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      m 6A mRNA Methylation Regulates Human β-Cell Biology in Physiological States and in Type 2 Diabetes

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          Abstract

          The regulation of islet cell biology is critical for glucose homeostasis 1 . N 6 -methyladenosine (m 6A) is the most abundant internal messenger RNA (mRNA) modification in mammals 2 . Here we report that the m 6A landscape segregates human type 2 diabetes (T2D) islets from controls significantly better than the transcriptome and that m 6A is vital for β-cell biology. m 6A-sequencing in human T2D islets reveals several hypomethylated transcripts involved in cell-cycle progression, insulin secretion, and the Insulin/IGF1-AKT-PDX1 pathway. Depletion of m 6A levels in EndoC-βH1 induces cell-cycle arrest and impairs insulin secretion by decreasing AKT phosphorylation and PDX1 protein levels. β-cell specific Mettl14 knock-out mice, which display reduced m 6A levels, mimic the islet phenotype in human T2D with early diabetes onset and mortality due 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 m 6A modulators to preserve β-cell survival and function in diabetes.

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          RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes.

          Yurong Xin, Jinrang Kim, Haruka Okamoto … (2016)
          Pancreatic islet cells are critical for maintaining normal blood glucose levels, and their malfunction underlies diabetes development and progression. We used single-cell RNA sequencing to determine the transcriptomes of 1,492 human pancreatic α, β, δ, and PP cells from non-diabetic and type 2 diabetes organ donors. We identified cell-type-specific genes and pathways as well as 245 genes with disturbed expression in type 2 diabetes. Importantly, 92% of the genes have not previously been associated with islet cell function or growth. Comparison of gene profiles in mouse and human α and β cells revealed species-specific expression. All data are available for online browsing and download and will hopefully serve as a resource for the islet research community.
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            Tissue-specific knockout of the insulin receptor in pancreatic beta cells creates an insulin secretory defect similar to that in type 2 diabetes.

            Rohit Kulkarni, Jens C Brüning, Jonathon N Winnay … (1999)
            Dysfunction of the pancreatic beta cell is an important defect in the pathogenesis of type 2 diabetes, although its exact relationship to the insulin resistance is unclear. To determine whether insulin signaling has a functional role in the beta cell we have used the Cre-loxP system to specifically inactivate the insulin receptor gene in the beta cells. The resultant mice exhibit a selective loss of insulin secretion in response to glucose and a progressive impairment of glucose tolerance. These data indicate an important functional role for the insulin receptor in glucose sensing by the pancreatic beta cell and suggest that defects in insulin signaling at the level of the beta cell may contribute to the observed alterations in insulin secretion in type 2 diabetes.
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              Computational assignment of cell-cycle stage from single-cell transcriptome data.

              Antonio Scialdone, Kedar Natarajan, Luis Saraiva … (2015)
              The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols.
              Article 0 comments Cited 193 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 101736592
                Journal ID (pubmed-jr-id): 48119
                Journal ID (nlm-ta): Nat Metab
                Journal ID (iso-abbrev): Nat Metab
                Title: Nature metabolism
                ISSN (Electronic): 2522-5812
                Publication date Nihms-submitted: 15 June 2019
                Publication date (Electronic): 29 July 2019
                Publication date (Print): August 2019
                Publication date PMC-release: 29 January 2020
                Volume: 1
                Issue: 8
                Pages: 765-774
                Affiliations
                [1 ]Islet Cell and Regenerative Biology, Joslin Diabetes Center, Department of Medicine, Brigham and Women’s Hospital, Harvard Stem Cell Institute, Harvard Medical School, Boston, MA 02215, USA.
                [2 ]Department of Chemistry, Department of Biochemistry and Molecular Biology, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL 60637, USA.
                [3 ]Howard Hughes Medical Institute, The University of Chicago, Chicago, IL 60637, USA.
                [4 ]Section of Genetic Medicine, Department of Medicine, Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA;
                Author notes

                AUTHOR CONTRIBUTIONS

                DFDJ conceived the idea, designed and performed the experiments, analyzed the data and wrote the manuscript. ZZ designed and performed the experiments, analyzed the data and wrote the manuscript. SK performed cell culture experiments and analyzed the data. NKB performed morphometric analyses of pancreases. JH performed immunohistochemistry. MKG performed RT-PCRs. CH contributed to conceptual discussions and designed the experiments. RNK contributed to conceptual discussions, designed the experiments, supervised the project and wrote the manuscript. All the authors have reviewed, commented and edited the manuscript.

                Correspondence to: Rohit N. Kulkarni M.D., Ph.D., Islet Cell and Regenerative Biology, Joslin Diabetes Center, One Joslin Place, Boston, 02215 MA, Tel: +1-617-309-3460 – Fax: +1-617-309-3476, Rohit.Kulkarni@ 123456joslin.harvard.edu , Chuan He Ph.D., Department of Chemistry, The University of Chicago, 929 E. 57 th St., Chicago, 60637 IL, Tel: +1-773-702-5061 -Fax: +1-773-702-0805, chuanhe@ 123456uchicago.edu
                [*]

                These authors contributed equally to this work.

                Article
                Manuscript ID: NIHMS1532031
                DOI: 10.1038/s42255-019-0089-9
                PMC ID: 6924515
                PubMed ID: 31867565
                SO-VID: d3f98a79-118e-4ef3-8822-d5c2de611edd
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                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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