Mining transcriptomic data to identifySaccharomyces cerevisiaesignatures related to improved and repressed ethanol production under fermentation

Saccharomyces cerevisiaeis known for its outstanding ability to produce ethanol in industry. Identifying the dynamic of gene expression inS. cerevisiaein response to fermentation is required for the establishment of any ethanol production improvement program. The goal of this study was 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. Through 11 machine learning based algorithms from RapidMiner employed on available microarray datasets related to yeast fermentation performance under Mg2+and Cu2+supplementation, 172 probe sets were identified by at least 5 AWAs. Some have been identified as being involved in carbohydrate metabolism, oxidative phosphorylation, and ethanol fermentation. Principal component analysis (PCA) and heatmap clustering were also validated the top-ranked selective probe sets. According to decision tree models, 17 roots with 100% performance were identified.OLI1andCYC3were 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.ADH5andPDA1are also recognized as differential top-ranked genes that contribute to ethanol production. According to the regulatory clustering analysis,Tup1has a significant effect on the top-ranked target genesCYC3andADH5genes. This study provides a basic understanding of theS. cerevisiaecell molecular mechanism and responses to two different medium conditions (Mg2+and Cu2+) during the fermentation process.