Tissues derive ATP from two pathways—glycolysis and the tricarboxylic acid (TCA) cycle coupled to the electron transport chain. Most energy in mammals is produced via TCA metabolism1. In tumours, however, the absolute rates of these pathways remain unclear. Here we optimize tracer infusion approaches to measure the rates of glycolysis and the TCA cycle in healthy mouse tissues, Kras-mutant solid tumours, metastases and leukaemia. Then, given the rates of these two pathways, we calculate total ATP synthesis rates. We find that TCA cycle flux is suppressed in all five primary solid tumour models examined and is increased in lung metastases of breast cancer relative to primary orthotopic tumours. As expected, glycolysis flux is increased in tumours compared with healthy tissues (the Warburg effect2,3), but this increase is insufficient to compensate for low TCA flux in terms of ATP production. Thus, instead of being hypermetabolic, as commonly assumed, solid tumours generally produce ATP at a slower than normal rate. In mouse pancreatic cancer, this is accommodated by the downregulation of protein synthesis, one of this tissue’s major energy costs. We propose that, as solid tumours develop, cancer cells shed energetically expensive tissue-specific functions, enabling uncontrolled growth despite a limited ability to produce ATP.
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All analysed data (including MS carbon-13-labelling timepoints and metabolite concentrations) used in this study are available in Supplementary Tables 1–12 and the source data. Source data are provided with this paper.
Code to calculate tissue TCA cycle fluxes is available at GitHub (https://github.com/weilandtd/tca_fluxes) and Zenodo (https://zenodo.org/record/7328216#.Y3VQsezMI6B).
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This work was funded by NIH R01CA163591 to J.D.R.; NIH DP1DK113643 to J.D.R.; Ludwig Cancer Research funding to J.D.R. and Y.K.; Stand Up to Cancer SU2CAACR-DT-20-16 to J.D.R.; American Cancer Society funding to Y.K.; NIH grants 35GM128813 and P30CA072720 to M.W.; NIH CA237347-01A1 and American Cancer Society 134036-RSG-19-165-01-TBG to J.Y.G.; NIH R01CA236936 and Leukemia and Lymphoma Society Scholar Award Scholar Award 1386-23 to D.H.; NIH R50CA211437 to W.L.; R35CA242379 to M.G.V.H.; Allen Foundation and Stand Up to Cancer funding to S.D.; Damon Runyon Foundation/Mark Foundation Postdoctoral Fellowship and K99CA273517 to C.R.B.; New Jersey Commission on Cancer Research grant COCR22PDF002 to V.d.S.-D.; the Charles H. Revson Senior Fellowship in Biomedical Science to Y.H.; NIH F32DK127843 to W.D.L.; New Jersey Commission on Cancer Research postdoctoral fellowship to A.R.; and T32GM007388 to C.S.R.J.
J.D.R. is an advisor and stockholder in Colorado Research Partners, L.E.A.F. Pharmaceuticals, Bantam Pharmaceuticals, Barer Institute and Rafael Pharmaceuticals; a paid consultant of Pfizer and Third Rock Ventures; a founder, director and stockholder of Farber Partners, Serien Therapeutics and Sofro Pharmaceuticals; a founder and stockholder in Empress Therapeutics; and a director of the Princeton University–PKU Shenzhen collaboration. M.G.V.H. is a scientific advisor for Agios Pharmaceuticals, iTeos Therapeutics, Sage Therapeutics, Drioa Ventures and Auron Therapeutics. Y.K. is a co-founder and chair of scientific advisory board of Firebrand Therapeutics and KayoThera.
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Extended data figures and tables
Extended Data Fig. 1 Kinetic primed [U-13C] lactate infusion.
(a) Rate constant of m+3 tissue lactate or m+2 tissue glutamate labelling from [U-13C] lactate primed infusion, calculated by fitting an exponential curve, bars show mean +/− standard deviation. For accurate TCA flux measurement, tissue lactate labelling (blue bars) must be faster than TCA labelling (red bars), lactate primed infusion timepoints in n = 12 mice per tissue (except n = 19 for intestine and n = 17 for soleus). (b) M+3 tissue lactate and m+2 tissue glutamate labelling in U-13C] lactate primed infusion, n = 12 mice each. (c) Arterial blood m+3 lactate enrichment in primed [U-13C] lactate infusion reaches steady state in around 90 s, while blood m+3 lactate enrichment in [U-13C] lactate infusion without a priming bolus takes around 20 min to reach steady state (primed: n = 8 mice, 4,4,5,5,6,6,6,and 6 blood timepoints each; unprimed: n = 2 mice, 6 blood timepoints each). (d) [U-13C] lactate primed infusion does not alter any arterial blood metabolite levels more than 1.5x, n = 3 mice (7,7, and 6 timepoints measured per mouse respectively). (e) M+2 and m+1 labelling of glutamate, malate, succinate, and aspartate from [U-13C] lactate primed infusion in colon and gastrocnemius muscle (not normalized). Curves are model fits, n = 12 timepoints for each tissue, each timepoint for one tissue type represents a tissue from a different mouse.
Extended Data Fig. 2 Five-minute tissue harvesting delay after primed [U-13C] lactate infusion does not greatly alter TCA metabolite labelling or concentration.
(a) TCA metabolite m+2 and m+1 labelling from primed [U-13C] lactate infusion in tissues harvested 0 or 5 min after euthanasia. (b) Tissue lactate m+3 labelling from same experiment as in (a). (c) Tissue TCA metabolite concentrations from the experiment in (a). (d) The mean m+2 and m+1 labelling of four TCA metabolites (glutamate, malate, succinate, aspartate) from the experiment in (a), each point represents the mean m+2 or m+1 labelling of four metabolites for one tissue from one mous e. (e) The sum of glutamate, malate, succinate, aspartate, citrate/isocitrate, and α-ketoglutarate concentrations from the experiment in (a), each point represents the sum of metabolite concentrations for one tissue from one mouse. All bar graphs show mean values, all t-tests are unpaired two-tailed t-tests; in all graphs, n = 3 mice for 0 min liver, 0 min lung, and 5 min quad; n = 4 for 0 min quad, 5 min liver, 5 min lung.
Extended Data Fig. 3 Non-stationary metabolic flux analysis (NMFA) model calculates in vivo tissue TCA fluxes.
(a) Schematic of model structure for calculating TCA flux, indicating the concentrations and labelling timepoints used as input data. (b) Tissue TCA metabolite concentrations fitted by NMFA model vs. measured experimentally, n = 4 mice per measurement except n = 3 for diaphragm, each point is one metabolite from one healthy tissue. (c) Labelling of m+3 lactate or m+5 glutamine in arterial blood during [U-13C] lactate or glutamine primed infusions respectively, used as input data for TCA flux model, n = 3 mice with 8 blood timepoints each for lactate, n = 2 mice with 6 timepoints each for glutamine. (d) Including collisions of multiple labelled metabolites in the model (‘full model’) does not alter calculated TCA fluxes compared to model without including such collisions (‘reduced model’) (see Methods and Supplementary Note for description of models). (e) Calculated pyruvate carboxylase flux as a fraction of total TCA flux (citrate synthase flux). (f) Omitting some late [U-13C] lactate timepoints lowers (i.e. improves) Bayesian information criterion for certain tissues. Error bars show mean +/− standard deviation, p-values from two-tailed t tests.
Extended Data Fig. 4 Healthy tissue TCA fluxes.
(a) Oxygen consumption calculated from TCA fluxes measured in this study correlates well to literature tissue slice oxygen consumption data (Martin & Fuhrmann, Phys Zool 1955), except for diaphragm and heart; heart correlates better to ex vivo beating heart oxygen consumption data from Boudina et al., Circulation 2005 (n = 12 mice per tissue for [U-13C] lactate infusion timepoints except n = 19 for brown adipose, n = 19 for intestine, n = 17 for soleus; n = 4 mice to measure tissue metabolite concentrations except n = 3 for diaphragm). (b) TCA fluxes calculated by non-stationary metabolic flux analysis (NMFA) model versus multiplying m+2 TCA metabolite labelling speed with summed TCA metabolite concentration are similar, except for heart, soleus, diaphragm, and brown adipose (n = 12 mice per tissue for [U-13C] lactate infusion timepoints except n = 19 for brown adipose, n = 19 for intestine, n = 17 for soleus; n = 4 mice to measure tissue metabolite concentrations except n = 3 for diaphragm). (c) TCA fluxes calculated using NMFA model from [U-13C] lactate infusion data or [U-13C] glutamine infusion data are similar (n = 12 mice per tissue for lactate primed infusion timepoints, n = 10 mice per tissue for glutamine primed infusion timepoints, n = 4 mice per tissue to measure tissue metabolite concentrations). (d) Mouse tissues as a percent of body mass (Brown et al., Toxicol Ind Health 1997). (e) Relative TCA flux in kidney cortex and medulla, measured using glutamate m+2 abundance after 1.5 min [U-13C] lactate primed infusion using imaging mass spectrometry, n = 2 mice, each point is a measurement in a distinct region in one of the images (showing 3 points per image per region). Error bars show median +/− standard deviation.
Extended Data Fig. 5 Kinetic 2-deoxyglucose infusion.
(a) The m/z of [1-13C] 2-deoxyglucose-phosphate has lower background signal in uninfused tissues compared to the m/z of unlabelled carbon-12 2-deoxyglucose-phosphate, n = 1 mouse shown, representative of n = 4 mice. (b) Arterial blood glucose concentration, n = 6 mice, error bar shows mean +/− standard deviation. (c) Brown adipose [1-13C] 2-deoxyglucose-phosphate concentration saturates after 10 min of [1-13C] 2-deoxyglucose infusion; therefore later timepoints were discarded in calculating glucose usage flux, n = 13 mice. (d) Waiting ten minutes after euthanasia to harvest tissues does not alter 2-deoxyglucose-phosphate concentrations after 10 min [1-13C] 2-deoxyglucose infusion, n = 4 mice per tissue per collection time, each point represents a pair of tissues from a single mouse (x and y axis values). (e) Colon and not liver (or other tissues) has a substantial background peak at the m/z of [1-13C] 2-deoxyglucose-phosphate (n = 1 mouse shown, representative of n = 13 mice); to measure glucose usage in this tissue, [U-13C] 2-deoxyglucose infusion was used. (f) [1-13C] or [U-13C] 2-deoxyglucose-phosphate concentrations in tissues at different timepoints after start of infusion (all infused with [1-13C] 2-deoxyglucose, except [U-13C] 2-deoxyglucose for colon); slope of line is the glucose usage flux, R2 calculated from the datapoints, requiring a y-intercept of 0 (n = 4 mice for colon and blood; n = 9 mice for gastrocnemius, intestine, lung, pancreas, skin, spleen, white adipose; n = 12 mice for kidney, liver, quad; n = 13 for brain, brown adipose, diaphragm, heart, soleus).
Extended Data Fig. 6 Calculated tissue lactate consumption and production.
(a) Non-normalized labelling of m+2 glutamate from primed [U-13C] lactate infusion after 90 min (n = 4 mice for each tissue except n = 6 for brown adipose and intestine) or 150 min (n = 2 mice for each tissue). Mean labelling from 90 min was used as pseudo-steady state to normalize labelling in figures, and to calculate tissue lactate consumption from TCA flux values. (b) Production and consumption of lactate by tissues, calculated from glucose usage flux and from TCA flux multiplied by lactate contribution to TCA cycle respectively, showing both y = x line (equal measured lactate production and consumption) and y = 0.5x line (equal production and consumption of lactate if glucose use rate is truly twice as high as we measured). (c) Tissues producing and consuming lactate in the whole body, considering mass of each mouse tissue as a fraction of the whole body. (d) Tissues producing and consuming lactate calculated as in (c) but if true glucose usage is twice as high as we measured. For (b–d), glucose usage fluxes were measured with n = 4–13 mice, TCA fluxes were measured with n = 12 mice to measure kinetic infusion timepoints and n = 4 mice to measure TCA metabolite concentrations (except n = 3 for diaphragm).
Extended Data Fig. 7 TCA flux measurement in tumours by kinetic [U-13C] lactate infusion.
(a) Growth of flank PDAC tumours, n = 7 mice per timepoint, error bars show mean +/− standard deviation. (b) Hematoxylin and eosin staining of GEMM PDAC tumours showing tumours are viable and not necrotic, showing n = 2 mice, representative of n = 5 mice. (c) Timepoints of m+3 lactate and m+2 glutamate labelling in flank PDAC from primed [U-13C] lactate infusion, n = 13 mice. (d) Rate constant of glutamate m+2 labelling compared to lactate m+3 labelling in tumours from [U-13C] lactate primed infusion suggests that lactate entry does not gate TCA turning (n = 6 mice for GEMM PDAC, 13 for flank PDAC, 9 for GEMM NSCLC, 10 for flank NSCLC, 7 for xenograft CRC). (e) M+2 and m+1 labelling of TCA metabolites from [U-13C] lactate primed infusion (points) and model fits (lines) in flank PDAC tumours, n = 13 mice. (f) Model fit versus measured metabolite concentrations in tumours, each point is a metabolite from one tumour type, measured in n = 4 mice. (g) TCA fluxes of pancreatic tumour models calculated from [U-13C] lactate or [U-13C] glutamine primed infusion are similar (for lactate primed infusion, n = 6 mice for GEMM PDAC, n = 13 mice for flank PDAC; for glutamine primed infusion, n = 7 mice for GEMM PDAC, n = 10 mice for flank PDAC, n = 4 mice per tumour type to measure tumour metabolite concentrations). Error bars show medians+/− standard deviation.
Extended Data Fig. 8 Tumour models used for TCA and glucose usage flux measurement.
(a) Weights of lungs from GEMM NSCLC mice compared to littermate controls, n = 5 mice for control lung and n = 4 mice for GEMM NSCLC. (b) Hematoxylin and eosin staining from GEMM NSCLC tumours showing tumours are viable and not necrotic, n = 2 mice. (c) Volume of xenograft CRC tumours, n = 8 mice at day 10 and n = 7 mice at day 14. (d) Percent of leukaemic cells in leukaemic spleens after red blood cell lysis, n = 6 mice. All error bars show mean +/− standard deviation.
Extended Data Fig. 9 TCA and glucose use flux measurement in primary breast cancer xenograft tumours and their spontaneous lung metastases.
(a) Volume of breast cancer xenograft primary tumours (n = 12 mice MDA-MB-231-LM2, n = 10 mice Sum159-M1a). (b) Weight of mouse lungs, either in non-tumour-bearing mice or mice with metastases seeded from primary breast cancer xenograft tumours (n = 6 mice no tumour, n = 11 mice Sum159-M1a, n = 2 mice MDA-MB-231-LM2). (c) Luciferase imaging showing metastases in lungs of mice with primary breast cancer xenograft tumours, n = 1 mouse per tumour type shown. (d) [1-13C] 2-deoxyglucose-phosphate concentration after 15 min [1-13C] 2-deoxyglucose infusion in primary breast xenograft tumours, and lungs with metastases from the same mice (n = 2 mice for MDA-MB-231-LM2 tumour and lung, n = 3 for Sum159-M1a tumour and lung). (e) Glucose use fluxes of primary tumour and lung from mice with MD-MBA-231-LM2 breast xenograft tumours, n = 2 mice per tissue. (f) Glucose use fluxes of primary tumour and lung from mice with Sum159-M1A breast xenograft tumours, n = 3 mice per tissue, p-value from two-tailed t test. (g) TCA metabolite m+2 and m+1 labelling from [U-13C] lactate primed infusion (points) and model fits (lines) in primary tumours and lungs of mice with MDA-MB-231-LM2 tumours, n = 6 mice per tissue. (h) Hematoxylin and eosin staining of lung from mouse with MDA-MB-231-LM2 primary tumour; arrows indicate metastatic nodules. (i) Glutamate m+2 labelling intensity after 10 min [U-13C] lactate primed infusion, same lung as in (h), arrows indicate metastatic nodules. (j) Glutamate m+2 labelling intensity from healthy and metastatic regions of lungs with MDA-MB-LM2 metastases, n = 3 mice, paired t-test. (k) Protein abundance of detected human mitochondrial oxidative-phosphorylation proteins in MDA-MB-231-LM2 primary tumours and lungs bearing metastases from the same mice, all values normalized to the median value of that protein in the primary tumour, n = 7 per sample type. All error bars show mean +/− standard deviation.
Extended Data Fig. 10 ATP usage in tumours versus healthy tissues, and experimental sample sizes.
(a) Total ATP synthesis flux calculated from glucose use flux and TCA flux is similar to the predicted cost of protein ATP synthesis calculated from protein synthesis rates measured using [U-13C] valine infusion in pancreas and pancreatic tumours; n = 13 mice for pancreas, n = 3 for GEMM PDAC, n = 4 for flank PDAC; bars shown mean +/− standard deviation. (b) Adjusted p values of two most significantly enriched KEGG pathways for the genes downregulated in human tumours compared to the corresponding healthy tissue; n = 167 patients for healthy pancreas, n = 178 pancreatic tumour, n = 140 for healthy kidney, n = 884 for kidney tumour, n = 50 for healthy liver, n = 360 for liver tumour; data from UCSC Xena database. (c) Experimental replicates for all experiments shown in this study. Each replicate is a separate mouse tissue. All healthy tissues are from non-tumour bearing mice.
TCA flux inference by non-stationary metabolic flux analysis.
Supplementary Tables 1–12.
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Bartman, C.R., Weilandt, D.R., Shen, Y. et al. Slow TCA flux and ATP production in primary solid tumours but not metastases. Nature 614, 349–357 (2023). https://doi.org/10.1038/s41586-022-05661-6
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