Slow TCA flux and ATP production in primary solid tumours but not metastases

doi:10.1038/s41586-022-05661-6

. 2023 Feb;614(7947):349-357.

doi: 10.1038/s41586-022-05661-6. Epub 2023 Feb 1.

Slow TCA flux and ATP production in primary solid tumours but not metastases

Caroline R Bartman^{1

2

3}, Daniel R Weilandt^{1

2

3}, Yihui Shen^{1

2}, Won Dong Lee^{1

2}, Yujiao Han^{3

4}, Tara TeSlaa^{1

2

3

5}, Connor S R Jankowski^{1

2

3}, Laith Samarah^{1

2

3}, Noel R Park^{2

4}, Victoria da Silva-Diz⁶, Maya Aleksandrova⁶, Yetis Gultekin^{7

8}, Argit Marishta^{1

4}, Lin Wang^{1

2

3

9}, Lifeng Yang^{1

2

3

10}, Asael Roichman^{1

2

3}, Vrushank Bhatt⁶, Taijin Lan⁶, Zhixian Hu⁶, Xi Xing^{1

2

3}, Wenyun Lu^{1

2

3}, Shawn Davidson², Martin Wühr^{1

4}, Matthew G Vander Heiden^{7

8}, Daniel Herranz⁶, Jessie Yanxiang Guo⁶, Yibin Kang^{3

4}, Joshua D Rabinowitz^{11

12

13}

Affiliations

¹ Department of Chemistry, Princeton University, Princeton, NJ, USA.
² Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
³ Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA.
⁴ Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
⁵ Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA.
⁶ Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA.
⁷ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Boston, MA, USA.
⁸ Department of Biology, Massachusetts Institute of Technology, Boston, MA, USA.
⁹ Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
¹⁰ Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China.
¹¹ Department of Chemistry, Princeton University, Princeton, NJ, USA. joshr@princeton.edu.
¹² Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA. joshr@princeton.edu.
¹³ Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA. joshr@princeton.edu.

PMID: 36725930
PMCID: PMC10288502
DOI: 10.1038/s41586-022-05661-6

Slow TCA flux and ATP production in primary solid tumours but not metastases

Caroline R Bartman et al. Nature. 2023 Feb.

. 2023 Feb;614(7947):349-357.

doi: 10.1038/s41586-022-05661-6. Epub 2023 Feb 1.

Authors

Affiliations

¹ Department of Chemistry, Princeton University, Princeton, NJ, USA.
² Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA.
³ Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA.
⁴ Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
⁵ Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA.
⁶ Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA.
⁷ Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Boston, MA, USA.
⁸ Department of Biology, Massachusetts Institute of Technology, Boston, MA, USA.
⁹ Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
¹⁰ Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China.
¹¹ Department of Chemistry, Princeton University, Princeton, NJ, USA. joshr@princeton.edu.
¹² Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA. joshr@princeton.edu.
¹³ Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA. joshr@princeton.edu.

PMID: 36725930
PMCID: PMC10288502
DOI: 10.1038/s41586-022-05661-6

Abstract

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 effect^2,3), 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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Conflict of interest statement

Competing interests 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.

Figures

**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, $R^{2}$ 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.

**Fig. 1 |. In vivo measurement of TCA flux by kinetic [U-¹³C]lactate infusion.**
a, Schematic of carbon labelling of TCA metabolites resulting from a [U-¹³C] lactate infusion. b, Schematic of the kinetics of blood lactate and tissue TCA metabolite labelling from a [U-¹³C]lactate primed infusion. c, Labelling of diaphragm glutamate M+2 and M+1 using a [U-¹³C]lactate-primed infusion, normalized to steady-state labelling. d, Labelling of colon glutamate M+2 and M+1 using a [U-¹³C]lactate primed infusion, normalized to steady-state labelling (that is, final timepoint measured). e, Labelling of gastrocnemius muscle glutamate M+2 and M+1 using a [U-¹³C]lactate-primed infusion, normalized to steady-state labelling. For c–e, n = 12 mice in each panel, two mice per timepoint, 6 timepoints. f, Tissue TCA fluxes. Data are best estimate of flux from computational modeling ± s.d. of the model fits. Fluxes were calculated for the primed infusion timepoints in n = 12 mice per tissue (except n = 19 for brown adipose and intestine, and n = 17 for soleus) and tissue metabolite concentrations measured in n = 4 mice for each tissue (except n = 3 for diaphragm). g, Oxygen consumption by each tissue calculated from tissue mass and TCA flux. The dotted line is the whole-body oxygen consumption measured using a metabolic cage. h, Immunofluorescence staining of the kidney proximal tubule using lotus tetragonolobus lectin. Scale bar, 2 mm. i, IMS analysis of glutamate M+2 labelling after 90 s [U-¹³C]lactate-primed infusion. Scale bar, 1 mm; labeling intensity in TIC, total ion counts. The experiments in h and i were performed on serial tissue slices; data are representative of n = 2 mice.

**Fig. 2 |. Kinetic 2-deoxyglucose infusion quantifies glucose use flux in vivo.**
a, Schematic of [1-¹³C]2-deoxyglucose infusion. b, The concentration of [1-¹³C]2-deoxyglucose in arterial blood during infusion. n = 2 mice, 7 timepoints each. c, The concentration of diaphragm and quadriceps muscle [1-¹³C]2-deoxyglucose phosphate versus the integral of blood [1-¹³C]2-deoxglucose with respect to time during [1-¹³C]2-deoxyglucose infusion. n = 12 mice (quadriceps) and n = 13 (diaphragm). d, Tissue glucose use flux. n = 4 mice (colon and blood), n = 9 (gastrocnemius (gast), intestine, lung, pancreas, skin, spleen, white adipose), n = 12 (kidney, liver, quadriceps) and n = 13 (brain, brown adipose, diaphragm, heart, soleus). Data are values determined by linear fit ± s.d. e, The correlation between absolute glucose use measured by [1-¹³C]2-deoxyglucose infusion and relative glucose use measured using FDG-PET. f, The calculated glucose use by each tissue at the whole-body level. The dotted line shows the whole-body glucose turnover rate measured by [U-¹³C]glucose infusion. g, The production and consumption of lactate by tissues, calculated from glucose use flux and from TCA flux multiplied by lactate contribution to TCA cycle, respectively.

**Fig. 3 |. Tumours have lower TCA flux compared with healthy tissues.**
a, Glutamate M+2 and M+1 labellingusing a [U-¹³C]lactate primed infusion in healthy pancreas, in a genetically engineered mouse model of pancreatic adenocarcinoma (*Pdx1-cre;LSL-Kras*^G12D/+*Trp53*^−/− (GEMM PDAC)) and in a flank-implanted model of pancreatic adenocarcinoma (implanted tumour fragments were *Pdx1-cre;LSL-Kras*^G12D/+*Trp53*^R172H/+ (flank PDAC)), normalized to steady-state labelling (labelling at the latest timepoint measured). b, TCA fluxes of pancreas and pancreatic tumour models. Primed infusion timepoints in n = 12 mice (pancreas), n = 6 (GEMM PDAC), n = 13 (flank PDAC); tissue metabolite concentrations in n = 4 mice per tissue or tumour. c,d, Glutamate labelling (c) and TCA fluxes (d) for healthy lung, a genetically engineered mouse model of non-small cell lung cancer (adenovirus-cre *LSL-Kras*^G12D/+; *Stk11*^−/−;*Trp53*^−/− (GEMM NSCLC)) and a flank-implanted model of non-small cell lung cancer (implanted cell line derived from tumour that was adenovirus-cre *LSL-Kras*^G12D/+;*Trp53*^−/− (flank NSCLC)). Primed infusion timepoints in n = 12 mice (lung), n = 9 (GEMM NSCLC), n = 10 (flank NSCLC); tissue metabolite concentrations in n = 4 mice per tissue. e,f, Glutamate labelling (e) and TCA fluxes (f) for healthy colon and a xenograft of the human colorectal cancer cell line HCT116 (xenograft CRC). Primed infusion timepoints in n = 12 mice (colon) and n = 7 mice (xenograft CRC); tissue metabolite concentrations in n = 4 mice per tissue. g,h, Glutamate labelling (g) and TCA fluxes (h) for healthy spleen and spleen from mice with transplanted NOTCH1-constitutively-active mouse T acute lymphocytic leukaemia (leukaemic spleen). Primed infusion timepoints in n = 12 mice (healthy spleen) and n = 15 mice (leukaemic spleen); tissue metabolite concentrations in n = 3 mice (healthy spleen) and n = 4 mice (leukaemic spleen). All measured healthy tissues were from non-tumour-bearing mice. For b, d, f and h, data are best estimates of flux from computational fitting of experimental data ± s.d. For b, d, f and h, P values were calculated using two-tailed t-tests.

**Fig. 4 |. Lung metastases have higher TCA flux than the primary tumours.**
a, Schematic of mammary-fat-pad-implanted breast cancer xenograft tumours, which lead to spontaneous lung metastases. b, The TCA flux of primary mammary fat pad tumour and of whole lung containing metastases from mice bearing MDA-MB-231-LM2 xenograft tumours. n = 6 mice per tissue type for lactate infusion and n = 4 mice per tissue type for tissue metabolite concentrations. c, The TCA flux of primary mammary fat pad tumour and whole lung containing metastases from mice bearing Sum159-M1A xenograft tumours. n = 8 mice per tissue type for lactate infusion and n = 4 mice per tissue type for tissue metabolite concentrations. d, Haematoxylin and eosin (H&E) staining of the lungs of a mouse bearing a Sum159-M1A mammary fat pad tumour. The arrows indicate metastatic regions. e, IMS analysis of glutamate M+2 labelling intensity in the lung of a mouse bearing Sum159-M1A mammary fat pad tumour after 10 min [U-¹³C]lactate primed infusion. Scale bar, 5 mm. For d and e, experiments were performed on the same tissue slice; the arrows indicate metastatic regions identified from d. f, Quantification of glutamate M+2 intensity (in units of total ion counts, TIC) from metastatic and non-metastatic regions of the lungs from d and e. n = 1 mouse. Each point represents a different region of the image in e. For b and c, data are best estimates of flux from computational fitting of experimental data ± s.d. For b and c, P values were calculated using two-tailed t-tests. The schematic in a was created using BioRender.

**Fig. 5 |. Tumours generate ATP slower than healthy tissues.**
a–h, Quantification of tumour glucose use flux. Tissue [1-¹³C]2-deoxyglucose phosphate concentration versus the integral of blood [1-¹³C]2-deoxyglucose concentration with respect to time in healthy pancreas, GEMM PDAC and flank PDAC (n = 9, 5 and 6 mice, respectively) (a), in healthy lung, GEMM NSCLC and flank NSCLC (n = 9, 5 and 6 mice, respectively) (b), in healthy colon and xenograft CRC (n = 4 and 6 mice, respectively) (c) and in healthy spleen and leukaemic spleen (n = 3 mice each) (d). e–h, The glucose use flux in healthy tissues and cancer models calculated from a–d (pancreas (e), lung (f), colon (g) and spleen (h)). n values are shown. Data are value determined by linear fit ± s.d. i–l, The calculated total ATP production flux in healthy tissues and cancer models (pancreas (i), lung (j), colon (k) and spleen (l)), calculated from the glucose use fluxes shown in e–h and TCA fluxes shown in Fig. 3b–h. Glucose use fluxes: n = 9 mice (pancreas), n = 5 (GEMM PDAC), n = 6 (flank PDAC), n = 9 (lung), n = 5 (GEMM NSCLC), n = 6 (flank NSCLC), n = 4 (colon), n = 6 (xenograft CRC), n = 3 (spleen), n = 3 (leukaemic spleen); lactate infusion timepoints: n = 12 mice (pancreas), n = 6 (GEMM PDAC), n = 13 (flank PDAC), n = 12 (lungs), n = 9 (GEMM NSCLC), n = 10 (flank NSCLC), n = 12 (colon), n = 7 (xenograft CRC), n = 12 (spleen), n = 15 (leukaemic spleen); tissue metabolite concentrations: n = 4 mice for all except n = 3 for healthy spleen. Data are best estimates of flux from computational fitting of experimental data ± s.d. m, The fraction of total ATP derived from glycolysis or the TCA cycle in tumours and healthy tissues. n, The calculated total ATP production flux in healthy tissues and cancer models. Data are best estimates of flux from computational fitting of experimental data ± s.d. All healthy tissues in this figure were from non-tumour-bearing mice. For e–l, P values were calculated using two-tailed t-tests; NS, not significant. Tumour name abbreviations are the same as in Fig. 3, with the addition of MDA-MB-231-LM2 triple-negative breast cancer xenograft tumour (xeno TNBC LM2) and Sum159-M1A triple-negative breast cancer xenograft tumour (xeno TNBC M1A).

**Fig. 6 |. Tumours downregulate ATP-costly tissue activities.**
a, Schematic of [U-¹³C]valine infusion to measure tissue protein synthesis. b,c, Healthy tissue (b) and GEMM PDAC and flank PDAC (c) protein synthesis was measured using [U-¹³C]valine infusion. Data are mean ± s.d. Healthy pancreas data are the same in b and c. n = 13 mice (pancreas), n = 4 (colon), n = 12 (liver), n = 6 (lungs), n = 2 (quadriceps), n = 3 (GEMM PDAC), n = 4 (flank PDAC). d–f, Gene expression changes between human tumours and their tissues of origin (pancreatic cancer versus healthy pancreas (d); kidney cancer versus healthy kidney (e); and liver cancer versus healthy liver (f)); each point represents the log₂(fold change) of one gene between cancer and corresponding healthy tissue. Genes are drawn from the indicated KEGG pathways; bicarbonate reclamation represents proximal tubule bicarbonate reclamation and sodium reabsorption represents aldosterone-regulated sodium reabsorption. Human tissue and tumour gene expression data are from the Xena database (https://xenabrowser.net/; n = 167 patients (healthy pancreas), n = 178 (pancreatic adenocarcinoma), n = 140 (healthy kidneys), n = 884 (kidney tumours, including n = 530 clear cell carcinoma and n = 354 papillary cell carcinoma), n = 50 (healthy liver) and n = 360 (hepatocellular carcinoma)). Colored lines show mean ± s.e.m. For c–f, P values were calculated using two-tailed t-tests.

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References

1. Frayn KN & Evans R Human Metabolism: A Regulatory Perspective (John Wiley & Sons, 2019).
1. Warburg O The metabolism of carcinoma cells. J. Cancer Res. 9, 148–163 (1925).
1. Warburg O The metabolism of tumors in the body. J. Gen. Physiol. 8, 519–530 (1927). - PMC - PubMed
1. Vander Heiden MG, Cantley LC & Thompson CB Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009). - PMC - PubMed
1. Liberti MV & Locasale JW The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016). - PMC - PubMed

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[1] Frayn KN & Evans R Human Metabolism: A Regulatory Perspective (John Wiley & Sons, 2019).

[2] Frayn KN & Evans R Human Metabolism: A Regulatory Perspective (John Wiley & Sons, 2019).

[3] Warburg O The metabolism of carcinoma cells. J. Cancer Res. 9, 148–163 (1925).

[4] Warburg O The metabolism of carcinoma cells. J. Cancer Res. 9, 148–163 (1925).

[5] Warburg O The metabolism of tumors in the body. J. Gen. Physiol. 8, 519–530 (1927). - PMC - PubMed

[6] Warburg O The metabolism of tumors in the body. J. Gen. Physiol. 8, 519–530 (1927). - PMC - PubMed

[7] Vander Heiden MG, Cantley LC & Thompson CB Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009). - PMC - PubMed

[8] Vander Heiden MG, Cantley LC & Thompson CB Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009). - PMC - PubMed

[9] Liberti MV & Locasale JW The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016). - PMC - PubMed

[10] Liberti MV & Locasale JW The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016). - PMC - PubMed

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Slow TCA flux and ATP production in primary solid tumours but not metastases

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Slow TCA flux and ATP production in primary solid tumours but not metastases

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