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Quantitative evolutionary dynamics using high-resolution lineage tracking

Author

Listed:
  • Sasha F. Levy

    (Stanford University
    Laufer Center for Physical and Quantitative Biology, Stony Brook University
    Stony Brook University)

  • Jamie R. Blundell

    (Stanford University
    Stanford University)

  • Sandeep Venkataram

    (Stanford University)

  • Dmitri A. Petrov

    (Stanford University)

  • Daniel S. Fisher

    (Stanford University
    Stanford University)

  • Gavin Sherlock

    (Stanford University)

Abstract

Evolution of large asexual cell populations underlies ∼30% of deaths worldwide, including those caused by bacteria, fungi, parasites, and cancer. However, the dynamics underlying these evolutionary processes remain poorly understood because they involve many competing beneficial lineages, most of which never rise above extremely low frequencies in the population. To observe these normally hidden evolutionary dynamics, we constructed a sequencing-based ultra high-resolution lineage tracking system in Saccharomyces cerevisiae that allowed us to monitor the relative frequencies of ∼500,000 lineages simultaneously. In contrast to some expectations, we found that the spectrum of fitness effects of beneficial mutations is neither exponential nor monotonic. Early adaptation is a predictable consequence of this spectrum and is strikingly reproducible, but the initial small-effect mutations are soon outcompeted by rarer large-effect mutations that result in variability between replicates. These results suggest that early evolutionary dynamics may be deterministic for a period of time before stochastic effects become important.

Suggested Citation

  • Sasha F. Levy & Jamie R. Blundell & Sandeep Venkataram & Dmitri A. Petrov & Daniel S. Fisher & Gavin Sherlock, 2015. "Quantitative evolutionary dynamics using high-resolution lineage tracking," Nature, Nature, vol. 519(7542), pages 181-186, March.
    • Handle: RePEc:nat:nature:v:519:y:2015:i:7542:d:10.1038_nature14279
    DOI: 10.1038/nature14279
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    Citations

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    Cited by:

    1. Marie Rescan & Daphné Grulois & Enrique Ortega Aboud & Pierre de Villemereuil & Luis-Miguel Chevin, 2021. "Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment," PLOS Genetics, Public Library of Science, vol. 17(6), pages 1-23, June.
    2. Sébastien Boyer & Lucas Hérissant & Gavin Sherlock, 2021. "Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment," PLOS Genetics, Public Library of Science, vol. 17(1), pages 1-27, January.
    3. Joseph J. Hale & Takeshi Matsui & Ilan Goldstein & Martin N. Mullis & Kevin R. Roy & Christopher Ne Ville & Darach Miller & Charley Wang & Trevor Reynolds & Lars M. Steinmetz & Sasha F. Levy & Ian M. , 2024. "Genome-scale analysis of interactions between genetic perturbations and natural variation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Daniel P. G. H. Wong & Benjamin H. Good, 2024. "Quantifying the adaptive landscape of commensal gut bacteria using high-resolution lineage tracking," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Li Xie & Wenying Shou, 2021. "Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Gaofei Jiang & Yuling Zhang & Min Chen & Josep Ramoneda & Liangliang Han & Yu Shi & Rémi Peyraud & Yikui Wang & Xiaojun Shi & Xinping Chen & Wei Ding & Alexandre Jousset & Yasufumi Hikichi & Kouhei Oh, 2024. "Effects of plant tissue permeability on invasion and population bottlenecks of a phytopathogen," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    7. 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.
    8. Xueying C. Li & Lautaro Gandara & Måns Ekelöf & Kerstin Richter & Theodore Alexandrov & Justin Crocker, 2024. "Rapid response of fly populations to gene dosage across development and generations," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    9. Miller, Craig R. & Van Leuven, James T. & Wichman, Holly A. & Joyce, Paul, 2018. "Selecting among three basic fitness landscape models: Additive, multiplicative and stickbreaking," Theoretical Population Biology, Elsevier, vol. 122(C), pages 97-109.
    10. Takeshi Matsui & Martin N. Mullis & Kevin R. Roy & Joseph J. Hale & Rachel Schell & Sasha F. Levy & Ian M. Ehrenreich, 2022. "The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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