Transcriptomic and Physiological Meta-Analysis of Multiple Stress-Resistant Saccharomyces cerevisiae Strains

Abstract

Meta-analysis is a beneficial approach to reevaluating the outcomes of independent previous studies in the same scope. Saccharomyces cerevisiae, or the baker’s yeast, is a commonly used unicellular and eukaryotic model organism. In this study, 12 evolved S. cerevisiae strains that became resistant to diverse stress conditions (boron, caffeine, caloric restriction, cobalt, coniferyl aldehyde, ethanol, iron, nickel, oxidative stress, 2-phenylethanol, and silver stress) by adaptive laboratory evolution were reassessed to reveal the correlated stress/stressor clusters based on their transcriptomic and stress–cross-resistance data. Principal Component Analysis (PCA) with k-means clustering was performed. Five clusters for the transcriptomic data of strains and six clusters for cross-resistance stressors were identified. Through statistical evaluations, critical genes pertinent to each cluster were elucidated. The pathways associated with these genes were investigated using the KEGG database. The findings demonstrated that caffeine and coniferyl aldehyde stressors exhibit clear distinctions from other stressors in terms of both physiological stress-cross-resistance responses and transcriptomic profiles. Pathway analysis showed that ribosome biogenesis was downregulated, and starch and sucrose metabolism was upregulated across all clusters. Gene and pathway analyses have shown that stressors lead to distinct changes in yeast gene expression, and these alterations have been systematically documented for each cluster. Several of the highlighted genes are pivotal for further exploration and could potentially clarify new aspects of stress response mechanisms and multiple stress resistance in yeast.