Glucose-dependent partitioning of arginine to the urea cycle protects β-cells from inflammation

Abstract

Chronic inflammation is linked to diverse disease processes, but the intrinsic mechanisms that determine cellular sensitivity to inflammation are incompletely understood. Here, we show the contribution of glucose metabolism to inflammation-induced changes in the survival of pancreatic islet β-cells. Using metabolomic, biochemical and functional analyses, we investigate the protective versus non-protective effects of glucose in the presence of pro-inflammatory cytokines. When protective, glucose metabolism augments anaplerotic input into the TCA cycle via pyruvate carboxylase (PC) activity, leading to increased aspartate levels. This metabolic mechanism supports the argininosuccinate shunt, which fuels ureagenesis from arginine and conversely diminishes arginine utilization for production of nitric oxide (NO), a chief mediator of inflammatory cytotoxicity. Activation of the PC–urea cycle axis is sufficient to suppress NO synthesis and shield cells from death in the context of inflammation and other stress paradigms. Overall, these studies uncover a previously unappreciated link between glucose metabolism and arginine-utilizing pathways via PC-directed ureagenesis as a protective mechanism.

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Fig. 1: Protective versus non-protective glucose metabolism in human islets undergoing inflammation and attendant metabolite signatures.
Fig. 2: Differential modulation of arginine metabolism by protective versus non-protective GK activation in human islets.
Fig. 3: Protective glucose metabolism directs arginine to the urea cycle away from NO synthesis in islets undergoing inflammation.
Fig. 4: Protective glucose metabolism links aspartate to the urea cycle to counter inflammation.
Fig. 5: PC supports aspartate and ureagenesis, and is required for the protective effects of glucose metabolism.
Fig. 6: PC is required for the protective effects of glucose metabolism in human islets in vivo and their capacity to reverse diabetes in mice.
Fig. 7: PC is sufficient to promote ureagenesis, restrict NO, and protect human islets from inflammation and glucotoxicity.

Data availability

The immunofluorescence data to support the conclusions of this study are available under https://doi.org/10.6084/m9.figshare.11956506 at https://figshare.com/. Uncropped western blots for Extended Data Figs. 1 and 57 are presented as source data with the paper. Proteomic data in Fig. 3a are available in Supplementary Table 1, and native mass spectrometry data files are available for download from the MassIVE archive at the University of California, San Diego (ftp://massive.ucsd.edu/MSV000085082/). RNA-seq data in Fig. 3b are available in https://doi.org/10.1038/s41467-017-00992-9. RNA-seq data in Extended Data Fig. 4 were obtained with permission from E. Dermitzakis (accession no. EGAS00001000442; http://www.ebi.ac.uk/ega/)51.

References

  1. 1.

    Dadon, D. et al. Glucose metabolism: key endogenous regulator of beta-cell replication and survival. Diabetes Obes. Metab. 14, 101–108 (2012).

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    De Nigris, V. et al. Short-term high glucose exposure impairs insulin signaling in endothelial cells. Cardiovasc. Diabetol. 14, 114 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  3. 3.

    Hu, C. M. et al. High glucose triggers nucleotide imbalance through o-glcnacylation of key enzymes and induces kras mutation in pancreatic cells. Cell Metab. 29, 1334–1349 e1310 (2019).

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Manzo, E. et al. Glycolysis upregulation is neuroprotective as a compensatory mechanism in ALS. eLife 8, e45114 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Atkinson, M. A. et al. How does type 1 diabetes develop?: the notion of homicide or beta-cell suicide revisited. Diabetes 60, 1370–1379 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Donath, M. Y., Dalmas, E., Sauter, N. S. & Boni-Schnetzler, M. Inflammation in obesity and diabetes: islet dysfunction and therapeutic opportunity. Cell Metab. 17, 860–872 (2013).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Eizirik, D. L., Colli, M. L. & Ortis, F. The role of inflammation in insulitis and beta-cell loss in type 1 diabetes. Nat. Rev. Endocrinol. 5, 219–226 (2009).

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Kolb, H. & von Herrath, M. Immunotherapy for type 1 diabetes: why do current protocols not halt the underlying disease process? Cell Metab. 25, 233–241 (2017).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Lee, Y. S., Wollam, J. & Olefsky, J. M. An integrated view of immunometabolism. Cell 172, 22–40 (2018).

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Mathis, D. Immunological goings-on in visceral adipose tissue. Cell Metab. 17, 851–859 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Matschinsky, F. M. Assessing the potential of glucokinase activators in diabetes therapy. Nat. Rev. Drug Discov. 8, 399–416 (2009).

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Ljubicic, S. et al. Phospho-BAD BH3 mimicry protects beta cells and restores functional beta cell mass in diabetes. Cell Rep. 10, 497–504 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Cuesta-Munoz, A. L. et al. Severe persistent hyperinsulinemic hypoglycemia due to a de novo glucokinase mutation. Diabetes 53, 2164–2168 (2004).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Grimsby, J. et al. Allosteric activators of glucokinase: potential role in diabetes therapy. Science 301, 370–373 (2003).

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Cullen, K. S., Matschinsky, F. M., Agius, L. & Arden, C. Susceptibility of glucokinase-MODY mutants to inactivation by oxidative stress in pancreatic beta-cells. Diabetes 60, 3175–3185 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Danial, N. N. et al. Dual role of proapoptotic BAD in insulin secretion and beta cell survival. Nat. Med. 14, 144–153 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Gimenez-Cassina, A. & Danial, N. N. Regulation of mitochondrial nutrient and energy metabolism by BCL-2 family proteins. Trends Endocrinol. Metab. 26, 165–175 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Szlyk, B. et al. A phospho-BAD BH3 helix activates glucokinase by a mechanism distinct from that of allosteric activators. Nat. Struct. Mol. Biol. 21, 36–42 (2014).

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Gimenez-Cassina, A. et al. Regulation of hepatic energy metabolism and gluconeogenesis by BAD. Cell Metab. 19, 272–284 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    McKerrecher, D. et al. Design of a potent, soluble glucokinase activator with excellent in vivo efficacy. Bioorg. Med. Chem. Lett. 16, 2705–2709 (2006).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Koppe, L. et al. Urea impairs beta cell glycolysis and insulin secretion in chronic kidney disease. J. Clin. Invest. 126, 3598–3612 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Stickings, P., Mistry, S. K., Boucher, J. L., Morris, S. M. & Cunningham, J. M. Arginase expression and modulation of IL-1β-induced nitric oxide generation in rat and human islets of langerhans. Nitric Oxide 7, 289–296 (2002).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Yoshimatsu, G. et al. Pancreatic beta-cell-derived IP-10/CXCL10 isletokine mediates early loss of graft function in islet cell transplantation. Diabetes 66, 2857–2867 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Bensellam, M., Laybutt, D. R. & Jonas, J. C. The molecular mechanisms of pancreatic beta-cell glucotoxicity: recent findings and future research directions. Mol. Cell Endocrinol. 364, 1–27 (2012).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Cappel, D. A. et al. Pyruvate-carboxylase-mediated anaplerosis promotes antioxidant capacity by sustaining tca cycle and redox metabolism in liver. Cell Metab. 29, 1291–1305 e1298 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Cardaci, S. et al. Pyruvate carboxylation enables growth of SDH-deficient cells by supporting aspartate biosynthesis. Nat. Cell Biol. 17, 1317–1326 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Jensen, M. V. et al. Metabolic cycling in control of glucose-stimulated insulin secretion. Am. J. Physiol. Endocrinol. Metab. 295, E1287–E1297 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Jitrapakdee, S., Wutthisathapornchai, A., Wallace, J. C. & MacDonald, M. J. Regulation of insulin secretion: role of mitochondrial signalling. Diabetologia 53, 1019–1032 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    MacDonald, P. E., Joseph, J. W. & Rorsman, P. Glucose-sensing mechanisms in pancreatic beta-cells. Philos. Trans. R Soc. Lond. B Biol. Sci. 360, 2211–2225 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Prentki, M., Matschinsky, F. M. & Madiraju, S. R. Metabolic signaling in fuel-induced insulin secretion. Cell Metab. 18, 162–185 (2013).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Garcia-Cazorla, A. et al. Pyruvate carboxylase deficiency: metabolic characteristics and new neurological aspects. Ann. Neurol. 59, 121–127 (2006).

    PubMed  Article  Google Scholar 

  32. 32.

    Lao-On, U., Attwood, P. V. & Jitrapakdee, S. Roles of pyruvate carboxylase in human diseases: from diabetes to cancers and infection. J. Mol. Med. (Berl) 96, 237–247 (2018).

    CAS  Article  Google Scholar 

  33. 33.

    Robitaille, K. et al. High-throughput functional genomics identifies regulators of primary human beta cell proliferation. J. Biol. Chem. 291, 4614–4625 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Nordmann, T. M. et al. The role of inflammation in beta-cell dedifferentiation. Sci. Rep. 7, 6285 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    Yang, Z. et al. Inflammatory blockade improves human pancreatic islet function and viability. Am. J. Transplant 5, 475–483 (2005).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Ficicioglu, C., Mandell, R. & Shih, V. E. Argininosuccinate lyase deficiency: longterm outcome of 13 patients detected by newborn screening. Mol. Genet. Metab. 98, 273–277 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Escudero, S. et al. Dynamic regulation of long-chain fatty acid oxidation by a noncanonical Interaction between the MCL-1 BH3 helix and VLCAD. Mol. Cell 69, 729–743 e727 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Fuhrer, T., Heer, D., Begemann, B. & Zamboni, N. High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. Anal. Chem. 83, 7074–7080 (2011).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Gravel, S. P., Andrzejewski, S., Avizonis, D. & St-Pierre, J. Stable isotope tracer analysis in isolated mitochondria from mammalian systems. Metabolites 4, 166–183 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40.

    Nanchen, A., Fuhrer, T. & Sauer, U. Determination of metabolic flux ratios from 13C-experiments and gas chromatography-mass spectrometry data: protocol and principles. Methods Mol. Biol. 358, 177–197 (2007).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    McGuirk, S. et al. PGC-1α supports glutamine metabolism in breast cancer. Cancer Metab. 1, 22 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Kerr, D., Grahame, G. & Nakouzi, G. Assays of pyruvate dehydrogenase complex and pyruvate carboxylase activity. Methods Mol. Biol. 837, 93–119 (2012).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Wang, H. et al. Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas. Nat. Commun. 8, 767 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Wang, P. et al. Combined Inhibition of DYRK1A, SMAD, and trithorax pathways synergizes to induce robust replication in adult human beta cells. Cell Metab. 29, 638–652 e635 (2019).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Hughes, C. S. et al. Ultrasensitive proteome analysis using paramagnetic bead technology. Mol. Syst. Biol. 10, 757–757 (2014).

    PubMed  Article  CAS  Google Scholar 

  46. 46.

    Zhou, F. et al. Genome-scale proteome quantification by DEEP SEQ mass spectrometry. Nat. Commun. 4, 2171 (2013).

  47. 47.

    Ma, B. et al. PEAKS: powerful software for peptidede novo sequencing by tandem mass spectrometry. Rapid Commun. Mass Spectrom. 17, 2337–2342 (2003).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Askenazi, M., Marto, J. A. & Linial, M. The complete peptide dictionary — a meta-proteomics resource. Proteomics 10, 4306–4310 (2010)..

  49. 49.

    Alexander, W. M., Ficarro, S. B., Adelmant, G. & Marto, J. A. multiplierz v2.0: a Python-based ecosystem for shared access and analysis of native mass spectrometry data. Proteomics 17, 1700091 (2017).

    Article  CAS  Google Scholar 

  50. 50.

    Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C. & Geromanos, S. J. Absolute quantification of proteins by LCMSE. Mol. Cell Proteom. 5, 144–156 (2006).

    CAS  Article  Google Scholar 

  51. 51.

    Nica, A. C. et al. Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Res. 23, 1554–1562 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank G. Yellen, B. Spiegelman, N. Kalaany and members of the Danial laboratory for helpful discussions and the Nikon Imaging Center at Harvard Medical School for access to imaging platforms. RNA expression data for comparing enrichment of genes in purified human β-cells compared to whole islets and non-β-cells (Extended Data Fig. 4) were generously provided by E. Dermitzakis. This work was supported by the US NIH grants R01DK078081 (N.N.D.), R01CA219850 (N.N.D. and J.A.M.), R01DK113079 (A.G.-O.), R01DK105015 and R01DK116873 (A.F.S.), P30DK020541 (Einstein-Sinai Diabetes Research Center) (A.G.-O. and A.F.S.), R35CA197583 (L.D.W.), R50CA211399 (G.H.B.), R01CA222218 (J.A.M.), Juvenile Diabetes Research Foundation Grant 2-SRA-2015-58-Q-R (N.N.D.) and Barry and Mimi Sternlicht Type 1 Diabetes Research Fund (N.N.D.). A.F. was supported by a postdoctoral fellowship from the Juvenile Diabetes Research foundation (JDRF). The Integrated Islet Distribution Program (IIDP) is supported by NIH Grant 2UC4DK098085. The Rosalind and Morris Goodman Cancer Research Centre Metabolomics Core Facility is supported by the Canada Foundation for Innovation, Dr. John R. and Clara M. Fraser Memorial Trust, the Terry Fox Foundation in partnership with the Foundation du Cancer du Sein du Quebec and McGill University. The Blais Proteomics Center is supported by the Dana-Farber Strategic Research Initiative. A.M.J.S. is a Fellow of the Royal Society of Canada, and is supported through a Canada Research Chair in Regenerative Medicine and Transplantation Surgery.

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Authors

Contributions

A.F. and N.N.D. designed experiments, performed and analysed metabolomics, biochemical and cell-based studies. T.K. and A.M.J.S. provided human donor islets. D.A., M.G.B., G.B., J.J.K. and R.G.J. provided expertise in metabolomics. A.G.-O. and A.F.S. provided expertise in β-cell biology. J.C.A.-P., C.R. and A.G.-O. carried out human islet xenotransplantation studies, and together with E.K. purified human β-cells. E.K. and A.F.S. performed RNA-seq analysis on purified human β-cells. S.B.F. and J.A.M. carried out proteomic analysis on purified human β-cells. G.H.B. and L.D.W. designed and synthesized stapled peptides. H-S.S. and S.D. performed isothermal titration calorimetry analyses. D.W.C. performed peptide pull-down studies. L.E. provided technical support for mouse islet isolation and in vitro studies. A.F. and N.N.D. wrote the manuscript, which was reviewed by all authors.

Corresponding author

Correspondence to Nika N. Danial.

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Competing interests

J.A.M. serves on the SAB of 908 Devices, and has received sponsored research support from AstraZeneca and Vertex. L.D.W. is a scientific co-founder and shareholder in Aileron Therapeutics. R.G.J. is a scientific advisory board member for Immunomet Therapeutics and consultant for Agios Pharmaceuticals. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Characterization of GK-modulating tools used in this study.

a, Western blots showing expression levels of full length MYC-tagged GK Y214C and BAD BH3 mutant proteins in islets used in Figs. 1b and 2f. Blots are representative of n = 2 independent experiments with similar results. b, Viability of human islets treated with increasing doses of RO0281675 or BAD SAHBA SD and exposed to cytokines as in Fig. 1c. Based on these dose response studies, we elected to use RO0281675 at 3 μM and BAD SAHBA SD at 5 μM throughout all studies. Data are means ± s.d. of 3 technical replicates of islet cultures from one human donor. c, GK activity in human islets treated with vehicle (DMSO), RO0281675, BAD SAHBA SD, BAD SAHBA AAA or a stapled peptide modeled after the BH3 domain of a related BCL-2 family protein (BIM SAHBA). Data are means ± s.d. with n = 4 (Veh and BAD SAHBA SD) or n = 3 (RO0281675, BAD SAHBA AAA or BIM SAHBA) technical replicates of islet cultures from one donor. d, Specific target engagement by BAD SAHBA SD as assessed by the capture of GK with biotinylated BAD SAHBA SD but not BAD SAHBA AAA or BIM SAHBA in INS-1 protein lysates. Western blot with the anti-PC antibody serves as negative control for GK. Input denotes INS-1 lysates not incubated with any stapled peptides or vehicle. Representative experiment is shown out of n = 2 experiments with similar results. e, Isothermal titration calorimetry (ITC) measurements showing the binding of recombinant human GK to BAD SAHBA SD in a 1:1 stoichiometry with binding affinity (dissociation constant, Kd) of ~580 nM (left). ITC using the corresponding unstapled peptide is shown for comparison with a log higher Kd (right). Data are representative of n = 3 independent ITC experiments with similar results. f, Western blots showing efficiency of GK knockdown in islets used in Figs. 1d and 2d. Blots are representative of n = 2 independent experiments with similar results. Source Data

Extended Data Fig. 2 Untargeted metabolomics analysis of human islets undergoing inflammation stress.

Heatmap presentation of LC-MS untargeted metabolomics analysis of human islets showing PBS and cytokine conditions corresponding to Fig. 1h, i. Data are transformed into log fold change for heatmap presentation with 8 technical replicates of total ion counts shown for islets pooled from n = 5 human donors.

Extended Data Fig. 3 Altered arginine metabolism in the context of protective vs non-protective glucose metabolism.

a, Urea and NO levels in human islets treated with the indicated compounds and exposed to cytokines (Fig. 2b), expanded to show the PBS data. PBS urea data are from n = 5 (Veh), n = 3 (RO0281675) and n = 4 (BAD SAHBA SD) human donors. Cytokine urea data are from n = 10 (Veh), n = 7 (RO0281675), and n = 12 (BAD SAHBA SD) donors. PBS NO data are from n = 8 (Veh, RO0281675), and n = 9 (BAD SAHBA SD) donors. Cytokine NO data are from n = 9 (Veh, RO0281675) and n = 8 (BAD SAHBA SD) donors. b, Viability of human islets treated with vehicle (DMSO), the allosteric GK activator (GKA50) or BAD SAHBA SD and exposed to inflammatory cytokines as in Fig. 2c, n = 4 donors. c, Urea and NO levels in human islets expressing the indicated GK and BAD mutants and treated with cytokines (Fig. 2f), expanded to show the PBS data. Urea data for PBS and cytokine conditions are from n = 6 (VC) and n = 7 (GK Y214C, BAD SD and BAD AAA) independent experiments using islet cultures from 2 donors. PBS NO data are from n = 4 (VC), n = 2 (GK Y214C) and n = 4 (BAD SD and BAD AAA) independent experiments using islet cultures from 2 donors. Cytokine NO data are from n = 4 (VC, GK Y214C, BAD SD and BAD AAA) independent experiments using islet cultures from 2 donors. Statistical analyses in (a) and (c) are two-way ANOVA and one-way ANOVA in (b), both with Tukey adjustment for multiple comparisons.

Extended Data Fig. 4 Expression of urea cycle enzymes and related pathways in FACS-purified human β-cells subjected to transcriptomic analyses.

RNAseq analysis of urea cycle enzymes and related pathways in sorted human β-cells and negative-sorted islet cells relative to whole islets. Read counts as RPKM (reads per kilobase per million mapped reads) are normalized to whole islet PKRM to assess enrichment. All urea cycle related enzymes and transporters are enriched (>1) in the β-cell fraction compared to whole islets and the negative-sorted cells.

Extended Data Fig. 5 Increased generation of aspartate from glucose following protective GK activation.

a, 13C fractional labelling of aspartate from13C6 glucose. Data are shown as non-normalized to vehicle PBS and display the fraction of each M+n mass isotopomer out of the total pool of aspartate for each condition. For clarity, statistical comparisons are only shown for each M+n of a given condition (RO0281675, BAD SAHBA SD and BAD SAHBA AAA) compared to the corresponding M+n of vehicle control. Data are pooled means from n = 6 (Veh), n = 5 (RO0281675), and n = 6 (BAD SAHBA SD, BAD SAHBA AAA) independent mouse islet isolations and experiments. b, Western blot analysis of GOT1/2 knockdown efficiency using multiple independent hairpins for data shown in Fig. 4d, e and Extended Data Fig. 5c,d. Blots are representative of n = 2 independent experiments with similar results. c, d, Aspartate (c), urea and NO (d) levels in human islets from the same experiments shown in Fig. 4d,e, displaying the complete set of data on all hairpins tested. Aspartate data are from n = 4 human donors for shCtrl samples and n = 3 donors for knockdown samples. Urea and NO data are from n = 4 and n = 3 donors, respectively. Statistical analyses in (a) are two-way ANOVA showing p-value comparisons for each condition to Veh, and one-way ANOVA in (c-d), both with Tukey adjustment for multiple comparisons. Source Data

Extended Data Fig. 6 Protective glucose metabolism increases pyruvate carboxylase activity in islets undergoing inflammation stress.

a, b, PDH (a) and the ratio of PC/PDH (b) activity in mouse islets labeled with 13C6 glucose, measured as M+2 citrate and the ratio of M+3 malate to M+2 citrate, respectively. Data are from analogous glucose tracer studies as in Fig. 5a, showing n = 8 (Veh), n = 5 (RO0281675, BAD SAHBA AAA) and n = 6 (BAD SAHBA SD) independent experiments for PDH, and n = 8 (Veh, BAD SAHBA AAA), n = 5 (RO0281675) and n = 6 (BAD SAHBA SD) independent experiments for PC/PDH. Statistical analyses were performed using one-way ANOVA with Tukey adjustment for multiple comparisons. c, PC activity in human islets treated with inflammatory cytokines in the context of protective vs non-protective glucose metabolism. Enzyme activity was measured as nmol 14CO2 generated from NaH14CO3, n = 2 human donors in duplicate. d, Validation of on-target PC knockdown and expression level of V5-tagged human PC (hPC) cDNA used to rescue PC expression in human islets treated with a 3′UTR-targeted shRNA against PC in experiments corresponding to Fig. 5d. Blots are representative of n = 2 independent experiments with similar results. e, The GLS inhibitor BPTES (Bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide) does not affect islet urea levels at concentrations that reduce the ratio of glutamate/glutamine (glu/gln, a readout of GLS activity), n = 2 human donors. Source Data

Extended Data Fig. 7 Validation of ARG2 and PC overexpression and knockdown.

a, Western blot analysis of ARG2 and PC expression levels in human islets corresponding to experiments shown in Fig. 7a,b,h,i. Blots are representative of n = 2 independent experiments with similar results. b, Western blot analysis of PC knockdown efficiency in experiments corresponding to Fig. 7c–e. Blots are representative of n = 3 independent experiments with similar results. Source data

Supplementary information

Supplementary Methods and Tables 2 and 3

Example of the contour plots or histograms generated by flow cytometry analysis and sorting of human islet cells for viability (a), NO levels (b) and RNAseq/proteomics studies (c). Supplementary Table 2. Details of the one-way ANOVA statistical analysis in Table 1 performed using Graphpad Prism 8. Supplementary Table 3. Multiple comparisons details for the one-way ANOVA analysis in Table 1 performed using Graphpad Prism 8.

Reporting Summary

Supplementary Table 1

Proteomic analysis of 8.7 × 104 FACS-purified human β-cells to profile protein expression by LC–MS/MS. Data are provided in an excel sheet with accession numbers.

Source data

Source Data Extended Data Fig. 1

Unprocessed western blots

Source Data Extended Data Fig. 5

Unprocessed western blots

Source Data Extended Data Fig. 6

Unprocessed western blots

Source Data Extended Data Fig. 7

Unprocessed western blots

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Fu, A., Alvarez-Perez, J.C., Avizonis, D. et al. Glucose-dependent partitioning of arginine to the urea cycle protects β-cells from inflammation. Nat Metab 2, 432–446 (2020). https://doi.org/10.1038/s42255-020-0199-4

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