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High-resolution 13C metabolic flux analysis

Nature Protocols volume 14, pages 2856–2877 (2019)Cite this article

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Abstract

Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. 13C metabolic flux analysis (13C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for 13C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with 13C-labeled glucose tracers, followed by gas chromatography–mass spectrometry (GC–MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for 13C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

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Fig. 1: Overview of procedure for high-resolution 13C metabolic flux analysis.
Fig. 2: Batch culture sampling to assess changes.
Fig. 3: Overview of procedures.
Fig. 4: Demonstration of the workflow for 13C-MFA for the E. coli ΔtpiA case study.

Data availability

All data relevant to this protocol are included in the Supplementary Information files.

Software availability

The Metran software is freely available for academic research and educational purposes. The technical licensing office at the Massachusetts Institute of Technology can be contacted to request a copy of the Metran software (https://tlo.mit.edu/technologies/metran-software-13c-metabolic-flux-analysis).

References

  1. Ishii, N. et al. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316, 593–597 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Buescher, J. M. et al. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  4. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Boghigian, B. A., Seth, G., Kiss, R. & Pfeifer, B. A. Metabolic flux analysis and pharmaceutical production. Metab. Eng. 12, 81–95 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Stephanopoulos, G. Metabolic fluxes and metabolic engineering. Metab. Eng. 1, 1–11 (1999).

    Article  CAS  PubMed  Google Scholar 

  7. Antoniewicz, M. R. Methods and advances in metabolic flux analysis: a mini-review. J. Ind. Microbiol. Biotechnol. 42, 317–325 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zamboni, N., Fendt, S. M., Ruhl, M. & Sauer, U. 13C-based metabolic flux analysis. Nat. Protoc. 4, 878–892 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. 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).

    Article  CAS  PubMed  Google Scholar 

  11. Wiechert, W., Mollney, M., Petersen, S. & de Graaf, A. A. A universal framework for 13C metabolic flux analysis. Metab. Eng. 3, 265–283 (2001).

    Article  CAS  PubMed  Google Scholar 

  12. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Antoniewicz, M. R. 13C metabolic flux analysis: optimal design of isotopic labeling experiments. Curr. Opin. Biotechnol. 24, 1116–1121 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Antoniewicz, M. R. Parallel labeling experiments for pathway elucidation and 13C metabolic flux analysis. Curr. Opin. Biotechnol. 36, 91–97 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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).

    Article  CAS  PubMed  Google Scholar 

  18. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    Article  CAS  PubMed  Google Scholar 

  20. Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. 14, 639–702 (2019).

    Article  CAS  PubMed  Google Scholar 

  22. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ahn, W. S., Crown, S. B. & Antoniewicz, M. R. Evidence for transketolase-like TKTL1 flux in CHO cells based on parallel labeling experiments and 13C-metabolic flux analysis. Metab. Eng. 37, 72–78 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Antoniewicz, M. R. A guide to 13C metabolic flux analysis for the cancer biologist. Exp. Mol. Med. 50, 19 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gonzalez, J. E. & Antoniewicz, M. R. Tracing metabolism from lignocellulosic biomass and gaseous substrates to products with stable-isotopes. Curr. Opin. Biotechnol. 43, 86–95 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. 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).

    Article  CAS  PubMed  Google Scholar 

  28. Hollinshead, W. D., Henson, W. R., Abernathy, M., Moon, T. S. & Tang, Y. J. Rapid metabolic analysis of Rhodococcus opacus PD630 via parallel 13C-metabolite fingerprinting. Biotechnol. Bioeng. 113, 91–100 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Wolfsberg, E., Long, C. P. & Antoniewicz, M. R. Metabolism in dense microbial colonies: 13C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding. Metab. Eng. 49, 242–247 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Cordova, L. T. & Antoniewicz, M. R. 13C metabolic flux analysis of the extremely thermophilic, fast growing, xylose-utilizing Geobacillus strain LC300. Metab. Eng. 33, 148–157 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Cordova, L. T. et al. Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by C metabolic flux analysis and whole genome sequencing. Metab. Eng. 37, 63–71 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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).

    Article  CAS  PubMed  Google Scholar 

  33. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. He, L. et al. Deciphering flux adjustments of engineered E. coli cells during fermentation with changing growth conditions. Metab. Eng. 39, 247–256 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Antoniewicz, M. R. Tandem mass spectrometry for measuring stable-isotope labeling. Curr. Opin. Biotechnol. 24, 48–53 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. van Winden, W., Schipper, D., Verheijen, P. & Heijnen, J. Innovations in generation and analysis of 2D [13C,1H] COSY NMR spectra for metabolic flux analysis purposes. Metab. Eng. 3, 322–343 (2001).

    Article  CAS  PubMed  Google Scholar 

  38. McCloskey, D., Young, J. D., Xu, S., Palsson, B. O. & Feist, A. M. MID Max: LC-MS/MS method for measuring the precursor and product mass isotopomer distributions of metabolic intermediates and cofactors for metabolic flux analysis applications. Anal. Chem. 88, 1362–1370 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 40, D742–D753 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Cordova, L. T., Cipolla, R. M., Swarup, A., Long, C. P. & Antoniewicz, M. R. 13C 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Long, C. P., Gonzalez, J. E., Cipolla, R. M. & Antoniewicz, M. R. Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis. Metab. Eng. 44, 191–197 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Au, J., Choi, J., Jones, S. W., Venkataramanan, K. P. & Antoniewicz, M. R. Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for 13C metabolic flux analysis. Metab. Eng. 26, 23–33 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Long, C. P. & Antoniewicz, M. R. Quantifying biomass composition by gas chromatography/mass spectrometry. Anal. Chem. 86, 9423–9427 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Leighty, R. W. & Antoniewicz, M. R. Parallel labeling experiments with [U-13C]glucose validate E. coli metabolic network model for 13C metabolic flux analysis. Metab. Eng. 14, 533–541 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. 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).

    Article  CAS  PubMed  Google Scholar 

  47. Gopalakrishnan, S. & Maranas, C. D. 13C metabolic flux analysis at a genome-scale. Metab. Eng. 32, 12–22 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Martin, H. G. et al. A method to constrain genome-scale models with 13C labeling data. PLoS Comput. Biol. 11, e1004363 (2015).

    Article  PubMed  Google Scholar 

  49. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Young, J. D. INCA: a computational platform for isotopically non-stationary metabolic flux analysis. Bioinformatics 30, 1333–1335 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Weitzel, M. et al. 13CFLUX2–high-performance software suite for 13C-metabolic flux analysis. Bioinformatics 29, 143–145 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. Shupletsov, M. S. et al. OpenFLUX2: 13C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments. Microb. Cell Fact. 13, 152 (2014).

    PubMed  PubMed Central  Google Scholar 

  53. Kajihata, S., Furusawa, C., Matsuda, F. & Shimizu, H. OpenMebius: an open source software for isotopically nonstationary 13C-based metabolic flux analysis. Biomed. Res. Int. 2014, 627014 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 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).

    Article  CAS  PubMed  Google Scholar 

  55. Long, C. P., Gonzalez, J. E., Feist, A. M., Palsson, B. O. & Antoniewicz, M. R. Dissecting the genetic and metabolic mechanisms of adaptation to the knockout of a major metabolic enzyme in Escherichia coli. Proc. Natl. Acad. Sci. USA 115, 222–227 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. Antoniewicz, M. R., Kelleher, J. K. & Stephanopoulos, G. Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis. Anal. Chem. 79, 7554–7559 (2007).

    Article  CAS  PubMed  Google Scholar 

  57. Fong, S. S., Nanchen, A., Palsson, B. O. & Sauer, U. Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes. J. Biol. Chem. 281, 8024–8033 (2006).

    Article  CAS  PubMed  Google Scholar 

  58. Long, C. P., Gonzalez, J. E., Feist, A. M., Palsson, B. O. & Antoniewicz, M. R. Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring. Metab. Eng. 44, 100–107 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 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).

    Article  CAS  PubMed  Google Scholar 

  60. Antoniewicz, M. R. Dynamic metabolic flux analysis–tools for probing transient states of metabolic networks. Curr. Opin. Biotechnol. 24, 973–978 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. Gebreselassie, N. A. & Antoniewicz, M. R. 13C-metabolic flux analysis of co-cultures: a novel approach. Metab. Eng. 31, 132–139 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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).

    Article  CAS  PubMed  Google Scholar 

  63. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Enjalbert, B., Millard, P., Dinclaux, M., Portais, J. C. & Letisse, F. Acetate fluxes in Escherichia coli are determined by the thermodynamic control of the Pta-AckA pathway. Sci. Rep. 7, 42135 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Taymaz-Nikerel, H., Borujeni, A. E., Verheijen, P. J., Heijnen, J. J. & van Gulik, W. M. Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry. Biotechnol. Bioeng. 107, 369–381 (2010).

    Article  CAS  PubMed  Google Scholar 

  66. Antoniewicz, M. R., Kelleher, J. K. & Stephanopoulos, G. Measuring deuterium enrichment of glucose hydrogen atoms by gas chromatography/mass spectrometry. Anal. Chem. 83, 3211–3216 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by grant NSF MCB-1616332.

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Author notes

  1. Christopher P. Long

    Present address: Ginkgo Bioworks, Boston, MA, USA

Authors and Affiliations

  1. Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA

    Christopher P. Long & Maciek R. Antoniewicz

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Contributions

C.P.L. performed all experiments. C.P.L. and M.R.A. analyzed the data and wrote the paper.

Corresponding author

Correspondence to Maciek R. Antoniewicz.

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Related links

Key references using this protocol

Orbán-Németh, Z. et al. Nat. Protoc. 13, 478–494 (2018): https://doi.org/10.1038/nprot.2017.146

Key data used in this protocol

Orbán-Németh, Z. et al. Nat. Protoc. 13, 478–494 (2018): https://doi.org/10.1038/nprot.2017.146

Supplementary information

Supplementary Data 1

Raw GC–MS data files in CDF file format for the E. coli ΔtpiA case study.

Reporting Summary

Supplementary Data 2

Integrated mass isotopomer distributions for the E. coli ΔtpiA case study.

Supplementary Data 3

Metabolic network model used for 13C-MFA for the E. coli ΔtpiA case study.

Supplementary Data 4

Metran file with default E. coli model.

Supplementary Data 5

Metran file for the E. coli ΔtpiA case study.

Supplementary Data 6

Flux analysis results for the E. coli ΔtpiA case study.

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Long, C.P., Antoniewicz, M.R. High-resolution 13C metabolic flux analysis. Nat Protoc 14, 2856–2877 (2019). https://doi.org/10.1038/s41596-019-0204-0

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