Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

m6A mRNA methylation regulates human β-cell biology in physiological states and in type 2 diabetes


The regulation of islet cell biology is critical for glucose homeostasis1. N6-methyladenosine (m6A) is the most abundant internal messenger RNA (mRNA) modification in mammals2. Here, we report that the m6A landscape segregates human type 2 diabetes (T2D) islets from controls significantly better than the transcriptome and that m6A is vital for β-cell biology. m6A sequencing in human T2D islets reveals several hypomethylated transcripts that are involved in cell-cycle progression, insulin secretion, and the insulin/IGF1–AKT–PDX1 pathway. Depletion of m6A levels in EndoC-βH1 cells induces cell-cycle arrest and impairs insulin secretion by decreasing AKT phosphorylation and PDX1 protein levels. β-cell-specific Mettl14 knockout mice, which display reduced m6A levels, mimic the islet phenotype in human T2D with early diabetes onset and mortality owing to decreased β-cell proliferation and insulin degranulation. Our data underscore the significance of RNA methylation in regulating human β-cell biology, and provide a rationale for potential therapeutic targeting of m6A modulators to preserve β-cell survival and function in diabetes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: RNA N6-methyladenosine sequencing reveals a homogeneous m6A decoration in human T2D islets.
Fig. 2: m6A controls PDX1 expression and modulates insulin/IGF1-mediated AKT phosphorylation.
Fig. 3: β-cell-specific Mettl14 knockout results in early diabetes and mortality secondary to decreased Pdx1 expression and decreased phosphorylation of AKT.
Fig. 4: Functional protein–protein interaction network analyses reveal the central role of AKT in controlling the effects of Mettl14 ablation in β-cells.

Data availability

m6A sequencing and RNA sequencing data in human islets have been deposited with the National Center for Biotechnology Information Gene Expression Omnibus under accession code GSE120024. m6A sequencing and RNA sequencing data in EndoC-βH1 cells have been deposited under accession code GSE132306. RNA sequencing in mouse FACS-sorted β-cells have been deposited under the accession code GSE132306. Phospho-antibody microarray data performed in mouse whole islets have been deposited under the accession code GSE132111.The data that support the findings of this study are available from the corresponding author upon reasonable request. R package RADAR code is available upon request.


  1. 1.

    De Jesus, D. F. & Kulkarni, R. N. Epigenetic modifiers of islet function and mass. Trends Endocrinol. Metab. 25, 628–636 (2014).

    Article  Google Scholar 

  2. 2.

    Frye, M., Harada, B. T., Behm, M. & He, C. RNA modifications modulate gene expression during development. Science 361, 1346–1349 (2018).

    CAS  Article  Google Scholar 

  3. 3.

    Taneera, J. et al. Silencing of the FTO gene inhibits insulin secretion: an in vitro study using GRINCH cells. Mol. Cell. Endocrinol. 472, 10–17 (2018).

    CAS  Article  Google Scholar 

  4. 4.

    Xin, Y. et al. RNA sequencing of single human islet cells reveals type 2 diabetes genes. Cell Metab. 24, 608–615 (2016).

    CAS  Article  Google Scholar 

  5. 5.

    Wang, X. et al. N 6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117 (2013).

    CAS  Article  Google Scholar 

  6. 6.

    Fadista, J. et al. Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism. Proc. Natl Acad. Sci. 111, 13924–13929 (2014).

    CAS  Article  Google Scholar 

  7. 7.

    Gromada, J., Chabosseau, P. & Rutter, G. A. The α-cell in diabetes mellitus. Nat. Rev. Endocrinol. 14, 694–704 (2018).

    CAS  Article  Google Scholar 

  8. 8.

    Diedisheim, M. et al. Modeling human pancreatic beta cell dedifferentiation. Mol. Metab. 10, 74–86 (2018).

    CAS  Article  Google Scholar 

  9. 9.

    Laukkanen, O. et al. Polymorphisms in the SLC2A2 (GLUT2) gene are associated with the conversion from impaired glucose tolerance to type 2 diabetes: the finnish diabetes prevention study. The Finnish Diabetes Prevention Study 54, 2256–2260 (2005).

    CAS  Google Scholar 

  10. 10.

    Wang, Y. et al. N 6-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications. Nat. Neurosci. 21, 195–206 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    Kulkarni, R. N. et al. PDX-1 haploinsufficiency limits the compensatory islet hyperplasia that occurs in response to insulin resistance. J. Clin. Invest. 114, 828–836 (2004).

    CAS  Article  Google Scholar 

  12. 12.

    Stoffers, D. A., Zinkin, N. T., Stanojevic, V., Clarke, W. L. & Habener, J. F. Pancreatic agenesis attributable to a single nucleotide deletion in the human IPF1 gene coding sequence. Nat. Genet. 15, 106 (1997).

    CAS  Article  Google Scholar 

  13. 13.

    Guo, S. et al. Inactivation of specific β cell transcription factors in type 2 diabetes. J. Clin. Invest. 123, 3305–3316 (2013).

    CAS  Article  Google Scholar 

  14. 14.

    Humphrey, R. K., Yu, S.-M., Flores, L. E. & Jhala, U. S. Glucose regulates steady-state levels of PDX1 via the reciprocal actions of GSK3 and AKT kinases. J. Biol. Chem. 285, 3406–3416 (2010).

    CAS  Article  Google Scholar 

  15. 15.

    Elghazi, L. & Bernal-Mizrachi, E. Akt and PTEN: beta-cell mass and pancreas plasticity. Trends Endocrinol. Metab. 20, 243–251 (2009).

    CAS  Article  Google Scholar 

  16. 16.

    Ravassard, P. et al. A genetically engineered human pancreatic β cell line exhibiting glucose-inducible insulin secretion. J. Clin. Invest. 121, 3589–3597 (2011).

    CAS  Article  Google Scholar 

  17. 17.

    Tsonkova, V. G. et al. The EndoC-βH1 cell line is a valid model of human beta cells and applicable for screenings to identify novel drug target candidates. Mol. Metab. 8, 144–157 (2018).

    CAS  Article  Google Scholar 

  18. 18.

    Boucher, M.-J., Selander, L., Carlsson, L. & Edlund, H. Phosphorylation marks IPF1/PDX1 protein for degradation by glycogen synthase kinase 3-dependent mechanisms. J. Biol. Chem. 281, 6395–6403 (2006).

    CAS  Article  Google Scholar 

  19. 19.

    Wang, X. et al. N 6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).

    CAS  Article  Google Scholar 

  20. 20.

    Tang, L. et al. Suppression of sirtuin-1 increases IL-6 expression by activation of the Akt pathway during allergic asthma. Cell Physiol. Biochem. 43, 1950–1960 (2017).

    CAS  Article  Google Scholar 

  21. 21.

    Lu, H., Koshkin, V., Allister, E. M., Gyulkhandanyan, A. V. & Wheeler, M. B. Molecular and metabolic evidence for mitochondrial defects associated with β-cell dysfunction in a mouse model of type 2 diabetes. Diabetes 59, 448–459 (2010).

    CAS  Article  Google Scholar 

  22. 22.

    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, 11 (2008).

    Article  Google Scholar 

  23. 23.

    Cook, R. S. et al. ErbB3 ablation impairs PI3K/Akt-dependent mammary tumorigenesis. Cancer Res. 71, 3941–3951 (2011).

    CAS  Article  Google Scholar 

  24. 24.

    Rabinovsky, R. et al. p85 associates with unphosphorylated PTEN and the PTEN-associated complex. Mol. Cell Biol. 29, 5377–5388 (2009).

    CAS  Article  Google Scholar 

  25. 25.

    Vazquez, F., Ramaswamy, S., Nakamura, N. & Sellers, W. R. Phosphorylation of the PTEN tail regulates protein stability and function. Mol. Cell Biol. 20, 5010–5018 (2000).

    CAS  Article  Google Scholar 

  26. 26.

    Snel, B. et al. STRING: known and predicted protein–protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33, D433–D437 (2005).

    PubMed  Google Scholar 

  27. 27.

    Weng, H. et al. METTL14 inhibits hematopoietic stem/progenitor differentiation and promotes leukemogenesis via mRNA m6A modification. Cell Stem Cell 22, 191–205.e199 (2018).

    CAS  Article  Google Scholar 

  28. 28.

    Yoon, K.-J. et al. Temporal control of mammalian cortical neurogenesis by m6A methylation. Cell 171, 877–889.e817 (2017).

    CAS  Article  Google Scholar 

  29. 29.

    Liu, J. et al. m6A mRNA methylation regulates AKT activity to promote the proliferation and tumorigenicity of endometrial cancer. Nat. Cell Biol. 20, 1074–1083 (2018).

    CAS  Article  Google Scholar 

  30. 30.

    Min, K.-W. et al. Profiling of m6A RNA modifications identified an age-associated regulation of AGO2 mRNA stability. Aging Cell 17, e12753 (2018).

    Article  Google Scholar 

  31. 31.

    Thorens, B. et al. Ins1Cre knock-in mice for beta cell-specific gene recombination. Diabetologia 58, 558–565 (2015).

    CAS  Article  Google Scholar 

  32. 32.

    El Ouaamari, A. et al. Compensatory islet response to insulin resistance revealed by quantitative proteomics. J. Proteome Res. 14, 3111–3122 (2015).

    CAS  Article  Google Scholar 

  33. 33.

    Dirice, E. et al. Soluble factors secreted by T cells promote β-cell proliferation. Diabetes 63, 188–202 (2014).

    CAS  Article  Google Scholar 

  34. 34.

    El Ouaamari, A. et al. SerpinB1 promotes pancreatic β-cell proliferation. Cell Metab. 23, 194–205 (2016).

    CAS  Article  Google Scholar 

  35. 35.

    Dirice, E. et al. Increased β-cell proliferation before immune cell invasion prevents progression of type 1 diabetes. Nat. Metab. 1, 509–518 (2019).

    Article  Google Scholar 

  36. 36.

    Kulkarni, R. N. et al. Tissue-specific knockout of the insulin receptor in pancreatic β-cells creates an insulin secretory defect similar to that in type 2 diabetes. Cell 96, 329–339 (1999).

    CAS  Article  Google Scholar 

  37. 37.

    Kahraman, S., Dirice, E., De Jesus, D. F., Hu, J. & Kulkarni, R. N. Maternal insulin resistance and transient hyperglycemia impact the metabolic and endocrine phenotypes of offspring. Am. J. Physiol. Endocrinol. Metab. 307, E906–E918 (2014).

    CAS  Article  Google Scholar 

  38. 38.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. (2011).

    Article  Google Scholar 

  39. 39.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Meng, J., Cui, X., Rao, M. K., Chen, Y. & Huang, Y. Exome-based analysis for RNA epigenome sequencing data. Bioinformatics 29, 1565–1567 (2013).

    CAS  Article  Google Scholar 

  41. 41.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  Article  Google Scholar 

  42. 42.

    Cui, X. et al. Guitar: an R/Bioconductor package for gene annotation guided transcriptomic analysis of RNA-Related genomic features. Biomed. Res. Int. 2016, 8367534 (2016).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  Google Scholar 

  44. 44.

    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).

    Article  Google Scholar 

  45. 45.

    Herwig, R., Hardt, C., Lienhard, M. & Kamburov, A. Analyzing and interpreting genome data at the network level with consensus path DB. Nat. Protoc. 11, 1889 (2016).

    CAS  Article  Google Scholar 

  46. 46.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  47. 47.

    Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).

    CAS  Article  Google Scholar 

  48. 48.

    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).

    Article  Google Scholar 

  49. 49.

    Krishnamoorthy, K. & Lee, M. Improved tests for the equality of normal coefficients of variation. Comput. Stat. 29, 215–232 (2014).

    Article  Google Scholar 

  50. 50.

    Maere, S., Heymans, K. & Kuiper, M. BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448–3449 (2005).

    CAS  Article  Google Scholar 

Download references


The authors thank the Joslin Islet Isolation Core, Joslin Bioinformatics Core, and Joslin Advanced Microscopy Core (P30 DK36836). This work is supported by NIH grants R01 DK67536 (R.N.K.), UC4 DK116278 (R.N.K. and C.H.) and RM1 HG008935 (C.H.). R.N.K. acknowledges support from the Margaret A. Congleton Endowed Chair and C.H. is a Howard Hughes Medical Institute Investigator. M.K.G. acknowledges support from the JDRF Advanced Postdoctoral Fellowship Award 3-APF-2017-393-A-N. The authors sincerely thank the families of the human islet donors.

Author information




D.F.D.J. conceived the study, designed and performed the experiments, analyzed the data, and wrote the manuscript. Z.Z. designed and performed the experiments, analyzed the data, and wrote the manuscript. S.K. performed cell culture experiments and analyzed the data. N.K.B. performed morphometric analyses of pancreases. J.H. performed immunohistochemistry. M.K.G. performed real-time PCR. C.H. contributed to conceptual discussions and designed the experiments. R.N.K. contributed to conceptual discussions, designed the experiments, supervised the project, and wrote the manuscript. All the authors have reviewed, commented, and edited the manuscript.

Corresponding authors

Correspondence to Chuan He or Rohit N. Kulkarni.

Ethics declarations

Competing interests

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.

Additional information

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.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

De Jesus, D.F., Zhang, Z., Kahraman, S. et al. m6A mRNA methylation regulates human β-cell biology in physiological states and in type 2 diabetes. Nat Metab 1, 765–774 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing