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

Abstract:

BACKGROUND: 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 genotypic and phenotypic diversity to boost yeast's industrial value is still urgently needed. RESULTS: We analyzed 5,400 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 a wider range of resistance, which may be associated with their metabolome and fluxome signatures in the tricarboxylic acid cycle and fatty acid metabolism. By deep genomic sequencing, we found that industrial strains have more genomic duplications especially affecting transcription factors, showing that they result from disparate evolutionary paths in comparison with 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. CONCLUSIONS: These resistance-related engineering targets and strain rankings provide a valuable source for engineering significantly improved industrial platform strains.

SEEK ID: https://funginet.hki-jena.de/publications/128

PubMed ID: 30715293

Projects: B5

Journal: Gigascience

Citation: Gigascience. 2019 Apr 1;8(4). pii: 5304885. doi: 10.1093/gigascience/giz015.

Date Published: 1st Apr 2019

Authors: K. Kang, B. Bergdahl, D. Machado, L. Dato, T. L. Han, J. Li, S. Villas-Boas, M. J. Herrgard, J. Forster, Gianni Panagiotou

Help
help Creator
Activity

Views: 531

Created: 15th Feb 2021 at 09:05

help Attributions

None

Related items

Powered by
(v.1.9.1)
Copyright © 2008 - 2019 The University of Manchester and HITS gGmbH