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Issue 62, 2017, Issue in Progress
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Transcriptomic analysis and driver mutant prioritization for differentially expressed genes from a Saccharomyces cerevisiae strain with high glucose tolerance generated by UV irradiation

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Abstract

The Saccharomyces cerevisiae strain UV02_HG, which has faster growth and a higher ethanol yield in 40% (w/v) glucose culture medium than the wild type, is a mutant generated from the MF02 wild-type strain by UV irradiation. Transcriptome sequencing was performed to analyze the differential gene expression between the two strains. 1203 genes had significantly different expression levels in response to high sugar stress. Based on KEGG enrichment analysis, the identified genes were involved in pathways such as the ribosome, oxidative phosphorylation, protein processing, protein export, carbon metabolism, and the MAPK pathway. Real-time quantitative PCR (qPCR) was used to validate the reliability of the results obtained by RNA-Seq. The network-based gene expression Quantitative Trait Locus (eQTL) tool was used to interpret the driver mutations that elicited the adaptive phenotype. According to this network, we speculated that mutations in STE12 and TUP1 are the main drivers of the adaptive phenotype. Sixteen poorly understood genes whose expression was downregulated by STE12 were annotated by gene ontology, all of which are highly associated with the cell membrane.

Graphical abstract: Transcriptomic analysis and driver mutant prioritization for differentially expressed genes from a Saccharomyces cerevisiae strain with high glucose tolerance generated by UV irradiation

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Publication details

The article was received on 02 Jun 2017, accepted on 24 Jul 2017 and first published on 08 Aug 2017


Article type: Paper
DOI: 10.1039/C7RA06146C
RSC Adv., 2017,7, 38784-38797
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    Transcriptomic analysis and driver mutant prioritization for differentially expressed genes from a Saccharomyces cerevisiae strain with high glucose tolerance generated by UV irradiation

    Y. Chen, Z. Lu, D. Chen, Y. Wei, X. Chen, J. Huang, N. Guan, Q. Lu, R. Wu and R. Huang, RSC Adv., 2017, 7, 38784
    DOI: 10.1039/C7RA06146C

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