Yuta Yamazaki, Fumihiko Omura, Eiichiro Ono, Nozomu Kamada, Keiko Kono
{"title":"酿酒葡萄球菌和pastorianus葡萄球菌响应质膜胁迫的时间分辨转录组学。","authors":"Yuta Yamazaki, Fumihiko Omura, Eiichiro Ono, Nozomu Kamada, Keiko Kono","doi":"10.1038/s41597-025-05565-w","DOIUrl":null,"url":null,"abstract":"<p><p>Yeasts are beneficial microorganisms for human society and are utilized for academic and industrial purposes. For academic purposes, S. cerevisiae is a well-investigated model for studying eukaryotic cellular processes. For industrial purposes, S. pastorianus, which has a hybrid genome of S. cerevisiae and S. eubayanus, has been served for lager beer production. During fermentation, S. pastorianus produces ~7% of EtOH, which induces plasma membrane (PM)/cell wall stress in yeast. Therefore, S. pastorianus may experience PM stress and adapt to the self-forming environment during fermentation. However, how yeast adapts to PM stress remains unclear. Here, we investigated the temporal cellular responses of S. cerevisiae and S. pastorianus during adaptation to PM stresses by time-resolved mRNA-seq analysis. Our data showed different transcriptional phenotypes between S. cerevisiae and S. pastorianus during adaptation. The results may reflect the distinct nature of the two yeasts that have evolved in different nutritional environments. The dataset presented here would provide a promising resource for studying the characteristic nature of these differentially domesticated yeasts upon PM stresses.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1246"},"PeriodicalIF":6.9000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267428/pdf/","citationCount":"0","resultStr":"{\"title\":\"Time-resolved transcriptomics of S. cerevisiae and S. pastorianus in response to plasma membrane stresses.\",\"authors\":\"Yuta Yamazaki, Fumihiko Omura, Eiichiro Ono, Nozomu Kamada, Keiko Kono\",\"doi\":\"10.1038/s41597-025-05565-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Yeasts are beneficial microorganisms for human society and are utilized for academic and industrial purposes. For academic purposes, S. cerevisiae is a well-investigated model for studying eukaryotic cellular processes. For industrial purposes, S. pastorianus, which has a hybrid genome of S. cerevisiae and S. eubayanus, has been served for lager beer production. During fermentation, S. pastorianus produces ~7% of EtOH, which induces plasma membrane (PM)/cell wall stress in yeast. Therefore, S. pastorianus may experience PM stress and adapt to the self-forming environment during fermentation. However, how yeast adapts to PM stress remains unclear. Here, we investigated the temporal cellular responses of S. cerevisiae and S. pastorianus during adaptation to PM stresses by time-resolved mRNA-seq analysis. Our data showed different transcriptional phenotypes between S. cerevisiae and S. pastorianus during adaptation. The results may reflect the distinct nature of the two yeasts that have evolved in different nutritional environments. The dataset presented here would provide a promising resource for studying the characteristic nature of these differentially domesticated yeasts upon PM stresses.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"1246\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267428/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05565-w\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05565-w","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Time-resolved transcriptomics of S. cerevisiae and S. pastorianus in response to plasma membrane stresses.
Yeasts are beneficial microorganisms for human society and are utilized for academic and industrial purposes. For academic purposes, S. cerevisiae is a well-investigated model for studying eukaryotic cellular processes. For industrial purposes, S. pastorianus, which has a hybrid genome of S. cerevisiae and S. eubayanus, has been served for lager beer production. During fermentation, S. pastorianus produces ~7% of EtOH, which induces plasma membrane (PM)/cell wall stress in yeast. Therefore, S. pastorianus may experience PM stress and adapt to the self-forming environment during fermentation. However, how yeast adapts to PM stress remains unclear. Here, we investigated the temporal cellular responses of S. cerevisiae and S. pastorianus during adaptation to PM stresses by time-resolved mRNA-seq analysis. Our data showed different transcriptional phenotypes between S. cerevisiae and S. pastorianus during adaptation. The results may reflect the distinct nature of the two yeasts that have evolved in different nutritional environments. The dataset presented here would provide a promising resource for studying the characteristic nature of these differentially domesticated yeasts upon PM stresses.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.