Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community
Journal article, 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

Author

Chengyu Zhang

The State Key Laboratory of Bioreactor Engineering

State Key Laboratory of Microbial Metabolism

Benjamín José Sánchez

Technical University of Denmark (DTU)

Novo Nordisk Foundation

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, Systems and Synthetic Biology

Albert Tafur Rangel

Novo Nordisk Foundation

Chalmers, Life Sciences, Systems and Synthetic Biology

Sebastián N. Mendoza

Vrije Universiteit Amsterdam

University of Chile (UCH)

Lixin Zhang

The State Key Laboratory of Bioreactor Engineering

Jens B Nielsen

Chalmers, Life Sciences, Systems and Synthetic Biology

BioInnovation Institute

Hongzhong Lu

State Key Laboratory of Microbial Metabolism

Eduard Kerkhoven

Chalmers, Life Sciences, Systems and Synthetic Biology

Novo Nordisk Foundation

Molecular Systems Biology

17444292 (eISSN)

Vol. 20 10 1134-1150

Bioinformatics Services for Data-Driven Design of Cell Factories and Communities (DD-DeCaF)

European Commission (EC) (EC/H2020/686070), 2016-03-01 -- 2020-02-28.

Subject Categories

Microbiology

Bioinformatics (Computational Biology)

Software Engineering

Bioinformatics and Systems Biology

DOI

10.1038/s44320-024-00060-7

PubMed

39134886

More information

Latest update

10/28/2024