Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast

Abstract

Cells maintain reserves in their metabolic and translational capacities as a strategy to quickly respond to changing environments. Here we quantify these reserves by stepwise reducing nitrogen availability in yeast steady-state chemostat cultures, imposing severe restrictions on total cellular protein and transcript content. Combining multi-omics analysis with metabolic modeling, we find that seven metabolic superpathways maintain >50% metabolic capacity in reserve, with glucose metabolism maintaining >80% reserve capacity. Cells maintain >50% reserve in translational capacity for 2490 out of 3361 expressed genes (74%), with a disproportionately large reserve dedicated to translating metabolic proteins. Finally, ribosome reserves contain up to 30% sub-stoichiometric ribosomal proteins, with activation of reserve translational capacity associated with selective upregulation of 17 ribosomal proteins. Together, our dataset provides a quantitative link between yeast physiology and cellular economics, which could be leveraged in future cell engineering through targeted proteome streamlining.

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

“…Genes demonstrating the strongest uncoupling of transcription and translation as two distinct points of regulation-i.e. genes whose transcript and protein abundances were poorly correlated -were particularly enriched for CCM processes, consistent with previous indications that this central metabolic pathway is regulated by complex mechanisms to ensure robust output (8). We further showed that protein abundance is unlikely to be controlled by relative mRNA abundance, in line with recent large-scale proteomics studies showing that the organizing principle of the cell proteome can be substantially different from that of the transcriptome (43).…”

“…Previously we have shown that the cell growth rate determines the allocation of the proteome and transcriptome to different processes, including the relative abundance of RNA polymerases and ribosomes (8,21). Our results here indicate that these growth rate-dependent changes in the transcription and translation machineries directly influences the absolute abundance ~90% of genes.…”

“…Considering all gene expression measurements in GR and AA experiments combined, the protein-mRNA correlations remained poor, with a median ρ of 0.22 ( Fig 6A). To further examine these protein-mRNA relationships, we tested for enrichment of GO-slim terms in 200-gene sliding windows of increasing ρ (8,19). We found that genes with high correlations between protein and mRNA abundance (median ρ of 0.4 to 0.7 in 200-gene windows) are enriched in GO-slim terms related to amino acid metabolism, as well as processes related protein translation, including ribosome biogenesis; rRNA processing; tRNA aminoacylation; and so on ( Fig 6B).…”

“…in response to stress or during oncogenic transformation, often include changes in the cell growth rate and metabolism. In turn, both growth rate and metabolic parameters of the cell can exert global influences on gene expression, as demonstrated by landmark studies in E. coli (1-4) and in yeast (5)(6)(7)(8). Thus, the gene expression program following a perturbation reflects a joint effect of the specific regulatory circuits that are induced (or repressed) by the perturbation, as well as the global influence on gene expression by an altered physiological state ( Fig 1A).…”

Different Routes of Protein Folding Contribute to Improved Protein Production in Saccharomyces cerevisiae

“…Clearly, the transcriptome is remodeled in a global manner but at a smaller scale, while the proteome changes in a more specific manner that precisely impacts bioprocesses related to protein production, including amino acid transport, vitamin metabolism, protein modification, protein phosphorylation, and so on. The differences between transcriptome and proteome showed the reserves in translational capacity of yeast ( 18 ). Hence, we focused our analysis on differential expression at the proteome level between the three α-amylase production strains.…”