文章基本信息

标题：Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism
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作者：Saori Uematsu ; Satoshi Ohno ; Kaori Y. Tanaka 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：2
页码：1-36
DOI：10.1016/j.isci.2022.103787
语种：English
出版社：Elsevier
摘要：SummaryGlucose homeostasis is maintained by modulation of metabolic flux. Enzymes and metabolites regulate the involved metabolic pathways. Dysregulation of glucose homeostasis is a pathological event in obesity. Analyzing metabolic pathways and the mechanisms contributing to obesity-associated dysregulationin vivois challenging. Here, we introduce OMELET: Omics-Based Metabolic Flux Estimation without Labeling for ExtendedTrans-omic Analysis. OMELET uses metabolomic, proteomic, and transcriptomic data to identify relative changes in metabolic flux, and to calculate contributions of metabolites, enzymes, and transcripts to the changes in metabolic flux. By evaluating the livers of fastingob/obmice, we found that increased metabolic flux through gluconeogenesis resulted primarily from increased transcripts, whereas that through the pyruvate cycle resulted from both increased transcripts and changes in substrates of metabolic enzymes. With OMELET, we identified mechanisms underlying the obesity-associated dysregulation of metabolic flux in the liver.Graphical AbstractDisplay OmittedHighlights•We developed OMELET to infer metabolic flux from label-free multi-omic data•Contributions of metabolites, enzymes, and transcripts for flux were inferred•Gluconeogenic flux increased in fastingob/obmice by increased transcripts•Increased pyruvate cycle fluxes were led by increased transcripts and substratesSystems biology; Proteomics; Metabolomics; Transcriptomics