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Gigascience. 2019 Jan 31. doi: 10.1093/gigascience/giz015. [Epub ahead of print]

Linking genetic, metabolic and phenotypic diversity among S. cerevisiae strains using multi-omics associations.

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Systems Biology & Bioinformatics Group, School of Biological Sciences, The University of Hong Kong, Hong Kong S.A.R., China.
Systems Biology & Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany.
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs.Lyngby, Denmark.
Department of Biological Engineering, School of Engineering, University of Minho, Braga, Portugal.
The European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Centre for Microbial Innovation, School of Biological Sciences, University of Auckland, Auckland, New Zealand.
Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R., China.


The selection of bioengineering platform strains and engineering strategies to improve the stress resistance of Saccharomyces cerevisiae remains a pressing need in bio-based chemical production. Thus, a systematic effort to exploit the genotypic and phenotypic diversity to boost yeast's industrial value is still urgently needed. Here, we analyzed 5400 growth curves obtained from 36 S. cerevisiae strains and comprehensively profiled their resistances against 13 industrially relevant stresses. We observed that bioethanol and brewing strains exhibit higher resistance against acidic conditions, however, plant isolates tend to have wider range of resistance, which may be associated with their metabolome and fluxome signatures in TCA cycle and fatty acid metabolism. By deep genomic sequencing we found that industrial strains have more genomic duplications especially affecting transcription factors, presenting disparate evolutionary paths in comparison to the environmental strains which have more InDels, gene deletions and strain-specific genes. Genome-wide association studies coupled with protein-protein interaction networks uncovered novel genetic determinants of stress resistances. These resistance-related engineering targets and strain rankings provide a valuable source for engineering significantly improved industrial platform strains.


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