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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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 5–7 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.
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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.
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.
Peer review information Primary Handling Editor: Elena Bellafante.
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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
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
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.
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.
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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
Nature Metabolism (2020)