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Quantifying gene expression control by growth and metabolism reveals distinct regulation of central carbon metabolism genes in yeast

ABSTRACT: Here we quantitatively describe the influence of cell growth rate and amino acid metabolic context on gene expression in the eukaryal model organism Saccharomyces cerevisiae. We show that growth rate and metabolic cues regulate ~70% of the yeast transcriptome and proteome, thereby exerting gene expression control in a global manner. We find that the growth rate-dependent differential gene expression largely reflects changing availabilities of the mRNA and protein synthesis machineries, while metabolic cues influences gene expression through the availabilities of amino acids and nucleotides. Genes in central carbon metabolism, however, are regulated independently of these global physiological controls, demonstrating distinct mechanisms to control their expression levels.

INSTRUMENT(S): Orbitrap Fusion

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

SUBMITTER: Egor Vorontsov

LAB HEAD: Jens B Nielsen

PROVIDER: PXD021218 | Pride | 2021-03-15

REPOSITORIES: Pride

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Project description:The interaction between transcription factors and genomic DNA forms the basis for spatio-temporal control of gene expression. Therefore, these interactions and their impact on disease and cellular fate are extensively studied on a global level, mainly using techniques based on next-generation sequencing. These techniques, however, do not allow an unbiased study of proteins or entire protein complexes that bind to a certain DNA sequence. In recent years, DNA pull-downs followed by quantitative mass spectrometry were introduced to close this gap. Established protocols, however, are based on metabolic labeling techniques or require enormous amounts of cellular material, thus restricting the method to cell lines grown in culture. Furthermore, they require substantial amount of expertise, thus keeping this technique restricted to a limited number of laboratories. Here, we introduce a high-throughput compatible, LC-MS/MS based DNA pull-down that combines on-bead digestion with direct dimethyl labeling or label-free protein quantification. We demonstrate, that our method can efficiently identify transcription factors binding to their known consensus DNA motifs when using nuclear extracts from model cell lines. Subsequently, we apply the method to study DNA-protein interactions in primary foreskin fibroblasts and peripheral blood mononuclear cells (PBMCs) freshly isolated from human donors. We show that the same DNA sequence binds different sets of proteins in an established model cell line as opposed to PBMCs. This stresses the importance of selecting relevant cell extracts for any interaction in question. In-depth nuclear proteomes with absolute quantification of close to 7,000 proteins in K562 cells and PBMCs clearly link these differential interactions to differences in protein abundance. In conclusion, our approach, applicable to primary material and capable of profiling DNA-protein interactions in high-throughput, will likely prove itself as a useful screening platform and will provide invaluable functional data, for example through integration with large scale GWAS.

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Quantifying gene expression control by growth and metabolism reveals distinct regulation of central carbon metabolism genes in yeast

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