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Genome Biol. 2012 Jun 8;13(6):R41. doi: 10.1186/gb-2012-13-6-r41.

The multiplicity of divergence mechanisms in a single evolving population.

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1
TEDA School of Biological Sciences and Biotechnology, Nankai University, Tianjin, PR China.

Abstract

BACKGROUND:

Evolutionary divergence is common within bacterial species and populations, even during a single bacterial infection. We use large-scale genomic and phenotypic analysis to identify the extent of diversification in controlled experimental populations and apply these data to differentiate between several potential mechanisms of evolutionary divergence.

RESULTS:

We defined testable differences between five proposed mechanisms and used experimental evolution studies to follow eight glucose-limited Escherichia coli chemostat populations at two growth rates. Simple phenotypic tests identified 11 phenotype combinations evolving under glucose limitation. Each evolved population exhibited 3 to 5 different combinations of the 11 phenotypic clusters. Genome sequencing of a representative of each phenotypic cluster from each population identified 193 mutations in 48 isolates. Only two of the 48 strains had evolved identically. Convergent paths to the same phenotype occurred, but two pleiotropic mutations were unique to slow-growing bacteria, permitting them greater phenotypic variance. Indeed, greater diversity arose in slower-growing, more stressed cultures. Mutation accumulation, hypermutator presence and fitness mechanisms varied between and within populations, with the evolved fitness considerably more uniform with fast growth cultures. Negative frequency-dependent fitness was shown by a subset of isolates.

CONCLUSIONS:

Evolutionary diversity is unlikely to be explained by any one of the available mechanisms. For a large population as used in this study, our results suggest that multiple mechanisms contribute to the mix of phenotypes and evolved fitness types in a diversifying population. Another major conclusion is that the capacity of a population to diversify is a function of growth rate.

PMID:
22682524
PMCID:
PMC3446313
DOI:
10.1186/gb-2012-13-6-r41
[Indexed for MEDLINE]
Free PMC Article
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