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Mitochondrial ATP generation is more proteome efficient than glycolysis

Nature Chemical Biology (2024)Cite this article

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Abstract

Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

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Fig. 1: Central carbon metabolic fluxes and ATP sources in yeast and T cells.
Fig. 2: Proteome allocation and proteome efficiency in yeast and T cells.
Fig. 3: Metabolic flux, proteome and proteome efficiency in yeast across different nutrient conditions.
Fig. 4: Proteome efficiency of mammalian cells, tissues and tumors.
Fig. 5: Growth and glucose consumption of evolutionarily divergent budding yeasts.
Fig. 6: Yeast fitness under aerobic and anaerobic conditions.

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Data availability

All raw data, including metabolic flux, proteomics and proteome efficiency data, are provided in the Supplementary Tables or publicly available repositories. The following accession numbers were used to access publicly available proteomes: S. cerevisiae (S288C: UP000002311, 24 February 2021; CEN.PK, UP000013192, 20 August 2021), I. orientalis (UP000029867, 13 November 2019) and M. musculus (UP000000589, 11 October 2022). We also queried Mouse-GEM68 and the Mouse Genome Database69 for mouse genome information. Some of the healthy mouse tissue proteomics data are from PaxDb51. The MS proteomics data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE85 partner repository with the dataset identifiers PXD048012 (I. orientalis), PXD048018 (S. cerevisiae) and PXD048041 (M. musculus). Source data are provided with this paper.

Code availability

Data analysis and visualization were performed in R (version 3.5.1) and MATLAB (version 2021b). R code for multiomic integration and metabolic regulation analysis is available at https://github.com/yihuishen/metabolic_flux_regulation. Input data and metabolic models for MFA can be found at https://github.com/maranasgroup/yeastsMFA and https://github.com/yihuishen/T_cell_MFA. MATLAB code for yeast MFA can be found at https://github.com/maranasgroup/SteadyState-MFA. Some of the figures were made with BioRender.

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Acknowledgements

We thank members of the Rabinowitz lab for discussions about experiments and the manuscript, S. Silverman and J. Avalos for the yeast strains, L. Ryazanova for help with the proteomics experiment, P. F. Suthers for discussion on the genome-scale model, M. Gupta for discussion of protein regulation, N. Piyush and Z. Zhang for advice on competitive fitness and R. Knowles for discussions on chemical kinetics. This work was funded by Department of Energy (DOE) DE-SC0018260 to J.D.R., M.W., C.D.M., H.Z. and Y.Y.; the DOE Center for Advanced Bioenergy and Bioproducts Innovation DE-SC0018420 to J.D.R., C.D.M. and H.Z.; DOE DE-AC02-05CH1123 to Z.-Y.W., S.D., and Y.Y.; Ludwig Cancer Research funding to J.D.R.; NIH 35GM128813 and P30CA072720 to M.W.; Princeton Catalysis Initiative to M.W.; an NSF Graduate Research Fellowship to E.R.C.; Princeton University’s Summer Undergraduate Research Program to H.B. and A.S. and the Damon Runyon Foundation/Mark Foundation Postdoctoral Fellowship and NIH K99CA273517 to C.R.B. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the US DOE.

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Authors and Affiliations

  1. Department of Chemistry, Princeton University, Princeton, NJ, USA

    Yihui Shen, Zihong Chen, Caroline R. Bartman, Tianxia Xiao, Catherine M. Call, Rolf-Peter Ryseck, Jimmy Pratas, Daniel Weilandt, Lifeng Yang & Joshua D. Rabinowitz

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Yihui Shen, Edward R. Cruz, Zihong Chen, Caroline R. Bartman, Tianxia Xiao, Catherine M. Call, Rolf-Peter Ryseck, Jimmy Pratas, Daniel Weilandt, Heide Baron, Arjuna Subramanian, Lifeng Yang, Martin Wühr & Joshua D. Rabinowitz

  3. Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA

    Hoang V. Dinh, John I. Hendry & Costas D. Maranas

  4. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Edward R. Cruz & Martin Wühr

  5. Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA

    Zihong Chen, Caroline R. Bartman & Joshua D. Rabinowitz

  6. Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA

    Zia Fatma, Vinh G. Tran & Huimin Zhao

  7. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Zia Fatma, Vinh G. Tran & Huimin Zhao

  8. US Department of Energy Joint Genome Institute and Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Zong-Yen Wu, Sudharsan Dwaraknath & Yasuo Yoshikuni

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  1. Yihui Shen
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Y.S., M.W. and J.D.R. designed the study. Y.S. performed most of the experiments and data analysis. H.V.D. designed and performed genome-scale MFA with input from Y.S., J.I.H. and C.D.M. E.R.C., H.B. and A.S. performed proteomics measurements. Z.C. performed mouse T cell isolation. C.M.C. performed nutrient-limited cultures and measurements. R.-P.R. designed and performed enzyme purifications and qPCR. J.P. performed enzyme purifications and competitive growth experiments. C.R.B. provided mouse tissues. Z.F. and Z.-Y.W. created mutant yeast strains with input from H.Z. and Y.Y. S.D. and Y.Y. contributed to yeast growth measurements. V.G.T. contributed to enzyme purification. T.X. contributed to metabolomics measurements. D.W. contributed to enzyme-constrained modeling. L.Y. contributed to oxygen consumption measurement. Y.S. and J.D.R. wrote the manuscript with input from all coauthors.

Corresponding authors

Correspondence to Martin Wühr or Joshua D. Rabinowitz.

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Competing interests

J.D.R. is a paid adviser and/or stockholder in Colorado Research Partners, L.E.A.F. Pharmaceuticals, Faeth Therapeutics and Empress Therapeutics; a paid consultant of Pfizer; a founder and stockholder in Marea Therapeutics and a founder, director and stockholder of Farber Partners, Raze Therapeutics and Sofro Pharmaceuticals. The other authors declare no conflicts of interest.

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Extended data

Extended Data Fig. 1 Metabolic flux analysis in yeasts.

(a) Overview of 13C metabolic flux analysis in yeasts. (b) Experimentally measured growth rate, oxygen consumption rate, and carbon metabolite fluxes for S. cerevisiae (strains FY4 and CEN.PK) and I. orientalis (SD108) grown in YNB with 20g/L glucose. FY4 is derived from the widely used S288C background. Mean ± s.e. derived from fitting of 3 time points in n = 3 biological replicates. (c) Isotopomer ratio in TCA intermediates reveals higher oxidative TCA activity in I. orientalis. Isotopomer ratios show average [M+1]/[M+2] ratio from [1,2-13C2] glucose tracing (1:1 mixed with unlabeled glucose) in three TCA metabolites, Asp(artate); Fum(arate); Mal(ate), mean ± s.e.m., n = 3 or 4 biological replicates. Flux ratios (from 13C genome-scale MFA) are between oxidative TCA (average of citrate synthase, alpha-ketoglutarate dehydrogenase and succinate dehydrogenase) and anaplerotic TCA (pyruvate decarboxylase). Filled circles, 13C atom. (d) Isotopomer ratio in pyruvate reveals higher flux through pentose phosphate pathway (PPP) in I. orientalis. [M+1]/[M+2] ratio of pyruvate from same experiment in (c), mean ± s.e.m., n = 4 biological replicates. Flux ratios (from 13C genome-scale MFA) are between PPP (difference between glucose-6-phosphate dehydrogenase and phosphoribosylpyrophosphate synthetase) and glycolysis (phosphoglucose isomerase). Filled circles, 13C atom. (e) Consumption (−) and production (+) flux contributing to the balance of whole-cell NADH. ALCD, alcohol dehydrogenase; NADHqx, NADH quinone oxidoreductase (Nde and Ndi in S.cerevisiae, Nde in I. orientalis); GAPD, glyceraldehyde-3-phosphate dehydrogenase; TCA, reactions in the TCA cycle. Fluxes are best estimate from genome-scale 13C MFA. (f) Growth impact of NADH dehydrogenase deletions in S. cerevisiae and I. orientalis. Data for S. cerevisiae is from a previous study1; data for I. orientalis is determined in this study and shows mean ± s.d. from single exponential fitting of the growth curve (n = 4 time points). p value from two-sided student t test without adjustment. (g) Glucose uptake relative to its use to make ethanol or biomass. Fluxes are from the genome-scale flux analysis, with fluxes consuming pyruvate (for example ethanol production) counted as 3 carbon atoms. (h) Fraction of flux through 11 central metabolites partitioned in biosynthesis (not including the flux to another central metabolite).

Extended Data Fig. 2 Metabolic measurement for T cell metabolic flux analysis.

(a) Overview of 13C metabolic flux analysis in mouse T cells. Primary CD8+T cells were purified from murine spleen, and kept in IL7 to remain in naïve state or activated by αCD3 and αCD28 in the presence of IL2 for 24hrs. Marker expression (CD69-FITC, CD25-APC) was evaluated by flow cytometry. (b) Isotope enrichment in central metabolites with [U-13C6]glucose or [U-13C5]glutamine tracing. 13C enrichment shows the average 13C labeling per carbon atom. Mean ± s.e.m., n = 3. (c) Media nutrient exchange flux. Positive and negative values indicate uptake and excretion, respectively. Numbers show fold change between naïve and activated T cells, with negative values reflecting change in flux direction. Mean ± s.e.m., n = 12 (O2), n = 4 (others). (d) Oxygen consumption rate (OCR) were measured with Mito Stress test through the sequential addition of pyruvate (pyr, 1mM), oligomycin (oligo, 5uM), fluoro-carbonyl cyanide phenylhydrazone (FCCP, 1uM), and rotenone/ antimycin A (Rot/AA, 2 uM). Mean ± s.e.m., n = 12. (e) Biomass (DNA, protein, and RNA) renewal flux is the product of mass composition and fraction renewed measured by 13C enrichment in the biomass hydrolysate normalized to monomer in soluble metabolites. Mean ± s.e.m., n = 6. Flux, mean ± s.d. error propagated from mass and fraction renewed. (f) Fold change of metabolic fluxes (from 13C MFA) between activated and naïve T cells. (g) Flux balance of NADH and TCA four-carbon metabolites (TCA C4). LDH, lactate dehydrogenase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GLUN, glutaminase; ME, malic enzyme; PC, pyruvate carboxylase; Glu, glutamate. (h) Isotopomer ratio (from LC-MS) reveals flux ratio (best estimate from 13C MFA) between malic enzyme (ME) and glycolysis. M+3 pyruvate (Pyr) is produced by ME from M+4 malate (Mal), whereas M+0 Pyr is produced from glycolysis. Note that malic enzyme flux increases in activated T cells despite a reduced isotopomer ratio. Mean ± s.e.m., n = 3.

Extended Data Fig. 3 Quantitative proteomics in yeasts and T cells.

(a) Comparison between the S. cerevisiae proteomics generated in this study and in other studies2,3. Data show mass fraction of functional sectors. Median ± s.e.m., p value from two-sided student t test between literature data and our data without adjustment, n = 19 (literature data); n = 4 (biological replicates, this study); *, p < 0.05, n.s., p > 0.05. Pearson’s correlation R = 0.88 (p = 1E-5) between our proteome allocation and the median of reference (n = 14 sectors). (b) Protein abundance of yeasts. Total protein mass and breakdown in each functional sectors (left) and allocation to individual reactions (right). Fold change (FC) from I. orientalis to S. cerevisiae is shown on the bottom. Enzyme abundance (sum of isozymes, if any) of individual reactions in glycolysis, TCA, and OXPHOS pathways. Mean ± s.e.m., n = 4. Two-sided student t test with Bonferroni FDR correction, n.s., p > 0.05; *, p < 0.05; **, p < 0.005; ***, p < 0.0005. (c) Protein abundance of T cells. Total protein mass and breakdown in each functional sectors (left) and allocation to individual reactions (right). Fold change (FC) from naive to activated is shown on the bottom. Enzyme abundance (sum of isozymes, if any) of individual reactions in glycolysis, TCA, and OXPHOS pathways. Fold change of individual genes is also shown. Mean ± SEM, n = 3. Two-sided student t test with Bonferroni FDR correction, n.s., p > 0.05; ***, p < 0.0002.

Extended Data Fig. 4 Proteome efficiency with flux-partitioning or mitochondrial proteins.

(a) Repartitioning of glycolytic and respiratory proteomes in proportion to flux distribution to biomass, fermentation, and respiration (left), and the resultant proteome efficiency (right). Data from batch cultured yeasts, mean ± s.e.m., error propagated from 13C metabolic flux analysis and proteomics (n = 4). (b) Flux-partitioned proteome efficiency of respiration in naïve and activated T cells given by (JATP,resp+Cglyc→resp∙JATP,glyc)/(Mresp + Cglyc→resp∙Mglyc), where (Cglyc→resp) is the fraction of glycolytic flux that ends up in respiration, \({J}_{{ATP}}\) is the ATP flux and \(M\) is the total mass of proteins in each pathway. Mean ± s.d., error propagated from 13C metabolic flux analysis and proteomics (n = 3). (c) Proteome efficiency accounting for all mitochondrial proteins in respiration in yeasts. Data from batch cultured yeasts, mean ± s.d., error propagated from 13C metabolic flux analysis and proteomics (n = 4). (d) Proteome efficiency accounting for all mitochondrial proteins in respiration in T cells. Data from T cells, mean ± s.e.m., error propagated from 13C metabolic flux analysis and proteomics (n = 3).

Extended Data Fig. 5 Metabolic fluxes of yeasts across different growth conditions.

(a) Dependence of metabolic flux and enzyme concentration on growth rate. Flux and enzyme concentration are normalized to maximum across nutrient conditions. For each reaction, a linear regression is done for flux versus growth rate. % variance explained is calculated, and averaged across all reactions. 53% of flux variation in S. cerevisiae and 71% in I. orientalis can be explained by growth rate alone. 21% of enzyme variation in S. cerevisiae and 23% in I. orientalis can be explained by growth rate alone. (b) Central carbon fluxes of yeasts across nutrient conditions. Flux (j) through TCA and glycolysis, and PPP are represented by flux through citrate synthase (CS), pyruvate kinase (PYK), and glucose-6-phosphate dehydrogenase (G6PD), respectively, and normalized to growth rate (µ). Limiting nutrient for chemostat, C, carbon; N, nitrogen; P, phosphorus; B, none.

Extended Data Fig. 6 Proteomics of mouse tissues and tumors and flux-partitioned proteome efficiency.

(a) Total protein mass and mass of each functional sectors in pancreas and k-Ras-driven pancreatic ductal adenocarcinoma (PDAC) (GEMM, genetically engineered mouse model; flank, flank implanted). Mean ± s.e.m., n = 3. Two-sided student t test between tumor and healthy without adjustment, n.s., p > 0.05; *, p < 0.05; **, p < 0.005; ***, p < 0.0005. Fold changes (FC) are shown on the bottom of each graph. (b) Mass fraction of proteome sectors as in (a). (c) Differential protein expression between healthy pancreas and PDAC. Top, glycolytic and respiratory proteins (fold change between PDAC and healthy pancreas), mean, n = 3. Bottom, hypoxia-inducible factor 1α (Hif1α), individual replicates and boxplot (median with quartiles). (d) Total protein mass and mass of each functional sectors in spleen and spleen infiltrated with Notch1-driven leukemia (leukemic spleen). Mean ± s.e.m., n = 3. Two-sided student t test between tumor and healthy without adjustment, n.s., p > 0.05; *, p < 0.05; **, p < 0.005; ***, p < 0.0005. Fold changes (FC) are shown on the bottom of each graph. (e) Mass fraction of proteome sectors as in (d). (f) Differential expression of glycolytic and respiratory proteins between healthy and leukemic spleen (fold change between leukemic and healthy spleen), mean, n = 3. (g) Flux partitioned proteome efficiency considering flux contribution from glycolysis to respiration (Cglyc->resp), based on data in Fig. 4. For NCI60 cancer cells, Cglyc->resp is quantified as the ratio between mitochondrial pyruvate carrier flux and glucose uptake flux. For mouse tissues and tumors, Cglyc->resp is the flux ratio between glucose oxidation (estimated as 40% of TCA cycle) and glycolysis.

Extended Data Fig. 7 Metabolism of evolutionarily divergent budding yeasts.

(a) Growth rates (top left) and glucose consumption rates (bottom left) and their relation (right) of 13 yeasts cultured in minimal YNB media containing 20g/L glucose. 14 budding yeasts with top growth rates in YPD were selected, with C. petersonii not able to grow in YNB. Mean ± s.e. (error from regression), n = 3 time points for n = 2 or 4 biological replicates. (b) Ethanol production rate (left) and its relation with glucose consumption rate (right) of the top 16 fast-growing yeasts in YPD containing 20g/L glucose. Mean ± s.e. (error from regression), n = 3 time points for n = 2 biological replicates.

Extended Data Fig. 8 Alternative explanations for aerobic glycolysis.

(a) Fold change in metabolite abundance between I. orientalis and S. cerevisiae cultured in glucose YNB. Inset shows the ratio between NAD+ and NADH. Metabolite abundance was measured by LC-MS, mean, n = 6, p value from student t test with Bonferroni adjustment for multiple comparisons. Inset, mean ± s.e.m. (b) Reaction Gibbs energy under physiological concentrations (top), total Gibbs energy dissipation rate (in J/mol) for ATP synthesis and hydrolysis in I. orientalis and two strains of S. cerevisiae (bottom left), and dissipation per ATP production as a function of ATP:oxygen (PO) ratio (bottom right). In the equations for dissipation per ATP, 2 reflects 2 ATP made per glycolysis, 12 reflects 12 pairs high-energy electrons made per glucose by respiration, and 12 ∙ PO is ATP yield per glucose by respiration. Shaded areas show experimentally obtained PO ratio (95% interval, see ‘Assessment of PO ratio’ in Ext. Data Note) for S. cerevisiae and I. orientalis. Mean ± s.d. propagated from error of flux measurement and flux analysis.

Extended Data Fig. 9 Fitness and proteome allocation of yeasts in aerobic and anaerobic conditions.

(a) Dissolved oxygen measured at the bottom of a multi-well plate culture with OxoPlate (phosphorescent oxygen sensor). Fresh exponential culture was added to the plate at indicated density and allowed to adapt for 15min. Oxygen concentration was then measured with or without active shaking. The typical maximal cell density (OD) from fully aerated culture is about 4. (b) Competitive fitness of lab adapted I. orientalis mutants, ΔPdc (null mutant for pyruvate decarboxylase, essential for ethanol fermentation) and ΔNde (null mutant for cytosol-facing NADH dehydrogenase, which feeds into the electron transport chain). 3 colonies of mutants were adapted for 14 days before competitive coculture. Relative fitness, mean ± s.e.m., n = 8. (c) A coarse-grained model where yeast growth is constrained by flux balance, energy (ATP) limitation, and proteome allocation. G, glycolysis; R, respiration; T, translation. For explanation of parameters and the model, see Ext. Data Note. (d) Maximal aerobic and anaerobic growth rate (µ) under different glycolytic (fG) and respiratory protein abundance (fR) (mass fractions of whole cell dry weight). Stars, optimal proteome allocation in aerobic and anaerobic conditions. Circles and triangles indicate measured proteome fractions in glucose-fed batch cultures of I. orientalis (aerobic, +O2; or anaerobic, -O2) and S. cerevisiae (aerobic, +O2). (e) Experimental glucose consumption (JGLC) and ethanol excretion (JETOH) rates (symbols, in mmol/h/gDW) and prediction from proteome-constrained model (lines) under high (S. cerevisiae) or low (I. orientalis) glycolytic proteome capacity (rG). Literature data was obtained from Van Hoek 19985. (f) Growth rate of wild type (WT) and adapted ΔNde mutant I. orientalis in aerated and settled culture. Data show WT and three colonies of ΔNde mutant picked after a 14-day adaptation. Media is YPD with 20g/L glucose. Mean ± s.e.m., n = 3 (aerated) n = 4 (settled).

Supplementary information

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Supplementary Fig. 1 and Supplementary Note.

Reporting Summary

Supplementary Table 1–19

Raw data for metabolic flux data, proteomics data, and so on.

Supplementary Data 1

Metabolic flux and Escher maps for visualizing data on escher.github.io.

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Shen, Y., Dinh, H.V., Cruz, E.R. et al. Mitochondrial ATP generation is more proteome efficient than glycolysis. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01571-y

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