Evolutionary insight from whole‐genome sequencing of experimentally evolved microbes

Abstract

Experimental evolution (EE) combined with whole-genome sequencing (WGS) has become a compelling approach to study the fundamental mechanisms and processes that drive evolution. Most EE-WGS studies published to date have used microbes, owing to their ease of propagation and manipulation in the laboratory and relatively small genome sizes. These experiments are particularly suited to answer long-standing questions such as: How many mutations underlie adaptive evolution, and how are they distributed across the genome and through time? Are there general rules or principles governing which genes contribute to adaptation, and are certain kinds of genes more likely to be targets than others? How common is epistasis among adaptive mutations, and what does this reveal about the variety of genetic routes to adaptation? How common is parallel evolution, where the same mutations evolve repeatedly and independently in response to similar selective pressures? Here, we summarize the significant findings of this body of work, identify important emerging trends and propose promising directions for future research. We also outline an example of a computational pipeline for use in EE-WGS studies, based on freely available bioinformatics tools.

Rapid selective sweep of pre-existing polymorphisms and slow fixation of new mutations in experimental evolution of Desulfovibrio vulgaris

“…Mutations that conferred general adaptive benefits were also reported in other microbial experimental evolution studies (Dettman et al, 2012). Different alleles of DVU2571 or DVU2287 were identified in stressevolved ES8 and ES9 and non-stress-evolved EC3 (Figure 1, Table 1, Supplementary Figure S3).…”

“…Whether evolutionary changes occur through many mutations with small fitness effects or few mutations with larger effects is one of the oldest questions in biology (Dettman et al, 2012). Association between a few mutations/genes and dramatic phenotypic changes or fitness improvement have been reported in prokaryotes, eukaryotes and viruses (Taubenberger et al, 2005;Wang et al, 2005;Zeyl 2005;Herring et al, 2006;Schoustra et al, 2009).…”

“…Despite the widespread application of these approaches, only non-essential genes can be correlated with phenotypes, and extensive follow-up sequencing and characterization of mixed populations are required to assess genotype-phenotype relationships. Microbial experimental evolution combined with whole-genome sequencing has the potential to bypass these limitations and provide direct evidence of functions for mutations in essential genes, non-essential genes and non-coding regions (Elena and Lenski, 2003;Brockhurst et al, 2011;Dettman et al, 2012;Barrick and Lenski, 2013). In this approach, populations are allowed to evolve under controlled laboratory conditions and the genetic changes are identified by sequence comparisons between the evolved strains or populations and the ancestral strain.…”

Host immune responses accelerate pathogen evolution

“…For strains K1 and K3, the ratio (o ¼ dN/dS) between the number of amino acid replacing codon changes (non-synonymous changes; N) and synonymous changes (S) is greater than 1, suggesting positive selection (Toft and Andersson, 2010). Previous studies of the genomic basis of adaptation in experimentally evolved bacterial populations have detected an average of 1.07 mutations/100 generations (Dettman et al, 2012). The mutations in strains K1 and K3 are higher than in the other strains used in this study (Supplementary Table S9) and those of previous studies.…”

“…Illumina sequencing has been used to follow the genome evolution of various bacterial pathogens (Brockhurst et al, 2011;Dettman et al, 2012). On an average, we obtained 15.09 million reads per strain with chromosome coverage ranging from 167 to 399 (Supplementary Table S1).…”