 ) ; thus , 86% of methylated cytosines accessible to our analyses were stably methylated across all HPG1 accessions ., The vast majority of variable sites ( 97% ) were detected in the CG context ( CG-DMPs ) ., As we have discussed previously 12 , this can be largely attributed to the lower average CHG and CHH methylation rates at individual sites compared to CG methylation , whereby differences in methylation rates are smaller and statistical tests of differential methylation fail more often for CHG and CHH sites ., ., Additionally , stable silencing-associated methylation of repeats and TEs , elements rich in CHG and CHH sites , may contribute to this pattern ., That only about 2% of all covered cytosines were differentially methylated in the relatively uniform HPG1 population contrasted with a previous epigenomic study , in which most cytosines in the genome were found to be differentially methylated in 140 genetically divergent accessions 10 ., Fewer than 10% of all cytosines in the genome were never methylated across these 140 accessions , although most methylation events were confined to single strains ( S9 Table of ref . 10 ) ., To make our data more comparable to this other study 10 , we identified DMPs of each HPG1 accession against the Col-0 reference genome ., On average we found 383 , 237 DMPs per accession , affecting a total of 1 , 046 , 892 unique sites ., We estimated that we would have detected 3 . 6 million DMPs , if we had sequenced 140 accessions from the HPG1 lineage ( see Materials and Methods; S8 Fig . ) ., The considerably larger number of DMPs in the 140 accessions 10 is likely due both to different methodology and to the higher degree of genetic variation between the analyzed accessions ., For example , Schmitz and colleagues 10 did not directly test for differential methylation at individual sites nor did they apply multiple testing correction , which might contribute to the high number of CHH-DMPs reported in that study ., Using the geographic outlier LISET-036 as a reference strain , we found that 61% of CG-DMPs as well as 36% of the small number of CHG- and CHH-DMPs were present in at least two independent accessions ( S9A Fig . ) , many of them shared between accessions from the same site ., As is typical for A . thaliana 38 , most methylated positions clustered around the centromere and localized to TEs and intergenic regions ( Fig . 2A; S9B Fig . ) ., In contrast , differential methylation in the CG context was over-represented on chromosome arms , localizing predominantly to coding sequences ( Fig . 2A; S9B Fig . ) , similar to what we had previously observed in the greenhouse-grown MA lines 12 ., We asked whether DMPs had accumulated more quickly in natural environments than in the greenhouse , using DNA mutations in the HPG1 and MA populations as a molecular clock ( SOM: Estimating DMP accumulation rates ) ., Our null hypothesis was that a variable and highly fluctuating natural environment increases the rate of heritable methylation changes ., In contrast to this expectation , DMPs appear to have accumulated in sub-linear fashion in both the HPG1 and MA populations 12 ( Fig . 2B ) – with similar trends for DMPs in all three contexts – and the number of DMPs did not increase more rapidly in the HPG1 than in the MA lines ., The steeper initial increase relative to SNP differences as well as the broader distribution of MA line differences relative to HPG1 differences were most likely the result of having compared individual plants in the MA experiment 12 , rather than pools of siblings , as in the HPG1 experiment ., The effect of pooling individuals , as shown by simulation ( S10 Fig . ) , and a potentially higher genetic mutation rate in the wild than in the greenhouse , for example because of increased stress 39 , could lead to a slight underestimation of the true HPG1 epimutation rate , but it remains unlikely that it greatly exceeds the one of the MA lines ( SOM: Estimating DMP accumulation rates ) ., Because it is unclear whether variation at individual methylated cytosines has any consequences in plants , we next focused on differentially methylated regions ( DMRs ) in the HPG1 population ., A limitation of previous plant methylome studies using short read sequencing has been that these relied on integration over methylated or single differentially methylated sites , or on the analysis of fixed sliding windows along the genome to identify DMRs ., What appears intuitively to be more appropriate is to first identify regions that are methylated in individual strains ( SOM: Differentially methylated regions ) 40 , and to test only these for differential methylation ., We therefore adapted a Hidden Markov Model ( HMM ) , which had been developed for segmentation of animal methylation data 41 , to the more complex DNA methylation patterns in plants ., We identified on average 32 , 529 methylated regions ( MRs ) per strain ( median length 122 bp ) , with the unified set across all strains covering almost a quarter of the HPG1 reference genome , 22 . 6 Mb ( Fig . 2A , C; S11A Fig . ; S6 Table ) ., MRs overlapping with coding regions were over-represented in genes responsible for basic cellular processes ( p-value <<0 . 001 ) , in agreement with gene body methylation being a hallmark of constitutively expressed genes 42 ., Only 1% of mCHH and 2% of mCHG positions were outside of methylated regions ( Fig . 2D ) , consistent with the dense CHH and CHG methylation found in repeats and silenced TEs 38 ., Compared to mCGs within methylated regions , mCGs in unmethylated space localized almost exclusively to genes ( 94% ) , were spaced much farther apart , and were separated by many more unmethylated loci ( Fig . 2E; S11B-C Fig . ) ., This explains why sparsely methylated genes were under-represented in HMM-determined methylated regions , even though gene body methylation accounts for a large fraction of methylated CG sites ., The accuracy of our MR detection method was well supported by independent methods ( SOM: Validation of methylated regions ) ., Using the unified set of MRs , we tested all pairs of accessions for differential methylation , identifying 4 , 821 DMRs with an average length of 159 bp ( S12 Fig . ; S11A Fig . ; S7 Table ) ., Of the total methylated genome space , only 3% were identified as being differentially methylated , indicating that the heritable methylation patterns had remained largely stable in this set of geographically dispersed accessions ., Indeed , 91% of genic and 98% of the TE sequence space were devoid of DMRs ., Of the DMRs , 3 , 199 were classified as highly differentially methylated ( hDMRs; S8 Table ) , i . e . they had a more than three-fold change in methylation rate and were longer than 50 bp ., The DMR allele frequency spectrum was similar to that of variably methylated single sites ( Fig . 2F ) ., Most DMRs and hDMRs showed statistically significant methylation variation in only one cytosine context , often CG ( Fig . 2G ) , even though DMRs were dominated by CHG and CHH methylation ( Fig . 2D , S13 Fig . ) ., Different from individual sites ( DMPs ) , the densities for DMRs and hDMRs were highest in centromeric and pericentromeric regions , and overlapped more often with TEs than with genes ( Fig . 2A , C ) ., Relative to all methylated regions , genic regions were two-fold overrepresented in the genome sequence covered by DMRs , and three-fold in hDMRs ( Fig . 2C ) ., Currently , we do not know whether this simply reflects the greater power of detecting differential methylation at the typically more highly methylated CG sites compared to CHG or CHH sites , or whether this reflects actual biology ., DNA methylation in gene bodies has been proposed to exclude H2A . Z deposition and thereby stabilize gene expression levels 42 ., We therefore asked what impact differential methylation had on transcriptional activity ., We identified 269 differentially expressed genes across all possible pairwise combinations ( S9-S10 Table ) , most of which were found in more than one comparison ., When we clustered accessions by differentially expressed genes , closely related pairs were placed together ( S14 Fig . ) ., We identified 28 differentially expressed genes that overlapped with an hDMR either in their coding or 1 kb upstream region , but the relationship between methylation and expression was variable ( S11 Table ) ., By visual examination , we found not more than five instances of demethylation that were associated with increased expression; examples are shown in S15 Fig . With the caveat that there are uncertainties about the genetic mutation rate in the wild , and therefore how the number of SNPs relates to the number of generations since the last common ancestor , there was no evidence for faster accumulation of variably methylated sites in the HPG1 population , nor for very different epimutation rates among HPG1 lines ( Fig . 2B ) ., Importantly , the overlap of differential methylation between the two populations was much greater than expected by chance: the probability of a random mC site in the MA population of being variably methylated in the HPG1 population was only 7% , but it was 41% among sites that were also variably methylated in the MA population – a six-fold enrichment ( four-fold enrichment in the reciprocal comparison; Fig . 3A ) ., In other words , almost half of the DMPs in the MA lines were also polymorphic in the HPG1 lines , and almost a third of HPG1 DMPs were also variably methylated in the MA population ., These shared DMPs were more heavily biased towards the chromosome arms and towards genic sequences than population-specific epimutations ( S16A-S16B Fig . ) ., Conversely , DMPs unique to one population were more likely to be unmethylated throughout the other population when compared to random methylated sites ( Fig . 3A ) , as one might expect for sites that sporadically gain methylation ., DMPs unique to the HPG1 lineage appeared to be less frequent in the pericentromere compared to MA- line-specific DMPs ( S16A Fig . ) , which was also reflected in an apparently higher epimutation frequency in the MA lines for these regions ( S16B Fig . ) ., We therefore investigated whether the annotation spectrum differed between these two classes of differentially methylated sites ., Even though MA-specific DMPs were more often found in TEs compared to HPG1-specific DMPs , this bias was also observed for all cytosines accessible to our methylome analyses ( S16C Fig . ) , and can therefore be explained by a more accurate read mapping and better TE annotation in the Col-0 reference compared to the HPG1 pseudo-reference genome ., Indeed , except for chromosome 4 , the average sequencing depth in the pericentromere was higher in the MA lines ( S16B Fig . ) ., DMPs distinguishing MA lines that were separated from each other by only a few generations were more frequently variably methylated in the HPG1 lineage than DMPs identified between distant MA lines ( S17 Fig . ) ., We interpret this observation as an indication of privileged sites that are more labile and therefore more likely to have already changed in status after a small number of generations ., We used the methods implemented for the HPG1 population to detect DMRs also in the MA strains ., Similar to variable single positions , or DMPs , the overlap between 2 , 523 DMRs of the MA lines that we could map to the HPG1 methylated genome space with the 4 , 821 DMRs of the HPG1 accessions was greater than expected and highly significant ( Ζ-score =\u200a32 . 9; 100 , 000 permutations ) ., HPG1 DMRs were four-fold more likely to coincide with MA DMRs than with a random methylated region from this set ( Fig . 3B ) ., We observed similar degrees of overlap independently of sequence context ., Shared DMRs between both lineages were , in contrast to shared DMPs , not biased towards genic regions ( S18 Fig . ) ., Differentially methylated regions in the HPG1 lineage , however , overlapped with genic sequences more often than MA DMRs ( S18 Fig . ) , which might again be explained by the different efficiencies in mapping to repetitive sequences and TEs ( S16B Fig . ) ., We next wanted to know how this short-term variation compared to methylation variation across much deeper splits ., To this end , we identified variably methylated regions between a randomly chosen MA line and a randomly chosen HPG1 line; these DMRs , which differentiate distantly related accessions , were also enriched in each of the two sets of within-population DMRs ( MA or HPG1 ) ( Fig . 3C ) ., Finally , we compared DMRs found in the HPG1 population to DMRs that had been identified with a different method among 140 natural accessions from the global range of the species 10 ( Fig . 3D ) ., Although only 9 , 994 , less than one fifth , of the variable regions from the global accessions were covered by methylated regions in the HPG1 strains , the overlap of DMRs was highly significant ( Ζ-score =\u200a19 . 8; 100 , 000 permutations ) ., Together , the high recurrence of differentially methylated sites and regions from different datasets points to the same loci being inherently biased towards undergoing changes in DNA methylation independently of genetic background and growth environment ., To explore potential sources of such lability , we compared variation in the HPG1 lines to that caused by mutations in various components of epigenetic silencing pathways 43 ., Almost all variable sites and regions in CG-methylated parts of the HPG1 genome were hypomethylated in mutants deficient in DNA methylation maintenance , most notably in the met1 single and the vim123 triple mutants ( S19 Fig . ) ., This is consistent with polymorphic methylation arising primarily because of errors in the maintenance of symmetrical CG methylation during DNA replication ., Hypermethylated sites in the rdd triple mutant , which shows impaired demethylation , were also found slightly more often within variably methylated regions of all contexts ( S19D Fig . ) ., To quantify how many methylation differences were co-segregating with genome-wide genetic changes at both linked and unlinked sites , we estimated heritability for each highly differentially methylated region by applying a linear mixed model-based method ., We used segregating sequence variants with complete information as genotypic data and average methylation rates of hDMRs with complete information as phenotypes ., The median heritability of all hDMRs was 0 . 41 ( mean 0 . 44 ) , which means that genetic variance across the entire genome contributed less than half of methylation variance ( Fig . 4A ) ., hDMRs in the HPG1 strains that were not methylated in the greenhouse-grown MA lines had a higher median heritability , 0 . 48 , than HPG1 hDMRs also found among MA DMRs ( 0 . 29 ) , which held true for all sequence contexts ( Fig . 4A; S20 Fig . ) ., Regions of highly differential methylation found only in the HPG1 population , especially those in unmethylated regions of the MA lines , were thus more likely to be linked to whole-genome sequence variation than hDMRs found in both populations ., For 19% of all hDMRs ( 21% CG-hDMRs , 14% CHG-hDMRs , 7% CHH-hDMRs ) , the whole-genome genotype explained more than 90% of their methylation differences ( with a standard error of at most 0 . 1 ) ., Of these hDMRs , half had a heritability of greater than 0 . 99 ., That 6 . 7% of the sequence space of these heritable hDMRs still overlapped with MA DMRs ( versus 9 . 4% for the less heritable hDMRs ) was in agreement with the hypothesis that there are regions that vary highly in their methylation status independently of genetic background ., To identify genetic variants that potentially directly cause methylation changes in their local genomic neighborhood , we focused on variably methylated regions that were within 1 kb of segregating SNPs or indels ., Of 191 such DMRs , only three showed a systematic correlation with nearby sequence polymorphisms ., We noticed , however , that coding regions with structural variants larger than 20 bp that distinguished the MA and HPG1 populations were more likely to be methylated in both lineages than non-polymorphic coding regions ( Fig . 4B ) ., Consequently , DMPs unique to the HPG1 lines were on average closer to insertions or deletions than DMPs shared between the HPG1 and MA populations ( Fig . 4C ) ., Lastly , we asked whether the genome-wide methylation pattern reflected genetic relatedness , i . e . , population structure ., Hierarchical clustering by methylation rates of variable sites and regions grouped strains by sampling location ( Fig . 4D , E ) ., This result was largely independent of the sequence or the annotation context of these loci , and not seen with sites that our statistical tests had identified as stably methylated ( S21 Fig . ) ., That variably methylated regions grouped the accessions similar to DMPs , albeit with less confidence ( shorter branch lengths; S21 Fig . ) , suggested that our DMR calling algorithm was conservative ., Methylation data thus paralleled similarity between accessions at the genetic level , in agreement with the interpretation that methylation differences primarily reflect the number of generations since the last common ancestor ., We have tested the hypothesis that accumulation of epigenetic variation under natural conditions proceeds over the short term in a very different manner than the clock-like behavior of genetic variation 24–27 ., To this end , we have taken advantage of a unique natural experiment , the A . thaliana HPG1 lineage , which has likely diverged for at least a century throughout North America ., Our analyses have revealed little evidence for broad-scale and durable epigenetic differentiation that might have been induced by the variable and fluctuating environmental conditions experienced by the HPG1 accessions since they separated from each other ., While the exact conditions these plants have been subjected to since their separation from a common ancestor remain unknown , the time scale and diversity of geographic provenance are strong indicators of the variability of the environment between the different sampling sites , supported by temperature and precipitation data from nearby weather monitoring stations ., The general analytical framework enabled by the HPG1 lineage – nearly isogenic lines grown for more than a century under variable and fluctuating conditions – could not have been achieved in a controlled greenhouse experiment ., Studies of epiRIL populations have shown that pure epialleles can be stably transmitted across several generations 5 , 19 , but how often this is the case for environmentally induced epigenetic changes has been heavily debated 33 , 44–46 ., The recent excitement about the transmission of induced epigenetic variants comes from such variants having been proposed to be more often adaptive than random genetic mutations 24–26 ., Contrary to the expectations discussed above , we found that epimutation rates under natural growth conditions at different sites did not differ substantially from those observed in a controlled greenhouse environment , with polymorphisms accumulating sub-linearly in both situations , apparently because of frequent reversions ., Note that we grew the HPG1 plants under controlled conditions for two generations after sampling at the natural site , to reduce the range of epigenetic variation to the long-term heritable fraction ., Given that the environment can induce acute methylation changes 23 , 47 , it is likely that we would have observed greater epigenetic variation , if we had sampled field-grown individuals directly ., However , most of such variation induced during ontogeny does not appear to be heritable , as we did not find evidence for it after two extra generations in the greenhouse ., Additional studies that directly compare plants grown outdoors to their progeny grown in a stable and controlled environment will help to further clarify this issue ., We found that positions of differential methylation in the HPG1 population are more likely to overlap with DMPs detected between closely related MA lines than between more distantly related MA lines ., This observation supports the hypothesis that there are different classes of polymorphic sites ., One of these includes ‘high lability’ sites that are independent of the genetic background , that change with a high epimutation rate , and that are therefore more likely to appear in each population ., Another class of DMPs comprises more stable sites that gain or lose methylation more slowly and that therefore are less likely to be shared between different populations ., Differences between accessions in terms of DNA methylation recapitulated their genetic relatedness , further corroborating our hypothesis that heritable epigenetic variants arise predominantly as a function of time rather than as a consequence of rapid local adaptation ., Epigenetic divergence thus does not become uncoupled from genetic divergence when plants grow in varying environments , nor does the rate of epimutation noticeably increase ., A minor fraction of heritable epigenetic variants may be related to habitat , which could be responsible for LISET-036 being epigenetically a slight outlier ( Fig . 4E ) , even though it is not any more genetically diverged from the most recent common ancestor of HPG1 than other lines ., Such local epigenetic footprints could also explain fluctuations in epimutation frequency between the MA and HPG1 lineages ., Subtle adaptive changes at a limited number of loci would go unnoticed in the present analysis of genome-wide patterns and can therefore not be excluded ., However , on a genome-wide scale there was little indication of adaptive change: neither were LISET-036 specific regions of differential methylation in and near genes enriched for GO terms with an obvious connection to environmental adaptation , nor were there overlapping differentially expressed genes ( S22 Fig . , SOM: Analysis of LISET-036 specific hDMRs ) ., In combination with the general lack of correlation between differential methylation and changes in gene expression , our findings suggest that epigenetic changes in nature are mostly neutral , and thus comparable to genetic mutations ., We point out that an annual species such as A . thaliana might be differently disposed to record environmental signals in its epigenome compared to more long-lived species ., From an evolutionary perspective , in perennial species the advantage of epigenetically mediated local adaptation to changing conditions could be more pronounced , and future studies are warranted to address this question ., Because of the near-isogenic background of the HPG1 accessions , we were also able to gauge how much of epigenetic variation is either caused by , or stably co-segregates with genetic differences ., HPG1-specific highly differentially methylated regions were more often linked to genotype variation than re

Introduction, Results, Discussion, Materials and Methods

There has been much excitement about the possibility that exposure to specific environments can induce an ecological memory in the form of whole-sale , genome-wide epigenetic changes that are maintained over many generations ., In the model plant Arabidopsis thaliana , numerous heritable DNA methylation differences have been identified in greenhouse-grown isogenic lines , but it remains unknown how natural , highly variable environments affect the rate and spectrum of such changes ., Here we present detailed methylome analyses in a geographically dispersed A . thaliana population that constitutes a collection of near-isogenic lines , diverged for at least a century from a common ancestor ., Methylome variation largely reflected genetic distance , and was in many aspects similar to that of lines raised in uniform conditions ., Thus , even when plants are grown in varying and diverse natural sites , genome-wide epigenetic variation accumulates mostly in a clock-like manner , and epigenetic divergence thus parallels the pattern of genome-wide DNA sequence divergence .

It continues to be hotly debated to what extent environmentally induced epigenetic change is stably inherited and thereby contributes to short-term adaptation ., It has been shown before that natural Arabidopsis thaliana lines differ substantially in their methylation profiles ., How much of this is independent of genetic changes remains , however , unclear , especially given that there is very little conservation of methylation between species , simply because the methylated sequences themselves , mostly repeats , are not conserved over millions of years ., On the other hand , there is no doubt that artificially induced epialleles can contribute to phenotypic variation ., To investigate whether epigenetic differentiation , at least in the short term , proceeds very differently from genetic variation , and whether genome-wide epigenetic fingerprints can be used to uncover local adaptation , we have taken advantage of a near-clonal North American A . thaliana population that has diverged under natural conditions for at least a century ., We found that both patterns and rates of methylome variation were in many aspects similar to those of lines grown in stable environments , which suggests that environment-induced changes are only minor contributors to durable genome-wide heritable epigenetic variation .

biogeography, ecology and environmental sciences, plant science, model organisms, plant genomics, epigenetics, theoretical ecology, plant ecology, dna methylation, arabidopsis thaliana, research and analysis methods, gene expression, plant genetics, phylogeography, dna modification, plant and algal models, ecology, genetics, biology and life sciences

journal.ppat.1005611

Antibiotic-Resistant Neisseria gonorrhoeae Spread Faster with More Treatment, Not More Sexual Partners

Antibiotic-resistant Neisseria gonorrhoeae can evolve and spread rapidly 1 ., Resistance is commonly observed against the antibiotic classes penicillin , tetracycline and fluoroquinolones 2–4 ., Resistance also emerged against cefixime , an oral third generation cephalosporin , in recent years 2 , 3 ., Since 2010 , cefixime is no longer recommended as first-line treatment 5 following guidelines from the World Health Organization ( WHO ) that an antibiotic should not be used when more than 5% of N . gonorrhoeae isolates are resistant 6 ., Injectable ceftriaxone , in combination with oral azithromycin , is now the last antibiotic remaining as recommended first-line treatment 7 ., Although other antibiotics are being tested for their safety and efficacy for N . gonorrhoeae treatment 8 , no new classes of antibiotics are currently available 4 and management of antibiotics is urgently needed to preserve their efficacy ., The current management strategy tries to reduce the overall burden of N . gonorrhoeae infection by expanded screening and treatment of hosts 9 , 10 , but the outcome of this strategy for resistance is uncertain ., Understanding the drivers of resistance spread and anticipating future resistance trends will provide rationales for antibiotic management and help to improve antibiotic treatment strategies ., Men who have sex with men ( MSM ) are host populations that have higher levels of antibiotic-resistant N . gonorrhoeae than heterosexual host populations 3 ., In a study 5 based on the Gonococcal Resistance to Antimicrobials Surveillance Programme ( GRASP ) in England and Wales , cefixime-resistant N . gonorrhoeae were mainly found in MSM until 2011 ., The authors suggested that cefixime resistance was circulating in a distinct sexual network of highly active MSM and that bridging between MSM and heterosexuals was necessary for subsequent spread among heterosexual hosts ., However , cefixime-resistant N . gonorrhoeae might have already been spreading undetected in the heterosexual host population ., Mathematical models can help explain the differential observations of antibiotic-resistant N . gonorrhoeae in different host populations ., In 1978 , Yorke et al . 11 introduced the concept of core groups to model the transmission of N . gonorrhoeae ., The concept of core groups posits that an infection can only be maintained in a host population if a highly sexually active group of hosts is responsible for a disproportionate amount of transmissions ., More recent modeling studies have examined the transmission of antibiotic-resistant N . gonorrhoeae ., Chan et al . 12 found that prevalence rebounds more quickly to a pre-treatment baseline when treatment is focused on the core group ., Xiridou et al . 13 developed a N . gonorrhoeae transmission model to determine the impact of different treatment strategies on the prevalence of N . gonorrhoeae in Dutch MSM ., They found that increased treatment rates could increase the spread of resistance , whereas re-treatment could slow it down ., Hui et al . 14 used an individual-based N . gonorrhoeae transmission model in a heterosexual host population to investigate the effect of a molecular resistance test on the time until 5% resistance are reported ., None of these studies has investigated or explained the differences in the spread of antibiotic-resistant N . gonorrhoeae in MSM and heterosexual host populations ., In this study , we investigated the dynamics and determinants of antibiotic-resistant N . gonorrhoeae spread using surveillance data and mathematical modeling ., We estimated the rates at which resistance spreads in heterosexual men ( HetM ) and MSM using surveillance data from the USA and from England and Wales ., We then developed a mathematical model of N . gonorrhoeae transmission to reconstruct the observed dynamics of resistance spread ., This allowed us to determine the major driver of resistance spread , and to explore the expected rates at which resistance spreads in MSM and heterosexual host populations ., We fitted a logistic growth model to the proportion of antibiotic-resistant N . gonorrhoeae as observed in the two gonococcal surveillance programs ( Fig 2 ) ., The proportion of cefixime-resistant N . gonorrhoeae in GRASP appears to increase for both HetM and MSM after 2006 ., Ciprofloxacin-resistant N . gonorrhoeae in HetM and MSM were spreading in all observed host populations after the year 2000 ., For a given antibiotic and surveillance program , the rates of resistance spread were consistently higher for MSM than for HetM ( Table 4 ) ., The average rate of resistance spread was 0 . 53 y−1 for HetM and 1 . 46 y−1 for MSM , corresponding to doubling times of 1 . 3 y ( HetM ) and 0 . 5 y ( MSM ) during the initial exponential growth phase ., Next , we studied the transmission of N . gonorrhoeae and the spread of resistance in the dynamic transmission model ., We calibrated five model parameters to expected prevalence and incidence in MSM and HMW host populations ., The posterior distributions of the parameters were based on 2 , 779 parameter sets for HMW and 65 , 699 parameter sets for MSM ( Fig 3 , Table 1 ) ., Distributions of the modeled prevalence and incidence of diagnosed infections after calibration are provided as Supporting Information ( S1 and S2 Figs , S3 Table ) ., The sexual mixing coefficient showed a tendency towards assortative mixing in both MSM and HMW ( Fig 3a ) ., The fraction of diagnosed and treated infections tended to be higher in MSM compared to HMW ( Fig 3b ) , whereas the infectious duration was considerably shorter in MSM ( median: 2 . 3 months , IQR: 1 . 7–3 . 0 months ) than in HMW ( median: 6 . 6 months , IQR: 5 . 5–7 . 9 months ) ( Fig 3c ) ., The transmission probabilities per partnership were generally higher in HMW than in MSM ( Fig 3d and 3e ) ., After calibration , we used the dynamic transmission model to study the spread of antibiotic-resistant N . gonorrhoeae ., The proportion of antibiotic-resistant N . gonorrhoeae increased faster in MSM than in HMW ( Fig 4 ) ., In HMW , the median of all simulations reached 5% resistance in fewer than 4 . 5 y and 50% resistance in fewer than 7 . 8 y after appearance of the first antibiotic-resistant N . gonorrhoeae infection ., In the MSM population , the median of all simulations reached a resistance level of 5% in fewer than 1 . 7 y and 50% in fewer than 2 . 6 y after resistance first appears in the population ., The range spanned by all simulations was much wider in HMW than in MSM: 95% of all simulations reached the 5% threshold in fewer than 2 . 7–7 . 7 y ( HMW ) , compared with 1 . 1–2 . 2 y ( MSM ) ., Antibiotic-sensitive and -resistant N . gonorrhoeae share the same resource for growth , i . e . the susceptible hosts ., The rate at which one strain replaces the other strain in the host population is given by the difference in their net growth rates ., We assume that the transmission probabilities and the infectious duration of the two strains are the same ., Since the probability of resistance during treatment is very small ( μ ≪ 1 ) , the difference in net growth rates between the strains is approximated by the treatment rate τ and corresponds to the rate of spread of antibiotic-resistant N . gonorrhoeae ., The observed distributions of treatment rates from the transmission model hardly overlap between HMW and MSM ( Fig 5 ) ., The median treatment rates , i . e . the approximated median rates of resistance spread in the transmission model are 3 . 12 y−1 ( MSM ) and 0 . 88 y−1 ( HMW ) ., We tested whether changes in the probability of resistance during treatment , μ , and fitness costs in the antibiotic-resistant strain alter the model outcomes ., Higher probabilities of resistance during treatment accelerate the establishment of antibiotic-resistant N . gonorrhoeae in the population and hence reduce the time until 5% resistance is reached ( S3 Fig ) ., Higher probabilities of resistance during treatment , however , do not affect rates of spread , unless the probability of resistance during treatment is unrealistically high ( 10% ) ( S4 Fig ) ., Fitness costs in the antibiotic-resistant strain result in rates of resistance spread that are lower than the treatment rate τ ( Fig . B in S1 Appendix ) ., Fitness costs that reduce the transmission probability per partnership , βij , have a stronger effect than fitness costs that reduce the duration of infection ., The effects of fitness costs are independent of the sexual partner change rate , πi , and βij if they affect the duration of infection , but can vary with πi and βij if they affect the transmission probability per partnership ( Fig . C in S1 Appendix ) ., While high fitness costs can prevent the spread of antibiotic-resistant strains ( Fig . A in S1 Appendix ) , fitness costs between 0%–10% have only small effects on the rates of resistance spread ( Fig . B in S1 Appendix ) ., In this study , we quantified the rate at which antibiotic-resistant N . gonorrhoeae spread in heterosexual and MSM populations ., We used data from two different surveillance programs and estimated that the proportion of ciprofloxacin- and cefixime-resistant N . gonorrhoeae doubles on average every 1 . 3 y in HetM and 0 . 5 y in MSM ., The faster spread of antibiotic-resistant N . gonorrhoeae in MSM than in heterosexual hosts was corroborated using a dynamic transmission model , which was calibrated to observed prevalence and incidence rates ., The model allowed us to identify the higher treatment rates in MSM , compared with heterosexual hosts , as the major driver for the faster spread of antibiotic-resistant N . gonorrhoeae ., To our knowledge , this is the first study to have analyzed and interpreted N . gonorrhoeae antibiotic resistance surveillance data in a dynamic and quantitative manner ., The transmission model was parameterized using sexual behavior data for HMW and MSM from Natsal-2 22 , a large probability sample survey of sexual behavior ., Calibrating the model to observed prevalence and incidence rates allowed us to use largely uninformative priors for the model parameters ., The calibration makes our model more robust to changes in parameters than using fixed parameter values , especially since for N . gonorrhoeae available parameter values are very uncertain 31 ., It also allowed us to rely on few assumptions about the natural history of N . gonorrhoeae infection ., The limitations to our study need to be taken into consideration when interpreting the findings ., First , we used data from different sources , although all were collected in high income countries ., The antibiotic resistance surveillance data are from programs in England and Wales and the USA ., The mathematical transmission model was parameterized using British sexual behavior data 22 and calibrated to prevalence and incidence rates from the USA ( HMW ) 26 , 27 and Australia ( MSM ) 28 , 29 ., For simplicity , we modeled the heterosexual and MSM host populations separately although there is some mixing between them ., We assumed the sexual behavior of heterosexual men and women to be the same and pooled their behavioral data ., Second , we assumed complete resistance against the antibiotic , i . e . 100% treatment failure ., We further assumed that treatment of the sensitive strain is 100% efficacious ., Both assumptions might explain why antibiotic-resistant N . gonorrhoeae spread at somewhat higher rates in the dynamic transmission model than estimated from data ., Third , we restricted our model to resistance to one antibiotic with no alternative treatment or interventions ., This is why we observe complete replacement of the antibiotic-sensitive strain in the model , a phenomenon that has not been observed in surveillance data ., Fourth , resistance in our model is treated as a generic trait , but it likely depends on the underlying molecular mechanisms and possibly the genetic background of the N . gonorrhoeae strain ., Different resistance mechanisms might explain some of the differences in the rates of resistance spread between the model and the different antibiotics from the surveillance data ., Fifth , we did not include co- and superinfection with antibiotic-sensitive and -resistant N . gonorrhoeae strains ., Since genetic typing provides evidence for mixed infections 32 , it is worth speculating how they would affect the rate of spread from the transmission model ., If antibiotic-sensitive and -resistant strains co-existed in a host and acted independently , we would not expect significant effects on the rate of spread ., In contrast , if there was competition between the two strains within a host , the rate of spread would increase if the antibiotic-resistant strain outcompetes the -sensitive strain , and decrease otherwise ., Sixth , we do not consider importation of resistance from another population ., For example , importation of resistance from other countries might play a particularly important role during the early phase of resistance spread , when stochastic events can lead to extinction of the antibiotic-resistant strain ., We expect that a high rate of importation of antibiotic resistance shortens the time to reach 5% resistance drastically , but that once the resistant strain is established in the population , importation hardly affects the rate of resistance spread ., Finally , we assumed that the transmission probabilities per partnership and the durations of infection in the model represent average values for N . gonorrhoeae infections at different infection sites ( urethral , pharyngeal , anal , cervical ) ., The estimated posterior distributions of the parameters fit within the range of previously used values , and provide some insights into sexual mixing and the natural history of N . gonorrhoeae ., The sexual mixing coefficient tends to be assortative for both HMW and MSM host populations in our model ., Quantifying the degree of sexual mixing is difficult and largely depends on the study population , but our finding is consistent with other studies indicating assortative sexual mixing in the general population 30 , 33 ., The posterior estimates of the fraction of diagnosed and treated infections are consistent with the notion that a large proportion of N . gonorrhoeae infections are symptomatic , and that this proportion is expected to be higher in men than in women 34–36 ., The average duration of infection was the only parameter with an informative prior , but we found marked differences between the duration of infection in HMW ( 6 . 6 months ) and MSM ( 2 . 3 months ) ., Per sex act transmission probabilities are generally considered to be lower from women to men than vice versa 37–39 ., In our model , the median of the transmission probability per partnership was lower in MSM hosts than in HMW for both sexual activity groups ., This could be explained by different numbers of sex acts per partnership in the two populations ., The low transmission probability within the highly active MSM group ( median: 30% ) could reflect a single or a small number of sex acts per partnership ., In contrast , the high transmission probability for HMW within the low sexual activity group ( median: 87% ) could be a result of a larger number of sex acts per partnership in those individuals ., Furthermore , condom use is more frequent in MSM than in HMW 22 , which could explain part of the observed differences in transmission probabilities ., Our study found that the treatment rate is the driving force of resistance spread ., Xiridou et al . 13 found that resistance could spread faster when the treatment rate was higher , but they did not identify the treatment as the major driver of resistance spread ., Chan et al . 12 found that focusing treatment on the core group leads to a faster rebound to pre-treatment prevalence than equal treatment of the entire host population ., Unfortunately , our findings cannot be compared with Chan et al . because they do not report the proportion of antibiotic-resistant N . gonorrhoeae ., It was shown previously that treatment is the main selective force acting on resistance evolution due to the selective advantage to the resistant pathogen 40 , 41 ., We now expand this concept by showing that , assuming no fitness costs , treatment rates determine the rates of resistance spread even when the host populations has a heterogeneous contact structure ., The intuitive argument that a faster spread of an infection , due to a higher number of sexual partners , will result in a faster spread of resistance does not hold ., Instead , the proportion of resistant infections spreads equally in host populations with different number of partners as long as they receive treatment at the same rate and there are no fitness costs associated with the transmission probability per partnership ., For N . gonorrhoeae , this insight challenges the current management strategy that aims to lower the overall burden of infection by expanding screening and treatment of hosts 9 , 10 ., As soon as antibiotic-resistant pathogens are frequent enough to evade stochastic extinction , expanded treatment will foster their spread and increase the burden of N . gonorrhoeae ., Additionally , we show that fitness costs can decelerate or even prevent the spread of antibiotic-resistant N . gonorrhoeae strains ., Fitness costs therefore might explain why highly resistant strains , such as the ceftriaxone-resistant N . gonorrhoeae strain H041 , do not spread in the host population after their first detection 42 ., Our findings also show that bridging between the HetM and the MSM host populations might not have been necessary for cefixime-resistance to spread in the HetM population after 2010 5 ., It is likely that cefixime-resistant N . gonorrhoeae had already been present in the HetM population but were spreading at a lower rate than in the MSM population ., The results of our study will be useful for future N . gonorrhoeae research and for guiding treatment recommendations ., The N . gonorrhoeae transmission model describes observed prevalence and incidence rates well and can reconstruct the spread of antibiotic-resistant N . gonorrhoeae ., Estimating rates of resistance spread is useful for projecting future resistance levels and the expected time it will take until a certain threshold in the proportion of antibiotic-resistant N . gonorrhoeae is reached ., Until now , treatment recommendations for N . gonorrhoeae are subject to change when 5% of N . gonorrhoeae isolates show resistance against a given antibiotic 6 ., Our study shows the importance of the rate of spread: a level of 5% resistance results in a marginal increase to 8% in the following year if resistance spreads logistically at rate 0 . 53 y−1 ( HetM mean estimate from Table 4 ) , but reaches 18% in the next year if resistance spreads at rate 1 . 46 y−1 ( MSM mean estimate from Table 4 ) ., Public health authorities could use surveillance data and adapt thresholds for treatment recommendation change to specific host populations using the method we describe ., Our study challenges the currently prevailing notion that more screening and treatment will limit the spread of N . gonorrhoeae , as higher treatment rates will ultimately result in faster spread of antibiotic resistance ., Future treatment recommendations for N . gonorrhoeae should carefully balance prevention of N . gonorrhoeae infection and avoidance of the spread of resistance .

Introduction, Results, Discussion

The sexually transmitted bacterium Neisseria gonorrhoeae has developed resistance to all antibiotic classes that have been used for treatment and strains resistant to multiple antibiotic classes have evolved ., In many countries , there is only one antibiotic remaining for empirical N . gonorrhoeae treatment , and antibiotic management to counteract resistance spread is urgently needed ., Understanding dynamics and drivers of resistance spread can provide an improved rationale for antibiotic management ., In our study , we first used antibiotic resistance surveillance data to estimate the rates at which antibiotic-resistant N . gonorrhoeae spread in two host populations , heterosexual men ( HetM ) and men who have sex with men ( MSM ) ., We found higher rates of spread for MSM ( 0 . 86 to 2 . 38 y−1 , mean doubling time: 6 months ) compared to HetM ( 0 . 24 to 0 . 86 y−1 , mean doubling time: 16 months ) ., We then developed a dynamic transmission model to reproduce the observed dynamics of N . gonorrhoeae transmission in populations of heterosexual men and women ( HMW ) and MSM ., We parameterized the model using sexual behavior data and calibrated it to N . gonorrhoeae prevalence and incidence data ., In the model , antibiotic-resistant N . gonorrhoeae spread with a median rate of 0 . 88 y−1 in HMW and 3 . 12 y−1 in MSM ., These rates correspond to median doubling times of 9 ( HMW ) and 3 ( MSM ) months ., Assuming no fitness costs , the model shows the difference in the host population’s treatment rate rather than the difference in the number of sexual partners explains the differential spread of resistance ., As higher treatment rates result in faster spread of antibiotic resistance , treatment recommendations for N . gonorrhoeae should carefully balance prevention of infection and avoidance of resistance spread .

More and more infectious disease treatments fail because the causative pathogens are resistant to the drugs used for treatment ., For the treatment of Neisseria gonorrhoeae , a sexually transmitted bacterium , drug resistance is a particularly big problem: there is only a single antibiotic left that is recommended for treatment ., We aimed to understand how antibiotic-resistant N . gonorrhoeae spread in a sexually active host population and how the spread of resistance can be slowed ., From antibiotic resistance surveillance data , we first estimated the rate at which antibiotic-resistant N . gonorrhoeae spread ., Second , we reproduced the observed dynamics in a mathematical model describing the transmission between hosts ., We found that antibiotic-resistant N . gonorrhoeae spread faster in host populations of men who have sex with men than in host populations of heterosexuals ., We could attribute the faster spread of resistant pathogens to higher treatment rates ., This finding implies that promoting screening to control antibiotic-resistant N . gonorrhoeae could in fact accelerate their spread .

antimicrobials, medicine and health sciences, pathology and laboratory medicine, pathogens, drugs, microbiology, neisseria gonorrhoeae, antibiotic resistance, probability distribution, mathematics, antibiotics, sexually transmitted diseases, pharmacology, bacteria, bacterial pathogens, infectious diseases, antimicrobial resistance, neisseria, sexual preferences, medical microbiology, microbial pathogens, men who have sex with men, probability theory, people and places, microbial control, biology and life sciences, population groupings, physical sciences, organisms, heterosexuals

journal.pbio.1001886

Fusion of Protein Aggregates Facilitates Asymmetric Damage Segregation

A dividing cell can deal with damaged material in two different ways ., First , the damaged material can be segregated asymmetrically during division , such that it is concentrated in one of the two newborn daughter cells , while the other cell is born clean ., The damage is then removed from the population when the cell retaining the damaged material dies ., Second , in phases of rapid growth , damaged material can be segregated randomly , leading to less asymmetric segregation of damage between daughters ., In this case , accumulation of damage within any cell is prevented by rapid divisions that dilute the damaged material ., Protein aggregates are a type of damaged material , composed of insoluble and dense protein particles 1 ., These aggregates , instead of being degraded , accumulate in the cell during stress and aging 2–4 ., Once formed , aggregates can interfere with cell cycle progression and cell function 5 and correlate with cell death 6 ., To deal with protein aggregates during cell division , Escherichia coli and Saccharomyces cerevisiae , as well as stem cells , use asymmetric segregation , where aggregates are retained by one cell , generating a clean sister cell 2 , 3 , 7–10 ., In E . coli , protein aggregates accumulate at the cell poles and often segregate with the old cell pole 3 ., In the case of S . cerevisiae , asymmetric segregation of aggregates is achieved through a combination of retention in specialized compartments 8 , 11–14 , active transport 8 , and limited diffusion through the bud neck 9 ., However , the mechanisms underlying aggregate segregation in eukaryotic cells that divide symmetrically are unclear ., We have recently shown that the symmetrically dividing fission yeast Schizosaccharomyces pombe does not show aging under favorable conditions , which suggests that aggregates are segregated symmetrically 6 ., After stress , however , the cells inheriting large aggregates do age and eventually die , while their sisters with small or no aggregates do not age 6 ., How a large aggregate arises after stress , and how the generation of aggregate-free cells is achieved , remained unknown ., Here we study the mechanism underlying the transition from symmetric to asymmetric aggregate segregation ., By combining in vivo quantification of aggregate nucleation , movement , fusion , and segregation with a mathematical model , we show that under favorable conditions aggregates fuse rarely and segregate symmetrically at division ., Using the total amount of aggregates , measured as the total fluorescence intensity in puncta of the GFP-tagged Hsp104 disaggregase 6 , to identify different levels of aggregation in response to stress , our experiments show that an increase in fusion facilitates asymmetric segregation of aggregates and production of aggregate-free cells ., These results are consistent with the predictions of our model , which provides support for the conclusion that the formation of damage-free cells is promoted by aggregate fusion ., We monitored protein aggregates using the Hsp104 disaggregase , a chaperone that binds and separates aggregated proteins 15 , labeled with GFP ( Figure 1A , Figure S1 , and Text S1 ) ., We have shown before that Hsp104 from S . pombe is active as a disaggregase in vitro and in vivo 6 and that the puncta of Hsp104-GFP observed in the cytoplasm represent endogenous aggregates ., We also observed diffuse Hsp104-GFP in the nucleus ( Figure 1A ) and in the cytoplasm ( see Figure S2F ) , as shown previously in S . cerevisiae 16 ., While the lower disaggregase activity of Hsp104 from S . pombe , when compared to its S . cerevisiae homolog 6 , likely accounts for the presence of aggregates under favorable conditions , deleting hsp104 resulted in increased aggregation ( Figure S1F–I ) and increased cell death after stress 6 , while labeling the endogenous Hsp104 with GFP has no effect on stress recovery 6 ., The Hsp104-GFP puncta are composed of aggregated proteins and chaperones ( Figure S1 ) , as reported for other organisms 5 ., To study aggregate dynamics during the cell cycle , we followed Hsp104-associated aggregates with wide-field fluorescence microscopy ( Materials and Methods ) ., Aggregates nucleated equally often in each of the two respective cytoplasmic regions ( compartments ) between the nucleus and the old cell pole , and the nucleus and the new cell pole , generated in the previous division ( 1 . 3±0 . 2 nucleation events/cell cycle , n\u200a=\u200a162 cells; Figure S2A and S2B ) ., After nucleation , aggregates typically remained in the same compartment ( only 3 . 2±1 . 5% of aggregates moved between the compartments , n\u200a=\u200a126 cells ) ., Aggregates moved and contact between them resulted in their fusion ( 94/100 contacts resulted in fusion; 0 . 40±0 . 06 fusion events/cell cycle , n\u200a=\u200a200 cells; Figure 1A , Movie S1 ) ., Fission of aggregates was rare ( 0 . 006±0 . 005 events/cell cycle ) , and disappearance of aggregates was not observed ( n\u200a=\u200a498 cells ) ., We tracked individual aggregates on time scales from milliseconds to tens of minutes and observed dynamics suggesting diffusive motion ( Figures 1B and S2C–F and Movie S2 ) ., To test whether aggregate movement was diffusive and not linked with the movement of other subcellular components , we performed a combination of tests , which confirmed that aggregates ( 1 ) move according to Stokes diffusion ( Figure 1B , inset ) , ( 2 ) do not co-localize with the cytoskeleton ( actin or microtubules ) or a wide range of lipid structures ( cellular membrane , endosomes , Golgi , vacuoles , and nuclear membrane ) ( Figure S2G and S2H ) , and ( 3 ) still undergo diffusion and fusion when the cytoskeleton is depolymerized ( Figures S2I–K; see also Text S1 ) ., We next studied how aggregates are segregated between cells at division ., Because aggregates nucleate and move randomly , we hypothesized that sister cells arising from a morphologically symmetrical division inherit the same number of aggregates on average ., Indeed , the aggregates did not segregate specifically to a cell inheriting the new or the old pole ( Figure S2B; the small bias can be a consequence of the displacement of aggregates towards the old pole by the nucleus during anaphase ) ., In the wild type , the two equally sized sister cells inherited on average the same number of aggregates ( Figure 1C and 1D ) ., Because asymmetric cell division may lead to biased segregation of aggregates towards the larger sister cell , we enforced asymmetry in cell division by using a Δpom1 mutant , in which the division plane is displaced off-center , resulting in two cells of different size 17 ., We observed that cells were up to 70% larger than their smaller sisters , and larger cells retained correspondingly more aggregates ( Figure 1C and 1D and Movie S3 ) ., These results show that aggregate segregation in S . pombe is unbiased ., We conclude that aggregate nucleation and movement is random , resulting in random aggregate segregation at division ., Based on our experimental observations , we developed a stochastic aggregation model ( Figure 2A ) that allows for the simulation of aggregate size distributions ( Figure 2B ) , which can be compared with the experimentally observed size distributions ( measured by the intensity of Hsp104-GFP in each puncta , a . u . ) ., A key feature distinguishing the proposed model from other models 18–20 is that aggregate segregation asymmetry is an output rather than an input of our model ., Three key processes operate on size distributions of aggregates in each of the two compartments of a cell ( Figure 2A ) : ( 1 ) generation of the smallest size aggregates at rate r; ( 2 ) fusion of aggregates of sizes i and j at rate K ( i , j ) to create an aggregate of size i+j; and ( 3 ) random segregation of aggregates to two new compartments at division ., We use the Brownian kernel: ( 1 ) where k\u200a=\u200aK ( 1 , 1 ) is a parameter to be determined ., This well-established kernel 21 , 22 can be derived from Brownian diffusion of aggregates with Stokes friction , a fusion rate increasing in proportion to the sum of the aggregates radii , and aggregate packing such that size ( volume ) is proportional to radius cubed ., In this manner , the effect of spatial diffusion on fusion rate is incorporated into the model , without explicitly simulating spatial diffusion 9 ., We introduce a visibility threshold ν below which aggregates cannot be detected by wide-field fluorescence imaging ( Figure S3A ) ., A visible nucleation event occurs when two nondetectable aggregates fuse , forming a detectable one ., Generation and fusion of aggregates within compartments were simulated with a stochastic aggregation algorithm 23 , which in turn was embedded within another algorithm that implemented random aggregate segregation among compartments at division ( Text S1 ) ., The testable predictions of our model are, ( i ) large aggregates are rare , while small ones are more abundant;, ( ii ) an increase in the number of aggregates at cell birth gives rise to a decrease in aggregate nucleation and, ( iii ) to an increase in fusion;, ( iv ) at cell division , the pattern of aggregate segregation into the daughter cells is between a completely symmetric and a random one; and, ( v ) aggregate fusion increases their segregation asymmetry at cell division and promotes the birth of aggregate-free cells ., These model predictions are general features of the model behavior and are not dependent on specific parameter values ., We will now compare predictions i–iv with our experimental results ., Prediction v will be tested in the response-to-stress extension of the model described below ., The experimentally measured size distribution of aggregates shows that small aggregates are found more frequently than large ones ( Figure 2B ) , confirming prediction i ., Whereas the experimentally measured number of fusion events increases with the total number of aggregates ( Figure 2C ) , the number of nucleation events shows the opposite trend ( Figure 2D ) , confirming predictions ii and iii ., The model therefore shows that in the presence of a high number of visible aggregates , an invisible aggregate is increasingly likely to fuse with a visible aggregate rather than fusing with another invisible aggregate to create a visible aggregate , which is observed as nucleation ., Parameter values were then fitted ( Figure S3A ) to obtain quantitative as well as qualitative consistency for these three predictions ( Text S1 ) ., The parameter values were additionally corroborated by theoretical arguments ( Text S1 ) ., The parameterized model predicts a pattern of aggregate segregation at cell division by aggregate number that is between completely symmetric segregation , where the difference in the aggregate number is the minimal possible , and fully random segregation , where each aggregate can segregate to either of the two newborn cells , corresponding to the model without compartmentalization ., The experimentally measured segregation pattern closely matches that predicted by the model , thereby confirming prediction iv ( Figure 2E ) ., Thus , our results do not support a biased segregation ( by compartment ) of aggregates in fission yeast ., If the average aggregate amount formed per cell cycle is substantially less than the amount which affects cell growth ( death threshold “d” , 5 a . u . ) 6 , symmetric segregation at division is sufficient to dilute the aggregates and allow survival , but if the average amount is more than what would be required to kill both daughter cells , asymmetric segregation may be necessary for one of the daughter cells to survive ., We tested the effect of a range of aggregate levels on segregation dynamics and on cell viability ., To increase the aggregate amount , we used stress conditions such as oxidative stress ( H2O2 ) and transient or continuous heat stress ( T\u200a=\u200a40°C ) ( Figure 3A ) ., Both types of stress increased the number of aggregate nucleation and fusion events ( Figure 3B ) ., As in the control situation , aggregate movement after heat stress was consistent with Stokes diffusion ( Figure S4A and S4B ) and 97 out of 103 aggregate contacts resulted in fusion ., During recovery from stress , aggregates did not co-localize significantly with actin structures or microtubules ( Figure S4C ) ., As under control conditions ( Figure S2J ) , nucleation and fusion of aggregates after stress occurred also in the absence of actin or microtubule structures ( for cells treated with Lat . B or MBC , 94/102 or 90/97 contacts resulted in fusion , respectively; Figure S4D ) ., Remarkably , fusion converted the aggregates into a single large one within the first few cell cycles after stress ( Figure 3A ) ., This single aggregate was asymmetrically segregated to one of the sister cells at division ( Figure 3A ) , while the other sister cell was born without aggregates ( segregation was not biased towards the old or the new cell pole; Figure S4E ) ., Cells with an aggregate amount greater than d typically died ( 28/49 cells ) , whereas their sisters survived ( 48/49 cells ) , indicating that the clearance of aggregates through asymmetric segregation is important for viability ., To address whether the aggregate number has an effect on the cell cycle 7 of cells born with similar aggregate amounts , we compared the division time of cells inheriting only one aggregate with that of cells inheriting two or more aggregates ( Figure S4F ) ., We observed no significant difference in the division time of cells containing one or more aggregates ( Figure S4F ) , which agrees with our previous observation that the total aggregate amount correlates more strongly with cell death than aggregate number 6 ., To test whether the transition to asymmetric segregation could be reproduced theoretically , we introduced stress into the model , using the parameters fitted for control conditions ., We raised the aggregate generation rate r to obtain the experimentally observed aggregate nucleation upon heat stress ( Figure 3B ) in one simulated cell cycle , and then returned r to the control value and simulated for another cycle before the first cell division ( r values are shown in Figure S3A ) , to account for the duration of the experimental stress recovery ., The experimentally observed size distributions ( Figure S4G ) , dependence of fusion on the number of aggregates ( Figure S4H ) , and aggregate segregation patterns ( Figure S4I ) were consistent with the model including stress , indicating that the model is robust ., The model shows a 10-fold increase in the number of fusion events compared to the control situation , which is explained by the increased aggregate number ( Figure S4H ) ., Fusion causes a shift toward large aggregate sizes after stress , and faster recovery to the control size distribution for small aggregate sizes at division 2 , in both model predictions and experimental results ( Figure S4G ) ., Thus , the stochastic aggregation model is consistent with the observed aggregate behavior after stress ., To understand which segregation modes maximize daughter cell survival for a given total aggregate amount , we model the effect of the segregation asymmetry on cell survival by assuming that , as observed experimentally 6 , a cell dies if it has a total aggregate amount at birth above the death threshold d ., This leads to three distinct optimal segregation regimes that maximize the number of surviving cells: ( 1 ) any segregation asymmetry when the total aggregate amount at division is below d , ( 2 ) low segregation asymmetry when the amount is between d and 2d , and ( 3 ) high asymmetry when the amount is above 2d ( Figure 3C , scheme and corresponding gray regions in graph ) ., The model predicts that fusion facilitates asymmetric segregation in response to different levels of stress , where high asymmetry is optimal ( Figure 4C , filled circles ) ., This behavior was also observed experimentally for a range of stresses ( Figure 4C , filled squares ) ., We observed that in divisions 2 and 3 after stress , the percentage of cells born without aggregates was higher for stress conditions that originated in a higher aggregate amount ( Figure S4J ) ., This phenomenon can be explained by the higher number of fusion events observed for high stress levels ( e . g . , heat stress as opposed to oxidative stress; Figure 3B ) , which can result in the faster generation of a single large aggregate ., Once large aggregates are formed , nucleation of aggregates decreases in favor of the growth of the large aggregates: as observed for unstressed cells ( Figure S4C ) , the nonvisible aggregates have a higher probability to fuse with large preexisting aggregates ., We conclude that in response to increased aggregate amount , an increase in fusion leads to fewer aggregates and thus more asymmetric segregation , which promotes the formation of aggregate-free cells ., The model predicts that reducing fusion decreases segregation asymmetry ( Figure 3C , empty circles ) ., To test the prediction , we needed to identify a molecular factor that would reduce fusion ., Small heat shock proteins are a special class of chaperones , which bind and sequester misfolded proteins 24 ., The fission yeast small heat-shock protein 16 ( Hsp16 ) was described to co-aggregate with misfolded proteins during stress 25; therefore , we hypothesized that Hsp16 has a role in the fusion of aggregated proteins in vivo ., Indeed , we observed that when we deleted Hsp16 , the number of aggregate contacts resulting in fusion decreased ( Figure 4A and 4E ) and aggregate fusion per cell cycle also decreased ( Figure 4B ) , which correlated with an increase in the number of cells containing aggregates in the population ( Figure S4M ) ., Aggregate nucleation ( Figure 4C ) and fission ( Figure 4D ) per cell cycle was not significantly altered in the absence of Hsp16 ., The total amount of aggregates was unaffected by the deletion of Hsp16 ( Figure S4L ) , which argues against the possibility that in the absence of Hsp16 there are generally more damaged proteins ., Thus , Hsp16 is primarily an aggregate fusion factor ., The decrease in fusion efficiency was specific to Hsp16 deletion , as deleting Hsp40 or Hsp70 , molecular chaperones that participate in protein disaggregation 26 , did not interfere with fusion or fission significantly ( Figure 4A , 4B , and 4D ) ., Contrary to Hsp16 deletion , deleting Hsp40 or Hsp70 caused an increase in aggregate nucleation ( Figure 4C ) and total amount per cell ( Figure S4L ) , whereas an increase in total aggregate number per cell was observed in all three deletions ( Figure S4M ) ., Taken together , these results suggest that the increase in the number of aggregates in Δhsp16 cells compared to the wild type is a consequence of reduced fusion ., We proceeded to test the prediction of the model in the strain deleted for Hsp16 ., We observed that decrease in fusion resulted in a decrease in the segregation asymmetry of aggregate amount ( Figure 4F and 4G ) , as expected from the model where , as a qualitative approximation , aggregates were not allowed to fuse after stress ( Figure 3C ) ., The model including aggregate fusion also precisely predicted the fraction of cells born without stress-induced aggregates at each division after stress in the wild type ( Figure 4H ) ., Remarkably , in spite of the fact that 10 aggregates on average were formed after stress ( Figure 3B ) , by the second and third division , ∼15% and 50% of the cells were born clean of aggregates , respectively ( Figure 4H ) ., Importantly , when the aggregates were not allowed to fuse in the model including stress , the fraction of cells born free of aggregates was halved ( Figure 4H ) ., Parameter sensitivity analysis shows that the fraction of cells born clean after stress is highly sensitive to the strength of the fusion process during recovery ( k ) , and is also decreased by a faster generation of aggregates ( r ) during stress ( Figure S3B ) , as would be intuitively expected ., The average number of aggregates per cell immediately after stress is increased by generation during stress ( r ) and decreased by fusion combining aggregates together ( k ) ( Figure S3C ) ., Both the fraction of cells born clean and the number of aggregates after stress are insensitive to the generation rate and fusion rate before stress was applied , as well as to the number of aggregates with which the first cells in the simulations were initialized ., As predicted by the model without fusion , we observed in the experiments a ∼50% decrease in the fraction of aggregate-free cells in Δhsp16 compared to wild-type cells ( Figure 4H ) , which correlated with an increase in the fraction of dead cells after heat stress ( 17±2% in Δhsp16 versus 9±1% in wild type , mean ± SEM , n\u200a=\u200a123 and 140 cells , respectively ) ., We conclude that fusion facilitates asymmetric damage segregation and accelerates the generation of cells clean of stress-induced aggregates , as stated in prediction v described above ., We have demonstrated that the symmetrically dividing cells of S . pombe undergo a transition to highly asymmetric segregation of protein aggregates , which is facilitated by aggregate fusion ., As we observed that aggregates occur in the absence of Hsp104 , both under favorable and stress conditions ( Figure S1F–H ) , fusion is likely occurring for aggregated proteins in general , and is not specific of Hsp104-associated aggregates ., In response to increased aggregate nucleation , two distinct mechanisms—stochastic movement and chaperone-mediated fusion of aggregates—combine to generate a single large unit of damage , which has to be segregated asymmetrically , resulting in the birth of a damage-free cell ( Figure 4I ) ., Creation of a single large unit requires extensive fusion , which is promoted by an increase in the number of aggregates and a higher Hsp16 chaperone level ( Figure S1F ) , as a consequence of heat stress 27 ., It is possible that fusion has a cytoprotective effect 28 by merging the aggregates in a single unit , such as during the first two cell cycles following stress recovery , before a clean cell is born ., However , irrespective of the number of aggregates , if the cell is born with a total aggregate amount above the death threshold , this cell is likely to die 6 ., Due to the geometry of cell division in S . pombe , the asymmetry in segregation can only be established at the second division after stress ., This becomes clear when considering the extreme scenario where all aggregates fuse into a unit in both cell compartments within the first cell cycle after stress ., In this case , each sibling receives one large aggregate after the first division ., In the second division , 50% of cells inherit this single aggregate , while their sisters are born clean ., This , however , occurred only in a smaller percentage of the cells ., The cells took , on average , one extra cell cycle to generate an aggregate-free cell , at the third division ., This delay may be because the frequency of aggregate fusion events decreases over the first and second division , as the total number of aggregates is reduced ., It is likely that the activated stress response promotes survival of cells with a high total aggregate amount for more than two divisions after stress , to ensure survival until cells with nonlethal amounts of aggregates are generated ., How do protein aggregate dynamics and segregation in S . pombe compare to those in other organisms ?, In S . cerevisiae and in kidney and ovary cells , aggregates are anchored to or transported by the cytoskeleton 8 , 10 , 29 , 30 and localize to functionally distinct protein quality control compartments 11 , 13 , 31 , 32 , which may also be involved in the asymmetric segregation of aggregates 11 , 12 ., In budding yeast , the sorting of misfolded proteins into these compartments is dependent on a small heat-shock protein , Hsp42 12 , 14 , 31 ., Hsp42 carries an N-terminal extension , which may promote anchoring of aggregates to the cytoskeleton 14 or membrane compartments 11 , thus ensuring their selective retention in the mother cell ., Small heat-shock proteins in S . pombe , however , lack this N-terminal domain and do not interact with the cytoskeleton or organelles , which agrees with our observation that aggregate movement is random ., The specific role of Hsp16 in aggregate fusion and cell survival after stress 6 suggests that fusion is a regulated process that is essential for the cell , as opposed to the consequence of an unregulated aggregate seeding process , observed in cells lacking Hsp40 or Hsp70 ., Taken together , these findings suggest that an organisms mode of cell division—morphologically symmetric versus asymmetric—generates specific evolutionary constraints , which may be counterbalanced by the invention or refinement of molecular pathways for concentrating and inheriting protein aggregates ., While in S . cerevisiae 11–13 and mammalian neurons 29 aggregates associate with subcellular structures , in E . coli and neuroblast cells aggregates localize to nucleoid-free 33 or organelle-free cytoplasmic regions 34 , respectively ., A common aspect of aggregate behavior in all these different systems is movement—either by diffusion 9 , 28 , 31 , 35 or active transport 8 , 29—which may allow for contacts and fusion between aggregates to occur ., Therefore , fusion might be a conserved mechanism that contributes to asymmetric segregation of aggregates ., Fusing a number of molecules/components in a cell represents an opportunity to segregate asymmetrically ., In mathematical terms , fusion increases the difference between the number of aggregates inherited by daughter cells at segregation ., While low numbers of a component that is randomly segregated at division assures a higher variability in individual cells in the population , the formation of a unitary component assures a complete asymmetry in segregation that might be important when minimizing damage or maximizing resources ., Fusion might also be a mechanism to establish asymmetry in the localization of aggregated functional molecules within the cell 36 , 37 , as an increase in the size of the aggregate will lower its diffusion or cause it to be physically trapped between large organelles ., The concept of fusion as a mechanism to achieve asymmetry may extend to other phase-partitioned molecules , such as prions 38 , metabolic enzymes 39 , 40 , or RNA granules 41 ., In general , fusion of cellular factors may represent a general mechanism to achieve asymmetric localization and segregation at cell division ., Cells were grown as described before 42 ., For imaging , cells were transferred to a MatTek dish ( MatTek , Ashland , USA ) and imaged in liquid media ( YE5 or EMM ) or covered with a solid agarose pad ( YE5-4% Agarose , SeaKem , Hessisch Oldendorf , Germany ) at 30°C ., For stress resistance , assays cells were treated with water , as a control , or oxidative stress with 1 mM H2O2 ( Sigma-Aldrich , Hannover , Germany ) followed by growth at T\u200a=\u200a30°C ( 70% of cells undergo mitosis , n\u200a=\u200a30 ) , heat stress of 40°C for 30 min followed by growth at T\u200a=\u200a30°C ( 67% of cells undergo mitosis , n\u200a=\u200a30 ) , or continuous heat ( stress of 40°C for 1 h followed by growth at 37°C , 53% of cells undergo mitosis , n\u200a=\u200a30 ) ., Under favorable conditions , 99 . 7% of cell complete mitosis successfully 6 ., Strains were constructed using a PCR-based gene targeting technique 43 , where the label was inserted in the C-terminal region of the target gene in the native genomic locus , keeping it under the control of native expression regulators ., Cells were imaged in a DeltaVision core microscope , with a motorized XYZ stage ( AppliedPrecision , USA ) ., An Olympus UPlanSApo 100× 1 . 4 NA Oil ( R . I . 1 . 516 ) immersion objective was used ( Olympus , Tokyo , Japan ) ., The illumination was provided by a LED ( transmitted light ) and Lumicore solid-state illuminator ( SSI-Lumencore , fluorescence ) , and the images were acquired with a Cool Snap HQ2 camera ( Photometrics , Tucson , AZ , USA ) and the SoftWorx software ( AppliedPrecision , USA ) , using 2×2 pixel binning , to minimize light exposure ( pixel size\u200a=\u200a0 . 1288 µm ) ., For long-term time lapse imaging , Z-stacks for 6–12 nonoverlapping imaging areas in the sample were acquired every 10 min ( total time\u200a=\u200a20 h ) and in short time-lapses every minute ( total time\u200a=\u200a1–3 h ) ., For single Z-stacks cells were imaged with exposure\u200a=\u200a0 . 05–0 . 20 s , 2%–50% transmission , depending on the protein and fluorescent label ., As a control for photo-toxicity , cell cycle duration and protein aggregate number were measured and found similar in the presence and absence of continuous illumination ., To quantify the total number of aggregates and to visualize small fast-moving aggregates and fusion events , we used highly inclined and laminated optical sheet microscopy ( HILO ) 44 with a high laser power , on a total internal reflection fluorescence ( TIRF ) microscopy setup ., Whereas TIRF illuminates up to 200 nm from the surface of the coverslip , HILO allowed us to image deeper in the cytoplasm , up to a depth of about 1 . 5 µm 44 ., An Olympus-IX71 ( Olympus , Tokyo , Japan ) inverted microscope was used ., Incidence angle of a DPSS 491 nm laser was changed to allow for excitation of the fluorophores in the sample up to 1 µm deep ( 1/3 of the cell volume was illuminated ) ., Cells close to the glass surface of a MatTek dish ( MatTek , Ashland , USA ) were imaged , one at a time , with continuous excitation and laser power of 80% for fast imaging ( 200 frames/s , duration 20 s ) and 10% for slow imaging ( 10 frames/s ) ., An Olympus PlanApo 100×1 . 45 NA TIRFM objective ( Olympus , Tokyo , Japan ) and an Andor iXon EM+ DU-897 BV EMCCD ( Andor , Belfast , UK ) camera were used ., Images were acquired while incubating the cells in EMM at 25°C , in order to decrease autofluorescence ., Protein aggregates and subcellular structures were imaged simultaneously to test for co-localization and coordinated movement using bright field , a complementary set of fluorescent proteins ( GFP , RFP , or mCherry ) and dyes ( Phalloidin and FM-464 ) ., We labeled protein aggregates indirectly with Hsp104-GFP or Hsp104-mCherry ., Bright field was used to directly visualize cell poles and the division plane ., Actin was indirectly labeled in vivo by expressing a calmodulin domain coupled to an N-terminal GFP ( GFP-CHD ) and directly labeled ex vivo in formaldehyde fixed cells with 2 . 5 µM phalloidin ., Microtubules and the microtubule nucleating center ( the spindle-pole body , SPB ) were directly labeled using two structural components , atb2-mCherry and sid4-RFP , respectively ., The nuclear membrane was directly labeled with bqt4-mCherry , an integral nuclear membrane protein ., Incubating cells in 1 mM FM-464 for 10 h resulted in the direct labeling of several lipid structures 45 ( cellular membrane , vacuoles , endosomes , and the Golgi complex ) .

Introduction, Results, Discussion, Materials and Methods

Asymmetric segregation of damaged proteins at cell division generates a cell that retains damage and a clean cell that supports population survival ., In cells that divide asymmetrically , such as Saccharomyces cerevisiae , segregation of damaged proteins is achieved by retention and active transport ., We have previously shown that in the symmetrically dividing Schizosaccharomyces pombe there is a transition between symmetric and asymmetric segregation of damaged proteins ., Yet how this transition and generation of damage-free cells are achieved remained unknown ., Here , by combining in vivo imaging of Hsp104-associated aggregates , a form of damage , with mathematical modeling , we find that fusion of protein aggregates facilitates asymmetric segregation ., Our model predicts that , after stress , the increased number of aggregates fuse into a single large unit , which is inherited asymmetrically by one daughter cell , whereas the other one is born clean ., We experimentally confirmed that fusion increases segregation asymmetry , for a range of stresses , and identified Hsp16 as a fusion factor ., Our work shows that fusion of protein aggregates promotes the formation of damage-free cells ., Fusion of cellular factors may represent a general mechanism for their asymmetric segregation at division .

During their lifetime , cells accumulate damage that is inherited by the daughter cells when the mother cell divides ., The amount of inherited damage determines how long the daughter cell will live and how fast it will age ., We have discovered fusion of protein aggregates as a new strategy that cells use to apportion damage asymmetrically during division ., By combining live-cell imaging with a mathematical model , we show that fission yeast cells divide the damage equally between the two daughter cells , but only as long as the amount of damage is low and harmless ., However , when the cells are stressed and the damage accumulates to higher levels , the aggregated proteins fuse into a single clump , which is then inherited by one daughter cell , while the other cell is born clean ., This form of damage control may be a universal survival strategy for a range of cell types , including stem cells , germ cells , and cancer cells .

computer and information sciences, mathematical computing, model organisms, mathematics, cell biology, theoretical biology, biology and life sciences, computing methods, physical sciences, yeast and fungal models, molecular cell biology, biophysics, research and analysis methods

Fusion of harmful aggregated proteins into larger clumps increases the asymmetry of segregation of damage at cell division, favoring the production of rejuvenated cells.

journal.ppat.1005043

Virulence of Group A Streptococci Is Enhanced by Human Complement Inhibitors

Streptococcus pyogenes , also known as Group A Streptococcus ( GAS ) is an important human bacterial pathogen that is widespread and responsible for more than 700 million infections globally each year 1 ., GAS causes a spectrum of diseases , ranging from milder pharyngitis and superficial skin infections to more severe illnesses that include acute rheumatic fever ( that may be complicated by rheumatic heart disease ) , post-streptococcal glomerulonephritis and invasive infections ., The latter may be accompanied by life-threatening sepsis , streptococcal toxic shock syndrome and/or necrotizing fasciitis 2 , 3 ., The burden , worldwide , of invasive GAS infection is high , with at least 663 , 000 new cases and 163 , 000 deaths each year ( 25% mortality ) ., In the absence of effective vaccines against GAS , the outcome of streptococcal infection is determined by the status of the host’s immune system 4 ., A key first line of defense against bacterial pathogens involves the complement system , which comprises over 30 soluble proteins and several membrane-associated complement receptors and inhibitors ., Complement can be activated on ‘non-self’ cells , such as bacteria , by one or more of three different activation pathways ., The classical pathway is initiated by binding of antibodies to the microbial surface , the lectin pathway is triggered by binding of one or more lectins to specific carbohydrate structures and the alternative pathway is activated by a ‘tickover’ mechanism followed by amplification through a positive feedback loop 5 ., All three pathways converge at the level of C3 deposition; formation of C3 convertases generates chemoattractant anaphylatoxins and further amplifies deposition of C3 fragments on microbes , which opsonizes the microbial target for efficient phagocytosis ., Formation of the lytic membrane attack complex ( MAC ) may result in direct lysis of gram-negative bacteria ., Gram-positive bacteria such as GAS are resistant to MAC-mediated lysis , but are eliminated by phagocytes following opsonization with C3b and iC3b ., The complement cascade is tightly regulated by surface bound and soluble inhibitors ( or regulators ) ; C4b-binding protein ( C4BP ) and Factor H ( FH ) are two examples of the latter which serve to prevent damage to host tissues ., GAS has evolved several virulence factors , which allow the pathogen to colonize its human host , escape the immune system and successfully establish infection 6 , 7 ., GAS infection is human-specific; in the context of its interaction with the innate immune system , GAS interacts with several human proteins , including fibrinogen , albumin and the Fc portion of IgG ., Fibrinogen binding to GAS reduces opsonization , while IgG Fc binding to GAS may prevent recognition by phagocyte Fc receptors 8 , 9 ., GAS surface molecules that are important for these interactions include the M protein and other members of the M protein family 10 ., M protein family members share high DNA sequence identity ( >70% ) , but are encoded by different genes ( enn , mrp , fcrA , arp , protH and others; reviewed in 11 ) ., Certain M or M-like proteins mediate GAS binding of human C4BP and/or human FH 12 , 13 ., A particularly virulent GAS strain called AP1 binds human C4BP and FH through protein H , which is a member of M protein family 14–16 ., Studies in vitro have shown that inhibition of complement activation through surface bound human FH and C4BP enables GAS to evade opsonization 17 ., However , in vivo evidence implicating C4BP and Factor H in GAS infections has been lacking because a suitable animal model has not been tested ., Several GAS bind only human , but not mouse C4BP and/or FH 18 ., Thus , wild-type mouse models are not suitable to evaluate the roles of these human complement inhibitors in GAS infection ., To circumvent these limitations in vivo 19 , we have employed novel transgenic mice that express human C4BP and FH ., Complement activation plays a key role in clearance of certain GAS by phagocytes 20 ., The binding of serum complement inhibitors to bacterial surfaces regulates complement activation ., Certain GAS bind human C4BP ( hu-C4BP ) and human FH ( hu-FH ) exclusively , but not the corresponding mouse complement inhibitors ., Therefore , we hypothesized that mice that express these human complement inhibitors would manifest increased severity of infection with GAS compared to wild type mice ., The α-chain of hu-C4BP was cloned into a pCAGS vector ( Fig 1A ) , which was then used to generate hu-C4BP transgenic animals in a BALB/c background ., Using a similar approach , previously we had generated hu-FH tg mice in a BALB/c background , ( Fig 1A and 21 ) ., Hu-C4BPxFH tg animals were generated by crossing hu-C4BP and hu-FH single transgenic animals ., These mice also express endogenous mouse FH and C4BP ., Genotyping confirmed the presence of the human genes in the respective tg animals ( Fig 1B; C4BP , upper panel and FH , lower panel ) ., Western blot analysis confirmed expression of the human proteins in the corresponding strains of mice ( Fig 1C; C4BP , upper panel and FH , lower panel ) ., As expected , hu-C4BP protein in tg mouse serum displayed a lower molecular mass compared to C4BP in normal human serum ( NHS ) because these mice lack the human C4BP β-chain gene ., The hu-C4BP molecule lacking the β-chain ( as expressed by our tg animals ) is fully functional as a complement inhibitor ( see below; 22 ) ., Human FH expressed by tg mice migrated in a manner similar to FH present in NHS on SDS-PAGE ., ELISA measurements of both human inhibitors in mouse serum with antisera specific for human FH and C4BP revealed levels that were comparable to those in NHS ( Fig 1D; C4BP , upper panel and FH , lower panel ) ., To ensure that activation of the mouse complement system in hu-C4BPxFH tg serum was relatively unimpaired on a complement activator surface , we compared mouse C3 deposition on zymosan particles ( zymosan is an activator of the alternative pathway of complement 23 ) using BALB/c and hu-C4BPxFH tg serum ., Both sera at concentrations of 20% deposited similar amounts of mouse C3 on zymosan , indicating that the complement system in ‘double’ transgenic mouse serum was not unduly inhibited by concomitantly expressed human complement inhibitors ( Fig 1E ) ., Experiments using 50% and 100% serum concentrations also did not show any differences between wt and tg sera ., To exclude major defects in the major innate immune pathways in the tg animals , we compared the ability of wt and C4BPxFH tg macrophages to respond to infection by culturing peritoneal macrophages with several different TLR and cGAS stimulating ligands including LPS ( TLR4 ligand ) , Pam2CSK4 ( TLR2 ligand ) , cytosolic dsDNA ( lipofectamine + dAdT , STING ligand ) , Sendai virus ( RIG-I ligand ) , live Gram-positive ( GAS AP1 ) and Gram-negative bacteria ( Neisseria gonorrhoeae; N . G . ) ., We collected supernatants after 18h and measured IL-6 secretion to assess NF-κB activation and RANTES ( an IFN-stimulated gene ) secretion to assess TRIF/STING activation ., Levels of IL-6 and RANTES were similar in all tested animals ( S1 Fig ) confirming that expression of the human tg proteins did not affect innate immune signaling networks for cytokine synthesis ., Taken together , expression of hu-C4BP and hu-FH in tg mice does not result in any apparent immune defects ., Evading complement attack through binding of host inhibitors to prevent opsonization can be an early and crucial step in the pathogenesis of GAS ( reviewed in 6 ) ., Activation of the complement system marks the pathogen for removal ., Certain GAS bind hu-C4BP and hu-FH but not the mouse counterparts ( Fig 2A and 2B ) ., Wild-type mouse serum complement is activated on GAS strain AP1 and results in C3 fragment ( C3b/iC3b ) deposition on the bacterial surface in a dose-dependent manner ( Fig 2C ) ., GAS strain AP1 binds hu-C4BP and hu-FH to its surface via protein H , a member of the M-protein family 12 , 15 ., Consistent with prior data , bacteria incubated in sera from both hu-C4BP and hu-C4BPxFH tg animals bound hu-C4BP in a dose dependent manner ( Fig 2D ) ., Similarly , we detected surface bound hu-FH on bacteria incubated in hu-FH and hu-C4BPxFH tg sera ( Fig 2E ) ., As expected , neither hu-C4BP nor hu-FH were detected on GAS incubated in wild type BALB/c serum ( Fig 2D and 2E; blue line ) ., Consistent with the ability of hu-C4BP and hu-FH to inhibit mouse complement , bacteria incubated in hu-C4BP , hu-FH or hu-C4BPxFH tg mouse sera showed significantly reduced C3 fragment deposition compared to wt BALB/c serum at serum concentrations ≥5% ( Fig 2F ) ., These results provide evidence in vitro of the importance of the binding of soluble human complement inhibitors to limit C3 deposition and opsonization ., The data above demonstrates that hu-C4BP and hu-FH limit C3 deposition on GAS strain AP1 ., To assess the impact of these two human complement inhibitors on phagocytosis , we infected mouse bone marrow derived macrophages in vitro with GAS strain AP1 in the presence of mouse sera with and without different human complement inhibitors ., The presence of hu-C4BP or hu-FH decreased phagocytosis by more than 65% ., Both inhibitors together reduced bacterial uptake by 75% compared to wild type mouse serum lacking human complement inhibitors ( Fig 3A ) ., To determine whether the presence of hu-C4BP and hu-FH affected GAS opsonophagocytosis in vivo , we infected wt and hu-C4BPxFH tg mice with strain AP1 i . p . and harvested peritoneal cells 2 hours post-infection ., Using flow cytometry we identified the proportion of neutrophils in peritoneal exudate cells ( S2 Fig shows the gating strategy ) ., We found that in wt BALB/c animals infected with GAS strain AP1 , more than 55% of all cells obtained were neutrophils , while significantly fewer neutrophils were recruited in hu-C4BPxFH tg animals during infection ( S3A Fig ) ., As a control we infected wt and hu-C4BPxFH tg mice infected with GAS mutant strain BM27 . 6 that is unable to bind either hu-C4BP or hu-FH ( Table 1 ) ., Strain BM27 . 6 recruited similar amounts of neutrophils in both types of animals ( S3B Fig ) ., Notably , AP1 uptake by neutrophils from BALB/c mice was significantly higher than that seen in hu-C4BPxFH tg mice ( S3C Fig ) while BM27 . 6 uptake by neutrophils was similar in BALB/c and hu-C4BPxFH tg mice ( S3D Fig ) ., We calculated a phagocytic index , which multiplies the proportion of neutrophils recruited to the peritoneum times the percent of neutrophils that ingest bacteria ., The phagocytic index of AP1 infected BALB/c wild type mice was 2-fold higher than the index in hu-C4BPxFH tg animals , indicating that binding of the complement inhibitors influences the uptake of AP1 ( Fig 3B ) ., The phagocytic indices of the two mouse strains that were infected with BM27 . 6 were similar ( Fig 3C ) , consistent with the inability of BM27 . 6 to bind to hu-FH or hu-C4BP ( S3 Fig and 15 ) ., Taken together , hu-C4BP and hu-FH expressed in mouse serum bind to strain AP1; decrease mouse C3 fragment deposition on the bacterial surface , which leads to diminished recruitment of phagocytes and reduced phagocytosis both in vitro and in vivo ., We next asked whether human complement inhibitors affected the survival of mice infected with GAS ., We infected single transgenic hu-C4BP , hu-FH mice and double tg hu-C4BPxFH tg mice intravenously ( i . v . ) and monitored animals for signs of disease for 8 days ., Based on in vitro data and the results of in vivo phagocytosis experiments , we hypothesized that double tg mice would be more susceptible to GAS infection with human complement inhibitor binding GAS strains ( hu-FH- and hu-C4BP-binding ) than single tg and normal control mice ., Indeed , we observed significant differences across single hu-tg and double-C4BPxFH tg mouse strains: C4BPxFH tg mice were the most susceptible to lethal GAS disease caused by hu-inhibitor binding strains ., At a dose of 5x106 CFU/mouse ( i . v . ) , both single C4BP tg and wt animals survived for 8 days and showed no signs of disease ( Fig 4A , blue and dotted black line , respectively ) ; hu-FH tg animals were more susceptible than wt or C4BP tg mice with a median survival of 6 . 5 days and a 50% fatality rate at 8 days ( Fig 4A , brown line ) ., At high-dose infection with strain AP1 GAS ( 5x107 CFU/mouse i . v . ) , ~83% of wt mice survived for 8 days compared to 20% survival of C4BP ( single ) tg mice ( Fig 4B ) ., Hu-C4BP tg mice showed a median survival of only 4 days ., Notably , BALB/c mice are relatively resistant to infections with GAS , necessitating high inocula to induce disease in wt 24 and single C4BP tg mice ., Transgenic animals that expressed both hu-FH and hu-C4BP were the most susceptible and all mice given the lower dose ( 5x106 CFU/mouse i . v ) , died within 6 days of inoculation ( Fig 4A , red line ) ., These data indicate that simultaneous inhibition of the classical and alternative pathways on the bacterial surface by hu-C4BP and hu-FH , respectively , greatly enhances GAS strain AP1 virulence and highlights the importance of regulation of complement activation by the bacteria ., Because hu-C4BP and hu-FH together displayed an additive effect in down-regulating complement in mice and were the most susceptible to lethal infection , we performed all subsequent experiments using hu-C4BPxFH tg mice ., We hypothesized that the increased lethality observed in the experiments above would not be unique to GAS strain AP1 ( binds hu-C4BP and hu-FH through protein H ) and tested additional GAS strains in our animal model ( listed in Table 1 ) ., We also examined whether the mortality-enhancing effects of the two human complement inhibitors were restricted only to GAS strains that bound hu-C4BP and hu-FH and determined the ability of these bacterial strains to survive infection ., We first infected BALB/c and hu-C4BPxFH tg animals with GAS strain BM27 . 6 , an isogenic mutant of AP1 , lacking both M protein and protein H , or with the wild-type strain AP3 strain ( Table 1 ) ., Neither BM27 . 6 nor AP3 bind hu-C4BP or hu-FH ( S4 Fig ) ., All 10 BALB/c and 9 out of 10 hu-C4BPxFH tg mice infected with 5x107 CFU BM27 . 6 survived ( Fig 4C ) ., Infections with either 1x107 or 5x108 CFU BM27 . 6 also revealed no difference in mortality between 10 BALB/c and 10 hu-C4BPxFH tg mice ( S5A and S5B Fig ) ., Although infections with GAS AP3 at an inoculum of 5x107 CFU/animal produced disease in both wt and hu-C4BPxFH tg mice , differences in survival across groups was not significant ( 67% mortality at day 8 in BALB/c and 100% in hu-C4BPxFH tg; Fig 4D ) ., Lower ( 2x107 ) and higher ( 1x108 ) inocula of AP3 also showed similar mortality in both groups ( S5C and S5D Fig ) ., By contrast , GAS AP18 , which like AP1 , binds both hu-C4BP and hu-FH , showed significantly increased virulence in hu-C4BPxFH tg compared to wt mice; like AP1 , all AP18-infected animals had died by 6 days , while all wt BALB/c control mice survived ( Fig 4E ) and did not show signs of morbidity ., Using 4 strains of GAS ( 2 that bind C4BP and FH and 2 that do not ) , these results indicate that GAS strains that bind these complement inhibitors show significantly increased virulence in mice that express human transgenes for both of the inhibitors , singly or in combination ., We next quantified the bacterial burden in the blood , kidneys , liver and spleen of hu-C4BPxFH and BALB/c mice infected with AP1 GAS ., Mice were sacrificed either at 2h or 24h post-infection and organs were homogenized and plated to enumerate bacterial CFUs ., As early as 2h , we noted significantly higher bacterial loads ( up to 1 . 5 log10 higher ) in blood , kidney and spleen of hu-C4BPxFH ( ‘double’ ) tg mice compared to wt BALB/c animals; liver samples from both strains showed similar bacterial loads ( CFUs ) ( Fig 5A ) ., At 24h post-infection , the liver , spleen and kidneys of hu-C4BPxFH tg mice showed significantly greater bacterial loads compared to loads in BALB/c mice ( Fig 5B ) ., In contrast to bacterial loads in the organs , bacterial loads from wt BALB/c blood were similar to levels in the blood of hu-C4BPxFH mice ( Fig 5B ) ., The greater bacterial burden in hu-C4BPxFH mice early in the course of infection points to altered innate immune defenses , which may have been the result of decreased opsonophagocytotic potential of GAS in tg animals ( Fig 3 and S3 Fig ) ., Taken together , GAS avoids early phagocytic clearance and establishes a more severe invasive infection in the transgenic animals ., Sepsis typically is associated with highly elevated levels of serum cytokines that lead to the systemic inflammatory response syndrome ( SIRS ) or cytokine storm , which often precedes multi-organ failure and eventually death 30 ., We analyzed serum cytokines during the course of infection ., Based on initial screening for 23 different serum cytokines in infected BALB/c and C4BP tg animals , we selected the following 11 cytokines for further analysis: IL-1β , IL-6 , IL-13 , G-CSF , IFN-γ , KC , MCP-1 , MIP-1α , MIP-1β , RANTES and TNF-α ., Using a multiplex analysis for these 11 cytokines , we analyzed serum samples from C4BPxFH tg and BALB/c wt mice 24h prior to , as well as 2h and 24h post infection ., At 2h after infection we identified significantly increased serum levels of MIP-1β , MCP-1 , TNF-α and MIP-1α in hu-C4BPxFH compared to wt mice ( Fig 6A–6D ) ., After 24h we observed a shift in the cytokine pattern with MIP-1β , MCP-1 , TNF-α , KC and MIP-1α becoming strongly down regulated ., In addition to KC and RANTES , which remained significantly higher in transgenic mice both at 2h and 24h post-infection ( Fig 6E and 6F ) , MIP-1β , MCP-1 , TNF-α , MIP-1α and KC peaked at 2h post-infection; levels of MIP-1β , MCP-1 , TNF-α , MIP-1α and KC were also significantly increased in hu-C4BPxFH tg compared to wt mice ( Fig 6A–6E ) at 2h post-infection ., G-CSF , IFN-γ and IL-6 , exhibited similar levels in BALB/c and C4BPxFH tg mice at 2h but were elevated significantly at 24h in hu-C4BPxFH tg compared to wt BALB/c mice ( Fig 6G , 6H and 6I ) ., GAS can bind both hu-C4BP and hu-FH like other pathogens , including Neisseria meningitidis and Neisseria gonorrhoeae 31 , 32 , Moraxella catarrhalis 33 , Candida albicans 34 and Haemophilus influenzae 35 , 36 ., Based on studies in vitro that have shown down-regulation of C3 fragment deposition mediated by binding of FH and/or C4BP 17 , 37 , 38 , it has been presumed that GAS may exploit these soluble inhibitors to escape complement attack in vivo , although direct evidence has been lacking ., Here we present evidence that bacteria-bound complement inhibitors increase virulence and accelerate fatal infections in vivo ., We have employed a novel mouse model that expresses hu-C4BP and/or hu-FH and have infected these animals with several GAS strains that differ in their ability to bind to these complement inhibitors ., Our data provide evidence to support a general mechanism whereby recruitment of C4BP and FH to the GAS surface protects bacteria from clearance by phagocytes in vivo and contributes to increased morbidity and mortality in the infected experimental host ., Mice are not natural hosts for GAS infection , but can be experimentally infected with relatively high bacterial inocula 39 ., We hypothesized that a GAS strain such as AP1 , which binds human complement inhibitors via surface protein H , an M-like protein 14 , 15 , would show enhanced virulence in mice that expressed hu-C4BP and hu-FH ., Indeed , the ‘double’ tg animals sustained higher bacterial burdens , displayed symptoms of bacterial sepsis and died more quickly than wt animals ., Of note , the inoculum required to induce a lethal infection in the ‘double’ tg mice was reduced by more than 1 log10 compared to the inoculum required to kill wild-type animals ., A second GAS strain ( AP18 ) with similar hu-C4BP and hu-FH binding capacity as AP1 yielded similar survival results as AP1 ., In this case AP18 bind hu-C4BP and hu-FH directly via surface M protein 27 , 29 ., As a result , hu-C4BP and hu-FH binding GAS strains produced significantly more disease in hu-C4BPxFH tg animals than in wt mice ., As ‘negative’ controls , we used GAS strains that were unable to bind hu-C4BP and hu-FH ., We showed reduced mortality even at high inocula in the ‘double’ tg mice when compared to hu-C4BP and hu-FH-binding GAS strains in this model ., Furthermore , we did not detect any differences in survival between wild type and hu-C4BPxFH tg animals that were challenged with strains unable to bind to these inhibitors ( strain AP3 and the isogenic mutant derived from AP1 , BM27 . 6 ) ., Taken together , these data strongly suggest that complement inhibitors exacerbate disease by binding GAS , but do not influence the course of GAS infection if the bacteria cannot recruit C4BP or FH to their surface ., Increased mortality of the double tg mice that were challenged with hu-C4BP/hu-FH-binding strains , AP1 and AP18 , was not attributed to generalized defects in the immune systems caused by introduction of the human complement inhibitor transgenes for the following reasons ., First , analysis of innate immune ligand-dependent cytokine release from peritoneal exudate cells ( PECs ) did not demonstrate differences between tg and wild type mice ., Second , complement deposition on zymosan that resulted from incubation of zymosan with tg or wt mouse sera did not demonstrate differences between the sera ., These findings suggest that our mouse model does not suffer from an apparent immune defect ., Second , and as discussed above , the double tg mice did not suffer increased mortality compared to wt mice when challenged with strains that did not bind to hu-C4BP and hu-FH ., We postulate that exacerbation of infection in tg mice infected with GAS strains that bound complement inhibitors , resulted in impaired opsonization with mouse C3 fragments ., We have shown previously that purified hu-C4BP injected in wt mice decreases complement activation via the classical pathway 22 , which confirms that hu-C4BP regulates mouse complement ., The β-chain of C4BP is not required for binding to GAS 26 and is not required for complement inhibition 40; therefore the hu-C4BP molecule that lacks the β-chain—the form expressed by our tg animals , was fully functional as a complement inhibitor on the surface of GAS ., 41 ., Similarly , hu-FH bound to bacteria also inhibits non-human complement via the alternative pathway42 , 43 ., Most pathogens activate complement via a combination of classical , lectin and alternative pathways ( reviewed in 44 ) ., Upon using hu-C4BPxFH double tg mice , we observed an additive effect of the two complement inhibitors , compared to using either hu-C4BP or hu-FH transgenic mice singly ., Infection of singly transfected mice resulted in increased mortality in the respective mice but time to death was accelerated in the double tg mice ., Opsonization with C3 fragments is required for efficient uptake by phagocytes ( reviewed in 20 ) ., Thus , inhibiting complement activation impairs opsonization , results in diminished phagocytic uptake and decreases killing of pathogens ., We showed that GAS strain AP1 recruited hu-C4BP and hu-FH to its surface , which reduced C3b/iC3b deposition on the bacterial surface and resulted in decreased phagocytosis of GAS both in vitro and in vivo ., We saw diminished recruitment of neutrophils by GAS inoculated into the peritoneal cavity of ‘double’ tg mice and decreased uptake of bacteria by neutrophils that had been recruited ., Diminished production of C3b results in decreased generation of both C5 convertase and C5a , a potent chemoattractant for neutrophils 45 ., Impaired clearance of hu-C4BP and hu-FH-binding GAS was also reflected by greater CFU recovered from blood and other organs ., Several of the cytokine levels that we measured were elevated in tg compared to wt mice , consistent with greater loads of organisms in tg mice 46 ., Cytokines generated early , may be important in controlling bacterial dissemination but excessive and persistent production may be detrimental 47 ., High levels of G-CSF in particular , generated within the first 24h have been reported to confer protection in mice infected with GAS 48 but in children , higher levels of pro-inflammatory cytokines generally , correlate with higher mortality from invasive GAS infections 49 ., Infection of hu-C4BPxFH tg animals with strain AP1 resulted in elevation of most cytokine levels early at 2 hours , compared to wt animals; G-CSF levels at two hours were not different in C4BPxFH tg vs . wt mice but increased markedly in double tg animals at 24h ., Cytokine levels , morbidity and fewer days to death , accompanied by increased bacterial burdens , were more pronounced in hu-C4BPxFH tg compared to wt mice ., We hypothesize that failure to opsonize GAS and consequent reduced phagocytosis results in uncontrolled replication of GAS , which kills the host ., A number of bacterial virulence factors are released , which lead to systemic toxicity , coagulopathy , hypotension , septicemia , tissue damage and finally multi organ failure 11 , 50 , 51 ., Our data differ from a previously published study that did not demonstrate accelerated mortality during acute GAS infection in C57BL/6 , mouse-FH KO , transgenic ( tg ) mice that expressed only chimeric human/mouse FH ( SCRs 6–8 were derived from human FH ) 28 ., Mortality was not affected despite evidence of binding of hu-SCR 6–8 to the M protein ( M5 ) of the infecting strain 28 ., This study used a C57BL/6 tg mouse model whose levels ( 200–210 μg/ml ) of chimeric FH had been reported earlier 52 to be similar to FH levels in wt C57BL/6 mice ., These FH levels were lower than those in our tg mice; 379 . 9 μg/ml in FH tg mice and 291 . 5 μg /ml in ‘double’ tg C4BPxFH ., These levels were similar to levels reported in human ( 320 ± 71 . 4 μg/ml in plasma taken from 358 individuals 53 ) ., The higher levels may have been important to display the completely virulent phenotype in mice ., Furthermore , chimeric FH , expressing hu-SCRs 6–8 28 , may also have undergone unique conformational changes , distinct from those that occur with native hu-FH 54 , which may be important in maintaining physiologic function ., Differences in mouse strains ( C57BL/6 mice were used in the chimeric FH study 28; we used BALB/c mice ) , bacterial strains and routes of inoculation all could have contributed to differences in our results compared to those of the previous study 28 ., In conclusion , we have demonstrated a detrimental influence of human complement inhibitors FH and C4BP in overcoming experimental GAS sepsis in vivo ., Our data suggest a pivotal role for complement inhibitors on GAS strains that bind these inhibitors to their surface ., Our novel hu-C4BPxFH tg animal infection model may prove invaluable in studies of GAS pathogenesis and in the development of vaccines and therapeutics that incorporate a ‘human’ context ., The following antibodies were used for ELISA measurements: 10 μg/ml rabbit anti hu-C4BP PK9008 , ( homemade , capture Ab ) ; 0 . 5 μg/ml mouse anti hu-C4BP MK104 , ( homemade , detection Ab ) ; 10 μg/ml mouse anti hu-fH MRC OX24 , ( homemade 55 , capture Ab ) ; 5 μg/ml sheep anti human-Factor H ( Abcam , ab8842; detection Ab ) ., C4BP and FH detection antibodies were secondarily detected using anti sheep IgG-HRP or anti mouse IgG-HRP ( DAKO , P0163 and P0260 ) ., For flow cytometry analysis , the following antibodies were used: mouse anti human-C4BP MK104 either unconjugated or conjugated to biotin; mouse anti human-Factor H MRC OX24 unconjugated or conjugated to biotin; rabbit anti mouse-C4BP ( homemade ) conjugated to Dylight 647; mouse monoclonal anti mouse-Factor H ( Hycult , HM1119 ) conjugated to biotin; goat anti mouse-C3c ( Nordic Immunology , GAM/C3c/7S ) ; anti mouse C3 FITC ( MP Biomedicals #0855500 ) anti mouse Ly-6G brilliant violet 421 ( BioLegend , #127627 ) ; anti mouse Ly-6C PerCP/Cy5 . 5 ( BioLegend , #128011 ) ; anti mouse CD11c ( BioLegend , #117317 ) ; anti mouse I-A/I-E brilliant violet 510 ( BioLegend , #107635 ) ; anti mouse CD64 APC ( BioLegend , #139305 ) ; anti mouse/human CD11b APC/Cy7 ( BioLegend , #101225 ) ., Unlabeled primary antibodies used for detection of the nominal targets in FACS were themselves bound and detected using donkey F ( ab’ ) 2-anti mouse IgG-PE ( Thermo , #31860 ) or donkey F ( ab’ ) 2-anti goat-IgG-PE ( eBioscience , #12-4012-87 ) ., Final reactions that measured biotin labeled antibody binding were disclosed with streptavidin-Dylight 650 ( Pierce , #84547 ) or streptavidin-PE ( eBioscience , #12-4317-87 ) ., For western blot analysis of human C4BP in mouse serum we used mouse anti hu-C4BP MK104 coupled to biotin detected by Dylight 649 Streptavidin ( BioLegend , 405224 ) ., Hu-FH in mouse serum was detected using goat anti human FH ( Calbiochem , #341276 ) and Alexa Fluor 647 donkey anti goat IgG ( Life Technologies , A21447 ) ., Western blots were read using Typhoon FLA 9500 ( GE Healthcare ) ., Streptococcus pyogenes AP1 ( strain 40/58 , serotype M1 ) , AP3 ( strain 4/55 , serotype M3 ) and AP18 ( strain 8/69 , serotype M18 ) were obtained from the WHO Collaborating Centre for Reference and Research on Streptococci , Prague , Czech Republic ., BM27 . 6 is an isogenic mutant of AP1 lacking protein H 56 ., Binding of human soluble complement inhibitors , C4BP and FH , to each strain is summarized in Table 1 ., Streptococcal strains were grown in Todd-Hewitt broth ( THB ) and Moraxella catarrhalis RH4 ( control strain ) in brain-heart infusion ( BHI ) broth overnight at 37°C and 5% CO2 without shaking ., Cultures were then diluted to OD600 = 0 . 1 in corresponding fresh medium and incubated again at 37°C and 5% CO2 without shaking , until exponential growth at OD600 = 0 . 3–0 . 4 was achieved ., Bacteria were harvested and washed with 1× PBS prior to use ., Genomic DNA from GAS AP1 , AP3 and AP18 strains was isolated using a DNeasy blood and tissue kit ( Qiagen ) according to manufacturers instructions ., The covRS operon was amplified ( for primers used see S1 Table ) by PCR and subsequently subjected to Sanger sequencing ., All animals were housed and bred under SPF conditions in the animal facility at the University of Massachusetts Medical School Worcester ( UMMS ) , USA ., Production of hu-FH transgenic mice has been described previously 21 ., To generate human C4BP transgenic mice , full-length cDNA encoding human C4BP ( 1 . 8 kbp ) was subcloned into the EcoRI site of the expression vector pCAGGS 57 ., A CMV enhancer and chicken β-actin promoter sequences are located upstream of the EcoRI site in pCAGGS and a rabbit β-globin polyA sequence is located downstream of the EcoRI site ., The resultant plasmid , pCAGGS-human C4BP , was digested with SalI and HindIII to isolate the transgenic cassette fragment that consisted of the CMV enhancer , the chicken β-actin promotor , the human C4BP cDNA and the rabbit β-globin poly ( A ) sequence ., The isolated 4 kb SalI and HindIII fragment was purified and microinjected into mouse embryos from BALB/c mice ., Mouse embryos were implanted into pseudo-pregnant female BALB/c mice ( Charles River Breeding Laboratories ) at the UMMS Transgenic Facility ., Human C4BP transgenic mice initially were identified by PCR analysis using genomic DNA prepared from mouse-tails ., A region inside human C4BP was amplified by PCR using primers C4BP-EcoRI and C4BP-NotI to yield a 383-bp product ( Fig 1B; for primer sequence see S1 Table ) ., Amplified products were resolved by electrophoresis on 2% TAE agarose gels and visualized with ethidium bromide staining under UV light ., Expression of human C4BP in sera of pups was detected by Western blotting using affinity purified rabbit anti-human C4BP ., FH and C4BP transgenic mice were bred together to create double transgenic mice ., To assess serum levels of hu-C4BP and hu-FH sandwich ELISAs ( see

Introduction, Results, Discussion, Materials and Methods

Streptococcus pyogenes , also known as Group A Streptococcus ( GAS ) , is an important human bacterial pathogen that can cause invasive infections ., Once it colonizes its exclusively human host , GAS needs to surmount numerous innate immune defense mechanisms , including opsonization by complement and consequent phagocytosis ., Several strains of GAS bind to human-specific complement inhibitors , C4b-binding protein ( C4BP ) and/or Factor H ( FH ) , to curtail complement C3 ( a critical opsonin ) deposition ., This results in diminished activation of phagocytes and clearance of GAS that may lead to the host being unable to limit the infection ., Herein we describe the course of GAS infection in three human complement inhibitor transgenic ( tg ) mouse models that examined each inhibitor ( human C4BP or FH ) alone , or the two inhibitors together ( C4BPxFH or ‘double’ tg ) ., GAS infection with strains that bound C4BP and FH resulted in enhanced mortality in each of the three transgenic mouse models compared to infection in wild type mice ., In addition , GAS manifested increased virulence in C4BPxFH mice: higher organism burdens and greater elevations of pro-inflammatory cytokines and they died earlier than single transgenic or wt controls ., The effects of hu-C4BP and hu-FH were specific for GAS strains that bound these inhibitors because strains that did not bind the inhibitors showed reduced virulence in the ‘double’ tg mice compared to strains that did bind; mortality was also similar in wild-type and C4BPxFH mice infected by non-binding GAS ., Our findings emphasize the importance of binding of complement inhibitors to GAS that results in impaired opsonization and phagocytic killing , which translates to enhanced virulence in a humanized whole animal model ., This novel hu-C4BPxFH tg model may prove invaluable in studies of GAS pathogenesis and for developing vaccines and therapeutics that rely on human complement activation for efficacy .

Streptococcus pyogenes is an important cause of human infections worldwide , ranging from mild and superficial disease to life-threatening invasive infections ., Development of new and efficient therapies for infections requires animal models that faithfully recapitulate infection in humans ., Humans are the only natural host of S . pyogenes; thus , infection in wild-type mice may not reflect infection in humans ., Mice that are humanized in ways that are relevant to the studied pathogen would better reproduce human infection ., Because S . pyogenes bind only human , but not mouse complement inhibitors , we used novel strains of humanized mice that produce two human complement inhibitory proteins which allowed us to analyze the impact of human-specific human complement inhibition on the severity of S . pyogenes infections in mice ., Here , we show that expression of human complement inhibitors significantly worsens the outcome of infection in humanized mice ., This animal model will permit studies of infection and disease and aid the development of novel therapies and vaccines against S . pyogenes infections , with emphasis on the human complement system .

journal.pcbi.1000400

The Voice of Bats: How Greater Mouse-eared Bats Recognize Individuals Based on Their Echolocation Calls

Voice is defined as the entirety of all acoustic signals produced by the vocal organs, of an organism and its ability to produce them ., Vocalizations are mostly used for, communication ., They can contain information about identity , gender , maturity ,, health , behavioural context , etc 1–3 ., Specific properties of, the sound production and articulation apparatus are responsible for the, individual-specific spectral properties of vocalizations ., The human voice , for, instance , reveals the identity of individuals and lately it has been shown that, other animals can also recognize individuals according to their social vocalizations, 4–10 ., Social vocalizations, constitute an important part of the vocal repertoire of bats ., These vocalizations, have been characterized for many species and contexts and were shown to contain, individual signatures 11–23 ., In addition to social, vocalizations , microchiropteran bats constantly emit echolocation calls and use the, returning echoes to perceive their surroundings 24 ., These echolocation, calls are tonal signals that exhibit a structured change in frequency over time that, is normally less variable than that of the social vocalizations ., The ability to, recognize individuals based on echolocation calls might explain many of the social, behaviours observed in bats e . g . , 16 ., Several studies tried to find, individual-specific cues in bat echolocation calls 2 , 25–28 ., Recently , the response of bats to the echolocation calls of different individuals, has been tested and the results suggested that they could recognize individuals, according to their echolocation calls 29 ., The echolocation calls of the greater mouse-eared bats ( Myotis, myotis ) used in this study are ∼3 ms long frequency-modulated ( FM ), down-sweeps ranging from ∼100 kHz to ∼30 kHz ., The exact, spectral-temporal structure of the calls changed depending on the task ., We, hypothesize that , despite this variability , the echolocation signals might contain, individual-specific characteristics , generated by the bats vocal, apparatus , which are sufficient for individual recognition ., We first tested whether, bats can distinguish between individuals according to their echolocation calls using, the most direct approach used until today: training greater mouse-eared bats to, classify echolocation calls of other individuals played back to them in a two, alternative forced choice ( 2-AFC ) experiment ., After showing that the bats can, clearly recognize their conspecifics , we used a statistical approach , new in this, field , to train statistical classifiers to reproduce the bats behaviour ,, namely to make similar correct and incorrect decisions as the bats ., Our approach, offers two main advantages in comparison to former unsuccessful attempts to, statistically identify individual bats according to their echolocation calls 30 ., First , our method is almost unlimited in the number of parameters that can be fed, into it ., This enabled us to use the raw representations of the calls and not to, limit ourselves to a set of parameters as was always the case before ., Second , we, used a large data set containing ca ., 800 calls per bat ., Such a large data set, enables us to create a good model of the individuals call despite its, large variability ., We used the statistical classifier as a model of the, bats underlying decision process to show how classification is, statistically possible and to understand how the bats might be able to recognize, other individuals ., All bats emitted calls typical for flying in confined spaces with a very, characteristic spectral-temporal structure ., Despite this repeating pattern , the, spectral content of the calls varied largely among individuals for both, behavioral and technical reasons ( see Materials, and Methods and Figure, 1 ) ., There was also some intra-individual variability of the sweep rate, ( Table 1 ) depicting, the differences in the time structure of the calls ., Finally , it is worth, emphasizing that the SNR of the calls varied dramatically ( Table 1 ) as a result of the, varying distance from the microphone ., The bats required 15–24 days before they were able to stably correctly, recognize the individuals in more than 75% of the trials ., The, learning curves ( Figure 2 ), fluctuated between days ., After training , all bats were able to recognize, S+ ( a single call of the bat they learned to recognize ) with much, higher accuracy than chance level ( Table 2 ) ., Bats were able to generalize from the learned task to recognize S+ or, avoid S− ( a single call of the bat that they learned to avoid ) when, presented with calls of new bats that were never heard during training ( Table 2 ) ., Most of the bats, showed both a preference for S+ and an avoidance of S− ., The, higher percentage of approaching S+ when presented with S0 ( a single, call of a bat that they did not encounter during training ) can be a result of, the fact that the S+ calls in these experiments were taken from the, training set and thus - the bats might have already heard them during training ., The lower avoidance of S− when presented with S0 could result from the, fact that they were familiar to the bats and the bats were even rewarded when, approaching them during the test phase ., A linear classifier ( Support Vector Machine – SVM ) learned to classify, the calls with high accuracy ( correct decision rates of, 81–90% ) ., This was the case for both types of, representations of the calls , i . e . the temporal-spectral spectrograms and the, spectral power spectrum densities ( PSD , Table 3 ) although in the case of the PSDs the, performance was a bit lower ( 77–84% ) ., This indicates that, individual-specific information is abundant in the calls ., The overall, performance of the linear machines was similar to that of the bats ., Our main goal was to model the behavior of the bats ., Therefore , more than the, overall performance , we were interested to find a classifier that behaves like, the bat in the sense that it makes more errors in trials that the model, considers to be more difficult and vice versa ., We assessed the similarity, between the bat and its model by measuring the correlation between the, performance of the bat and the performance of the model on the same test set, ( see Materials and Methods ) ., The, performance of the model was indirectly measured by calculating the distances, between the pairs of calls in the test set ., This reflects the metric of the, model ., A high correlation between the two indicates that the bat made more, errors in trials that are considered to be difficult by the machine and vice, versa ., Except for a single case ( using the PSD for the classification task of, bat 6 vs . bat 1 ) the metrics ( distances to the hyperplane ) of the linear, classifiers are actually negatively correlated with the error rate of the bats ,, implying that they were using different features than the model to classify the, calls ( Table 3 ) ., We were ,, however , able to train non-linear SVMs that correlated with the bats, behavior in each of the classification tasks ., This was true both for the, spectrograms and the PSDs , although the correlation seems a bit less salient in, the case of the PSDs ( Figure, 3 ) ., The overall performance of the non-linear SVMs behaving most, similarly to the bats was very close to that of the bats , when using the, spectrograms and was a bit lower when using the PSDs ( Table 3 ) ., In one case ( classification of bat, 5 vs . bat 2 ) the performance when using the PSDs was much lower ., To eliminate the possibility that a single simple cue was sufficient for, classification we analyzed the commonly used call parameters, ( starting/terminal/maximum energy frequencies , bandwidth and call duration ,, Table 1 ) and tested, the performance when relying solely on each of them ., We used exactly the same, pairs of calls that were presented to the bats in the testing phase and measured, the percent of correct decisions if the bat would rely on one of the above, parameters , ( e . g . always go to the call with a lower or higher terminal, frequency ) ., In almost all cases , relying on any single cueresulted in a, performance at chance level ( 45–55% ) ., For the, classification task of bat 2 vs . bat 5 , using two single cues ( the bandwidth or, the initial frequency ) was sufficient to correctly classify, 60–65% of the calls - higher than chance but much lower, than the observed performance ., This last similarity implies that the decisions of the bats can be modeled as a, prototype classifier 31 in the sense that the bat learns the mean, calls of the bat pair as a prototype for the two classes, ( S+/S− ) ., To test this hypothesis we applied a simple, prototype classifier to our data ., We used the nearest mean-of class prototype, classifier , in which each class is represented by its mean and each call is, assigned to the class whose mean PSD is closer to its PSD using the Euclidean, distance ., The means were calculated from the training data exclusively ., Since, the bats heard two calls in each trial , we calculated the sum of distances, between the PSDs of these calls and the mean PSDs for both the correct and the, incorrect assignments ., We considered any case for which the correct sum of, distances was smaller than the incorrect sum of distances as a correct decision, of the classifier ., We repeated this for the spectrograms as well ., Despite its simplicity , the prototype classifier achieved a classification, performance significantly higher than chance level for both the PSDs and the, spectrograms ( Table 4 ) ., The lower performance compared to the non-linear SVM is not surprising due to, the simplicity of this classifier ., The overall performance however , is less, important in our case ., It could probably be increased by a more sophisticated, prototype classifier , for instance one that only learns the means of features, that have a large inter-bat variability ., Much more important is the very high, correlation between the distance metric of this classifier ( sum of prototype, distances ) and the bat performance , meaning that the bats tend to make more, errors when the calls presented to them are farther from the mean calls ( Figure 5A ) ., An interpretation of the SVM decision rule regarding the spectrograms is not easy, due to their high dimensionality , but the above analysis suggests a prototype, classifier as well ( Figure ., 5 and Table 4 ) ., To, validate this idea we ranked the spectrograms of the presented call pairs of Bat, 1 and Bat 3 according to distances between them ( based on the non-linear SVM, metric ) ., The closer the two spectrograms are to each other , the more difficult, they should be to classify ., To test the prototype hypothesis we next measured, how similar each spectrogram pair is to the pair created by the two class means ., We calculated the linear correlation between, a ) the difference between the pairs, and, b ) the difference between the mean spectrograms ., We found a strong positive, correlation between the two . which shows that the more similar the difference, between two spectrograms is to the mean difference , the easier it is to classify, by the trained SVM ., As this SVM was trained to imitate the bats, behavior , this once again supports the hypothesis that the bats are using some, sort of a prototype classifier ( Figure 5B ) ., In summary , for both PSDs and spectrograms , we found evidence that the bats use a, prototype classifier in which they evaluate the mean difference between the, calls of the bat couple as a reference to which they compare the difference, between any new pair of calls they hear ., This hypothesis is strengthened by the, results of the generalization experiments , which suggest that the bats are using, both S+ and S− to classify ( Table 3 ) ., We did not observe the exact PSDs, of all classification tasks , mainly because the amount of errors for the other, tasks was very small ., The application of a prototype classifier ( Table 4 and Figure 5A ) however , implies, that all of them were using a sort of a prototype classifier ., Researchers were always fascinated by the social behaviors exhibited by bats ., There are , for instance , some reports of bats leaving the roost and flying to, and between foraging sites in groups of between two and six individuals 16 , 22 ., Little is known, about how bats might perform the strenuous task of remaining in a group when, flying at high speeds in darkness , or about how they avoid interference between, each others echolocation calls ., The finding that bats can recognize, their conspecifics based on their echolocation calls might have some significant, implications in this context ., Despite their stereotyped spectrograms , echolocation calls show a large, task-dependent variability that obscures possible features in the calls that, might facilitate the recognition of individual bats 30 ., For this reason ,, we had to use statistical classifiers as a new method of analysis in a context, that requires a minimal set of restrictive assumptions on candidate, discriminative features ., The results pointed strongly towards a prototype, strategy ., This now enables us to design additional behavioral experiments to, test this hypothesis ., To test the prototype hypothesis one could , for instance ,, divide the calls of one of the bats into 2 subgroups that are selected such that, their prototype ( mean ) is very different ., The tested bat should then be trained, using calls from one subgroup and tested using calls from the other ., If the, prototype hypothesis holds , the bat would be expected to have a very high error, rate ., An alternative approach could be to use the hyperplane learnt by the SVM, to simulate artificial calls at known distances from the hyperplane and, therefore known difficulty see 32 for more details ., Comparing the performance of the tested classifiers on the PSDs or on the, spectrograms reveals that the performance when using the PSDs does not drop as, we would expect from taking into account the drop of information ( Table 3 ) ., This implies that, most of the information necessary for classification already exists in the, frequency domain ., Along with the above analysis of PSDs , this suggests that the, filtering properties of the vocal tracts of the individuals , which reflect vocal, tract resonances ( formants ) provide sufficient acoustic cues for individual, recognition ., These findings are in line with some recent evidence supporting the, presence of formants in animal calls 8–10 ,, 33–35 ., It is quite, probable that for the classification of the complete repertoire of M ., myotis calls , including calls emitted in different behavioral, situations that show a much higher variation of temporal-spectral relations , the, PSDs might even be advantageous compared to the spectrograms since they provide, a time-independent set of cues ., We conducted the experiments using five adult male M . myotis, ( Borkhausen , 1797 ) , captured in Bulgaria ( license from the Ministry of, Environment and Waters , 34/04 . 07 . 2005 , Sofia , Bulgaria ) and housed under, standardized conditions ( 16∶8 h light∶ dark cycle ,, 24±2°C and 65±5% humidity ) ., Bats were, fed on mealworms ( larvae of Tenebrio molitor ) only during, training and experimental sessions ., The diet was supplemented with minerals, ( Korvimin® , WDT ) and vitamins ( Nutrical© , Albrecht ) and, freshwater was accessible all the time ., The animals used in the experiments were, kept together for a few months in a flight cage that enabled them to fly, regularly ., Five bats were recorded separately while freely flying in a flight room, ( 3 . 6×6 . 0×2 . 8 m ) covered with acoustic foam to reduce echoes, from the walls and floor ., The flight behavior consisted of two patterns: The, animals either circled in the room ca ., 2 m above ground , or they flew to one of, the walls and hung on it ., In the latter case we encouraged them to fly again by, clapping the hands or gently poking them with a butterfly net ., The sound, recordings were performed with custom-made equipment ( Universität, Tübingen , Germany ) including an ultrasonic microphone ( flat response, ±3 dB between 18 and 200 kHz ) in a stationary position pointing, 45° upwards at one end of the room and a digital recorder ( PCTape ) , with, a sampling rate of 480 kHz ., The order of the animals was selected using the, Latin squares method 36 to mitigate undesired effects caused by the, order or time of the day ., The recordings lasted 20 minutes in total , collected on two consecutive days ., This procedure provided us with a large data set of over 2000 calls per bat ., The, characteristics of the calls varied greatly within each individual even though, they were emitted under the same conditions ., This variability had at least two, causes: 1 ) Behavioral - the bats were constantly changing their distance from, the walls , especially when approaching them to land and adjusted their, echolocation accordingly 37 , 38 ., 2 ) Acoustical - the, calls were recorded when the bats were at different distances from the, microphone and with different aspect angles to it ., This resulted in substantial, changes in the signal to noise ratio ( SNR: see Results for more details ) ., We, discarded all calls that were shorter than 2 ms since they were severely, affected by the directionality of the microphone ( i . e . calls with a strong, attenuation at high frequencies ) ., This procedure left us with approximately 800, calls for each bat ., In the behavioral experiments each bat was trained to distinguish between two, other specific bats in a 2-AFC paradigm ., Each experimental bat was assigned two, other bats between whose calls it had to distinguish ., We will refer to the bat, it had to approach as S+ and to the other one as S− ., The bats, had to sit on a Y-shaped platform and crawl to the side where the calls of, S+ were played ., The stimuli consisted of alternately playing a single, call of S+ on one side of the platform and a single call of, S− on the other side with a 0 . 5 s pause between them until the bat, made a decision ., All calls were normalized in the time domain to have the same, maximum amplitude ., We used custom-made equipment ( Universität, Tübingen , Germany ) to play back the calls with a sampling rate of 480, kHz ., The loudspeakers ( Thiel Diamond Driver D2 20-6 ) were positioned, 1 . 35 m from the platform and 1 . 35 m apart from each other , forming an, equilateral triangle together with the platform ., The side on which S+, was presented varied randomly between the trials ., The experiments were divided, into a training phase and a testing phase ., In the training phase the bats were, trained to perform the task using a subset of the data composed of, 80% of the calls ( the training set ) chosen randomly ., During training ,, when the bat crawled to S+ , it was rewarded with a mealworm ., The bats, needed ∼4 days of training to get used to sitting on the Y-platform, ( they were fed on it ) ., They needed another ∼3 days to learn to crawl to, one of the sides of the Y-platform to get the reward ., To do this , we placed the, bat in the starting arm and played back S+ from one side and, S− from the other one , showing the mealworm at the end of the correct, arm and rewarding the bat for crawling towards it ., The next step ( the training, phase ) consisted of the training on the task ., S+ and S− were, played back as described above and the bats were rewarded for crawling to the, correct side ., When they made an error the trial would be repeated up to 3 times ., If the bat continued misclassifying we moved to the next pair of calls ., Once a, bat made more than 75% correct decisions\\3 days in a row it was, transfered into the testing phase ., The training phase lasted ∼20 days on, average so that each bat performed ∼25 trials per day so that in total, the bats heard ∼500 calls of each bat before starting the testing phase ., In the testing phase , we used the remaining 20% of the calls that had, never been heard by the bats before ., Each pair of calls was played back during a, single trial ., The decision of the bats was always rewarded , so that the, experimenter could not give the bats a hint about the correct answer ( a double, blind paradigm ) ., The assignment of bat pairs ( S+ vs . S− ) were, as following: bat1–bat2 vs . bat6 , bat3–bat6 vs . bat1 ,, bat4–bat5 vs . bat2 and bat5–bat3 vs . bat1 ., We used four, different pair of bats ( rather than testing all bats on the same task ) assuming, that all tasks were more or less equally hard and thus a high performance in all, of them would imply high performance for any chosen pair of bats ., We recorded the calls that were played back by the speakers to validate that the, system was working properly with the same recording equipment mentioned, above ., To test the ability of the bats to generalize and to estimate whether they, learned to recognize S+ or to avoid S− we conducted another, set of control experiments ., Here S+ or S− were presented on, one side and S0 , which consisted of a call of one of two novel bats never played, back to that animal before , on the other side ., The S+/S−, calls were randomly selected from the training set , since the bats recently, heard all of the testing calls and were not exposed to training calls for at, least 2 weeks ., The order of presentation of S+ or S− and S0, was random as well as the side on which they were played ., The rest of the, procedure was the same as in the testing session ., We used Support Vector Machines SVM , 39 , 40 , a well-known classification algorithm in the, field of machine learning , to classify the calls of the different bats ., This, method is suitable for dealing with multi-dimensional data and uses the raw data, in order to learn the best features for classification , with minimal prior, assumptions on the data distribution ., We tested the performance of the classifier using two different representations, of the calls: spectrograms and power spectral densities ( PSD ) ., The spectrograms, are a time-frequency decomposition of the calls and therefore represent both, types of information the bats possess after the basic filtering in the ear 41 ., The, spectrograms were calculated using a Hann FFT window of 240 points with 0 . 9, overlap between consecutive windows , providing a frequency resolution of 2 kHz, and a time resolution of 0 . 5 ms . The part of the spectrogram containing the call, was segmented from the background noise using Otsus method 42 ., This, was done for each spectrogram separately and provided us with the call segments, that were clearly above noise ., We should emphasize that this was done for the, machine classification only ., The bats had to face noisy calls with a large, variability of background noise ., We restricted the spectrograms to the frequency range between 21–140, kHz , which contains the entire frequency range of the calls ., This left us with, very high-dimensional data ( 4200 dimensions: 60 frequencies times 70 time, points ) ., We aligned all spectrograms in the time axis such that in all calls the, maximal energy at 30 kHz was at the same time instant of the spectrogram ., We, used Principal Component Analysis ( PCA ) to reduce the dimensionality of the, data ., Each data point ( representing a single call ) was projected on the 300, eigenvectors with the highest eigenvalues ., This reduced the dimensionality of, the data to 300 dimensions ., In a spectrogram of a frequency-modulated M ., myotis call most of the values of each spectrogram contain, background noise ., Reducing the dimensionality in a way that preserves the, directions of the greatest variance ( using PCA ) should therefore get rid of a, large amount of noise ., In every experiment , the eigenvectors were exclusively, calculated from the covariance matrix of the training set ( see below ) ., The PSD contains only the frequency information of the calls , leading to a, classification that is independent of temporal information ( e . g . , call duration ,, sweep rate ) which tends to vary widely in nature ., Throughout the paper they will, sometimes be referred to as spectra ., The PSDs were calculated with, Welchs method with a 2 ms window with 0 . 5 overlap ., We then, under-sampled the PSDs so that their frequency resolution was identical to that, of the spectrograms , ensuring that they contained the same spectral information, as the spectrograms but no temporal information ., All data points ( spectrograms, after PCA and PSDs ) were normalized ( divided by the maximum ) so that each of, them had a maximum of 1 before they were used for classification ., SVMs are state-of-the-art learning algorithms based on statistical learning, theory ., A linear SVM uses a training data set to learn a hyperplane ( a, multidimensional decision boundary ) that divides the data set into two classes ., It does so by minimizing the classification error and at the same time by, maximizing the distance between the hyperplane and the data points that are, closest to it ., A non-linear SVM is used when the data cannot be separated, linearly ., It first transforms the data non-linearly into a higher-dimensional, space ( feature space ) and then finds a hyperplane that divides the data into the, two classes in this space ., In both cases the hyperplane is simply a geometrical, multidimensional plane either in the original or in the feature space ., Since in, many cases a perfect separation of the data into two classes is not possible ,, the learning algorithm is adjusted to enable a certain amount of, misclassification ., This is controlled by a constant ( C ) that defines the penalty, for misclassified points ., This constant is known as the free parameter of the, SVM ., We applied SVM classifiers on both types of data ( i . e . , spectrograms and PSDs ) ., We used the same training set of calls that was used to train the bats in order, to train the classification machines and the same test set to test them ., We, tested both linear and non-linear SVMs ., For the non-linear SVMs , we trained, non-linear machines using the radial basis Gaussian kernel RBF , 39 , 40 , 43 to transform the, data nonlinearly before computing the separating hyperplane ., This is a standard, choice in machine learning that usually performs well in a wide range of, applications ., The use of the RBF kernel introduces a second parameter, ( σ ) that sets the width of the Gaussian ., In order to optimize the, classifier to perform like the bat ( see below ) we tested 8 different values for, each of the two parameters ( 0 . 1 , 1 , 10 , 50 , 100 , 500 , 1000 , 10000 ) and trained, linear SVMs with all possible C values and non-linear SVMs with all possible, combinations of the two in order to find a classifier with a performance that is, most similar to that of the bats ., There are several possibilities to optimize the model such that it behaves like a, bat ., The overall performance ( error rate ) is not a sufficient criterion since it, does not provide any information about the classification strategy - e . g . , the, bat and model could do the exact opposite right and wrong decisions but still, have the same error rate ., An exact comparison between the decisions of the bat, and the decisions of the model ( percent of identical right/wrong decisions ) is a, better criterion , but it is also limited since it divides the trials into, identical decisions and non-identical decisions but provides no information, about how difficult each decision was ., We therefore chose a different criterion ,, one which is , to our understanding , more informative ., For each model, ( linear/non-linear SVM ) we computed the distances between the pairs of test, calls the bat had to classify according to the model ., This can be done by, computing the distance of each call from the hyperplane ., The distance from the, hyperplane can be thought of as an estimation of how difficult the call is to, classify ., The closer a call is to the hyperplane , the more difficult it is to, classify , since it is closer to the boundary between the two classes ., We refer, to this measure as the metric of the model and it reflects how difficult/easy, each trial is considered to be according to the model ., We assumed that if the, machine captured the features used by the bats for classification , the distance, between the calls should positively correlate with the performance of the bats ,, meaning that the farther apart the two calls presented to the bat were , the, easier it should be for the bats to classify them correctly ., In practice we, divided the entire distance range into 4 distance classes , each containing an, equal number of calls and plotted the error rate of the bats for each of these, distance ranges ., We then calculated the correlation between the performance of, the bat and the difficulty of the trials it performed , represented by the, average distances of the group of trials ., We searched for the parameters that, yielded a classifier that maximizes this correlation ., To choose the best, parameters we divided the test set into 3 equally sized sub-sets of data ., We, then used only two thirds of the test set to choose the best model ( this set is, called the validation set ) and we measured the results on the un-used third ., This process was repeated three times and ensures that the test set did not, influence our decision ., This procedure also provided us with an estimation of, the variance of the models performance ., We implemented the SVM classifier using the free “spider”, software ( http://www . kyb . mpg . de/bs/people/spider ) ., For more details about, the application of SVMs on a data set of spectrograms see Yovel et al 31 .

Introduction, Results, Discussion, Materials and Methods

Echolocating bats use the echoes from their echolocation calls to perceive their, surroundings ., The ability to use these continuously emitted calls , whose main, function is not communication , for recognition of individual conspecifics might, facilitate many of the social behaviours observed in bats ., Several studies of, individual-specific information in echolocation calls found some evidence for, its existence but did not quantify or explain it ., We used a direct paradigm to, show that greater mouse-eared bats ( Myotis myotis ) can easily, discriminate between individuals based on their echolocation calls and that they, can generalize their knowledge to discriminate new individuals that they were, not trained to recognize ., We conclude that , despite their high variability ,, broadband bat-echolocation calls contain individual-specific information that is, sufficient for recognition ., An analysis of the call spectra showed that, formant-related features are suitable cues for individual recognition ., As a, model for the bats decision strategy , we trained nonlinear statistical, classifiers to reproduce the behaviour of the bats , namely to repeat correct and, incorrect decisions of the bats ., The comparison of the bats with the model, strongly implies that the bats are using a prototype classification approach:, they learn the average call characteristics of individuals and use them as a, reference for classification .

Animals must recognize each other in order to engage in social behaviour ., Vocal, communication signals could be helpful for recognizing individuals , especially, in nocturnal organisms such as bats ., Echolocating bats continuously emit special, vocalizations , known as echolocation calls , and perceive their surroundings by, analyzing the returning echoes ., In this work we show that bats can use these, vocalizations for the recognition of individuals , despite the fact that their, main function is not communication ., We used a statistical approach to analyze, how the bats could do so ., We created a computer model that reproduces the, recognition behaviour of the bats ., Our model suggests that the bats learn the, average calls of other individuals and recognize individuals by comparing their, calls with the learnt average representations .

neuroscience/behavioral neuroscience, neuroscience/cognitive neuroscience, neuroscience/sensory systems

journal.pgen.1000105

Low Levels of DNA Polymerase Alpha Induce Mitotic and Meiotic Instability in the Ribosomal DNA Gene Cluster of Saccharomyces cerevisiae

The maintenance of genetic stability during DNA replication is of critical importance ., DNA polymerases can stall at DNA lesions such as crosslinks , strand breaks , natural pause sites , and regions that can form secondary structures 1 , 2 ., Stalled replication forks are a potential source of genetic instability , because they can be processed to a double-strand break ( DSB ) 3 , 4 ., Recombination proteins form foci at stalled forks , and homologous recombination ( HR ) is thought to be one mechanism by which collapsed forks are re-initiated 5 , 6 ., Almost 10% of the S . cerevisiae genome is within the rDNA array , a cluster of 150–200 tandemly repeated 9 kb units on the right arm of chromosome XII 7 , 8 ., Each 9 kb unit has a natural replication fork barrier ( RFB ) site ., The RFB prevents replication fork progression in the direction opposite 35S transcription , presumably to prevent collisions between DNA and RNA polymerases 9–11 ., The Fob1p binds directly to the RFB sequence and is required for replication fork blocking 12 ., Double-strand breaks ( DSBs ) are observed near the RFB site in logarithmically growing cells 13 , 14 and are a source of genetic instability within the array , leading to high levels of unequal sister-chromatid exchange , unequal gene conversion , and intra-chromatid recombination 15–18 ., Cells that lack the Fob1p do not experience fork stalling at the RFB and have reduced mitotic rDNA recombination 14 , 17–19 ., In these studies , the effect of the fob1 mutation on rDNA recombination between homologues was not examined ., In contrast to the relatively high levels of mitotic recombination in the rDNA , meiotic recombination between rDNA arrays on homologous chromosomes is suppressed 70- to 100-fold 8 , 20 ., The mechanism preventing meiotic rDNA recombination between homologs is not yet fully understood ., Meiosis-specific DSBs are undetectable in the array 21 , and Spo11p , which catalyzes meiotic DSBs , is at low levels within the array 22 ., Strains that lack Sir2p have increased Spo11p-associated DSBs in the rDNA 22 and significantly elevated meiotic and mitotic unequal sister-chromatid rDNA recombination 22–24 ., In this study , we designed a system that allows us to measure rDNA recombination both between homologues and between sister chromatids ., Using this system , we examined the relationship between DNA replication and recombination by investigating mitotic and meiotic rDNA recombination in cells with low levels of Pol1p , the catalytic subunit of the lagging strand DNA polymerase alpha 25 ., Reduced levels of Pol1p were previously shown to elevate the rates of translocations , chromosome loss events , microsatellite alterations , deletions and point mutations in non-rDNA regions 26 , 27 ., Below , we show that low levels of Pol1p significantly increase recombination in the rDNA array , both between homologues and between sister chromatids ., This increase is observed in both mitosis and meiosis ., These data suggest that the mechanisms controlling rDNA recombination are closely coordinated with the replication machinery ., In order to investigate the effect of low Pol1p levels on rDNA instability , we used a diploid strain homozygous for the GAL-POL1 allele , in which the POL1 gene is fused to the GAL1/10 promoter 26 ., Low galactose levels ( 0 . 005% ) induce Pol1p expression at ∼10% of the wild-type level , whereas high galactose ( 0 . 05% ) induces ∼300% expression of Pol1p compared to wild-type ., For our analysis , we constructed strains containing heterozygous markers surrounding and within the rDNA array ., On one homologue , we inserted the URA3 gene at the centromere-distal junction of the rDNA ., On the other homologue , we inserted the HPH gene ( encoding hygromycin resistance ) centromere-proximal to the rDNA , and a copy of TRP1 within the rDNA array ( Figure 1 ) ., The rDNA cluster sizes in these strains were determined by clamped homogenous electric field ( CHEF ) gel electrophoresis of genomic DNA digested with BamHI which does not cut within the array or the TRP1 insertion ., The location of TRP1 within the array was determined by digestion with NgoMIV that cuts in TRP1 but not within the rDNA ( Figure 1 ) ., To measure the rate of mitotic rDNA recombination , we incubated GAL-POL1 diploids in media containing high or low galactose levels for six hours , followed by plating onto medium with high galactose ., Wild-type diploids were grown in rich growth medium media for six hours , followed by plating on rich growth medium ., Colonies formed on the plates were replica plated to media lacking uracil or tryptophan , or containing hygromycin ., Cells undergoing a recombination event within the rDNA at the time of plating will appear as sectored colonies on the diagnostic media ., Using the phenotypes of the sectors , we can diagnose reciprocal crossovers ( RCOs; Figure 2A–C ) as well as various other types of recombination ( Figure 2D–F ) ., We can only detect half of RCO events , because only one of the two possible chromosome segregation patterns will produce a sectored colony ., Since these two patterns of segregation are equally frequent 28 , we calculate that the rate of RCO is twice the frequency of sectored colonies ., Non-reciprocal recombination events ( Break-Induced Replication BIR 29 ) can result in a loss of one marker ( for example , URA3 as shown in Figure 2D ) and duplication of another ( TRP1 ) ., Loss of the TRP1 marker by intrachromatid “pop-out” exchange ( Figure 2E ) , single-strand annealing ( not depicted ) , or unequal sister-chromatid exchange ( Figure 2F ) can also be detected ., To analyze further the type of recombination event responsible for sectoring , we did several types of analysis ., First , we purified all sectored colonies and , in colonies with Ura+/Ura− sectors , we determined whether the Ura+ cells had a high rate of 5-fluoro-orotate-resistant ( 5-FOAR ) derivatives ( indicating that the cells were heterozygous for the URA3 insertion ) or had a very low rate of 5-FOAR derivatives ( indicating that the cells were homozygous for the insertion ) ., In strains heterozygous for the insertion , a 5-FOAR derivative could arise by loss of the wild-type URA3 allele by a subsequent mitotic crossover or by chromosome loss ., We also subjected each sector side of the colony to tetrad analysis ., Some Trp+/Trp− sectored colonies were further analyzed by Southern analysis to determine whether sectoring arose by unequal crossover , intrachromatid recombination , or other events; this analysis is discussed in detail in Text S1 ., Based on this analysis ( summarized in Table 1 ) , we grouped these mitotic events into two categories: rDNA recombination between homologues and between sister chromatids ., In cells with low levels of Pol1p ( Figure 3A ) , we observed a four- to five-fold increase relative to wild-type cells or cells with high levels of Pol1p in both of these categories ( p< . 0001 and p\u200a=\u200a0 . 0031 , respectively ) ., To determine whether forks blocked at the RFB are the primary source of rDNA instability in cells with low Pol1p , we analyzed mitotic recombination rates in fob1 mutant derivatives of our strains ., Previous studies reported decreased extrachromosomal rDNA circle ( ERC ) formation and an approximately three-fold reduction in internal rDNA marker loss in fob1 mutants 14 , 17–19 , presumably due to the decrease in recombinogenic DSBs at the RFB ., We found that the fob1 mutation alone resulted in a 10-fold reduction , relative to wild-type , in the rate of mitotic rDNA recombination between homologs ( p\u200a=\u200a0 . 011 ) , but a statistically insignificant ( p\u200a= . 671 ) reduction in sister chromatid recombination ( Figure 3B ) ., Surprisingly , we showed that the fob1 mutation resulted in a significant increase in recombination between homologues ( p\u200a=\u200a0 . 027 ) in the GAL-POL1 strain grown on low galactose compared to the level observed in the FOB1 GAL-POL1 strain grown under the same conditions ., The classes of mitotic recombination events in strains with the fob1 mutation ( Table S1 ) were similar to those shown in Table, 1 . Thus , the hyper-Rec phenotype associated with low levels of DNA polymerase alpha does not reflect elevated levels of Fob1p-mediated stalling at the RFB ., We also directly investigated the level of DSBs at the RFB in our cells by Southern analysis ( Figure 3C ) ., In BglII-treated DNA from wild-type cells , the DSB associated with RFB ( indicated by an arrow ) is observed as a 2 . 2 kb fragment hybridizing to an rDNA probe ( lane 1 , Figure 3C ) , as reported previously 13 , 14 ., The amount of this fragment ( normalized to the 4 . 6 kb unbroken BglII fragment ) was about the same in the GAL-POL1 strain with low levels of DNA polymerase alpha ( lane 2 , Figure 3C ) as in the wild-type strain ., The fob1 mutation , as expected , reduced the amount of the 2 . 2 kb fragment ( lane 3 , Figure 3C ) ., The pair of 3 . 0–3 . 5 kb bands observed in all samples have been observed previously 13 , 14 and represent either a Fob1p-independent DSB in the rDNA or junction fragments of non-rDNA with rDNA ., Unequal sister-chromatid recombination will result in loss of the TRP1 marker only if the crossover occurs between the misaligned insertions ( Figure 2F ) ., In contrast , all unequal crossovers or intrachromatid crossovers that alter the size of the rDNA array by 50 kb or more can be detected by CHEF gel electrophoresis of BamHI-treated DNA samples ., To determine whether low DNA polymerase alpha resulted in increased size variability of the rDNA , we examined the sizes of rDNA clusters in sub-cultured isolates of the GAL-POL1 diploid grown on plates containing high or low levels of galactose ( Figure 4 ) ., The rDNA clusters in the initial GAL-POL1 diploid colony were about 1100 kb and 855 kb ., We observed slight size variation following two cycles of high galactose subculturing ( lanes 3–7 ) ., The size variation in cultures sub-cultured in low galactose ( lanes 9–13 ) was considerably greater , and two of the five colonies had three rDNA clusters , reflecting either chromosome XII trisomy or the presence of sub-populations within the culture with varying array sizes ., The rDNA bands derived from strains subcultured on low galactose were blurry in comparison to those subcultured in high galactose ., It is likely that this blurring reflects a very high rate of recombination resulting in small changes in cluster size ., We also observed that , in DNA samples isolated from the GAL-POL1 cells in exponential phase , most of the chromosome XII DNA molecules were retained in the well of the gel rather than migrating in the normal position ( Figure S1 ) ., Since branched DNA molecules remain in the well of CHEF gels , it is likely that the observation indicates that GAL-POL1 strains have increased levels of DNA replication and/or recombination intermediates ., Using two-dimensional gel electrophoresis , Zou and Rothstein 30 showed that certain mutants of DNA polymerases alpha and delta resulted in increased levels of an rDNA structure ( termed “xDNA” ) that had the properties expected for a recombination intermediate ., Meiotic recombination between rDNA clusters on homologous chromosomes is greatly suppressed ., Although the rDNA is about 10% of the genome and the yeast genome has a genetic length of about 4200 cM , the rDNA cluster is only 2 . 5 cM in length 8 , 20 ., Unequal sister chromatid meiotic recombination is less suppressed , with loss of an internal marker occurring in up to 10% of tetrads 31 ., To evaluate the effect of Pol1p levels on meiotic rDNA recombination , we sporulated the wild-type and GAL-POL1 strains on plates containing either high or low galactose ., Tetrads were dissected and scored for parental ditype ( PD ) , non-parental ditype ( NPD ) and tetratype ( T ) for three intervals: HPH-TRP1 , TRP1-URA3 , and HPH-URA3 ., The genetic distances between markers were calculated by standard procedures 32 and are shown in Table, 2 . As expected , recombination in the rDNA in wild-type cells was extremely low , 1 cM for the entire cluster ( HPH-URA3 interval ) ., Since the difference in recombination rates between the wild-type cells sporulated in high and low levels of galactose was not significant , the data were combined ., In contrast , the genetic distance between HPH and URA3 was increased to 28 cM in cells with low Pol1p ( p< . 0001 ) ., In the equation used to calculate map distances , in two-point crosses , NPD events are assumed to reflect four-strand double meiotic crossovers between markers 32 ., For a two-point cross , however , an NPD event could also be a consequence of a mitotic crossover prior to meiosis ., In general , the frequency of mitotic crossovers was lower than the observed frequency of meiotic NPD events , suggesting that at least some of the NPD tetrads reflect double meiotic crossovers ., For example , in the strain with low levels of alpha DNA polymerase for the HPH-TRP1 interval , we observed a rate of mitotic crossovers of 1% ( Table 1 ) , whereas the rate of NPDs for the same interval was 6% ( Table 2 ) ., In addition , as will be discussed further below , in analyzing the HPH-URA3 interval , we detected NPD tetrads that had one crossover in the HPH-TRP1 interval and a second crossover in the TRP1-URA3 interval , demonstrating that some NPD tetrads reflect meiotic exchanges ., Nonetheless , we also calculated map distances for the three intervals excluding all of the NPD tetrads that could represent mitotic crossovers ( values shown in parentheses in Table 2 ) ., Even with this conservative assumption , the genetic distance in the rDNA cluster is more than 10-fold elevated in the strain with low levels of alpha polymerase compared to the wild-type ( p< . 0001 ) ., The genetic distances in the rDNA in cells with low Pol1p were not additive since the HPH-TRP1 distance is 30 cM , the TRP1-URA3 is 12 cM , and the HPH-URA3 distance is only 28 cM ., There are two likely interpretations of this non-additivity ., First , as described above , some of the NPD tetrads used in calculated the HPH-TRP1 and TRP1-URA3 distances may reflect mitotic crossover events ., Second , since the equation used to calculate map distance 32 is based on the assumption that the interval examined has two or fewer crossovers , map distances for intervals that have more than two crossovers are underestimated ., We observed four tetrads from low Pol1p cells that had marker segregation patterns consistent with triple crossovers surrounding TRP1; no such tetrads were found in wild-type cells or in GAL-POL1 cells sporulated on high galactose ., We also noted a significant increase in the HPH-URA3 distance in GAL-POL1 cells sporulated on high galactose relative to the wild-type strain , 11 and 1 cM , respectively ( p< . 0001 ) ., Pol1p is overexpressed about three-fold relative to wild-type under these conditions 26 ., It is possible that the overexpression of this single unit of DNA polymerase alpha complex perturbs its assembly ., We previously observed that overexpression of Pol1p resulted in elevated levels of chromosome rearrangements and chromosome loss 26 ., We detected an elevation of the frequency of tetrads with one Trp+ and three Trp− spores ( instead of the expected 2∶2 marker segregation ) in the GAL-POL1 strain ., Loss of the TRP1 insertion can occur by unequal crossing-over between sister chromatids , intra-chromatid recombination , and gene conversion ( either between homologues , or unequally between sister chromatids ) ., Of the 280 four-spore tetrads from GAL-POL1 cells sporulated on high galactose , 20 had one Trp+ to three Trp− spores ( 7% ) ., In GAL-POL1 cells sporulated on low galactose , this level was 25% ( 51 out of 204 tetrads ) ., We observed only one tetrad that had three Trp+ to one Trp− spore , and this tetrad was from GAL-POL1 cells sporulated on low galactose ., This bias toward TRP1 marker loss indicates that the majority of these events are intrachromatid events ( for example , unequal crossovers between sisters ) , rather than “classic” gene conversion events ., We did not observe TRP1 marker loss in any tetrads from wild-type cells ( total of 362 four-spore tetrads ) ., This result differs from an earlier report in which an internal rDNA marker was lost in ∼10% of wild-type tetrads 31 ., This variance may be due to differences in strain background or in the location of the inserted marker ., To clarify whether reduced Pol1p resulted in elevated meiotic crossovers in non-rDNA regions of the genome , we also investigated meiotic crossovers in a non-rDNA interval on chromosome II , between LYS2 and TYR1 ., The LYS2-TYR1 genetic distance in our wild-type strain is 32 cM , in agreement with the 35 cM average distance between these loci reported in the Saccharomyces Genome Database ., In cells with low Pol1p , the distance between these markers was 41 cM ( Table 2 ) ., Although this increase relative to wild-type was statistically significant ( p\u200a=\u200a0 . 02 by a Chi-square test ) , it is far less dramatic than that observed within the rDNA ., In S . cerevisiae , as in most other eukaryotes , crossovers in one region reduce the probability of a nearby crossover 33 ., In organisms in which tetrad analysis is possible , interference can be calculated in two-point crosses by analyzing the relative frequencies of PD , NPD , and T tetrads ( 34; modifications introduced by Stahl 35; also , http://molbio . uoregon . edu/~fstahl/ ) ., For most genetic intervals in S . cerevisiae , NPD tetrads ( representing four-strand double crossovers ) are significantly less frequent than expected on the basis of the number of T tetrads ( representing single crossovers as well as certain types of double crossovers ) ., We used two procedures to calculate the expected number of NPD tetrads ., First , using our observed numbers of PD , T , and NPD tetrads , we calculated the expected number of NPD tetrads by a direct application of the equation described in the Stahl Web site ( NPD exp in Table 2 ) ., We then used a chi-square analysis to compare the observed and expected numbers of tetrads in all three classes , converting the chi square value to a p value ( p in Table 2 ) ., We also calculated the degree of interference as 1− ( NPDobserved/NPDexpected ) ., For all three intervals in the strain with low levels of alpha DNA polymerase , interference was negative , suggesting that a crossover in one region of the rDNA increases the probability of a second crossover in the rDNA ., This effect is specific to the rDNA , as a non-rDNA LYS2-TYR1 interval on chromosome II has significant crossover interference in both the wild-type and GAL-POL1 strains ( Table 2 ) ., Because ( as discussed above ) , NPD tetrads in two-point crosses can reflect a mitotic exchange rather than two meiotic crossovers , we also examined interference in a more traditional way ., From Table 2 , we calculated that the frequency of tetratype tetrads ( mostly representing single crossovers ) in the HPH-TRP1 interval in the GAL-POL1 strain with low levels of DNA polymerase is about 0 . 25 ( 32 of 126 tetrads , excluding NPD tetrads from the total ) ., The frequency of tetratype tetrads in the TRP1-URA3 interval is 0 . 15 ., Thus , the expected frequency of tetrads with crossovers in both intervals ( assuming no interference ) is 0 . 04 ., In a sample of 134 tetrads examined in the GAL-POL1 strain , we expect five DCOs in the HPH-URA3 interval ., We observed ten ( Table S2 ) ., This calculation confirms that the crossovers observed in the rDNA in the GAL-POL1 strain with low levels of alpha polymerase have no interference or negative interference ., If the increased meiotic rDNA recombination in cells with low Pol1p is initiated from DSBs at the RFB site , we would expect the fob1 mutation to reduce this recombination ., Instead , we observed the opposite: the rate of recombination in cells that lack Fob1p and that have low Pol1p is significantly greater than observed in cells with only low Pol1p ( Table 3 ) , with a total HPH-URA3 genetic distance of 50 cM ( p\u200a= . 04 ) ., We also found that fob1 mutation alone significantly increased recombination relative to that observed in the wild-type strain ( p\u200a= . 0006 ) ., This finding is unexpected because , in mitosis , the fob1 deletion reduces the rate of rDNA recombination 14 , 17–19 ., This difference in phenotype indicates that Fob1p has a unique role in meiosis separate from its role in mitosis ., The frequencies of tetrads segregating one Trp+ to three Trp− spores ( indicating unequal sister chromatid or intrachromatid recombination ) were 0 . 23 ( fob1 GAL-POL1 strain in low galactose ) , 0 . 12 ( fob1 GAL-POL1 strain in high galactose ) , and 0 . 014 ( fob1 strain ) ., We also examined interference in these strains and , in the GAL-POL1 fob1 cells , the observed number of NPDs was usually equal to or more than the expected number , indicating a lack of crossover interference ( Table 3 ) ., In addition , in the GAL-POL1 fob1 strain sporulated in low galactose , the expected frequency of double crossovers calculated from the frequencies of single crossovers in the HPH-TRP1 interval ( 0 . 22 ) and the TRP1-URA3 interval ( 0 . 26 ) is 0 . 06 ., Since the expected number of DCO tetrads is 10 ( 0 . 06×164 tetrads ) and observed number is 11 , there is no detectable crossover interference ., Summing the data from the GAL-POL1 and fob1 GAL-POL1 strains sporulated in high or low galactose , we found 7 two-strand DCOs , 14 three-strand DCOs , and 9 four-strand double crossovers ( Table S2 ) ., These numbers are close to the ratio predicted ( 1∶2∶1 ) if there is no chromatid interference ., Meiotic recombination is initiated in S . cerevisiae by Spo11p-dependent DSBs 29 ., The number of Spo11p-dependent DSBs in the rDNA is low , as expected based on the genetic data 22 ., The hyper-Rec phenotype associated with low DNA polymerase could reflect either Spo11p-independent DSBs ( perhaps generated during the meiotic S-period ) or increased Spo11p-dependent DSBs ., To distinguish between these two possibilities , we sporulated our strains under low-galactose conditions and used chromatin immunoprecipitation to purify Spo11p-associated DNA , followed by quantitative real-time PCR analysis ., Since the chromatin immunoprecipitation experiments were done without formaldehyde treatment of the chromatin , these experiments monitor the covalent attachment of Spo11p to target DNA , reflecting Spo11p catalyzed DSBs ., In both POL1 and GAL-POL1 cells , Spo11p-catalyzed DSBs were at same level at HIS4 , a previously identified hotspot for Spo11p-mediated DSBs 36 , with no significant difference between these strains ( p\u200a= . 487 ) ., There is an approximately 4-fold increase in Spo11p-associated rDNA in cells with low Pol1p compared to wild-type cells ( p\u200a= . 0003 ) ( Figure S2 A–C ) ., Thus , low levels of Polp1 disrupt mechanisms required for suppression of Spo11p entry into the rDNA array , leading to increased Spo11p-catalyzed DSBs ., Loss of the histone deacetylase Sir2p results in elevated rates of unequal meiotic 23 and mitotic unequal crossing over in the rDNA , and increased levels of Spo11p cleavage in the rDNA in meiotic cells 22 ., We used quantitative real-time PCR of immunoprecipitated meiotic DNA to measure Sir2p in the GAL-POL1 strain sporulated in low levels of galactose ., In logarithmically-growing cells , there are two sites of Sir2p binding in each rDNA unit , one near the RFB site , and the other at the 5′ end of the 35S transcript 37 ., We found that there is a significant decrease in Sir2p bound near the RFB site in GAL-POL1 meiotic cells as compared to wild-type meiotic cells ( p\u200a= . 022 ) ( Figure S2D ) ., We also investigated Sir2p binding in logarithmically-growing cells with low Pol1p; we did not find a significant decrease in the level of Sir2p ( data not shown ) ., Lastly , by chromatin immunoprecipitation , we looked for an alteration in the binding of the cohesin subunit Mcd1p in vegetative wild-type cells and in cells with low Pol1p ., We found no significant difference ( data not shown ) ., The rate of reciprocal mitotic crossovers ( RCOs ) between homologues in the 120 kb CEN5–CAN1 interval of chromosome V is about 4×10−5 per cell division 38 ., Assuming this rate is representative of mitotic recombination throughout the genome , we would expect the rate of RCOs in the rDNA , which is about ten times larger than the CEN5–CAN1 interval , to be approximately 4×10−4 per cell division ., Since we observe a rate of RCOs of about 3×10−3 per cell division in the wild-type strain , mitotic crossovers in the rDNA are not suppressed and , in fact , appear somewhat elevated relative to non-rDNA sequences ., In cells with low Pol1p , rDNA recombination between both clusters on homologues and clusters on sister chromatids was increased about five-fold ., To determine whether stalling of replication forks at the RFB site is responsible for the elevated rDNA recombination , we examined strains that lacked the RFB-binding Fob1p ., Although loss of Fob1p reduces the hyper-Rec rDNA phenotype associated with sgs1 and dna2 helicase mutants 13 , 39–41 , the fob1 mutation did not decrease mitotic recombination in our strains with low polymerase ., We also directly compared the level of DSBs at the RFB in wild-type and low Pol1p strains , and found no difference ., In previous studies , an elevated level of unequal sister-strand mitotic recombination in the rDNA was observed in strains lacking Sir2p 14 , 23 ., It is unlikely that the hyper-Rec effect of low alpha polymerase in mitotic cells reflects a reduction in the level of Sir2p for several reasons ., First , loss of Sir2p specifically elevates rDNA recombination 23 but , as discussed below , loss of alpha polymerase elevates recombination in other regions of the genome ., Second , the hyper-Rec phenotype caused by the sir2 mutation is dependent on Fob1p 14 , unlike the hyper-Rec phenotype resulting from low DNA polymerase alpha ., Third , Kobayashi et al . 14 showed that intragenic recombination within a single rDNA gene was not elevated in sir2 strains , although unequal sister-strand recombination was elevated ., These researchers also found a defect in the level of the cohesin subunit Mcd1p in sir2 strains and suggested that the loss of sister-strand cohesion in sir2 strains led to elevated levels of unequal sister-strand recombination without an elevated level of recombinogenic lesions ., Finally , we failed to see any effect of low alpha polymerase on the level of Sir2p binding in the rDNA in mitotic cells by chromatin immunoprecipitation experiments ., Although the hyper-Rec phenotype in our experiments is not correlated with elevated DSBs at the RFB , we suggest that the hyper-Rec phenotype is likely to reflect elevated DNA lesions ( perhaps distributed randomly ) based on several arguments ., First , we found an elevated rate of RCO in an interval of chromosome XII ( CEN12-HPH , Table 1 ) that does not contain rDNA , although only a small number of events were detected ., Second , strains with low levels of alpha polymerase are hyper-Rec in non-rDNA regions; mitotic recombination in the CEN5-CAN1 interval is elevated about twenty-fold by low alpha DNA polymerase 26 ., A general hyper-Rec phenotype is associated with mutations affecting many components of the DNA replication system ( reviewed by 42 ) ., Third , an elevated level of DSBs in strains with low alpha DNA polymerase was physically demonstrated at a fragile site on chromosome III 26 ., Fourth , increased levels of Holliday junctions , presumably representing repair of DNA lesions , are observed in the rDNA of polymerase alpha mutants 30 ., Fifth , in analyzing intact chromosomal DNA samples by CHEF gels , chromosome XII was often trapped in the wells , characteristic of the behavior of branched DNA molecules ., Strains that lack Rrm3 also have elevated levels of rDNA recombination and chromosome XII molecules that are trapped in the gel wells 43 , 44 ., Low levels of Pol1p very substantially increase meiotic recombination in the rDNA between homologues and sister-chromatids ., We also observe a small , but significant , elevation of recombination in the LYS2-TYR1 interval ( Table 1 ) ., The much greater stimulation of recombination in the rDNA and the observed increase in Spo11p-mediated DSBs in the rDNA in strains with low levels of DNA polymerase argue that the stimulation is not primarily a consequence of DSBs associated with problems with DNA replication ., The stimulation is also independent of Fob1p ., In contrast to its effect in mitosis , loss of Fob1p results in increased rather than decreased meiotic recombination in the rDNA ., Fob1p is involved in the recruitment of Sir2p to the rDNA 45 ., Since we found that strains with low alpha DNA polymerase have somewhat reduced meiotic levels of Sir2p in the rDNA , two different methods of reducing the concentration of Sir2p in the nucleolus result in a hyper-Rec meiotic phenotype ., Based on our observations and those of others , a relatively simple model can be proposed ., A reduction in the level of Sir2p in the rDNA results in a reduction in the level of the Pch2p ., San-Segundo and Roeder 24 showed that Sir2p was required for the localization of Pch2p to the nucleolus and pch2 mutants had elevated rates of meiotic recombination ., These researchers also showed that Pch2p excludes Hop1p from the nucleolus ., Since hop1 strains have reduced levels of Spo11p-catalyzed DSBs 46 , increased entry of Hop1p into the nucleolus would be expected to elevate Spo11p-induced DSBs ., There are several alternative explanations of our data ., First , breaks in the rDNA of cells with low Pol1p during meiotic S-phase may be re-located outside the nucleolus for repair , and during this time of re-location , Spo11-induced DSBs could be formed 47 ., Second , DNA lesions ( for example , single-strand nicks ) in the rDNA in cells with low polymerase may recruit the Mre11p/Rad50p/Xrs2p complex that subsequently associates with Spo11p 48 , resulting in an increased level of Spo11p-catalyzed DSBs ., Finally , we cannot rule out the possibility that some of the meiotic recombinogenic lesions are a consequence of DNA lesions resulting from low DNA polymerase during meiotic recombination that are independent of Spo11p ., In summary , we suggest that fob1 mutations have different recombination phenotypes in mitosis ( hypo-Rec ) and meiosis ( hyper-Rec ) because of the different effects of Sir2p ., In mitosis , the primary recombination-related role of Sir2p is to help maintain sister-chromatid cohesion and loss of Sir2p results in elevated unequal sister-strand recombination ., In meiotic recombination , Sir2p acts to prevent recombination-stimulating proteins such as Hop1p and Spo11p from entering the nucleolus and , consequently , loss of Sir2p elevates meiotic recombination ., These explanations leave two important questions unanswered ., First , why does a low level of alpha DNA polymerase reduce Sir2p binding in the rDNA of meiotic cells ?, Second , why does low alpha DNA polymerase reduce Sir2p binding in meiotic , but not mitotic cells ?, Although we cannot provide definitive answers to either of these questions , it is possible that the role of Pol1p in chromatin assembly is relevant ., Pol1p interacts with Spt16p-Pob3p ( components of the nucleosome reorganization complex ) and Ctf4p , a protein involved in sister-chromatid cohesion 49 ., Consequently , a severe reduction in the level of alpha DNA polymerase might affect the replication-associated assembly of DNA-interacting proteins , including Sir2p , within the rDNA ., Although it is not clear why this effect would be observed in meiotic , but not mitotic cells , there are a substantial number of differences between the meiotic and mitotic S-phases including the length of the

Introduction, Results, Discussion, Materials and Methods

The ribosomal DNA ( rDNA ) genes of Saccharomyces cerevisiae are located in a tandem array of about 150 repeats ., Using a diploid with markers flanking and within the rDNA array , we showed that low levels of DNA polymerase alpha elevate recombination between both homologues and sister chromatids , about five-fold in mitotic cells and 30-fold in meiotic cells ., This stimulation is independent of Fob1p , a protein required for the programmed replication fork block ( RFB ) in the rDNA ., We observed that the fob1 mutation alone significantly increased meiotic , but not mitotic , rDNA recombination , suggesting a meiosis-specific role for this protein ., We found that meiotic cells with low polymerase alpha had decreased Sir2p binding and increased Spo11p-catalyzed double-strand DNA breaks in the rDNA ., Furthermore , meiotic crossover interference in the rDNA is absent ., These results suggest that the hyper-Rec phenotypes resulting from low levels of DNA polymerase alpha in mitosis and meiosis reflect two fundamentally different mechanisms: the increased mitotic recombination is likely due to increased double-strand DNA breaks ( DSBs ) resulting from Fob1p-independent stalled replication forks , whereas the hyper-Rec meiotic phenotype results from increased levels of Spo11-catalyzed DSBs in the rDNA .

In many organisms , the genes that encode the ribosomal RNAs are present in multiple copies arranged in tandem ., This unique section of the genome is under strict cellular control to minimize changes in the number of ribosomal DNA ( rDNA ) genes as a consequence of unequal crossover between repeats ., In addition , the rate of unequal crossovers and gene conversion in the rDNA influence the level of sequence divergence between repeats ., Crossovers can result from repair processes initiated at stalled replication forks , and in this study we investigated the effect of a low level of DNA polymerase on rDNA stability ., We found that low levels of DNA polymerase modestly increase rDNA recombination in mitosis and strongly elevate rDNA recombination in meiosis ., We suggest that in mitotic cells the increased recombination is likely due to increased double strand DNA breaks ( DSBs ) resulting from stalled replication forks ., However , in meiotic cells , we found evidence that the high level of recombination results from increased levels of Spo11-catalyzed DSBs in the rDNA ., Our results indicate that there are two fundamentally different mechanisms in mitosis and meiosis for the maintenance of rDNA stability .

molecular biology/dna replication, molecular biology/recombination, genetics and genomics, molecular biology/dna repair

journal.pgen.1006862

Distinguishing functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B and -C alleles

Present in all jawed vertebrates , the Major Histocompatibility Complex ( MHC ) is a genomic region that encodes fundamental components of the immune system ., Hallmarks of the MHC are highly polymorphic genes that encode diverse MHC class I and II antigen-presenting molecules 1 , 2 ., The human MHC is called the HLA region and is present on the short arm of chromosome 6 3 ., HLA class I and II glycoproteins have homologous structures and complementary functions in binding peptide antigens and presenting them to lymphocyte receptors 4 , 5 ., HLA class II is dedicated to adaptive immunity and engagement of the αβ antigen receptors of CD4 T cells 6 ., In contrast , HLA class I contributes both to innate immunity , by engaging Natural Killer ( NK ) cell receptors , and to adaptive immunity , through engagement of the αβ antigen receptors of CD8 T cells 7 ., Correlating with these functional differences , polymorphism within the antigen-binding site is restricted to one of the two domains that form the site for HLA class II whereas HLA class I polymorphism is spread throughout the two domains 8 , 9 ., Consequently , the number of alleles and the differences between them are greater for HLA class I , the subject of our investigation , than HLA class II 10 ., Within the HLA region , three genes , HLA-A , HLA-B and HLA-C , encode highly polymorphic HLA class I molecules ., Sequence variation is concentrated in the α1 and α2 domains that are encoded by exon 2 and 3 , respectively ., These two domains contain the binding sites for peptide antigens and lymphocyte receptors 11 ., The functional effects of the polymorphism are first to increase the breadth of an individual’s immune response to a pathogen , and second to diversify that response within families and populations ., One clinical corollary of HLA polymorphism is that numerous diseases are associated with particular HLA alleles and haplotypes , and are frequently the strongest genetic associations 7 , 12 ., Another clinical corollary is that the success of allogeneic transplantation of tissues and organs improves with the extent of HLA match between donor and recipient 13 ., HLA class I typing for clinical transplantation was begun in the 1960s using low-resolution serological methods ., Nucleotide sequencing of HLA class I alleles began in the 1980s and by 1988 had led to establishment of the HLA database as the source for accurate , curated HLA sequence data 10 , 14–16 ., Since that time , improvements in methods 17 have progressively increased the discovery rate of novel alleles ., By July 2016 sequences for more than 10 , 000 HLA-A , -B and -C alleles were deposited in the database ., These alleles represent a worldwide sampling of many , but not all , human populations ., They provide a unique data set for analysis of HLA class I variation ., To analyze this variation , we developed new and general methods for handling and analyzing these large numbers of homologous sequences ., Using these tools we examined variation in exons 2 and 3 of HLA-A , -B , and –C , which encode α1 and α2 , with the goal of identifying those aspects of HLA class I variation that have most impact on the diversity of human immune function ., The methods used here to study exons 2 and 3 of HLA class I are directly applicable to polymorphic HLA class II genes ., They can also be applied to other regions of HLA genes , which are known to harbor functionally relevant polymorphism 18–20 , when sufficient sequence data become available ., A general method of multi-sequence dot-plot analysis was developed ( see Materials and methods ) and used to compare the exon 2 and 3 sequences of HLA-A , HLA-B and HLA-C individually ( Fig 1A–1C ) , and in combination ( Fig 1D ) ., The mean intragenic distances of the three genes differ significantly ( p<1 x 10−10 , One-Way ANOVA ) , with HLA-C showing the shortest average distance of 16 . 60 nucleotide differences ( 3% ) compared to HLA-B , which has the largest with a mean 27 . 65 differences between alleles ( 5% ) ., HLA-A is intermediate with 22 . 82 differences between alleles ( 4% ) ., The average number of differences between alleles of the same gene is 23 . 75 , whereas the average between alleles of different genes is significantly higher at 51 . 12 ( p<1 x 10−10 , One-Way ANOVA ) ., The HLA-A and HLA-B dot plots show well-defined triangular clusters of closely related alleles ( Fig 1A and 1B ) ., These clusters correspond to the HLA-A and HLA-B antigens defined by serological typing , the method first used to define HLA class I polymorphisms 21 ., Most pairwise differences are greater than 20 nucleotides , producing an extensive white background on which there are well-defined triangles of color ., The dot-plot comparison of HLA-C alleles also has well-defined clusters corresponding to serological HLA-C types ( Fig 1C ) ., However , in contrast to the HLA-A and HLA-B dot plots , white areas do not dominate because HLA-C alleles have diverged to lesser extent than HLA-A or HLA-B alleles ., One likely cause of this difference is that MHC-A and MHC-B are ten million years older than MHC-C , another is that HLA-C has distinctive functions in reproduction , which are not shared with HLA-A or -B ., In particular , HLA-C expressed on fetal trophoblast interacts with KIR on maternal uterine NK cells to facilitate placental development 22 ., Fig 1D , shows all pairwise comparisons of HLA-A , –B and -C alleles ., The color patterns show how HLA-B and HLA-C are more closely related to each other than either is to HLA-A ., The median number of differences between sequences of HLA-B and HLA-C is 42 compared to 55–56 for differences between HLA-A and HLA-B or HLA-C ( S9 Fig ) ., These results are consistent with MHC-C having originated with duplication of an MHC-B allele ., Each of the 546 positions in exons 2 and 3 can have five alternative forms , the four different nucleotides and insertion/deletion ( indel ) ., The distribution of the variability is shown as histograms in S2 Fig and the numbers per exon for each gene are given in S3 Fig . In summing the data for the three genes , we find only 4 . 5% of the positions are invariant , whereas 23 . 2% , 34 . 3% and 32 . 2% positions have two , three and four forms , in HLA-A , –B and –C , respectively ., All five forms are present at 5 . 7% of positions ., The pattern of variability is similar for HLA-A , -B and -C ( S2 Fig ) ., Variation was thus found at almost every position in exons 2 and 3 of these genes ., We performed similar analysis of the amino-acid sequences of the α1 and α2 domains ., The results are displayed as histograms in S4 Fig and summarized in Table, 1 . The striking result is that , for each of the three genes , there are no positions in the sequences of their protein products that exhibit only one or two amino acids ., The number of residues at a given position varies from 3 to 14 , with 149 of the 181 positions having between 5 and 9 alternative amino acid residues ( Table 1 ) ., To distinguish positions having a balanced polymorphism between two or more nucleotides , from positions dominated by one nucleotide , we determined the incidence ( in the dataset of allelic sequences ) for the second-most common nucleotide at each position in the exon 2 and 3 sequence ( Fig 2 ) ., Positions where the incidence of the second nucleotide exceeded 1% were considered polymorphic , whereas positions with lower incidence were considered to exhibit rare variation ., The second nucleotide occurs in more than 1% of the alleles for 70 positions in HLA-A , 85 in HLA-B and 54 in HLA-C ( S5 Fig ) ., These comprise a minority of positions in the 546 bp sequence of exon 2 and 3 , demonstrating that the variation observed at most positions in exons 2 and 3 ( S2 Fig ) is due to the contribution of nucleotide substitutions that are present in one or a few alleles ., Analyzing the incidence of the second most common amino acid residue showed that all 181 positions in the α1 and α2 domains of HLA-A , -B and -C exhibit some variation ., Of these positions , however , only 45 in HLA-A , 46 in HLA-B and 32 in HLA-C have a second amino acid incidence of >1% and are thus considered polymorphic ( Fig 3 , S6 Fig ) ., Twelve of these positions are shared by HLA-A , -B and -C: four in α1 ( residues 9 , 66 , 77 and 80 ) and eight in α2 ( 95 , 97 , 99 , 114 , 116 , 152 , 156 and 163 ) ., Larger numbers of polymorphic positions are shared by two of the three HLA class I: 26 by HLA-A and -B , 20 by HLA-B and -C , and 14 by HLA-A and -C ., On the other hand , 17 polymorphic positions are unique to HLA-A , 12 to HLA-B and 10 to HLA-C ., These 39 positions impart considerable gene-specific character to the polymorphism ( Fig 4 ) ., This reflects functional specialization of the three HLA class I ., For polymorphic positions with a second nucleotide incidence of >1% , the mean number of different nucleotides is 3 . 8 for HLA-A , 3 . 7 for HLA-B and 3 . 6 for HLA-C ., The values are higher than the mean differences for all other variable positions: 3 . 1 for HLA-A and HLA-B and 2 . 9 for HLA-C ., The polymorphic positions have a significantly increased incidence of three or more nucleotides at each position ( 91% ) when compared to the other positions in the dataset ( 73% ) ( Chi squared test , p = 2 . 08 x 10−6 ) ., Additionally there are polymorphic positions with three or more nucleotides with an incidence of >1% ., There are nine positions in HLA-A , 14 in HLA-B and nine in HLA-C having three nucleotides with an incidence >1% ., With four nucleotides at an incidence >1% are position 527 ( codon 152 ) in HLA-A , positions 206 ( codon 45 ) , 272 ( codon 67 ) and 362 ( codon 97 ) in HLA-B , and position 368 ( codon 99 ) in HLA-C ., These results suggest that variation arising at these sites is more likely to be retained in the population ., This is consistent with the sequence variation at such sites serving to diversify the functional interactions of HLA class I with peptide antigens and lymphocyte receptors ., Crystallographic analyses have identified 70 residues in α1 and α2 domains of HLA class I that are involved in binding peptide antigens and/or lymphocyte receptors 11 , 23–27 ., These functionally defined residues overlap considerably with the set of polymorphic residues defined by the incidence of the second nucleotide ., Thus , 35 of 45 polymorphic HLA-A positions , 32 of 46 polymorphic HLA-B positions and 19 of 33 polymorphic HLA-C positions are functionally important sites ., This correlation of function with polymorphism is highly significant for HLA-A ( p = 6 . 52 x 10−7 ) and HLA-B ( p = 1 . 18 x 10−6 ) , but less so for HLA-C ( p = 0 . 0124 ) ( 2x2 Fisher’s Exact test ) ., The difference is consistent with highly polymorphic HLA-A and -B molecules interacting mainly with highly diverse αβ CD8 T cell receptors , and less polymorphic HLA-C molecules interacting mainly with the less diverse killer cell immunoglobulin–like receptors ( KIR ) of NK cells ., The striking correlation between immunological function and genetic polymorphism was further investigated by testing the polymorphic sites for evidence of positive selection ., Our null hypothesis was that polymorphic sites are not subject to positive selection ., If correct there would be no bias in the rates of synonymous and non-synonymous nucleotide substitutions , as measured by the parameters dS and dN ., For each test performed , the probability for rejecting the null hypothesis of neutral variation ( dN = dS ) is shown in Table, 2 . Values of P<0 . 05 , following a Bonferroni correction and bootstrapping of 1 , 000 replicates , were considered significant at the 5% level and are highlighted ., We first compared the 70 codons encoding functionally critical α1 and α2 domain residues ( Binding site codons in Table 2 ) , as defined previously 11 , to the other 112 codons of exons 2 and, 3 . For the 70 functional positions , the dN-dS values all point in the direction of positive selection ( 3 . 58 for HLA-A , 2 . 89 for HLA-B and 2 . 58 for HLA-C ) and are statistically significant for HLA-A ( p = 0 . 0031 ) and HLA-B ( p = 0 . 0275 ) but not for HLA-C ( p = 0 . 0720 ) ( statistical significance is achieved at p<0 . 05 , after application of Bonferroni correction to the tests on a per gene basis ) ., In contrast , the 112 other positions ( Not binding site codons ) have negative dN-dS values consistent with the null hypothesis: -1 . 78 for HLA-A ( p = 1 . 0 ) , -1 . 73 for HLA-B ( p = 1 . 0 ) and -1 . 25 for HLA-C ( p = 1 . 0 ) ., These results argue strongly against positive selection at the other positions ., Having validated the selection analysis on functional sites , we compared the polymorphic codons , as defined by having at least one nucleotide position where the incidence of the second nucleotide >1% , with the remaining codons of exons 2 and, 3 . For the polymorphic codons the dN-dS values pointed clearly in the direction of positive selection and were statistically significant: 4 . 98 for HLA-A ( p = 0 . 0001 ) , 4 . 55 for HLA-B ( p = 0 . 0001 ) but not for HLA-C ( 2 . 20 , p = 0 . 1800 ) ., In contrast , the values for the codons where the second nucleotide was present at less than 1% were all decidedly negative: -2 . 66 for HLA-A ( p = 1 . 0 ) , -3 . 06 for HLA-B ( p = 1 . 0 ) and -2 . 78 for HLA-C ( p = 1 . 0 ) ., These data strongly support positive selection at the polymorphic positions ., Independent analysis of the α1 and α2 domains ( Table 2 ) shows that dN-dS for HLA-A is higher in α2 for both binding sites and polymorphic positions ( 3 . 342 , p = 0 . 0067; 4 . 517 p = 0 . 0008 ) than α1 where selection is detected only for polymorphic positions ( 1 . 359 p = 1 . 0000; 3 . 135 p = 0 . 0130 ) which represent a subset of the functionally important residues ., For HLA-B selection was detected for the polymorphic positions in both α1 ( 3 . 467 , p = 0 . 0044 ) and α2 ( 2 . 875 , p = 0 . 0286 ) and for complete set of binding site codons ( 2 . 889 , p = 0 . 0275 ) but not the individual domains ., The HLA-C sequences show no significant selection differences between the α1 and α2 domains , with neither the functional nor polymorphic positions showing significant positive selection ., Assessment of selection at gene-specific positions of polymorphism ( Fig 5 ) showed there has been positive selection only for HLA-C specific polymorphisms and those are limited to one of the two domains ., The α1 domain has been subject to strong positive selection ( dN-dS = 3 . 65 , p = 0 . 0023 ) , but that is not the case for HLA-C specific sites of α2 ( dN-dS = -0 . 16 , p = 1 . 00 ) ., The gene-specific sites of HLA-A and HLA-B show no evidence for significant positive selection ., Previous analysis of HLA class I variation , studied small numbers of alleles and relied on visual inspection to discern the relationships between them 5 ., To analyze the current dataset of 10 , 956 HLA class I sequences , we developed the Sq2 algorithm ( see Materials and methods ) , which provides a quicker , more objective and largely automated approach ., In two separate phases of analysis , Sq2 divided the alleles into three categories ., In the first phase , Sq2 identified all SNP alleles , which constitute ~85% of the dataset ., These are alleles of more recent origin that differ from an older allele by just one nucleotide substitution ., After identifying and removing the SNP alleles , the reduced database of 1 , 555 alleles was subjected to the second phase of analysis ., This identified all alleles that are recombinants of other alleles ., To do this , Sq2 identified motifs of several substitutions that are present in multiple allelic backgrounds as a consequence of recombination ( Fig 5 ) ., The iconic example is the Bw4 motif ., Present in codons 76–83 of one third of HLA-B alleles , Bw4 defines the ligand recognized by a major NK cell receptor , KIR3DL1 28 , 29 ., As well as being present in 12 of the 33 HLA-B allele families , Bw4 was transferred by a gene conversion from HLA-B to HLA-A , where it spread by recombination to four HLA-A allele families 30 ., By comparing the distribution of such motifs among alleles , Sq2 identified pairs of alleles differing only by presence or absence of a particular motif ., In this way 1 , 171 recombinants were identified ., Of these 1 , 092 were formed by recombination between alleles of the same gene ( intragenic recombinants ) , and 79 were recombinants formed by recombination between alleles of different genes ( intergenic recombinants ) ., Of the latter , 16 are products of single recombination ( crossover ) and 63 ( 10 HLA-A , 37 HLA-B , and 16 HLA-C ) are products of double recombination ( conversion ) ., HLA-B is clearly seen as the more frequent beneficiary of recombination ( Table 3 ) ., Among intragenic recombinants , double recombinants ( N = 735 ) outnumber single recombinants ( N = 357 ) by a factor of two ., It is likely that some alleles assigned as single recombinants are actually double recombinants , for which the second recombination is not in exon 2 or 3 but in a flanking intron , for which we had no sequence ., Both forms of recombinant are more prevalent at HLA-B ( N = 728 ) than either HLA-A ( N = 226 ) or HLA-C ( N = 138 ) ., The frequency of double recombination for HLA-B is similar in exons 2 and 3 , whereas it is greater in exon 2 of HLA-A and in exon 3 of HLA-C ., A similar hierarchy is observed for the single recombinants ., Removal of SNP and recombinant alleles , reduced the database to <1% of its original size ., This left 11 HLA-A , 17 HLA-B and 14 HLA-C alleles ( Fig 6A ) ., Because these 42 alleles represent all functionally significant variation ( polymorphism ) in exons 2 and 3 of HLA-A , -B and -C , we call them ‘core’ alleles ( Fig 6B ) ., Although they are older in their origins than the SNP alleles and recombinant alleles , they are unlikely to represent , or reflect , any particular human population , either ancient or modern ., Core alleles vary widely in their contribution to the total set of alleles ( Fig 6A ) , in their geographical distribution ( S7 Fig ) and in their abundance in the modern human population ., A substantial proportion of the core alleles , 5 HLA-A , 8 HLA-B and 6 HLA-C , are likely derived from archaic humans ( Fig 6A ) 31 ., A dot plot analysis of the core alleles ( S8 Fig ) has similar substructure to that of the complete set of alleles ( Fig 1D ) and for each gene the mean pairwise differences for core alleles and all alleles is remarkably similar ( S9 Fig ) ., Analysis of selection on the polymorphic and functional sites of core HLA-A , -B and -C alleles ( Table 4 ) gives comparable results to those obtained for the full sets of alleles ( Table 2 ) for HLA-A ., For HLA-B and -C the results are comparable when looking at the full-length sequence , but some differences are seen for the individual domains ., This could , however , be due to the small number of sequences analyzed ., The effects of applying the Sq2 algorithm to the HLA class I data set are seen in histograms constructed from the pairwise differences of nucleotide sequences ( Fig 7 , top row ) ., For complete sets of HLA-A , -B and -C alleles , the histograms have a characteristic bimodal distribution with one peak at 2 nucleotide differences and a second peak at 20–30 nucleotide differences ., The first peak contains the large number of pairwise comparisons between alleles differing by one or two nucleotide substitutions ., Pairs differing by one nucleotide substitution usually involve an older , common allele and a rare SNP variant ., Pairs differing by two nucleotide substitutions involve two rare SNP variants that differ from the same parental allele by different SNPs ., Taking the SNP alleles out of the analysis , led to loss of the first peak and retention of the second peak ( Fig 7 , middle row ) ., For HLA-A and -B the loss is complete , but for HLA-C it is not ., HLA-A gives a bimodal distribution , which differs from that observed in the complete dataset ., This is because HLA-A comprises a small number of large and divergent allele families ., Thus the minor distribution , seen as the shoulder at 4–12 nucleotide differences , comprises the differences between members of the same allele family , whereas the major distribution is formed from the larger differences between members of different allele families ., In contrast to HLA-A , HLA-B comprises a large number of less divergent allele families than HLA-A , as well as a few highly divergent alleles with no close family ties ., This gives HLA-B both a more symmetrical and broader distribution ., Histograms for the pairwise differences between core alleles ( Fig 7 , bottom row ) represent much of the range of difference seen with the larger data sets , with the notable absence of allele pairs differing by small numbers of substitutions ., That the HLA-C core allele histogram has a distribution with a more coherent shape , than the HLA-A and -B core histograms , probably reflects the more recent origin of HLA-C 4 , 32 ., Because we have detected variation at all nucleotide positions in exons 2 and 3 of HLA-A , -B and -C ( S2 Fig ) the maximum number of possible HLA class I alleles is 5546 ( 4 . 3 x 10381 ) ., This calculation is based on observing all four nucleotides or an indel at each of the 546 positions in the exon 2 and 3 sequence ., This number far exceeds the size of the modern human population , which is estimated to be 7 . 5 billion ( http://www . worldometers . info/world-population/ ) ., This difference means that the number of variants present in a population is limited only by the size of that population ., To estimate the total number of HLA-A , -B and -C alleles now present in the human population , we first determined the rate at which novel alleles are being identified ., In this context , the rate is simply the ratio between the number of individuals typed and the number of new alleles discovered ., For each gene , the product of the rate and the population size ( 7 . 5 billion ) gives an estimate of the total number of alleles ., To provide an internally consistent dataset , we analyzed HLA typing data from donor cohorts recruited by various transplantation registries , but all typed at the same sequencing center ( Histogenetics ) ., Similar rates , of 1 . 80 , 2 . 13 , and 2 . 18 x 10−4 , were observed for the acquisition of novel HLA-A , -B and –C alleles , respectively ( Table 5 ) ., Using these rates , we estimate there are 2 . 7 million HLA-A , 3 . 3 million HLA-B and 3 . 2 million HLA-C alleles in today’s human population ., These estimates are comparable to the 3 . 5 million alleles per HLA gene predicted by Klitz , et al 33 , using estimates of effective population size and mutation rates ., Our method for estimating the total numbers of HLA-A , -B and -C alleles used a constant rate for the discovery of novel alleles ., This assumption was based on the results of two recently published studies 34 , 35 , which both indicated that the rate of discovery of new alleles is not tapering off over time , even for European populations 34 , 35 , which have been intensively studied compared to the populations of other continents ., Analyses show that the human population has a small number of common HLA class I alleles ( 68 HLA-A , 125 HLA-B , 44 HLA-C ) that are present at appreciable frequency in different populations 36 ., In contrast , the overwhelming majority of HLA class I alleles are very rare and highly localized in their distribution ., Consistent with these properties , each newly sampled cohort or population is expected to harbor a subset of HLA-A , -B and -C alleles that are novel and present in only one or a few individuals ., Because of their rarity and population specificity , the relative frequency of novel alleles will not diminish in time as further cohorts of donor are HLA typed at high resolution ., We studied sequence variation in exons 2 and 3 that encode the highly polymorphic α1 and α2 domains of HLA-A , -B and -C ., The analysis was restricted to these exons and genes to enable an in depth study of the maximum number of sequences ., The tools developed for this analysis can , and should , be extended to study the remaining exons of these genes , which are known to contain functionally relevant polymorphism 18–20 , when sufficient data becomes available ., These analyses can also be applied to the study of polymorphism in the HLA class II genes ., Sequence differences in the α1 and α2 domains of HLA-A , -B and -C determine the peptide antigens that are bound by an HLA class I allotype , as well as the lymphocyte receptors that can engage the complex of peptide and HLA class I . HLA-A , -B and -C are candidates for being the most polymorphic of human genes 22 ., Moreover , their polymorphisms are associated with numerous clinical factors including infectious diseases , autoimmune and inflammatory diseases , pregnancy syndromes and success in the transplantation of allogeneic organs and tissues 7 , 37–42 ., Transplantation of bone marrow , and other sources of hematopoietic stem cells , is a successful and widely used therapy for leukemias , lymphomas and other malignancies of hematopoietic cells ., The preferred donor is an HLA identical sibling , but in the absence of such a donor , the next best choice is an unrelated individual having the same , or very similar , HLA type as the patient ., To identify such donors , there exists an international network of donor registries , which has HLA typed more than 30 million potential HCT donors 43 ., During the last ten years , less precise methods of HLA typing have been superseded by nucleotide sequencing exons 2 and 3 of the HLA-A , –B and –C genes ., The set of HLA class I sequences we studied derive from sequence-based typing of >3 million individuals , as well as earlier studies in which typing at lower levels of resolution identified variants , which were followed up with targeted sequence analysis ., The prospective donors of hematopoietic stem cells were recruited to registries in varied countries and continents , but demographically and anthropologically they are not , in the main , well characterized ., A total of 10 , 956 different exon 2 and 3 sequences were analyzed: 3 , 489 HLA-A , 4 , 356 HLA-B and 3 , 111 HLA-C alleles ., In our analysis of these three sets of alleles , each sequence was given equal weight , irrespective of its abundance or scarcity in any human population ., At the nucleotide level , we found substitutions at >95% of all positions in each of the three genes ., As the exceptions are at different positions in each gene , we predict that substitution at these positions will soon be identified ., At the amino-acid level , we found substitutions at every position in the α1 and α2 domains of HLA-A , -B and –C ., A majority of the substitutions , >84% , are in rare alleles , which in many cases have been detected in only one individual or one family ., Most of the alleles differ from a common allele by the single substitution that defines them ., The obvious interpretation of these data is that these substitutions reflect the germ line mutation rate of the HLA-A , –B and –C genes ., Consistent with this thesis , there is no evidence for positive selection at these sites , many of which are , otherwise , highly conserved ., The remaining alleles are formed by intragenic or , rarely , intergenic recombination events ., From the rate at which new alleles in exons 2 and 3 have been defined by sequence-based typing we estimate there are 2–3 million each of HLA-A , -B and -C alleles in the human population worldwide ., The majority of the variable nucleotide positions are characterized by one dominant and one or more rare nucleotides ., However , variation at a smaller number of nucleotide positions , ( 70 , 85 and 54 in HLA-A , -B and -C , respectively ) has a very different character ., These positions have two , three or four nucleotides at appreciable frequency ., They have also been spread by recombination throughout the population of alleles and are thus found in numerous combinations ., There is good evidence for positive selection at these sites , which has over time , given them a balanced polymorphism ., Supporting this conclusion , numerous immunological studies have correlated substitution at polymorphic sites with modulation of HLA-A , -B and –C function 7 , 37 , 40 , 41 , 44–46 ., Thus we can divide the alleles into two distinctive groups ., Firstly SNP alleles , defined by substitution that confers no functional benefit , but could be detrimental in the context of transplantation ., Secondly , functional alleles , with functional benefit conferred by combinations of substitutions at positions with balanced polymorphism ., We further divided the functional alleles into two subgroups: 1 , 171 recombinant alleles that were derived by recombination from other alleles and 42 core alleles ( 11 HLA-A , 17 HLA-B and 14 HLA-C ) that cannot be derived by simple events of recombination from other alleles ., The core alleles , many of which were passed by introgression from archaic to modern humans 31 , contain all elements of HLA-A , -B and –C polymorphism present in the modern human population ., Although the core alleles are probably older than the SNP alleles and the recombinant alleles , they are very unlikely to represent the HLA-A , -B and -C alleles carried by any particular ancestral human population ., Because polymorphic MHC class I and II genes have no wild-type , understanding their genetics and biology in any species requires extensive study of populations ., For reasons of cost and logistics this has been rarely , if ever , achieved ., Many population studies have recruited only small numbers of individuals ( therefore , likely missing rare alleles ) and until recently have reliably assayed only known alleles ., Because the HLA class I and II genes contribute to so many numerous and diverse aspects of human health and disease 7 , 37–42 , the MHC of the human species is by far the most studied and , by default , provides the model for studies of other placental mammals 4 , 32 , 47–49 ., The capacity to acquire large datasets , of the type we have analyzed and reported here , should enable HLA population genetics and disease associations to be studied to increasingly higher definition , resolution and coverage of the world’s human populations ., The minimum requirement for naming an HLA class I allele and depositing it in the IPD-IMGT/HLA Database , is the nucleotide sequence of exons 2 and, 3 . Because of this requirement , a majority of deposited sequences ( ~65% of HLA-A and -B alleles and ~80% of HLA-C alleles ) consist of only exons 2 and 3 , encoding residues 2-182 of the mature HLA class I protein ., Thus to maximize the number of alleles analyzed we limited this study to the sequences of exons 2 and, 3 . Our analysis used all sequences in the IPD-IMGT/HLA database as of July 2016 ( Release 3 . 25 . 0 ) ., All analyses used custom written Perl scripts , http://www . perl . org 50 , with graphical outputs generated using the Perl::GD modules or R , http://www . r-project . org 51 ., Where appropriate , statistical analysis was also completed in R . For F distribution analyses with df1 and df2 exceeding 1 , 000 , R outputs a p-value of 0 , these have been reported as p<1 x 10−10 ., The set of scripts developed constitutes the Sq2 package ., Individual scripts perform different steps of the algorithm ., The individual algorithms are listed and described below: The scripts are available from the ANHIG Gitlab repository which can be found at; https://github . com/ANHIG ., Alleles of an HLA class I gene are of three types: core alleles , recombinant alleles and SNP alleles ., Core alleles comprise the set of alleles that cannot be related to each other by single events of recombination or point mutation ., Recombinant alleles are the products of one or more recombination events between core alleles ., SNP alleles differ from another allele by a single nucleoti

Introduction, Results, Discussion, Materials and methods

HLA class I glycoproteins contain the functional sites that bind peptide antigens and engage lymphocyte receptors ., Recently , clinical application of sequence-based HLA typing has uncovered an unprecedented number of novel HLA class I alleles ., Here we define the nature and extent of the variation in 3 , 489 HLA-A , 4 , 356 HLA-B and 3 , 111 HLA-C alleles ., This analysis required development of suites of methods , having general applicability , for comparing and analyzing large numbers of homologous sequences ., At least three amino-acid substitutions are present at every position in the polymorphic α1 and α2 domains of HLA-A , -B and -C ., A minority of positions have an incidence >1% for the ‘second’ most frequent nucleotide , comprising 70 positions in HLA-A , 85 in HLA-B and 54 in HLA-C ., The majority of these positions have three or four alternative nucleotides ., These positions were subject to positive selection and correspond to binding sites for peptides and receptors ., Most alleles of HLA class I ( >80% ) are very rare , often identified in one person or family , and they differ by point mutation from older , more common alleles ., These alleles with single nucleotide polymorphisms reflect the germ-line mutation rate ., Their frequency predicts the human population harbors 8–9 million HLA class I variants ., The common alleles of human populations comprise 42 core alleles , which represent all selected polymorphism , and recombinants that have assorted this polymorphism .

The HLA complex is a region of the human genome containing immune system genes ., Our study concerns those HLA genes that orchestrate defense against viral infections ., Distinguishing HLA genes from other human genes is their extensive variation within individuals , families and populations ., One advantage of this genetic variation is to increase the depth and breadth of the weaponry used against viruses; another is to impede the spread of infection within families and communities ., A drawback to HLA variation is that bone-marrow transplants between donors and patients of different HLA type trigger immune reactions that attack and can kill the patient ., For some patients an HLA identical family member can be the donor , but for others an unrelated HLA identical donor is sought ., Facilitating these searches are registries , listing millions of possible donors whose HLA types were determined by gene sequencing ., During the last ten years , this effort produced exponential growth in the number of HLA variants sequenced ., This gave us the unprecedented opportunity to compare more than 10 , 000 sequences and distinguish aspects of the variation that are important for immune functions , from those that are not ., First , however , we needed to develop software that could handle this mass of data .

sequencing techniques, split-decomposition method, alleles, multiple alignment calculation, sequence motif analysis, molecular biology techniques, research and analysis methods, sequence analysis, sequence alignment, bioinformatics, biological databases, molecular biology, genetic loci, nucleotide sequencing, dna sequence analysis, sequence databases, computational techniques, database and informatics methods, genetics, biology and life sciences

journal.pgen.1002293

A Genome-Wide Meta-Analysis of Six Type 1 Diabetes Cohorts Identifies Multiple Associated Loci

Diabetes impacts approximately 200 million people worldwide 1 , with microvascular and cardiovascular disease being the primary complications ., Approximately 10% of cases are type 1 diabetes ( T1D ) sufferers , with ∼3% increase in the incidence of T1D globally per year 2 ., It is expected that the incidence is 40% higher in 2010 than in 1998 3 ., T1D is a clear example of a complex trait that results from the interplay between environmental and genetic factors ., There are many lines of evidence that there is a strong genetic component to T1D , primarily due to the fact that T1D has high concordance among monozygotic twins 4 and runs strongly in families , together with a high sibling risk 5 ., Prior to the era of GWAS , only five loci had been fully established to be associated with T1D ., However , the majority of the other reported associations in the pre-GWAS era 6–8 remain highly doubtful , where an initial report of association does not hold up in subsequent replication attempts by other investigative groups ., This previous hazy picture of the genetics of T1D can be put down to the use of the only methodologies that were available at the time and which were much more limited than GWAS i . e . the candidate gene approach ( where genomic regions were studied based on biological reasoning ) and family-based linkage methodologies ., Inconsistent findings can also be attributed to small sample sizes i . e . when power is low the false discovery rate tends to be high; GWAS per se has not improved consistency , rather it has leveraged large , well powered sample sizes combined with sound statistical analyses ., It has been long established that approximately half of the genetic risk for T1D is conferred by the genomic region harboring the HLA class II genes ( primarily HLA-DRB1 , -DQA1 and -DQB1 genes ) , which encode the highly polymorphic antigen-presenting proteins ., Other established loci prior to the application of GWAS are the genes encoding insulin ( INS ) 9-12 , cytotoxic T-lymphocyte-associated protein 4 ( CTLA4 ) 13–16 , protein tyrosine phosphatase , non-receptor type 22 ( PTPN22 ) gene 17 , 18 , interleukin 2 receptor alpha ( IL2RA ) 19–21 and ubiquitin-associated and SH3 domain-containing protein A ( UBASH3A ) 22 ., The application of genome wide association studies ( GWAS ) has robustly revealed dozens of genetic contributors to T1D 23–29 , the results of which have largely been independently replicated 30–36 ., The most recently reported meta-analysis of this trait identified in excess of forty loci 29 , including 18 novel regions plus confirmation of a number of loci uncovered through cross-disease comparisons 34–36 ., As such , the risks conferred by these additional loci are relatively modest compared to the ‘low-hanging fruit’ described in the first studies and could only be ultimately uncovered when larger sample sizes were utilized ., We sought to expand further on this mode of analysis by combining our cohort with all publically released genome wide SNP datasets to identify additional loci contributing to the etiology of T1D ., Unfortunately , there is a relative paucity of control genotype data in these publically available sources ., To circumvent this problem , we combined individual level data from each available cohort and we then compared the cases with controls from two sources ., We next separated all the individual level data into two groups , characterized by the type of genotyping platform that was used to genotype the samples , which would later be recombined using inverse-variance meta-analysis ., The 6 , 523 cases genotyped on an Illumina BeadChip included subjects from McGill University , The Childrens Hospital of Philadelphia ( CHOP ) , The Diabetes Control and Complications Trial – Epidemiology of Diabetes Interventions and Complications ( DCCT-EDIC ) cohort , and the Type 1 Diabetes Genetics Consortium ( T1DGC ) , which in turn were compared with 6 , 648 similarly genotyped controls recruited at CHOP ., The 3 , 411 cases genotyped on Affymetrix arrays included subjects from the Genetics of Kidneys in Diabetes Study ( GoKinD ) and the Wellcome Trust Cases Control Consortium ( WTCCC ) that were then compared with 10 , 308 similarly genotyped controls , including being derived from non-autoimmune disease related cases from the WTCCC , as well as from the British 1958 Birth Cohort and the UK National Blood Service 24 ., We compared the power of our meta-analysis to that of the previous largest meta-analysis to date ., We have more than double the power of the Barrett et al . meta-analysis to find variants with a relative risk of 1 . 2 and approximately three times the power to detect variants with a relative risk of 1 . 1 29 ( Figure S1 ) ., We used principal components analysis ( PCA ) 37 in order to minimize the potential impact of population stratification in our case/control sample sets ., Eigenstrat 3 . 0 was employed to remove outliers and to subsequently calculate the principal components in the Illumina and Affymetrix assigned groups separately ., The principal components were then used as covariates in a logistic regression , using the software PLINK 38 , to compute the P-values , betas and standard errors which were combined in our fixed effects inverse variance meta-analysis ., After controlling for population stratification , the λ in the Affymetrix and Illumina cohorts was 1 . 11 and 1 . 17 , respectively ( see Figure 1 for Q-Q plot ) ., A full description of the correlation of each eigenvector with known continental ancestry appears in Text S1 ., Mach was used to impute ∼2 . 54 million SNPs , including HapMap Phase 2 SNPs in the Illumina and Affymetrix datasets in order for the statistics to be uniform and amenable to being combined 39 ., Results from the meta-analysis of this resulting ‘discovery’ cohort are shown Table 1 and graphically in Figure 2 ., 53 SNPs were brought forward to the replication stage based on satisfying the following criteria; however one of these SNPs consistently failed genotyping in the replication effort ., The most significantly associated SNP at a given locus if the meta-analysis P-value was less than 1×10−5 ( an independent locus was defined as a region for a given focal SNP , where we extended the region in both directions until either 250 kb had been traversed or until reaching another SNP with P<10−5 ) , the Cochrans Q statistic P-value was greater than 0 . 05 and the locus had not been already reported from a previous GWAS of T1D ., A table outlining the results for all previously described T1D associated SNPs plus our strongest associations for known regions associated with the disease are shown in Table 2 and Table S1 , respectively ., The replication cohort consisted of additional T1D affected trios from the T1DGC and McGill which had not been part of the original discovery cohort ., The replication cohort was genotyped using the Sequenom iPLEX system and the results were analyzed using the transmission disequilibrium test in PLINK ., Results for both the discovery and replication cohorts for the six SNPs that replicated with P≤0 . 05 are shown in Table 1 ( the full outcomes for all SNPs tested are in Table S2 ) ., We combined the ‘discovery’ and ‘replication’ meta-analysis P-values using Fishers combined P-value method implemented in Haploview , comparable to what has been previously employed by others 40 ., Three of the SNPs , namely rs539514 , rs478222 and rs924043 , had combined P-values <5×10−8 , the statistical threshold for genome wide significance , while the remaining three , namely rs550448 , rs12679857 and rs6547853 , failed to reach this bar but were suggestive of association as the alleles yielded both a consistent direction of effect and P-values <0 . 05 in the replication cohort ., These two categories of outcome are summarized in Table 1; in addition , these six SNPs were further investigated with respect to adjustments of the discovery and met-analysis P-values based on the lambdas of each respective cohort ( Table S3 ) ., We have carried out the largest meta-analysis of genome wide genotyped datasets for T1D to date ., The replication of three loci using the stratification-free TDT with minimal Mendelian error clearly indicates that they are not false positives due to artifacts such as uncorrected systematic error from stratification or genotyping bias ., The most significantly associated SNP ( rs539514 , P\u200a=\u200a5 . 66×10−11 ) resides in an intronic region of the LMO7 ( LIM domain only 7 ) gene on 13q22 ., We investigated the associated region using LocusZoom 41 and determined that it is the only gene residing within the block of linkage disequilibrium harboring the signal ( Figure S3 ) ., Regional plots showing P-values , linkage disequilibrium , and recombination rate for all SNPs in Table 1 are outlined in the Figures S2 , S3 , S4 , S5 , S6 , S7 ., LMO7 encodes a protein that contains multiple domains , including a calponin homology domain , a PDZ domain and a LIM domain ., There are multiple LMO7 isoforms already known but their full nature and the actual extent of different isoforms remains unclear 42 ., Mice with homozygous deletions of LMO7 display retinal , muscular , and growth retardation 43 ., Although the function of LMO7 doesnt clearly relate to the etiology of T1D , LMO7 is expressed in pancreatic islets and thus is a possible biological candidate at this locus 44; however it should be noted that the retinal , muscular development and islet patterns are a key element in Emery-Dreifuss Muscular Dystrophy , caused by mutations in LMO7 45 , but bears very little similarity to T1D ., The second most significantly associated SNP ( rs478222 , P\u200a=\u200a3 . 50×10−9 ) resides in an intronic region of the EFR3B ( protein EFR3 homolog B ) gene on 2p23; however the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes , including 3NCOA1 , C2orf79 , CENPO , ADCY3 , DNAJC27 , POMC , and DNMT3A ., ( Figure S2 ) ., A previous meta-analysis of a subset of the data used in this current study found suggestive association with T1D in the same LD block with the independent SNP , rs2165738 ( r2\u200a=\u200a0 . 115 ) , but did not achieve genome wide significance at that time ( P\u200a=\u200a3 . 65×10−6 ) 27; however , we only found modest evidence of association with rs2165738 ( P\u200a=\u200a4 . 78×10−3 ) in our discovery cohort ., There has also been association to inflammatory bowel disease 46 height 47 , 48 and BMI 49 reported at this locus , where in both cases the risk allele for increased height or BMI was protective for T1D risk ., The third most significantly associated SNP ( rs924043 , P\u200a=\u200a8 . 06×10−9 ) lies in an intergenic region on 6q27 , where the region of association is approximately 900 kb and harbors multiple genes including WDR27 , C6orf120 , PHF10 , TCTE3 , C6orf208 , LOC154449 , DLL1 , FAM120B , PSMB1 , TBP and PCD2 ( Figure S5 ) ., In addition , despite not reaching the bar for genome wide significance , we did observe evidence for association at three additional loci ( Table 1 ) containing the candidate genes LOC100128081 , TNFRSF11B and FOSL2 ., Of these , it is notable that TNFRSF11B is a strongly associated locus with bone mineral density , also as a consequence of GWAS 50 , 51 ., In addition , the locus harboring LOC100128081 has also been reported in the context of a GWAS of SLE 52 ., Further work will be required to fully validate the role of these particular loci in the pathogenesis of T1D ., The Barrett et al . meta-analysis was able to use British controls with British cases and American controls with American cases 29 ., We did not have the same control data to be able to make the same comparisons ., In the case of the Affymetrix analysis , some American cases were analyzed with purely British controls and , in the case of the Illumina analysis , some British cases with purely American controls ., As such , we were forced to make our corrections using eigenvectors as covariates in our analysis; this will have the effect of modestly weakening the level of significance for associations that vary in allele frequency between the cases and controls , as now the case and controls will both vary with the eigenvectors to some degree ., This in effect will make our analysis overly conservative with estimating the true effect of a SNP , and in fact every SNP that had a P-value less than 0 . 05 in the replication set did indeed have a greater effect than that which was estimated from the discovery set ., In summary , we provide convincing evidence for the existence of three additional loci associated with the T1D , adding to the repertoire of over 50 loci already demonstrated to be associated with the disease ., The study was approved by the institutional review board and the ethics committee of each institution ., Written informed consent was obtained from each participant in accordance with institutional requirements and the Declaration of Helsinki Principles ., Cases in the discovery set were obtained from four publically available resources and combined with those from a previous publication for the meta-analysis ., Samples descriptions are available on dbgap ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=gap ) for the T1DGC ( http://www . ncbi . nlm . nih . gov/projects/gap/cgi-bin/study . cgi ? study_id=phs000180 . v1 . p1 ) , GoKinD ( http://www . ncbi . nlm . nih . gov/projects/gap/cgi-bin/study . cgi ? study_id=phs000088 . v1 . p1 ) , and DCCT-EDIC ( http://www . ncbi . nlm . nih . gov/projects/gap/cgi-bin/study . cgi ? study_id=phs000086 . v2 . p1 ) patients ., The WTCCC sample information is available from 24 ., Samples from the T1D segment of the WTCCC were used as cases , while controls were derived from the 1958 Birth Cohort , UK Blood Service , Bipolar disorder , Coronary heart disease , Hypertension , and Type 2 Diabetes segments ., The remaining cases used in the meta-analysis were previously described 23 ., The total number of individuals used in the meta-analysis discovery set was 26 , 890 ( 9 , 934 cases/16 , 956 controls ) ., The replication set consisted of 1120 case-parent trios from the T1DGC and those identified through pediatric diabetes clinics in Canada ., The replication set was identical to that used in Hakonarson et al . with an extension of patients identified through pediatric diabetes clinics in Montreal , Toronto , Ottawa , and Winnipeg ., All individuals were of Caucasian ancestry ., A breakdown of the number of samples in each cohort in the discovery phase and a comparison with the numbers used in the Barrett et al . meta-analysis are shown in Table 3 29 ., The minor variation in the number of cases reflects that , despite using slight differences in both quality control and methods for dealing with population stratification , we have comparable numbers of cases from this cohort remaining in our analysis ., Primarily , this small difference is due to the fact that we strictly accounted for relatedness and duplicates within and across cohorts in this current setting ., Power analysis was performed with the genetic analysis calculator which can be found at ( http://pngu . mgh . harvard . edu/~purcell/gpc/ ) 53 ., Various assumption were made included perfect LD between the causative variant and the markers that were genotyped , an additive genetic model , a disease prevalence of 0 . 0033 and an alpha of 1×10−5 ., Discovery samples from Philadelphia , Canada , T1DGC , and DCCT-EDIC were genotyped on a mixture of the Illumina HumanHap 550v1 , 2 , and 3 , whereas samples from GoKinD and WTCCC were genotyped on the Affymetrix 500 K Chip ., Sequenom iPlex was used to replicate the findings of the meta-analysis in 1 , 120 affected offspring trios from the T1DGC and from Canada ., All individuals needed an individual genotyping call rate greater than 0 . 98 to be included in the analysis pre-imputation and individuals were removed that showed evidence of cryptic relatedness and duplication within and across cohorts using identity-by-state ., SNP quality control was performed on all samples pre-imputation ., SNPs were excluded from the analysis if the minor allele was below 1% , the genotyping call rate was less than 95% , or the Hardy Weinberg equilibrium P-value was less than 0 . 00001 ., To control for population stratification , Eigenstrat 3 . 0 was used to compute the top 10 principal components of the individuals genotyped on the Illumina SNP chips and the Affymetrix SNP chips separately 37 ., Individuals were removed from the analysis if they were 6 standard deviations away from the mean of one of the top 10 principal components ., After controlling for population stratification , the estimated lambda in the Affymetrix data was 1 . 11 and 1 . 17 in the Illumina data ., Mach 1 . 0 was used to impute ∼2 . 54 millions SNPs from the HapMap CEU panel for all individuals 39 ., SNPs were excluded after imputation if they had a minor allele frequency less than 0 . 01 and an r2 value less than 0 . 3 ., PLINK 38 was used to perform a logistic regression using the 10 principal components as covariates , T1D status as the outcome , and in the case of the Affymetrix cohort , an extra dummy covariate specifying WTCCC or GoKinD cohort membership ., Results from the logistic regression of 2 , 436 , 110 SNPs from the Affymetrix samples and 2 , 062 , 307 SNPs from the Illumina samples separately were combined using inverse-variance meta-analysis in PLINK ., A fixed effects meta-analysis was performed and 53 SNPs were chosen for replication who had a fixed effects P-value <0 . 00001 , a Cochrans Q statistic P-value greater than 0 . 05 and were not previously known to be associated with type 1 diabetes ., However one of the SNPs consistently failed during the replication effort .

Introduction, Results, Discussion, Materials and Methods

Diabetes impacts approximately 200 million people worldwide , of whom approximately 10% are affected by type 1 diabetes ( T1D ) ., The application of genome-wide association studies ( GWAS ) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D , with the most recent meta-analysis identifying in excess of 40 loci ., To identify additional genetic loci for T1D susceptibility , we examined associations in the largest meta-analysis to date between the disease and ∼2 . 54 million SNPs in a combined cohort of 9 , 934 cases and 16 , 956 controls ., Targeted follow-up of 53 SNPs in 1 , 120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance ., The most significantly associated SNP ( rs539514 , P\u200a=\u200a5 . 66×10−11 ) resides in an intronic region of the LMO7 ( LIM domain only 7 ) gene on 13q22 ., The second most significantly associated SNP ( rs478222 , P\u200a=\u200a3 . 50×10−9 ) resides in an intronic region of the EFR3B ( protein EFR3 homolog B ) gene on 2p23; however , the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes , including NCOA1 , C2orf79 , CENPO , ADCY3 , DNAJC27 , POMC , and DNMT3A ., The third most significantly associated SNP ( rs924043 , P\u200a=\u200a8 . 06×10−9 ) lies in an intergenic region on 6q27 , where the region of association is approximately 900 kb and harbors multiple genes including WDR27 , C6orf120 , PHF10 , TCTE3 , C6orf208 , LOC154449 , DLL1 , FAM120B , PSMB1 , TBP , and PCD2 ., These latest associated regions add to the growing repertoire of gene networks predisposing to T1D .

Despite the fact that there is clearly a large genetic component to type 1 diabetes ( T1D ) , uncovering the genes contributing to this disease has proven challenging ., However , in the past three years there has been relatively major progress in this regard , with advances in genetic screening technologies allowing investigators to scan the genome for variants conferring risk for disease without prior hypotheses ., Such genome-wide association studies have revealed multiple regions of the genome to be robustly and consistently associated with T1D ., More recent findings have been a consequence of combining of multiple datasets from independent investigators in meta-analyses , which have more power to pick up additional variants contributing to the trait ., In the current study , we describe the largest meta-analysis of T1D genome-wide genotyped datasets to date , which combines six large studies ., As a consequence , we have uncovered three new signals residing at the chromosomal locations 13q22 , 2p23 , and 6q27 , which went on to be replicated in independent sample sets ., These latest associated regions add to the growing repertoire of gene networks predisposing to T1D .

genome-wide association studies, genetics, biology, genetics of disease, genetics and genomics