Dataset Information

Exploiting natural variation in Saccharomyces cerevisiae to identify genes for increased ethanol resistance.

ABSTRACT: Ethanol production from lignocellulosic biomass holds promise as an alternative fuel. However, industrial stresses, including ethanol stress, limit microbial fermentation and thus prevent cost competitiveness with fossil fuels. To identify novel engineering targets for increased ethanol tolerance, we took advantage of natural diversity in wild Saccharomyces cerevisiae strains. We previously showed that an S288c-derived lab strain cannot acquire higher ethanol tolerance after a mild ethanol pretreatment, which is distinct from other stresses. Here, we measured acquired ethanol tolerance in a large panel of wild strains and show that most strains can acquire higher tolerance after pretreatment. We exploited this major phenotypic difference to address the mechanism of acquired ethanol tolerance, by comparing the global gene expression response to 5% ethanol in S288c and two wild strains. Hundreds of genes showed variation in ethanol-dependent gene expression across strains. Computational analysis identified several transcription factor modules and known coregulated genes as differentially expressed, implicating genetic variation in the ethanol signaling pathway. We used this information to identify genes required for acquisition of ethanol tolerance in wild strains, including new genes and processes not previously linked to ethanol tolerance, and four genes that increase ethanol tolerance when overexpressed. Our approach shows that comparative genomics across natural isolates can quickly identify genes for industrial engineering while expanding our understanding of natural diversity.

SUBMITTER: Lewis JA

PROVIDER: S-EPMC2998304 | biostudies-other | 2010 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Exploiting natural variation in Saccharomyces cerevisiae to identify genes for increased ethanol resistance.

Lewis Jeffrey A JA Elkon Isaac M IM McGee Mick A MA Higbee Alan J AJ Gasch Audrey P AP

Genetics 20100920 4

Ethanol production from lignocellulosic biomass holds promise as an alternative fuel. However, industrial stresses, including ethanol stress, limit microbial fermentation and thus prevent cost competitiveness with fossil fuels. To identify novel engineering targets for increased ethanol tolerance, we took advantage of natural diversity in wild Saccharomyces cerevisiae strains. We previously showed that an S288c-derived lab strain cannot acquire higher ethanol tolerance after a mild ethanol pretr ...[more]

PMID: 20855568

Similar Datasets

Project description:Acute severe ethanol stress (10% [vol/vol]) damages proteins and causes the intracellular accumulation of insoluble proteins in Saccharomyces cerevisiae On the other hand, a pretreatment with mild stress increases tolerance to subsequent severe stress, which is called acquired stress resistance. It currently remains unclear whether the accumulation of insoluble proteins under severe ethanol stress may be mitigated by increasing protein quality control (PQC) activity in cells pretreated with mild stress. In the present study, we examined the induction of resistance to severe ethanol stress in PQC and confirmed that a pretreatment with 6% (vol/vol) ethanol or mild thermal stress at 37°C significantly reduced insoluble protein levels and the aggregation of Lsg1, which is prone to denaturation and aggregation by stress, in yeast cells under 10% (vol/vol) ethanol stress. The induction of this stress resistance required the new synthesis of proteins; the expression of proteins comprising the bichaperone system (Hsp104, Ssa3, and Fes1), Sis1, and Hsp42 was upregulated during the pretreatment and maintained under subsequent severe ethanol stress. Since the pretreated cells of deficient mutants in the bichaperone system (fes1Δ hsp104Δ and ssa2Δ ssa3Δ ssa4Δ) failed to sufficiently reduce insoluble protein levels and Lsg1 aggregation, the enhanced activity of the bichaperone system appears to be important for the induction of adequate stress resistance. In contrast, the importance of proteasomes and aggregases (Btn2 and Hsp42) in the induction of stress resistance has not been confirmed. These results provide further insights into the PQC activity of yeast cells under severe ethanol stress, including the brewing process.IMPORTANCE Although the budding yeast S. cerevisiae, which is used in the production of alcoholic beverages and bioethanol, is highly tolerant of ethanol, high concentrations of ethanol are also stressful to the yeast and cause various adverse effects, including protein denaturation. A pretreatment with mild stress improves the ethanol tolerance of yeast cells; however, it currently remains unclear whether it increases PQC activity and reduces the levels of denatured proteins. In the present study, we found that a pretreatment with mild ethanol upregulated the expression of proteins involved in PQC and mitigated the accumulation of insoluble proteins, even under severe ethanol stress. These results provide novel insights into ethanol tolerance and the adaptive capacity of yeast. They may also contribute to research on the physiology of yeast cells during the brewing process, in which the concentration of ethanol gradually increases.

Project description:Saccharomyces cerevisiae is known for its outstanding ability to produce ethanol in industry. Underlying the dynamics of gene expression in S. cerevisiae in response to fermentation could provide informative results, required for the establishment of any ethanol production improvement program. Thus, representing a new approach, this study was conducted to identify the discriminative genes between improved and repressed ethanol production as well as clarifying the molecular responses to this process through mining the transcriptomic data. The significant differential expression probe sets were extracted from available microarray datasets related to yeast fermentation performance. To identify the most effective probe sets contributing to discriminate ethanol content, 11 machine learning algorithms from RapidMiner were employed. Further analysis including pathway enrichment and regulatory analysis were performed on discriminative probe sets. Besides, the decision tree models were constructed, the performance of each model was evaluated and the roots were identified. Based on the results, 171 probe sets were identified by at least 5 attribute weighting algorithms (AWAs) and 17 roots were recognized with 100% performance Some of the top ranked presets were found to be involved in carbohydrate metabolism, oxidative phosphorylation, and ethanol fermentation. Principal component analysis (PCA) and heatmap clustering validated the top-ranked selective probe sets. In addition, the top-ranked genes were validated based on GSE78759 and GSE5185 dataset. From all discriminative probe sets, OLI1 and CYC3 were identified as the roots with the best performance, demonstrated by the most weighting algorithms and linked to top two significant enriched pathways including porphyrin biosynthesis and oxidative phosphorylation. ADH5 and PDA1 were also recognized as differential top-ranked genes that contribute to ethanol production. According to the regulatory clustering analysis, Tup1 has a significant effect on the top-ranked target genes CYC3 and ADH5 genes. This study provides a basic understanding of the S. cerevisiae cell molecular mechanism and responses to two different medium conditions (Mg2+ and Cu2+) during the fermentation process.

Dataset Information

Exploiting natural variation in Saccharomyces cerevisiae to identify genes for increased ethanol resistance.

Publications

Exploiting natural variation in Saccharomyces cerevisiae to identify genes for increased ethanol resistance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets