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Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes

Nature Genetics volume 48, pages 519–527 (2016)Cite this article

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Abstract

Type 1 (T1D) and type 2 (T2D) diabetes share pathophysiological characteristics, yet mechanistic links have remained elusive. T1D results from autoimmune destruction of pancreatic beta cells, whereas beta cell failure in T2D is delayed and progressive. Here we find a new genetic component of diabetes susceptibility in T1D non-obese diabetic (NOD) mice, identifying immune-independent beta cell fragility. Genetic variation in Xrcc4 and Glis3 alters the response of NOD beta cells to unfolded protein stress, enhancing the apoptotic and senescent fates. The same transcriptional relationships were observed in human islets, demonstrating the role of beta cell fragility in genetic predisposition to diabetes.

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Figure 1: NOD mouse susceptibility to immune-independent diabetes demonstrated through a sensitized transgenic model.
Figure 2: Transgene-induced beta cell stress results in disturbed insulin processing and glucose intolerance.
Figure 3: Qualitative rather than quantitative differences in the UPR on the B10 and NOD backgrounds.
Figure 4: Genetic control of NOD mouse susceptibility to transgene-induced diabetes.
Figure 5: Xrcc4 mutation drives enhanced susceptibility to senescence.
Figure 6: Reduced Glis3 expression results in enhanced susceptibility to apoptosis.
Figure 7: Dietary change recapitulates the effect of the NOD genetic background on a resistant mouse strain.
Figure 8: Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.

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Acknowledgements

The authors thank P. Jeggo, C. Mathieu, D. Gray and A. Goris for critical insights and D. Pombal, J. Sreenivasan, A. Bullman, M. Koina and T. Dagpo for technical assistance. We acknowledge the Human Tissue Laboratory (HTL) of the Lund University Diabetes Centre (LUDC) for providing high-quality data from human pancreatic islets. This work was supported by the VIB, European Research Council (ERC) and a Juvenile Diabetes Research Foundation (JDRF) Career Development Award (A.L.) and by the National Health and Medical Research Council of Australia (project grant 1028108; C.J.N.). N.O. and V. Lyssenko acknowledge support by a Strategic Research Grant from the Swedish Research Council (2009-1039).

Author information

Author notes

  1. James Dooley, Lei Tian, Susann Schonefeldt and Viviane Delghingaro-Augusto: These authors contributed equally to this work.

  2. Susan M Schlenner and Adrian Liston: These authors jointly supervised this work.

Authors and Affiliations

  1. Center for the Biology of Disease, VIB, Leuven, Belgium

    James Dooley, Lei Tian, Susann Schonefeldt, Josselyn E Garcia-Perez, Emanuela Pasciuto, Dean Franckaert, Vasiliki Lagou, Susan M Schlenner & Adrian Liston

  2. Department of Microbiology and Immunology, University of Leuven, Leuven, Belgium

    James Dooley, Lei Tian, Susann Schonefeldt, Josselyn E Garcia-Perez, Emanuela Pasciuto, Dean Franckaert, Vasiliki Lagou, Susan M Schlenner & Adrian Liston

  3. Australian National University Medical School, Canberra, Australian Capital Territory, Australia

    Viviane Delghingaro-Augusto, Jane E Dahlstrom & Christopher J Nolan

  4. Department of Informatics, Università della Svizzera Italiana, Lugano, Switzerland

    Daniele Di Marino

  5. Lymphocyte Signaling and Development Institute Strategic Programme, Babraham Institute, Cambridge, UK

    Edward J Carr & Michelle A Linterman

  6. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Malmö, Sweden

    Nikolay Oskolkov & Valeriya Lyssenko

  7. Department of Translational Pathophysiology, Steno Diabetes Center, Gentofte, Denmark

    Valeriya Lyssenko

  8. Department of Neurosciences, University of Leuven, Leuven, Belgium

    Vasiliki Lagou

  9. Department of Clinical and Experimental Medicine, University of Leuven, Leuven, Belgium

    Lut Overbergh

  10. Department of Medical Protein Research, VIB, Ghent, Belgium.,

    Jonathan Vandenbussche & Kris Gevaert

  11. Department of Biochemistry, Ghent University, Ghent, Belgium

    Jonathan Vandenbussche & Kris Gevaert

  12. VIB Nucleomics Core, University of Leuven, Leuven, Belgium

    Joke Allemeersch

  13. Immunology-Oncology Section, Maisonneuve-Rosemont Hospital, Montreal, Quebec, Canada

    Genevieve Chabot-Roy & Sylvie Lesage

  14. Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, Quebec, Canada

    Genevieve Chabot-Roy & Sylvie Lesage

  15. Department of Anatomical Pathology, Canberra Hospital, Garran, Australian Capital Territory, Australia

    Jane E Dahlstrom

  16. Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia

    D Ross Laybutt & Chris C Goodnow

  17. Department of Endocrinology, Flinders University, Adelaide, South Australia, Australia

    Nikolai Petrovsky

  18. John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia

    Luis Socha

  19. Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, US National Institutes of Health, Research Triangle Park, North Carolina, USA

    Anton M Jetten

  20. Vesalius Research Center, VIB, Leuven, Belgium

    Diether Lambrechts

  21. Department of Oncology, University of Leuven, Leuven, Belgium

    Diether Lambrechts

  22. Department of Endocrinology, Canberra Hospital, Garran, Australian Capital Territory, Australia

    Christopher J Nolan

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  1. James Dooley
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  2. Lei Tian
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  3. Susann Schonefeldt
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  4. Viviane Delghingaro-Augusto
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  7. Daniele Di Marino
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  8. Edward J Carr
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  9. Nikolay Oskolkov
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  10. Valeriya Lyssenko
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  11. Dean Franckaert
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  12. Vasiliki Lagou
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  14. Jonathan Vandenbussche
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  25. Chris C Goodnow
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  26. Christopher J Nolan
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  27. Sylvie Lesage
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  28. Susan M Schlenner
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  29. Adrian Liston
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Contributions

A.L., S.M.S., S.L., N.P., C.J.N., D.R.L. and C.C.G. designed the study. J.D., L.T., S.S., V.D.-A., J.E.G.-P., E.P., D.D.M., D.F., L.O., J.V., G.C.-R., J.D., L.S., S.M.S., J.E.D. and A.L. performed the experiments. E.J.C., N.O., V. Lyssenko, V. Lagou, J.A., K.G., D.L., M.A.L. and A.L. analyzed results. A.M.J. provided reagents. A.L. wrote the manuscript. All authors discussed results and read and approved the manuscript.

Corresponding author

Correspondence to Adrian Liston.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Equivalent islet insHEL expression on the B10 and NOD backgrounds.

RNA-seq was performed on the islets of wild-type (wt) B10k.Rag−/−, B10k.Rag−/−.insHEL, wt NODk.Rag−/− and (pre-diabetic) NODk.Rag−/−.insHEL mice (n = 3/group). (a) Reads were aligned to a custom genome build combining chicken chromosome 1 with the mouse genome, with normalized counts displayed for chicken Lyz, the gene encoding HEL. (b) Expression of H2k1, of which the transmembrane region is included in the insHEL construct to encode a membrane anchor. Means ± s.e.m.

Supplementary Figure 2 No effect of insHEL insertion on chemokine gene expression from the endogenous locus.

RNA-seq was performed on the islets of B10k.Rag−/−, B10k.Rag−/−.insHEL, NODk.Rag−/− and (pre-diabetic) NODk.Rag−/−.insHEL mice (n = 3/group). Expression of chemokines is given for each strain. Means ± s.e.m.: *P < 0.05.

Supplementary Figure 3 No effect of insHEL insertion on gene expression from the endogenous locus.

The gene insertion site of insHEL was defined by the congenic region remaining in the backcross to the NOD background. The outer boundaries of the integration site were identified on chromosome 12 (64,900,000–71,900,000). RNA-seq was performed on the islets of B10k.Rag−/−, B10k.Rag−/−.insHEL, NODk.Rag−/− and (pre-diabetic) NODk.Rag−/−.insHEL mice (n = 3/group). (a) Expression of genes within the integration boundaries in B10k.Rag−/− islets. (b) For all genes with reliable expression detection within the boundaries, the effect of insHEL expression was assessed as the expression in transgenic islets as a percentage of the expression in non-transgenic islets on the B10k strain (black), NODk strain (white) or average of the two strains (gray). No genes in the interval showed a significant change in expression.

Supplementary Figure 4 Altered insulin expression in the islets of insHEL transgenic mice.

(a) Immunofluorescence analysis was performed on the islets of wild-type (wt) B10k.Rag−/−, B10k.Rag−/−.insHEL, wt NODk.Rag−/− and (pre-diabetic) NODk.Rag−/−.insHEL mice with a polyclonal antibody to insulin, antibody to glucagon and DAPI. Staining is representative of three experiments. Scale bar, 100 μm. (b,c) RNA-seq analysis was performed on the islets of B10k.Rag−/−, B10k.Rag−/−.insHEL, NODk.Rag−/− and (pre-diabetic) NODk.Rag−/−.insHEL mice (n = 3/group). Expression is shown for Ins1 (b) and Ins2 (c). (d,e) ERAI mice were crossed to B10k.Rag−/−.insHEL and NODk.Rag−/−.insHEL mice, and islets were analyzed by flow cytometry. Histograms are shown of insulin (d) and side scatter in insulin-expressing beta cells (e). Results are representative of three experiments. Means ± s.e.m. **p<0.001.

Supplementary Figure 5 Transgene-induced beta cell stress results in disturbed insulin processing.

(a,b) Islets from B10k.Rag0/0, B10k.Rag0/0.insHEL, NODk.Rag0/0 and NODk.Rag0/0.insHEL mice were cultured in vitro in the presence of a high (25 mM) glucose concentration (n = 5/group) and assessed for insulin (a) and proinsulin (b) secretion by ELISA. (c,d) RNA-seq analysis was performed on the islets of B10k.Rag0/0, B10k.Rag0/0.insHEL, NODk.Rag0/0 and (pre-diabetic) NODk.Rag0/0.insHEL mice (n = 3/group). Expression is shown for Pcsk1 (prohormone convertase 1-3) (c) and Pcsk2 (prohormone convertase 2) (d). (e,f) Pancreas immunofluorescence with a polyclonal antibody to insulin, antibody to PC1 and PC3, and DAPI, with mice of the B10k.Rag0/0 and NODk.Rag0/0 backgrounds. Quantification is shown for islet raw fluorescence in the PC1-PC3 channel (n = 10, 16, 17, 16) (e), with representative sections (f). Scale bar, 100 μm. (g–i) Quantification of immunoblot analysis of islets from B10k, B10k.insHEL, NODk and NODk.insHEL mice for PC1-PC3 (g) and PC2 (h), with representative blots (n = 3/group) (i). Means ± s.e.m.: *P < 0.05, **P < 0.001, ***P < 0.0001.

Supplementary Figure 6 Glucose intolerance in insHEL transgenic mice.

(a,b) Blood glucose levels following a glucose tolerance test in B10k.Rag−/− (n = 15) and B10k.Rag−/−.insHEL (n = 17) mice at 12 (a) and 24 (b) weeks of age. (c,d) Blood glucose levels following a glucose tolerance test in (B10k × NODk).F1.Rag−/− (n = 11) and pre-diabetic (B10k × NODk)F1.Rag−/−.insHEL (n = 19) mice at 12 (c) and 24 (d) weeks of age. (e) Blood insulin levels following a glucose tolerance test in B10k (n = 5), B10k.insHEL (n = 4), NODk (n = 15) and NODk.insHEL (n = 9) mice at 12 weeks of age. Means ± s.e.m.: *P < 0.05, **P < 0.001, ***P < 0.0001.

Supplementary Figure 7 Sex hormones control susceptibility to islet stress and diabetes.

(a) Table of the incidence of diabetes at 26 weeks of age in female insHEL transgenic mice. (b) Representative electron microscopy images of beta cells from male and female NODk and NODk.insHEL mice at 12 weeks of age. Scale bar, 5 μm. (c) Images were used to assess the number of insulin granules per cellular cross-section (n = 3/group). (d,e) ERAI mice were crossed to NODk.Rag−/−.insHEL mice, and islets were analyzed by flow cytometry. Histograms show side scatter in insulin-expressing beta cells (d) and insulin (e). Results are representative of three experiments. (f–h) Fasting serum samples from male and female NODk and NODk.insHEL mice at 24 weeks of age were assessed by ELISA for insulin (n = 11, 11, 12, 7) (f), proinsulin (n = 11, 11, 12, 8) (g) and C-peptide (n = 11, 11, 9, 8) (h). (i,j) Pancreas immunofluorescence with a polyclonal antibody to insulin, antibody to PC1 and PC3, and DAPI, with female NODk.Rag−/− mice. Representative sections (scale bar, 100 μm) are shown (i), with quantification of islet raw fluorescence in the PC1-PC3 channel (n = 15, 25) (j). (k) Blood glucose levels in 12-week-old female NODk (n = 10) and NODk.insHEL (n = 9) mice following glucose tolerance test. (l) Average number of islets per pancreatic section in female NODk and NODk.insHEL mice at 28 weeks of age (n = 3 mice/group). (m) Incidence of diabetes in NODk insHEL transgenic male mice, with (n = 24) and without (n = 24) castration. Means ± s.e.m.: *P < 0.05, **P < 0.001, ***P < 0.0001.

Supplementary Figure 8 Expression of insHEL activates similar gene sets in B10 and NOD islets.

(a) Genes with significant expression changes between B10k.Rag−/− and NODk.Rag−/− islets were plotted for average expression. Outliers are annotated, Scg2 is not shown. (b,c) Cytoscape enrichment map for significant gene sets (FDR < 0.001) between B10k.Rag−/− islets and B10k.Rag−/−.insHEL islets (b) or NODk.Rag−/− islets and NODk.Rag−/−.insHEL islets (c). UPR and Xbp1 response gene sets are shown in yellow.

Supplementary Figure 9 Expression of insHEL induces similar transcriptional changes in qualitatively different B10 and NOD islets.

(a,b) Expression of genes significantly upregulated between B10k.Rag−/− islets and B10k.Rag−/−.insHEL islets (a) or between NODk.Rag−/− islets and NODk.Rag−/−.insHEL islets (b), with the average expression shown across all samples. Data represented are normalized to the expression in B10k.Rag−/− islets, with the average percentage and outliers annotated. H2k1 is not shown. (c,d) Expression of genes significantly downregulated between B10k.Rag−/− islets and B10k.Rag−/−.insHEL islets (c) or between NODk.Rag−/− islets and NODk.Rag−/−.insHEL islets (d), with the average expression shown across all samples. Data represented are normalized to the expression in B10k.Rag−/− islets, with the average percentage and outliers annotated.

Supplementary Figure 10 Molecular modeling of the effect of p.Ala27Thr and p.Glu125Asp mutations on Xrcc4 stability.

Simulation of the movements of the DNA ligase IV complex during 100-ns simulation. (a,b) Representative snapshots of the B10 allele (a) and NOD allele (b) for the DNA ligase IV complex extracted from the two trajectories at 0 ns, 60 ns, 80 ns and 100 ns. (c) Root-mean-square deviation of the BRCT2 domain of ligase 4 calculated with respect to the fixed position of BRCT1. Movements after the initial 70 ns of equilibrium were considered reliable. The black and gray lines represent ligase IV from the B10 and NOD alleles, respectively. Results are representative of four simulations.

Supplementary Figure 11 DNA ligase 4 hypomorph enhances susceptibility to diabetes.

B10k.insHEL mice were intercrossed with the ligase 4 hypomorph Tyr288Cys, and diabetes incidence was analyzed in B10 (n = 6), B10.Lig4Y228C/+ (n = 5), B10.insHEL (n = 7) and B10.Lig4Y228C/+.insHEL (n = 11) littermates. **P < 0.01.

Supplementary Figure 12 Transgene-induced beta cell stress results in disturbed insulin processing and glucose intolerance on the Glis3 heterozygous background.

(a–d) Fasting serum samples from wild-type (wt) B10 (n = 30), wt B10.Glis3+/– (n = 6), B10.insHEL (n = 51) and B10.Glis3+/–.insHEL (n = 7) mice at 10 weeks of age were assessed by ELISA for insulin (a), proinsulin (b), C-peptide (c) and proinsulin/insulin ratio (d). (e,f) Blood glucose levels in 12-week-old B10 (n = 6), B10.insHEL (n = 1), B10.Glis3+/– (n = 3) and (non-diabetic) B10.Glis3+/–.insHEL (n = 4) mice following glucose tolerance test (n = 28, 47, 9, 21) (e) or insulin tolerance test (n = 8, 17, 3, 13) (f). Means ± s.e.m.: *P < 0.05, **P < 0.001, ***P < 0.0001.

Supplementary Figure 13 Expression of GLIS3 and MANF in the islets of patients with T2D.

mRNA expression in human pancreatic islets from healthy individuals (n = 105) and individuals diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a,b) Expression of GLIS3 (a) and MANF (b) in islets from healthy individuals as compared to islets from individuals with T2D. The median and interquartile range (box) are shown, with error bars indicating 1.5xIQR (interquartile range). Individual values are shown if beyond 1.5xIQR.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Tables 1 and 2. (PDF 4390 kb)

Supplementary Data Set 1

Transcriptional analysis of the effect of insHEL on the B10 and NOD backgrounds. (XLSX 17127 kb)

Supplementary Data Set 2

Network analysis of the effect of insHEL on the B10 and NOD backgrounds. (XLSX 2954 kb)

Supplementary Data Set 3

Proteomics analysis of the effect of insHEL on the B10 and NOD backgrounds. (XLSX 153 kb)

Supplementary Data Set 4

Genomic variation analysis of islet-expressed genes on the B10 and NOD backgrounds. (XLSX 1842 kb)

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Dooley, J., Tian, L., Schonefeldt, S. et al. Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes. Nat Genet 48, 519–527 (2016). https://doi.org/10.1038/ng.3531

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