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BMC Evol Biol. 2008 Mar 17;8:85. doi: 10.1186/1471-2148-8-85.

Experimental evolution and genome sequencing reveal variation in levels of clonal interference in large populations of bacteriophage phiX174.

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Department of Biological Sciences, University of Idaho, Moscow, ID, 83844-3051, USA.



In large asexual populations where beneficial mutations may co-occur and recombination is absent, the fate of beneficial mutations can be significantly affected by competition (i.e., clonal interference). Theoretical models predict that clonal interference (CI) can slow adaptation, alter the distribution of fixed beneficial mutations, and affect disease progression by impacting within-host evolution of pathogens. While phenotypic data support that CI is a significant determinant of adaptive outcome, genetic data are needed to verify the patterns and to inform evolutionary models. We adapted replicate populations of the bacteriophage phiX174 under two levels of CaCl2 to create benign and harsh environments. Genomic sequences of multiple individuals from evolved populations were used to detect competing beneficial mutations.


There were several competing genotypes in most of the populations where CaCl2 was abundant, but no evidence of CI where CaCl2 was scarce, even though rates of adaptation and population sizes among the treatments were similar. The sequence data revealed that observed mutations were limited to a single portion of one gene in the harsh treatment, but spanned a different and larger region of the genome under the benign treatments, suggesting that there were more adaptive solutions to the benign treatment.


Beneficial mutations with relatively large selection coefficients can be excluded by CI. CI may commonly determine the fate of beneficial mutations in large microbial populations, but its occurrence depends on selective conditions. CI was more frequent in benign selective conditions possibly due to a greater number of adaptive targets under this treatment. Additionally, the genomic sequence data showed that selection can target different types and numbers of phenotypes in environments that differ by only a single continuous variable.

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