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In vivo screen identifies a SIK inhibitor that induces β cell proliferation through a transient UPR

Nature Metabolism volume 3, pages 682–700 (2021)Cite this article

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Abstract

It is known that β cell proliferation expands the β cell mass during development and under certain hyperglycemic conditions in the adult, a process that may be used for β cell regeneration in diabetes. Here, through a new high-throughput screen using a luminescence ubiquitination-based cell cycle indicator (LUCCI) in zebrafish, we identify HG-9-91-01 as a driver of proliferation and confirm this effect in mouse and human β cells. HG-9-91-01 is an inhibitor of salt-inducible kinases (SIKs), and overexpression of Sik1 specifically in β cells blocks the effect of HG-9-91-01 on β cell proliferation. Single-cell transcriptomic analyses of mouse β cells demonstrate that HG-9-91-01 induces a wave of activating transcription factor (ATF)6-dependent unfolded protein response (UPR) before cell cycle entry. Importantly, the UPR wave is not associated with an increase in insulin expression. Additional mechanistic studies indicate that HG-9-91-01 induces multiple signalling effectors downstream of SIK inhibition, including CRTC1, CRTC2, ATF6, IRE1 and mTOR, which integrate to collectively drive β cell proliferation.

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Fig. 1: In vivo chemical screen identifies stimulators of β cell proliferation in zebrafish.
Fig. 2: HG increases the proliferation of both mouse and human β cells.
Fig. 3: Genetic modulation of Sik-family members regulates β cell proliferation.
Fig. 4: scRNA-seq of β cells reveals characteristic transcriptional signatures.
Fig. 5: Treatment-dependent clustering of UPR+ and proliferative β cells reveals genetic networks in the ATF6 and IRE1 pathways.
Fig. 6: Pseudotemporal ordering of β cells treated with HG or harmine reveals different paths to proliferation.
Fig. 7: The UPR is necessary for HG-induced β cell proliferation.
Fig. 8: HG engages several downstream effectors, leading to β cell proliferation.

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Data availability

Raw and processed Smart-seq2 data are accessible at the Gene Expression Omnibus under accession GSE163972. UMAP plots for β cells treated with DMSO, harmine, HG or harmine and HG and gene-expression levels can be found at http://rshiny.nbis.se/shiny-server-apps/o_andersson/. Source data are provided with this paper.

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Acknowledgements

We thank J. Avila for assistance with flow cytometry, F. Salomons for assistance with ImageXpress and C. Karampelias and K.-C. Liu for discussions. Human islets for research were provided by the Alberta Diabetes Institute IsletCore at the University of Alberta (www.bcell.org/adi-isletcore) and the Integrated Islet Distribution Program (https://iidp.coh.org). Single-cell transcriptome data were generated at the Eukaryotic Single-Cell Genomics facility at SciLifeLab in Stockholm, Sweden. Computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at the UPPMAX, partially funded by the Swedish Research Council through grant agreement no. 2018-05973. This work was supported by grants from the following organisations: the Swedish Research Council, the Novo Nordisk Foundation, the Swedish Diabetes Foundation, the Ragnar Söderberg’s Foundation and Strategic Research Programmes in Diabetes, Stem Cell Research & Regenerative Medicine at the Karolinska Institutet to O.A.; and NIH/NIDDK (R01DK114686, R01DK113300) and the George F. and Sybil H. Fuller Foundation to L.C.A.; A.J. and R.Å. were financially supported by the Knut and Alice Wallenberg Foundation as part of the National Bioinformatics Infrastructure Sweden at SciLifeLab.

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Author notes

  1. These authors contributed equally: Lipeng Ren, Rohit B. Sharma.

Authors and Affiliations

  1. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden

    Jérémie Charbord, Lipeng Ren, Lianhe Chu, Dominika Tworus, Nadja Schulz & Olov Andersson

  2. Division of Endocrinology, Diabetes and Metabolism, Weill Cornell Medicine, New York, NY, USA

    Rohit B. Sharma & Laura C. Alonso

  3. Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden

    Anna Johansson

  4. Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Göteborg, Sweden

    Rasmus Ågren

  5. Sorbonne Université, Institut de Biologie Paris-Seine, CNRS UMR 7622, Inserm, Paris, France

    Pierre Charbord

  6. Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Andrew F. Stewart & Peng Wang

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Contributions

J.C., L.R. and D.T. performed and analysed results from the zebrafish experiments. L.R. performed the mechanistic biochemical and cell culture experiments. J.C., L.C. and N.S. performed in vivo mouse experiments. P.W. and A.F.S. performed the human islet experiments and analysed the data. J.C., A.J., R.Å. and P.C. analysed scRNA-seq data. R.B.S. and L.C.A performed the mouse experiments displayed in Fig. 7 and analysed the data. J.C. and O.A. designed, performed and analysed zebrafish and mouse experiments and wrote the manuscript with input from all authors. O.A. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Olov Andersson.

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Peer review information Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Elena Bellafante; Isabella Samuelson.

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Extended data

Extended Data Fig. 1 In vivo chemical screen identifies stimulators of β-cell proliferation in zebrafish (related to Fig. 1).

(a) Validation of the LUCCI assay using NECA as a positive control in the basal state and during β-cell regeneration. Data in the basal state: N = 28 (DMSO), N = 13 (NECA 50 µM), and N = 32 (NECA 100 µM) wells. Data during regeneration: N = 30 (DMSO), N = 33 (NECA 50 µM), and N = 34 (NECA 100 µM) wells. One-way ANOVA with Tukey’s multiple comparisons test, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Data are represented as the mean ± SEM. (b) Scatterplot of the efficacy of the hits identified during the basal state. PI3K: phosphoinositide 3-kinase. RTK: receptor tyrosine kinase. S/TK: serine/threonine kinase. (c) Dose-response activity of Lini and of HG. Tg(ins:NLuc-gmnn) or Tg(ins:NLuc-gmnn);Tg(ins:flag-NTR) larvae were treated with Lini or HG for 2 days (from 4 to 6 dpf), respectively. For Lini: N = 8 wells for X = −6.5; N = 11 wells for X = −6 and X = −5.3; N = 12 wells for X = −5; N = 15 for X = −5.7 and X = −4.7. For HG: N = 5 wells for X = −4.4, X = −4.7, X = −5 and X = −5.3. Data are represented as the mean ± SEM. A second order polynomial equation was used for modelling the bell-shaped curve. R2 = 0.13 for Lini and R2 = 0.32 for HG. (d) Effect of HG and Lini on the LUCCI signal under two conditions: in the basal state and during β-cell regeneration. Data in the basal state: N = 8 (DMSO), N = 8 (HG), and N = 6 (Lini) wells. Data during regeneration: N = 16 (DMSO), N = 10 (HG) and N = 5 (Lini) wells. One-way ANOVA with Tukey’s multiple comparisons test, ***P ≤ 0.001, ****P ≤ 0.0001. Data are represented as the mean ± SEM. (e) Number of β-cells in Tg(ins:H2B-GFP) larvae treated with DMSO or Lini from 4 to 6 dpf. Two-sided student’s t-test, ****P ≤ 0.0001. N = 23 (DMSO) and 9 (Lini) larvae. Data are represented as the mean ± SEM.

Extended Data Fig. 2 HG increases the proliferation of both mouse and human β-cells (related to Fig. 2).

(a) Effect of combined treatment with Harm and HG on β-cell proliferation in regenerating zebrafish. N = 5 wells per condition except for Harm 10 µM where N = 4. One-way ANOVA with Tukey’s multiple comparisons test, *P ≤ 0.05. Data are represented as the mean ± SEM. (b) Quantification of the percentage of proliferating α-, δ-, and β-cells after treatment with HG in regenerating zebrafish and mouse. Two-sided student’s t-test, *** P ≤ 0.001, ****P ≤ 0.0001. ****P ≤ 0.0001. Data are represented as the mean ± SEM. Data with the number of cells in zebrafish: N = 29 (gcga:GFP, DMSO), N = 27 (gcga:GFP, HG), N = 22 (sst2:RFP, DMSO), N = 29 (sst2:RFP, HG), N = 33 (ins:GFP, DMSO) and N = 34 (ins:GFP, HG) larvae. Data with the number of EdU+ cells in zebrafish: N = 47 (gcga:GFP, DMSO), N = 43 (gcga:GFP, HG), N = 36 (sst2:RFP, DMSO), N = 45 (sst2:RFP, HG), N = 46 (ins:GFP, DMSO) and N = 50 (ins:GFP, HG) larvae. Data with the percentage of proliferative cells from mouse: N = 11 (for sst and gcg, DMSO and HG), N = 38 (ins, DMSO), and N = 29 (ins, HG) wells. (c) Detection of apoptosis in mouse β-cells after 48 and 96 h of treatment. DNase I was used as positive control. N = 2 mice per treatment and time, except for the positive control DNAse I (N = 1). (d) Quantification of the percentage proliferative β-cells from 1.5-year-old mice after 96 h of culture of dissociated mouse islets. One-way ANOVA with Tukey’s multiple comparisons test, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. For data with Ki-67 and EdU: N = 8 (DMSO), N = 7 (Lini) and N = 9 (Harm, HG, and Harm + HG) mice. Data are represented as the mean ± SEM. (e) Assessment of β-cell functionality after 4 days treatment with DMSO or HG in low or high glucose medium. N = 4 wells per condition. One-way ANOVA with Tukey’s multiple comparisons test, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. Data are represented as the mean ± SEM. (f) Quantification of the percentage proliferative β-cells in cultures of dissociated human islets after treatment with DMSO or Lini for 96 h. N = 4 donors.

Extended Data Fig. 3 Single-cell RNA-seq of β-cells reveals characteristic transcriptional signatures (related to Fig. 4).

(a) FACS of wild-type islet cells. An example is shown with HG-treated cells. The 7-AAD was added to deselect proapoptotic cells. Different gates based on the forward-scatter vs. side-scatter (P1), width of the forward-scatter (P2) and the side-scatter (P3) and Cy5neg (7-AADneg, P4) cells were applied before sorting the cells into 384-well plates. (b-j) Pre-processing: Quality Control (QC) and feature selection. (b) Experimental setup: Each plate (PU: plate unit) corresponds to one experiment. Similar conditions between experiments are outlined in different colors, showing partial experimental overlap from one plate to another. (c) QC covariates: Filtering criterion (upper panel) histogram for count gene/count depth (middle panel), histogram for gene detection (lower panel). Failed cells are shown in red, and the cutoff value is indicated in the upper panel. (d) Summary of QC statistics. The total number of cells below the cutoff value for each criterion is shown along the diagonal. (e) The number of cells per identity: the molecular marker for each cellular identity and the gene cutoff value for each marker are indicated. (f) The number of β-cells per condition (plate unit, treatment time, and compound treatment). (g) Principal component analysis underscoring the cell distribution according to 3 conditions: PU, treatment time, and compound treatment. Note the marked distribution heterogeneity according to PU and treatment time as compared to the compound treatment. (h) Explained variance for different QC covariates/batch information. The density plots show how much of the variance in expression of the genes can be explained by the various QC covariates/batch information. The vertical line indicates 1%, meaning that the genes to the left of that line have a small correlation with the covariate/batch information. The gene detection covariate explains a large amount of the variance, in contrast to PU. (i) Correlations of the most important features to principal components 1 and 2 (PC1 and PC2). (j) Feature selection: The 500 most highly variable genes are shown in blue.

Extended Data Fig. 4 The UPR is necessary for HG-induced β-cell proliferation (related to Fig. 7).

(a) mRNA levels of genes belonging to the ATF6, IRE1, or PERK pathways after treatment of mouse islet cells with DMSO or HG in low glucose medium. N = 3 independent experiments. Two-sided student’s t-test. **P = 0.001 for Sec24d, ***P ≤ 0.001 for Pdia4. (b) In vivo effect of HG (10 mg/kg) on UPR activation in wild-type mice. Left panel: Representative images of islets from control and HG-treated mice after 14 h of treatment. Scale bar: 20 µm. In blue: DAPI; in green: insulin; in red: BIP. The arrows show BIP+ β-cells. Right panel: Quantification of the percentage of mouse β-cells expressing BIP per islet. Data are represented as the mean ± SEM. Two-sided student’s t-test. **P = 0.0032. N = 123 islets from 6 HG-treated mice and 131 islets from 6 control-treated mice.

Extended Data Fig. 5 HG engages several downstream effectors leading to β-cell proliferation (related to Fig. 8).

(a) Time-course of the Tm effect on ATF6-N and BIP protein levels in MIN6 cells. Left panel: Representative immunoblot with GAPDH used as a loading control. Right panel: Quantification of ATF6-N and BIP relative protein expression over time. N = 3 independent experiments per time point. (b) Immunoprecipitation experiments to examine a potential direct interaction of ATF6 with SIK1. Upper panel: Experimental workflow. HEK cells were transfected with a Myc-Flag-SIK1 plasmid and treated with DMSO or HG for 6 h. After incubation of cell lysates with anti-Flag antibodies and immunoprecipitation, samples were immunoblotted using anti-ATF6 or anti-Flag antibodies. Lower-left panel: The negative control corresponds to non-transfected HEK cells (Neg). The input displays an ATF6 band that is not detected in the samples after immunoprecipitation with anti-Flag (SIK1). Lower-right panel: The input and IP show the presence of Flag-tagged SIK1 in the samples. One representative experiment out of 2 is shown. (c) Immunoprecipitation experiments to examine the phosphorylation of ATF6 at Ser/Thr residues. Upper panel: Experimental workflow. HEK cells were transfected with a Flag-ATF6 plasmid and treated with DMSO or HG for 6 h. After incubation of cell lysates with anti-Flag antibodies and immunoprecipitation, samples were immunoblotted using anti-phospho-Ser/Thr or anti-ATF6. Lower-left panel: The negative control corresponds to IP lysis buffer (Neg). The input shows phosphorylation marks (indicated by arrows, ph) in both DMSO or HG-treated cells, marks that are no longer detected in the samples after immunoprecipitation with anti-Flag (ATF6). Lower-right panel: The input and IP show the presence of an ATF6 band in the samples. One representative experiment out of 2 is shown.

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Unprocessed western blots.

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Charbord, J., Ren, L., Sharma, R.B. et al. In vivo screen identifies a SIK inhibitor that induces β cell proliferation through a transient UPR. Nat Metab 3, 682–700 (2021). https://doi.org/10.1038/s42255-021-00391-x

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