The distribution of fitness effects of beneficial mutations in Pseudomonas aeruginosa

Understanding how beneficial mutations affect fitness is crucial to our understanding of adaptation by natural selection. Here, using adaptation to the antibiotic rifampicin in the opportunistic pathogen Pseudomonas aeruginosa as a model system, we investigate the underlying distribution of fitness...

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Bibliographic Details
Published in:PLoS genetics 2009-03, Vol.5 (3), p.e1000406-e1000406
Main Authors: MacLean, R Craig, Buckling, Angus
Format: Article
Language:English
Subjects:
Anti-Bacterial Agents - pharmacology
Antibiotics
Competition
DNA-Directed RNA Polymerases - genetics
DNA-Directed RNA Polymerases - metabolism
Evolutionary Biology
Evolutionary Biology/Microbial Evolution and Genomics
Gene mutations
Genetic aspects
Genetics
Genetics and Genomics/Population Genetics
Growth rate
Hypotheses
Mutation
Natural selection
Phenotype
Population
Population genetics
Pseudomonas aeruginosa
Pseudomonas aeruginosa - drug effects
Pseudomonas aeruginosa - genetics
Pseudomonas aeruginosa - growth & development
Rifampin - pharmacology
RNA polymerase
Selection, Genetic
Studies
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Summary:Understanding how beneficial mutations affect fitness is crucial to our understanding of adaptation by natural selection. Here, using adaptation to the antibiotic rifampicin in the opportunistic pathogen Pseudomonas aeruginosa as a model system, we investigate the underlying distribution of fitness effects of beneficial mutations on which natural selection acts. Consistent with theory, the effects of beneficial mutations are exponentially distributed where the fitness of the wild type is moderate to high. However, when the fitness of the wild type is low, the data no longer follow an exponential distribution, because many beneficial mutations have large effects on fitness. There is no existing population genetic theory to explain this bias towards mutations of large effects, but it can be readily explained by the underlying biochemistry of rifampicin-RNA polymerase interactions. These results demonstrate the limitations of current population genetic theory for predicting adaptation to severe sources of stress, such as antibiotics, and they highlight the utility of integrating statistical and biophysical approaches to adaptation.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1000406