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Circulating metabolite homeostasis achieved through mass action

Nature Metabolism volume 4, pages 141–152 (2022)Cite this article

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Abstract

Homeostasis maintains serum metabolites within physiological ranges. For glucose, this requires insulin, which suppresses glucose production while accelerating its consumption. For other circulating metabolites, a comparable master regulator has yet to be discovered. Here we show that, in mice, many circulating metabolites are cleared via the tricarboxylic acid cycle (TCA) cycle in linear proportionality to their circulating concentration. Abundant circulating metabolites (essential amino acids, serine, alanine, citrate, 3-hydroxybutyrate) were administered intravenously in perturbative amounts and their fluxes were measured using isotope labelling. The increased circulating concentrations induced by the perturbative infusions hardly altered production fluxes while linearly enhancing consumption fluxes and TCA contributions. The same mass action relationship between concentration and consumption flux largely held across feeding, fasting and high- and low-protein diets, with amino acid homeostasis during fasting further supported by enhanced endogenous protein catabolism. Thus, despite the copious regulatory machinery in mammals, circulating metabolite homeostasis is achieved substantially through mass action-driven oxidation.

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Fig. 1: Serum concentration increases linearly with metabolite infusion rate.
Fig. 2: Metabolic consumption flux is linearly proportional to circulating concentration.
Fig. 3: Only for glucose, unlabelled levels fall on perturbative 13C-labelled infusion, indicating active homeostatic regulation.
Fig. 4: The same mass action relationship largely holds across fasting and feeding.
Fig. 5: TCA oxidation mediates mass action consumption.
Fig. 6: Mass action-driven oxidation clears amino acid influx from food.
Fig. 7: Mass action explains amino acid homeostasis across high- and low-protein diets.

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Data availability

All data and materials will be provided on reasonable request to the lead corresponding author (joshr@princeton.edu). Source data are provided with this paper.

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Acknowledgements

S.H. was supported by a National Institutes of Health (NIH) grant (no. 4R00DK117066). T.G.A. was supported by NIH grant no. DK109714 and a U.S. Department of Agriculture National Institute of Food and Agriculture grant no. NC1184-NJ14240. C.J. was supported by NIH grant no. 1R01AA02912. This work was supported by the NIH Pioneer (no. 1DP1DK113643) and Paul G. Allen Family Foundation grants (no. 0034665) and Ludwig Cancer Research.

Author information

Authors and Affiliations

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

    Xiaoxuan Li & Joshua D. Rabinowitz

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

    Sheng Hui, Won Dong Lee, Cholsoon Jang & Joshua D. Rabinowitz

  3. Department of Molecular Metabolism, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Sheng Hui

  4. Department of Nutritional Sciences, Rutgers University, New Brunswick, NJ, USA

    Emily T. Mirek, William O. Jonsson & Tracy G. Anthony

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

    Won Dong Lee, Xianfeng Zeng & Joshua D. Rabinowitz

  6. Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA

    Cholsoon Jang

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

    Joshua D. Rabinowitz

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Contributions

X.L., S.H., C.J. and J.D.R. designed the study. X.L. performed most of the experiments and data analysis. E.T.M. and T.G.A. contributed to the breeding and BCAA infusions on BCKDK knockout mice. W.O.J. performed the comprehensive laboratory animal monitoring system study on the BCKDK knockout mice. W.D.L. contributed to the hyperinsulinaemic–euglycaemic clamp study. X.Z. provided the portal vein data for the amino acids. X.L., C.J. and J.D.R. wrote the manuscript. All authors discussed and commented on the manuscript.

Corresponding authors

Correspondence to Cholsoon Jang or Joshua D. Rabinowitz.

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Competing interests

J.D.R. is a cofounder and stockholder in Toran and Serien Therapeutics and advisor to and stockholder in Agios Pharmaceuticals, Kadmon, Bantam Pharmaceutical, Colorado Research Partners, Rafael Pharmaceuticals, Barer Institute and L.E.A.F. Pharmaceuticals. The other authors declare no competing interests.

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Nature Metabolism thanks Adam Rose and the other, anonymous, reviewers for their contribution to the peer review of this work. Alfredo Gimenez-Cassina and George Caputa were the primary handling editors.

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Extended data

Extended Data Fig. 1 Perturbative infusion outcomes for different regulatory mechanisms.

Green arrows reflect labeled metabolite fluxes including the experimenter-controlled influx from infusion. Blue arrows reflect unlabeled metabolite fluxes, including endogenous production. Green and blue circles are labeled and unlabeled metabolites, respectively. Red circles represent red blood cells. (a) Mass action. Labeled metabolites accumulate linearly with infusion rate, with unlabeled metabolite concentrations and fluxes not altered. (b) Active consumption induction. Labeled metabolites accumulate less than linearly with infusion rate, with unlabeled metabolite levels decreased but fluxes unaltered. (c) Consumption saturation. Labeled metabolites accumulate more than linearly with infusion rate, with unlabeled metabolites levels increased but fluxes unaltered. (d) Feedback inhibition of production. Unlabeled metabolite levels and fluxes are decreased.

Extended Data Fig. 2 Clearance of metabolite boluses is consistent with mass action kinetics.

(a) Mice were fasted from 9 AM to 5 PM (8 h fasting). At 5 PM, mice were injected with an intravenous bolus of the indicated [U-13C] metabolite at a low, medium, or high dose (as specified in the methods). Blood was taken 5, 15, 30, and 60 min after the injection, and the concentration of labeled metabolite in serum was measured by LC-MS. For experiments involving branched-chain amino acids, all three were given together, with only the indicated amino acid in labeled form. Labeled metabolite concentration was plotted against time post bolus. Lines are exponential decay curves fitted with mean value of each group. (b) Pseudo-first-order consumption rate constants from bolus and perturbative infusions. The slopes (α) calculated from the infusion experiments were plotted against the elimination constants (γ) calculated from the bolus experiments for each circulating metabolite. Line is linear regression fit.

Source data

Extended Data Fig. 3 Elevated portal vein alanine in fed mice.

(a) Circulating metabolite concentrations in the fasted and refed state. Fasting and feeding schedules were the same as Fig. 4a. Blood was taken at 5 PM for the fasted group and 11 PM for the refed group. Mean±SD. n = 4 mice. (b, c) Insulin does not alter concentrations or consumption fluxes of valine, lysine, and alanine. (b) Serum metabolite levels from hyperinsulinemic-euglycemic clamp (2.5 mU/kg/min insulin) and control (saline) experiments. Mice were fasted from 10 AM to 5 PM. The clamp was performed from 3 PM to 5 PM and blood was collected at 5 PM. Mean±s.d. n = 4 mice. (c) Consumption fluxes in the above clamp condition based on non-perturbative infusion of a mixture of 13C-valine, 13C-lysine, and 13C-alanine. The 13C-infusion was initiated 2.5 h prior to starting insulin to induce the hyperinsulinemic clamp and continued throughout the clamp experiment. Blood samples were taken immediately prior to or 120-min after initiation of the clamp. Mean±s.d. n = 4 mice. (d) Metabolite concentration ratios between the portal vein and tail vein of fasted (7.5 h fast starting at 9:30 AM, with sampling at 5 PM) or ad lib fed mice (with sampling at 11 PM). Mean±s.d. n = 4 mice. (e) Calculation of alanine consumption flux-concentration relationship using tail or portal vein data. Portal vein concentrations were calculated by multiplying alanine data from Fig. 4a by the portal/tail vein TIC ratio from (d). Lines are linear fits to each dataset.

Source data

Extended Data Fig. 4 BCKDK whole-body KO mice are lethargic when fed low protein diet.

(a) Valine consumption flux versus circulating concentration in whole-body BCKDK knockout (KO) mice and littermate control mice (WT). Both groups were infused with [U-13C]valine as in Fig. 2. Lines are linear fits to each dataset. (b) KO and WT mice were fed either 20% or 5% protein diet for 7 days and blood was taken at 5 PM on the last day. Mean±SD. n = 5 WT and 4 KO mice. (c) Activity of KO and WT mice fed 5% protein diet as measured using metabolic chambers. Mean±SD. n = 6 mice.

Source data

Extended Data Fig. 5 TCA oxidation mediates mass action-driven consumption.

Tissue TCA labeling-concentration relationship for fasted perturbative infusions. Data are as in Fig. 5, except for (a) Use of succinate rather than malate to read out tissue TCA labeling or (b) Measurement of malate labeling across additional organs. Lines are linear fits to the data with intercept set to zero.

Source data

Extended Data Fig. 6 Protein synthesis rates are insensitive to branched-chain amino acid infusion.

(a) Calculation of protein synthesis rate. Mice were infused with [U-13C]valine as described in Fig. 6a (fasted group). Tissues were harvested and valine labeling fraction in hydrolyzed tissue proteins were plotted against time (left). Protein synthesis rate was then calculated based on the slopes (right) using the equation shown (below). Lines are linear fitting with mean values of each tissue. n = 2 mice per time point for each condition. (b) Tissue protein synthesis rates do not increase with infusion rate. n = 2 mice per time point for each condition. (c) Tissue protein synthesis rate does not change in response to perturbative valine infusion. Line is mean of the data as the slope is not significant. (d) Tissue protein labeling was consistent from different BCAAs. Mice were infused with [U-13C]valine, [U-13C]leucine, or [U-13C]isoleucine as in (a). n = 2 mice per time point for each condition.

Source data

Extended Data Fig. 7 Protein degradation rate does not change in response to perturbative valine infusion.

Data were from the experiments in Fig. 2 (valine panel). Line is mean of the data as the slope is not significant.

Source data

Extended Data Fig. 8 Raw data supporting the determination of protein synthesis rates after feeding.

Mice were infused with [U-13C]valine at the same condition as described in Fig. 6a (refed group). Lines are linear fitting with mean values of each tissue. n = 2 mice per timepoint.

Source data

Extended Data Fig. 9 Over 2 weeks, dietary protein fraction has little effect on body weight or food intake.

Mice were fed high- (HP), normal- (NP), and low-protein (LP) diets ad lib for 2 weeks. (a) Body weight gain. (b) Food intake. Mean ± SD, n = 6 mice.

Source data

Extended Data Fig. 10 TCA oxidation mediates mass action-driven valine consumption under high-, medium-, and low-protein diet.

Tissue malate labeling relative to serum valine labeling from non-perturbative infusion of [U-13C]valine, as in Fig. 7c. Lines are linear fits to the data with intercept set to zero.

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Li, X., Hui, S., Mirek, E.T. et al. Circulating metabolite homeostasis achieved through mass action. Nat Metab 4, 141–152 (2022). https://doi.org/10.1038/s42255-021-00517-1

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