Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Abstract

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

“…The k cat prediction for 343 yeast/fungi species. We previously reconstructed GEMs for 332 yeast species plus 11 out-group fungi, but only expanded 14 of them to ecGEMs using the original pipeline 10 due to the limited available k cat data 14 . As DLKcat allows prediction of almost all k cat values for metabolic enzymes against any substrates for any species, this enabled the generation of ecGEMs for all 343 yeast/fungi species, predicting k cat values for around three million enzyme-substrate pairs (Supplementary Fig.…”

“…Prediction of k cat values for 343 yeast/fungi species. The GEMs of 343 yeast/fungi species were automatically reconstructed in our previous paper 14 from a yeast/ fungi 'pan-GEM' , which was derived from the well-curated Yeast8 of S. cerevisiae combined with the pan-genome annotation. For each model, all reversible enzymatic reactions were split into forward and backward reactions.…”

“…Over the past decade, ecGEMs (or models following the concept of enzyme constraints) have been separately developed for several well-studied organisms 7 including Escherichia coli 8,9 , Saccharomyces cerevisiae 2,10 , Chinese hamster ovary cells 11 and Homo sapiens 12 . Due to the limitations of k cat measurements 13 and the reliance on enzyme commission (EC) number annotations to search for k cat values in those developed pipelines 2,8,10 , the reconstruction of ecGEMs for lesser-studied organisms or large-scale reconstruction for multiple organisms has remained a challenge 7,14 . Moreover, even for those well-studied organisms, the k cat coverage is far from complete 13,15,16 .…”

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

“…Moreover, the large-scale reconstruction of species-specific GEMs for 343 fungal species enabled comprehensive analyses of evolutionary diversification of substrate utilization (Lu et al . 2021 ).…”

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

“…GEMs of S. pombe have previously been constructed. However, several issues, including incompatibility with current Systems Biology Markup Language (SBML) standards (Pitkänen et al, 2014; Sohn et al, 2012), a lack of gene-protein-reaction (GPR)-associations, or automated reconstruction without additional curation (Lu et al, 2021; Pitkänen et al, 2014), significantly limited their utility. Furthermore, recent extensions of the GEM framework to include regulation and resource allocation dynamics now enable the exploration of complex metabolic behaviours such as the Crabtree-effect (analogous to the Warburg-effect seen in human cells) that cannot be explained with conventional GEMs.…”