Near-equilibrium glycolysis supports metabolic homeostasis and energy yield

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Abstract

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: https://github.com/jopark/GibbsIT

Code availability

The code for metabolic flux and free energy analysis is available on the GitHub public repository: https://github.com/jopark/GibbsIT

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Acknowledgements

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.

Author information

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.

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

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Supplementary Figures 1–15, Supplementary Tables 1–13 and Supplementary Notes.

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