Inference of Host-Pathogen Interaction Matrices from Genome-Wide Polymorphism Data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype × genotype (G×G) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of hosts and pathogen genomes (co-genome-wide association studies) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an approximate Bayesian computation method to pinpoint loci with relevant G×G interactions and to infer their underlying interaction matrix. In a combined single nucleotide polymorphism dataset of 451 European humans and their infecting hepatitis C virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (G×G) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of G×G interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key G×G interactions underpinning HCV adaptation to the European human population after a recent expansion.

Unraveling coevolutionary dynamics using ecological genomics.

Coevolutionary interactions, from the delicate co-dependency in mutualistic interactions to the antagonistic relationship of hosts and parasites, are a ubiquitous driver of adaptation. Surprisingly, little is known about the genomic processes underlying coevolution in an ecological context. However, species comprise genetically differentiated populations that interact with temporally variable abiotic and biotic environments. We discuss the recent advances in coevolutionary theory and genomics as well as shortcomings, to identify coevolving genes that take into account this spatial and temporal variability of coevolution, and propose a practical guide to understand the dynamic of coevolution using an ecological genomics lens.

Evolution of gene networks underlying adaptation to drought stress in the wild tomato Solanum chilense.

Drought stress is a key limitation for plant growth and colonization of arid habitats. We study the evolution of gene expression response to drought stress in a wild tomato, Solanum chilense, naturally occurring in dry habitats in South America. We conduct a transcriptome analysis under standard and drought experimental conditions to identify drought-responsive gene networks and estimate the age of the involved genes. We identify two main regulatory networks corresponding to two typical drought-responsive strategies: cell cycle and fundamental metabolic processes. The metabolic network exhibits a more recent evolutionary origin and a more variable transcriptome response than the cell cycle network (with ancestral origin and higher conservation of the transcriptional response). We also integrate population genomics analyses to reveal positive selection signals acting at the genes of both networks, revealing that genes exhibiting selective sweeps of older age also exhibit greater connectivity in the networks. These findings suggest that adaptive changes first occur at core genes of drought response networks, driving significant network re-wiring, which likely underpins species divergence and further spread into drier habitats. Combining transcriptomics and population genomics approaches, we decipher the timing of gene network evolution for drought stress response in arid habitats.

Correction: Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data.

[This corrects the article DOI: 10.1371/journal.pgen.1008698.].

Approximate Bayesian Computation Untangles Signatures of Contemporary and Historical Hybridization between Two Endangered Species.

Contemporary gene flow, when resumed after a period of isolation, can have crucial consequences for endangered species, as it can both increase the supply of adaptive alleles and erode local adaptation. Determining the history of gene flow and thus the importance of contemporary hybridization, however, is notoriously difficult. Here, we focus on two endangered plant species, Arabis nemorensis and A. sagittata, which hybridize naturally in a sympatric population located on the banks of the Rhine. Using reduced genome sequencing, we determined the phylogeography of the two taxa but report only a unique sympatric population. Molecular variation in chloroplast DNA indicated that A. sagittata is the principal receiver of gene flow. Applying classical D-statistics and its derivatives to whole-genome data of 35 accessions, we detect gene flow not only in the sympatric population but also among allopatric populations. Using an Approximate Bayesian computation approach, we identify the model that best describes the history of gene flow between these taxa. This model shows that low levels of gene flow have persisted long after speciation. Around 10 000 years ago, gene flow stopped and a period of complete isolation began. Eventually, a hotspot of contemporary hybridization was formed in the unique sympatric population. Occasional sympatry may have helped protect these lineages from extinction in spite of their extremely low diversity.

Selective sweeps linked to the colonization of novel habitats and climatic changes in a wild tomato species.

Positive selection is the driving force underpinning local adaptation and leaves footprints of selective sweeps on the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plant species occurring in steep and varying environmental gradients are crucial to predict a species' ability to colonize new niches. We use whole-genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events and distribution shifts. Several selective sweeps occur at regulatory networks involved in root-hair development in low altitude and response to photoperiod and vernalization in high-altitude populations. These sweeps appear to occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. Using a unique combination of genome scans and modelling of past climatic data, we quantify the timing of selection at genes likely underpinning local adaptation to semiarid habitats.

Determinants of rapid adaptation in species with large variance in offspring production.

The speed of population adaptation to changing biotic and abiotic environments is determined by the interaction between genetic drift, positive selection and linkage effects. Many marine species (fish, crustaceans), invertebrates and pathogens of humans and crops, exhibit sweepstakes reproduction characterized by the production of a very large amount of offspring (fecundity phase) from which only a small fraction may survive to the next generation (viability phase). Using stochastic simulations, we investigate whether the occurrence of sweepstakes reproduction affects the efficiency of a positively selected unlinked locus, and thus, the speed of adaptation since fecundity and/or viability have distinguishable consequences on mutation rate, probability and fixation time of advantageous alleles. We observe that the mean number of mutations at the next generation is always the function of the population size, but the variance increases with stronger sweepstakes reproduction when mutations occur in the parents. On the one hand, stronger sweepstakes reproduction magnifies the effect of genetic drift thus increasing the probability of fixation of neutral allele and decreasing that of selected alleles. On the other hand, the time to fixation of advantageous (as well as neutral) alleles is shortened by stronger sweepstakes reproduction. Importantly, fecundity and viability selection exhibit different probabilities and times to fixation of advantageous alleles under intermediate and weak sweepstakes reproduction. Finally, alleles under both strong fecundity and viability selection display a synergistic efficiency of selection. We conclude that measuring and modelling accurately fecundity and/or viability selection are crucial to predict the adaptive potential of species with sweepstakes reproduction.

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population.

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

Weak seed banks influence the signature and detectability of selective sweeps.

Seed banking (or dormancy) is a widespread bet-hedging strategy, generating a form of population overlap, which decreases the magnitude of genetic drift. The methodological complexity of integrating this trait implies it is ignored when developing tools to detect selective sweeps. But, as dormancy lengthens the ancestral recombination graph (ARG), increasing times to fixation, it can change the genomic signatures of selection. To detect genes under positive selection in seed banking species it is important to (1) determine whether the efficacy of selection is affected, and (2) predict the patterns of nucleotide diversity at and around positively selected alleles. We present the first tree sequence-based simulation program integrating a weak seed bank to examine the dynamics and genomic footprints of beneficial alleles in a finite population. We find that seed banking does not affect the probability of fixation and confirm expectations of increased times to fixation. We also confirm earlier findings that, for strong selection, the times to fixation are not scaled by the inbreeding effective population size in the presence of seed banks, but are shorter than would be expected. As seed banking increases the effective recombination rate, footprints of sweeps appear narrower around the selected sites and due to the scaling of the ARG are detectable for longer periods of time. The developed simulation tool can be used to predict the footprints of selection and draw statistical inference of past evolutionary events in plants, invertebrates, or fungi with seed banks.

Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data.

Several methods based on the Sequential Markovian coalescence (SMC) have been developed that make use of genome sequence data to uncover population demographic history, which is of interest in its own right and is a key requirement to generate a null model for selection tests. While these methods can be applied to all possible kind of species, the underlying assumptions are sexual reproduction in each generation and non-overlapping generations. However, in many plants, invertebrates, fungi and other taxa, those assumptions are often violated due to different ecological and life history traits, such as self-fertilization or long term dormant structures (seed or egg-banking). We develop a novel SMC-based method to infer 1) the rates/parameters of dormancy and of self-fertilization, and 2) the populations' past demographic history. Using simulated data sets, we demonstrate the accuracy of our method for a wide range of demographic scenarios and for sequence lengths from one to 30 Mb using four sampled genomes. Finally, we apply our method to a Swedish and a German population of Arabidopsis thaliana demonstrating a selfing rate of ca. 0.87 and the absence of any detectable seed-bank. In contrast, we show that the water flea Daphnia pulex exhibits a long lived egg-bank of three to 18 generations. In conclusion, we here present a novel method to infer accurate demographies and life-history traits for species with selfing and/or seed/egg-banks. Finally, we provide recommendations for the use of SMC-based methods for non-model organisms, highlighting the importance of the per site and the effective ratios of recombination over mutation.

Novel genomic approaches to study antagonistic coevolution between hosts and parasites.

Host-parasite coevolution is ubiquitous, shaping genetic and phenotypic diversity and the evolutionary trajectory of interacting species. With the advances of high throughput sequencing technologies applicable to model and non-model organisms alike, it is now feasible to study in greater detail (a) the genetic underpinnings of coevolution, (b) the speed and type of dynamics at coevolving loci, and (c) the genomic consequences of coevolution. This review focuses on three recently developed approaches that leverage information from host and parasite full genome data simultaneously to pinpoint coevolving loci and draw inference on the coevolutionary history. First, co-genome-wide association study (co-GWAS) methods allow pinpointing the loci underlying host-parasite interactions. These methods focus on detecting associations between genetic variants and the outcome of experimental infection tests or on correlations between genomes of naturally infected hosts and their infecting parasites. Second, extensions to population genomics methods can detect genes under coevolution and infer the coevolutionary history, such as fitness costs. Third, correlations between host and parasite population size in time are indicative of coevolution, and polymorphism levels across independent spatially distributed populations of hosts and parasites can reveal coevolutionary loci and infer coevolutionary history. We describe the principles of these three approaches and discuss their advantages and limitations based on coevolutionary theory. We present recommendations for their application to various host (prokaryotes, fungi, plants, and animals) and parasite (viruses, bacteria, fungi, and macroparasites) species. We conclude by pointing out methodological and theoretical gaps to be filled to extract maximum information from full genome data and thereby to shed light on the molecular underpinnings of coevolution.

Inference of coevolutionary dynamics and parameters from host and parasite polymorphism data of repeated experiments.

There is a long-standing interest in understanding host-parasite coevolutionary dynamics and associated fitness effects. Increasing amounts of genomic data for both interacting species offer a promising source to identify candidate loci and to infer the main parameters of the past coevolutionary history. However, so far no method exists to perform the latter. By coupling a gene-for-gene model with coalescent simulations, we first show that three types of biological costs, namely, resistance, infectivity and infection, define the allele frequencies at the internal equilibrium point of the coevolution model. These in return determine the strength of selective signatures at the coevolving host and parasite loci. We apply an Approximate Bayesian Computation (ABC) approach on simulated datasets to infer these costs by jointly integrating host and parasite polymorphism data at the coevolving loci. To control for the effect of genetic drift on coevolutionary dynamics, we assume that 10 or 30 repetitions are available from controlled experiments or several natural populations. We study two scenarios: 1) the cost of infection and population sizes (host and parasite) are unknown while costs of infectivity and resistance are known, and 2) all three costs are unknown while populations sizes are known. Using the ABC model choice procedure, we show that for both scenarios, we can distinguish with high accuracy pairs of coevolving host and parasite loci from pairs of neutrally evolving loci, though the statistical power decreases with higher cost of infection. The accuracy of parameter inference is high under both scenarios especially when using both host and parasite data because parasite polymorphism data do inform on costs applying to the host and vice-versa. As the false positive rate to detect pairs of genes under coevolution is small, we suggest that our method complements recently developed methods to identify host and parasite candidate loci for functional studies.

Neutral genomic signatures of host-parasite coevolution.

BACKGROUND: Coevolution is a selective process of reciprocal adaptation in hosts and parasites or in mutualistic symbionts. Classic population genetics theory predicts the signatures of selection at the interacting loci of both species, but not the neutral genome-wide polymorphism patterns. To bridge this gap, we build an eco-evolutionary model, where neutral genomic changes over time are driven by a single selected locus in hosts and parasites via a simple biallelic gene-for-gene or matching-allele interaction. This coevolutionary process may lead to cyclic changes in the sizes of the interacting populations. RESULTS: We investigate if and when these changes can be observed in the site frequency spectrum of neutral polymorphisms from host and parasite full genome data. We show that changes of the host population size are too smooth to be observable in its polymorphism pattern over the course of time. Conversely, the parasite population may undergo a series of strong bottlenecks occurring on a slower relative time scale, which may lead to observable changes in a time series sample. We also extend our results to cases with 1) several parasites per host accelerating relative time, and 2) multiple parasite generations per host generation slowing down rescaled time. CONCLUSIONS: Our results show that time series sampling of host and parasite populations with full genome data are crucial to understand if and how coevolution occurs. This model provides therefore a framework to interpret and draw inference from genome-wide polymorphism data of interacting species.

Persistent seed banking as eco-evolutionary determinant of plant nucleotide diversity: novel population genetics insights.

Contents Summary 725 I. Introduction 725 II. Seed banks decrease the population extinction rate 726 III. Seed banks define the effective population size 727 IV. Seed banks affect the mutation rate 728 V. Seed banks affect the effective recombination rate 728 VI. Seed banks influence the rate and signatures of natural selection 729 VII. Conclusion 729 Acknowledgements 729 References 729 SUMMARY: Long-term persistent seed banking is a common temporal bet-hedging strategy in plants to adapt to unpredictable environments. The population genomics perspective developed in this article suggests that seed banking determines plant nucleotide diversity by decreasing the rate of genetic drift and the effect of linked selection while increasing mutational input. As a result, persistent seed banks are important factors determining the magnitude of the discrepancy between the census size of the above-ground plant population and its genetic diversity, an effect known as the Lewontin paradox. The theoretical population genetics predictions presented here can be tested by combining genome-wide polymorphism data with ecological studies of dormancy.

Subsets of NLR genes show differential signatures of adaptation during colonization of new habitats.

Nucleotide binding site, leucine-rich repeat receptors (NLRs) are canonical resistance (R) genes in plants, fungi and animals, functioning as central (helper) and peripheral (sensor) genes in a signalling network. We investigate NLR evolution during the colonization of novel habitats in a model tomato species, Solanum chilense. We used R-gene enrichment sequencing to obtain polymorphism data at NLRs of 140 plants sampled across 14 populations covering the whole species range. We inferred the past demographic history of habitat colonization by resequencing whole genomes from three S. chilense plants from three key populations and performing approximate Bayesian computation using data from the 14 populations. Using these parameters, we simulated the genetic differentiation statistics distribution expected under neutral NLR evolution and identified small subsets of outlier NLRs exhibiting signatures of selection across populations. NLRs under selection between habitats are more often helper genes, whereas those showing signatures of adaptation in single populations are more often sensor-NLRs. Thus, centrality in the NLR network does not constrain NLR evolvability, and new mutations in central genes in the network are key for R-gene adaptation during colonization of different habitats.

Strengths and potential pitfalls of hay transfer for ecological restoration revealed by RAD-seq analysis in floodplain Arabis species.

Achieving high intraspecific genetic diversity is a critical goal in ecological restoration as it increases the adaptive potential and long-term resilience of populations. Thus, we investigated genetic diversity within and between pristine sites in a fossil floodplain and compared it to sites restored by hay transfer between 1997 and 2014. RAD-seq genotyping revealed that the stenoecious floodplain species Arabis nemorensis is co-occurring with individuals that, based on ploidy, ITS-sequencing and morphology, probably belong to the close relative Arabis sagittata, which has a documented preference for dry calcareous grasslands but has not been reported in floodplain meadows. We show that hay transfer maintains genetic diversity for both species. Additionally, in A. sagittata, transfer from multiple genetically isolated pristine sites resulted in restored sites with increased diversity and admixed local genotypes. In A. nemorensis, transfer did not create novel admixture dynamics because genetic diversity between pristine sites was less differentiated. Thus, the effects of hay transfer on genetic diversity also depend on the genetic make-up of the donor communities of each species, especially when local material is mixed. Our results demonstrate the efficiency of hay transfer for habitat restoration and emphasize the importance of prerestoration characterization of microgeographic patterns of intraspecific diversity of the community to guarantee that restoration practices reach their goal, that is maximize the adaptive potential of the entire restored plant community. Overlooking these patterns may alter the balance between species in the community. Additionally, our comparison of summary statistics obtained from de novo- and reference-based RAD-seq pipelines shows that the genomic impact of restoration can be reliably monitored in species lacking prior genomic knowledge.

The Current Epidemic of the Barley Pathogen Ramularia collo-cygni Derives from a Population Expansion and Shows Global Admixture.

Ramularia leaf spot is becoming an ever-increasing problem in main barley-growing regions since the 1980s, causing up to 70% yield loss in extreme cases. Yet, the causal agent Ramularia collo-cygni remains poorly studied. The diversity of the pathogen in the field thus far remains unknown. Furthermore, it is unknown to what extent the pathogen has a sexual reproductive cycle. The teleomorph of R. collo-cygni has not been observed. To study the genetic diversity of R. collo-cygni and get more insights in its biology, we sequenced the genomes of 19 R. collo-cygni isolates from multiple geographic locations and diverse hosts. Nucleotide polymorphism analyses of all isolates shows that R. collo-cygni is genetically diverse worldwide, with little geographic or host specific differentiation. Next, we used two different methods to detect signals of recombination in our sample set. Both methods find putative recombination events, which indicate that sexual reproduction happens or has happened in the global R. collo-cygni population. Lastly, we used these data on recombination to perform historic population size analyses. These suggest that the effective population size of R. collo-cygni decreased during the domestication of barley and subsequently grew with the rise of agriculture. Our findings deepen our understanding of R. collo-cygni biology and can help us to understand the current epidemic. We discuss how our findings support possible global spread through seed transfer, and we highlight how recombination, clonal spreading, and lack of host specificity could amplify global epidemics of this increasingly important disease and suggest specific approaches to combat the pathogen.

Effects of population- and seed bank size fluctuations on neutral evolution and efficacy of natural selection.

Population genetics models typically consider a fixed population size and a unique selection coefficient. However, population dynamics inherently generate fluctuations in numbers of individuals and selection acts on various components of the individuals' fitness. In plant species with seed banks, the size of both the above- and below-ground compartments induce fluctuations depending on seed production and the state of the seed bank. We investigate if this fluctuation has consequences on (1) the rate of genetic drift, and (2) the efficacy of selection. We consider four variants of two-allele Moran-type models defined by combinations of presence and absence of fluctuations in the population size in above-ground and seed bank compartments. Time scale analysis and dimension reduction methods allow us to reduce the corresponding Fokker-Planck equations to one-dimensional diffusion approximations of a Moran model. We first show that if the fluctuations of above-ground population size classically affect the rate of genetic drift, fluctuations of below-ground population size reduce the diversity storage effect of the seed bank. Second, we consider that selection can act on four different components of the plant fitness: plant or seed death rate, seed production or seed germination. Our striking result is that the efficacy of selection for seed death rate or germination rate is reduced by fluctuations in the seed bank size, whereas selection occurring on plant death rate or seed production is not affected. We derive the expected site-frequency spectrum reflecting this heterogeneity in selection efficacy between genes underpinning different plant fitness components. Our results highlight the importance to consider the effect of ecological noise to predict the impact of seed banks on neutral and selective evolution.

The escalatory Red Queen: Population extinction and replacement following arms race dynamics in poplar rust.

Host-parasite systems provide convincing examples of Red Queen co-evolutionary dynamics. Yet, a key process underscored in Van Valen's theory - that arms race dynamics can result in extinction - has never been documented. One reason for this may be that most sampling designs lack the breadth needed to illuminate the rapid pace of adaptation by pathogen populations. In this study, we used a 25-year temporal sampling to decipher the demographic history of a plant pathogen: the poplar rust fungus, Melampsora larici-populina. A major adaptive event occurred in 1994 with the breakdown of R7 resistance carried by several poplar cultivars widely planted in Western Europe since 1982. The corresponding virulence rapidly spread in M. larici-populina populations and nearly reached fixation in northern France, even on susceptible hosts. Using both temporal records of virulence profiles and temporal population genetic data, our analyses revealed that (i) R7 resistance breakdown resulted in the emergence of a unique and homogeneous genetic group, the so-called cultivated population, which predominated in northern France for about 20 years, (ii) selection for Vir7 individuals brought with it multiple other virulence types via hitchhiking, resulting in an overall increase in the population-wide number of virulence types and (iii) - above all - the emergence of the cultivated population superseded the initial population which predominated at the same place before R7 resistance breakdown. Our temporal analysis illustrates how antagonistic co-evolution can lead to population extinction and replacement, hence providing direct evidence for the escalation process which is at the core of Red Queen dynamics.