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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Adaptation to High Ethanol Reveals Complex Evolutionary Pathways
PLoS Genetics, Volume 11, No. 11, Article e1005635, Year 2015
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Description
Tolerance to high levels of ethanol is an ecologically and industrially relevant phenotype of microbes, but the molecular mechanisms underlying this complex trait remain largely unknown. Here, we use long-term experimental evolution of isogenic yeast populations of different initial ploidy to study adaptation to increasing levels of ethanol. Whole-genome sequencing of more than 30 evolved populations and over 100 adapted clones isolated throughout this two-year evolution experiment revealed how a complex interplay of de novo single nucleotide mutations, copy number variation, ploidy changes, mutator phenotypes, and clonal interference led to a significant increase in ethanol tolerance. Although the specific mutations differ between different evolved lineages, application of a novel computational pipeline, PheNetic, revealed that many mutations target functional modules involved in stress response, cell cycle regulation, DNA repair and respiration. Measuring the fitness effects of selected mutations introduced in non-evolved ethanol-sensitive cells revealed several adaptive mutations that had previously not been implicated in ethanol tolerance, including mutations in PRT1, VPS70 and MEX67. Interestingly, variation in VPS70 was recently identified as a QTL for ethanol tolerance in an industrial bio-ethanol strain. Taken together, our results show how, in contrast to adaptation to some other stresses, adaptation to a continuous complex and severe stress involves interplay of different evolutionary mechanisms. In addition, our study reveals functional modules involved in ethanol resistance and identifies several mutations that could help to improve the ethanol tolerance of industrial yeasts. © 2015 Voordeckers et al.
Authors & Co-Authors
de Maeyer, Dries
Belgium, Leuven
Ku Leuven
Belgium, Ghent
Universiteit Gent
Marchal, Kathleen
Belgium, Leuven
Ku Leuven
Belgium, Ghent
Universiteit Gent
van Noort, Vera
Belgium, Leuven
Ku Leuven
Verstrepen, Kevin J.
Belgium, Ghent
Vlaams Instituut Voor Biotechnologie
Belgium, Leuven
Ku Leuven
Statistics
Citations: 113
Authors: 4
Affiliations: 4
Identifiers
Doi:
10.1371/journal.pgen.1005635
ISSN:
15537390
Research Areas
Cancer
Genetics And Genomics
Study Approach
Quantitative