TY - JOUR
T1 - Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community
AU - Zhang, Chengyu
AU - Sánchez, Benjamín J.
AU - Li, Feiran
AU - Eiden, Cheng Wei Quan
AU - Scott, William T.
AU - Liebal, Ulf W.
AU - Blank, Lars M.
AU - Mengers, Hendrik G.
AU - Anton, Mihail
AU - Rangel, Albert Tafur
AU - Mendoza, Sebastián N.
AU - Zhang, Lixin
AU - Nielsen, Jens
AU - Lu, Hongzhong
AU - Kerkhoven, Eduard J.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Genome-scale Metabolic Models
KW - Machine Learning
KW - Multi-omics Integration
KW - Saccharomyces cerevisiae
U2 - 10.1038/s44320-024-00060-7
DO - 10.1038/s44320-024-00060-7
M3 - Article
AN - SCOPUS:85200947353
SN - 1744-4292
VL - 20
SP - 1134
EP - 1150
JO - Molecular Systems Biology
JF - Molecular Systems Biology
IS - 10
ER -