Abstract
Control of insulin mRNA translation is crucial for energy homeostasis, but the mechanisms remain largely unknown. We discovered that insulin mRNAs across invertebrates, vertebrates and mammals feature the modified base N6-methyladenosine (m6A). In flies, this RNA modification enhances insulin mRNA translation by promoting the association of the transcript with polysomes. Depleting m6A in Drosophila melanogaster insulin 2 mRNA (dilp2) directly through specific 3′ untranslated region (UTR) mutations, or indirectly by mutating the m6A writer Mettl3, decreases dilp2 protein production, leading to aberrant energy homeostasis and diabetic-like phenotypes. Together, our findings reveal adenosine mRNA methylation as a key regulator of insulin protein synthesis with notable implications for energy balance and metabolic disease.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Data availability
All data are available in the main text or supplementary materials. RNA sequencing reads were deposited in GEO under accession number GSE207547. dilp2 3′ UTR mutants are available upon request. The D. melanogaster genome sequence is available in Ensembl under BDGP6.32, Mus musculus Ins2 is available in GenBank under NM_001185083.2, and Salmo salar ins is available in GenBank under XM_014198195.2. Source data are provided with this paper.
Code availability
All custom scripts for data analysis can be found at https://github.com/dwilinski/m6A-fly-insulin.git.
References
Zaccara, S., Ries, R. J. & Jaffrey, S. R. Reading, writing and erasing mRNA methylation. Nat. Rev. Mol. Cell Biol. 20, 608–624 (2019).
He, P. C. & He, C. m6A RNA methylation: from mechanisms to therapeutic potential. EMBO J. 40, e105977 (2021).
Men, L., Sun, J., Luo, G. & Ren, D. Acute deletion of METTL14 in β-cells of adult mice results in glucose intolerance. Endocrinology 160, 2388–2394 (2019).
Liu, J. et al. METTL14 is essential for β-cell survival and insulin secretion. Biochim. Biophys. Acta Mol. Basis Dis. 1865, 2138–2148 (2019).
Li, X., Yang, Y. & Chen, Z. Downregulation of the m6A reader protein YTHDC1 leads to islet β-cell failure and diabetes. Metabolism 138, 155339 (2023).
De Jesus, D. F. et al. m6A mRNA methylation regulates human β-cell biology in physiological states and in type 2 diabetes. Nat. Metab. 1, 765–774 (2019).
Yang, Y. et al. Glucose is involved in the dynamic regulation of m6A in patients with type 2 diabetes. J. Clin. Endocrinol. Metab. 104, 665–673 (2019).
Jahr, H., Schröder, D., Ziegler, B., Ziegler, M. & Zühlke, H. Transcriptional and translational control of glucose-stimulated (pro)insulin biosynthesis. Eur. J. Biochem. 110, 499–505 (1980).
Magro, M. G. & Solimena, M. Regulation of β-cell function by RNA-binding proteins. Mol. Metab. 2, 348–355 (2013).
Lence, T., Soller, M. & Roignant, J.-Y. A fly view on the roles and mechanisms of the m6A mRNA modification and its players. RNA Biol. 14, 1232–1240 (2017).
Das, R. & Dobens, L. L. Conservation of gene and tissue networks regulating insulin signalling in flies and vertebrates. Biochem. Soc. Trans. 43, 1057–1062 (2015).
Rulifson, E. J., Kim, S. K. & Nusse, R. Ablation of insulin-producing neurons in flies: growth and diabetic phenotypes. Science 296, 1118–1120 (2002).
Kannan, K. & Fridell, Y.-W. C. Functional implications of Drosophila insulin-like peptides in metabolism, aging, and dietary restriction. Front. Physiol. 4, 288 (2013).
Semaniuk, U. V. et al. Insulin-like peptides regulate feeding preference and metabolism in Drosophila. Front. Physiol. 9, 1083 (2018).
Brogiolo, W. et al. An evolutionarily conserved function of the Drosophila insulin receptor and insulin-like peptides in growth control. Curr. Biol. 11, 213–221 (2001).
Lence, T. et al. m6A modulates neuronal functions and sex determination in Drosophila. Nature 540, 242–247 (2016).
Kan, L. et al. The m6A pathway facilitates sex determination in Drosophila. Nat. Commun. 8, 15737 (2017).
Grozhik, A. V., Linder, B., Olarerin-George, A. O. & Jaffrey, S. R. Mapping m6A at individual-nucleotide resolution using crosslinking and immunoprecipitation (miCLIP). Methods Mol. Biol. 1562, 55–78 (2017).
Kan, L. et al. A neural m6A/Ythdf pathway is required for learning and memory in Drosophila. Nat. Commun. 12, 1458 (2021).
Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).
Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).
Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).
Liu, H. et al. Accurate detection of m6A RNA modifications in native RNA sequences. Nat. Commun. 10, 4079 (2019).
Parker, M. T. et al. Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification. eLife 9, e49658 (2020).
Chen, X. & Dickman, D. Development of a tissue-specific ribosome profiling approach in Drosophila enables genome-wide evaluation of translational adaptations. PLoS Genet. 13, e1007117 (2017).
Kim, J. & Lee, G. Metabolic control of m6A RNA modification. Metabolites 11, 80 (2021).
Li, X., Jiang, Y., Sun, X., Wu, Y. & Chen, Z. METTL3 is required for maintaining β-cell function. Metabolism 116, 154702 (2021).
Zhong, H., Tang, H.-F. & Kai, Y. N6-methyladenine RNA modification (m6A): an emerging regulator of metabolic diseases. Curr. Drug Targets 21, 1056–1067 (2020).
Zarnegar, B. J. et al. irCLIP platform for efficient characterization of protein–RNA interactions. Nat. Methods 13, 489–492 (2016).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Uren, P. J. et al. Site identification in high-throughput RNA–protein interaction data. Bioinformatics 28, 3013–3020 (2012).
Olarerin-George, A. O. & Jaffrey, S. R. MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites. Bioinformatics 33, 1563–1564 (2017).
Crooks, G. E., Hon, G., Chandonia, J.-M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Carlson, M. org.Dm.eg.db: genome wide annotation for Fly. Bioconductor https://doi.org/10.18129/B9.BIOC.ORG.DM.EG.DB (2017).
Essers, P. et al. Reduced insulin/insulin-like growth factor signaling decreases translation in Drosophila and mice. Sci. Rep. 6, 30290 (2016).
Wilinski, D. et al. Rapid metabolic shifts occur during the transition between hunger and satiety in Drosophila melanogaster. Nat. Commun. 10, 4052 (2019).
May, C. E. et al. High dietary sugar reshapes sweet taste to promote feeding behavior in Drosophila melanogaster. Cell Rep. 27, 1675–1685.e7 (2019).
Géminard, C., Rulifson, E. J. & Léopold, P. Remote control of insulin secretion by fat cells in Drosophila. Cell Metab. 10, 199–207 (2009).
Buhler, K. et al. Growth control through regulation of insulin signalling by nutrition-activated steroid hormone in Drosophila. Development 145, dev165654 (2018).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Vaziri, A. et al. Persistent epigenetic reprogramming of sweet taste by diet. Sci. Adv. 6, eabc8492 (2020).
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
Stoiber, M. et al. De novo identification of DNA modifications enabled by genome-guided nanopore signal processing. Preprint at bioRxiv https://doi.org/10.1101/094672 (2017).
Tennessen, J. M., Barry, W. E., Cox, J. & Thummel, C. S. Methods for studying metabolism in Drosophila. Methods 68, 105–115 (2014).
Acknowledgements
We thank P. Léopold (Institut Curie) for the kind gift of the dilp2 antibody, P. Callaerts (KU Leuven) for the gift of the dilp3 antibody, R. Seeley and C. Cras-Méneur for mouse islets (University of Michigan), B. Peterson (National Cold Water Marine Aquaculture Center) for salmon tissue, J.-Y. Roignant (University of Lausanne) for Mettl3 mutant flies, and the Bloomington Drosophila Stock Center for other flies used in this study. We are grateful to P. Todd and S. Miller for training and the use of their polysome fractionation equipment, C. Lapointe for thoughtful comments on the manuscript, and C. Duan for advice. We also thank J. Kuhl for designing some of the graphics in this manuscript. This work was supported by National Institutes of Health grants R00 DK-097141 (M.D.), 1DP2DK-113750 (M.D.), T32 DA007268 (D.W.), P30 DK089503 (M.D. and D.W.), and K99 DK128539 (D.W.); the Rita Allen Foundation (M.D.); and National Science Foundation CAREER 1941822 (M.D.).
Author information
Authors and Affiliations
Contributions
Conceptualization: D.W. and M.D. Methodology: D.W. Investigation: D.W. and M.D. Visualization: D.W. and M.D. Funding acquisition: M.D. and D.W. Project administration: M.D. Supervision: M.D. Writing – original draft: D.W. and M.D. Writing – review & editing: D.W. and M.D.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Structural & Molecular Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Carolina Perdigoto and Dimitris Typas were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.
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 The effects of Mettl3 KD on energy homeostasis are not developmental.
(a) Quantification of Mettl3 mRNA from heads of control nSyb > w1118CS (n = 3 sets of 20 flies) and nSyb > Mettl3RNAi flies (n = 6 sets of 20 flies). Unpaired two-tailed Student’s t-test, p = 0.045. (b) Triglyceride levels normalized to protein in male control (Tubulin-GAL80ts, dilp2-GAL4 / +) and Tubulin-GAL80ts; dilp2 > Mettl3RNAi flies. n = 8, 6 pools of two flies. Unpaired two-tailed Student’s t-test, p = 0.0003. Error bars are SEM. * p < 0.05, ** p < 0.005.
Extended Data Fig. 2 Additional phenotyping of Mettl3 mutants.
(a, b, c) Quantification of (a) dilp2, (b) dilp3, and (c) dilp5 mRNA from heads of control (w1118CS) and Mettl3−/− mutant flies. n = 6 sets of 20 flies. Unpaired two-tailed Student’s t-test; p = 0.594 (a), p = 0.778 (b), and p = 0.073 (c). (d) Quantification of insulin cells n = 3 brains. Unpaired two-tailed Student’s t-test, p = 0.374. (e) Representative confocal images of immunofluorescence of dilp3 protein in control (w1118CS) and Mettl3−/− mutant flies. Scale bar, 20um. Quantification of median dilp3 fluorescence of individual insulin-producing cells from (d), n = 6 brains per genotype. Unpaired two-tailed Student’s t-test, p = 0.333. Error bars are SEM. ns = not significant.
Extended Data Fig. 3 Reproducibility of biological CLIP replicates.
(a) Correlation plots of log2 normalized reads per CLIP peak. Each dot represents a CLIP peak found in all three biological replicates. Pearson’s correlation coefficient (r). (b) Gene ontology (GO) enrichment analysis of genes that harbor a CLIP peak. Circle size represents the number of genes with CLIP peaks in the corresponding GO categories. The color represents the significance of the enrichment (Benjamini–Hochberg corrected p-value from Gene Set Enrichment Analysis (GSEA)). (c) Metagene plot of CLIP peaks from D. melanogaster head mRNA. Representing the position of 4,506 CLIP peaks. (d) The sequence context of 5,485 cross-linked mutational sites (CIMS) contained within CLIP peaks. (e, f) miCLIP (blue) and input (gray) traces mapped to the dilp3 (e) and dilp5 (f) loci (Reads Per Million mapped reads, RPM).
Extended Data Fig. 4 Direct RNA sequencing of in vitro transcribed control RNAs and dilp2 RNA.
(a) Schematic of the randomly generated random-1 (rand1) in vitro transcribed RNAs. The RNAs were transcribed with A (gray) and m6A (red). The sequences were identical except for the 6 nt molecular barcode depicted by the green block to distinguish between the unmethylated and methylated RNA unambiguously. (b) Normalized direct RNA sequencing signal derived from in vitro transcribed RNA with A (gray) and with m6A (red). n = 50 reads plotted. Green triangles represent the expected current level based on the base calling mode (see Methods). (c) EpiNano significance trace across the in vitro transcribed RNA sequence. Significant position 239 (red) corresponds to the base following the methylated A (238). Other significant bases labeled ‘barcode’ correspond to the green barcode in (a). (d) Normalized direct-RNA sequencing signal derived from in vitro transcribed dilp2 RNA (left, gray, n = 50 reads) and native dilp2 mRNA from fly heads (right, red n = 50 reads) of the region corresponding to the miCLIP peak in (Fig. 2a). Green triangles represent the expected current level based on the base calling model.
Extended Data Fig. 5 Creation of dilp2m6A mutant flies.
(a) Diagram of CRISPR strategy to replace 11 AC dinucleotides in 3′ UTR of the dilp2 transcript (dilp2m6A−/− gray). (b) Sanger sequencing of genomic DNA from positive transgenic line 364-4 showing all 11 AC dinucleotides replaced with UC.
Extended Data Fig. 6 Polysome profiles of control and dilp2m6A−/− mutant flies.
(a, b) Representative polysome profile from sucrose gradient of (a) control (w1118) and (b) dilp2m6A−/− mutant fly heads in Fig. 2c. (c) Representative confocal images of immunofluorescence of dilp2 protein in control (w1118CS) and Mettl3−/− mutant flies. Scale bar, 20um. (d) Triglyceride levels normalized to protein in control (w1118) and mutant dilp2m6A−/− flies. n = 8. Error bars SEM. Unpaired two-sided Student’s t-test, p = 0.001. ** p<0.005.
Extended Data Fig. 7 Vertebrate insulin mRNA is enriched by m6A RIP.
(a, b) Quantification of salmon ins (a) and mouse Ins2 (b) mRNA by qPCR from mock-treated or m6A RIP samples of pancreatic tissue. n = 3 salmon and n = 3 of groups of two male mice. Error bars SEM. Unpaired two-tailed Student’s t-test, p <0.0001 (a) and p = 0.001 (b). (c) Normalized Transcripts Per Kilobase Million (TPM) counts of RNA-sequencing reads from the human insulin (INS) gene6. Paired two-tailed Student′s t-test p <0.0001. *p < 0.05, ** p < 0.005.
Supplementary information
Supplementary Tables 1–4
Supplementary Table 1. Oligonucleotide sequences. DNA sequences used for qPCR primers, Sanger sequencing, CLIP adaptors, and in vitro transcription of control RNAs. Supplementary Table 2. Sequence context of significant direct RNA sequence differences. Sequences are listed for random in vitro transcribed RNA, and regions from native transcripts from each organism tested. Supplementary Table 3. Fly stocks. Supplementary Table 4. Summary of sequencing reads.
Supplementary Data 1
Complete list of peaks from each biological replicate and the union of the data sets. CIMS output of C-to-T transitions.
Source data
Source Data Fig. 1
Data that underlie figure.
Source Data Fig. 2
Data that underlie figure.
Source Data Extended Data Fig. 1
Data that underlie figure.
Source Data Extended Data Fig. 2
Data that underlie figure.
Source Data Extended Data Fig. 3
Data that underlie figure.
Source Data Extended Data Fig. 4
Data that underlie figure.
Source Data Extended Data Fig. 6
Data that underlie figure.
Source Data Extended Data Fig. 7
Data that underlie figure.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wilinski, D., Dus, M. N6-adenosine methylation controls the translation of insulin mRNA. Nat Struct Mol Biol 30, 1260–1264 (2023). https://doi.org/10.1038/s41594-023-01048-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41594-023-01048-x
