Tempo and mode of genome evolution in a 50,000-generation experiment

Olivier Tenaillon, Jeffrey E. Barrick, Noah Ribeck, Daniel E. Deatherage, Jeffrey L. Blanchard, Aurko Dasgupta, Gabriel C. Wu, Sébastien Wielgoss, Stéphane Cruveiller, Claudine Médigue, Dominique Schneider and Richard E. Lenski ()
Additional contact information
Olivier Tenaillon: IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité
Jeffrey E. Barrick: Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin
Noah Ribeck: BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing
Daniel E. Deatherage: Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin
Jeffrey L. Blanchard: University of Massachusetts
Aurko Dasgupta: Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin
Gabriel C. Wu: Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin
Sébastien Wielgoss: Institute of Integrative Biology, ETH Zürich, Universitätstrasse 16
Stéphane Cruveiller: UMR 8030, CNRS, Université Évry-Val-d’Essonne, CEA, Institut de Génomique, Laboratoire d’Analyses Bioinformatiques pour la Génomique et le Métabolisme
Claudine Médigue: UMR 8030, CNRS, Université Évry-Val-d’Essonne, CEA, Institut de Génomique, Laboratoire d’Analyses Bioinformatiques pour la Génomique et le Métabolisme
Dominique Schneider: Université Grenoble Alpes, Laboratoire Technologies de l’Ingénierie Médicale et de la Complexité — Informatique, Mathématiques et Applications (TIMC-IMAG)
Richard E. Lenski: BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing

Nature, 2016, vol. 536, issue 7615, 165-170

Abstract: Abstract Adaptation by natural selection depends on the rates, effects and interactions of many mutations, making it difficult to determine what proportion of mutations in an evolving lineage are beneficial. Here we analysed 264 complete genomes from 12 Escherichia coli populations to characterize their dynamics over 50,000 generations. The populations that retained the ancestral mutation rate support a model in which most fixed mutations are beneficial, the fraction of beneficial mutations declines as fitness rises, and neutral mutations accumulate at a constant rate. We also compared these populations to mutation-accumulation lines evolved under a bottlenecking regime that minimizes selection. Nonsynonymous mutations, intergenic mutations, insertions and deletions are overrepresented in the long-term populations, further supporting the inference that most mutations that reached high frequency were favoured by selection. These results illuminate the shifting balance of forces that govern genome evolution in populations adapting to a new environment.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.nature.com/articles/nature18959 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:536:y:2016:i:7615:d:10.1038_nature18959

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/nature18959

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().