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

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. Synopsis: Yeast9 is the latest release of the community-consensus genome-scale model of Saccharomyces cerevisiae metabolism. Among others, Yeast9 has corrected mass & charge balancing; curated gene associations and is annotated with Gibbs free energies. When used to analyse 163 single-cell transcriptomes, Yeast9 reveals a high osmotic adaptation mechanism. Yeast9 is able to elucidate the organism's nitrogen source preference and its metabolic rewiring under nitrogen limitation. When constraint with transcriptomics from 1229 mutants, Yeast9 can predict fluxes that inform machine learning-based gene function prediction. Yeast9 is the latest release of the community-consensus genome-scale model of Saccharomyces cerevisiae metabolism. [ABSTRACT FROM AUTHOR]