Abstract
SummaryA challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.
Highlights
Despite the fact that metabolism is intensively studied, one still debates about how much of metabolic regulation is explained by metabolic self-regulation and by regulation of enzyme activity and how much is dependent on enzyme abundance changes
Using metabolic control analysis (MCA), we revealed the importance of largely overlooked mechanisms in metabolic regulation
The measurement of the 97 proteomes mounted to 397 whole-proteome samples processed using the data-independent acquisition method SWATH-MS (Gillet et al, 2012) and the workflow optimized for achieving high quantification precision at large sample numbers (Vowinckel et al, 2018)
Summary
Despite the fact that metabolism is intensively studied, one still debates about how much of metabolic regulation is explained by metabolic self-regulation and by regulation of enzyme activity and how much is dependent on enzyme abundance changes. Metabolite concentrations seem to correlate much better with metabolic fluxes than with enzyme expression levels (Chubukov et al, 2013; Hackett et al, 2016; Millard et al, 2017). These results seem to suggest that the post-translational regulation, metabolic self-regulation, and allostery are dominant in metabolism regulation. The expression changes are centered on metabolites that change in concentration (Patil and Nielsen, 2005; Zelezniak et al, 2010), while systematically recorded transcriptomes and proteomes of metabolically perturbed yeast correlate with metabolic flux distributions (Alam et al, 2016). All metabolism-regulating transcriptional and signaling networks identified to date, such as AMP-activated protein kinase (AMPK) (Mihaylova and Shaw, 2011), mechamTOR (Gonzalez and Hall, 2017), or GCN2/4 (Zaborske et al, 2010), trigger metabolic gene expression changes
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