Neutral genomic signatures of host-parasite coevolution

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.

Hatchability of Fascioloides magna Eggs in Cervids

The giant liver fluke (Fascioloides magna) is an invasive parasite found permanently in three foci in Europe. The fluke has an indirect life cycle involving a final and an intermediate host. The currently accepted terminology determines three types of final hosts: definitive, dead-end, and aberrant hosts. Recently, roe deer (Capreolus capreolus) has been classified as an aberrant host, which cannot contribute to the reproduction of F. magna. This study investigated the hatchability of F. magna eggs of red deer (Cervus elaphus) and roe deer origin to compare the suitability of the two host species for the maintenance of the parasite. The study was carried out on a newly invaded area, two years after the first reported observation of F. magna. The prevalence of the parasite proved to be 68.4% (CI95% 44.6-85.3%) in red deer and 36.7% (CI95% 24.8-50.0%) in roe deer. The difference between the two species was confirmed to be significant (p = 0.02). The mean intensity proved to be 10.0 (CI95% 4.9-22.6) and 7.59 (CI95% 2.7-24.2) in the red deer and the roe deer, respectively. The difference of the mean intensities did not prove to be significant (p = 0.72). Of the 70 observed pseudocysts, 67 originated from red deer and 3 from roe deer. Most of the pseudocysts contained two flukes, while a few pseudocysts contained one or three parasites. Egg production was observed in all three types of pseudocysts. We did not find more than three flukes in any pseudocyst. The apparent proportion of self-fertilisation in flukes without mating partners was 23.5% and 100% in red deer and roe deer, respectively. The survival of single-parent eggs was not confirmed to be worse than that of gregarious parents. The viability of offspring originating from roe and red deer differed significantly. Our findings suggest that F. magna adapted to the new populations of susceptible hosts rather than vice versa.

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.

Taking Advantage of Pathogen Diversity and Immune Priming to Minimize Disease Prevalence in Host Mixtures: A Model

Host mixtures are a promising method for agroecological plant disease control. Plant immunity is key to the success of host mixtures against polymorphic pathogen populations. This immunity results from priming-induced cross-protection, whereby plants able to resist infection by specific pathogen genotypes become more resistant to other pathogen genotypes. Strikingly, this phenomenon was absent from mathematical models aiming at designing host mixtures. We developed a model to specifically explore how priming affects the coexistence of two pathogen genotypes in host mixtures composed of two host genotypes and how it affects disease prevalence. The main effect of priming is to reduce the coexistence region in the parameter space (due to the cross-protection) and to generate a singular mixture of resistant and susceptible hosts corresponding to the maximal reduction disease prevalence (in absence of priming, a resistant pure stand is optimal). The epidemiological advantage of host mixtures over a resistant pure stand thus appears as a direct consequence of immune priming. We also showed that there is indirect cross-protection between host genotypes in a mixture. Moreover, the optimal mix prevents the emergence of a resistance-breaking pathogen genotype. Our results highlight the importance of considering immune priming to design optimal and sustainable host mixtures.

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.

Host-parasite co-evolution and its genomic signature

Studies in diverse biological systems have indicated that host-parasite co-evolution is responsible for the extraordinary genetic diversity seen in some genomic regions, such as major histocompatibility (MHC) genes in jawed vertebrates and resistance genes in plants. This diversity is believed to evolve under balancing selection on hosts by parasites. However, the mechanisms that link the genomic signatures in these regions to the underlying co-evolutionary process are only slowly emerging. We still lack a clear picture of the co-evolutionary concepts and of the genetic basis of the co-evolving phenotypic traits in the interacting antagonists. Emerging genomic tools that provide new options for identifying underlying genes will contribute to a fuller understanding of the co-evolutionary process.

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.