Literature DB >> 33493203

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

Sébastien Boyer1, Lucas Hérissant1, Gavin Sherlock1.   

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.

Entities:  

Year:  2021        PMID: 33493203      PMCID: PMC7861553          DOI: 10.1371/journal.pgen.1009314

Source DB:  PubMed          Journal:  PLoS Genet        ISSN: 1553-7390            Impact factor:   5.917


  45 in total

1.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Authors:  Aaron McKenna; Matthew Hanna; Eric Banks; Andrey Sivachenko; Kristian Cibulskis; Andrew Kernytsky; Kiran Garimella; David Altshuler; Stacey Gabriel; Mark Daly; Mark A DePristo
Journal:  Genome Res       Date:  2010-07-19       Impact factor: 9.043

2.  Single nucleotide mapping of trait space reveals Pareto fronts that constrain adaptation.

Authors:  Yuping Li; Dmitri A Petrov; Gavin Sherlock
Journal:  Nat Ecol Evol       Date:  2019-10-14       Impact factor: 15.460

3.  Tandem repeats finder: a program to analyze DNA sequences.

Authors:  G Benson
Journal:  Nucleic Acids Res       Date:  1999-01-15       Impact factor: 16.971

4.  Exclusion rules, bottlenecks and the evolution of stochastic phenotype switching.

Authors:  Eric Libby; Paul B Rainey
Journal:  Proc Biol Sci       Date:  2011-04-13       Impact factor: 5.349

Review 5.  Antibiotic resistance and its cost: is it possible to reverse resistance?

Authors:  Dan I Andersson; Diarmaid Hughes
Journal:  Nat Rev Microbiol       Date:  2010-03-08       Impact factor: 60.633

6.  The evolution of phenotypic switching in subdivided populations.

Authors:  Oana Carja; Uri Liberman; Marcus W Feldman
Journal:  Genetics       Date:  2014-02-04       Impact factor: 4.562

7.  Spatiotemporal microbial evolution on antibiotic landscapes.

Authors:  Michael Baym; Tami D Lieberman; Eric D Kelsic; Remy Chait; Rotem Gross; Idan Yelin; Roy Kishony
Journal:  Science       Date:  2016-09-09       Impact factor: 47.728

8.  iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast.

Authors:  Mia Jaffe; Gavin Sherlock; Sasha F Levy
Journal:  G3 (Bethesda)       Date:  2017-01-05       Impact factor: 3.154

9.  The dynamics of molecular evolution over 60,000 generations.

Authors:  Benjamin H Good; Michael J McDonald; Jeffrey E Barrick; Richard E Lenski; Michael M Desai
Journal:  Nature       Date:  2017-10-18       Impact factor: 49.962

10.  Genomics of cellular proliferation in periodic environmental fluctuations.

Authors:  Jérôme Salignon; Magali Richard; Etienne Fulcrand; Hélène Duplus-Bottin; Gaël Yvert
Journal:  Mol Syst Biol       Date:  2018-03-05       Impact factor: 11.429

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  1 in total

1.  Strong selective environments determine evolutionary outcome in time-dependent fitness seascapes.

Authors:  Johannes Cairns; Florian Borse; Tommi Mononen; Teppo Hiltunen; Ville Mustonen
Journal:  Evol Lett       Date:  2022-05-26
  1 in total

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