A guide to 13C metabolic flux analysis for the cancer biologist

Experimental and Molecular Medicine

Volumn 50, Issue 4, 2018, Pages

  Antoniewicz, Maciek R ^a  

^a UNIVERSITY OF DELAWARE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARBON 13; DIHYDROXYACETONE PHOSPHATE; FRUCTOSE 6 PHOSPHATE; GLUCOSE; GLYCEROPHOSPHATE; LACTIC ACID; PHOSPHOENOLPYRUVATE; PHOSPHOGLYCERATE KINASE; PYRUVIC ACID; REDUCED NICOTINAMIDE ADENINE DINUCLEOTIDE PHOSPHATE; TRICARBOXYLIC ACID; CARBON; CARBON-13;

CARBON METABOLISM; CARCINOGENESIS; CELL COUNT; CELL GROWTH; CELL METABOLISM; GLUCOSE METABOLISM; HUMAN; ISOTOPE LABELING; LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY; MASS SPECTROMETRY; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; METABOLIC FLUX ANALYSIS; PROTEIN DEGRADATION; REVIEW; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; ENERGY METABOLISM; METABOLISM; NEOPLASM; PROCEDURES; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; CARBON ISOTOPES; ENERGY METABOLISM; HUMANS; ISOTOPE LABELING; METABOLIC FLUX ANALYSIS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NEOPLASMS;

EID: 85050857299     PISSN: 12263613     EISSN: 20926413     Source Type: Journal    
DOI: 10.1038/s12276-018-0060-y     Document Type: Review

References (100)

1. DeBerardinis, R. J. & Thompson, C. B. Cellular metabolism and disease: what do metabolic outliers teach us? Cell 148, 1132–1144 (2012).
2. Metallo, C. M. & Vander Heiden, M. G. Understanding metabolic regulation and its influence on cell physiology. Mol. Cell 49, 388–398 (2013).
3. Dong, W., Keibler, M. A. & Stephanopoulos, G. Review of metabolic pathways activated in cancer cells as determined through isotopic labeling and network analysis. Metab. Eng. 43, 113–124 (2017).
4. Badur, M. G. & Metallo, C. M. Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease. Metab. Eng. 45, 95–108 (2017).
5. Metallo, C. M. & Deberardinis, R. J. Engineering approaches to study cancer metabolism. Metab. Eng. 43, 93 (2017).
6. Hiller, K. & Metallo, C. M. Profiling metabolic networks to study cancer metabolism. Curr. Opin. Biotechnol. 24, 60–68 (2013).
7. Boroughs, L. K. & DeBerardinis, R. J. Metabolic pathways promoting cancer cell survival and growth. Nat. Cell Biol. 17, 351–359 (2015).
8. Vander Heiden, M. G. & DeBerardinis, R. J. Understanding the Intersections between metabolism and cancer biology. Cell 168, 657–669 (2017).
9. Warburg, O. On the origin of cancer cells. Science 123, 309–314 (1956).
10. Liberti, M. V. & Locasale, J. W. The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016).
11. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
12. Pavlova, N. N. & Thompson, C. B. The emerging hallmarks of cancer metabolism. Cell Metab. 23, 27–47 (2016).
13. Metallo, C. M. et al. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature 481, 380–384 (2011).
14. Vander Heiden, M. G. et al. Evidence for an alternative glycolytic pathway in rapidly proliferating cells. Science 329, 1492–1499 (2010).
15. Possemato, R. et al. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 476, 346–350 (2011).
16. DeBerardinis, R. J. Serine metabolism: some tumors take the road less traveled. Cell Metab. 14, 285–286 (2011).
17. Pacold, M. E. et al. A PHGDH inhibitor reveals coordination of serine synthesis and one-carbon unit fate. Nat. Chem. Biol. 12, 452–458 (2016).
18. Locasale, J. W. Serine, glycine and one-carbon units: cancer metabolism in full circle. Nat. Rev. Cancer 13, 572–583 (2013).
19. Yang, M. & Vousden, K. H. Serine and one-carbon metabolism in cancer. Nat. Rev. Cancer 16, 650–662 (2016).
20. Coy, J. F., Dressler, D., Wilde, J. & Schubert, P. Mutations in the transketolase-like gene TKTL1: clinical implications for neurodegenerative diseases, diabetes and cancer. Clin. Lab. 51, 257–273 (2005).
21. Diaz-Moralli, S. et al. A key role for transketolase-like 1 in tumor metabolic reprogramming. Oncotarget 7, 51875–51897 (2016).
22. Schug, Z. T. et al. Acetyl-CoA synthetase 2 promotes acetate utilization and maintains cancer cell growth under metabolic stress. Cancer Cell 27, 57–71 (2015).
23. Mashimo, T. et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell 159, 1603–1614 (2014).
24. Comerford, S. A. et al. Acetate dependence of tumors. Cell 159, 1591–1602 (2014).
25. Kamphorst, J. J., Chung, M. K., Fan, J. & Rabinowitz, J. D. Quantitative analysis of acetyl-CoA production in hypoxic cancer cells reveals substantial contribution from acetate. Cancer Metab. 2, 23 (2014).
26. Davidson, S. M. et al. Environment impacts the metabolic dependencies of Ras-driven non-small cell lung cancer. Cell Metab. 23, 517–528 (2016).
27. Achreja, A. et al. Exo-MFA—a 13C metabolic flux analysis framework to dissect tumor microenvironment-secreted exosome contributions towards cancer cell metabolism. Metab. Eng. 43(Pt B), 156–172 (2017).
28. Hensley, C. T. et al. Metabolic heterogeneity in human lung tumors. Cell 164, 681–694 (2016).
29. Patra, K. C. & Hay, N. The pentose phosphate pathway and cancer. Trends Biochem. Sci. 39, 347–354 (2014).
30. Nilsson, A. & Nielsen, J. Genome scale metabolic modeling of cancer. Metab. Eng. 43, 103–112 (2017).
31. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425 (2013).
32. Swarup, A., Lu, J., DeWoody, K. C. & Antoniewicz, M. R. Metabolic network reconstruction, growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8. Metab. Eng. 24, 173–180 (2014).
33. Long, C. P. & Antoniewicz, M. R. Metabolic flux analysis of Escherichia coli knockouts: lessons from the Keio collection and future outlook. Curr. Opin. Biotechnol. 28, 127–133 (2014).
34. Haverkorn van Rijsewijk, B. R., Nanchen, A., Nallet, S., Kleijn, R. J. & Sauer, U. Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli. Mol. Syst. Biol. 7, 477 (2011).
35. Young, J. D. Metabolic flux rewiring in mammalian cell cultures. Curr. Opin. Biotechnol. 24, 1108–1115 (2013).
36. Jiang, L. et al. Reductive carboxylation supports redox homeostasis during anchorage-independent growth. Nature 532, 255–258 (2016).
37. Templeton, N. et al. Application of (13)C flux analysis to identify high-productivity CHO metabolic phenotypes. Metab. Eng. 43(Pt B), 218–225 (2017).
38. Maier, K. et al. Quantification of statin effects on hepatic cholesterol synthesis by transient (13)C-flux analysis. Metab. Eng. 11, 292–309 (2009).
39. Antoniewicz, M. R., Kelleher, J. K. & Stephanopoulos, G. Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions. Metab. Eng. 9, 68–86 (2007).
40. Antoniewicz, M. R., Kelleher, J. K. & Stephanopoulos, G. Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements. Metab. Eng. 8, 324–337 (2006).
41. Young, J. D., Walther, J. L., Antoniewicz, M. R., Yoo, H. & Stephanopoulos, G. An elementary metabolite unit (EMU) based method of isotopically nonstationary flux analysis. Biotechnol. Bioeng. 99, 686–699 (2008).
42. Yoo, H., Antoniewicz, M. R., Stephanopoulos, G. & Kelleher, J. K. Quantifying reductive carboxylation flux of glutamine to lipid in a brown adipocyte cell line. J. Biol. Chem. 283, 20621–20627 (2008).
43. Young, J. D. INCA: a computational platform for isotopically non-stationary metabolic flux analysis. Bioinformatics 30, 1333–1335 (2014).
44. Crown, S. B. & Antoniewicz, M. R. Publishing 13C metabolic flux analysis studies: a review and future perspectives. Metab. Eng. 20, 42–48 (2013).
45. Crown, S. B. & Antoniewicz, M. R. Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies. Metab. Eng. 16, 21–32 (2013).
46. Antoniewicz, M. R. Methods and advances in metabolic flux analysis: a mini-review. J. Ind. Microbiol. Biotechnol. 42, 317–325 (2015).
47. Ahn, W. S. & Antoniewicz, M. R. Towards dynamic metabolic flux analysis in CHO cell cultures. Biotechnol. J. 7, 61–74 (2012).
48. Antoniewicz, M. R., Stephanopoulos, G. & Kelleher, J. K. Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway. Metabolomics 2, 41–52 (2006).
49. Crown, S. B., Long, C. P. & Antoniewicz, M. R. Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: a new precision and synergy scoring system. Metab. Eng. 38, 10–18 (2016).
50. Antoniewicz, M. R. 13C metabolic flux analysis: optimal design of isotopic labeling experiments. Curr. Opin. Biotechnol. 24, 1116–1121 (2013).
51. Buescher, J. M. et al. A roadmap for interpreting (13)C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).
52. Schmidt, K., Carlsen, M., Nielsen, J. & Villadsen, J. Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices. Biotechnol. Bioeng. 55, 831–840 (1997).
53. Zupke, C. & Stephanopoulos, G. Intracellular flux analysis in hybridomas using mass balances and in vitro (13)C nmr. Biotechnol. Bioeng. 45, 292–303 (1995).
54. Wiechert, W., Mollney, M., Isermann, N., Wurzel, M. & de Graaf, A. A. Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems. Biotechnol. Bioeng. 66, 69–85 (1999).
55. Ahn, W. S. & Antoniewicz, M. R. Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry. Metab. Eng. 13, 598–609 (2011).
56. Ahn, W. S. & Antoniewicz, M. R. Parallel labeling experiments with [1,2-(13)C]glucose and [U-(13)C]glutamine provide new insights into CHO cell metabolism. Metab. Eng. 15, 34–47 (2013).
57. Crown, S. B., Ahn, W. S. & Antoniewicz, M. R. Rational design of (1)(3)C-labeling experiments for metabolic flux analysis in mammalian cells. BMC Syst. Biol. 6, 43 (2012).
58. Metallo, C. M., Walther, J. L. & Stephanopoulos, G. Evaluation of 13C isotopic tracers for metabolic flux analysis in mammalian cells. J. Biotechnol. 144, 167–174 (2009).
59. Antoniewicz, M. R. Parallel labeling experiments for pathway elucidation and 13C metabolic flux analysis. Curr. Opin. Biotechnol. 36, 91–97 (2015).
60. Crown, S. B., Kelleher, J. K., Rouf, R., Muoio, D. M. & Antoniewicz, M. R. Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts. Am. J. Physiol. Heart Circ. Physiol. 311, H881–H891 (2016).
61. DeWaal, D. et al. Hexokinase-2 depletion inhibits glycolysis and induces oxidative phosphorylation in hepatocellular carcinoma and sensitizes to metformin. Nat. Commun. 9, 446 (2018).
62. Crown, S. B., Marze, N. & Antoniewicz, M. R. Catabolism of branched chain amino acids contributes significantly to synthesis of odd-chain and even-chain fatty acids in 3T3-L1 adipocytes. PLoS ONE 10, e0145850 (2015).
63. Crown, S. B., Long, C. P. & Antoniewicz, M. R. Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli. Metab. Eng. 28, 151–158 (2015).
64. Long, C. P., Gonzalez, J. E., Sandoval, N. R. & Antoniewicz, M. R. Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism. Metab. Eng. 37, 102–113 (2016).
65. Long, C. P. & Antoniewicz, M. R. Quantifying biomass composition by gas chromatography/mass spectrometry. Anal. Chem. 86, 9423–9427 (2014).
66. McConnell, B. O. & Antoniewicz, M. R. Measuring the composition and stable-isotope labeling of algal biomass carbohydrates via gas chromatography/mass spectrometry. Anal. Chem. 88, 4624–4628 (2016).
67. Long, C. P., Au, J., Sandoval, N. R., Gebreselassie, N. A. & Antoniewicz, M. R. Enzyme I facilitates reverse flux from pyruvate to phosphoenolpyruvate in Escherichia coli. Nat. Commun. 8, 14316 (2017).
68. Nakahigashi, K. et al. Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism. Mol. Syst. Biol. 5, 306 (2009).
69. Cordova, L. T., Cipolla, R. M., Swarup, A., Long, C. P. & Antoniewicz, M. R. (13)C metabolic flux analysis of three divergent extremely thermophilic bacteria: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252. Metab. Eng. 44, 182–190 (2017).
70. Clasquin, M. F. et al. Riboneogenesis in yeast. Cell 145, 969–980 (2011).
71. Crown, S. B. et al. Resolving the TCA cycle and pentose-phosphate pathway of Clostridium acetobutylicum ATCC 824: isotopomer analysis, in vitro activities and expression analysis. Biotechnol. J. 6, 300–305 (2011).
72. Feng, X. et al. Characterization of the central metabolic pathways in Thermoanaerobacter sp. strain X514 via isotopomer-assisted metabolite analysis. Appl. Environ. Microbiol. 75, 5001–5008 (2009).
73. Ahn, W. S., Crown, S. B. & Antoniewicz, M. R. Evidence for transketolase-like TKTL1 flux in CHO cells based on parallel labeling experiments and (13)C-metabolic flux analysis. Metab. Eng. 37, 72–78 (2016).
74. Kharroubi, A. T., Masterson, T. M., Aldaghlas, T. A., Kennedy, K. A. & Kelleher, J. K. Isotopomer spectral analysis of triglyceride fatty acid synthesis in 3T3-L1 cells. Am. J. Physiol. 263(4 Pt 1), E667–E675 (1992).
75. Green, C. R. et al. Branched-chain amino acid catabolism fuels adipocyte differentiation and lipogenesis. Nat. Chem. Biol. 12, 15–21 (2016).
76. Fendt, S. M. et al. Metformin decreases glucose oxidation and increases the dependency of prostate cancer cells on reductive glutamine metabolism. Cancer Res. 73, 4429–4438 (2013).
77. Jiang, L. et al. Quantitative metabolic flux analysis reveals an unconventional pathway of fatty acid synthesis in cancer cells deficient for the mitochondrial citrate transport protein. Metab. Eng. 43(Pt B), 198–207 (2017).
78. Crown, S. B. & Antoniewicz, M. R. Selection of tracers for 13C-metabolic flux analysis using elementary metabolite units (EMU) basis vector methodology. Metab. Eng. 14, 150–161 (2012).
79. DeBerardinis, R. J. et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc. Natl Acad. Sci. USA 104, 19345–19350 (2007).
80. Previs, S. F. et al. New methodologies for studying lipid synthesis and turnover: looking backwards to enable moving forwards. Biochim. Biophys. Acta 1842, 402–413 (2014).
81. Landau, B. R. et al. Contributions of gluconeogenesis to glucose production in the fasted state. J. Clin. Invest. 98, 378–385 (1996).
82. Hasenour, C. M. et al. Mass spectrometry-based microassay of (2)H and (13)C plasma glucose labeling to quantify liver metabolic fluxes in vivo. Am. J. Physiol. Endocrinol. Metab. 309, E191–E203 (2015).
83. Sandberg, T. E. et al. Evolution of E. coli on [U-13C]glucose reveals a negligible isotopic influence on metabolism and physiology. PLoS ONE 11, e0151130 (2016).
84. Liu, L. et al. Malic enzyme tracers reveal hypoxia-induced switch in adipocyte NADPH pathway usage. Nat. Chem. Biol. 12, 345–352 (2016).
85. Lewis, C. A. et al. Tracing compartmentalized NADPH metabolism in the cytosol and mitochondria of mammalian cells. Mol. Cell 55, 253–263 (2014).
86. Fan, J. et al. Quantitative flux analysis reveals folate-dependent NADPH production. Nature 510, 298–302 (2014).
87. Wahrheit, J., Nicolae, A. & Heinzle, E. Eukaryotic metabolism: measuring compartment fluxes. Biotechnol. J. 6, 1071–1085 (2011).
88. Niklas, J., Schneider, K. & Heinzle, E. Metabolic flux analysis in eukaryotes. Curr. Opin. Biotechnol. 21, 63–69 (2010).
89. Gebreselassie, N. A. & Antoniewicz, M. R. (13)C-metabolic flux analysis of co-cultures: a novel approach. Metab. Eng. 31, 132–139 (2015).
90. Chen, W. W., Freinkman, E., Wang, T., Birsoy, K. & Sabatini, D. M. Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism. Cell 166, 1324–1337 e11 (2016).
91. Nicolae, A., Wahrheit, J., Nonnenmacher, Y., Weyler, C. & Heinzle, E. Identification of active elementary flux modes in mitochondria using selectively permeabilized CHO cells. Metab. Eng. 32, 95–105 (2015).
92. Nonnenmacher, Y. et al. Analysis of mitochondrial metabolism in situ: combining stable isotope labeling with selective permeabilization. Metab. Eng. 43, 147–155 (2017).
93. Wasylenko, T. M. & Stephanopoulos, G. Kinetic isotope effects significantly influence intracellular metabolite (13) C labeling patterns and flux determination. Biotechnol. J. 8, 1080–1089 (2013).
94. Wiechert, W. & Noh, K. From stationary to instationary metabolic flux analysis. Adv. Biochem. Eng. Biotechnol. 92, 145–172 (2005).
95. Antoniewicz, M. R. Dynamic metabolic flux analysis--tools for probing transient states of metabolic networks. Curr. Opin. Biotechnol. 24, 973–978 (2013).
96. Leighty, R. W. & Antoniewicz, M. R. Dynamic metabolic flux analysis (DMFA): a framework for determining fluxes at metabolic non-steady state. Metab. Eng. 13, 745–755 (2011).
97. Antoniewicz, M. R. et al. Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol. Metab. Eng. 9, 277–292 (2007).
98. Long, C. P., Au, J., Gonzalez, J. E. & Antoniewicz, M. R. 13C metabolic flux analysis of microbial and mammalian systems is enhanced with GC-MS measurements of glycogen and RNA labeling. Metab. Eng. 38, 65–72 (2016).
99. Gonzalez, J. E., Long, C. P. & Antoniewicz, M. R. Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis. Metab. Eng. 39, 9–18 (2017).
100. Guzman, S. et al. (13)C metabolic flux analysis shows that resistin impairs the metabolic response to insulin in L6E9 myotubes. BMC Syst. Biol. 8, 109 (2014).