Near-equilibrium glycolysis supports metabolic homeostasis and energy yield


Glycolysis plays a central role in producing ATP and biomass. Its control principles, however, remain incompletely understood. Here, we develop a method that combines 2H and 13C tracers to determine glycolytic thermodynamics. Using this method, we show that, in conditions and organisms with relatively slow fluxes, multiple steps in glycolysis are near to equilibrium, reflecting spare enzyme capacity. In Escherichia coli, nitrogen or phosphorus upshift rapidly increases the thermodynamic driving force, deploying the spare enzyme capacity to increase flux. Similarly, respiration inhibition in mammalian cells rapidly increases both glycolytic flux and the thermodynamic driving force. The thermodynamic shift allows flux to increase with only small metabolite concentration changes. Finally, we find that the cellulose-degrading anaerobe Clostridium cellulolyticum exhibits slow, near-equilibrium glycolysis due to the use of pyrophosphate rather than ATP for fructose-bisphosphate production, resulting in enhanced per-glucose ATP yield. Thus, near-equilibrium steps of glycolysis promote both rapid flux adaptation and energy efficiency.

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Fig. 1: Visualizing the extent of glycolysis reversibility using [5-2H1]glucose.
Fig. 2: Simultaneous 2H and 13C labeling reveals ∆G.
Fig. 3: Nitrogen upshift drives glycolysis forward.
Fig. 4: Phosphorus upshift drives glycolysis forward.
Fig. 5: Oligomycin enhances the forward driving force in glycolysis.
Fig. 6: Slow glycolysis of C. cellulolyticum operates near equilibrium using PPi-dependent PFK.

Data availability

Source data for Figs. 16 are provided in Supplementary Tables 113 and on the GitHub public repository:

Code availability

The code for metabolic flux and free energy analysis is available on the GitHub public repository:


  1. 1.

    Tanner, L. B. et al. Four key steps control glycolytic flux in mammalian cells. Cell Syst. 7, 49–62.e48 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Thermodynamics-based metabolic flux analysis. Biophys. J. 92, 1792–1805 (2007).

    CAS  Article  Google Scholar 

  3. 3.

    Fell, D. Understanding the Control of Metabolism (Portland Press, 1997).

  4. 4.

    Hackett, S. R. et al. Systems-level analysis of mechanisms regulating yeast metabolic flux. Science 354, aaf2786 (2016).

    Article  Google Scholar 

  5. 5.

    Flamholz, A., Noor, E., Bar-Even, A., Liebermeister, W. & Milo, R. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc. Natl Acad. Sci. USA 110, 10039–10044 (2013).

    CAS  Article  Google Scholar 

  6. 6.

    Dona, A. C. et al. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Computat. Struct. Biotechnol. J. 14, 135–153 (2016).

    CAS  Article  Google Scholar 

  7. 7.

    Bennett, B. D., Yuan, J., Kimball, E. H. & Rabinowitz, J. D. Absolute quantitation of intracellular metabolite concentrations by an isotope ratio-based approach. Nat. Protoc. 3, 1299–1311 (2008).

    CAS  Article  Google Scholar 

  8. 8.

    Lu, W. et al. Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer. Anal. Chem. 82, 3212–3221 (2010).

    CAS  Article  Google Scholar 

  9. 9.

    Katz, L. A., Swain, J. A., Portman, M. A. & Balaban, R. S. Intracellular pH and inorganic phosphate content of heart in vivo: a 31P-NMR study. Am. J. Physiol. 255, H189–H196 (1988).

    CAS  PubMed  Google Scholar 

  10. 10.

    Lu, W. et al. Metabolite measurement: pitfalls to avoid and practices to follow. Annu. Rev. Biochem. 86, 277–304 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Noor, E., Haraldsdóttir, H. S., Milo, R. & Fleming, R. M. T. Consistent estimation of Gibbs energy using component contributions. PLoS Comput. Biol. 9, e1003098 (2013).

    CAS  Article  Google Scholar 

  12. 12.

    Du, B. et al. Temperature-dependent estimation of gibbs energies using an updated group-contribution method. Biophys. J. 114, 2691–2702 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Park, J. O. et al. Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nat. Chem. Biol. 12, 482–489 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Beard, D. A. & Qian, H. Relationship between thermodynamic driving force and one-way fluxes in reversible processes. PLoS One 2, e144 (2007).

    Article  Google Scholar 

  15. 15.

    Harris, T. K., Abeygunawardana, C. & Mildvan, A. S. NMR studies of the role of hydrogen bonding in the mechanism of triosephosphate isomerase. Biochemistry 36, 14661–14675 (1997).

    CAS  Article  Google Scholar 

  16. 16.

    Poyner, R. R., Laughlin, L. T., Sowa, G. A. & Reed, G. H. Toward identification of acid/base catalysts in the active site of enolase: comparison of the properties of K345A, E168Q, and E211Q variants. Biochemistry 35, 1692–1699 (1996).

    CAS  Article  Google Scholar 

  17. 17.

    Xu, Y.-F., Lu, W. & Rabinowitz, J. D. Avoiding misannotation of in-source fragmentation products as cellular metabolites in liquid chromatography–mass spectrometry-based metabolomics. Anal. Chem. 87, 2273–2281 (2015).

    CAS  Article  Google Scholar 

  18. 18.

    Antoniewicz, M. R., Kelleher, J. K. & Stephanopoulos, G. Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions. Metab. Eng. 9, 68–86 (2007).

    CAS  Article  Google Scholar 

  19. 19.

    Bren, A. et al. Glucose becomes one of the worst carbon sources for E. coli on poor nitrogen sources due to suboptimal levels of cAMP. Sci. Rep. 6, 24834 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Doucette, C. D., Schwab, D. J., Wingreen, N. S. & Rabinowitz, J. D. α-Ketoglutarate coordinates carbon and nitrogen utilization via enzyme I inhibition. Nat. Chem. Biol. 7, 894–901 (2011).

    CAS  Article  Google Scholar 

  21. 21.

    Yuan, J. et al. Metabolomics-driven quantitative analysis of ammonia assimilation in E. coli. Mol. Syst. Biol. 5, 302–302 (2009).

    Article  Google Scholar 

  22. 22.

    Kustu, S., Hirschman, J., Burton, D., Jelesko, J. & Meeks, J. C. Covalent modification of bacterial glutamine-synthetase—physiological significance. Mol. Gen. Genet. 197, 309–317 (1984).

    CAS  Article  Google Scholar 

  23. 23.

    Ikeda, T. P., Shauger, A. E. & Kustu, S. Salmonella typhimurium apparently perceives external nitrogen limitation as internal glutamine limitation. J. Mol. Biol. 259, 589–607 (1996).

    CAS  Article  Google Scholar 

  24. 24.

    Xu, Y.-F., Amador-Noguez, D., Reaves, M. L., Feng, X.-J. & Rabinowitz, J. D. Ultrasensitive regulation of anapleurosis via allosteric activation of PEP carboxylase. Nat. Chem. Biol. 8, 562–568 (2012).

    CAS  Article  Google Scholar 

  25. 25.

    Pike Winer, L. S. & Wu, M. Rapid analysis of glycolytic and oxidative substrate flux of cancer cells in a microplate. PLoS One 9, e109916 (2014).

    Article  Google Scholar 

  26. 26.

    Desvaux, M. Clostridium cellulolyticum: model organism of mesophilic cellulolytic clostridia. FEMS Microbiol. Rev. 29, 741–764 (2005).

    CAS  Article  Google Scholar 

  27. 27.

    Zhou, J. L. et al. Atypical glycolysis in Clostridium thermocellum. Appl. Environ. Microbiol. 79, 3000–3008 (2013).

    CAS  Article  Google Scholar 

  28. 28.

    Chen, J. et al. Pyrophosphatase is essential for growth of Escherichia coli. J. Bacteriol. 172, 5686–5689 (1990).

    CAS  Article  Google Scholar 

  29. 29.

    Mertens, E. Pyrophosphate-dependent phosphofructokinase, an anaerobic glycolytic enzyme? FEBS Lett. 285, 1–5 (1991).

    CAS  Article  Google Scholar 

  30. 30.

    Beg, Q. K. et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl Acad. Sci. USA 104, 12663–12668 (2007).

    CAS  Article  Google Scholar 

  31. 31.

    Basan, M. et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528, 99–104 (2015).

    CAS  Article  Google Scholar 

  32. 32.

    Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. G. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010).

    CAS  Article  Google Scholar 

  33. 33.

    Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M. & Sauer, U. Multidimensional optimality of microbial metabolism. Science 336, 601–604 (2012).

    CAS  Article  Google Scholar 

  34. 34.

    Dekel, E. & Alon, U. Optimality and evolutionary tuning of the expression level of a protein. Nature 436, 588–592 (2005).

    CAS  Article  Google Scholar 

  35. 35.

    Tian, L. et al. Metabolome analysis reveals a role for glyceraldehyde 3-phosphate dehydrogenase in the inhibition of C. thermocellum by ethanol. Biotechnol. Biofuels 10, 276 (2017).

    Article  Google Scholar 

  36. 36.

    Heinrich, R. & Schuster, S. The Regulation of Cellular Systems (Chapman & Hall, 1996).

  37. 37.

    Hofmeyr, J. H. & Cornish-Bowden, A. Quantitative assessment of regulation in metabolic systems. Eur. J. Biochem. 200, 223–236 (1991).

    CAS  Article  Google Scholar 

  38. 38.

    Gutnick, D., Calvo, J. M., Klopotow., T. & Ames, B. N. Compounds which serve as sole source of carbon or nitrogen for Salmonella typhimurium LT-2. J. Bacteriol. 100, 215–219 (1969).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Bennett, B. D. et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5, 593–599 (2009).

    CAS  Article  Google Scholar 

  40. 40.

    Mathew, R., Degenhardt, K., Haramaty, L., Karp, C. M. & White, E. Immortalized mouse epithelial cell models to study the role of apoptosis in cancer. Methods Enzymol. 446, 77–106 (2008).

    CAS  Article  Google Scholar 

  41. 41.

    Pisithkul, T., Jacobson, T. B., O’Brien, T. J., Stevenson, D. M. & Amador-Noguez, D. Phenolic amides are potent inhibitors of de novo nucleotide. Appl. Environ. Microbiol. 81, 5761–5772 (2015).

    CAS  Article  Google Scholar 

  42. 42.

    Clasquin, M. F., Melamud, E. & Rabinowitz, J. D. LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr. Protoc. Bioinformatics 37, 14 11.1–14.11.23 (2012).

    Google Scholar 

  43. 43.

    Su, X., Lu, W. & Rabinowitz, J. D. Metabolite spectral accuracy on orbitraps. Anal. Chem. 89, 5940–5948 (2017).

    CAS  Article  Google Scholar 

  44. 44.

    Fan, J. et al. Glutamine-driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxia. Mol. Syst. Biol. 9, 712 (2013).

    CAS  Article  Google Scholar 

  45. 45.

    Carrieri, D. et al. Identification and quantification of water-soluble metabolites by cryoprobe-assisted nuclear magnetic resonance spectroscopy applied to microbial fermentation. Magn. Reson. Chem. 47, S138–S146 (2009).

    CAS  Article  Google Scholar 

  46. 46.

    Hwang, T. L. & Shaka, A. J. Water suppression that works. Excitation sculpting using arbitrary wave-forms and pulsed-field gradients. J. Magn. Reson. A 112, 275–279 (1995).

    CAS  Article  Google Scholar 

  47. 47.

    Antoniewicz, M. R., Kelleher, J. K. & Stephanopoulos, G. Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements. Metab. Eng. 8, 324–337 (2006).

    CAS  Article  Google Scholar 

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This work was supported by a Department of Energy (DOE) grant (no. DE-SC0012461 to J.D.R.), the Center for Advanced Bioenergy and Bioproducts Innovation (grant no. DE-SC0018420, subcontract to J.D.R.), the Center for Bioenergy Innovation (grant no. DE-AC05-00OR22725, subcontract to D.A.-N.) and ExxonMobil through its membership in the Princeton E-ffiliates Partnership of the Andlinger Center for Energy and the Environment. The Center for Advanced Bioenergy and Bioproducts Innovation and the Center for Bioenergy Innovation are both U.S. Department of Energy Bioenergy Research Centers supported by the Office of Biological and Environmental Research in the DOE Office of Science. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the U.S. Department of Energy.

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J.O.P., L.B.T., D.A.-N. and J.D.R. designed the study. J.O.P., D.B.K., T.B.J., S.H.-J.L. and M.B.H. carried out the E. coli experiments. J.O.P., L.B.T. and M.H.W. carried out the mammalian cell experiments. Z.Z., D.M.S. and D.A.-N. carried out the Clostridia experiments. J.O.P., M.H.W. and S.A.R. developed the computational tools for reaction flux, reversibility and Gibbs free energy quantitation. J.O.P., D.A.-N. and J.D.R. wrote the paper.

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Correspondence to Daniel Amador-Noguez or Joshua D. Rabinowitz.

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Park, J.O., Tanner, L.B., Wei, M.H. et al. Near-equilibrium glycolysis supports metabolic homeostasis and energy yield. Nat Chem Biol 15, 1001–1008 (2019).

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