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Proc Natl Acad Sci U S A. Apr 20, 2010; 107(16): 7359–7364.
Published online Apr 5, 2010. doi:  10.1073/pnas.1003113107
PMCID: PMC2867683

Multiple reciprocal adaptations and rapid genetic change upon experimental coevolution of an animal host and its microbial parasite


The coevolution between hosts and parasites is predicted to have complex evolutionary consequences for both antagonists, often within short time periods. To date, conclusive experimental support for the predictions is available mainly for microbial host systems, but for only a few multicellular host taxa. We here introduce a model system of experimental coevolution that consists of the multicellular nematode host Caenorhabditis elegans and the microbial parasite Bacillus thuringiensis. We demonstrate that 48 host generations of experimental coevolution under controlled laboratory conditions led to multiple changes in both parasite and host. These changes included increases in the traits of direct relevance to the interaction such as parasite virulence (i.e., host killing rate) and host resistance (i.e., the ability to survive pathogens). Importantly, our results provide evidence of reciprocal effects for several other central predictions of the coevolutionary dynamics, including (i) possible adaptation costs (i.e., reductions in traits related to the reproductive rate, measured in the absence of the antagonist), (ii) rapid genetic changes, and (iii) an overall increase in genetic diversity across time. Possible underlying mechanisms for the genetic effects were found to include increased rates of genetic exchange in the parasite and elevated mutation rates in the host. Taken together, our data provide comprehensive experimental evidence of the consequences of host–parasite coevolution, and thus emphasize the pace and complexity of reciprocal adaptations associated with these antagonistic interactions.

Keywords: Caenorhabditis elegans, parasite–host coevolution, Red Queen, trade-offs, Bacillus thuringiensis

Host–parasite coevolution provides an ideal opportunity to experimentally study the consequences of natural selection, because it is often associated with rapid adaptive changes in a variety of life-history traits (1, 2). An immediate response is predicted for the traits that are directly involved in the coevolutionary interaction, such as parasite virulence and host resistance. These traits may continuously increase in value or show persistent variation as a result of recurrent selective sweeps or negative frequency-dependent selection, respectively (35). Changes in virulence or resistance may be energetically costly if they are due to the increased production of, for example, effector molecules (e.g., parasite toxins or host antimicrobial peptides). In these cases, the changes may lead to life-history tradeoffs, and thus an indirect consequence of host-parasite coevolution is predicted for the traits that compete with virulence and resistance for resources [e.g., competitive ability and reproductive rate (68)].

Host-parasite coevolution may also favor increased activity of specific genetic processes such as recombination or mutation, which can break up existing genetic linkage groups (i.e., recombination) or give rise to new alleles (i.e., recombination and mutation). Therefore, these processes are predicted to lead to the continuous generation of novel genotypes to which the respective antagonist is not adapted (5, 9, 10). Furthermore, the spread of new favorable alleles is predicted to cause an at least temporary increase in genetic diversity within the population (11). As coevolutionary trajectories are likely to differ across populations, host–parasite coevolution is also predicted to increase among-population diversity, potentially leading to speciation (11, 12).

These coevolutionary interactions have been studied under natural conditions in a variety of host systems, including the snail Potamopyrgus antipodarum, the waterflea Daphnia magna, Drosophila fruit flies, the wild flax Linum marginale, the ribwort plantain Plantago lanceolata, or the annual weed Arabidopsis thaliana (reviewed in refs. 1317). A particular challenge in these field studies is that the evolution of any trait may be influenced by a multitude of selective processes that are not directly related to the coevolutionary interaction (e.g., fluctuating environmental conditions or competition, cf. ref. 17). Therefore, the identification of coevolutionary consequences is often difficult and requires complex study designs, such as in long-term analyses across locations and habitats, as performed, among others, for Potamopyrgus snails (5, 18) or the ribwort plantain (19).

A different approach is based on experimental evolution, during which organisms evolve under controlled conditions, and which offers an opportunity to minimize confounding environmental variation (20). This approach has been extensively used under laboratory conditions to study host–parasite coevolution in microbial host systems, for example the bacteria Pseudomonas fluorescens, Escherichia coli, or the ciliate Paramecium caudatum (3, 7, 8, 11, 12, 21). These studies provided evidence for the multifaceted consequences of coevolution, especially at the phenotypic level [(e.g., resistance, virulence, life-history tradeoffs, or biodiversity (3, 7, 8, 11, 12)], whereas the genetic underpinnings have not as yet been evaluated directly with the help of molecular markers. Several previous coevolution experiments also focused on multicellular hosts, which are characterized by more complex defense responses than microbes. These experiments were performed under laboratory and field conditions and included Drosophila fruit flies, the red flour beetle Tribolium castaneum, the waterflea D. magna, and different snail species (6, 9, 2225). Particular studies in these host systems usually focused on only one set of predictions in only one of the antagonists, such as host resistance and associated tradeoffs in Drosophila flies (6) or infectivity of the bacterial parasite of D. magna (22).

In the present study, we simultaneously tested the basic predictions of the consequences of coevolution outlined at the beginning of the present article. For this purpose, we established a model of experimental host–parasite coevolution based on a multicellular host, the nematode Caenorhabditis elegans, and a microbial parasite, the Gram-positive bacterium Bacillus thuringiensis. Both antagonists show specific adaptations to each other, including the involvement of a characteristic set of host defense genes and a toxin-mediated process of persistent infection and host killing that is required for bacterial transmission (2632).

Our analysis was based on the comparison of a host–parasite coevolution treatment, during which parasites and hosts were forced to adapt to each other over 48 host generations, with respective control treatments, during which either the parasite or host evolved for the same time period without the antagonist (parasite and host control, respectively; Fig. 1). For both antagonists, we evaluated two types of phenotypic responses to coevolution: reciprocal increases in parasite virulence/host resistance and concurrent reciprocal reductions in other fitness traits resulting from life-history tradeoffs. Phenotypic treatment differences were examined for a single time point, namely the end of the evolution experiment, when changes under coevolution conditions were expected in response to recurrent selective sweeps and possibly also the recent spread of a favorable allele during negative frequency-dependent selection (35). For both antagonists, we further evaluated whether coevolution caused reciprocal increases in four types of genetic characteristics (912): the rate of genetic change (across time; Fig. 1C), genetic diversities within and among populations (at the end of experimental evolution and across time; Fig. 1 B and C), and also the activity of genetic diversity-generating mechanisms (at the end of experimental evolution; Fig. 1B).

Fig. 1.
Schematic overview of experimental setup. (A) Treatment conditions were identical except for the presence or absence of an antagonist. In the host control (blue), C. elegans (squiggles) adapted to the general experimental conditions (20 replicate populations). ...


We first tested whether, after experimental evolution, coevolved parasites show higher virulence than control parasites whereas coevolved hosts show higher resistance than control hosts. Coevolved parasites and hosts were compared with their respective controls in a paired setup (Fig. 1B) in which each pair was exposed to an identical replicate population of the respective antagonist (e.g., one parasite pair was exposed to one particular replicate population of control hosts, a second parasite pair was exposed to another replicate population of hosts, and so on). Parasite virulence was measured as the proportion of dead hosts after 3 d of parasite exposure (killing rate). The inverse of this measure (survival rate) was used as a proxy for host resistance. We found that coevolved parasites did indeed express significantly higher virulence than their controls, whereas coevolved hosts showed a trend toward higher resistance than the control hosts (Fig. 2). We conclude that the presence of a coadapting antagonist leads to reciprocal changes in traits directly relevant to the interaction, whereby the response is more pronounced for the parasite than for the host.

Fig. 2.
Phenotypic consequences of 48 host generations of experimental evolution. Phenotypes examined for parasite (black) and host (gray) included parasite virulence, parasite growth rate, host resistance, host population growth rate, and host body size. The ...

We next tested whether, in the subsequent absence of the antagonist, coevolved parasites and hosts are less fit (i.e., have lower reproductive outputs) than their controls. Parasite fitness was based on growth rate on a high-nutrition medium. Host fitness was approximated under ad libitum food conditions using population growth rate and adult body size, which correlates positively with fecundity (33). Indeed, coevolved parasites and hosts had lower fitness than their controls (Fig. 2). We conclude that evolution in the presence of a coadapting antagonist incurs fitness reductions under “optimal” conditions, consistent with a cost of adaptation. Again, the effects are stronger for the parasite than for the host.

We subsequently evaluated the consequences of coevolution on genetic characteristics both over time and at the end of experimental evolution (Fig. 1 B and C). We studied three plasmid-encoded B. thuringiensis toxin genes, each present in only one of the three original parasite genotypes, and nine C. elegans microsatellites located in different parts of the genome. Changes across time were calculated as relative toxin gene (parasite) or allele (host) frequencies from pooled population DNA samples (i.e., each sample contained DNA of several bacteria/individuals from the same replicate population), which were isolated every fourth host generation (Fig. 1C). Changes at the end of experimental evolution were inferred from individual parasite and host lines per replicate population (i.e., for each replicate population, 10 microbial clones or 20 individual host lines were separated at the end of the experiment, followed by molecular analysis; Fig. 1B). We evaluated four different genetic characteristics that are predicted to be affected by host–parasite coevolution (912): the rate of genetic change, genetic diversity within populations from the same treatment, genetic diversity across populations from the same treatment, and the mechanisms that may contribute to genetic diversity.

First, changes in both parasite toxin gene prevalence and host allele frequencies (averaged across loci) were significantly more frequent across time during coevolution than the respective controls (P < 0.001; Fig. 3A). Separate analyses of host microsatellites showed the same pattern for five individual loci (P ≤ 0.014 for loci II-R, 4001, IV-L, X004, X-R; Fig. 3A and Table S1).

Fig. 3.
Evolution across time. Temporal changes in allele frequency/gene prevalence (A), gene diversity within replicate populations (B), and gene diversity between replicate populations within treatments (C), resulting in three main evolutionary patterns (D ...

Second, although populations in the different treatments were initiated with identical genotype mixtures, parasite gene diversity within populations was significantly higher under coevolution than control conditions both over time and at the end (P ≤ 0.005; Fig. 3B). For the host, overall gene diversity within populations did not differ between treatments, neither across time nor at the end of experimental evolution (P > 0.2; Fig. 3B and Table S1). However, analyses of individual microsatellites revealed higher diversity for the host–parasite treatment across time for the two chromosome IV loci (P ≤ 0.017 for loci 4001 and IV-L; Fig. 3B) and lower diversity at three loci across time (P ≤ 0.018, loci II-R, V-L, X004; Fig. 3B) and at one locus at the end (P = 0.003, locus 3003).

Third, parasite gene diversity across populations was significantly higher under coevolution than under control conditions when measured over time (P < 0.001; Fig. 3C), but not at the end (P = 0.165). For the host, gene diversity across populations was significantly higher in the coevolving populations over time (P < 0.001; Fig. 3C), but not at the end of the experiment (P = 0.3). Separate analyses of individual microsatellites showed higher between-population diversity under coevolution over time at four loci (P ≤ 0.010, loci II-R, 4001, IV-L, X004; Fig. 3C and Table S1) and lower values over time at three loci (P ≤ 0.010, loci 3003, V-L, X003; Fig. 3C).

Fourth, variation in the rates of genetic reassortment, recombination, and mutation was used to evaluate possible changes in genetic diversity-generating processes. These processes cannot be characterized from the pooled population DNA samples collected over time, because their analysis requires allele data from individual lines. Thus, we took snap-shot looks using the genetic data obtained at the end. Coevolved parasites were significantly more likely to possess more than one toxin gene (P = 0.003; Fig. 4A), suggesting increased genetic exchange of the plasmid-encoded toxins. Host genetic reassortment and recombination were inferred from the number of linkage disequilibria between microsatellites from either different or the same chromosomes, respectively, and always displayed insignificant treatment variations (P ≥ 0.015; critical significance level adjusted to an α of 0.007 according to the false discovery rate (FDR); Table S1). Mutation rates could be determined only for the host. The population-average number of microsatellites with unique mutated alleles not present among the original genotypes revealed a trend toward more mutations upon coevolution (P = 0.02; FDR-adjusted α of 0.01; Fig. 4B). Separate analyses of individual microsatellites identified one locus that tended to contain more mutations in coevolved hosts (P = 0.017; FDR-adjusted α of 0.005; locus 3003; Table S1).

Fig. 4.
Snap-shot look at possible diversity-generating mechanisms after 48 host generations of experimental evolution. (A) Genetic exchange in B. thuringiensis (average number of clones with more than one toxin gene per replicate) was significantly higher upon ...


We demonstrated with the help of a single controlled experiment that host–parasite coevolution caused multiple phenotypic and genetic changes in both interacting antagonists. In the following, we will first discuss the phenotypic results and then turn to the changes observed at the genetic level.

Two types of phenotypic responses were identified upon coevolution: reciprocal increases in the traits of direct relevance to the interaction (virulence, resistance) and reciprocal reductions in other life-history traits (growth rate and related traits), indicative of adaptation costs. These findings were made at the end of our evolution experiment, demonstrating that host–parasite coevolution can leave a multifaceted phenotypic signature at a single time point during the coevolutionary interaction.

The presence of both types of phenotypic responses in both antagonists had not been previously demonstrated within a single laboratory- or field-based evolution experiment. However, consistent findings were made across studies. For example, in a microbial host system (P. fluorescens and its phage), coevolution led to reciprocal increases in host resistance and parasite virulence (3) and additionally reduced host growth rates or host competitive ability (11, 34). In studies that used animal hosts and focused on host responses, coevolution resulted in increased resistance and either reduced competitive ability [e.g., in Drosophila hosts (6)] or increased competitive ability irrespective of parasite presence but reduced growth rates in the absence of parasites and low nutrient conditions [e.g., in D. magna water fleas (35)]. Other studies with animal hosts focused on parasite responses and demonstrated that coevolution led to fast adaptations in infectivity [e.g., in bacterial parasites of D. magna (22)]. Moreover, rapid reciprocal changes in resistance and infectivity were identified for coevolving Potamopyrgus snail hosts and their trematode parasites: over six host generations, the parasite was found to show time-lagged adaptations in infectivity to the coevolving host, whereas the host from the sixth host generation exhibited increased resistance to the coadapted antagonist (23). This result indicates that resistance to coevolving parasites may be traded off with resistance to other, noncoevolving parasites. Reciprocal responses were also observed for a coevolving Tribolium beetle–Nosema microsporidian system: here, the host increased in resistance, whereas the parasite showed reduced host killing and, at the same time, no changes in spore production, possibly indicating a tradeoff between virulence and transmission potential for the parasite (25).

The phenotypic responses in our study were more pronounced for the parasite than the host. A possible explanation for this pattern is that the bacterial parasites were able to adapt faster as a result of their haploid genome, their generally larger population size, and their shorter generation time, which is consistent with the many previous observations of rapid virulence evolution during serial passage experiments (36).

Our study also revealed reciprocal changes in genetic characteristics. These genetic characteristics had not been evaluated for both antagonists in previous coevolution experiments (laboratory or field-based). However, some of these studies made consistent findings in the host, showing, for example, increased allele fluctuations in coevolving D. magna water fleas (37), a decreased frequency of common genotypes in coevolving Potamopyrgus snails (38), and increased recombination rates in coevolving T. castaneum beetles (9). Furthermore, morphological variation in P. fluorescens bacteria was used to calculate changes in host diversities, revealing increases in both within- and between-population diversities during coevolution with phages in homogenous environments (11). Moreover, an increase in P. fluorescens mutation rate was deduced from the higher frequency of antibiotic resistant mutants under coevolution conditions (10).

In our study, the predicted increases in genetic diversities under coevolution conditions were mainly identified across time, but not at the end. A possible explanation for this result is that continuous coevolutionary interactions can momentarily lower diversity at a particular time point in response to a selective advantage of a particular allele, even if diversity is on average higher over time (2). This observation is consistent with previous studies in which significantly elevated diversities were recorded especially during the coevolutionary adaptation (11, 12).

Our analysis across time revealed three evolutionary patterns that are defined by the significance of differences between coevolution and control treatments in three variables: allele/gene frequency changes, within-population gene diversities, and across-population gene diversities (Fig. 3D). These evolutionary patterns can be explained by different underlying selection dynamics. The first evolutionary pattern is characterized by significantly increased rates of change and gene diversities under coevolution conditions and was found for the parasite toxins and the two host microsatellites on chromosome IV (loci 4001, IV-L; pattern I in Fig. 3D). It is consistent with the scenario most often associated with host–parasite coevolution in the literature, which suggests that continuous host–parasite interactions favor rare alleles and thus their repeated spread to intermediate frequencies within the population, such as via cycles of negative frequency–dependent selection or incomplete selective sweeps (2). Such dynamics should then associate with continuous allele frequency changes (4, 5) and, in turn, increased genetic diversities within populations (11). Moreover, genetic diversities should increase across populations that are likely subject to independent coevolutionary trajectories (11, 12).

This pattern is specifically expected for the B. thuringiensis toxins that are likely relevant to virulence (28). In turn, our results may point to a role in resistance evolution for sites on chromosome IV. Intriguingly, this chromosome harbors several genes involved in resistance against B. thuringiensis, including the p38 MAPK (pmk-1), c-Jun N-terminal kinase (jnk-1), and BT toxin resistance genes bre-1 and bre-5 (26, 27, 29) (Fig. S1). Based on this association, it may be speculated that particular alleles of these defense genes were under selection in the coevolving populations.

The second pattern is characterized by significantly higher evolutionary rates and diversities across populations, but significantly lower within-population diversities under coevolution conditions (pattern II; Fig. 3D). It was identified for microsatellites II-R and X004 (Table S1). It may be caused by the repeated spread of favorable alleles close to fixation, for example via complete selective sweeps or negative frequency-dependent cycles with large amplitudes. Patterns I and II are related because both should apply to virulence/resistance genes directly involved in the coevolutionary interaction. The third pattern was found when coevolution produced a significant decrease in both within- and across-population diversities and no increased rates of change, as observed for host microsatellite V-L (pattern III; Fig. 3D). It could be caused by directional selection under coevolution conditions, as possibly expected for genes involved in general defense or other life-history traits (e.g., reproductive rate).

Genetic drift has likely played only a minor role in shaping these patterns. Such drift effects may result from the limited population sizes usually used in evolution experiments (SI Materials and Methods). They are particularly expected for the coevolution treatment, for which population size may be further reduced as a result of interaction with the antagonist, potentially preventing coevolutionary adaptation or causing linkage disequilibrium and reduction of within-population genetic diversity, especially for the host X chromosome, for which population size is only 50% of that of autosomes. However, our study did demonstrate that coevolution leads to reciprocal adaptations (Fig. 2), and that it reduces within-population diversities for only some host loci (Fig. 3), including only one of the three X chromosome loci (Table S1). Moreover, linkage disequilibria were not significantly different between treatments (Table S1), and the final host populations consisted of genotypes that were almost all different from each other (757 of 759 multilocus genotypes were different), further suggesting an absence of drift. It is interesting to note that, in contrast to our results, a recent study recorded an increased recombination rate (equivalent to reduced linkage disequilibrium) during experimental evolution of C. elegans in the presence of the Gram-negative pathogen Serratia marcescens (39). This contrast is likely a result of a different study approach in the published work in which pathogens did not coevolve with their hosts and in which recombination was not directly inferred from molecular marker analysis (39).

Consequently, the observation of the various evolutionary patterns in the genetic data may point to more complex evolutionary trajectories than currently considered in theoretical models and during which different parts of the genome are subject to different selective constraints. Similar genome-wide variations in selective dynamics were reported for Arabidopsis resistance and Drosophila immunity genes (4042), even though it is as yet unclear whether they are caused by coevolution with parasites or other selective constraints. A particular future challenge will be to characterize in detail the distinctive properties of the various evolutionary patterns. A promising approach is to identify the alleles of loci subject to alternative evolutionary dynamics, and to characterize their role during coevolutionary adaptation, for example through a combination of genome-wide association mapping and functional genetic analysis (43). Interestingly, our results already indicated that rapid change is influenced by increased toxin gene exchange in the parasite as well as increased mutation rates in the host (Fig. 4).

In summary, we identified distinct phenotypic and genetic consequences of host–parasite coevolution with the help of a single controlled experiment, based on a multicellular host system. Coevolution was associated with consistently elevated rates of genetic change, thus confirming the original formulation of the Red Queen hypothesis by Van Valen (44) that persistence in a variable environment (such as that generated by interacting parasites and hosts) requires continuously high evolutionary rates. Presence of the respective antagonists also favored high diversity in parasite toxins and some host loci, which is generally consistent with the idea put forward by Charles Darwin that parasites (among others) drive diversification (45) and thus contribute to the maintenance of biological diversity (2, 12, 34). Importantly, our study yielded experimental support for reciprocity during coevolution, which had not been previously demonstrated for possible adaptation costs, rapid genetic change, or increased genetic diversities, even though reciprocal changes in these characteristics are central to many theories on parasite-mediated selection and the evolution of sex (e.g., refs. 4648).

Materials and Methods

Experimental evolution was performed for 48 host generations in “wormballs” (49) using three selection regimes that were identical except in terms of the presence of the antagonist (host–parasite coevolution; 20 replicates) or its absence (parasite or host control; 10 or 20 replicates, respectively; Fig. 1A). The potential for evolution was enhanced by using genetically diverse starting populations for both antagonists, each derived from three original genotypes known to vary in their interactions and other life-history traits (Tables S2 and S3). To minimize the risk of random loss of genetic material, immigration was simulated by regular addition of original genotypes. At the end of experimental evolution, phenotypic changes were evaluated by comparing randomly paired replicate populations from the coevo-lution and control treatments (parasite comparisons, pairs of coevolved vs. control parasite populations; host comparisons, pairs of coevolved vs. control host populations; Fig. 1B). Phenotypic traits measured included parasite virulence (measured as the proportion of killed worms after 3 d exposure), host resistance (proportion of surviving animals after 3 d parasite exposure), parasite growth rate (inverse generation time on high nutrient medium), host population growth rate (mean offspring number per day and worm in absence of parasites), and host body size (mean body area in absence of parasites; SI Materials and Methods). Genetic changes were assessed for pooled population samples across time, isolated every fourth host generation from each replicate population (Fig. 1C) and for individual lines from the end of experimental evolution (Fig. 1B). The parasite analysis was based on three toxin genes, each of which were present in only one of the three original strains (Table S4). For the host, we studied nine microsatellites known to vary among the starting strains and located in different genomic regions (Table S5 and Fig. S1). A more detailed description is provided in SI Materials and Methods.

Supplementary Material

Supporting Information:


We are most grateful for support and advice from N. Anthes, C. Boehnisch, M. Bundman, S. Cremer, A. Cutter, I. Dankert, T. D'Souza, A. Dubuffet, S. Gandon, M. Hasshoff, S. Hering, M. Hohloch, R. Iserlohe, G. Jansen, I. Jost, O. Kaltz, R. Klassen, U. Kuntz, J. Kurtz, F. Meinhardt, G. Rauch, T. Reusch, S. Riss, G. Schulte, H. Schwitte, C.-P. Stelzer, H. Teotonio, N. Timmermeyer, D. Tonn, J. Waldeck, M. Wegner, and K. Dierking. We also thank the three referees for valuable advice. We acknowledge financial support from German Science Foundation Grants 1415/1-2 and 1415/5-1.


The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/cgi/content/full/1003113107/DCSupplemental.


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