Yeast adapts to diverse ecological niches driven by genomics and metabolic reprogramming

生态位 酵母 模式生物 转录组 酿酒酵母 利基 生物 生态学 基因组学 比较基因组学 适应(眼睛) 有机体 人口 代谢网络 表型 计算生物学 基因组 遗传学 基因 基因表达 栖息地 社会学 人口学 神经科学
作者
Haoyu Wang,Jens Nielsen,Yongjin J. Zhou,Hongzhong Lu
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [National Academy of Sciences]
卷期号:122 (32): e2502044122-e2502044122 被引量:2
标识
DOI:10.1073/pnas.2502044122
摘要

The famous model organism Saccharomyces cerevisiae is widely present in a variety of natural and human-associated habitats. Despite extensive studies of this organism, the metabolic mechanisms driving its adaptation to varying niches remain elusive. We here gathered genomic resources from 1,807 S. cerevisiae strains and assembled them into a high-quality pangenome, facilitating the comprehensive characterization of genetic diversity across isolates. Utilizing the pangenome, 1,807 strain-specific genome-scale metabolic models (ssGEMs) were generated, which performed well in quantitative predictions of cellular phenotypes, thus helping to examine the metabolic disparities among all S. cerevisiae strains. Integrative analyses of fluxomics and transcriptomics with ssGEMs showcased ubiquitous transcriptional regulation of metabolic flux in specific pathways (i.e., amino acid synthesis) at a population level. Additionally, the gene/reaction inactivation analysis through the ssGEMs refined by transcriptomics showed that S. cerevisiae strains from various ecological niches had undergone reductive evolution at both the genomic and metabolic network levels when compared to wild isolates. Finally, the compiled analysis of the pangenome, transcriptome, and metabolic fluxome revealed remarkable metabolic differences among S. cerevisiae strains originating from distinct oxygen-limited niches, including human gut and cheese environments, and identified convergent metabolic evolution, such as downregulation of oxidative phosphorylation pathways. Together, these results illustrate how yeast adapts to distinct niches modulated by genomic and metabolic reprogramming, and provide computational resources for translating yeast genotype to fitness in future studies.
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