DOI: 10.5061/dryad.2v6wwpzsv
title: Diet can alter the cost of resistance to a natural parasite in Caenorhabditis elegans

description: Resistance to parasites confers a fitness advantage, yet hosts show
 substantial variation in resistance in natural populations. Evolutionary
 theory indicates that resistant and susceptible genotypes can coexist if
 resistance is costly, but there is mixed evidence that resistant
 individuals have lower fitness in the absence of parasites. One
 explanation for this discrepancy is that the cost of resistance varies
 with environmental context. We tested this hypothesis using Caenorhabditis
 elegans and its natural microsporidian parasite, Nematocida ironsii. We
 used multiple metrics to compare the fitness of two near-isogenic host
 genotypes differing at regions associated with resistance to N. ironsii.
 To quantify the effect of the environment on the cost associated with
 these known resistance regions, we measured fitness on three microbial
 diets. We found that the cost of resistance varied with both diet and the
 measure of fitness. We detected no cost to resistance, irrespective of
 diet, when fitness was measured as fecundity. However, we detected a cost
 when fitness was measured in terms of population growth, and the magnitude
 of this cost varied with diet. These results provide a proof-of-concept
 that, by mediating the cost of resistance, environmental context may
 govern the rate and nature of resistance evolution in heterogeneous
 environments.
description: Fecundity Assay We conducted two assays to
 assess different components of fitness across diet conditions. First, to
 determine if the resistant genotype has lower fecundity than the
 susceptible genotype, we counted the number of offspring produced by
 ERT250 and N2 hosts raised on the three bacterial diets. For each
 genotype, we isolated eggs using a standard bleach wash (Porta-de-la-Riva
 et al., 2012), then added approximately 100 eggs per plate to DA837-,
 OP50-, or HB101-seeded 100 mm plates. We allowed hosts to reach the fourth
 larval (L4) stage at 20°C then moved hosts individually to 35 mm plates
 seeded with the same bacteria as their original plate (n = 60 hosts, with
 one host per 35 mm plate; 10-14 hosts per genotype*food combination). We
 moved each host to a new plate every day for six days, at which point
 reproduction had finished. Plates with eggs were incubated at 20°C for 24
 hours to hatch, then we counted viable offspring.  We censored data from
 hosts that were lost or suffered damage unrelated to the test conditions
 (n = 4). Data from an additional three hosts were excluded from analysis
 because of reporting errors.  Population Expansion
 Assay To test whether resistant populations grow at a
 slower rate than susceptible populations, we measured the size of
 resistant and susceptible populations after a standardized period of
 expansion. To ensure sufficient replication for each treatment, we limited
 this assay to DA837 and HB101. For both N2 and ERT250, we picked L4 hosts
 individually onto 100 mm plates seeded with either DA837 or HB101 (n = 100
 hosts, with one host per 100 mm plate; 25 hosts per genotype*food
 combination). Plates were monitored daily. The populations expanded for
 six days (~2-3 generations), at which point we collected entire
 populations in M9 buffer, diluted the populations to 14.5 mL, and counted
 six 20 uL aliquots for each population. The total population size could be
 estimated by multiplying these aliquot counts by 725. Replicate
 populations were removed from the analysis if the original host died
 before producing offspring (n = 2) or if there was an error during the
 washing and resuspension steps (n = 4). Statistical
 Analysis We performed all analyses in R Version
 1.4.1106 (R Core Team, 2021). To test for variation in fitness, we used
 the lme4 package (Bates et al., 2015) to fit generalized linear models or
 mixed models (GLMM). For the fecundity data, we analyzed both variation in
 lifetime fecundity (the total number of offspring summed over all days)
 and variation in the number of offspring per day, which can reveal
 differences in reproductive timing. For the fecundity models, we found
 evidence of overdispersion under the Poisson distribution, so we assumed a
 negative binomial distribution. We compared multiple candidate models
 including host genotype, food type, and their interaction as predictors.
 For the daily number of offspring, we also included the day of observation
 and its interaction with each of the aforementioned predictors, as well as
 a random effect to account for repeated measures of the same host. For the
 population expansion data, we fit Poisson GLMMs, including host genotype,
 food type, and their interaction as predictors of hosts per aliquot, plus
 a random effect for the replicate population to account for repeated
 measures. For all analyses, we used AIC scores to compare candidate
 models.