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Loss of ZnT8 function protects against diabetes by enhanced insulin secretion

Nature Genetics volume 51, pages 1596–1606 (2019)Cite this article

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Abstract

A rare loss-of-function allele p.Arg138* in SLC30A8 encoding the zinc transporter 8 (ZnT8), which is enriched in Western Finland, protects against type 2 diabetes (T2D). We recruited relatives of the identified carriers and showed that protection was associated with better insulin secretion due to enhanced glucose responsiveness and proinsulin conversion, particularly when compared with individuals matched for the genotype of a common T2D-risk allele in SLC30A8, p.Arg325. In genome-edited human induced pluripotent stem cell (iPSC)-derived β-like cells, we establish that the p.Arg138* allele results in reduced SLC30A8 expression due to haploinsufficiency. In human β cells, loss of SLC30A8 leads to increased glucose responsiveness and reduced KATP channel function similar to isolated islets from carriers of the T2D-protective allele p.Trp325. These data position ZnT8 as an appealing target for treatment aimed at maintaining insulin secretion capacity in T2D.

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Fig. 1: A flowchart describing the study design.
Fig. 2: SLC30A8 p.Arg138* enhances insulin secretion and proinsulin processing during test meal.
Fig. 3: SLC30A8 p.Arg138* and p.Trp325 enhance insulin secretion during OGTT.
Fig. 4: β-like cells derived from SLC30A8 p.Arg138* iPSCs display haploinsufficiency of SLC30A8.
Fig. 5: SLC30A8 knockdown leads to enhanced insulin secretion, proinsulin processing and cell viability in the human pancreatic EndoC-βH1 cells.
Fig. 6: Male Slc30a8 p.Arg138* mice on HFD show enhanced insulin secretion and proinsulin processing.
Fig. 7: SLC30A8- p.Trp325 leads to enhanced insulin secretion in human islets.

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Data availability

Individual level data for the human study can only be obtained via the Biobank of The Institute of Health and Welfare in Finland (https://thl.fi/en/web/thl-biobank). Next-generation sequencing data have been deposited in the SRA database (PRJNA563975) and the processed counts data can be found in the Supplementary Dataset 1. The individual processed data from cell lines (Figs. 4 and 5), mice studies (Fig. 6) and human islet work (Fig. 7) are available in the Source Data files. Additional data supporting the findings of this study are available on request from the corresponding author. Source data for Figs. 2 and 4–7 and Extended Data Figs. 2, 3, 6 and 8 are available online.

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Acknowledgements

We thank the Botnia Study Group for recruiting and studying the participants, J. J. Holst for measuring GLP-1 concentrations and L. Boselli for carrying out mathematical modeling of the OGTT studies. We thank D. Fu (Department of Physiology, The Johns Hopkins School of Medicine) for providing monoclonal anti-ZnT8 antibody. We thank W.-h. Li (Departments of Cell Biology and of Biochemistry, University of Texas Southwestern Medical Center) for providing zinc probe ZIMIR. We thank E. Na for her help with the mouse immunohistochemistry and histology, and C. Green and the Chromosome Dynamics & Genome Engineering Cores at the Wellcome Centre for Human Genetics for support with karotyping and genome editing (funded by the Welcome Trust grant no. 203141). We thank the Sequencing Unit core facility at FIMM Technology Centre supported by University of Helsinki and Biocenter Finland. The Botnia and the PPP-Botnia studies (L.G., T.T.) have been financially supported by grants from Folkhälsan Research Foundation, the Sigrid Juselius Foundation, the Academy of Finland (grant nos 263401, 267882, 312063, to L.G., 312072, to T.T. and 317599, to O.P.D.), Nordic Center of Excellence in Disease Genetics, EU (no. EXGENESIS, EUFP7-MOSAIC FP7-600914), Ollqvist Foundation, Swedish Cultural Foundation in Finland, Finnish Diabetes Research Foundation, Foundation for Life and Health in Finland, Signe and Ane Gyllenberg Foundation, Finnish Medical Society, Paavo Nurmi Foundation, Helsinki University Central Hospital Research Foundation, Perklén Foundation, Närpes Health Care Foundation and Ahokas Foundation, as well as by the Ministry of Education in Finland, Municipal Heath Care Center and Hospital in Jakobstad and Health Care Centers in Vasa, Närpes and Korsholm. The work described in this paper has been supported with funding from collaborative agreements with Pfizer Inc., as well as with Regeneron Genetics Center LLC. The work was also supported by Hjelt Foundation (L.G. and C.B.W.) and Rhapsody (L.G.). J.O.L. was supported by Vinnova (Sweden’s Innovation Agency) (grant no. 2015-01549), Swedish Diabetes Foundation, Albert Påhlsson Foundation, Hjelt Foundations, Crafoord Foundation, Royal Physiographic Society in Lund, Swedish Foundation for Strategic Research (grant no. IRC15-0067), Swedish Research Council (grant no. 2009-1039, Strategic research area Exodiab). E.A. was supported by Crafoord Foundation, Påhlsson Foundation, Swedish Research Council (grant no. Dnr: 2017-02688). O.H. was supported by Diabetes Research Foundation. R.C.B. was supported by Italian Ministry of University and Research (grant no. PRIN 2015373Z39_004) and University of Parma Research Funds. G.R. was supported by a Wellcome Trust Senior Investigator Award (no. WT098424AIA), MRC Programme grants (nos. MR/R022259/1, MR/J0003042/1, MR/L020149/1) and Experimental Challenge Grant (no. DIVA, MR/L02036X/1), MRC (grant no. MR/N00275X/1), Diabetes UK (grant nos. BDA/11/0004210, BDA/15/0005275, BDA 16/0005485) and Imperial Confidence in Concept grants and a Royal Society Wolfson Research Merit Award. A.L.G. is a Wellcome Trust Senior Fellow in Basic Biomedical Science. M.I.M. and P.R. are Wellcome Senior Investigators. This work was funded in Oxford by the Wellcome Trust (grant nos. 095101 and 200837 to A.L.G., 098381 to M.I.M., 106130 to A.L.G. and M.I.M., 203141 to A.L.G., B.D. and M.I.M., 203141 to. M.I.M. and 090531 to P.R.), Medical Research Council (grant no. MR/L020149/1 to M.I.M., A.L.G. and P.R.), European Union Horizon 2020 Programme (T2D Systems) (A.L.G.) and NIH (grant nos. U01-DK105535 and U01-DK085545 to M.I.M. and A.L.G.). The research was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (A.L.G., M.I.M., P.R.). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Author notes

  1. These authors contributed equally: Om Prakash Dwivedi, Mikko Lehtovirta, Benoit Hastoy.

  2. These authors jointly supervised this work: Anna L. Gloyn, Tiinamaija Tuomi, Leif Groop.

Authors and Affiliations

  1. Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland

    Om Prakash Dwivedi, Mikko Lehtovirta, Ola Hansson, Anu Suoranta, Tiinamaija Tuomi & Leif Groop

  2. Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK

    Benoit Hastoy, Nicola L. Beer, Antje Grotz, Soren K. Thomsen, Ioannis Spiliotis, Reshma Ramracheya, Martijn van de Bunt, Patrik Rorsman, Mark I. McCarthy & Anna L. Gloyn

  3. Stem Cells and Metabolism Research Program and Biomedicum Stem Cell Centre, Faculty of Medicine, University of Helsinki, Helsinki, Finland

    Vikash Chandra & Timo Otonkoski

  4. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Nicole A. J. Krentz, Fernando Abaitua, Benoite Champon, Anthony J. Payne, Daniela Moralli, Philipp Kramer, Martijn van de Bunt, Benjamin Davies, Mark I. McCarthy & Anna L. Gloyn

  5. Regeneron Pharmaceuticals, Tarrytown, NY, USA

    Sandra Kleiner, Daniel Gomez, Aris Baras & Jesper Gromada

  6. Department of Experimental Medical Science, Lund University, Lund, Sweden

    Deepak Jain & Jens O. Lagerstedt

  7. Pfizer Inc., Cambridge, MA, USA

    Ann-Marie Richard & Julia Brosnan

  8. Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden

    Rashmi B. Prasad, Ola Hansson, Emma Ahlqvist, Ulrika Krus, Isabella Artner, Claes B. Wolheim & Leif Groop

  9. Section of Cell Biology, Department of Medicine, Imperial College London, Imperial Centre for Translational and Experimental Medicine, Hammersmith, Hospital, London, UK

    Pauline Chabosseau, Andria Theodoulou, Rebecca Cheung & Guy A. Rutter

  10. Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA

    Jason Flannick

  11. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    Jason Flannick

  12. Department of Medicine, University of Verona and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy

    Maddalena Trombetta & Enzo Bonora

  13. Folkhälsan Research Center, Helsinki, Finland

    Leena Sarelin & Tiinamaija Tuomi

  14. Department of Medicine and Surgery, University of Parma School of Medicine and Azienda Ospedaliera Universitaria of Parma, Parma, Italy

    Riccardo C. Bonadonna

  15. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK

    Mark I. McCarthy & Anna L. Gloyn

  16. Abdominal Center, Endocrinology, Helsinki University Central Hospital, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland

    Tiinamaija Tuomi

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  1. Om Prakash Dwivedi
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  40. Mark I. McCarthy
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  46. Tiinamaija Tuomi
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  47. Leif Groop
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M.L., L.S., T.T. and L.G. conducted the human study. E.A., O.H., A.B., O.P.D. and J.F. analyzed the genotype data. M.L., O.P.D., M.T., E.B., R.C.B., T.T. and L.G. analyzed the human data. B.H., A.G., N.L.B., S.K.T., M.v.d.B., V.C., O.P.D., T.O. and A.L.G. characterized the human β cell model. N.A.J.K., F.A., N.L.B., B.C., D.M., P.K., B.D., O.P.D., A.S., M.I.M. and A.L.G. characterized the human iPSC-derived model. U.K., R.B.P., O.P.D., B.H., A.J.P., I.S., R.R., I.A., P.R., M.I.M. and A.L.G. characterized the human islets. S.K., D.G. and J.G. characterized the Slc30a8 p.Arg138* mice. D.J., J.O.L., P.C., A.T., R.C., A.-M.R., J.B. and G.A.R. characterized the rat insulinoma cell line. M.I.M., A.L.G., T.T. and L.G. supervised the project. O.P.D., M.L., B.H., N.A.J.K., S.K., P.R., C.B.W., A.L.G., T.T. and L.G. wrote the manuscript. All authors revised the manuscript.

Corresponding author

Correspondence to Leif Groop.

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Competing interests

L.G. has received research funding from Pfizer Inc., Regeneron Pharmaceuticals, Eli Lilly and Astra Zeneca. N.L.B. and M.v.d.B are now employees of Novo Nordisk, although all experimental work was carried out under employment at the University of Oxford. A.L.G. has received honoraria from Novo Nordisk and Merck. M.I.M. serves on advisory panels for Pfizer, Novo Nordisk, Zoe Global; has received honoraria from Pfizer, Novo Nordisk and Eli Lilly; has stock options in Zoe Global; has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier, Takeda. G.A.R. is a consultant for Sun Pharma and has received grant funding from Servier. J.O.L. has received research funding from Pfizer Inc. and Novo Nordisk A/S.

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Extended data

Extended Data Fig. 1 Families of SLC30A8-p.Arg138* carriers involved in genotype-based recall for human in vivo study.

Families (≥ 2 members per family) of SLC30A8-p.Arg138* carriers participated in genotype-based recall (test meal) study. To protect anonymity of the carriers, the gender of the offspring is not revealed and some pedigrees have been split to smaller nuclear families. The carrier status of p.Arg138* is shown by yellow (p.Arg138Arg) or magenta (p.Arg138*), that of p.Trp325Arg by vertical (p.Arg325Arg), horizontal (p.Trp325Trp) or diagonal (p.Trp325Arg) lines. The white color (with no lines) represents individuals with unknown status for genotype and phenotype.

Extended Data Fig. 2 Association of SLC30A8 p.Arg138* and p.Trp325Arg with free fatty acids, hormones, and insulin clearance during test meal.

Association of SLC30A8 p.Arg138* and p.Trp35Arg variant with a, serum (S)-C-peptide b, S-proinsulin c, plasma (P)-Glucagon d, Total S-GLP-1 e, S-free fatty acid (FFA) concentrations f, Insulin-C-peptide ratio and g, model-based insulin clearance index during test meal. Left panel: Carriers (red, n=50-54) vs. non-carriers (black, n=37-47) of p.Arg138*. Middle panel: Carriers of p.Arg138* (red, n=50-54) vs. p.Arg138Arg having the common risk variant p.Arg325 (blue, n=25-31). Right panel: Carriers of p.Trp325Trp (grey, n=12-16) vs. p.Arg325 (blue, n=25-31). Exact numbers used for genetic association analysis are available in Source Data files. Data are Mean ±SEM; A star (*p < 0.05, ** p < 0.01) indicates significance in family based association (using QTDT35) after 100,000 permutations, adjusted for age, sex and BMI for left panel and age, sex, BMI and genotype of p.Trp325Arg for middle panel. A hash sign (# p < 0.05) indicates significance in QFAM (as implemented in PLINK36) test using 100,000 permutations (see Methods).

Source data

Extended Data Fig. 3 Effect of p.Trp325Arg genotype on insulin secretion during intravenous glucose tolerance test (IVGTT) and β-cell sensitivity to glucose during OGTT.

a-b, (a) Serum (S-) insulin concentrations, p.Trp325Trp (grey, n=116) and p.Arg325 (blue, n=733), and (b) S-insulin -plasma glucose ratio, p.Trp325Trp (grey, n=86) and p.Arg325 (blue, n=458) during IVGTT. Data are Mean ± SEM. Analysis was performed using mixed model adjusting for age, sex, BMI and genetic relatedness. * p < 0.05. c, β-cell sensitivity to glucose is presented as insulin secretion rate in response to plasma glucose during oral glucose tolerance test (OGTT) in people with newly diagnosed type 2 diabetes58. Data are Mean ± SEM. Analysis was performed using a generalized linear model (log-transformed data) for repeated measures, adjusting for age, sex and BMI.

Source data

Extended Data Fig. 4 Generation of SLC30A8-p.Lys34Serfs50* and SLC30A8-p.Arg138* hiPSC lines.

a, CRISPR-Cas9 strategy to generate SLC30A8-p.Lys34Serfs50* (Exon 2) and SLC30A8-p.Arg138* (Exon 3) hiPSC lines. Orange font highlights the nucleotide changes: c.101-107del; p.Lys34Serfs50* and c.412C>T; p.Arg138*. The gRNA (blue font) and PAM sequences (red font) are indicated on the partial genomic sequence of SLC30A8. b-c, FACS data from undifferentiated b, SLC30A8-p.Arg138* and c, SLC30A8-p.Lys34Serfs50* iPSCs and relevant isotype controls using antibodies against: OCT3/4, SSEA, SOX2, and NANOG. d, Expression of INSULIN in hiPSC-derived beta-like cells. Black bars represent p.Arg138Arg control cells, red bars represent p.Arg138*, and yellow bars represent p.Lys34Serfs50*. (n=6-8 wells from three differentiations) e-g, RNAscope analysis of the number of e, INSULIN- and f, SLC30A8- transcript positive cells in hiPSC-derived beta-like cells. 7-21 image fields were quantified and presented as % of DAPI+ cells. Representative images used for quantification shown in g (scale bar = 50 µm). Data are presented as Mean±SEM. Statistical analysis was performed using the one-way ANOVA and Tukey’s multiple comparison test (n = 5-9 wells from three differentiations, ****p<0.0001).

Extended Data Fig. 5 Confirmation of the ddPCR probe specificity and target SLC30A8 mRNA sequencing.

a, R138 (pGEM_CT) and X138 (pGEM_TT) sequences were inserted in pGEM vector and used as template for digital droplet PCR. Original probe configuration confirmed specificity as R138 droplets were only detected by FAM (CT-FAM; channel 1) and X138 droplets were only detected by VIC (TT-VIC; channel 2). In the swapped probe configuration, FAM and VIC probes were swapped and ddPCR was performed using pGEM_CT or pGEM_TT as template. b, Detection of R138 allele (Channel 1) and X138 allele (Channel 2) using cDNA from the heterozygous hiPSC-derived beta-like cells (B1 clone) as a template. In the swapped probe configuration, FAM and VIC probes were swapped and ddPCR was performed using cDNA from hiPSC-derived beta-like cells as template. c, Depicting the unique sequencing reads coverage at p.Arg138* and p.Ala139Ala obtained by SLC30A8 target mRNAs sequencing in edited clones (B1 and A3) and unedited cells (wildtype).

Extended Data Fig. 6 Silencing of ZnT8 tends to lower granule Zn2+ content.

EndoC-βH1 cells were transfected with siRNA control (siCtrl) or targeted against SLC30A8 (siZnT8) for 72 hours prior to imaging. a, ZnT8 knock-down was confirmed at the protein level by Western-blot. b, Control cells were incubated for 20 min. with the membrane-targeted zinc probe ZIMIR15 to monitor zinc secretion after cell stimulation with 20 mM KCl using total internal reflection of fluorescence (TIRF) microscopy (see accompanying movies, Supplementary video 1–4). c, Fluorescence intensity at the membrane was monitored upon time and traces obtained were averaged for cells transfected with siCtrl (14 cells) or with siZnT8 (14 cells). d, Fluorescence intensity increase due to zinc secretion after stimulation with KCl was determined for each cell. An outlier data point in the siZnT8 condition, likely to reflect release from a non-silenced cell, was excluded by Grubb’s test and statistical significance determined by Student’s t-test (Graph Pad Prism 7.0). Scale bar in b, 5 µm. Blots have been cropped and corresponding full blots are available in Source Data files.

Extended Data Fig. 7 RNA (mRNAs) sequencing of SLC30A8 knock down and control EndoC-βH1 cells.

Effect of SLC30A8 knock down (KD) on expression of genes involved in a, proinsulin processing, b, insulin production and β-cell development, c, β-cell excitability and insulin exocytosis (Supplementary Dataset 1). d, Over-representation analysis of differentially expressed genes (red dots) and depiction of most enriched WNT pathway genes (Supplementary Dataset 1) along with a gene set enrichment analysis (GSEA) considering all expressed genes. *p<0.05, ***p<0.001; Differential expression analysis was done using sequencing reads count based data using method similar to Fisher’s Exact test as implemented in edgR software package59 and further corrected for multiple testing using Bonferroni correction (see Supplementary Note).

Extended Data Fig. 8 Expression and localization of p.Arg138* and impact on cytosolic free Zn2+ concentrations in cultured INS1 β-cells.

a-d, Rat INS1e cells were transiently transfected with p.Arg138*-mCherry fusion construct followed by fluorescence microscopy imaging and immunodetection. a, Fusion protein localized to distinct subcellular compartments in INS1e cells at 48 h and 96 h after transfection. b, Expression of mCherry in control INS1e cells indicated cytoplasmic localization. c, Control experiments with immunostaining of p.Arg138* with HA or Myc-His (both are significantly smaller additions than mCherry) confirmed localization of fusion proteins to distinct subcellular compartments in the INS1e cells. d, Immunological detection (anti-mCherry) of the fusion protein at indicated time points after transfection confirms protein expression and indicate protein stability. Tubulin is used as control. e-f, INS1(832/13) cells were transfected constructs expressing p.Arg138*-Myc-His or eCALWY-4, or co-transfected with both, followed by (e) immunostaining or (f) immunofluorescence imaging at 24 h post-transfection using anti-c-Myc antibody. g, Cytosolic free Zn2+ concentrations in INS-1 (832/13) cells. Data are combined from three fully independent experiments. Scale bars are 50 μm (a, b), 10 μm (c) and 25 μm (f). Blots have been cropped and corresponding full blots are available in Source Data files.

Supplementary information

Supplementary Information

Supplementary Tables 1–8 and Note

Reporting Summary

Supplementary Dataset 1

Supplementary Video 1

Supplementary Video 1

Supplementary Video 2

Supplementary Video 2

Supplementary Video 3

Supplementary Video 3

Supplementary Video 4

Supplementary Video 4

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Dwivedi, O.P., Lehtovirta, M., Hastoy, B. et al. Loss of ZnT8 function protects against diabetes by enhanced insulin secretion. Nat Genet 51, 1596–1606 (2019). https://doi.org/10.1038/s41588-019-0513-9

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