Identificação e dinâmica de uma mutação benéfica em um experimento de longo tempo com Escherichia coli

quarta-feira, janeiro 27, 2010

Identification and dynamics of a beneficial mutation in a long-term evolution experiment with Escherichia coli

Mark T Stanek1* , Tim F Cooper2* and Richard E Lenski1

1 Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan 48824-4320, USA

2 Department of Biology and Biochemistry, University of Houston, Houston, Texas 77204, USA

 author email corresponding author email* Contributed equally

BMC Evolutionary Biology 2009, 9:302doi:10.1186/1471-2148-9-302

Published: 29 December 2009

Abstract

Background

Twelve populations of E. coli were serially propagated for 20,000 generations in a glucose-supplemented minimal medium in order to study the dynamics of evolution. We sought to find and characterize one of the beneficial mutations responsible for the adaptation and other phenotypic changes, including increased cell size, in one of these populations.
Results

We used transposon-tagging followed by P1-transduction into the ancestor, screening for increased cell size and fitness, co-transduction analysis, and DNA sequencing. We identified a 1-bp insertion in the BoxG1 region located upstream of glmUS, an operon involved in cell-wall biosynthesis. When transduced into the ancestor, this mutation increased competitive fitness by about 5%. This mutation spread through its population of origin between 500 and 1500 generations. Mutations in this region were not found in the other 11 evolving populations, even after 20,000 generations.

Conclusion

The 1-bp insertion in the BoxG1 region near glmUS was demonstrably beneficial in the environment in which it arose. The absence of similar mutations in the other evolved populations suggests that they substituted other mutations that rendered this particular mutation unimportant. These results show the unpredictability of adaptive evolution, whereas parallel substitutions at other loci in these same populations reveal the predictability.

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