Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast

Abstract

In addition to specific regulatory circuits, gene expression is also regulated by global physiological cues such as the cell growth rate and metabolic parameters. Here we examine these global control mechanisms by analyzing an orthogonal multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and intracellular amino acids in 22 steady-state yeast cultures. Our model indicates that transcript and protein abundance are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but are independently controlled by metabolic parameters relating to amino acid and nucleotide availability. Genes in central carbon metabolism, however, are regulated independently of these global physiological cues. Our findings can be used to augment gene expression profiling analyses in the distantly related yeast Schizosaccharomyces pombe and a human cancer cell model. Our results provide a framework to analyze gene expression profiles to gain novel biological insights, a key goal of systems biology.

Non-random organization of flux control mechanisms in yeast central metabolic pathways

“…In yeast, genes related to nitrogen and amino acid metabolism are among the best known to be transcriptionally co-regulated, in the nitrogen catabolite repression (NCR) and general amino acid control (GAAC) regulons (12,26). Consistently, our results show that pathways in AA metabolism are predominantly regulated by enzyme abundance (Fig 2A ) stemming from transcriptional regulation (16). A rational extension of these observations is the hypothesis that post-transcriptional control mechanisms may be similarly coordinated by pathway, which is supported by previous reports of coordinated protein translation (9) as well as transcript and protein turnover rates (46) in central metabolic pathways.…”

“…To estimate metabolic flux, we performed flux balance analysis (FBA) (18) using the consensus yeast genome-scale metabolic model (GEM), Yeast8.4.0 (19), constraining the model using measured metabolite exchange fluxes and dilution rates (16). The feasible region of each chemostat condition was randomly sampled 1,000 times, allowing statistical significance to be calculated when fluxes were compared across conditions (20,21).…”

“…Culture conditions are as previously described (16). The yeast Saccharomyces cerevisiae CEN.PK113-7D (MATa, MAL2-8c, SUC2) was used for all experiments.…”

“…Herein we test this hypothesis using the eukaryal model organism Saccharomyces cerevisiae, through a combination of chemostat cultivation, metabolic modeling, and mass spectrometry (MS)based proteomics and phosphoproteomics. By correlating enzyme and phosphoenzyme levels with metabolic fluxes (14,15) across a wide range of conditions designed to perturb metabolism at the global scale (16), we identified reactions that are regulated by three mechanisms: (i) enzyme abundance, (ii) activating enzyme phosphorylation, and (iii) inactivating enzyme phosphorylation.…”

Adaptations in metabolism and protein translation give rise to the Crabtree effect in yeast

“…The total RP abundance were 19.5 to 23.6% (g per g total protein) in the faster-growing species (S. cerevisiae, K. marxianus, and S. stipitis),and 16.1% in the slower-growing S. pombe (SI Appendix, Fig. S11C), in line with previous studies showing that RP abundance increases linearly with specific growth rate (40,41). We then calculated the overall ribosomal translation efficiency as mg total protein per nmol ribosomes per hour and found that S. pombe has less-efficient ribosomes compared to S. stipitis, while K. marxianus and S. cerevisiae exhibited similar efficiencies (Fig.…”