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

Yu, Rong; Vorontsov, Egor; Sihlbom, Carina; Nielsen, Jens

doi:10.1101/2020.12.22.423946

Cited by 8 publications

(27 citation statements)

References 54 publications

(82 reference statements)

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“…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.…”

Section: Fig 3bsupporting

confidence: 83%

“…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).…”

Section: Data Descriptionmentioning

confidence: 99%

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

Section: Culture Conditionsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Vorontsov

Sihlbom

et al. 2021

Preprint

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Metabolic flux can be regulated by a variety of different mechanisms, but the organization of these mechanisms within the metabolic network has remained unknown. Here we test the hypothesis that flux control mechanisms are not distributed randomly in the metabolic network, but rather organized according to pathway. Combining proteomics, phosphoproteomics, and metabolic modeling, we report the largest collection of flux-enzyme-phosphoenzyme relationships to date in Saccharomyces cerevisiae. In support of the hypothesis, we show that (i) amino acid metabolic pathways are predominantly regulated by enzyme abundance stemming from transcriptional regulation; (ii) upper glycolysis and associated pathways, by inactivating enzyme phosphorylation; (iii) lower glycolysis and associated pathways, by activating enzyme phosphorylation; and (iv) glycolipid/glycophospholipid pathways, by a combination of enzyme phosphorylation and metabolic compartmentalization. We delineate the evolutionary history for the observed organization of flux control mechanisms in yeast central metabolic pathways, furthering our understanding of the regulation of metabolism and its evolution.

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Section: Fig 3bsupporting

confidence: 83%

Section: Data Descriptionmentioning

confidence: 99%

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

Section: Culture Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Vorontsov

Sihlbom

et al. 2021

Preprint

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“…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.…”

Section: The Crabtree Effect Is Accompanied By Differences In Rp Content Andsupporting

confidence: 90%

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

Malina

Björkeroth

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Aerobic fermentation, also referred to as the Crabtree effect in yeast, is a well-studied phenomenon that allows many eukaryal cells to attain higher growth rates at high glucose availability. Not all yeasts exhibit the Crabtree effect, and it is not known why Crabtree-negative yeasts can grow at rates comparable to Crabtree-positive yeasts. Here, we quantitatively compared two Crabtree-positive yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and two Crabtree-negative yeasts, Kluyveromyces marxianus and Scheffersomyces stipitis, cultivated under glucose excess conditions. Combining physiological and proteome quantification with genome-scale metabolic modeling, we found that the two groups differ in energy metabolism and translation efficiency. In Crabtree-positive yeasts, the central carbon metabolism flux and proteome allocation favor a glucose utilization strategy minimizing proteome cost as proteins translation parameters, including ribosomal content and/or efficiency, are lower. Crabtree-negative yeasts, however, use a strategy of maximizing ATP yield, accompanied by higher protein translation parameters. Our analyses provide insight into the underlying reasons for the Crabtree effect, demonstrating a coupling to adaptations in both metabolism and protein translation.

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Data integration across conditions improves turnover number estimates and metabolic predictions

Wendering

Arend

Nikoloski

2022

Preprint

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Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by ranking of estimates obtained by integrating reaction rate and enzyme abundance measurements from individual experiments; yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly in the ultimate test scenario when protein abundances are integrated in the model. We then demonstrate that in vitro turnover numbers can be corrected via a constraint-based approach that simultaneously leverages heterogeneous physiological data from multiple experiments. We find that the resulting estimates of in vivo turnover numbers lead to improved prediction of condition-specific growth rates, particularly when protein abundances are used as constraints, and are more precise than the available in vitro turnover numbers. Therefore, our approach provides the means to decrease the bias of in vivo turnover numbers and paves the way towards cataloguing in vivo kcatomes of other organisms.Significance StatementIntegration of turnover numbers in protein-constrained metabolic models has provided insights in enzyme allocation and can improve prediction of metabolic phenotypes. While in vivo turnover numbers are estimated by ranking the outcomes from the integration of condition-specific proteomics data and reaction rates, simultaneous consideration of physiological read-outs over multiple conditions has not been considered, yet. Here we designed and tested a constraint-based approach that leverages heterogeneous data to improve estimates of in vivo turnover numbers in model unicellular organisms. We showed that the resulting turnover numbers are more precise than in vitro estimates from public databases and lead to improved prediction of condition-specific growth. The approach is readily applicable to non-model organisms and paves the way to documenting their kcatomes.

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

Cited by 8 publications

References 54 publications

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

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

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

Data integration across conditions improves turnover number estimates and metabolic predictions

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