DOI: 10.5061/dryad.pvmcvdnn3
title: Effect of mutation supply on population dynamics and trait evolution in experimental microbial community

description: Mutation supply can influence eco-evolutionary dynamics in important ways
 which have received little attention. Mutation supply determines key
 features of population genetics, such as the pool of adaptive mutations,
 evolutionary pathways available, and importance of processes such as
 clonal interference. The resultant trait evolutionary dynamics, in turn,
 can alter population size and species interactions. However, controlled
 experiments testing for the importance of mutation supply on rapid
 adaptation and thereby population and community dynamics are lacking. To
 close this knowledge gap, we performed a serial passage experiment with
 wild-type Pseudomonas fluorescens and an isogenic xerD mutant with reduced
 mutation rate. Bacteria were grown at two resource levels in combination
 with the presence of a ciliate predator. We found that a higher mutation
 supply enabled faster adaptation to the low-resource environment and
 anti-predatory defense. This was associated with higher population size at
 the ecological level and better access to high-recurrence mutational
 targets at the genomic level for the strain with higher mutation supply.
 In contrast, mutation rate did not affect growth under high-resource
 level, possibly because of more permissive conditions or high population
 size saturated in mutations. Our results demonstrate that intrinsic
 mutation rate influences population dynamics and trait evolution
 particularly when population size is constrained by extrinsic conditions.
description: The dataset was collected as part of an evolutionary experiment
 performed with the bacterial prey species Pseudomonas fluorescens F113 and
 the ciliate predator species Tetrahymena thermophila at two resource
 levels of King's B medium (0.5 and 2 %, representing low and high
 resource levels, respectively). The prey species had two substrains: a
 wild-type-like ("WTL") strain and mutant supply defective strain
 (insertional inactivation mutant, or "IM"). The experiment was
 performed using a full-factorial design, with four replicates per
 treatment combination. The data includes phenotypic and ecological data
 (F113_all_data.txt) and downstream genomic variant data from whole-genome
 sequencing (variants.txt).
description: The metadata in F113_all_data.txt  fileincludes the following
 fields: day = day in serial transfer evolutionary
 experiment bact_OD = bacterial population density (OD
 at 600 nm wavelength) strain = bacterial strain
 (WTL/IM) res_level = resource level
 (low/high) predator = presene/absence of ciliate
 predation (yes/no) rep = replicate (1-4)
 id = unique sample id over time pasted from above fields except
 for day (e.g. "WTLhighno1") pred_pop =
 ciliate predator population size (cells/ml)
 xerD_expression = expression level of xerD (to control for gene
 function related to mutation supply in this model system)
 delta_xerD_expression = change in expression level of
 xerD res_use = growth ability (see manuscript for
 details) delta_res_use = change in growth
 ability D = anti-predatory defense level
 delta_D = change in anti-predatory defense level
 NA = data not measured for particular treatment or time
 point   The metadata in the
 variants.txt file includes the following fields for whole population (pool
 seq) samples from the evolutionary experimental end-point (day
 58): SAMPLE = unique sample id
 STRAIN = bacterial strain (WTL/IM) RESOURCE
 = resource level (low/high) REPLICATE = replicate
 (1-4) CHROM = name of F113 reference genome (one
 contiguous chromosome)    POS = allele position (i.e.
 locus) on reference genome     REF = reference allele
 (baseline)    ALT = alternative allele (mutation
 against baseline)    ANN_ALLELE = annotated allele
 (same as ALT as only those mutated positions kept with one ALT allele in
 an individual population)    EFFECT = predicted effect
 of alternative allele (from variant effect prediction, VEP, using
 SnpEff)  IMPACT = predicted impact of alternative
 allele (from variant effect prediction, VEP, using SnpEff)    
 GENE = affected gene name     GENEID =
 affected gene code    HGVS_C = locus and base change in
 affected gene     HGVS_P = locus and amino acid change
 in affected gene    GT = genotype = alternative allele
 frequency in population (pool seq samples; 0-1)
 EFFECT_CAT = EFFECT field simplified into three categories:
 nonsynonymous, synonymous and intergenic