Quantitative flux analysis reveals folate-dependent NADPH production (original) (raw)

References

1. Voet, D. V. & Voet, J. G. Biochemistry 3rd edn (John Wiley & Sons, 2004)
   Google Scholar
2. Jiang, P., Du, W., Mancuso, A., Wellen, K. E. & Yang, X. Reciprocal regulation of p53 and malic enzymes modulates metabolism and senescence. Nature 493, 689–693 (2013)
   Article ADS CAS Google Scholar
3. Son, J. et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature 496, 101–105 (2013)
   Article ADS CAS Google Scholar
4. Lee, W. N. et al. Mass isotopomer study of the nonoxidative pathways of the pentose cycle with [1,2–13C2]glucose. Am. J. Physiol. 274, E843–E851 (1998)
   Article CAS Google Scholar
5. 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 Google Scholar
6. Fan, T. W. et al. Rhabdomyosarcoma cells show an energy producing anabolic metabolic phenotype compared with primary myocytes. Mol. Cancer 7, 79 (2008)
   Article Google Scholar
7. Brekke, E. M., Walls, A. B., Schousboe, A., Waagepetersen, H. S. & Sonnewald, U. Quantitative importance of the pentose phosphate pathway determined by incorporation of 13C from [2–13C]- and [3–13C]glucose into TCA cycle intermediates and neurotransmitter amino acids in functionally intact neurons. J. Cereb. Blood Flow Metab. 32, 1788–1799 (2012)
   Article CAS Google Scholar
8. Lu, W. et al. Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer. Anal. Chem. 82, 3212–3221 (2010)
   Article CAS Google Scholar
9. Circu, M. L., Maloney, R. E. & Aw, T. Y. Disruption of pyridine nucleotide redox status during oxidative challenge at normal and low-glucose states: implications for cellular adenosine triphosphate, mitochondrial respiratory activity, and reducing capacity in colon epithelial cells. Antioxid. Redox Signal. 14, 2151–2162 (2011)
   Article CAS Google Scholar
10. Shreve, D. S. & Levy, H. R. Kinetic mechanism of glucose-6-phosphate dehydrogenase from the lactating rat mammary gland. Implications for regulation. J. Biol. Chem. 255, 2670–2677 (1980)
    CAS PubMed Google Scholar
11. Price, N. E. & Cook, P. F. Kinetic and chemical mechanisms of the sheep liver 6-phosphogluconate dehydrogenase. Arch. Biochem. Biophys. 336, 215–223 (1996)
    Article CAS Google Scholar
12. Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl Acad. Sci. USA 104, 1777–1782 (2007)
    Article ADS CAS Google Scholar
13. Degenhardt, K., Chen, G., Lindsten, T. & White, E. BAX and BAK mediate p53-independent suppression of tumorigenesis. Cancer Cell 2, 193–203 (2002)
    Article CAS Google Scholar
14. Folger, O. et al. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011)
    Article Google Scholar
15. Tibbetts, A. S. & Appling, D. R. Compartmentalization of mammalian folate-mediated one-carbon metabolism. Annu. Rev. Nutr. 30, 57–81 (2010)
    Article CAS Google Scholar
16. Christensen, K. E. & Mackenzie, R. E. Mitochondrial methylenetetrahydrofolate dehydrogenase, methenyltetrahydrofolate cyclohydrolase, and formyltetrahydrofolate synthetases. Vitam. Horm. 79, 393–410 (2008)
    Article CAS Google Scholar
17. Locasale, J. W. et al. Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nature Genet. 43, 869–874 (2011)
    Article CAS Google Scholar
18. Possemato, R. et al. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 476, 346–350 (2011)
    Article ADS CAS Google Scholar
19. Maddocks, O. D. et al. Serine starvation induces stress and p53-dependent metabolic remodelling in cancer cells. Nature 493, 542–546 (2013)
    Article ADS CAS Google Scholar
20. Zhang, W. C. et al. Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis. Cell 148, 259–272 (2012)
    Article CAS Google Scholar
21. Nilsson, R. et al. Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer. Nature Commun. 5, 3128 (2014)
    Article ADS Google Scholar
22. Ayromlou, H., Hajipour, B., Hossenian, M. M., Khodadadi, A. & Vatankhah, A. M. Oxidative effect of methotrexate administration in spinal cord of rabbits. J. Pak. Med. Assoc. 61, 1096–1099 (2011)
    PubMed Google Scholar
23. Bradley, K. K. & Bradley, M. E. Purine nucleoside-dependent inhibition of cellular proliferation in 1321N1 human astrocytoma cells. J. Pharmacol. Exp. Ther. 299, 748–752 (2001)
    CAS PubMed Google Scholar
24. Tedeschi, P. M. et al. Contribution of serine, folate and glycine metabolism to the ATP, NADPH and purine requirements of cancer cells. Cell Death Dis. 4, e877 (2013)
    Article CAS Google Scholar
25. Ye, J. et al. Pyruvate kinase M2 promotes de novo serine synthesis to sustain mTORC1 activity and cell proliferation. Proc. Natl Acad. Sci. USA 109, 6904–6909 (2012)
    Article ADS CAS Google Scholar
26. Chaneton, B. et al. Serine is a natural ligand and allosteric activator of pyruvate kinase M2. Nature 491, 458–462 (2012)
    Article ADS CAS Google Scholar
27. Anastasiou, D. et al. Inhibition of pyruvate kinase M2 by reactive oxygen species contributes to cellular antioxidant responses. Science 334, 1278–1283 (2011)
    Article ADS CAS Google Scholar
28. Jain, M. et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336, 1040–1044 (2012)
    Article ADS CAS Google Scholar
29. Warburg, O. On the origin of cancer cells. Science 123, 309–314 (1956)
    Article ADS CAS Google Scholar
30. 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)
    Article ADS CAS Google Scholar
31. Mathew, R., Degenhardt, K., Haramaty, L., Karp, C. M. & White, E. Immortalized mouse epithelial cell models to study the role of apoptosis in cancer. Methods Enzymol. 446, 77–106 (2008)
    Article CAS Google Scholar
32. Munger, J. et al. Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy. Nature Biotechnol. 26, 1179–1186 (2008)
    Article CAS Google Scholar
33. Lemons, J. M. et al. Quiescent fibroblasts exhibit high metabolic activity. PLoS Biol. 8, e1000514 (2010)
    Article Google Scholar
34. Melamud, E., Vastag, L. & Rabinowitz, J. D. Metabolomic analysis and visualization engine for LC-MS data. Anal. Chem. 82, 9818–9826 (2010)
    Article CAS Google Scholar
35. Millard, P., Letisse, F., Sokol, S. & Portais, J. C. IsoCor: correcting MS data in isotope labeling experiments. Bioinformatics 28, 1294–1296 (2012)
    Article CAS Google Scholar
36. Yuan, Z. & Hammes, G. G. Elementary steps in the reaction mechanism of chicken liver fatty acid synthase. pH dependence of NADPH binding and isotope rate effect for beta-ketoacyl reductase. J. Biol. Chem. 259, 6748–6751 (1984)
    CAS PubMed Google Scholar
37. Yuan, J., Bennett, B. D. & Rabinowitz, J. D. Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nature Protocols 3, 1328–1340 (2008)
    Article CAS Google Scholar
38. Eruslanov, E. & Kusmartsev, S. Identification of ROS using oxidized DCFDA and flow-cytometry. Methods Mol. Biol. 594, 57–72 (2010)
    Article CAS Google Scholar
39. Lorans, G. & Phang, J. M. Proline synthesis and redox regulation: differential functions of pyrroline-5-carboxylate reductase in human lymphoblastoid cell lines. Biochem. Biophys. Res. Commun. 101, 1018–1025 (1981)
    Article CAS Google Scholar
40. Pawelek, P. D. & MacKenzie, R. E. Methenyltetrahydrofolate cyclohydrolase is rate limiting for the enzymatic conversion of 10-formyltetrahydrofolate to 5,10-methylenetetrahydrofolate in bifunctional dehydrogenase-cyclohydrolase enzymes. Biochemistry 37, 1109–1115 (1998)
    Article CAS Google Scholar

Download references

Acknowledgements

The iBMK parental and Akt cell lines were generously provided by E. White. The 14C-labelled CO2 release experiments were conducted with the help of E. Suh and H. Coller. NMR measurement of formate was carried out with the help of I. Lewis. We thank H. Djaballah and the High-Throughput Drug Screening Facility at MSKCC for supplying the hairpins, and M. Vander Heiden and his laboratory members for discussions. This work was supported by Stand Up To Cancer and NIH R01 grants CA163591, AI097382, and CA105463, P01 grant CA104838 and P50 grant GM071508. J.F. is a Howard Hughes Medical Institute (HHMI) international student research fellow. J.J.K. is a Hope Funds for Cancer Research fellow (HFCR-11-03-01).

Author information

Author notes

1. Jing Fan and Jiangbin Ye: These authors contributed equally to this work.

Authors and Affiliations

1. Department of Chemistry and Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, 08540, New Jersey, USA
   Jing Fan, Jurre J. Kamphorst, Tomer Shlomi & Joshua D. Rabinowitz
2. Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
   Jiangbin Ye & Craig B. Thompson
3. Department of Computer Science, Technion – Israel Institute of Technology, Haifa 32000, Israel,
   Tomer Shlomi

Authors

1. Jing Fan
   You can also search for this author inPubMed Google Scholar
2. Jiangbin Ye
   You can also search for this author inPubMed Google Scholar
3. Jurre J. Kamphorst
   You can also search for this author inPubMed Google Scholar
4. Tomer Shlomi
   You can also search for this author inPubMed Google Scholar
5. Craig B. Thompson
   You can also search for this author inPubMed Google Scholar
6. Joshua D. Rabinowitz
   You can also search for this author inPubMed Google Scholar

Contributions

J.F. and J.D.R. conceived the study. J.F., J.Y., C.B.T. and J.D.R. designed the experiments. J.F., J.Y. and J.J.K. performed the experiments. T.S. and J.F. conducted the computational analyses. J.D.R. and J.F., assisted by J.Y., T.S. and C.B.T., wrote the manuscript.

Corresponding author

Correspondence toJoshua D. Rabinowitz.

Ethics declarations

Competing interests

J.D.R. is the only author with a competing financial interest with respect to the current manuscript. He is involved in the founding of Raze Therapeutics.

Extended data figures and tables

Extended Data Figure 1 Probing the fractional contribution of the oxidative pentose phosphate pathway to NADPH production with [2H]glucose.

a, Example of LC–MS chromatogram of M+0 and M+1 forms of NADPH and NADP+. Plotted values are 5 p.p.m. mass window around each compound. b, Extent of NADPH labelling must be corrected for extent of glucose-6-phosphate labelling. Incomplete labelling can occur due to influx from glycogen or hydrogen-deuterium exchange. c, Labelling fraction of glucose-6-phosphate and fructose-1,6-phosphate in iBMK cells with and without activated Akt (20 min after switching into [1-2H]glucose). d, Labelling fraction of fructose-1,6-phosphate and 6-phosphogluconate after feeding [1-2H]glucose. Labelling fraction of fructose-1,6-phosphate reflects the labelling of glucose-6-phosphate, whose peak after addition of the [2H]glucose was not sufficiently resolved from other LC–MS peaks in HEK293T and MDA-MB-468 cells to allow precise quantification of its labelling directly. The difference in the labelling fraction between glucose-6-phosphate and 6-phosphogluconate reflects the fraction of deuterium labelling specifically at position 1 of glucose-6-phosphate. e, Due to the kinetic isotope effect, feeding of deuterium tracer can potentially alter pathway fluxes. To assess whether the feeding of [1-2H]glucose creates a bottleneck in the oxidative pentose phosphate pathway, we measured the relative concentration of oxidative pentose phosphate pathway intermediates with or without feeding of [1-2H]glucose. No significant changes were observed. f, Effect of different mechanisms of correcting for the deuterium kinetic isotope effect on fractional contribution of oxidative pentose phosphate pathway to NADPH production. g, Effect of different mechanisms of correcting for the deuterium kinetic isotope effect on calculated total NADPH production rate. The correction mechanisms are: (1) no kinetic isotope effect (_C_KIE = 1), (2) no effect on total pathway flux but preferential utilization of 1H over 2H-labelled substrate (equation (4) of main text) (the smallest reasonable correction, and the one applied in the main text), or (3) full kinetic isotope effect observed for the isolate enzyme with associated decrease in total pathway flux (Eqn. 6 of Methods) (the largest reasonable correction). All results are mean ± s.d., n ≥ 2 biological replicates from a single experiment and results were confirmed in multiple experiments.

Extended Data Figure 2 Two independent measurement methods give consistent oxidative pentose phosphate pathway fluxes.

a, Diagram of [1-14C]glucose and [6-14C]glucose metabolism through glycolysis and the oxidative pentose phosphate pathway. The oxidative pentose phosphate pathway specifically releases glucose C1 as CO2, whereas all other CO2-releasing reactions are downstream of triose phosphate isomerase (TPI). As TPI renders C1 and C6 of glucose indistinguishable (both positions become C3 of glyceraldehyde-3-phosphate), the difference in CO2 release from C1 versus C6, multiplied by two, gives the absolute rate of NADPH production via oxidative pentose phosphate pathway. A potential complication involves carbon scrambling via the reactions of the non-oxidative pentose phosphate pathway, but this was negligible (see Extended Data Fig. 3). b, Complete carbon labelling of glucose-6-phosphate. Glucose-6-phosphate was labelled completely (> 99%) within 2 h of switching cells into [U-13C]glucose. c, CO2 release rate from [1-14C]glucose and [6-14C]glucose. d, Pool size of 6-phosphogluconate. e, Kinetics of glucose-6-phosphate and 6-phosphogluconate labelling upon switching cells to [U-13C]glucose. f, Overlay upon the 6-phosphogluconate data from e of simulated labelling curves based on the flux that best fits the labelling kinetics (blue) (see Methods), and the flux from 14CO2 release measurements (green). g, Calculated fluxes and 95% confidence intervals based on kinetics of 6-phosphogluconate labelling from [U-13C-]glucose, compared to radioactive CO2 release from [1-14C]glucose and [6-14C]glucose. The two approaches give consistent results, with the 14CO2 release data being more precise. Mean ± s.d., n = 3.

Extended Data Figure 3 The extent of carbon scrambling via non-oxidative pentose phosphate pathway is insufficient to substantially affect oxidative pentose phosphate pathway flux determination using [1-14C]glucose and [6-14C]glucose, with most carbon entering oxidative pentose phosphate pathway directed towards nucleotide synthesis.

a, Schematic of glycolysis and pentose phosphate pathway showing fate of glucose C6. Note that glucose C6 occupies the phosphorylated position (that is, the last carbon) in every intermediate. Thus, upon catabolism to pyruvate, glucose C6 always becomes pyruvate C3, irrespective of any potential scrambling reactions. b, Schematic of glycolysis and pentose phosphate pathway showing fate of glucose C1. Glucose C1 can be scrambled via the non-oxidative pentose phosphate pathway, moving to C3 (red boxes) or C6 as shown here. The forms shown in the green boxes were not experimentally observed. As glucose C3 becomes pyruvate C1 (the carboxylic acid carbon of pyruvate), which is selectively released as CO2 by pyruvate dehydrogenase, scrambling of C1 to C3 can potentially increase CO2 release from glucose C1 relative to C6. This is ruled out in panels d and e. c, Feeding [1-13C]glucose or [6-13C]glucose results in 50% labelling of 3-phosphoglycerate without any double labelling (that is, M+2), as expected in the absence of scrambling. d, MS/MS method to analyse positional labelling of 1-labelled pyruvate. Collision induced dissociation breaks pyruvate to release the carboxylic acid group as CO2. If the daughter peak of 1-labelled pyruvate does not contain labelled carbon (m/z = 43), the labelling is at the C1 position; otherwise, it is at C2 or C3. e, After feeding [1-13C]glucose or [6-13C]glucose, pyruvate is not labelled at the C1 position (< 0.5%), ruling out extensive scrambling. f, Oxidative pentose phosphate pathway flux is similar to or smaller than ribose demand for nucleotide synthesis. Mean ± s.d., n = 3.

Extended Data Figure 4 Probing the contribution of alternative NADPH producing pathways.

a, Pathway diagram showing potential for [2,3,3,4,4-2H]glutamine to label NADPH via glutamate dehydrogenase and via malic enzyme. Labelled hydrogens are shown in red. b, NADP+ and NADPH labelling patterns (without correction for natural 13C-abundance) after 48 h incubation with [2,3,3,4,4-2H]glutamine. The indistinguishable labelling of NADP+ and NADPH implies lack of NADPH redox active hydrogen labelling. c, Pathway diagram showing potential for [2,3,3-2H]aspartate to label NADPH via isocitrate dehydrogenase. d, NADP+ and NADPH labelling patterns (without correction for natural 13C-abundance) after 48 h incubation with [2,3,3-2H]aspartate. The indistinguishable labelling of NADP+ and NADPH implies lack of redox active hydrogen labelling. e, Diagram of [2,3,3,4,4-2H]glutamine metabolism through TCA cycle, tracing labelled hydrogen. Hydrogen atoms of lighter shade indicate potential H/D exchange with water. f, Malate labelling fraction after cells were supplied with [2,3,3,4,4-2H]glutamine for 48 h. g, Pathway diagram showing potential for [1,2,3-13C]malate (made by feeding [U-13C]glutamine) to label pyruvate and lactate via malic enzyme. h, Extent of malate and pyruvate/lactate 13C-labelling. Cells were incubated with [U-13C]glutamine for 48 h. M+3 pyruvate indicates malic enzyme flux, which may generate either NADH or NADPH. Similar results were obtained also for M+3 lactate, which was used as a surrogate for pyruvate in cases in which lactate was better detected. The corresponding maximal possible malic enzyme-driven NADPH production rate ranges, depending on the cell line, from < 2 nmol µl−1 h−1 (based on the limit of detection of M+3 pyruvate) to 6 nmol µl−1 h−1. Mean ± s.d., n ≥ 2.

Extended Data Figure 5 Computational and experimental evidence for THF-dependent NADPH production.

a, Predicted contribution of folate metabolism to NADPH production based on flux balance analysis, using minimization of total flux as the objective function, across different biomass compositions. The biomass fraction of cell dry weight consisting of protein, nucleic acid and lipid was varied as follows: protein 50–90% with a step size of 10%; RNA/DNA 3–20% with step size of 1%, and lipids 3–20% with step size of 1% (considering only those combinations that sum to no more than 100%). With this range of physiologically possible biomass compositions, the model predicts a median contribution of folate metabolism of 24%. Note that with the constraint of experimentally measured biomass composition, yet without constraining the uptake rate of amino acids other than glutamine to be ≤ 1/3 of the glutamine uptake rate, the contribution of folate pathway to total NADPH production is predicted to be 23%. b, Range of feasible flux through NADPH producing reactions in Recon1 model computed via flux variability analysis under the constraint of maximal growth rate. As shown, the model predicts that each NADPH producing reaction can theoretically have zero flux, with all NADPH production proceeding through alternative pathways. Only reactions whose flux upper bound is greater than zero are shown. Reactions producing NADPH via a thermodynamically infeasible futile cycle were manually removed. As shown, among all NADPH producing reactions, MTHFD has the highest flux consistent with maximal growth. c, Pathway diagram showing potential for [2,3,3-2H]serine to label NADPH via methylene tetrahydrofolate dehydrogenase. d, NADP+ and NADPH labelling pattern after 48 h incubation with [2,3,3-2H]serine (no glycine present in the media). The greater abundance of more heavily labelled forms of NADPH relative to NADP+ indicates redox active hydrogen labelling. Results are mean ± s.d., n ≥ 2 biological replicates from a single experiment and were confirmed in n ≥ 2 experiments. Based on the data in panel d, the contribution of MTHFD1 to cytosolic NADPH production spans a broad range (10–40% of total cytosolic NADPH; the range is due to variation across cell lines, experimental noise, and the large KIE40). This range includes the flux calculated based on purine biosynthetic rate and 14CO2 release from serine (Fig. 3d). Note that the total contribution of the cytosolic folate metabolism to NADPH production can exceed that of MTHFD1, as 10-formyl-THF dehydrogenase also produces NADPH.

Extended Data Figure 6 One-carbon units used in purine and thymidine synthesis are derived from serine.

a, Serine and ATP labelling pattern after 24 h incubation of HEK293T cells with [U-13C]serine. The presence of M+1 to M+4 ATP indicates that serine contributes carbon to purines both through glycine and through one-carbon units derived from serine C3. b, Quantitative analysis of cytosolic one-carbon unit labelling from measured the intracellular ATP, glycine, and serine labelling reveals that most cytosolic 10-formyl-THF assimilated into purines comes from serine. c, [U-13C]serine labels the methyl group that distinguishes dTTP from UTP. d, [U-13C]glycine does not label dTTP. e, The extent of dTTP labelling mirrors the extent of intracellular serine labelling. f, Methionine does not label from [U-13C]glycine. In all experiments, cells were grown in [U-13C]serine or glycine for 48 h. Mean ± s.d., n = 3.

Extended Data Figure 7 Measurement of CO2 release rate from serine and glycine by combination of 14C- and 13C-labelling.

a, 14CO2 release rate when cells are supplied with a medium with a trace amount of [3-14C]serine, [1-14C]glycine or [2-14C]glycine. b, Fraction of intracellular serine labelled in cells grown in DMEM media containing 0.4 mM [3-13C]serine in place of unlabelled serine. The residual unlabelled serine is presumably from de novo synthesis. c, Fraction of intracellular glycine labelled in cells grown in DMEM medium containing 0.4 mM [U-13C]glycine in place of unlabelled glycine. d, CO2 release rates from serine C3, glycine C1 or C2. e, Potential alternative pathway to metabolize glycine or serine into CO2, via pyruvate. f, Pyruvate labelling fraction after 48 h labelling with [U-13C]serine or [U-13C]glycine. The lack of labelling in pyruvate indicates that serine and glycine are not metabolized through this pathway. g, Knockdown of MTHFD2 or ALDH1L2 decreases CO2 release from glycine C2. h, Knockdown of ALDH1L2 decreases the GSH/GSSG ratio. Mean ± s.d., n = 3.

Extended Data Figure 8 In the absence of serine, elevated concentrations of glycine inhibit cell growth and decrease the NADPH/NADP+ ratio.

a, Schematic of serine hydroxymethyltransferase reaction. High glycine may either inhibit forward flux (product inhibition) or drive reserve flux. b, Relative cell number after culturing HEK293T cells for 3 days in regular DMEM, DMEM with no serine or DMEM with no serine and 12.5-times the normal concentration of glycine (5 mM instead of 0.4 mM). c, Relative NADPH/NADP+ ratio (normalized to cells grown in DMEM) after culturing HEK293T cell for 3 days in regular DMEM, DMEM with no serine or DMEM with no serine and 12.5-times the normal concentration of glycine. d, e, Labelling of serine and glycine after feeding [U-13C]serine or [U-13C]glycine reveals reverse serine hydroxymethyltransferase flux. Mean ± s.d., n = 3.

Extended Data Figure 9 Quantitative analysis of NADPH consumption for biomass production and antioxidant defence.

a, Cell doubling times, which are inversely proportional to biomass production rates. b, Cellular protein content. c, Cellular fatty acid content (from saponification of total cellular lipid). d, Quantification of fatty acid synthesis versus import, with synthesis but not import requiring NADPH. HEK293T cells were cultured in [U-13C]glucose and [U-13C]glutamine until pseudo-steady state, and fatty acids saponified from total cellular lipids and their labelling patterns measured (green bars), and production versus import of each fatty acid was stimulated based on this experimental data. The fractional contribution of each route was determined by least square fitting, with the theoretical labelling pattern based on the elucidated routes shown (pink bars). Similar data were obtained also for MD-MBA-468, iBMK-parental, and iBMK-Akt cells (not shown) and used to calculate associated NADPH consumption by fatty acid synthesis. e, Cellular DNA and RNA contents. f, NADPH consumption by de novo DNA synthesis. g, Proline and glutamate labelling patterns after 24 h in [U-13C]glutamine media, which was used to quantitate different proline synthesis routes and associated NADPH consumption. h, Quantitative analysis of cytosolic NADPH consumption in normally growing HEK293T cells (control) and non-growing cell under oxidative stress (150 µM H2O2, 5 h). Total cytosolic NADPH turnover was measured based on the absolute oxidative pentose phosphate pathway flux divided by the fractional contribution of the oxidative pentose phosphate pathway to total NADPH as measured using [2H]NADPH formation from [1-2H]glucose. Mean ± s.d., n = 3.

Extended Data Figure 10 Confirmation of knockdown efficiency by western blot or qPCR.

a, Western blot for G6PD knockdown. b, Western blot for MTHFD1 and MTHFD2 knockdown. c, mRNA level for ME1 knockdown. d, mRNA level for NNT knockdown. e, Western blot for IDH1 and IDH2 knockdown. f, Western blot for ALDH1L2 knockdown. g, Cell doubling times of HEK293T with stable knockdown of indicated genes (results for different hairpins of the same gene were indistinguishable).

PowerPoint slides

Rights and permissions

About this article

Cite this article

Fan, J., Ye, J., Kamphorst, J. et al. Quantitative flux analysis reveals folate-dependent NADPH production.Nature 510, 298–302 (2014). https://doi.org/10.1038/nature13236

Download citation

• Received: 11 March 2013
• Accepted: 06 March 2014
• Published: 04 May 2014
• Issue Date: 12 June 2014
• DOI: https://doi.org/10.1038/nature13236