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

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...

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Autores principales: Kang, Kang, Bergdahl, Basti, Machado, Daniel, Dato, Laura, Han, Ting-Li, Li, Jun, Villas-Boas, Silas, Herrgård, Markus J, Förster, Jochen, Panagiotou, Gianni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6446221/
https://www.ncbi.nlm.nih.gov/pubmed/30715293
http://dx.doi.org/10.1093/gigascience/giz015
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author Kang, Kang
Bergdahl, Basti
Machado, Daniel
Dato, Laura
Han, Ting-Li
Li, Jun
Villas-Boas, Silas
Herrgård, Markus J
Förster, Jochen
Panagiotou, Gianni
author_facet Kang, Kang
Bergdahl, Basti
Machado, Daniel
Dato, Laura
Han, Ting-Li
Li, Jun
Villas-Boas, Silas
Herrgård, Markus J
Förster, Jochen
Panagiotou, Gianni
author_sort Kang, Kang
collection PubMed
description 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.
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spelling pubmed-64462212019-04-09 Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations Kang, Kang Bergdahl, Basti Machado, Daniel Dato, Laura Han, Ting-Li Li, Jun Villas-Boas, Silas Herrgård, Markus J Förster, Jochen Panagiotou, Gianni Gigascience Research 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. Oxford University Press 2019-01-31 /pmc/articles/PMC6446221/ /pubmed/30715293 http://dx.doi.org/10.1093/gigascience/giz015 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Kang, Kang
Bergdahl, Basti
Machado, Daniel
Dato, Laura
Han, Ting-Li
Li, Jun
Villas-Boas, Silas
Herrgård, Markus J
Förster, Jochen
Panagiotou, Gianni
Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations
title Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations
title_full Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations
title_fullStr Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations
title_full_unstemmed Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations
title_short Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations
title_sort linking genetic, metabolic, and phenotypic diversity among saccharomyces cerevisiae strains using multi-omics associations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6446221/
https://www.ncbi.nlm.nih.gov/pubmed/30715293
http://dx.doi.org/10.1093/gigascience/giz015
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