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Redox regulation of m6A methyltransferase METTL3 in β-cells controls the innate immune response in type 1 diabetes

Nature Cell Biology volume 26, pages 421–437 (2024)Cite this article

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Abstract

Type 1 diabetes (T1D) is characterized by the destruction of pancreatic β-cells. Several observations have renewed the interest in β-cell RNA sensors and editors. Here, we report that N6-methyladenosine (m6A) is an adaptive β-cell safeguard mechanism that controls the amplitude and duration of the antiviral innate immune response at T1D onset. m6A writer methyltransferase 3 (METTL3) levels increase drastically in β-cells at T1D onset but rapidly decline with disease progression. m6A sequencing revealed the m6A hypermethylation of several key innate immune mediators, including OAS1, OAS2, OAS3 and ADAR1 in human islets and EndoC-βH1 cells at T1D onset. METTL3 silencing enhanced 2′-5′-oligoadenylate synthetase levels by increasing its mRNA stability. Consistently, in vivo gene therapy to prolong Mettl3 overexpression specifically in β-cells delayed diabetes progression in the non-obese diabetic mouse model of T1D. Mechanistically, the accumulation of reactive oxygen species blocked upregulation of METTL3 in response to cytokines, while physiological levels of nitric oxide enhanced METTL3 levels and activity. Furthermore, we report that the cysteines in position C276 and C326 in the zinc finger domains of the METTL3 protein are sensitive to S-nitrosylation and are important to the METTL3-mediated regulation of oligoadenylate synthase mRNA stability in human β-cells. Collectively, we report that m6A regulates the innate immune response at the β-cell level during the onset of T1D in humans.

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Fig. 1: m6A writer METTL3 levels peak at T1D onset but decrease drastically with disease progression.
Fig. 2: Co-treatment of human β-cells with IL-1β and IFN-α recapitulates the METTL3 upregulation seen at human T1D onset.
Fig. 3: m6A landscape analyses of human islets treated with IL-1β and IFN-α reveal hypermethylation of 2′,5′-OAS genes.
Fig. 4: m6A controls the mRNA stability of 2′–5′-OAS genes.
Fig. 5: OAS upregulation leads to an extensive downregulation of metallothioneins in β-cells.
Fig. 6: m6A landscape of established T1D is enriched in β-cell identity and function genes.
Fig. 7: In vivo AAV8-mediated overexpression of Mettl3 in NOD mouse β-cells delays T1D progression.
Fig. 8: OAS mRNA stability is regulated by the SNO of METTL3 in human β-cells.

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Data availability

m6A-seq and RNA-seq data in human islets and EndoC-βH1 cells have been deposited with the National Center for Biotechnology Information Gene Expression Omnibus under accession code GSE228267. RNA-seq in NOD mouse β- and non-β-cells sorted by fluorescence-activated cell sorting have been deposited under the accession code GSE228219. RNA-seq data in EndoC-βH1 cells harbouring OAS overexpression have been deposited under the accession code GSE244273. Mass spectrometry data on METTL3 SNO have been deposited with Mass Spectrometry Interactive Visual Environment (MassIVE) under the accession code MSV000091547. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank F. Bosch (Universitat Autònoma de Barcelona) and her lab members for discussions about AAV design and in vivo administration. We thank A. Op de Beeck (ULB Center for Diabetes Research, Université Libre de Bruxelles) for helpful discussions related to viral infections. We thank E. Dirice (New York Medical College) for discussions related to co-culture of islets and splenocytes. We thank B. Kunkemoeller (Brigham and Women’s Hospital) for assistance with fluorescence-activated cell sorting analysis and E. Mandato (Dana-Farber Cancer Institute) and A. Ferreira (Brigham and Women’s Hospital) for discussions regarding site mutagenesis. The authors thank the Joslin Islet Isolation Core, Joslin Flow Cytometry Core and Joslin Bioinformatics Core (P30 DK36836). This work is supported by NIH grants R01 DK67536 (R.N.K.), UC4 DK116278 (R.N.K. and C.H.), RM1 HG008935 (C.H.) and R01 DK122160 (W.-J.Q. and R.N.K.). Portions of the mass spectrometry work were performed in the Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, a national scientific user facility sponsored by the Department of Energy under contract DE-AC05-76RL0 1830. R.N.K. acknowledges support from the Margaret A. Congleton Endowed Chair and C.H. is a Howard Hughes Medical Institute Investigator. D.F.D.J. acknowledges support from Mary K. Iacocca Junior Postdoctoral Fellowship, American Diabetes Association grant #7-21-PDF-140 and NIH K99 DK135927. The authors sincerely thank the families of the human islet donors.

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Author notes

  1. Jiangbo Wei

    Present address: Department of Chemistry and Department of Biological Sciences, National University of Singapore, Singapore, Singapore

Authors and Affiliations

  1. Section of Islet Cell and Regenerative Biology, Joslin Diabetes Center; Department of Medicine, Beth Israel Deaconess Medical Center; Harvard Stem Cell Institute, Harvard Medical School, Boston, MA, USA

    Dario F. De Jesus, Natalie K. Brown, Ling Xiao, Jiang Hu, Garrett Fogarty, Sevim Kahraman, Giorgio Basile & Rohit N. Kulkarni

  2. Department of Chemistry, Department of Biochemistry and Molecular Biology, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA

    Zijie Zhang, Jiangbo Wei & Chuan He

  3. Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA

    Zijie Zhang, Jiangbo Wei & Chuan He

  4. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Xiaolu Li, Matthew J. Gaffrey & Wei-Jun Qian

  5. Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA

    Tariq M. Rana

  6. Department of Pathology, The University of Florida College of Medicine, Gainesville, FL, USA

    Clayton Mathews & Mark A. Atkinson

  7. Department of Medicine, and Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, USA

    Alvin C. Powers

  8. Diabetes Center, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Audrey V. Parent

  9. Department of Biological Chemistry, and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

    Sirano Dhe-Paganon

  10. ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium

    Decio L. Eizirik

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Contributions

D.F.D.J. conceived the study, designed and performed experiments, analysed the data, assembled figures and wrote the manuscript. Z.Z. performed RNA-seq, m6A-seq and m6A LC–MS/MS experiments. N.K.B. and G.F. performed morphometric analyses of pancreases and assisted with animal experiments. X.L. and M.J.G. performed LC–MS/MS on SNO. S.K. performed cell culture experiments. J.W. assisted with omics data handling. J.H. performed immunohistochemistry. G.B. assisted with in vivo experiments. L.X. assisted with immune cell profiling by fluorescence-activated cell sorting. T.M.R. provided reagents. C.M. provided microarray data on T1D islets. A.C.P. provided T1D islets. M.A.A. contributed to conceptual discussions and assisted with nPOD pancreatic sections. D.L.E. contributed to conceptual discussion and shared protocols. S.D.-P. assisted with METTL3 structural modelling. A.V.P. assisted with human pseudoislet experiments. W.-J.Q. performed LC–MS/MS data analysis and contributed to conceptual discussions. C.H. contributed to conceptual discussions, designed the experiments and wrote the manuscript. R.N.K. conceived the study, designed the experiments, supervised the project and wrote the manuscript. All the authors have reviewed, commented on and edited the manuscript.

Corresponding authors

Correspondence to Chuan He or Rohit N. Kulkarni.

Ethics declarations

Competing interests

R.N.K is on the scientific advisory board of Novo Nordisk, Biomea and REDD. C.H. is a scientific founder and a member of the scientific advisory board of Accent Therapeutics. The remaining authors have no conflicts of interest.

Peer review

Peer review information

Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 T1D pathways are upregulated in Mettl14 deficient mouse β-cells and pre-diabetic NOD β-cells show enrichment in pathways associated with Type 1 diabetes (related to Fig. 1).

a, Diagram of differentially expressed genes in Mettl14 KO β-cells compared to controls. b, Pathway enrichment analyses of genes upregulated in Mettl14 KO β-cells compared to controls (Controls, n = 4 pools, 2 animals/pool; M14KO, n = 4 pools, 4 animals/pool). c, Body weight of NOR and NOD females at 4- or 8-weeks of age (n = 6 mice/group). d, Random-fed serum glucose levels of NOR and NOD females at 4- or 8-weeks of age (n = 6 mice/group). e, Representative FACS sorting strategy to deplete CD45 positive cells, and obtain an enriched β-cell population based on size, granularity, and autofluorescence (FITC) from NOD female mice. f, Immunofluorescence staining of sorted β- and non-β-cell fractions showing insulin staining in red, glucagon in green, and somatostatin in blue (n = 3 independent experiments/group). Scale bar = 20μM. g, PCA plot of RNA-seq samples of 4- or 8-week-old FACS sorted β-cells or non-β-cells (n = 3 pools of 3 mice per pool). h, Heat-map representation of islet identity genes, showing enrichment for β cell identity genes in the β-cell fraction compared to non-β-cells. i, Heat-map of represented genes associated with Type 1 diabetes, innate immunity, and antigen processing and presentation and upregulated in pre-diabetic 8-week-old β-cells compared to 4-week-old. All samples in each panel are biologically independent. Data were expressed as means ± SEM. Heat maps represent clipped Z-scored log CPM. Statistical analyses were performed using the Benjamini-Hochberg procedure and genes were filtered for FDR<0.05 or 0.10 in ‘a’. P values of pathway enrichment analysis were calculated according to the hypergeometric test based on the number of physical entities present in both the predefined set and user-specified list of physical entities. Data in ‘a’ and ‘b’ were downloaded and reanalyzed from dataset GSE132306 (ref. 35). Source numerical data are available in source data.

Source data

Extended Data Fig. 2 scRNA-seq re-analyses of established T1D islets, upregulation of METTL3 in human β-cells at T1D onset, and Mettl3 downregulation in β-cells with T1D progression in female NOD mice (related to Fig. 1).

a-b, t-SNE representation of β-cells (a) (high insulin expression) or α-cells (b) (high glucagon expression) in control and established Type 1 diabetes (T1D) (GSE121863)40. c, Schematic representation of the Image J pipeline used to quantify the METTL3 nuclear intensity in proinsulin positive area in human pancreatic sections from the Network for Pancreatic Organ Donors with Diabetes (nPOD). d, Representative low magnification pictures of immunofluorescence staining of METTL3 (green) and Proinsulin (red) in pancreatic sections from human control and T1D onset showing a robust islet enriched upregulation of METTL3 at T1D onset. (Control, n = 9 patients; T1D Onset, n = 4 patients; established T1D, n = 7 patients). e, Representative pictures of immunofluorescence staining of Mettl3 (green), Insulin (blue), and Cd3 (red) in pancreatic sections from NOD male mice without any histological pancreatic immune cell infiltration patterns (n = 3 mice/group). Scale bar = 100μM. Source numerical data are available in source data.

Source data

Extended Data Fig. 3 m6A landscape analyses of human islets treated with IL-1β and IFN-α reveal differential methylation of class I MHC-mediated antigen processing and presentation genes (related to Fig. 3).

a, Venn diagram representation of the m6A hypermethylated, m6A hypomethylated, upregulated, or downregulated genes in human islets treated with IL-1β and IFN-α compared to PBS. Statistical analyses were performed using the Benjamini-Hochberg procedure and genes were filtered for FDR<0.05. b-e, Pathway enrichment analyses of m6A hypermethylated and upregulated (a), m6A hypermethylated and downregulated (c), m6A hypomethylated and upregulated (d), or m6A hypomethylated and downregulated genes (e) in human islets treated with IL-1β and IFN-α compared to PBS. P values were calculated according to the hypergeometric test based on the number of physical entities present in both the predefined set and user-specified list of physical entities. f, MHC class I differentially m6A methylated genes in human islets treated with IL-1β and IFN-α compared to PBS. g, Protein-protein interactions network of class I MHC-mediated antigen processing and presentation differentially m6A methylated in human islets treated with IL-1β and IFN-α compared to PBS. h-j, Coverage plots of m6A peaks ERAP1in human islets treated with IL-1β and IFN-α or PBS (h) (n = 15 biological independent samples/group), EndoC-βH1 cells treated with IL-1β and IFN-α or PBS (i) (n = 6 independent experiments/group), or human T1D and Control islets (j) (Controls, n = 20; T1D, n = 7 biological independent samples). Plotted coverages are the median of the n replicates presented. All samples in each panel are biologically independent. P values of pathway enrichment analysis were calculated according to the hypergeometric test based on the number of physical entities present in both the predefined set and user-specified list of physical entities. Source numerical data are available in source data.

Source data

Extended Data Fig. 4 Human islets and EndoC-βH1 cells present an extensive overlap in the innate immune response to IL-1β and IFN-α (related to Figs. 3 and 4).

a, Diagram representation of the upregulated (red), downregulated (blue), and unchanged genes (black) in EndoC-βH1 cells treated with IL-1β and IFN-α compared to PBS. Statistical analyses were performed using the Benjamini-Hochberg procedure and genes were filtered for FDR<0.05. b, Pathway enrichment analyses of upregulated and downregulated genes in EndoC-βH1 cells treated with IL-1β and IFN-α compared to PBS. c, Venn diagram representation of the commonly upregulated (red), downregulated (blue), and unchanged genes (black) of the intersected genes in EndoC-βH1 and human islets cells treated with IL-1β and IFN-α compared to PBS. Statistical analyses were performed using the Benjamini-Hochberg procedure and genes were filtered for FDR<0.05. d, Pathway enrichment analyses of commonly upregulated and downregulated genes in human islets and EndoC-βH1 cells treated with IL-1β and IFN-α compared to PBS. e-f, Volcano-plot representation of differentially expressed genes in human islets (e) and EndoC-βH1 cells (f) treated with IL-1β and IFN-α compared to PBS. Innate immune genes are depicted in red and show a near absolute overlap between human islets and EndoC-βH1 cells. Human islets: n = 15 biologically independent samples. EndoC-βH1 cells: n = 6 independent experiments/group. Statistical analyses were performed using the Benjamini-Hochberg procedure. P values of pathway enrichment analysis were calculated according to the hypergeometric test based on the number of physical entities present in both the predefined set and user-specified list of physical entities. Source numerical data are available in source data.

Source data

Extended Data Fig. 5 OAS upregulation is more prominent in β-cells, and METTL3 silencing leads to the downregulation of ADAR1 p150 isoform in β-cells (related to Fig. 4).

a, Metagene of m6A enriched peaks in PBS (blue) or IL-1β + IFN-α-treated (red) EndoC-βH1 cells. b, Enrichment for known m6A consensus motif RRACH. c, Histogram of the distribution of differential m6A loci log2 fold changes from IL-1β plus IFN-α-treated versus PBS in EndoC-βH1 cells. d, m6A-IP-qPCR showing increased m6A in OAS1, OAS2, and OAS3 in IL-1β + IFN-α-treated EndoC-βH1 cells compared to PBS (Basal) (n = 3 independent experiments/group). e-g, Western-blot analyses of indicated proteins in EndoC-βH1 cells and 266-6 cells (e), or PANC-1 (f), or αTC-6 (g) treated with PBS or IL1-β plus IFN-α for 24h (n = 3 independent experiments/group). h, Western-blot analyses of indicated proteins EndoC-βH1 cells harboring METTL3 silencing or Scramble and treated with PBS or IL1-β plus IFN-α (n = 3 independent experiments/group). Same experiment of Fig. 4h, with same loading control. i, Protein quantification of indicated proteins related to (h). j, qRT-PCR analyses of YTHDF genes after IL-1β plus IFN-α stimulation in scramble, YTHDF1, YTHDF2, or YTHDF3 KD EndoC-βH1 cells (n = 4 independent experiments/group). All samples in each panel are biologically independent. Data were expressed as means ± SEM. Statistical analysis was performed by two-tailed unpaired t-test. Source numerical data and unprocessed gel images are available in source data.

Source data

Extended Data Fig. 6 m6A landscape analyses of established T1D reveal differential methylation of master regulators of β-cell identity and function (related to Fig. 6).

a, Venn diagram representation of the m6A hypermethylated, m6A hypomethylated, upregulated, or downregulated genes in human islets from patients with established T1D or non-diabetic Controls. Statistical analyses were performed using the Benjamini-Hochberg procedure and genes were filtered for FDR<0.05. Human islets: Controls n = 20 and T1D n = 7 biologically independent samples. b-e, Pathway enrichment analyses of m6A hypermethylated and upregulated (a), m6A hypermethylated and downregulated (c), m6A hypomethylated and upregulated (d), or m6A hypomethylated and downregulated genes (e) in human islets from established T1D compared to Controls. P values of pathway enrichment analysis were calculated according to the hypergeometric test based on the number of physical entities present in both the predefined set and user-specified list of physical entities. Source numerical data are available in source data.

Source data

Extended Data Fig. 7 Mettl3 overexpression in NOD β-cells (related to Fig. 7).

a, Schematic diagram showing the vector design of AAV8 driving eGFP or Mettl3 under the control of a rat insulin promoter II. b, Representative immunofluorescence images of pancreas, white adipose tissue (WAT), muscle, liver, and intestine showing insulin (red), eGFP (green), and DAPI (blue) (n = 4 mice/group). Scale bar = 100μM.

Source data

Extended Data Fig. 8 Immune cell profiling of NOD pancreatic lymph nodes (related to Fig. 7).

a-n, Flow cytometry analyses of CD4 (a,b), CD8 (c,d), B-cells (e,f), dendritic cells (g,h), activated dendritic cells (i,j), tolerogenic dendritic cells (k,l), or th1 cells (m,n) isolated from pancreatic lymph nodes of NOD mice receiving PBS, eGFP, or Mettl3 overexpression (n = 4 mice/group). All samples in each panel are biologically independent. Data were expressed as means ± SEM. Statistical analysis was performed by One-Way ANOVA with Holm-Sidak test. Source numerical data are available in source data.

Source data

Extended Data Fig. 9 Immune cell profiling of NOD splenocytes (related to Fig. 7).

a-n, Flow cytometry analyses of CD4 (a,b), CD8 (c,d), B-cells (e,f), dendritic cells (g,h), activated dendritic cells (i,j), tolerogenic dendritic cells (k,l), or th1 cells (m,n) isolated from spleens of NOD mice receiving PBS, eGFP, or Mettl3 overexpression (n = 4 mice/group). All samples in each panel are biologically independent. Data were expressed as means ± SEM. Statistical analysis was performed by One-Way ANOVA with Holm-Sidak test. Source numerical data are available in source data.

Source data

Extended Data Fig. 10 Summary schemes of experimental approaches and METTL3 protein structural modeling (related to Fig. 8).

a, Western-blot analyses of indicated proteins in human islets cells treated with DMSO, 1μM thapsigargin, or 2μg/mL tunicamycin for 24h (n = 2 biological independent samples/group). b, Protein quantification of indicated proteins related to (a). c, Western-blot analyses of indicated proteins in EndoC-βH1 cells treated with DMSO, 25μM H2O2, or 25μM H2O2 + 5μM MG132 for 24h (n = 3 independent experiments/group). d, Protein quantification of indicated proteins related to (c). e, Summary scheme of experimental approach to measure METTL3 SNO by LC-MS/MS. f, MS/MS spectra of S-nitrosylated METTL3 peptides and the identification of cysteines C276, C294, C326, and C336 (n = 2 replicates). g, Predicted aligned error related to Fig. 8i–k. The color at position (x, y) indicates AlphaFold’s expected position error at residue x, when the predicted and true structures are aligned on residue y. h, CLUSTAL (1.2.4) multiple sequence alignment of METTL3 in represented species. i, Conceptual model of the role of METTL3 in controlling the OAS innate antiviral immune response by regulating the formation of deleterious dsRNAs and/or controlling viral response. The METTL3 response is decreased by the presence of ER stress and increased ROS levels, resulting from several potential factors such as hyperglycemia, dyslipidemia, genetic, or other environmental mediators such as pollution. Data were expressed as means ± SEM. Statistical analysis was performed by Two-Way ANOVA with Holm-Sidak test in b, and One-Way ANOVA with Holm-Sidak test in d. Source unprocessed gel images and numerical data are available in source data.

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Human islet patient information and primer sequences.

Source data

Source Data

Statistical source data for Figs. 1–8.

Source Data

Statistical source data for Extended Data Figs. 1–10.

Source Data

Unprocessed western blots for Figs. 2, 4, 5, 7 and 8 and Extended Data Figs. 5 and 10.

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F. De Jesus, D., Zhang, Z., Brown, N.K. et al. Redox regulation of m6A methyltransferase METTL3 in β-cells controls the innate immune response in type 1 diabetes. Nat Cell Biol 26, 421–437 (2024). https://doi.org/10.1038/s41556-024-01368-0

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