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

Author

Listed:
  • 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
    BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing)

  • Noah Ribeck

    (BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing
    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
    †Present address: Department of Internal Medicine, Washington University School of Medicine, St Louis, Missouri 63110, USA.)

  • 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
    Université Grenoble Alpes, Laboratoire Technologies de l’Ingénierie Médicale et de la Complexité — Informatique, Mathématiques et Applications (TIMC-IMAG))

  • 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)
    Centre National de la Recherche Scientifique, TIMC-IMAG)

  • Richard E. Lenski

    (BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing
    Michigan State University, East Lansing)

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.

Suggested Citation

  • Olivier Tenaillon & Jeffrey E. Barrick & Noah Ribeck & Daniel E. Deatherage & Jeffrey L. Blanchard & Aurko Dasgupta & Gabriel C. Wu & Sébastien Wielgoss & Stéphane Cruveiller & Claudine Médigue & Domi, 2016. "Tempo and mode of genome evolution in a 50,000-generation experiment," Nature, Nature, vol. 536(7615), pages 165-170, August.
  • Handle: RePEc:nat:nature:v:536:y:2016:i:7615:d:10.1038_nature18959
    DOI: 10.1038/nature18959
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    Cited by:

    1. N. Frazão & A. Konrad & M. Amicone & E. Seixas & D. Güleresi & M. Lässig & I. Gordo, 2022. "Two modes of evolution shape bacterial strain diversity in the mammalian gut for thousands of generations," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Joao A. Ascensao & Kelly M. Wetmore & Benjamin H. Good & Adam P. Arkin & Oskar Hallatschek, 2023. "Quantifying the local adaptive landscape of a nascent bacterial community," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. Claas Kirchhelle, 2023. "The Antibiocene – towards an eco-social analysis of humanity’s antimicrobial footprint," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    4. Ryo Mizuuchi & Taro Furubayashi & Norikazu Ichihashi, 2022. "Evolutionary transition from a single RNA replicator to a multiple replicator network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Jacob J. Valenzuela & Selva Rupa Christinal Immanuel & James Wilson & Serdar Turkarslan & Maryann Ruiz & Sean M. Gibbons & Kristopher A. Hunt & Nejc Stopnisek & Manfred Auer & Marcin Zemla & David A. , 2024. "Origin of biogeographically distinct ecotypes during laboratory evolution," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Piaopiao Chen & Jianzhi Zhang, 2024. "The loci of environmental adaptation in a model eukaryote," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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