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

• Main Authors: Zhang, Chengyu, Sánchez, Benjamín J., Li, Feiran, Cheng, Eiden Wei Quan, Scott, William T., Liebal, Ulf W., Blank, Lars M., Mengers, Hendrik G., Anton, Mihail, Rangel, Albert Tafur, Mendoza, Sebastián N., Zhang, Lixin, Nielsen, Jens, Lu, Hongzhong, Kerkhoven, Eduard J.
• Other Authors: School of Chemistry, Chemical Engineering and Biotechnology
• Format: Article
• Language: English
• Published: 2024
• Subjects: Medicine, Health and Life Sciences; Machine Learning; Multi-omics Integration
• Online Access: https://hdl.handle.net/10356/181838
• Institution: Nanyang Technological University

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.