April 26, 2024
Slow TCA flux and ATP production in primary solid tumours but not metastases – Nature

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

  • Frayn, K. N. & Evans, R. Human Metabolism: A Regulatory Perspective (John Wiley & Sons, 2019).

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

    CAS 

    Google Scholar
     

  • Warburg, O. The metabolism of tumors in the body. J. Gen. Physiol. 8, 519–530 (1927).

    CAS 

    Google Scholar
     

  • Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).


    Google Scholar
     

  • Liberti, M. V. & Locasale, J. W. The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016).

    CAS 

    Google Scholar
     

  • Cori, C. F. & Cori, G. T. The carbohydrate metabolism of tumors: III. The rate of glycolysis of tumor tissue in the living animal. J. Cancer Res. 12, 301–313 (1928).


    Google Scholar
     

  • Crabtree, H. G. Observations on the carbohydrate metabolism of tumours. Biochem. J. 23, 536–545 (1929).

    CAS 

    Google Scholar
     

  • Fletcher, J. W. et al. Recommendations on the use of 18F-FDG PET in oncology. J. Nucl. Med. 49, 480–508 (2008).


    Google Scholar
     

  • Birsoy, K. et al. An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell 162, 540–551 (2015).

    CAS 

    Google Scholar
     

  • Ju, Y. S. et al. Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer. eLife 3, e02935 (2014).


    Google Scholar
     

  • Weinberg, F. et al. Mitochondrial metabolism and ROS generation are essential for Kras-mediated tumorigenicity. Proc. Natl Acad. Sci. USA 107, 8788–8793 (2010).

    CAS 

    Google Scholar
     

  • Sullivan, L. B. et al. Supporting aspartate biosynthesis is an essential function of respiration in proliferating. Cell 162, 552–563 (2015).

    CAS 

    Google Scholar
     

  • Viale, A. et al. Oncogene ablation-resistant pancreatic cancer cells depend on mitochondrial function. Nature 514, 628–632 (2014).

    CAS 

    Google Scholar
     

  • Gorelick, A. N. et al. Respiratory complex and tissue lineage drive recurrent mutations in tumour mtDNA. Nat Metab. 3, 558–570 (2021).

    CAS 

    Google Scholar
     

  • Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115–118 (2017).


    Google Scholar
     

  • Hensley, C. T. et al. Metabolic heterogeneity in human lung tumors. Cell 164, 681–694 (2016).

    CAS 

    Google Scholar
     

  • Mason, G. F. et al. Simultaneous determination of the rates of the TCA cycle, glucose utilization, α-ketoglutarate/glutamate exchange, and glutamine synthesis in human brain by NMR. J. Cereb. Blood Flow Metab. 15, 12–25 (1995).

    CAS 

    Google Scholar
     

  • Jucker, B. M., Lee, J. Y. & Shulman, R. G. In vivo 13C NMR measurements of hepatocellular tricarboxylic acid cycle flux. J. Biol. Chem. 273, 12187–12194 (1998).

    CAS 

    Google Scholar
     

  • Petersen, K. F. et al. Mitochondrial dysfunction in the elderly: possible role in insulin resistance. Science 300, 1140–1142 (2003).

    CAS 

    Google Scholar
     

  • Wijnen, J. P. et al. In vivo 13C magnetic resonance spectroscopy of a human brain tumor after application of 13C-1-enriched glucose. Magn. Reson. Imaging 28, 690–697 (2010).

    CAS 

    Google Scholar
     

  • Yuan, J., Bennett, B. D. & Rabinowitz, J. D. Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nat. Protoc. 3, 1328–1340 (2008).

    CAS 

    Google Scholar
     

  • Befroy, D. E. et al. Direct assessment of hepatic mitochondrial oxidative and anaplerotic fluxes in humans using dynamic 13 C magnetic resonance spectroscopy. Nat. Med. 20, 98–102 (2014).

    CAS 

    Google Scholar
     

  • Nöh, K., Wahl, A. & Wiechert, W. Computational tools for isotopically instationary 13C labeling experiments under metabolic steady state conditions. Metab. Eng. 8, 554–577 (2006).


    Google Scholar
     

  • Martin, A. W. & Fuhrman, F. A. The relationship between summated tissue respiration and metabolic rate in the mouse and dog. Physiol. Zool. 28, 18–34 (1955).


    Google Scholar
     

  • Sokoloff, L. et al. The [14c]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino Rat. J. Neurochem. 28, 897–916 (1977).

    CAS 

    Google Scholar
     

  • Hostetler, K. Y. & Landau, B. R. Estimation of the pentose cycle contribution to glucose metabolism in tissue in vivo. Biochemistry 6, 2961–2964 (1967).

    CAS 

    Google Scholar
     

  • Munger, J. et al. Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy. Nat. Biotechnol. 26, 1179–1186 (2008).

    CAS 

    Google Scholar
     

  • Fueger, B. J. et al. Impact of animal handling on the results of 18F-FDG PET studies in mice. J. Nucl. Med. 47, 999–1006 (2006).

    CAS 

    Google Scholar
     

  • Wolfe, R. R. Tracers in Metabolic Research: Radioisotope and Stable Isotope/Mass Spectometry Methods (A.R. Liss, 1984).

  • Donovan, C. M. & Brooks, G. A. Endurance training affects lactate clearance, not lactate production. Am. J. Physiol. Endocrinol. Metab. 244, E83–E92 (1983).

    CAS 

    Google Scholar
     

  • Levy, M. N. Uptake of lactate and pyruvate by intact kidney of the dog. Am. J. Physiol. 202, 302–308 (1962).

    CAS 

    Google Scholar
     

  • Murashige, D. et al. Comprehensive quantification of fuel use by the failing and nonfailing human heart. Science 370, 364–368 (2020).

    CAS 

    Google Scholar
     

  • Jang, C. et al. Metabolite exchange between mammalian organs quantified in pigs. Cell Metab. 30, 594–606 (2019).

    CAS 

    Google Scholar
     

  • Piskounova, E. et al. Oxidative stress inhibits distant metastasis by human melanoma cells. Nature 527, 186–191 (2015).

    CAS 

    Google Scholar
     

  • Ubellacker, J. M. et al. Lymph protects metastasizing melanoma cells from ferroptosis. Nature 585, 113–118 (2020).

    CAS 

    Google Scholar
     

  • Fischer, G. M. et al. Molecular profiling reveals unique immune and metabolic features of melanoma brain metastases. Cancer Discov. 9, 628–645 (2019).

    CAS 

    Google Scholar
     

  • Rodrigues, M. F. et al. Enhanced OXPHOS, glutaminolysis and β-oxidation constitute the metastatic phenotype of melanoma cells. Biochem. J. 473, 703–715 (2016).

    CAS 

    Google Scholar
     

  • Momcilovic, M. et al. In vivo imaging of mitochondrial membrane potential in non-small-cell lung cancer. Nature 575, 380–384 (2019).

    CAS 

    Google Scholar
     

  • Nicholls, D. G. & Locke, R. M. Thermogenic mechanisms in brown fat. Physiol. Rev. 64, 1–64 (1984).

    CAS 

    Google Scholar
     

  • Divakaruni, A. S. & Brand, M. D. The regulation and physiology of mitochondrial proton leak. Physiology 26, 192–205 (2011).

    CAS 

    Google Scholar
     

  • Brown, G. C. Control of respiration and ATP synthesis in mammalian mitochondria and cells. Biochem. J. 284, 1–13 (1992).

    CAS 

    Google Scholar
     

  • Pavlova, N. N. & Thompson, C. B. The emerging hallmarks of cancer metabolism. Cell Metab. 23, 27–47 (2016).

    CAS 

    Google Scholar
     

  • Bauduin, H., Colin, M. & Dumont, J. E. Energy sources for protein synthesis and enzymatic secretion in rat pancreas in vitro. Biochim. Biophy. Acta 174, 722–733 (1969).

    CAS 

    Google Scholar
     

  • Campagne, R. N. & Gruber, M. Amino acid and energy requirements of protein synthesis in rat pancreatic tissue in vitro. Biochim. Biophys. Acta 55, 353–360 (1962).

    CAS 

    Google Scholar
     

  • Neinast, M. D. et al. Quantitative analysis of the whole-body metabolic fate of branched-chain amino acids. Cell Metab. 29, 417–429 (2019).

    CAS 

    Google Scholar
     

  • Lassen, N. A., Munck, O. & Thaysen, J. H. Oxygen consumption and sodium reabsorption in the kidney. Acta Physiol. Scand. 51, 371–384 (1961).

    CAS 

    Google Scholar
     

  • Müller, M. J. Hepatic fuel selection. Proc. Nutr. Soc. 54, 139–150 (1995).


    Google Scholar
     

  • Frauwirth, K. A. et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 16, 769–777 (2002).

    CAS 

    Google Scholar
     

  • Lapidot, T. et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367, 645–648 (1994).

    CAS 

    Google Scholar
     

  • Storz, P. Acinar cell plasticity and development of pancreatic ductal adenocarcinoma. Nat. Rev. Gastroenterol. Hepatol. 14, 296–304 (2017).

    CAS 

    Google Scholar
     

  • Rajasekaran, S. A. et al. Reduced expression of beta-subunit of na,k-atpase in human clear-cell renal cell carcinoma. J. Urol. 162, 574–580 (1999).

    CAS 

    Google Scholar
     

  • Chang, C.-H. et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162, 1229–1241 (2015).

    CAS 

    Google Scholar
     

  • Kamphorst, J. J. et al. Human pancreatic cancer tumors are nutrient poor and tumor cells actively scavenge extracellular protein. Cancer Res. 75, 544–553 (2015).

    CAS 

    Google Scholar
     

  • Tasdogan, A. et al. Metabolic heterogeneity confers differences in melanoma metastatic potential. Nature 577, 115–120 (2020).

    CAS 

    Google Scholar
     

  • Reinfeld, B. I. et al. Cell-programmed nutrient partitioning in the tumour microenvironment. Nature 593, 282–288 (2021).

    CAS 

    Google Scholar
     

  • Brindle, K. M. Imaging metabolism with hyperpolarized 13C-labeled cell substrates. J. Am. Chem. Soc. 137, 6418–6427 (2015).

    CAS 

    Google Scholar
     

  • Davidson, S. M. et al. Environment impacts the metabolic dependencies of Ras-driven non-small cell lung cancer. Cell Metab. 23, 517–528 (2016).

    CAS 

    Google Scholar
     

  • Herranz, D. et al. Metabolic reprogramming induces resistance to anti-NOTCH1 therapies in T cell acute lymphoblastic leukemia. Nat. Med. 21, 1182–1189 (2015).

    CAS 

    Google Scholar
     

  • Kang, Y. et al. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3, 537–549 (2003).

    CAS 

    Google Scholar
     

  • Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).

    CAS 

    Google Scholar
     

  • Esposito, M. et al. TGF-β-induced DACT1 biomolecular condensates repress Wnt signalling to promote bone metastasis. Nat. Cell Biol. 23, 257–267 (2021).

    CAS 

    Google Scholar
     

  • Chiles, E. et al. Fast LC-MS quantitation of glucose and glycerol via enzymatic derivatization. Anal. Biochem. 575, 40–43 (2019).

    CAS 

    Google Scholar
     

  • Wang, L. et al. Spatially resolved isotope tracing reveals tissue metabolic activity. Nat. Methods 19, 223–230 (2022).


    Google Scholar
     

  • Gupta, M., Sonnett, M., Ryazanova, L., Presler, M. & Wühr, M. Quantitative proteomics of Xenopus embryos I, sample preparation. Methods Mol. Biol. 1865, 175–194 (2018).

    CAS 

    Google Scholar
     

  • Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).

    CAS 

    Google Scholar
     

  • Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).

    CAS 

    Google Scholar
     

  • Li, J. et al. TMTpro-18plex: The expanded and complete set of TMTpro reagents for sample multiplexing. J. Proteome Res. 20, 2964–2972 (2021).

    CAS 

    Google Scholar
     

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

    CAS 

    Google Scholar
     

  • Levenberg, K. A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math. 2, 164–168 (1944).

    MATH 

    Google Scholar
     

  • Marquardt, D. W. An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963).

    MATH 

    Google Scholar
     

  • Hui, S. et al. Quantitative fluxomics of circulating metabolites. Cell Metab. 32, 676–688 (2020).

    CAS 

    Google Scholar
     

  • Ghergurovich, J. M. et al. Local production of lactate, ribose phosphate, and amino acids by human triple-negative breast cancer. Med 2, 736–754 (2021).

    CAS 

    Google Scholar
     

  • Petersen, M. C., Vatner, D. F. & Shulman, G. I. Regulation of hepatic glucose metabolism in health and disease. Nat. Rev. Endocrinol. 13, 572–587 (2017).

    CAS 

    Google Scholar
     

  • Brown, R. P., Delp, M. D., Lindstedt, S. L., Rhomberg, L. R. & Beliles, R. P. Physiological parameter values for physiologically based pharmacokinetic models. Toxicol. Ind. Health 13, 407–484 (1997).

    CAS 

    Google Scholar
     

  • West, D. B., Boozer, C. N., Moody, D. L. & Atkinson, R. L. Dietary obesity in nine inbred mouse strains. Am. J. Physiol. 262, R1025–R1032 (1992).

    CAS 

    Google Scholar
     

  • Burkholder, T. J., Fingado, B., Baron, S. & Lieber, R. L. Relationship between muscle fiber types and sizes and muscle architectural properties in the mouse hindlimb. J. Morphol. 221, 177–190 (1994).

    CAS 

    Google Scholar
     

  • Mathewson, M. A., Chapman, M. A., Hentzen, E. R., Fridén, J. & Lieber, R. L. Anatomical, architectural, and biochemical diversity of the murine forelimb muscles. J. Anat. 221, 443–451 (2012).


    Google Scholar
     

  • Kim, Y. S. Human tissues: chemical composition and photon dosimetry data. Radiat. Res. 57, 38–45 (1974).

    CAS 

    Google Scholar
     

  • Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).

    CAS 

    Google Scholar
     

  • Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS 

    Google Scholar
     

  • Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 14, 128 (2013).


    Google Scholar
     

  • Sonnett, M., Gupta, M., Nguyen, T. & Wühr, M. Quantitative proteomics for Xenopus embryos II, data analysis. Methods Mol. Biol. 1865, 195–215 (2018).

    CAS 

    Google Scholar
     

  • Sonnett, M., Yeung, E. & Wühr, M. Accurate, sensitive, and precise multiplexed proteomics using the complement reporter ion cluster. Anal. Chem. 90, 5032–5039 (2018).

    CAS 

    Google Scholar
     

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