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Targeted pharmacological therapy restores β-cell function for diabetes remission

Nature Metabolism volume 2, pages 192–209 (2020)Cite this article

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An Author Correction to this article was published on 14 April 2020

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Abstract

Dedifferentiation of insulin-secreting β cells in the islets of Langerhans has been proposed to be a major mechanism of β-cell dysfunction. Whether dedifferentiated β cells can be targeted by pharmacological intervention for diabetes remission, and ways in which this could be accomplished, are unknown as yet. Here we report the use of streptozotocin-induced diabetes to study β-cell dedifferentiation in mice. Single-cell RNA sequencing (scRNA-seq) of islets identified markers and pathways associated with β-cell dedifferentiation and dysfunction. Single and combinatorial pharmacology further show that insulin treatment triggers insulin receptor pathway activation in β cells and restores maturation and function for diabetes remission. Additional β-cell selective delivery of oestrogen by Glucagon-like peptide-1 (GLP-1–oestrogen conjugate) decreases daily insulin requirements by 60%, triggers oestrogen-specific activation of the endoplasmic-reticulum-associated protein degradation system, and further increases β-cell survival and regeneration. GLP-1–oestrogen also protects human β cells against cytokine-induced dysfunction. This study not only describes mechanisms of β-cell dedifferentiation and regeneration, but also reveals pharmacological entry points to target dedifferentiated β cells for diabetes remission.

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Fig. 1: GLP-1–oestrogen and PEG–insulin treatment regenerates functional β-cell mass.
Fig. 2: Sustained effects of GLP-1–oestrogen to ameliorate mSTZ diabetes in mice.
Fig. 3: GLP-1–oestrogen improves function of human micro-islets.
Fig. 4: β-cell dedifferentiation in mSTZ-diabetic mice.
Fig. 5: mSTZ-derived β cells are similar to immature β cells from embryonic or immature islets.
Fig. 6: β-cell redifferentiation upon insulin and GLP-1–oestrogen treatment.
Fig. 7: Treatment specific effects of β-cell regeneration.
Fig. 8: Origin and fate of treated endocrine cells.

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Data availability

Custom python scripts written for performing scRNA-seq data analysis are available in a github repository (https://github.com/theislab/pancreas-targeted_pharmacology). Versions of packages that might influence numerical results are indicated in the scripts. Raw data and gene expression matrices of scRNA-seq are deposited in GEO under the accession number GSE128565. Source data for Figs. 1–3 and 7 and Extended Data Figs. 1–5 and 10 are provided with the paper.

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Acknowledgements

We thank L. Müller, L. Sehrer, E. Malogajski and M. Kilian from the Helmholtz Diabetes Center in Munich for excellent assistance with in vivo mouse experiments. We thank J. Jaki, C. Salinno, F. Volta, J. Beckenbauer, A. Savoca and R. Fimmen for excellent assistance with in vitro experiments. We thank C. Pyke and P. Gottrup Mortensen for providing the GLP-1R antibody. We thank V. Bergen, M. Lücken, L. Simon and D. Fischer for fruitful discussions on the computational analysis. This work was supported in part by funding to M.H.T. from the Alexander von Humboldt Foundation and the Initiative and Networking Fund of the Helmholtz Association and funding by the European Research Council (AdG HypoFlam grant no. 695054). In addition, this work was supported by funds from the Helmholtz future topic ‘Aging and Metabolic programming’, the Helmholtz Alliance ICEMED, the Helmholtz Initiative on Personalised Medicine, iMed, by the Helmholtz Association, and the Helmholtz cross-programme topic ‘Metabolic Dysfunction’. M.A.S.-G. was funded by a Marie Sklodowska-Curie individual Fellowship (grant no. 706965, GCG-T3 Dyslipidemia). F.J.T. acknowledges support by the BMBF (grant no. 01IS18036A and grant no. 01IS18053A), by the German Research Foundation (DFG) within the Collaborative Research Centre 1243, Subproject A17, by the Helmholtz Association (Incubator grant sparse2big, grant no. ZT-I-0007) and by the Chan Zuckerberg Initiative DAF (advised fund of Silicon Valley Community Foundation, 182835).

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Author notes

  1. These authors contributed equally: Stephan Sachs, Aimée Bastidas-Ponce, Sophie Tritschler.

Authors and Affiliations

  1. Institute of Diabetes and Regeneration Research, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany

    Stephan Sachs, Aimée Bastidas-Ponce, Sophie Tritschler, Mostafa Bakhti, Anika Böttcher, Marta Tarquis-Medina, Erik Bader, Sara Roscioni, Susanna M. Hofmann & Heiko Lickert

  2. Institute of Diabetes and Obesity, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany

    Stephan Sachs, Miguel A. Sánchez-Garrido, Maximilian Kleinert, Katrin Fischer, Sigrid Jall, Alexandra Harger, Matthias H. Tschöp & Timo D. Müller

  3. Division of Metabolic Diseases, Department of Medicine, Technical University of Munich, Munich, Germany

    Stephan Sachs, Katrin Fischer, Sigrid Jall & Matthias H. Tschöp

  4. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Stephan Sachs, Aimée Bastidas-Ponce, Sophie Tritschler, Mostafa Bakhti, Anika Böttcher, Marta Tarquis-Medina, Siegfried Ussar, Matthias H. Tschöp, Susanna M. Hofmann, Timo D. Müller & Heiko Lickert

  5. Institute of Stem Cell Research, Helmholtz Center Munich, Neuherberg, Germany

    Aimée Bastidas-Ponce, Mostafa Bakhti, Anika Böttcher, Marta Tarquis-Medina & Heiko Lickert

  6. Department of Medicine, Technical University of Munich, Munich, Germany

    Aimée Bastidas-Ponce, Marta Tarquis-Medina, Siegfried Ussar & Heiko Lickert

  7. Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany

    Sophie Tritschler & Fabian J. Theis

  8. School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany

    Sophie Tritschler & Fabian J. Theis

  9. Section of Molecular Physiology, Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark

    Maximilian Kleinert

  10. RG Adipocytes & Metabolism, Institute for Diabetes & Obesity, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany

    Siegfried Ussar

  11. Research Unit Analytical Pathology, Helmholtz Center Munich, Neuherberg, Germany

    Annette Feuchtinger

  12. InSphero AG, Schlieren, Switzerland

    Burcak Yesildag & Aparna Neelakandhan

  13. Global Drug Discovery, Novo Nordisk A/S, Maaloev, Denmark

    Christine B. Jensen & Marion Cornu

  14. Novo Nordisk Research Center Indianapolis, Indianapolis, IN, USA

    Bin Yang, Brian Finan & Richard D. DiMarchi

  15. Department of Chemistry, Indiana University, Bloomington, IN, USA

    Richard D. DiMarchi

  16. Department of Mathematics, Technical University of Munich, Munich, Germany

    Fabian J. Theis

  17. Medical Clinic and Polyclinic IV, Ludwig Maximilian University of Munich, Munich, Germany

    Susanna M. Hofmann

  18. Department of Pharmacology and Experimental Therapy, Institute of Experimental and Clinical Pharmacology and Toxicology, Eberhard Karls University Hospitals and Clinics, Tübingen, Germany

    Timo D. Müller

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  1. Stephan Sachs
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Contributions

S.S. performed in vivo and ex vivo rodent experiments, pancreas histology, analysed and interpreted all data, interpreted scRNA-seq data, and wrote the manuscript. A.B-P. performed ex vivo rodent experiments, pancreas histology, analysed and interpreted data, and cowrote the manuscript. S.T. analysed and interpreted scRNA-seq data and cowrote the manuscript. M.B. performed ex vivo experiments and helped to draft the manuscript. A.B. performed ex vivo experiments and helped to prepare the single-cell suspensions for scRNA-seq. M.A.S.-G. performed in vivo experiments. M.T-M. performed ex vivo experiments. M.K., K.F., S.J, and A.H. performed in vivo experiments and helped to interpret data. E.B. and S.R. performed ex vivo experiments and helped to interpret data. S.U. helped to interpret data. A.F. conducted and analysed automatic pancreatic histology. B.Y. and A.N. performed, analysed, and interpreted human micro-islet experiments. C.B.J. designed, analysed, interpreted and supervised the rat study, interpreted in vivo data and helped to write the manuscript. M.C. designed and oversaw human micro-islet experiments and helped to interpret data. B.Y. synthesised and characterised compounds. B.F. designed the in vivo rodent experiment, synthesised and characterised compounds, interpreted the data, and helped to write the manuscript. R.D.D. and M.H.T conceptualised and interpreted all studies and helped to write the manuscript. F.J.T. conceptualised, supervised, and interpreted the scRNA-seq analysis and helped to write the manuscript. S.M.H, T.D.M, and H.L. conceptualised, designed, supervised, and interpreted all studies and wrote the manuscript.

Corresponding authors

Correspondence to Fabian J. Theis, Susanna M. Hofmann, Timo D. Müller or Heiko Lickert.

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Competing interests

C.B.J., M.C., B.Y, B.F., and R.D.D. are current employees of Novo Nordisk. Novo Nordisk has licensed from Indiana University intellectual property pertaining to this report. M.H.T. serves as a scientific advisory board member of ERX Pharmaceuticals, Inc., Cambridge, MA. F.J.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc. and Dermagnostix. S.T. reports receiving consulting fees from Cellarity, Inc. The Institute for Diabetes and Obesity receives research support from Novo Nordisk. All other authors declare no conflict of interest.

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Peer review information Primary Handling Editor: Elena Bellafante.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Remaining β cells lose cell identity 10 days after last STZ injection.

Effects of either vehicle or the mSTZ treatment on a, fasting blood glucose (No STZ: n = 20, mSTZ: n = 107; unpaired two-sided t-test; t = 14.64, df = 125), b, pancreatic islets histology (No STZ: 179 islets of n = 3 mice, mSTZ: 182, n = 3; unpaired two-sided t-test; β: t = 11.44, df = 358; α: t = 10.98, df = 356; δ: t = 4.27, df = 338; images are representative from no STZ n = 3 and mSTZ n = 3 mice), c, the insulin positive area within pancreatic sections (No STZ: 27 sections of n = 3 mice; STZ: 27, n = 3; unpaired two-sided t- test; t = 3.646, df = 52), d, the proliferation (No STZ: 58 islets of n = 3 mice; STZ: 69, n = 3) and e, apoptosis rate in β cells (No STZ: 46 islets of n = 3 mice; STZ: 42, n = 3; unpaired two-sided t-test, t = 3.955, df = 86), f, the expression of β-cell functional marker Ucn3 and Glut2 (images are representative of dataset plotted in b from no STZ n = 3 and mSTZ n = 3 mice), g, the homoeostatic model assessment of β-cell function (HOMA-β) (No STZ: n = 20, STZ: n = 107; unpaired two-sided t-test; t = 20.65, df = 124) and h, the ratio of fasting C-peptide to fasting blood glucose (No STZ: n = 20, STZ: n = 106; unpaired two-sided t-test; t = 14.03, df = 122). Boxplots covering all data points are depicted. Line indicates the median. Scale bar, 50 μm. Scale bar zoom-in, 20 µm.

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Extended Data Fig. 2 Benefits of polypharmcotherapy to ameliorate mSTZ diabetes in mice.

Effect of treatment with indicated compounds and doses on a, fasting plasma insulin levels at week 12 of treatment (mSTZ-vehicle, n = 12; oestrogen, n = 10; GLP-1, n = 11; GLP-1/oestrogen, n = 11; one-way ANOVA with Tukey post-hoc; F (3, 39) = 10.66) and b, body weight in the end of the study (no STZ-vehicle, n = 12; mSTZ-vehicle, n = 13; GLP-1, n = 11; oestrogen, n = 11; GLP-1/oestrogen, n = 11; PEG-insulin, n = 9; GLP-1/oestrogen and PEG-insulin, n = 10; unpaired two-sided t-test; t = 2.436, df = 17). (c, d) Comparison of PEG-insulin and GLP-1/oestrogen plus PEG-insulin co-treated mice. c, Blood glucose after intraperitoneal glucose (0.5 g/kg) at week 12 (no STZ-vehicle, n = 12; PEG-insulin, n = 9; GLP-1/oestrogen and PEG-insulin, n = 10; one-way ANOVA with Tukey post-hoc (F (2, 27) = 24.71)). d, Pancreatic insulin content in the end of the study (no STZ-vehicle, n = 4; PEG-insulin, n = 4; GLP-1/oestrogen and PEG-insulin, n = 4; unpaired two-sided t-test; t = 4.534, df = 6). All data are mean ± SEM.

Source data

Extended Data Fig. 3 Tissue specificity and β-cell selectivity of the GLP-1/oestrogen conjugate.

(a, b) Treatment of female OVX Sprague-Dawley rats. a, Study scheme. b, Dry uterus weight. Data are mean ± SEM. N = 8 female rats per group. One-way ANOVA with Tukey post-hoc (F (4, 34) = 44.89). (c-g) Treatment of male FVFPBFDHom mice. (c) FACS gating strategy of dispersed endocrine cells based on granularity (Side Scatter Cell (SSC)) and PBF (405 nm) intensity. d, qPCR analysis confirmed sorting strategy of endocrine cells. Data are values of sorted cells from n = 2 mice. e, Study scheme; FVFPBFDHom male mice were treated with vehicle (n = 7), oestrogen (n = 5), GLP-1 (n = 9), or GLP-1/oestrogen (n = 11) at the indicated doses for four weeks. f, Fasting blood glucose. Data are mean ± SEM. g, Sorted endocrine cell populations after treatment (vehicle (n = 4, cells of n = 2 mice each were pooled), oestrogen (n = 4, cells of n = 2 mice each were pooled), GLP-1 (n = 5, cells of n = 2 and n = 3 mice were pooled), or GLP-1/oestrogen (n = 6, islets of n = 3 mice each were pooled)). Data are mean ± SD.

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Extended Data Fig. 4 Viability and cell death of human micro-islets.

Measurement of human micro-islet viability and cell death with and without cytokine exposure and in the present of different compounds at the indicated doses. (a) ATP content and (b) Caspase 3/7 activity of human micro-islets. a, N = 6 micro-islets of n = 3 human donors for each condition. Boxplot of all data points. Line indicates the median. #P indicates P-value to vehicle no stress condition. *P indicates P-value to vehicle cytokine exposure. No stress versus cytokines stress by unpaired two-sided t-test (t = 1.756, df = 33). Otherwise one-way ANOVA with donor as random effect followed by Tukey post-hoc (Flow dose(4, 81) = 6.68; Fmedium dose(4, 82) = 10.68; Fhigh dose(4, 81) = 6.30). b, N = 3–4 micro-islets of n = 3 human donors for each condition. Boxplot of all data points. Line indicates the median. #P indicates P-value to vehicle no stress condition. *P indicates P-value to vehicle cytokine exposure. No stress versus cytokines stress by unpaired two-sided t-test (t = 2.567, df = 22). Otherwise one-way ANOVA with donor as random effect followed by Tukey post-hoc (Flow dose(4, 52) = 5.58; Fmedium dose(4, 52) = 4.44; Fhigh dose(4,53) = 4.23).

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Extended Data Fig. 5 Physiological characteristics of mice used for scRNA-seq.

Representative mice (n = 3) of each treatment were used for scRNA-seq. a, Fasting glucose levels. One-way ANOVA with Tukey post-hoc test among mSTZ, oestrogen, GLP-1 and GLP- 1/oestrogen treated mice (F (3, 8) = 21.23). One-way ANOVA with Tukey post-hoc among no STZ, GLP-1/oestrogen, PEG-insulin, and co-treated mice (F (3, 8) = 94.06). b, Fasting C-peptide levels. One-way ANOVA with Tukey post-hoc test among STZ, oestrogen, GLP-1 and GLP- 1/oestrogen treated mice (F (3, 8) = 9.073). All data are mean ± SEM.

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Extended Data Fig. 6 β-cell heterogeneity in healthy mice.

a, Endocrine cell annotation is based on the hormone expression of insulin (Ins), glucagon (Gcg), somatostatin (Sst), and pancreatic polypeptide (PP). b, UMAP plot showing all endocrine cells (7578 cells in total) from healthy mice. The cell number and proportion of each endocrine cluster is indicated. c, Redefined clustering of the Ins+ β cells revealed two main β-cell subpopulations. d, Expression changes of genes from selected pathways along a pseudotime trajectory from β2- to β1 cells. β1-cells were downsampled to 1000 cells for better visualisation. e, GO term and KEGG pathway enrichment analysis of up- (log(fold change) > 0.25) and downregulated (log(fold change) < -0.25) genes in β2- (278 cells) compared to β1 cells (5380 cells). Cells were pooled from n = 3 mice. Representative terms from Supplementary table 2 are depicted. We used limma-trend to find differentially expressed genes (M&M). Gene enrichment was done with EnrichR using Fisher’s exact test to identify regulated ontologies/pathways (M&M). f, Violin plots showing the distribution of the expression of proliferation and β-cell maturation genes suggesting an immature phenotype of cycling β-cells. Accordingly, 16/403 of the β2 cells, whereas only 2/5319 of the mature β1 cells were classified as cycling (M&M). Cells were pooled from n = 3 mice. Violin shows the distribution as a kernel density estimate fit. Points in violin interior show individual data points. Boxplot in violin interior shows median, quartile and whisker values. g, Measured proportion and expected doublet frequency of polyhormonal cell clusters. Expected doublet frequency is calculated given a doublet rate of 10% (M&M). h, Boxplot displaying the doublet score distribution of mono- and polyhormonal cell clusters. A high score indicates a high doublet probability. Cells were pooled from n = 3 mice. Boxplot shows the quartile values and extreme values. Whiskers extend to 1.5 IQRs of the lower and upper quartile. Outliers are displayed individually.

Extended Data Fig. 7 β-cell dedifferentiation in mSTZ-diabetic mice.

(a–d), Volcano plots showing differential expression and its significance (-log10(adjusted p-Value), limma-trend) for each gene in (a) β-, (b) α-, (c) PP-, and (d) δ-cells from mSTZ treated versus healthy mice. Red line indicates thresholds used on significance level and gene expression change. Significantly regulated genes are highlighted in black. Genes significantly regulated in only one cell type but not the others are highlighted in blue. p-values were correct for multiple testing using BH. Cells were pooled from no STZ (n = 3) and mSTZ-vehicle (n = 3) treated mice. (e, f) GO term and KEGG pathway enrichment analysis of up- (log(fold change) > 0.25) and downregulated (log(fold change) < -0.25) genes in (e) α- and (f) δ cells in mSTZ treated versus healthy mice. We used limma-trend to find differentially expressed genes (M&M). Gene enrichment was done with EnrichR using Fisher’s exact test to identify regulated ontologies/pathways (M&M). Cells were pooled from no STZ (n = 3) and mSTZ-vehicle (n = 3) treated mice. (g, h) Comparison between dysregulated genes in mSTZ-β cells in mice with (g) data from RNA-seq of human T2D pancreata and (h) from scRNA-seq of human T1D β cells. Gene names of overlapping genes and identified dedifferentiation markers in Fig. 4e) are listed. i, Violin plots showing the distribution of the expression of endocrine developmental genes in beta cells of mSTZ treated and healthy mice. Violin shows the distribution as a kernel density estimate fit. Points in violin interior show individual data points. Boxplot in violin interior shows median, quartile and whisker values. Cells were pooled from no STZ (n = 3) and mSTZ-vehicle (n = 3) treated mice.

Extended Data Fig. 8 Common and distinct pathways of embryonic and dedifferentiated β cells.

Gene ontologies (Pvalue < 0.0001) and KEGG pathways (Pvalue < 0.05) that are commonly and specifically (a) down- and (b) upregulated in embryonic and mSTZ-derived β-cells. Representative terms from Supplementary Table 4 are depicted. Gene enrichment was done with EnrichR using Fisher’s exact test to identify regulated ontologies/pathways (M&M). Cells were pooled from mSTZ-vehicle (n = 3) treated mice.

Extended Data Fig. 9 Effects on endocrine cells of different treatments.

UMAP plot of all endocrine cells after 100 days of treatment showing endocrine cell distribution in each individual treatment. Total cell number for (a) mSTZ diabetic mice 5001, for (b) oestrogen treated mice 4889, for (c) GLP-1 treated mice 3874, for (d) GLP-1/oestrogen treated mice 5201, for (e) PEG-insulin treated mice 3217, and for (f) GLP-1/oestrogen (GLP-1/E) and PEG-insulin (PEG-ins) co-treated mice 3276. Values indicate the proportions of each cell cluster. Cells of n = 3 mice for each treatment were pooled.

Extended Data Fig. 10 β-cell maturation after compound treatment.

a, Immunohistochemical analysis of Ucn3 expression during the course of the study. Scale bar, 50 μm. Day 0: Images are representative of no STZ-vehicle (n = 3) and mSTZ-vehicle (n = 3) treated mice. Day 25: Images are representative of no STZ-vehicle (n = 3), mSTZ-vehicle (n = 3), GLP-1/oestrogen (n = 3), PEG-insulin (n = 3), and GLP-1/oestrogen and PEG-insulin (n = 3) co-treated mice. Day 100: Images are representative of no STZ-vehicle (n = 3), mSTZ-vehicle (n = 3), GLP-1/oestrogen (n = 2), PEG-insulin (n = 2), and GLP-1/oestrogen and PEG-insulin (n = 3) co-treated mice. b, Plasma proinsulin/C-peptide ration in the end of the study (no STZ-vehicle, n = 8; mSTZ-vehicle, n = 8; oestrogen, n = 6; GLP-1, n = 5; GLP-1/oestrogen, n = 6; PEG-insulin, n = 6, GLP-1/oestrogen and PEG-insulin, n = 6; one-way ANOVA with Tukey post-hoc: F(6, 36) = 8.12). Data are mean ± SEM. c, Representative staining for insulin and Sel1l after 25 days of treatment. Arrow indicates Sel1l + insulin + -cells, which were especially found in GLP-1/oestrogen and PEG-insulin co-treated mice. Sel1l + insulin–cells (arrow head) were more common in mSTZ-diabetic and PEG-insulin treated mice. Images are representative of no STZ-vehicle (n = 3), mSTZ-vehicle (n = 3), PEG-insulin (n = 3), and GLP-1/oestrogen + PEG-insulin (n = 3) co-treated mice. Scale bar, 20μm. d, Expression of selected ER stress and ERAD-associated genes by scRNA-seq at study end.

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Sachs, S., Bastidas-Ponce, A., Tritschler, S. et al. Targeted pharmacological therapy restores β-cell function for diabetes remission. Nat Metab 2, 192–209 (2020). https://doi.org/10.1038/s42255-020-0171-3

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