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Cytosolic and mitochondrial NADPH fluxes are independently regulated

Nature Chemical Biology volume 19, pages 837–845 (2023)Cite this article

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Abstract

Although nicotinamide adenine dinucleotide phosphate (NADPH) is produced and consumed in both the cytosol and mitochondria, the relationship between NADPH fluxes in each compartment has been difficult to assess due to technological limitations. Here we introduce an approach to resolve cytosolic and mitochondrial NADPH fluxes that relies on tracing deuterium from glucose to metabolites of proline biosynthesis localized to either the cytosol or mitochondria. We introduced NADPH challenges in either the cytosol or mitochondria of cells by using isocitrate dehydrogenase mutations, administering chemotherapeutics or with genetically encoded NADPH oxidase. We found that cytosolic challenges influenced NADPH fluxes in the cytosol but not NADPH fluxes in mitochondria, and vice versa. This work highlights the value of using proline labeling as a reporter system to study compartmentalized metabolism and reveals that NADPH homeostasis in the cytosolic and mitochondrial locations of a cell are independently regulated, with no evidence for NADPH shuttle activity.

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Fig. 1: Metabolic pathway diagrams.
Fig. 2: Mutations in IDH1 and IDH2 lead to compartment-specific alterations in NADPH metabolism.
Fig. 3: NADPH is produced by different pathways depending on the location of the NADPH demand.
Fig. 4: TPNOX expression in the cytosol and mitochondria induces compartment-specific alterations in NADPH metabolism.
Fig. 5: Chemotherapeutics induce compartmentalized changes in NADPH fluxes.

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Data availability

Source data are provided for figures, extended data figures and supplementary figures. All supplementary figures, tables and notes are included in the Supplementary Information. The LC-MS files have been deposited in the MassIVE database under the accession code MSV000090926 (ftp://massive.ucsd.edu/MSV000090926/).

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Acknowledgements

This work was supported by funding from National Institutes of Health grant nos. R35ES028365 (G.J.P.) and R24OD024624 (G.J.P.).

Author information

Authors and Affiliations

  1. Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA

    Xiangfeng Niu, Ethan Stancliffe, Susan J. Gelman, Lingjue Wang, Michaela Schwaiger-Haber, Joe L. Rowles III, Leah P. Shriver & Gary J. Patti

  2. Washington University Center for Metabolomics and Isotope Tracing, St. Louis, MO, USA

    Xiangfeng Niu, Ethan Stancliffe, Susan J. Gelman, Lingjue Wang, Michaela Schwaiger-Haber, Joe L. Rowles III, Leah P. Shriver & Gary J. Patti

  3. Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA

    Ethan Stancliffe, Leah P. Shriver & Gary J. Patti

  4. Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA

    Gary J. Patti

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Contributions

X.N. and G.J.P. designed the study. X.N. prepared the samples and collected most of the experimental data. E.S. built the metabolic flux models and performed the computational analysis. S.J.G. contributed to experiments assessing relative PPP flux in IDH1 and IDH2 cells. L.W. conducted dose–response analysis by using the TOXcms R package. J.L.R. helped prepare HCT116 cells expressing TPNOX. X.N., E.S., L.P.S. and G.J.P. wrote the paper. M.S.-H. and all authors contributed to data analysis, data interpretation and approved the paper.

Corresponding author

Correspondence to Gary J. Patti.

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Competing interests

G.J.P. is a scientific advisory board member for Cambridge Isotope Laboratories and founder of Panome Bio. The Patti Laboratory has a research collaboration agreement with Thermo Fisher Scientific.

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Nature Chemical Biology thanks Li Chen, Seth Parker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Model of cytosolic and mitochondrial NADPH fluxes.

a, Model to assess the distribution of cytosolic NADPH fluxes in cells labeled with 3-2H glucose. b, Model to assess the distribution of mitochondrial NADPH fluxes in cells labeled with 4-2H glucose. PPP, pentose phosphate pathway; G6P, glucose 6-phosphate; P5C, pyrroline 5-carboxylic acid; NADPH[c], cytosolic NADPH; NADPH[m], mitochondrial NADPH; NNT, nicotinamide nucleotide transhydrogenase; PYCR, P5C reductase; PYCR1/2, subtypes of PYCR in mitochondria; PYCRL, subtype of PYCR in cytosol; MDH, malate dehydrogenase; ME, malic enzyme.

Extended Data Fig. 2 Effect of IDH1 and IDH2 mutations on proline, G6P, P5C, and malate labeling.

a, Time course of proline M + 2H enrichment in HCT116 wild-type cells labeled with 3-2H glucose. b, Time course of proline M + 2H enrichment in HCT116 wild-type cells labeled with 4-2H glucose. c, Proline M + 2H enrichment in cells labeled with 3-2H glucose for 48 hours. d, G6P M + 2H enrichment in cells labeled with 3-2H glucose for 48 hours. e, P5C M + 2H enrichment in cells labeled with 4-2H glucose for 48 hours. f, Malate M + 2H enrichment in cells labeled with 4-2H glucose for 48 hours. (c–f) Although differences in the ratio of proline to G6P labeling and the ratio of P5C to malate labeling are driven by the numerator in our cells, we include G6P and malate in our calculations to normalize potential differences in upstream metabolic processes that are independent of NADPH (for example, a change in glucose consumption). WT, wild-type HCT116 cells; mIDH1, mutant IDH1 cells; mIDH2, mutant IDH2 cells; G6P, glucose 6-phosphate; P5C, pyrroline 5-carboxylic acid; 2HG, 2-hydroxyglutarate. Values are mean ± s.d; n = 3 biologically independent samples (a–f). Statistically significant differences were calculated by using a one-way ANOVA followed by Dunnett’s multiple comparison test (c-f). NS= no significant difference.

Source data

Extended Data Fig. 3 Mutant IDH protein inactivation by inhibitors alters NADPH fluxes induced by mutant IDH.

To confirm that the observed changes in cytosolic and mitochondrial NADPH metabolism were caused by compartmentalized (D) 2-hydroxyglutarate (2HG) production, we repeated the analysis in the presence of pharmacological inhibitors that selectively inactivate mutant IDH protein. We used AGI-5198 to inhibit 2HG production by mutant IDH1 and enasidenib to inhibit 2HG production by mutant IDH2. As anticipated, AGI-5198 reduced intracellular 2HG levels in IDH1 mutants but did not influence intracellular 2HG levels in IDH2 mutants (a). Enasidenib had the opposite effect, reducing intracellular 2HG levels in IDH2 mutants but not IDH1 mutants (a). We treated cells with either AGI-5198 or enasidenib for 48 hours, while they were being labeled with 2H glucose. Neither inhibitor influenced the distribution of cytosolic or mitochondrial NADPH fluxes in wild-type cells. AGI-5198, but not enasidenib, did lead to a statistically significant change in the distribution of cytosolic NADPH fluxes in IDH1 mutants (b-d). Only enasidenib, on the other hand, influenced the distribution of mitochondrial NADPH fluxes in IDH2 mutants (e-h). These rescue experiments provide additional evidence that localized production of 2HG has compartment-specific effects on NADPH metabolism. a, Relative levels of intracellular 2HG from HCT116 wild-type cells, IDH1 mutants, and IDH2 mutants treated with AGI-5198 or Enasidenib. b, Ratio of proline to G6P enrichment in HCT116 wild-type cells labeled with 3-2H glucose is not affected by inhibitors of mutant IDH. c, Ratio of proline to G6P enrichment in IDH1 mutants labeled with 3-2H glucose. Inhibition of mutant IDH1 with AGI-5198 alters the distribution of cytosolic NADPH fluxes, but enasidenib does not. d, Ratio of proline to G6P enrichment in IDH2 mutants labeled with 3-2H glucose is not affected by inhibitors of mutant IDH. e, Ratio of P5C to malate enrichment in HCT116 wild-type cells labeled with 4-2H glucose is not affected by inhibitors of mutant IDH. f, Ratio of P5C to malate enrichment in IDH1 mutants labeled with 4-2H glucose is not affected by inhibitors of mutant IDH. g, Ratio of P5C to malate enrichment in IDH2 mutants labeled with 4-2H glucose. Inhibition of mutant IDH2 with enasidenib alters the distribution of mitochondrial NADPH fluxes, but AGI-5198 does not . WT, wild-type HCT116 cells; mIDH1, mutant IDH1 cells; mIDH2, mutant IDH2 cells. A concentration of 0.2 μM AGI-5198 and 0.1 μM enasidenib was used. Values are mean ± s.d; n = 3 biologically independent samples (a-g). Statistically significant differences were calculated by using a one-way ANOVA followed by Dunnett’s multiple comparison test (a-g).NS= no significant difference.

Source data

Extended Data Fig. 4 IDH1 and IDH2 mutations in GL261 and TF1 cells lead to compartment-specific alterations in NADPH metabolism.

We applied the same strategy outlined for HCT116 cells to assess changes in the distribution of cytosolic and mitochondrial NADPH fluxes in GL261 murine glioma cells harboring gain-of-function mutations in IDH1 and TF-1 erythroleukemia cells harboring gain-of-function mutations in IDH2. Consistent with our results from HCT116 cells, IDH1 mutations caused alterations in the distribution of cytosolic NADPH fluxes but did not lead to a statistically significant change in the distribution of mitochondrial NADPH fluxes (a-b). IDH2 mutations had the opposite effect of producing statistically significant changes in the distribution of mitochondrial NADPH fluxes but not cytosolic NADPH fluxes (c-d). a, Ratio of proline to G6P enrichment in GL261 cells labeled with 3-2H glucose. b, Ratio of P5C to malate enrichment in GL261 cells labeled with 4-2H glucose. c, Ratio of proline to G6P enrichment in TF-1 cells labeled with 3-2H glucose. d, Ratio of P5C to malate enrichment in TF-1 cells labeled with 4-2H glucose. Values are mean ± s.d; n = 3 biologically independent samples (a-d). Statistically significant differences were calculated by using a two-tailed t-test (a-d). NS= no significant difference.

Source data

Extended Data Fig. 5 Evaluating changes in PPP and one-carbon metabolism.

a, Relative lactate enrichment in cells labeled with 1,2-13C2 glucose for 12 hours. b, Relative glucose uptake as measured by the depletion of glucose from media after culturing cells for 12 hours. c, Enrichment of AMP in cells labeled with 2,3,3-2H3 serine for 48 hours. Values are mean ± s.d; n = 6 biologically independent samples (WT in a and b, c) and n = 3 biologically independent samples (mIDH1 and mIDH2 in a and b). WT, HCT116 wild-type cells; mIDH1, mutant IDH1 cells; mIDH2, mutant IDH2 cells; AMP, adenosine monophosphate. Statistically significant differences were calculated by using a one-way ANOVA followed by Dunnett’s multiple comparison test (a-b). NS= no significant difference.

Source data

Extended Data Fig. 6 Characterizing the effects of cisplatin and doxorubicin.

a, Dose-response analysis for cisplatin administered to HCT116 wild-type cells. Cell proliferation was assessed at increasing concentrations of drug. b, Dose-response analysis for doxorubicin administered to HCT116 wild-type cells. Cell proliferation was assessed at increasing concentrations of drug. c, Percent decreased viability of cells treated with 10 μM cisplatin for 48 hours. Data were obtained by using an MTT assay. Values are mean ± s.d; n = 8 biologically independent samples (a-c, n = 7 biologically independent samples for WT in c). Statistically significant differences were calculated by using a one-way ANOVA followed by Dunnett’s multiple comparison test (c). NS= no significant difference.

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–4, Figs. 1–4 and original TIF images of Supplementary Fig. 3a–c.

Reporting Summary

Supplementary Tables

The peak areas and calculated labeling percentages for proline, malate, P5C and G6P and the raw enrichment of lactate.

Supplementary Data 1

Source data for Supplementary Fig. 1.

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Niu, X., Stancliffe, E., Gelman, S.J. et al. Cytosolic and mitochondrial NADPH fluxes are independently regulated. Nat Chem Biol 19, 837–845 (2023). https://doi.org/10.1038/s41589-023-01283-9

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