Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies

Combining GWAS and population genomic analyses to characterize coevolution in a legume-rhizobia symbiosis

The mutualism between legumes and rhizobia is clearly the product of past coevolution. However, the nature of ongoing evolution between these partners is less clear. To characterize the nature of recent coevolution between legumes and rhizobia, we used population genomic analysis to characterize selection on functionally annotated symbiosis genes as well as on symbiosis gene candidates identified through a two-species association analysis. For the association analysis, we inoculated each of 202 accessions of the legume host Medicago truncatula with a community of 88 Sinorhizobia (Ensifer) meliloti strains. Multistrain inoculation, which better reflects the ecological reality of rhizobial selection in nature than single-strain inoculation, allows strains to compete for nodulation opportunities and host resources and for hosts to preferentially form nodules and provide resources to some strains. We found extensive host by symbiont, that is, genotype-by-genotype, effects on rhizobial fitness and some annotated rhizobial genes bear signatures of recent positive selection. However, neither genes responsible for this variation nor annotated host symbiosis genes are enriched for signatures of either positive or balancing selection. This result suggests that stabilizing selection dominates selection acting on symbiotic traits and that variation in these traits is under mutation-selection balance. Consistent with the lack of positive selection acting on host genes, we found that among-host variation in growth was similar whether plants were grown with rhizobia or N-fertilizer, suggesting that the symbiosis may not be a major driver of variation in plant growth in multistrain contexts.

Decreased coevolutionary potential and increased symbiont fecundity during the biological invasion of a legume-rhizobium mutualism

Although most invasive species engage in mutualism, we know little about how mutualism evolves as partners colonize novel environments. Selection on cooperation and standing genetic variation for mutualism traits may differ between a mutualism's invaded and native ranges, which could alter cooperation and coevolutionary dynamics. To test for such differences, we compare mutualism traits between invaded- and native-range host-symbiont genotype combinations of the weedy legume, Medicago polymorpha, and its nitrogen-fixing rhizobium symbiont, Ensifer medicae, which have coinvaded North America. We find that mutualism benefits for plants are indistinguishable between invaded- and native-range symbioses. However, rhizobia gain greater fitness from invaded-range mutualisms than from native-range mutualisms, and this enhancement of symbiont fecundity could increase the mutualism's spread by increasing symbiont availability during plant colonization. Furthermore, mutualism traits in invaded-range symbioses show lower genetic variance and a simpler partitioning of genetic variance between host and symbiont sources, compared to native-range symbioses. This suggests that biological invasion has reduced mutualists' potential to respond to coevolutionary selection. Additionally, rhizobia bearing a locus (hrrP) that can enhance symbiotic fitness have more exploitative phenotypes in invaded-range than in native-range symbioses. These findings highlight the impacts of biological invasion on the evolution of mutualistic interactions.

Evolution on the bright side of life: microorganisms and the evolution of mutualism

Mutualistic interactions, where two interacting species have a net beneficial effect on each other's fitness, play a crucial role in the survival and evolution of many species. Despite substantial empirical and theoretical work in past decades, the impact of these interactions on natural selection is not fully understood. In addition, mutualisms between microorganisms have been largely ignored, even though they are ecologically important and can be used as tools to bridge the gap between theory and empirical work. Here, I describe two problems with our current understanding of natural selection in mutualism and highlight the properties of microbial mutualisms that could help solve them. One problem is that bias and methodological problems have limited our understanding of the variety of mechanisms by which species may adapt to mutualism. Another problem is that it is rare for experiments testing coevolution in mutualism to address whether each species has adapted to evolutionary changes in its partner. These problems can be addressed with genome resequencing and time-shift experiments, techniques that are easier to perform in microorganisms. In addition, microbial mutualisms may inspire novel insights and hypotheses about natural selection in mutualism.

Identifying coevolving loci using interspecific genetic correlations

Evaluating the importance of coevolution for a wide range of evolutionary questions, such as the role parasites play in the evolution of sexual reproduction, requires that we understand the genetic basis of coevolutionary interactions. Despite its importance, little progress has been made identifying the genetic basis of coevolution, largely because we lack tools designed specifically for this purpose. Instead, coevolutionary studies are often forced to re-purpose single species techniques. Here, we propose a novel approach for identifying the genes mediating locally adapted coevolutionary interactions that relies on spatial correlations between genetic marker frequencies in the interacting species. Using individual-based multi-locus simulations, we quantify the performance of our approach across a range of coevolutionary genetic models. Our results show that when one species is strongly locally adapted to the other and a sufficient number of populations can be sampled, our approach accurately identifies functionally coupled host and parasite genes. Although not a panacea, the approach we outline here could help to focus the search for coevolving genes in a wide variety of well-studied systems for which substantial local adaptation has been demonstrated.

No evidence for adaptation to local rhizobial mutualists in the legume Medicago lupulina

Local adaptation is a common but not ubiquitous feature of species interactions, and understanding the circumstances under which it evolves illuminates the factors that influence adaptive population divergence. Antagonistic species interactions dominate the local adaptation literature relative to mutualistic ones, preventing an overall assessment of adaptation within interspecific interactions. Here, we tested whether the legume Medicago lupulina is adapted to the locally abundant species of mutualistic nitrogen-fixing rhizobial bacteria that vary in frequency across its eastern North American range. We reciprocally inoculated northern and southern M. lupulina genotypes with the northern (Ensifer medicae) or southern bacterium (E. meliloti) in a greenhouse experiment. Despite producing different numbers of root nodules (the structures in which the plants house the bacteria), neither northern nor southern plants produced more seeds, flowered earlier, or were more likely to flower when inoculated with their local rhizobia. We then used a pre-existing dataset to perform a genome scan for loci that showed elevated differentiation between field-collected plants that hosted different bacteria. None of the loci we identified belonged to the well-characterized suite of legume-rhizobia symbiosis genes, suggesting that the rhizobia do not drive genetic divergence between M. lupulina populations. Our results demonstrate that symbiont local adaptation has not evolved in this mutualism despite large-scale geographic variation in the identity of the interacting species.

Genotype specificity among hosts, pathogens, and beneficial microbes influences the strength of symbiont-mediated protection

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.

Genotype-specific interactions between parasitic arthropods

Despite the ubiquity of coinfection, we know little of the effects of intra-specific genetic variability on coinfection by distinct parasite species. Here we test the hypothesis that parasite multiplication depends on the combination of parasite genotypes that coinfect the host (that is Genotype._parasite × Genotype_.parasite interaction). To that aim, we infected tomato leaves with the ecto-parasitic mites Tetranychus urticae and Tetranychus evansi. We tested all possible combinations between four T. urticae and two T. evansi populations sampled on different hosts or localities. There was no universal (that is genotype-independent) effect of coinfection on mite multiplication; in many cases the two species had no effect on each other. However, several combinations of T. evansi and T. urticae populations led to elevated T. evansi numbers. Similarly, T. urticae reproduction largely depended on the interaction between T. urticae and T. evansi populations. This evidence for genotype-by-genotype interaction between coinfecting parasites indicates that the effect of coinfection on parasite epidemiology and evolution may vary in space according to the genetic composition of local parasite populations; it further suggests the possibility of coevolution between parasites species that share the same hosts.

Genotype-by-genotype interactions between an insect and its pathogen

Genotype-by-genotype (G×G) interactions are an essential requirement for the coevolution of hosts and parasites, but have only been documented in a small number of animal model systems. G×G effects arise from interactions between host and pathogen genotypes, such that some pathogen strains are more infectious in certain hosts and some hosts are more susceptible to certain pathogen strains. We tested for G×G interactions in the gypsy moth (Lymantria dispar) and its baculovirus. We infected 21 full-sib families of gypsy moths with each of 16 isolates of baculovirus and measured the between-isolate correlations of infection rate across host families for all pairwise combinations of isolates. Mean infectiousness varied among isolates and disease susceptibility varied among host families. Between-isolate correlations of infection rate were generally less than one, indicating nonadditive effects of host and pathogen type consistent with G×G interactions. Our results support the presence of G×G effects in the gypsy moth-baculovirus interaction and provide empirical evidence that correlations in infection rates between field-collected isolates are consistent with values that mathematical models have previously shown to increase the likelihood of pathogen polymorphism.

Epidemiological and Evolutionary Outcomes in Gene-for-Gene and Matching Allele Models

Gene-for-gene (GFG) and matching-allele (MA) models are qualitatively different paradigms for describing the outcome of genetic interactions between hosts and pathogens. The GFG paradigm was largely built on the foundations of Flor's early work on the flax-flax rust interaction and is based on the concept of genetic recognition leading to incompatible disease outcomes, typical of host immune recognition. In contrast, the MA model is based on the assumption that genetic recognition leads to compatible interactions, which can result when pathogens require specific host factors to cause infection. Results from classical MA and GFG models have led to important predictions regarding various coevolutionary phenomena, including the role of fitness costs associated with resistance and infectivity, the distribution of resistance genes in wild populations, patterns of local adaptation and the evolution and maintenance of sexual reproduction. Empirical evidence (which we review briefly here), particularly from recent molecular advances in understanding of the mechanisms that determine the outcome of host-pathogen encounters, suggests considerable variation in specific details of the functioning of interactions between hosts and pathogens, which may contain elements of both models. In this regard, GFG and MA scenarios likely represent endpoints of a continuum of potentially more complex interactions that occur in nature. Increasingly, this has been recognized in theoretical studies of coevolutionary processes in plant host-pathogen and animal host-parasite associations (e.g., departures from strict GFG/MA assumptions, diploid genetics, multi-step infection processes). However, few studies have explored how different genetic assumptions about host resistance and pathogen infectivity might impact on disease epidemiology or pathogen persistence within and among populations. Here, we use spatially explicit simulations of the basic MA and GFG scenarios to highlight qualitative differences between these scenarios with regard to patterns of disease and impacts on host demography. Given that such impacts drive evolutionary trajectories, future theoretical advances that aim to capture more complex genetic scenarios should explicitly address the interaction between epidemiology and different models of host-pathogen interaction genetics.

Specialization for resistance in wild host-pathogen interaction networks

Properties encompassed by host-pathogen interaction networks have potential to give valuable insight into the evolution of specialization and coevolutionary dynamics in host-pathogen interactions. However, network approaches have been rarely utilized in previous studies of host and pathogen phenotypic variation. Here we applied quantitative analyses to eight networks derived from spatially and temporally segregated host (Linum marginale) and pathogen (Melampsora lini) populations. First, we found that resistance strategies are highly variable within and among networks, corresponding to a spectrum of specialist and generalist resistance types being maintained within all networks. At the individual level, specialization was strongly linked to partial resistance, such that partial resistance was effective against a greater number of pathogens compared to full resistance. Second, we found that all networks were significantly nested. There was little support for the hypothesis that temporal evolutionary dynamics may lead to the development of nestedness in host-pathogen infection networks. Rather, the common patterns observed in terms of nestedness suggests a universal driver (or multiple drivers) that may be independent of spatial and temporal structure. Third, we found that resistance networks were significantly modular in two spatial networks, clearly reflecting spatial and ecological structure within one of the networks. We conclude that (1) overall patterns of specialization in the networks we studied mirror evolutionary trade-offs with the strength of resistance; (2) that specific network architecture can emerge under different evolutionary scenarios; and (3) network approaches offer great utility as a tool for probing the evolutionary and ecological genetics of host-pathogen interactions.