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Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts.

Cell systems (2018-09-10)
Aleksej Zelezniak, Jakob Vowinckel, Floriana Capuano, Christoph B Messner, Vadim Demichev, Nicole Polowsky, Michael Mülleder, Stephan Kamrad, Bernd Klaus, Markus A Keller, Markus Ralser
ABSTRACT

A 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.

MATERIALS
Product Number
Brand
Product Description

Supelco
L-Amino acids, analytical standard
Sigma-Aldrich
D(+)-2-Phosphoglyceric acid sodium salt hydrate, ≥75% (calc. on dry substance, enzymatic)
Sigma-Aldrich
Picropodophyllotoxin, ≥96% (HPLC), powder