CoChat
Sign Up
Cited by 56Diabetes and associated disordersRNA modifications and cancerPancreatic function and diabetes

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

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

Open access
De Jesus, Dario F., Zhang, Zijie, Brown, Natalie K., Li, Xiaolu, Xiao, Ling, Hu, Jiang, Gaffrey, Matthew J., Fogarty, Garrett, Kahraman, Sevim, Wei, Jiangbo, Basile, Giorgio, Rana, Tariq M., Mathews, Clayton, Powers, Alvin C., Parent, Audrey V., Atkinson, Mark A., Dhe-Paganon, Sirano, Eizirik, Decio L., Qian, Wei-Jun, He, Chuan, Kulkarni, Rohit N.·2024·Nature Cell Biology
DOI 10.1038/s41556-024-01368-0PMC PMC11042681

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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 N 6-methyladenosine (m 6 A) is an adaptive β-cell safeguard mechanism that controls the amplitude and duration of the antiviral innate immune response at T1D onset. m 6 A writer methyltransferase 3 (METTL3) levels increase drastically in β-cells at T1D onset but rapidly decline with disease progression. m 6 A sequencing revealed the m 6 A hyper methylation 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 T1 D. 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 m 6 A regulates the innate immune response at the β-cell level during the onset of T1D in humans.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41556-024-01368-0

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 85

  1. Crowl, J. T., Gray, E. E., Pestal, K., Volkman, H. E. & Stetson, D. B. Intracellular nucleic acid detection in autoimmunity. Annu. Rev. Immunol. 35, 313–336 (2017). · DOI
  2. Lang, K. S., Burow, A., Kurrer, M., Lang, P. A. & Recher, M. The role of the innate immune response in autoimmune disease. J. Autoimmun. 29, 206–212 (2007). · DOI
  3. Kallionpaa, H. et al. Innate immune activity is detected prior to seroconversion in children with HLA-conferred type 1 diabetes susceptibility. Diabetes 63, 2402–2414 (2014). · DOI
  4. Carry, P. M. et al. Changes in the coexpression of innate immunity genes during persistent islet autoimmunity are associated with progression of islet autoimmunity: Diabetes Autoimmunity Study in the Young (DAISY). Diabetes 71, 2048–2057 (2022). · DOI
  5. Apaolaza, P. S. et al. Islet expression of type I interferon response sensors is associated with immune infiltration and viral infection in type 1 diabetes. Sci. Adv. 7, eabd6527 (2021). · DOI
  6. Lundberg, M., Krogvold, L., Kuric, E., Dahl-Jorgensen, K. & Skog, O. Expression of interferon-stimulated genes in insulitic pancreatic islets of patients recently diagnosed with type 1. Diabetes 65, 3104–3110 (2016). · DOI
  7. Hornung, V., Hartmann, R., Ablasser, A. & Hopfner, K. P. OAS proteins and cGAS: unifying concepts in sensing and responding to cytosolic nucleic acids. Nat. Rev. Immunol. 14, 521–528 (2014). · DOI
  8. Pedersen, K. et al. Genetic predisposition in the 2′-5′A pathway in the development of type 1 diabetes: potential contribution to dysregulation of innate antiviral immunity. Diabetologia 64, 1805–1815 (2021). · DOI
  9. Field, L. L. et al. OAS1 splice site polymorphism controlling antiviral enzyme activity influences susceptibility to type 1 diabetes. Diabetes 54, 1588–1591 (2005). · DOI
  10. Tessier, M. C. et al. Type 1 diabetes and the OAS gene cluster: association with splicing polymorphism or haplotype? J. Med. Genet. 43, 129–132 (2006). · DOI
  11. Bonnevie-Nielsen, V., Buschard, K. & Dyrberg, T. Differential responsiveness to interferon-alpha in beta-cells and non-beta cells. Diabetes 45, 818–821 (1996). · DOI
  12. Li, M., Zheng, D. J., Field, L. L. & Bonnevie-Nielsen, V. Murine pancreatic beta TC3 cells show greater 2′, 5′-oligoadenylate synthetase (2′5′AS) antiviral enzyme activity and apoptosis following IFN-alpha or poly(I:C) treatment than pancreatic alpha TC3 cells. Exp. Diabetes Res. 2009, 631026 (2009). · DOI
  13. Dan, M., Zheng, D., Field, L. L. & Bonnevie-Nielsen, V. Induction and activation of antiviral enzyme 2′,5′-oligoadenylate synthetase by in vitro transcribed insulin mRNA and other cellular RNAs. Mol. Biol. Rep. 39, 7813–7822 (2012). · DOI
  14. Zeng, C. et al. RNase L contributes to experimentally induced type 1 diabetes onset in mice. J. Endocrinol. 223, 277–287 (2014). · DOI
  15. Frye, M., Harada, B. T., Behm, M. & He, C. RNA modifications modulate gene expression during development. Science 361, 1346–1349 (2018). · DOI
  16. Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012). · DOI · Paper page
  17. Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012). · DOI · Paper page
  18. Lee, J. H. et al. Enhancer RNA m6A methylation facilitates transcriptional condensate formation and gene activation. Mol. Cell 81, 3368–3385 e3369 (2021). · DOI
  19. Lasman, L. et al. Context-dependent functional compensation between Ythdf m6A reader proteins. Genes Dev. 34, 1373–1391 (2020). · DOI · Paper page
  20. Zou, Z., Sepich-Poore, C., Zhou, X., Wei, J. & He, C. The mechanism underlying redundant functions of the YTHDF proteins. Genome Biol. 24, 17 (2023). · DOI
  21. Wang, P., Doxtader, K. A. & Nam, Y. Structural basis for cooperative function of Mettl3 and Mettl14 methyltransferases. Mol. Cell 63, 306–317 (2016). · DOI
  22. Du, Y. et al. SUMOylation of the m6A-RNA methyltransferase METTL3 modulates its function. Nucleic Acids Res. 46, 5195–5208 (2018). · DOI
  23. Sun, H. L. et al. Stabilization of ERK-phosphorylated METTL3 by USP5 increases m6A methylation. Mol. Cell 80, 633–647 e637 (2020). · DOI
  24. Winkler, R. et al. m(6)A modification controls the innate immune response to infection by targeting type I interferons. Nat. Immunol. 20, 173–182 (2019). · DOI
  25. Terajima, H. et al. N6-methyladenosine promotes induction of ADAR1-mediated A-to-I RNA editing to suppress aberrant antiviral innate immune responses. PLoS Biol. 19, e3001292 (2021). · DOI
  26. Gao, Y. et al. m6A modification prevents formation of endogenous double-stranded RNAs and deleterious innate immune responses during hematopoietic development. Immunity 52, 1007–1021 e1008 (2020). · DOI
  27. Qiu, W. et al. N6-methyladenosine RNA modification suppresses antiviral innate sensing pathways via reshaping double-stranded RNA. Nat. Commun. 12, 1582 (2021). · DOI
  28. Makino, S. et al. Breeding of a non-obese, diabetic strain of mice. Jikken Dobutsu 29, 1–13 (1980).
  29. De Jesus, D. F. et al. m6A mRNA methylation regulates human beta-cell biology in physiological states and in type 2 diabetes. Nat. Metab. 1, 765–774 (2019). · DOI
  30. Prochazka, M., Serreze, D. V., Frankel, W. N. & Leiter, E. H. NOR/Lt mice: MHC-matched diabetes-resistant control strain for NOD mice. Diabetes 41, 98–106 (1992). · DOI
  31. Carrero, J. A., Calderon, B., Towfic, F., Artyomov, M. N. & Unanue, E. R. Defining the transcriptional and cellular landscape of type 1 diabetes in the NOD mouse. PLoS ONE 8, e59701 (2013). · DOI
  32. Smelt, M. J., Faas, M. M., de Haan, B. J. & de Vos, P. Pancreatic beta-cell purification by altering FAD and NAD(P)H metabolism. Exp. Diabetes Res 2008, 165360 (2008). · DOI
  33. Russell, M. A. et al. HLA class II antigen processing and presentation pathway components demonstrated by transcriptome and protein analyses of islet beta-cells from donors with type 1 diabetes. Diabetes 68, 988–1001 (2019). · DOI
  34. Sledz, P. & Jinek, M. Structural insights into the molecular mechanism of the m6A writer complex. eLife 5, e18434 (2016). · DOI
  35. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012). · DOI · Paper page
  36. Dirice, E. et al. Increased beta-cell proliferation before immune cell invasion prevents progression of type 1 diabetes. Nat. Metab. 1, 509–518 (2019). · DOI
  37. Eisenbarth, G. S. Type I diabetes mellitus. A chronic autoimmune disease. N. Engl. J. Med. 314, 1360–1368 (1986). · DOI
  38. Ramos-Rodriguez, M. et al. The impact of proinflammatory cytokines on the beta-cell regulatory landscape provides insights into the genetics of type 1 diabetes. Nat. Genet. 51, 1588–1595 (2019). · DOI
  39. Benazra, M. et al. A human beta cell line with drug inducible excision of immortalizing transgenes. Mol. Metab. 4, 916–925 (2015). · DOI
  40. York, I. A., Brehm, M. A., Zendzian, S., Towne, C. F. & Rock, K. L. Endoplasmic reticulum aminopeptidase 1 (ERAP1) trims MHC class I-presented peptides in vivo and plays an important role in immunodominance. Proc. Natl Acad. Sci. USA 103, 9202–9207 (2006). · DOI
  41. Thomaidou, S. et al. β-Cell stress shapes CTL immune recognition of preproinsulin signal peptide by posttranscriptional regulation of endoplasmic reticulum aminopeptidase 1. Diabetes 69, 670–680 (2020). · DOI
  42. Szymczak, F. et al. ADAR1-dependent editing regulates human β cell transcriptome diversity during inflammation. Front. Endocrinol. 13, 1058345 (2022). · DOI
  43. Hu, S.-B. et al. ADAR1p150 prevents MDA5 and PKR activation via distinct mechanisms to avert fatal autoinflammation. Mol. Cell 83, 3869–3884.e3867 (2023). · DOI
  44. Li, X., Chen, H. & Epstein, P. N. Metallothionein protects islets from hypoxia and extends islet graft survival by scavenging most kinds of reactive oxygen species. J. Biol. Chem. 279, 765–771 (2004). · DOI
  45. Bensellam, M., Laybutt, D. R. & Jonas, J.-C. Emerging roles of metallothioneins in beta cell pathophysiology: beyond and above metal homeostasis and antioxidant response. Biology 10, 176 (2021). · DOI
  46. Rui, J. et al. Tet2 controls the responses of beta cells to inflammation in autoimmune diabetes. Nat. Commun. 12, 5074 (2021). · DOI
  47. Kulkarni, A. et al. 12-Lipoxygenase governs the innate immune pathogenesis of islet inflammation and autoimmune diabetes. JCI Insight 6, e147812 (2021). · DOI
  48. Nelson, A. S. et al. Oral therapy with colonization factor antigen I prevents development of type 1 diabetes in non-obese diabetic mice. Sci. Rep. 10, 6156 (2020). · DOI
  49. Robinson, C. P., Yamamoto, H., Peck, A. B. & Humphreys-Beher, M. G. Genetically programmed development of salivary gland abnormalities in the NOD (nonobese diabetic)-scid mouse in the absence of detectable lymphocytic infiltration: a potential trigger for sialoadenitis of NOD mice. Clin. Immunol. Immunopathol. 79, 50–59 (1996). · DOI
  50. Elkhateeb, E. et al. The role of mouse 2′,5′-oligoadenylate synthetase 1 paralogs. Infect. Genet. Evol. 45, 393–401 (2016). · DOI
  51. Chen, J., Stimpson, S. E., Fernandez-Bueno, G. A. & Mathews, C. E. Mitochondrial reactive oxygen species and type 1 diabetes. Antioxid. Redox Signal 29, 1361–1372 (2018). · DOI
  52. Kim, Y. K., Sussel, L. & Davidson, H. W. Inherent beta cell dysfunction contributes to autoimmune susceptibility. Biomolecules 11, 512 (2021). · DOI
  53. Tersey, S. A. et al. Islet beta-cell endoplasmic reticulum stress precedes the onset of type 1 diabetes in the nonobese diabetic mouse model. Diabetes 61, 818–827 (2012). · DOI · Paper page
  54. Mallone, R. & Eizirik, D. L. Presumption of innocence for beta cells: why are they vulnerable autoimmune targets in type 1 diabetes? Diabetologia 63, 1999–2006 (2020). · DOI
  55. Wang, X. et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014). · DOI · Paper page
  56. Rubio, R. M., Depledge, D. P., Bianco, C., Thompson, L. & Mohr, I. RNA m6A modification enzymes shape innate responses to DNA by regulating interferon beta. Genes Dev. 32, 1472–1484 (2018). · DOI
  57. Balzano-Nogueira, L. et al. Integrative analyses of TEDDY Omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes. Genome Biol. 22, 39 (2021). · DOI
  58. Eizirik, D. L., Miani, M. & Cardozo, A. K. Signalling danger: endoplasmic reticulum stress and the unfolded protein response in pancreatic islet inflammation. Diabetologia 56, 234–241 (2013). · DOI
  59. Sahin, G. S., Lee, H. & Engin, F. An accomplice more than a mere victim: the impact of β-cell ER stress on type 1 diabetes pathogenesis. Mol. Metab. 54, 101365 (2021). · DOI
  60. Zhou, H. L., Premont, R. T. & Stamler, J. S. The manifold roles of protein S-nitrosylation in the life of insulin. Nat. Rev. Endocrinol. 18, 111–128 (2022). · DOI
  61. Garban, H. J., Marquez-Garban, D. C., Pietras, R. J. & Ignarro, L. J. Rapid nitric oxide-mediated S-nitrosylation of estrogen receptor: regulation of estrogen-dependent gene transcription. Proc. Natl Acad. Sci. USA 102, 2632–2636 (2005). · DOI
  62. Kroncke, K. D. Zinc finger proteins as molecular targets for nitric oxide-mediated gene regulation. Antioxid. Redox Signal 3, 565–575 (2001). · DOI
  63. Huang, J. et al. Solution structure of the RNA recognition domain of METTL3–METTL14 N6-methyladenosine methyltransferase. Protein Cell 10, 272–284 (2019). · DOI
  64. Ravassard, P. et al. A genetically engineered human pancreatic β cell line exhibiting glucose-inducible insulin secretion. J. Clin. Investig. 121, 3589–3597 (2011). · DOI
  65. El Ouaamari, A. et al. SerpinB1 promotes pancreatic beta cell proliferation. Cell Metab. 23, 194–205 (2016). · DOI
  66. El Ouaamari, A. et al. Compensatory islet response to insulin resistance revealed by quantitative proteomics. J. Proteome Res. 14, 3111–3122 (2015). · DOI
  67. Dirice, E. et al. Soluble factors secreted by T cells promote beta-cell proliferation. Diabetes 63, 188–202 (2014). · DOI
  68. De Jesus, D. F. et al. Parental metabolic syndrome epigenetically reprograms offspring hepatic lipid metabolism in mice. J. Clin. Invest. 130, 2391–2407 (2020). · DOI
  69. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). · DOI · Paper page
  70. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018). · DOI · Paper page
  71. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). · DOI · Paper page
  72. Zhang, Z. et al. RADAR: differential analysis of MeRIP-seq data with a random effect model. Genome Biol. 20, 294 (2019). · DOI
  73. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). · DOI · Paper page
  74. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). · DOI · Paper page
  75. Fisher, R. A. in Breakthroughs in Statistics (eds Kotz, S. & Johnson, N. L.) 66–70 (Springer, 1992). · DOI
  76. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014). · DOI · Paper page
  77. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016). · DOI · Paper page
  78. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015). · DOI · Paper page
  79. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010). · DOI · Paper page
  80. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014). · DOI · Paper page
  81. Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R. J. 8, 289–317 (2016). · DOI
  82. Herwig, R., Hardt, C., Lienhard, M. & Kamburov, A. Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nat. Protoc. 11, 1889 (2016). · DOI
  83. Saito, R. et al. A travel guide to Cytoscape plugins. Nat. Methods 9, 1069–1076 (2012). · DOI
  84. Snel, B. et al. STRING: known and predicted protein–protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33, D433–D437 (2005).
  85. Duan, J. et al. Stochiometric quantification of the thiol redox proteome of macrophages reveals subcellular compartmentalization and susceptibility to oxidative perturbations. Redox Biol. 36, 101649 (2020). · DOI

Metadata

Authors
De Jesus, D. F., Zhang, Z., Brown, N. K., Li, X., Xiao, L., Hu, J., Gaffrey, M. J., Fogarty, G., Kahraman, S., Wei, J., Basile, G., Rana, T. M., Mathews, C., Powers, A. C., Parent, A. V., Atkinson, M. A., Dhe-Paganon, S., Eizirik, D. L., Qian, W.-J., He, C., & Kulkarni, R. N.
Affiliations
  • Howard Hughes Medical Institute (US)
  • Joslin Diabetes Center (US)
  • Pacific Northwest National Laboratory (US)
  • University of California San Diego (US)
  • University of Florida Health (US)
  • Vanderbilt University Medical Center (US)
  • University of California, San Francisco (US)
  • Université Libre de Bruxelles (BE)
Corresponding author
  • He, Chuan
  • Kulkarni, Rohit N.
ORCID
Venue
Nature Cell Biology
Publisher
Springer Science and Business Media LLC
Published
2024-02-26
Volume
26
Issue
3
Pages
421-437
ISSN
  • 1465-7392 (ISSN-L)
  • 1476-4679
DOI
10.1038/s41556-024-01368-0
PMC
PMC11042681
OpenAlex
W4392157901
Cited by
56
References
7
Citations by year
2026: 18 · 2025: 20 · 2024: 18
FWCI
13.78
Type
journal article
Language
en
Update notices
correction (2025-11-17)
Open access
Licence
Locations
  • Nature Cell Biology (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
  • PubMed Central (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • American Diabetes Association (7-21-PDF-140)
  • U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (K99 DK135927, UC4 DK116278)
  • DOE | Advanced Research Projects Agency - Energy (DE-AC05-76RL0 1830)
  • U.S. Department of Health & Human Services | National Institutes of Health (RM1 HG008935)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
Keywords
type 1 diabetes, pancreatic beta cells, METTL3, N6-methyladenosine, RNA methylation, innate immune response, antiviral response, 2',5'-oligoadenylate synthetase, ADAR1, mRNA stability, reactive oxygen species, nitric oxide, S-nitrosylation, zinc finger, gene therapy, NOD mice, islets

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review