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Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment

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  • Sébastien Boyer
  • Lucas Hérissant
  • Gavin Sherlock

Abstract

The environmental conditions of microorganisms’ habitats may fluctuate in unpredictable ways, such as changes in temperature, carbon source, pH, and salinity to name a few. Environmental heterogeneity presents a challenge to microorganisms, as they have to adapt not only to be fit under a specific condition, but they must also be robust across many conditions and be able to deal with the switch between conditions itself. While experimental evolution has been used to gain insight into the adaptive process, this has largely been in either unvarying or consistently varying conditions. In cases where changing environments have been investigated, relatively little is known about how such environments influence the dynamics of the adaptive process itself, as well as the genetic and phenotypic outcomes. We designed a systematic series of evolution experiments where we used two growth conditions that have differing timescales of adaptation and varied the rate of switching between them. We used lineage tracking to follow adaptation, and whole genome sequenced adaptive clones from each of the experiments. We find that both the switch rate and the order of the conditions influences adaptation. We also find different adaptive outcomes, at both the genetic and phenotypic levels, even when populations spent the same amount of total time in the two different conditions, but the order and/or switch rate differed. Thus, in a variable environment adaptation depends not only on the nature of the conditions and phenotypes under selection, but also on the complexity of the manner in which those conditions are combined to result in a given dynamic environment.Author summary: The environments in which organisms evolve typically fluctuate, in both predictable ways, such as daily, seasonal and annual changes in, for example, temperature and photoperiod, as well as in hard to predict ways, such as changes in the weather. Most laboratory evolution experiments evolve organisms in either constant environments, or environments that change predictably, such as by serial dilution into fresh media periodically. To investigate how unpredictable changes in the environment can affect an organism’s evolution, we designed a series of experiments where the environment alternated between two conditions, either predictably, or randomly, with different timescales of switching. We then evolved the budding yeast, Saccharomyces cerevisiae under these different conditions. We found that the both the switch rate and the order of the conditions influences adaptation, and that switching can both speed up and slow down adaptation. We also observed different adaptive outcomes between populations, both at the genetic and phenotypic level, even if populations spent the same amount of total time in the two different conditions, but that the order and/or switch rate differed. These data suggest that adaptive outcomes are dependent on the exact nature of the prevailing environmental conditions.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pgen00:1009314
    DOI: 10.1371/journal.pgen.1009314
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    References listed on IDEAS

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    1. Gregory I. Lang & Daniel P. Rice & Mark J. Hickman & Erica Sodergren & George M. Weinstock & David Botstein & Michael M. Desai, 2013. "Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations," Nature, Nature, vol. 500(7464), pages 571-574, August.
    2. Alex N. Nguyen Ba & Ivana Cvijović & José I. Rojas Echenique & Katherine R. Lawrence & Artur Rego-Costa & Xianan Liu & Sasha F. Levy & Michael M. Desai, 2019. "High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast," Nature, Nature, vol. 575(7783), pages 494-499, November.
    3. 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.
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    5. Amir Mitchell & Gal H. Romano & Bella Groisman & Avihu Yona & Erez Dekel & Martin Kupiec & Orna Dahan & Yitzhak Pilpel, 2009. "Adaptive prediction of environmental changes by microorganisms," Nature, Nature, vol. 460(7252), pages 220-224, July.
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    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.

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