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Adaptation to High Ethanol Reveals Complex Evolutionary Pathways.

Voordeckers K, Kominek J, Das A, Espinosa-CantĂș A, De Maeyer D, Arslan A, Van Pee M, van der Zande E, Meert W, Yang Y, Zhu B, Marchal K, DeLuna A, Van Noort V, Jelier R, Verstrepen KJ - PLoS Genet. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: VIB Laboratory for Systems Biology, Leuven, Belgium.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Dynamics and linkage of mutations in evolved populations of reactor 2.Mutations (reaching a frequency of least 20% in the evolved population samples) and corresponding frequencies were identified from population sequencing data. Muller diagram represents the hierarchical clustering of these mutations, with each color block representing a specific group of linked mutations. Indels are designated with I, whereas heterozygous mutations are in italics. Mutations present as heterozygous mutations in all clones of a specific time point and present at a frequency of 50% in the population, are depicted as a frequency of 100% in the population, since it is expected that all cells in the population contain this mutation. After 80 generations, a mutator phenotype appeared in this reactor (indicated by arrow under graph), which coincides with the rise in frequency of an indel in the mismatch repair gene MSH2. Frequencies of haplotypes can be found in S2 Table. Dynamics and linkage for reactor 1 is shown in S7 Fig.
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pgen.1005635.g006: Dynamics and linkage of mutations in evolved populations of reactor 2.Mutations (reaching a frequency of least 20% in the evolved population samples) and corresponding frequencies were identified from population sequencing data. Muller diagram represents the hierarchical clustering of these mutations, with each color block representing a specific group of linked mutations. Indels are designated with I, whereas heterozygous mutations are in italics. Mutations present as heterozygous mutations in all clones of a specific time point and present at a frequency of 50% in the population, are depicted as a frequency of 100% in the population, since it is expected that all cells in the population contain this mutation. After 80 generations, a mutator phenotype appeared in this reactor (indicated by arrow under graph), which coincides with the rise in frequency of an indel in the mismatch repair gene MSH2. Frequencies of haplotypes can be found in S2 Table. Dynamics and linkage for reactor 1 is shown in S7 Fig.

Mentions: The higher number of sequenced samples from reactors 1 and 2 allowed us to further analyze these population sequences and group mutations based on correlations in the changes in their respective frequencies (based on the pipeline developed by [32]; see also Materials and Methods). This yields a more detailed picture of the different co-evolving sub-populations present in these reactors, which is depicted in the Muller diagrams of Fig 6 and S7 Fig, see also S2 Table for haplotype frequencies.


Adaptation to High Ethanol Reveals Complex Evolutionary Pathways.

Voordeckers K, Kominek J, Das A, Espinosa-CantĂș A, De Maeyer D, Arslan A, Van Pee M, van der Zande E, Meert W, Yang Y, Zhu B, Marchal K, DeLuna A, Van Noort V, Jelier R, Verstrepen KJ - PLoS Genet. (2015)

Dynamics and linkage of mutations in evolved populations of reactor 2.Mutations (reaching a frequency of least 20% in the evolved population samples) and corresponding frequencies were identified from population sequencing data. Muller diagram represents the hierarchical clustering of these mutations, with each color block representing a specific group of linked mutations. Indels are designated with I, whereas heterozygous mutations are in italics. Mutations present as heterozygous mutations in all clones of a specific time point and present at a frequency of 50% in the population, are depicted as a frequency of 100% in the population, since it is expected that all cells in the population contain this mutation. After 80 generations, a mutator phenotype appeared in this reactor (indicated by arrow under graph), which coincides with the rise in frequency of an indel in the mismatch repair gene MSH2. Frequencies of haplotypes can be found in S2 Table. Dynamics and linkage for reactor 1 is shown in S7 Fig.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636377&req=5

pgen.1005635.g006: Dynamics and linkage of mutations in evolved populations of reactor 2.Mutations (reaching a frequency of least 20% in the evolved population samples) and corresponding frequencies were identified from population sequencing data. Muller diagram represents the hierarchical clustering of these mutations, with each color block representing a specific group of linked mutations. Indels are designated with I, whereas heterozygous mutations are in italics. Mutations present as heterozygous mutations in all clones of a specific time point and present at a frequency of 50% in the population, are depicted as a frequency of 100% in the population, since it is expected that all cells in the population contain this mutation. After 80 generations, a mutator phenotype appeared in this reactor (indicated by arrow under graph), which coincides with the rise in frequency of an indel in the mismatch repair gene MSH2. Frequencies of haplotypes can be found in S2 Table. Dynamics and linkage for reactor 1 is shown in S7 Fig.
Mentions: The higher number of sequenced samples from reactors 1 and 2 allowed us to further analyze these population sequences and group mutations based on correlations in the changes in their respective frequencies (based on the pipeline developed by [32]; see also Materials and Methods). This yields a more detailed picture of the different co-evolving sub-populations present in these reactors, which is depicted in the Muller diagrams of Fig 6 and S7 Fig, see also S2 Table for haplotype frequencies.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: VIB Laboratory for Systems Biology, Leuven, Belgium.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus