文献类型：期刊文献

英文题名：Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

作者：Zhang, Chengyu[1,2];Sanchez, Benjamin J.[3,4];Li, Feiran[5];Eiden, Cheng Wei Quan[6];Scott, William T.[7,8];Liebal, Ulf W.[9];Blank, Lars M.[9];Mengers, Hendrik G.[9];Anton, Mihail[10];Rangel, Albert Tafur[3,11];Mendoza, Sebastian N.[12,13];Zhang, Lixin[2];Nielsen, Jens[11,14];Lu, Hongzhong[1];Kerkhoven, Eduard J.[3,15]

机构：[1]Shanghai Jiao Tong Univ, Sch Life Sci & Biotechnol, State Key Lab Microbial Metab, Shanghai 200240, Peoples R China;[2]East China Univ Sci & Technol ECUST, Sch Biotechnol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[3]Tech Univ Denmark, Novo Nordisk Fdn, Ctr Biosustainabil, DK-2800 Lyngby, Denmark;[4]Tech Univ Denmark, Dept Biotechnol & Biomed, DK-2800 Lyngby, Denmark;[5]Tsinghua Univ, Inst Biopharmaceut & Hlth Engn, Tsinghua Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China;[6]Nanyang Technol Univ, Sch Chem Chem Engn & Biotechnol, 62 Nanyang Dr, Singapore 637459, Singapore;[7]Wageningen Univ & Res, UNLOCK, Wageningen, Netherlands;[8]Wageningen Univ & Res, Lab Syst & Synthet Biol, Wageningen, Netherlands;[9]Rhein Westfal TH Aachen, Inst Appl Microbiol iAMB, Aachen Biol & Biotechnol ABBt, Aachen, Germany;[10]Chalmers Univ Technol, Dept Life Sci, Sci Life Lab, Natl Bioinformat Infrastruct Sweden, SE412 58, Gothenburg, Sweden;[11]Chalmers Univ Technol, Dept Life Sci, SE-41296 Gothenburg, Sweden;[12]Univ Chile, Ctr Math Modeling, Santiago, Chile;[13]Vrije Univ Amsterdam, Syst Biol Lab, Amsterdam, Netherlands;[14]BioInnovat Inst, Ole Maaloes Vej 3, DK-2200 Copenhagen N, Denmark;[15]Chalmers Univ Technol, Dept Life Sci, SE-41296 Gothenburg, Sweden

年份：2024

卷号：20

期号：10

起止页码：1134

外文期刊名：MOLECULAR SYSTEMS BIOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001289924900001)】；

基金：This work is supported by grant 2022YFA0913000 from the National Key R&D Program of China, Shanghai Pujiang Program, and grants 22208211 and 22378263 from the National Natural Science Foundation of China (NSFC). This work is also supported by the Novo Nordisk Foundation (grant no. NNF20CC0035580); the Knut and Alice Wallenberg Foundation, and the European Union's Horizon 2020 research and innovation program (grant agreements 686070 and 720824); National Key Research and Development Program of China (2020YFA0907800); the 111 Project (B18022); Dutch Research Council (Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)) for the UNLOCK initiative (NWO: 184.035.007); CN Yang Scholars Programme; Deutsche Forschungsgemein-schaft (DFG, German Research Foundation) under Germany's Excellence Strategy-Cluster of Excellence 2186; Centro de Modelamiento Matematico, ACE210010 and FB210005, BASAL funds for Centers of Excellence from ANID-Chile Project ICN2021 044 of the Millennium Scientific Initiative Grant Exploracion number 13220002; and CONICYT Becas Chile grant #72180373 (https://www.conicyt.cl/becasconicyt/). The funding bodies had no role in the design of the study, analysis and interpretation of the data, preparation of the manuscript, and decision to submit the manuscript for publication.

语种：英文

外文关键词：Saccharomyces cerevisiae; Genome-scale Metabolic Models; Machine Learning; Multi-omics Integration

摘要：Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.