Abstract
Rapid exaggeration of host and pathogen traits via arms race dynamics is one possible outcome of host–pathogen coevolution. However, the exaggerated traits are expected to incur costs in terms of resource investment in other life-history traits. The current study investigated the costs associated with evolved traits in a host–pathogen coevolution system. We used the Drosophila melanogaster (host)–Pseudomonas entomophila (pathogen) system to experimentally derive two selection regimes, one where the host and pathogen both coevolved, and the other, where only the host evolved against a non-evolving pathogen. After 17 generations of selection, we found that hosts from both selected populations had better post-infection survivorship than controls. Even though the coevolving populations tended to have better survivorship post-infection, we found no clear evidence that the two selection regimes were significantly different from each other. There was weak evidence for the coevolving pathogens being more virulent than the ancestral pathogen. We found no major cost of increased post-infection survivorship. The costs were not different between the coevolving hosts and the hosts evolving against a non-evolving pathogen. We found no evolved costs in the coevolving pathogens. Thus, our results suggest that increased host immunity and pathogen virulence may not be costly.
Keywords: experimental coevolution, life-history traits, Drosophila melanogaster, Pseudomonas entomophila
1. Introduction
An organism's ability to combat pathogens is a critical determinant of its fitness. However, immunity is costly [1] and can trade-off with other life-history traits [2,3] when resources are limiting. These trade-offs can occur at the physiological level and evolutionary level. Physiological trade-offs are investigated between immunity and other life-history traits such as reproduction [4,5], longevity [6–8] and metabolic activity [1,9,10] in a wide variety of taxa.
Evolutionary cost can be defined as the evolution of heightened immunity at the cost of other traits [11]. For instance, populations of Indian meal moth Plodia interpunctella selected against a virus evolved greater resistance along with increased development time and reduced egg viability [12]. Selected populations of Drosophila melanogaster evolved resistance against their endoparasite Leptopilina boulardi at the cost of larval competitive ability [13]. Evolved immunity was also costly in selected populations of D. melanogaster against Pseudomonas aeruginosa bacteria [14] or Bacillus cereus bacterial spores [15]. However, many other studies found no cost of increased immunity in either nematode [16] or fly populations [17,18] evolving against their bacterial pathogens. Thus, empirical results about the cost of immunity have been variable.
The studies mentioned above used an experimental system where only the host evolved better immunity against a static or non-evolving pathogen. However, evolutionary outcomes in a scenario where the host and the pathogen coevolve may differ. One possibility is the arms race model of coevolution, which predicts the evolution of increased resistance in hosts and virulence in pathogens [19,20]. As a second possibility, the pathogen's virulence and the coevolving host's resistance may be subject to fluctuating selection, leading to no net directional change in the mean value of these traits [21,22]. Multiple studies using a bacteria–phage coevolution system find a directional increase in resistance and infectivity [20,23] at the cost of competitive fitness [20,24,25] in the short term. Such costs prevent the indefinite escalation of arms race dynamics [23]. Experimental evidence shows that costs associated with the short-term arms race certainly alter the host–pathogen coevolutionary dynamics and lead to long-term fluctuating selection dynamics [23]. These results suggest that selection might be stronger or variable in the host–pathogen coevolution system, relative to the case where the host experiences selection by a static or non-evolving pathogen. Thus, if arms race dynamics predominate, the coevolving organisms are expected to evolve more extreme immunity-related traits and incur higher fitness costs in other life-history traits relative to a scenario where one organism evolves against a static (non-evolving) antagonist [26].
Much of our understanding of the costs associated with host–pathogen coevolution comes from studies focused on bacteria–phage models [20,23,27]. Some have used multi-cellular organisms as hosts in the coevolutionary studies and have assessed associated costs [22,28,29]. For instance, higher host resistance in Caenorhabditis elegans nematode host and higher virulence in Bacillus thuringiensis bacterial pathogen were accompanied by a reduction in host growth rate, body size and fecundity, and reduced growth rate in the coevolving pathogen [30]. Another coevolution study using Tribolium castaneum beetle hosts and Beauveria bassiana fungal parasite showed that the hosts evolved increased survival with no associated life-history costs [29]. Coevolution studies using plant hosts and their antagonists have reported trade-offs between evolved virulence and spore production in the pathogen [31] and between evolved resistance against pests and growth in plant hosts [32]. Such costs are also observed in naturally occurring interactions between predators and their prey. For instance, escalated resistance in common garter snake against a highly toxic newt Tarchia was found to negatively affect the crawling speed of the snakes [33].
In the present study, we examined if (i) evolved immunity traits in the hosts and pathogens extract a life-history cost, (ii) coevolving hosts pay a greater cost relative to hosts evolving against a non-evolving pathogen and (iii) coevolving pathogens pay a cost relative to the ancestral pathogen. We used the Drosophila melanogaster host and Pseudomonas entomophila pathogen system described by Ahlawat et al. [34] to address these questions. This study consisted of fly populations that were (i) coevolving with the pathogen, (ii) evolving against a non-evolving pathogen, and (iii) two controls. After 17 generations of coevolution, we evaluated whether post-infection survivorship had increased in the selected populations and if survivorship was higher in the coevolving populations relative to populations adapting to a static pathogen. At the same time, we also assayed several host life-history traits to assess if there were any costs of evolved immunity and if these costs were different in the two types of selected host populations. Finally, we assessed whether coevolved pathogens paid a fitness cost in terms of their growth rate. We hypothesized that (i) the coevolving hosts would evolve more extreme immunity traits (better post-infection survival) than hosts evolving against a static pathogen and consequently pay a higher life-history cost and (ii) the coevolving pathogens would also pay a fitness cost (lower growth rate) relative to ancestral pathogen.
2. Material and methods
(a) . Fly populations
We performed various life-history assays on replicate populations of four selection regimes of Drosophila melanogaster (host)–Pseudomonas entomophila (pathogen).
-
(i)
coevolution (Coev 1–4): (both host and pathogen coevolve with each other)
-
(ii)
adaptation (Adapt 1–4): (only host evolves in response to a non-evolving pathogen)
-
(iii)
sham control (Co.S 1–4): (infection or injury control)
-
(iv)
unhandled control (Co.U 1–4): (unhandled control)
We used the ancestral Blue Ridge Baseline (BRB 1–4) populations to derive these four selection regimes. We derived one replicate from each replicate BRB population for the four selection regimes (see electronic supplementary material, figure S1). For example, from BRB 1, we derived Coev 1, Adapt 1, Co.S 1 and Co.U 1; from BRB 2, we derived Coev 2, Adapt 2, Co.S 2 and Co.U 2 populations and so on. Different replicate populations of the same numeral were termed as ‘blocks’, which were maintained independently. Hence, populations with the same numeral are treated as statistical blocks. Thus, there were four blocks (block 1–4) in our experiment.
The detailed experimental protocol for these selection regimes can be found in the electronic supplementary material. Here, we briefly discuss their maintenance.
(i) . Coev (host and pathogen coevolution)
Every generation, 200 males and 200 females were randomly chosen and infected with a bacterial suspension of coevolving P. entomophila bacteria at an OD600 of 0.4, using a sterile needle under light CO2 anaesthesia. Twenty-four to 48 h post-infection, the number of dead flies was counted, and 10–15 dead flies per sex were collected and stored at 4°C for further use. These flies were later used to extract the bacteria to infect the next generation. Host mortality was recorded until 4 days post-infection [35]. We used a larger population size of 400 individuals, as approximately half of the individuals survived to contribute to the next generation.
The first coevolving pathogen was derived from the dead flies collected post-infecting the host with the non-evolving/ancestral P. entomophila strain as mentioned in the electronic supplementary material. Further, these dead flies were used to isolate the coevolved pathogen to infect the next host generation. We also preserved the coevolved pathogen for future assays.
(ii) . Adapt (only the host adapted to the pathogen)
One hundred and fifty males and 150 females were infected with P. entomophila pathogen at an OD600 of 0.5. The host was infected with a non-evolving/ancestral pathogen, i.e. only the host evolved in response to the non-changing or static pathogen. We monitored the host mortality for 4 days post-infection.
(iii) . Co.S (sham infection control)
One hundred males and 100 females were pricked by a needle dipped in sterile 10 mM MgSO4 solution. This regime was used as an infection/pricking control, and MgSO4 elicited almost 0% mortality in flies.
(iv) . Co.U (unhandled control)
One hundred males and 100 females were randomly sorted under light CO2 anaesthesia and were not infected or pricked with a needle.
(b) . Host and pathogen life-history assays
All life-history assays for hosts were performed between 17 and 20 coevolution generations using standardized flies. See electronic supplementary material for the fly standardization protocol and detailed protocols for life-history assays.
On the 12th day post-egg collection, the experimental flies from each selection regime were used to set up two experiments (a) longevity post-infection and (b) longevity under non-infected conditions (basal longevity).
(i) . Longevity post-infection
For every block, flies from each population were divided into three infection treatments: infected with coevolved Pe from the 15th generation of coevolution, infected with ancestral Pe, and sham infection. Seventy-five males and 75 females were randomly selected and infected/sham infected for each treatment. Flies were then transferred into cages, and mortality was monitored until the last fly died in each cage. We could record longevity for block 2 Adapt flies infected with ancestral Pe for only one month, as that cage was lost in an accident.
Mortality over the first 96 h post-infection was analysed separately to assess response to selection (since this period corresponds to the regular selection protocol). Mortality data after 96 h of infection until the death of the last fly was analysed separately to assess the long-term effects of infection on host longevity.
(ii) . Basal longevity
To measure basal longevity, flies from each population were randomly sorted using light CO2 anaesthesia in mixed sex groups (75 males and 75 females per cage). These flies were transferred into cages. Each cage was scanned twice a day for fly mortality until the last fly in the cage died.
(iii) . Host fecundity
Fecundity data were obtained from the cages set-up for the longevity experiment. Each cage was provided with a fresh food plate for 6 h to measure fecundity. For the first 5 days post-treatment, fecundity plates were provided every day. After this, fecundity was assessed once every 5 days. Plates were provided between 16.00 and 22.00 hours to account for the fecundity peak that we observe in our flies when switching to the dark part of the light cycle. After the 6 h interval, fecundity plates from each cage were removed and were stored at −20°C. They were later thawed, and eggs were counted.
(iv) . Host development time, dry body weight and lipid content
Eggs were collected from standardized flies at a density of 70 per vial. Ten such vials were set up per population and incubated under standard conditions. Eclosing flies were collected every 4 h until all the flies had eclosed and stored at −20°C. Later, flies were sexed and counted. These flies were also used to assay dry weight and lipid content (see electronic supplementary materials for details).
(v) . Host starvation resistance
As the ability to tolerate stress is affected by the mating status of the flies, this assay was done using mated and virgin flies. On the 12th day post-egg collection, 75 mated and virgin flies from each selection regime and for each sex (75 × sex × treatment × selection regime × block) were transferred into cages with access to water but no food. Mortality was scored every 7–8 h until all the flies died.
(vi) . Pathogen growth rate
We assessed the growth rate for ancestral Pe and coevolved Pe from the 15th and 20th generations in each of the four replicate populations. Flasks containing inoculated pathogen in LB medium were maintained at standard conditions. OD was measured every hour until the stationary phase was reached. The assay was repeated thrice.
(c) . Statistical analysis
All the analyses were performed in R (v. 3.6.0) (R Core Team [36]). To measure the evolutionary costs of development time, fecundity, dry body weight and lipid content, we performed mixed-model ANOVA using the R packages ‘lme4’ [37] and ‘lmerTest’ [38]. We used selection (Coev, Adapt, Co.S and Co.U) and sex as fixed factors and block (replicating populations) as a random factor. We calculated the mean and standard error for each sample using the ‘summarySE()’ function under ‘Rmisc’ [39] package. For basal longevity and starvation assays, the Cox proportional hazard model was used to analyse the results using ‘coxme’ [40] package, keeping block as random and selection and sex as fixed factors. (See electronic supplementary material for statistical models.)
To assess response to selection, we measured survivorship of flies post-infection with ancestral and coevolved pathogens for 96 h. We used two kinds of analyses to explore the response to selection: (i) we used the Cox proportional hazard model to analyse the data separately for males and females using selection and pathogenic treatment as a fixed factor and block as a random factor, and (ii) we calculated the proportion of flies surviving until the 96th hour post-infection with ancestral and coevolved pathogen using mixed-model ANOVA. We used selection and pathogen treatment as fixed factors and blocks as a random factor.
Longevity post-infection was measured from the same experiment as above. Flies that had survived post 96 h of infection were followed until their eventual death. The time between 96 h post-infection and eventual death was considered the post-infection longevity of flies. We performed Cox proportional hazard analysis with selection, treatment and sex as fixed factors and block as a random factor. Since one treatment was lost during the experiment, we measured longevity post 96 h of infection for three blocks only. Additionally, most of the flies in the control populations had died by 96 h post-infection. Therefore, we analysed longevity post-infection in only the two selected populations (Coev and Adapt).
Bacterial growth rate analysis was done using ‘growthcurver’ package [41] in R. It fits the data into a nonlinear least-square Levenberg–Marquardt algorithm to summarize growth dynamics and displays the growth rates, carrying capacity and doubling time of each strain. The logistic equation is [K/(1 + ((K−N0)/N0) × exp(−r × t))], where K is the maximum possible size or carrying capacity, r is the intrinsic growth rate of the population, N0 is a number of cells at the beginning of the growth curve and t is time. It also calculates the AUC (Area under the curve) and doubling time (time at which the bacteria or the absorbance doubles) [41]. We used these values and performed one-way ANOVA to compare bacterial populations to observe the correlation between growth rate and doubling time.
3. Results
(a) . Longevity
(i) . Longevity post-infection
During regular maintenance, flies were monitored for 96 h post-infection. To start the next generation, eggs were collected from flies that survived for 96 h. Therefore, survivorship over the first 96 h post-infection is important in these selection regimes. We measured the survivorship of the flies post-infection with ancestral or coevolved pathogen until 96 h. None of the sham infected flies died during the first 96 h post-infection. The two selected (Coev and Adapt) populations survived infection (from ancestral and coevolving pathogens) much better than the two control (Co.S and Co.U) populations (figure 1a; table 1a). Within the two selected populations, overall, we observed a consistent pattern where the Coev flies were better at surviving pathogenic infection (by ancestral as well as coevolved pathogen) relative to Adapt populations (figure 1a). However, this difference was not statistically significant (table 1a). The difference in the mortality caused by ancestral Pe and coevolved Pe was significantly different in males only (table 1a). However, there was again a trend across most comparisons where fly mortality caused by ancestral Pe (dotted lines) was either lower than the coevolved Pe (solid lines) or was equivalent to that of coevolved Pe (figure 1a).
Figure 1.
(a) Survivorship of selected (Coev and Adapt) and control (Co.S and Co.U) regimes until 96 h post-infection with ancestral Pe and coevolved Pe. The left panel (F) shows the survivorship of female individuals from each selection regime, and the right panel (M) shows the survivorship of male individuals. The dotted lines indicate the survivorship of flies when infected with ancestral Pe, whereas the solid lines indicate the survivorship of flies when infected with coevolved Pe. (b) Basal longevity of selected (Coev and Adapt) and control (Co.S and Co.U) regimes. The left panel (F) shows the longevity of female individuals from each selection regime, and the right panel (M) shows the longevity of male individuals. (Online version in colour.)
Table 1.
(a) The output of Cox proportional hazard models for survivorship of female and male hosts from the Adapt, Co.S, Co.U and Coev regimes for the first 96 h post-infection with ancestral Pe and coevolved Pe. The default level for ‘Selection’ is Adapt, while the default level for ‘Treatment’ is Anc Pe and their hazard ratios are constrained to be 1. Lower CI and Upper CI indicate lower and upper bounds of 95% confidence intervals. Confidence intervals that do not contain 1 signify statistical significance and are shown in italics. Higher hazard rates are equivalent to lower survivorship in the hosts. (b) The output of Cox's proportional hazards model for the basal longevity of the hosts from the Adapt, Co.S, Co.U and Coev regimes. The default level for ‘Selection’ is Adapt, while the default level for ‘Sex’ is Female (F) with a hazard ratio 1.
| (a) Longevity post-infection (96 h) | |||
|---|---|---|---|
| females | |||
| fixed coefficients | hazard rate | lower CI | upper CI |
| SelectionCo.S | 2.3365 | 1.314931 | 4.151783 |
| SelectionCo.U | 2.8332 | 1.597644 | 5.024541 |
| SelectionCoev | 0.6247 | 0.342948 | 1.138142 |
| TreatmentCoev Pe | 1.3293 | 0.92784 | 1.904518 |
| SelectionCo.S: TreatmentCoev Pe | 0.8814 | 0.545183 | 1.42525 |
| SelectionCo.U: TreatmentCoev Pe | 1.3019 | 0.809769 | 2.093293 |
| SelectionCoev: TreatmentCoev Pe | 0.7952 | 0.465024 | 1.360092 |
| random effects | |||
| group | variance | ||
| block/selection/treatment | 0.03300148 | ||
| block/selection | 0.10946617 | ||
| block | 0.19702231 | ||
| males | |||
| fixed coefficients | hazard rate | lower CI | upper CI |
| SelectionCo.S | 2.7429 | 1.507766 | 4.989849 |
| SelectionCo.U | 3.1169 | 1.71584 | 5.662009 |
| SelectionCoev | 0.7343 | 0.393803 | 1.369294 |
| TreatmentCoev Pe | 1.5956 | 1.121341 | 2.270577 |
| SelectionCo.S: TreatmentCoev Pe | 0.6603 | 0.414181 | 1.052847 |
| SelectionCo.U: TreatmentCoev Pe | 0.8543 | 0.538926 | 1.354235 |
| SelectionCoev: TreatmentCoev Pe | 0.9031 | 0.540564 | 1.508947 |
| random effects | |||
| group | variance | ||
| block/selection/treatment | 0.025 | ||
| block/selection | 0.1249 | ||
| block | 0.3325 | ||
| (b) Basal longevity | |||
| fixed coefficients | hazard rate | lower CI | upper CI |
| SelectionCoev | 0.81099 | 0.539021 | 1.220182 |
| SelectionCo.S | 1.36979 | 0.911194 | 2.059164 |
| SelectionCo.U | 1.33287 | 0.885237 | 2.006918 |
| SexMale | 1.12758 | 0.866407 | 1.467412 |
| SelectionCoev: SexM | 0.97971 | 0.674894 | 1.422193 |
| SelectionCo.S: SexM | 0.86122 | 0.593392 | 1.249946 |
| SelectionCo.U: SexM | 0.92108 | 0.634448 | 1.337363 |
| random effects | |||
| group | variance | ||
| block/selection/sex | 0.0210 | ||
| block/selection | 0.0504 | ||
| block | 0.0004 | ||
Consistent with these results, we observed that the proportion of flies that survived post 96 h of infection (by ancestral or coevolved Pe) was consistently higher in Coev populations relative to Adapt and control populations (electronic supplementary material, figure S2 and table S1). We also observed that the proportion of surviving flies in selected and control populations was lesser against coevolved Pe relative to ancestral Pe (electronic supplementary material, figure S2). However, the effect of ancestral versus coevolved Pe was non-significant (electronic supplementary material, table S1).
At the end of 96 h after infection, very few flies were left in the two control regimes. Therefore, we analysed data from only the two selected populations for longevity beyond 96 h post-infection. There was no mortality over the first 96 h in any of the regimes in the sham treatment. Therefore, we used data from all the selection regimes to calculate longevity under sham-infected conditions. Overall, we found that males and females in the Coev populations survived longer than the Adapt flies when infected with ancestral Pe or coevolved Pe (electronic supplementary material, figure S3a). The longevity of Adapt and Coev flies was unaffected by the type of pathogen treatment (electronic supplementary material, table S2a). We also found no difference in the longevity of selected (Coev and Adapt) and control (Co.S and Co.U) flies in sham-infected (flies treated with sterile MgSo4 solution) treatment (electronic supplementary material, figure S3b and table S2b).
(ii) . Basal longevity
We found no significant difference in the lifespan of males and females from each of the selection regimes (figure 1b). Overall, results from the Cox proportional hazard model showed no difference in basal longevity of selected (Coev and Adapt), as well as control (Co.S and Co.U) flies (table 1b).
(b) . Starvation resistance
The mated individuals from the different selection regimes did not differ in their starvation resistance (electronic supplementary material, figure S4a; table 2a). Again, the mated males and females had comparable starvation resistance (electronic supplementary material, figure S4a; table 2a).
Table 2.
Starvation resistance: the output of Cox's proportional hazards models for (a) mated treatment and (b) virgin treatment. The default level for ‘Selection’ is Adapt, while the default level for ‘Sex’ is female and their hazard ratios are constrained to be 1. Lower CI and upper CI indicate lower and upper bounds of 95% confidence intervals. Confidence intervals that do not contain 1 signify statistical significance and are shown in italics. Higher hazard rates are equivalent to lower survivorship in the hosts.
| (a) mated treatment | |||
|---|---|---|---|
| fixed coeffecients | hazard ratios | lower CI | upper CI |
| SelectionCoev | 0.893726 | 0.645584 | 1.237261 |
| SelectionCo.S | 0.988455 | 0.714552 | 2.537781 |
| SelectionCo.U | 0.838874 | 0.606227 | 1.152461 |
| SexMale | 1.1197104 | 0.850016 | 1.475062 |
| SelectionCoev:SexMale | 1.4321981 | 0.968894 | 2.117 |
| SelectionCo.S: SexMale | 1.367117 | 0.769665 | 1.787825 |
| SelectionCo.U: SexMale | 1.3971481 | 0.945917 | 2.117 |
| random effects | |||
| group | variance | ||
| block/selection/sex | 0.026136 | ||
| block/selection | 0.015259 | ||
| block | 0.246848 | ||
| (b) virgin treatment | |||
| fixed coeffecients | hazard ratios | lower CI | upper CI |
| SelectionCoev | 0.505572 | 0.361498 | 0.707159 |
| SelectionCo.S | 0.554523 | 0.396492 | 0.782626 |
| SelectionCo.U | 0.5097037 | 0.364583 | 0.712981 |
| SexMale | 1.7641164 | 1.262508 | 2.464774 |
| SelectionCoev: SexMale | 1.0247934 | 0.639096 | 1.643289 |
| SelectionCo.S: SexMale | 1.2514483 | 0.780438 | 2.006717 |
| SelectionCo.U: SexMale | 1.6893869 | 1.052954 | 2.71041 |
| random effects | |||
| group | variance | ||
| block/selection/sex | 0.044578 | ||
| block/selection | 0.000372 | ||
| block | 0.202629 | ||
The pattern of starvation resistance was different in the virgin individuals. Flies from the Adapt regime had significantly lower starvation resistance than flies from Coev, Co.S and Co.U regimes (electronic supplementary material, figure S4b; table 2b). Additionally, virgin females had higher starvation resistance than their male counterparts (table 2b; electronic supplementary material, figure S4b).
(c) . Fecundity
Females from the different selection regimes laid a comparable number of eggs, and we found no significant difference between them (table 3; electronic supplementary material, figure S5). We also observed no effect of treatment on the fecundity of females from selected (Coev and Adapt) and control (CoS and Co.U) regimes as the number of eggs laid by these females remained unaffected post-infection with ancestral Pe and coevolved Pe (electronic supplementary material, figure S5; table 3).
Table 3.
Analysis of variance table showing the effects of fixed factors (Selection and Treatment), random factor (Block) and their interaction on fecundity.
| fixed effects | sum sq | mean sq | num d.f. | den d.f. | F-value | Pr(>F) |
|---|---|---|---|---|---|---|
| selection | 4.75 | 1.58 | 3 | 137 | 0.365 | 0.777 |
| treatment | 29.891 | 3.268 | 3 | 137 | 1.617 | 0.198 |
| Selection : Treatment | 29.145 | 9.963 | 9 | 137 | 0.525 | 0.848 |
| random effects | npar | logLik | AIC | LRT | d.f. | Pr(>Chisq) |
| (Block : Selection : Treatment) | 6 | −316.88 | 624.21 | 10.59 | 1 | <0.0001*** |
| (Block : Selection) | 6 | −311.34 | 634.69 | 8.945 | 1 | 1.00 |
| (Block) | 6 | −315.82 | 643.63 | 0.00 | 1 | 0.0027** |
(d) . Development time
We compared mean development time (defined as the time gap between egg-laying and eclosion) of the different selection regimes. Overall, we found no differences in the development time of the different selection regimes. However, the development time of the different regimes varied across blocks, resulting in a significant Block:Selection interaction (table 4a; figure 2a).
Table 4.
Analysis of variance table showing effects fixed factors (Selection and Sex), random factor (Block) and their interaction on various traits.
| (a) development time | ||||||
| fixed effects | sum sq | mean sq | num d.f. | den d.f. | F-value | Pr(>F) |
|---|---|---|---|---|---|---|
| selection | 117.7 | 39.26 | 3 | 8.9971 | 0.7406 | 0.5541 |
| sex | 2996.9 | 2996.9 | 1 | 11.999 | 56.519 | <0.0001*** |
| Selection:Sex | 157.31 | 52.44 | 3 | 11.999 | 0.9892 | 0.4307 |
| random effects | npar | logLik | AIC | LRT | d.f. | Pr(>Chisq) |
| (Block : Selection:Sex) | 12 | −1105.7 | 2235.4 | 1.5150 | 1 | 0.2184 |
| (Block : Selection) | 12 | −1117.7 | 2259.4 | 25.574 | 1 | <0.0001*** |
| (Block : Sex) | 12 | −1104.9 | 2233.8 | 0.000 | 1 | 1.000 |
| (Block) | 12 | −1105.2 | 2234.5 | 0.6305 | 1 | 0.427 |
| (b) dry body weight | ||||||
| fixed effects | sum sq | mean sq | num d.f. | den d.f. | F-value | Pr(>F) |
| selection | <0.0001 | <0.0001 | 3 | 9.011 | 15.9667 | 0.0005*** |
| sex | <0.0001 | <0.0001 | 1 | 12.066 | 3390.20 | <0.0001*** |
| Selection : Sex | <0.0001 | <0.0001 | 3 | 12.066 | 0.4462 | 0.7244 |
| random effects | npar | logLik | AIC | LRT | Df | Pr(>Chisq) |
| (Block : Selection:Sex) | 12 | 2332.2 | −4640.4 | 0.576 | 1 | 0.4478 |
| (Block : Selection) | 12 | 2330.8 | −4637.5 | 3.475 | 1 | 0.0623 |
| (Block : Sex) | 12 | 2332.5 | −4641.0 | 0 | 1 | 0.999 |
| (Block) | 12 | 2328.5 | −4632.9 | 8.072 | 1 | 0.0044** |
| (c) lipid content | ||||||
| fixed effects | sum sq | mean sq | num d.f. | den d.f. | F-value | Pr(>F) |
| selection | <0.0001 | <0.0001 | 3 | 10.9074 | 1.6015 | 0.2455 |
| sex | <0.0001 | <0.0001 | 1 | 3.9243 | 248.510 | 0.0001*** |
| Selection : Sex | <0.0001 | <0.0001 | 3 | 8.7501 | 0.1734 | 0.9116 |
| random effects | npar | logLik | AIC | LRT | d.f. | Pr(>Chisq) |
| (Block : Selection:Sex) | 12 | 2279.5 | −4535.2 | 9.167 | 1 | 0.0024** |
| (Block : Selection) | 12 | 2280.8 | −4537.5 | 7.216 | 1 | 0.0072** |
| (Block : Sex) | 12 | 2282.5 | −4541.0 | 2.34 | 1 | 0.3281 |
| (Block) | 12 | 2284.1 | −4544.2 | 0 | 1 | 0.126 |
Figure 2.
Effect of selection on various other traits of female and male hosts: (a) development time; (b) dry body weight; (c) lipid content. (Online version in colour.)
(e) . Dry body weight
We observed that the selection regimes differ in dry body weight (table 4b). Co.U flies were the heaviest among selection regimes, followed by Co.S flies (figure 2b, table 4b). Coev and Adapt flies were comparatively lighter. There was no difference between the body weight of flies from the Coev and Adapt regimes (figure 2b). On average, Co.U flies were around 11% heavier than Adapt flies. Overall, females were nearly 33% heavier than males (figure 2b).
(f) . Lipid content
Overall, the lipid content did not differ among the different selection regimes (table 4c; figure 2c). However, the sexes differed in their lipid content. On average, females had around 35% higher lipid content than males (figure 2c).
(g) . Bacterial growth rate
We observed no difference in bacterial growth parameters, i.e. doubling time, area under the curve and growth rate ‘r’, for coevolving Pe of all four blocks and ancestral Pe (table 5). This trend was similar across ancestral pathogen and coevolved pathogens from generation 15th and 20th of host–pathogen coevolution (table 5). In brief, neither could we find any change in the growth rate of the coevolved bacteria with respect to its ancestral or non-evolving bacteria, nor could we detect any differences among the coevolved pathogens isolated from different host generations.
Table 5.
Analysis of variance table for three measures pathogen fitness. Fitness of ancestral Pe and coevolving Pe were assayed after 15 and 20 generations of coevolution.
| d.f. | sum Sq | mean Sq | F-value | Pr(>F) | |
|---|---|---|---|---|---|
| after 15 generations of coevolution | |||||
| doubling time | 4 | 0.08989 | 0.022460 | 1.1557 | 0.386 |
| area under curve | 4 | 4.1138 | 1.0284 | 0.8878 | 0.5056 |
| intrinsic growth rate ‘r’ | 4 | 0.051729 | 0.012932 | 1.247 | 0.3522 |
| after 20 generations of coevolution | |||||
| doubling time | 4 | 0.031027 | 0.0077567 | 0.2593 | 0.8974 |
| area under curve | 4 | 12.476 | 3.1189 | 0.2528 | 0.9014 |
| intrinsic growth rate ‘r’ | 4 | 0.021526 | 0.0053816 | 0.2767 | 0.8864 |
4. Discussion
One possible outcome of coevolution is the directional elaboration of host and pathogen traits. Such elaboration is likely to carry a cost and may ultimately limit arms race dynamics. In this study, we asked if such costs can be detected in a coevolving system of D. melanogaster host and P. entomophila pathogen.
The two selected regimes were clearly better at surviving pathogen infection than controls. However, we found no clear evidence that the coevolving populations had better post-infection survivorship relative to the hosts evolving against a static pathogen. While there were clear trends in this direction, they were not statistically significant. However, when we looked at the long-term effects of infection (longevity post-infection), the Coev regime had higher survivorship than the regime where the host evolved against a static pathogen (Adapt Regime). Therefore, while we have clear results for long-term differences, we do not have clear evidence that at least by 17 generations of selection, coevolution had led to the greater elaboration of traits in the hosts, specifically in the 96 h post-infection period that is important for the flies in our selection regime. However, we found some evidence for increased virulence of the coevolving pathogen, at least towards the males. Many previous studies have found that in coevolving systems of bacteria and their antagonists, typically, arms race dynamics predominate, at least in the short run, leading to the directional elaboration of costly traits in hosts and pathogens [20,23,24]. Thus, our results suggest that arms race dynamics may not predominate, even in the early stages of host–pathogen coevolution. A rapid exaggeration of traits would require such additive variation in the population of hosts and pathogens. In our fly hosts, given the population size and the duration of the experiment, most of the responses in terms of the exaggeration of traits are expected to come from the standing genetic variation (and recombination producing new trait variants) rather than novel mutations. It may limit the hosts to a rather limited trait space and may overall curb the rate of trait exaggeration in both the hosts and the pathogen. In coevolution studies that use microbes, given the generation time, population size and mutation rates, novel mutations may play a relatively more significant role in exaggerating the traits in both hosts and pathogens.
We found no difference in the life-history traits (basal longevity, fecundity, starvation resistance of mated individuals, development time, and mean lipid content at eclosion) between the selected (Coev and Adapt) and control regimes (Co.S and Co.U). However, we found that the selected populations (Coev and Adapt) had smaller body sizes relative to the control (Co.S and Co.U) regimes. This reduction in body size did not negatively affect lipid content or the ability to survive bacterial infection. Additionally, virgin flies from the Adapt regimes showed a lower starvation resistance compared to all other regimes. These results thus show that there is no other major cost to evolve increased survivorship against a bacterial pathogen.
Interestingly, Coev and Adapt regimes did not differ from each other in most life-history traits. This is probably not surprising given that the two selected regimes did not differ significantly in terms of their evolved immune traits (even though there was a tendency for the coevolving populations to be better at survival). In addition, coevolving pathogens from all four blocks were found to have no fitness costs related to the growth rate in LB media compared with the ancestral pathogen. Our results are contrary to those from previous studies [23,25,30], which have found both a cost of increased host resistance and pathogen virulence.
We observed no cost of coevolution in most of the life-history traits. Results from the current study are contrary to those observed in many previous studies [23,30,31]. While many previous studies of coevolution using bacteria–phage systems find high, short-term costs of coevolution [23,42–44], we found no such major costs. It may be possible that 17–20 generations of host–pathogen coevolution might be too few to find any significant cost of evolved immune responses. In fact, Ahlawat et al. [34] assayed post-infection survivorship of these same populations after 20 generations of coevolution. They also used coevolved bacteria isolated after 20 generations of coevolution. They found that the coevolving populations had significantly better survivorship compared to hosts evolving against a static pathogen. Additionally, there was clear evidence of the coevolving pathogen becoming more virulent [34]. Thus, within a few generations after our assay, the trends in survivorship that we saw evolved to be statistically significant differences. Therefore, one could expect that life-history costs suffered by the hosts might have also been evident after a few more generations. This argument does not hold for the pathogen since in the present study, we assayed the cost of virulence using pathogens collected after 20 generations of selection, by which time there is clear evidence of the increased virulence [34]. However, Gupta et al. [17], who used P. entomophila pathogen and the host D. melanogaster from the same base populations as the ones used in this study, found no life-history costs in the hosts even after 30 generations of selection (for host survival against infection from a non-evolving pathogen). Thus, it is possible that evolved immune response in hosts and virulence in pathogens may actually be cheap.
Since our populations are maintained on rich, ad libitum food, resource-based trade-offs may be obscured. Therefore, it remains to be seen if life-history costs of increased immunity are expressed under low resource conditions [4,45]. Finally, it is possible that in both the host and the pathogen, trade-offs might be in traits that we did not assay (for example, alternative immune pathways such as PO, haemocyte number and transmission of the pathogen within the host).
Acknowledgements
N.A. thanks IISER Mohali for Junior and Senior Research Fellowship. K.M. thanks UGC and M.A.G. thanks CSIR, Govt. of India for Junior and Senior Research Fellowship. J. thanks DST-INSPIRE for providing SHE (scholarship for higher education). A.M. thanks DST-INSPIRE and A.A. thanks KVPY for fellowship. We would like to thank Prof. Lemaitre, EPFL Switzerland, for providing Gfp tagged Pseudomonas entomophila bacteria. We also thank Naginder for taking care of the logistics during the experiments.
Data accessibility
Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.9cnp5hqkx [46].
The data are provided in the electronic supplementary material [47].
Authors' contributions
N.A.: conceptualization, formal analysis, investigation, project administration, writing—original draft and writing—review and editing; K.M.: investigation and writing—review and editing; J.: investigation and writing—review and editing; M.A.G.: formal analysis, investigation and writing—review and editing; A.M.: investigation and writing—review and editing; A.A.: investigation and writing—review and editing; N.G.P.: conceptualization and writing—review and editing.
All authors gave final approval for publication and agreed to be held accountable for the work performed therein.
Conflict of interest declaration
We declare we have no competing interests.
Funding
This work was funded by IISER Mohali.
References
- 1.Lochmiller RL, Deerenberg C. 2000. Trade-offs in evolutionary immunology: just what is the cost of immunity? Oikos 88, 87-98. ( 10.1034/j.1600-0706.2000.880110.x) [DOI] [Google Scholar]
- 2.Zuk M, Stoehr AM. 2002. Immune defense and host life history. Am. Nat. 160(Suppl. 4), S9-22. ( 10.1086/342131) [DOI] [PubMed] [Google Scholar]
- 3.McKean KA, Yourth CP, Lazzaro BP, Clark AG. 2008. The evolutionary costs of immunological maintenance and deployment. BMC Evol. Biol. 8, 76. ( 10.1186/1471-2148-8-76) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Schwenke RA, Lazzaro BP, Wolfner MF. 2016. Reproduction–immunity trade-offs in insects. Annu. Rev. Entomol. 61, 239-256. ( 10.1146/annurev-ento-010715-023924) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Schwartz A, Koella JC. 2004. The cost of immunity in the yellow fever mosquito, Aedes aegypti depends on immune activation. J. Evol. Biol. 17, 834-840. ( 10.1111/j.1420-9101.2004.00720.x) [DOI] [PubMed] [Google Scholar]
- 6.Little TJ, Killick SC. 2007. Evidence for a cost of immunity when the crustacean Daphnia magna is exposed to the bacterial pathogen Pasteuria ramosa. J. Anim. Ecol. 76, 1202-1207. ( 10.1111/j.1365-2656.2007.01290.x) [DOI] [PubMed] [Google Scholar]
- 7.Moret Y, Schmid-Hempel P. 2000. Survival for immunity: the price of immune system activation for bumblebee workers. Science 290, 1166-1168. ( 10.1126/science.290.5494.1166) [DOI] [PubMed] [Google Scholar]
- 8.Armitage SaO, Thompson JJW, Rolff J, Siva-Jothy MT. 2003. Examining costs of induced and constitutive immune investment in Tenebrio molitor. J. Evol. Biol. 16, 1038-1044. ( 10.1046/j.1420-9101.2003.00551.x) [DOI] [PubMed] [Google Scholar]
- 9.Ardia DR, Gantz JE, Schneider CB, Strebel S. 2012. Costs of immunity in insects: an induced immune response increases metabolic rate and decreases antimicrobial activity. Funct. Ecol. 26, 732-739. ( 10.1111/j.1365-2435.2012.01989.x) [DOI] [Google Scholar]
- 10.Dolezal T, Krejcova G, Bajgar A, Nedbalova P, Strasser P. 2019. Molecular regulations of metabolism during immune response in insects. Insect. Biochem. Mol. Biol. 109, 31-42. ( 10.1016/j.ibmb.2019.04.005) [DOI] [PubMed] [Google Scholar]
- 11.Schmid-Hempel P. 2005. Evolutionary ecology of insect immune defenses. Annu. Rev. Entomol. 50, 529-551. ( 10.1146/annurev.ento.50.071803.130420) [DOI] [PubMed] [Google Scholar]
- 12.Boots M, Begon M. 1993. Trade-offs with resistance to a granulosis virus in the Indian meal moth, examined by a laboratory evolution experiment. Funct. Ecol. 7, 529-534. ( 10.2307/2390128) [DOI] [Google Scholar]
- 13.Kraaijeveld AR, Godfray HC. 1997. Trade-off between parasitoid resistance and larval competitive ability in Drosophila melanogaster. Nature 389, 278-280. ( 10.1038/38483) [DOI] [PubMed] [Google Scholar]
- 14.Ye YH, Chenoweth SF, McGraw EA. 2009. Effective but costly, evolved mechanisms of defense against a virulent opportunistic pathogen in Drosophila melanogaster. PLoS Pathog. 5, e1000385. ( 10.1371/journal.ppat.1000385) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ma J, Benson AK, Kachman SD, Hu Z, Harshman LG. 2012. Drosophila melanogaster selection for survival of Bacillus cereus infection: life history trait indirect responses. Int. J. Evol. Biol. 2012, 935970. ( 10.1155/2012/935970) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Penley MJ, Greenberg AB, Khalid A, Namburar SR, Morran LT. 2018. No measurable fitness cost to experimentally evolved host defence in the Caenorhabditis elegans–Serratia marcescens host–parasite system. J. Evol. Biol. 31, 1976-1981. ( 10.1111/jeb.13372) [DOI] [PubMed] [Google Scholar]
- 17.Gupta V, Venkatesan S, Chatterjee M, Syed ZA, Nivsarkar V, Prasad NG. 2016. No apparent cost of evolved immune response in Drosophila melanogaster. Evolution 70, 934-943. ( 10.1111/evo.12896) [DOI] [PubMed] [Google Scholar]
- 18.Faria VG, Martins NE, Paulo T, Teixeira L, Sucena É, Magalhães S. 2015. Evolution of Drosophila resistance against different pathogens and infection routes entails no detectable maintenance costs. Evolution 69, 2799-2809. ( 10.1111/evo.12782) [DOI] [PubMed] [Google Scholar]
- 19.Woolhouse MEJ, Webster JP, Domingo E, Charlesworth B, Levin BR. 2002. Biological and biomedical implications of the coevolution of pathogens and their hosts. Nat. Genet. 32, 569-577. ( 10.1038/ng1202-569) [DOI] [PubMed] [Google Scholar]
- 20.Buckling A, Rainey PB. 2002. Antagonistic coevolution between a bacterium and a bacteriophage. Proc. Biol. Sci. 269, 931-936. ( 10.1098/rspb.2001.1945) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Agrawal A, Lively C. 2002. Infection genetics: gene-for-gene versus matching-alleles models and all points in between. Evol. Ecol. Res. 4, 79-90. [Google Scholar]
- 22.Decaestecker E, Gaba S, Raeymaekers JAM, Stoks R, Van Kerckhoven L, Ebert D, De Meester L. 2007. Host–parasite ‘Red Queen’ dynamics archived in pond sediment. Nature 450, 870-873. ( 10.1038/nature06291) [DOI] [PubMed] [Google Scholar]
- 23.Hall AR, Scanlan PD, Morgan AD, Buckling A. 2011. Host–parasite coevolutionary arms races give way to fluctuating selection. Ecol. Lett. 14, 635-642. ( 10.1111/j.1461-0248.2011.01624.x) [DOI] [PubMed] [Google Scholar]
- 24.Gandon S, Buckling A, Decaestecker E, Day T. 2008. Host–parasite coevolution and patterns of adaptation across time and space. J. Evol. Biol. 21, 1861-1866. ( 10.1111/j.1420-9101.2008.01598.x) [DOI] [PubMed] [Google Scholar]
- 25.Poullain V, Gandon S, Brockhurst MA, Buckling A, Hochberg ME. 2008. The evolution of specificity in evolving and coevolving antagonistic interactions between a bacteria and its phage. Evolution 62, 1-11. [DOI] [PubMed] [Google Scholar]
- 26.Masri L, et al. 2015. Host–pathogen coevolution: the selective advantage of Bacillus thuringiensis virulence and its cry toxin genes. PLoS Biol. 13, e1002169. ( 10.1371/journal.pbio.1002169) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Koskella B, Brockhurst MA. 2014. Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol. Rev. 38, 916-931. ( 10.1111/1574-6976.12072) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Bérénos C, Schmid-Hempel P, Wegner KM. 2009. Evolution of host resistance and trade-offs between virulence and transmission potential in an obligately killing parasite. J. Evol. Biol. 22, 2049-2056. ( 10.1111/j.1420-9101.2009.01821.x) [DOI] [PubMed] [Google Scholar]
- 29.Rafaluk-Mohr C, Wagner S, Joop G. 2018. Cryptic changes in immune response and fitness in Tribolium castaneum as a consequence of coevolution with Beauveria bassiana. J. Invertebr. Pathol. 152, 1-7. ( 10.1016/j.jip.2017.12.003) [DOI] [PubMed] [Google Scholar]
- 30.Schulte RD, Makus C, Hasert B, Michiels NK, Schulenburg H. 2010. Multiple reciprocal adaptations and rapid genetic change upon experimental coevolution of an animal host and its microbial parasite. Proc. Natl Acad. Sci. USA 107, 7359-7364. ( 10.1073/pnas.1003113107) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Thrall PH, Burdon JJ. 2003. Evolution of virulence in a plant host–pathogen metapopulation. Science 299, 1735-1737. ( 10.1126/science.1080070) [DOI] [PubMed] [Google Scholar]
- 32.Todesco M, et al. 2010. Natural allelic variation underlying a major fitness trade-off in Arabidopsis thaliana. Nature 465, 632-636. ( 10.1038/nature09083) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Hague MTJ, Toledo G, Geffeney SL, Hanifin CT, Brodie Jr ED, Brodie ED III. 2018. Large-effect mutations generate a trade-off between predatory and locomotor ability during arms race coevolution with deadly prey. Evol. Lett. 2, 406-416. ( 10.1002/evl3.76) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Ahlawat N, Geeta Arun M, Maggu K, Prasad NG. 2021. Enemies make you stronger: coevolution between fruit fly host and bacterial pathogen increases post-infection survivorship in the host. Ecol. Evol. 11, 9563-9574. ( 10.1002/ece3.7774) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Gupta V, Ali ZS, Prasad NG. 2013. Sexual activity increases resistance against Pseudomonas entomophila in male Drosophila melanogaster. BMC Evol. Biol. 13, 185. ( 10.1186/1471-2148-13-185) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.R Core Team. 2017. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. See http://www.R-project.org/. [Google Scholar]
- 37.Bates D, Mächler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1-48. ( 10.18637/jss.v067.i01) [DOI] [Google Scholar]
- 38.Kuznetsova A, Brockhoff PB, Christensen RHB. 2017. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1-26. ( 10.18637/jss.v082.i13) [DOI] [Google Scholar]
- 39.Hope RM. 2016. Package Rmisc. R Package Version 1.5 See https://CRAN.R-project.org/package=Rmisc. [Google Scholar]
- 40.Therneau T. Package ‘coxme’. Mixed effects Cox models. R package version, 2.24. See https://CRAN.R-project.org/package=coxme.
- 41.Sprouffske K, Wagner A. 2016. Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinform. 17, 172. ( 10.1186/s12859-016-1016-7) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Sasaki A. 2000. Host-parasite coevolution in a multilocus gene-for-gene system. Proc. Biol. Sci. 267, 2183-2188. ( 10.1098/rspb.2000.1267) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Zhang QG, Chu XL, Buckling A. 2021. Overcoming the growth–infectivity trade-off in a bacteriophage slows bacterial resistance evolution. Evol. Appl. 14, 2055-2063. ( 10.1111/eva.13260) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Huang W, Traulsen A, Werner B, Hiltunen T, Becks L. 2017. Dynamical trade-offs arise from antagonistic coevolution and decrease intraspecific diversity. Nat. Commun. 8, 2059. ( 10.1038/s41467-017-01957-8) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Cotter S, Simpson SJ, Raubenheimer D, Wilson K. 2011. Macronutrient balance mediates trade-offs between immune function and life-history traits. Funct. Ecol. 25, 186-198. ( 10.1111/j.1365-2435.2010.01766.x) [DOI] [Google Scholar]
- 46.Ahlawat N, Maggu K, Jigisha, Arun MG, Meena A, Agarwala A, Prasad NG. 2022. Data from: No major cost of evolved survivorship in Drosophila melanogaster populations coevolving with Pseudomonas entomophila. Dryad Digital Repository. ( 10.5061/dryad.9cnp5hqkx) [DOI]
- 47.Ahlawat N, Maggu K, Jigisha, Arun MG, Meena A, Agarwala A, Prasad NG. 2022. No major cost of evolved survivorship in Drosophila melanogaster populations coevolving with Pseudomonas entomophila. FigShare. ( 10.6084/m9.figshare.c.5974213) [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Citations
- Ahlawat N, Maggu K, Jigisha, Arun MG, Meena A, Agarwala A, Prasad NG. 2022. Data from: No major cost of evolved survivorship in Drosophila melanogaster populations coevolving with Pseudomonas entomophila. Dryad Digital Repository. ( 10.5061/dryad.9cnp5hqkx) [DOI]
- Ahlawat N, Maggu K, Jigisha, Arun MG, Meena A, Agarwala A, Prasad NG. 2022. No major cost of evolved survivorship in Drosophila melanogaster populations coevolving with Pseudomonas entomophila. FigShare. ( 10.6084/m9.figshare.c.5974213) [DOI] [PMC free article] [PubMed]
Data Availability Statement
Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.9cnp5hqkx [46].
The data are provided in the electronic supplementary material [47].