mSystemsMicrobiologyPeer-reviewed journal article
Identification of a crucial INO2 allele for enhancing ethanol resistance in an industrial fermentation strain of Saccharomyces cerevisiae
Sonia Albillos‐ArenaliD, Javier Alonso‐del‐RealiD, Ana Cristina AdamiD, María Lairón‐PerisiD, Eladio BarrioiD, Amparo QueroliD
Abstract
Ethanol toxicity is a major challenge for S. cerevisiae during fermentation, affecting its growth and influencing the process. This study investigated the molecular mechanisms of ethanol tolerance using transcriptomic analysis of three S. cerevisiae strains with varying ethanol resistance. We identified distinct responses in membrane lipid synthesis genes, particularly in ergosterol biosynthesis, regulated by the Ino2p transcription factor. A variant of Ino2p with V263I and H86R amino acid replacements was exclusive to ethanol-tolerant strains. CRISPR-Cas9-mediated point mutations in the INO2 gene of the highly tolerant strain AJ4 resulted in decreased ethanol tolerance. Our findings demonstrate the crucial role of Ino2p in ethanol tolerance through regulation of lipid synthesis and membrane composition, highlighting the complex interplay of trans elements in strain-specific ethanol resistance IMPORTANCEThis study provides critical insights into the molecular basis of ethanol tolerance in S. cerevisiae, a key trait for improving industrial fermentation processes. By identifying specific genetic variants in the Ino2p transcription factor and their impact on ethanol resistance, we reveal potential targets for enhancing yeast strain performance in high-ethanol environments. Our findings not only contribute to the fundamental understanding of stress response mechanisms in yeast but also offer practical implications for strain engineering in the biotechnology and beverage industries. The unexpected magnitude of the Ino2p variants effect on ethanol tolerance underscores the importance of considering strain-specific genetic backgrounds in metabolic engineering strategies
- organism: yeast
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: cell type annotation
- research focus: differential expression
- approach: experimental assay
- approach: statistical model
- Citations
- 4
- FWCI
- 0.99
- Field percentile
- top 26%
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