Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies.
ABSTRACT: Predicting how species interactions evolve requires that we understand the mechanistic basis of coevolution, and thus the functional genotype-by-genotype interactions (G × G) that drive reciprocal natural selection. Theory on host-parasite coevolution provides testable hypotheses for empiricists, but depends upon models of functional G × G that remain loosely tethered to the molecular details of any particular system. In practice, reciprocal cross-infection studies are often used to partition the variation in infection or fitness in a population that is attributable to G × G (statistical G × G). Here we use simulations to demonstrate that within-population statistical G × G likely tells us little about the existence of coevolution, its strength, or the genetic basis of functional G × G. Combined with studies of multiple populations or points in time, mapping and molecular techniques can bridge the gap between natural variation and mechanistic models of coevolution, while model-based statistics can formally confront coevolutionary models with cross-infection data. Together these approaches provide a robust framework for inferring the infection genetics underlying statistical G × G, helping unravel the genetic basis of coevolution.
Project description:Ehrlich and Raven formally introduced the concept of stepwise coevolution using butterfly and angiosperm interactions in an attempt to account for the impressive biological diversity of these groups. However, many biologists currently envision butterflies evolving 50 to 30 million years (Myr) after the major angiosperm radiation and thus reject coevolutionary origins of butterfly biodiversity. The unresolved central tenet of Ehrlich and Raven's theory is that evolution of plant chemical defenses is followed closely by biochemical adaptation in insect herbivores, and that newly evolved detoxification mechanisms result in adaptive radiation of herbivore lineages. Using one of their original butterfly-host plant systems, the Pieridae, we identify a pierid glucosinolate detoxification mechanism, nitrile-specifier protein (NSP), as a key innovation. Larval NSP activity matches the distribution of glucosinolate in their host plants. Moreover, by using five different temporal estimates, NSP seems to have evolved shortly after the evolution of the host plant group (Brassicales) ( approximately 10 Myr). An adaptive radiation of these glucosinolate-feeding Pierinae followed, resulting in significantly elevated species numbers compared with related clades. Mechanistic understanding in its proper historical context documents more ancient and dynamic plant-insect interactions than previously envisioned. Moreover, these mechanistic insights provide the tools for detailed molecular studies of coevolution from both the plant and insect perspectives.
Project description: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.
Project description:Many antagonistic interactions between hosts and their parasites result in coevolution. Although coevolution can drive diversity and specificity within species, it is not known whether coevolutionary dynamics differ among functionally similar species. We present evidence of coevolution within simple communities of Pseudomonas aeruginosa PAO1 and a panel of bacteriophages. Pathogen identity affected coevolutionary dynamics. For five of six phages tested, time-shift assays revealed temporal peaks in bacterial resistance and phage infectivity, consistent with frequency-dependent selection (Red Queen dynamics). Two of the six phages also imposed additional directional selection, resulting in strongly increased resistance ranges over the entire length of the experiment (ca. 60 generations). Cross-resistance to these two phages was very high, independent of the coevolutionary history of the bacteria. We suggest that coevolutionary dynamics are associated with the nature of the receptor used by the phage for infection. Our results shed light on the coevolutionary process in simple communities and have practical application in the control of bacterial pathogens through the evolutionary training of phages, increasing their virulence and efficacy as therapeutics or disinfectants.
Project description:We introduce and analyze a within-host dynamical model of the coevolution between rapidly mutating pathogens and the adaptive immune response. Pathogen mutation and a homeostatic constraint on lymphocytes both play a role in allowing the development of chronic infection, rather than quick pathogen clearance. The dynamics of these chronic infections display emergent structure, including branching patterns corresponding to asexual pathogen speciation, which is fundamentally driven by the coevolutionary interaction. Over time, continued branching creates an increasingly fragile immune system, and leads to the eventual catastrophic loss of immune control.
Project description:Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid sites within the multiple sequence alignment of a protein family. Here, we use the maximum entropy-based approach called mean field Direct Coupling Analysis (mfDCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family. We use the inferred pairwise statistical couplings to generate the sequence-dependent heterogeneous interaction energies of a structure-based model (SBM) where only native contacts are considered. Considering the ribosomal S6 protein and its circular permutants as well as the SH3 protein, we demonstrate that these models quantitatively agree with experimental data on folding mechanisms. This work serves as a new framework for generating coevolutionary data-enriched models that can potentially be used to engineer key functional motions and novel interactions in protein systems.
Project description:Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.
Project description:The microbial symbionts of eukaryotes influence disease resistance in many host-parasite systems. Symbionts show substantial variation in both genotype and phenotype, but it is unclear how natural selection maintains this variation. It is also unknown whether variable symbiont genotypes show specificity with the genotypes of hosts or parasites in natural populations. Genotype by genotype interactions are a necessary condition for coevolution between interacting species. Uncovering the patterns of genetic specificity among hosts, symbionts, and parasites is therefore critical for determining the role that symbionts play in host-parasite coevolution. Here, we show that the strength of protection conferred against a fungal pathogen by a vertically transmitted symbiont of an aphid is influenced by both host-symbiont and symbiont-pathogen genotype by genotype interactions. Further, we show that certain symbiont phylogenetic clades have evolved to provide stronger protection against particular pathogen genotypes. However, we found no evidence of reciprocal adaptation of co-occurring host and symbiont lineages. Our results suggest that genetic variation among symbiont strains may be maintained by antagonistic coevolution with their host and/or their host's parasites.
Project description:Reciprocal selective effects between coevolving species are often influenced by interactions with the broader ecological community. Community-level interactions may also influence macroevolutionary patterns of coevolution, such as cospeciation, but this hypothesis has received little attention. We studied two groups of ecologically similar feather lice (Phthiraptera: Ischnocera) that differ in their patterns of association with a single group of hosts. The two groups, "body lice" and "wing lice," are both parasites of pigeons and doves (Columbiformes). Body lice are more host-specific and show greater population genetic structure than wing lice. The macroevolutionary history of body lice also parallels that of their columbiform hosts more closely than does the evolutionary history of wing lice. The closer association of body lice with hosts, compared with wing lice, can be explained if body lice are less capable of switching hosts than wing lice. Wing lice sometimes disperse phoretically on parasitic flies (Diptera: Hippoboscidae), but body lice seldom engage in this behavior. We tested the hypothesis that wing lice switch host species more often than body lice, and that the difference is governed by phoresis. Our results show that, where flies are present, wing lice switch to novel host species in sufficient numbers to establish viable populations on the new host. Body lice do not switch hosts, even where flies are present. Thus, differences in the coevolutionary history of wing and body lice can be explained by differences in host-switching, mediated by a member of the broader parasite community.
Project description:CRISPR-Cas is an adaptive prokaryotic immune system that prevents phage infection. By incorporating phage-derived 'spacer' sequences into CRISPR loci on the host genome, future infections from the same phage genotype can be recognized and the phage genome cleaved. However, the phage can escape CRISPR degradation by mutating the sequence targeted by the spacer, allowing them to re-infect previously CRISPR-immune hosts, and theoretically leading to coevolution. Previous studies have shown that phage can persist over long periods in populations of Streptococcus thermophilus that can acquire CRISPR-Cas immunity, but it has remained less clear whether this coexistence was owing to coevolution, and if so, what type of coevolutionary dynamics were involved. In this study, we performed highly replicated serial transfer experiments over 30 days with S. thermophilus and a lytic phage. Using a combination of phenotypic and genotypic data, we show that CRISPR-mediated resistance and phage infectivity coevolved over time following an arms race dynamic, and that asymmetry between phage infectivity and host resistance within this system eventually causes phage extinction. This work provides further insight into the way CRISPR-Cas systems shape the population and coevolutionary dynamics of bacteria-phage interactions. This article is part of a discussion meeting issue 'The ecology and evolution of prokaryotic CRISPR-Cas adaptive immune systems'.
Project description:Antagonistic coevolution is a critical force driving the evolution of diversity, yet the selective processes underpinning reciprocal adaptive changes in nature are not well understood. Local adaptation studies demonstrate partner impacts on fitness and adaptive change, but do not directly expose genetic processes predicted by theory. Specifically, we have little knowledge of the relative importance of fluctuating selection vs. arms-race dynamics in maintaining polymorphism in natural systems where metapopulation processes predominate. We conducted cross-year epidemiological, infection and genetic studies of multiple wild host and pathogen populations in the Linum-Melampsora association. We observed asynchronous phenotypic fluctuations in resistance and infectivity among demes. Importantly, changes in allelic frequencies at pathogen infectivity loci, and in host recognition of these genetic variants, correlated with disease prevalence during natural epidemics. These data strongly support reciprocal coevolution maintaining balanced resistance and infectivity polymorphisms, and highlight the importance of characterising spatial and temporal dynamics in antagonistic interactions.