Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community
Artikel i vetenskaplig tidskrift, 2024
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.
Machine Learning
Multi-omics Integration
Saccharomyces cerevisiae
Genome-scale Metabolic Models
Författare
Chengyu Zhang
The State Key Laboratory of Bioreactor Engineering
State Key Laboratory of Microbial Metabolism
Benjamín José Sánchez
Danmarks Tekniske Universitet (DTU)
Novo Nordisk Fonden
Feiran Li
Tsinghua University
Cheng Wei Quan Eiden
School of Chemistry, Chemical Engineering and Biotechnology
William T. Scott
Wageningen University and Research
Ulf W. Liebal
RWTH Aachen University
L. M. Blank
RWTH Aachen University
Hendrik G. Mengers
RWTH Aachen University
Petre Mihail Anton
Chalmers, Life sciences, Systembiologi
Albert Tafur Rangel
Novo Nordisk Fonden
Chalmers, Life sciences, Systembiologi
Sebastián N. Mendoza
Vrije Universiteit Amsterdam
Universidad de Chile (UCH)
Lixin Zhang
The State Key Laboratory of Bioreactor Engineering
Jens B Nielsen
Chalmers, Life sciences, Systembiologi
BioInnovation Institute
Hongzhong Lu
State Key Laboratory of Microbial Metabolism
Eduard Kerkhoven
Chalmers, Life sciences, Systembiologi
Novo Nordisk Fonden
Molecular Systems Biology
17444292 (eISSN)
Vol. 20 10 1134-1150Bioinformatics Services for Data-Driven Design of Cell Factories and Communities (DD-DeCaF)
Europeiska kommissionen (EU) (EC/H2020/686070), 2016-03-01 -- 2020-02-28.
Ämneskategorier
Mikrobiologi
Bioinformatik (beräkningsbiologi)
Programvaruteknik
Bioinformatik och systembiologi
DOI
10.1038/s44320-024-00060-7
PubMed
39134886