Genome-wide analytical approaches for reverse metabolic engineering of industrially relevant phenotypes in yeast
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Successful reverse engineering of mutants that have been obtained by nontargeted strain improvement has long presented a major challenge in yeast biotechnology. This paper reviews the use of genome-wide approaches for analysis of Saccharomyces cerevisiae strains originating from evolutionary engineering or random mutagenesis. On the basis of an evaluation of the strengths and weaknesses of different methods, we conclude that for the initial identification of relevant genetic changes, whole genome sequencing is superior to other analytical techniques, such as transcriptome, metabolome, proteome, or array-based genome analysis. Key advantages of this technique over gene expression analysis include the independency of genome sequences on experimental context and the possibility to directly and precisely reproduce the identified changes in naive strains. The predictive value of genome-wide analysis of strains with industrially relevant characteristics can be further improved by classical genetics or simultaneous analysis of strains derived from parallel, independent strain improvement lineages.
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In nature as well as in biotechnological processes, microorganisms are challenged with changing environmental conditions, where they experience fluctuations in temperature, pH, oxygenation, atmospheric or hydrostatic pressure but also water and nutrient availability; thus, the selective pressures can be divided in two main categories: nutrient availability and environmental stress.
“In nature as well as in biotechnological processes, microorganisms are challenged with changing environmental conditions, where they experience fluctuations in temperature, pH, oxygenation, atmospheric or hydrostatic pressure but also water and nutrient availability; thus, the selective pressures can be divided in two main categories: nutrient availability and environmental stress [17].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [17]
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Self-citationThe proteome of a wine yeast strain during fermentation, correlation with the transcriptome
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Self-citationIndustrial Relevance of Chromosomal Copy Number Variation in Saccharomyces Yeasts
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Self-citationComparative genomics: a revolutionary tool for wine yeast strain development
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Self-citationQuantitative Trait Loci (QTL)-Guided Metabolic Engineering of a Complex Trait
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Self-citation
- Authors
- Oud, B., Maris, A. J. A., Daran, J.-M., & Pronk, J. T.
- Affiliations
- Delft University of Technology (NL)
- Corresponding author
- Pronk, Jack T.
- Venue
- FEMS Yeast Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01-10
- Volume
- 12
- Issue
- 2
- Pages
- 183-196
- ISSN
- 1567-1356 (ISSN-L)
- PMC
- PMC3615171
- OpenAlex
- W1973748500
- Cited by
- 82
- References
- 116
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 4 · 2021: 7 · 2020: 5 · 2019: 1 · 2018: 3 · 2017: 9 · 2016: 6 · 2015: 11 · 2014: 13 · 2013: 12 · 2012: 7
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- FEMS Yeast Research (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Commission
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Life Sciences › Agricultural and Biological Sciences › Food Science › Fermentation and Sensory Analysis
- Keywords
- Saccharomyces cerevisiae, whole genome sequencing, strain improvement, evolutionary engineering, random mutagenesis, industrial yeast strains, transcriptome, metabolome, proteome
- MeSH terms
- Biotechnology, Biological Evolution, Phenotype, Saccharomyces cerevisiae, Mutagenesis, Genome, Fungal, Genomics, Genome-Wide Association Study, Transcriptome, Metabolic Engineering
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