Institute of Biotechnology, Shiraz University, Shiraz, Fars, Iran.
Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Fars, Iran.
PLoS One. 2022 Jul 26;17(7):e0259476. doi: 10.1371/journal.pone.0259476. eCollection 2022.
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.
酿酒酵母以其在工业中生产乙醇的出色能力而闻名。在建立任何提高乙醇生产的方案之前，了解酿酒酵母基因表达对发酵的动态响应可为我们提供有用的信息。因此，本研究采用一种新方法，通过挖掘转录组数据，鉴定出改善和抑制乙醇生产之间的差异表达基因，并阐明该过程的分子响应。从与酵母发酵性能相关的可用微阵列数据集提取显著差异表达的探针集。为了鉴定有助于区分乙醇含量的最有效探针集，使用 RapidMiner 中的 11 种机器学习算法。对差异探针集进行通路富集和调控分析。此外，构建决策树模型，评估每个模型的性能并识别根节点。基于结果，至少有 5 个属性权重算法（AWAs）识别出 171 个探针集，100%性能识别出 17 个根节点。一些排名靠前的预设被发现与碳水化合物代谢、氧化磷酸化和乙醇发酵有关。主成分分析（PCA）和热图聚类验证了排名靠前的选择性探针集。此外，基于 GSE78759 和 GSE5185 数据集验证了排名靠前的基因。从所有差异表达探针集中，OLI1 和 CYC3 被鉴定为具有最佳性能的根节点，这是由最多的加权算法确定的，并与包括卟啉生物合成和氧化磷酸化在内的两个最重要的富集通路相关。ADH5 和 PDA1 也被认为是对乙醇生产有贡献的差异排名靠前的基因。根据调控聚类分析，Tup1 对排名靠前的靶基因 CYC3 和 ADH5 基因有显著影响。本研究为酿酒酵母细胞分子机制和对发酵过程中两种不同培养基条件（Mg2+和 Cu2+）的响应提供了基本的理解。
