Dataset Information

Proteomes of yeast kinase knock-outs analysed by SWATH-MS

ABSTRACT: It proves so far difficult to predict the metabolome, even when genome, transcriptome or proteome of a cell are known. In order to globally map enzyme-metabolite relationships, we systematically quantified enzyme expression and metabolite concentrations in Saccharomyces cerevisiae kinase knock-out strains. Enzymes expression changes did account for a major fraction of all differentially expressed proteins, and were non-redundant, implying that kinases act generally yet specifically in metabolic regulation. Differential enzyme expression was found to affect metabolite concentrations through the redistribution of flux control, resulting in a many-to-many relationship between enzyme abundance and the metabolome. Machine learning successfully mapped these relationships, allowing the precise prediction of metabolite concentrations, as well as identifying regulatory genes. Our study reveals that hierarchical metabolic regulation acts predominantly through adjustment of broad enzyme expression patterns rather than over rate-limiting enzymes, and may account for more than half of metabolic regulation.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Vadim Demichev

LAB HEAD: Markus Ralser

PROVIDER: PXD010529 | Pride | 2018-09-13

REPOSITORIES: pride

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

Zelezniak Aleksej A Vowinckel Jakob J Capuano Floriana F Messner Christoph B CB Demichev Vadim V Polowsky Nicole N Mülleder Michael M Kamrad Stephan S Klaus Bernd B Keller Markus A MA Ralser Markus M

Cell systems 20180905 3

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 pr ...[more]

PMID: 30195436

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