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N6-methyladenosine (m6A) recruits and repels proteins to regulate mRNA homeostasis

Nature Structural & Molecular Biology volume 24, pages 870–878 (2017)Cite this article

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Abstract

RNA modifications are integral to the regulation of RNA metabolism. One abundant mRNA modification is N6-methyladenosine (m6A), which affects various aspects of RNA metabolism, including splicing, translation and degradation. Current knowledge about the proteins recruited to m6A to carry out these molecular processes is still limited. Here we describe comprehensive and systematic mass-spectrometry-based screening of m6A interactors in various cell types and sequence contexts. Among the main findings, we identified G3BP1 as a protein that is repelled by m6A and positively regulates mRNA stability in an m6A-regulated manner. Furthermore, we identified FMR1 as a sequence-context-dependent m6A reader, thus revealing a connection between an mRNA modification and an autism spectrum disorder. Collectively, our data represent a rich resource and shed further light on the complex interplay among m6A, m6A interactors and mRNA homeostasis.

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Figure 1: A global m6A interactome.
Figure 2: G3BP1 and G3BP2 interact with thousands of transcripts.
Figure 3: G3BP1 protects target mRNAs from degradation.
Figure 4: FMR1 preferentially binds to m6A-containing mRNA in vitro and in vivo and affects the translation of its targets.

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References

  1. Dimock, K. & Stoltzfus, C.M. Sequence specificity of internal methylation in B77 avian sarcoma virus RNA subunits. Biochemistry 16, 471–478 (1977).

    Article  CAS  Google Scholar 

  2. Desrosiers, R.C., Friderici, K.H. & Rottman, F.M. Characterization of Novikoff hepatoma mRNA methylation and heterogeneity in the methylated 5′ terminus. Biochemistry 14, 4367–4374 (1975).

    Article  CAS  Google Scholar 

  3. Zheng, G. et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell 49, 18–29 (2013).

    Article  CAS  Google Scholar 

  4. Jia, G. et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat. Chem. Biol. 7, 885–887 (2011).

    Article  CAS  Google Scholar 

  5. Liu, J. et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014).

    Article  CAS  Google Scholar 

  6. Ping, X.L. et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 24, 177–189 (2014).

    Article  CAS  Google Scholar 

  7. Schwartz, S. et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5′ sites. Cell Rep. 8, 284–296 (2014).

    Article  CAS  Google Scholar 

  8. Aguilo, F. et al. Coordination of m6A mRNA methylation and gene transcription by ZFP217 regulates pluripotency and reprogramming. Cell Stem Cell 17, 689–704 (2015).

    Article  CAS  Google Scholar 

  9. Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).

    Article  CAS  Google Scholar 

  10. Meyer, K.D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).

    Article  CAS  Google Scholar 

  11. Zhao, X. et al. FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res. 24, 1403–1419 (2014).

    Article  CAS  Google Scholar 

  12. Zhong, S. et al. MTA is an Arabidopsis messenger RNA adenosine methylase and interacts with a homolog of a sex-specific splicing factor. Plant Cell 20, 1278–1288 (2008).

    Article  CAS  Google Scholar 

  13. Agarwala, S.D., Blitzblau, H.G., Hochwagen, A. & Fink, G.R. RNA methylation by the MIS complex regulates a cell fate decision in yeast. PLoS Genet. 8, e1002732 (2012).

    Article  CAS  Google Scholar 

  14. Lin, S., Choe, J., Du, P., Triboulet, R. & Gregory, R.I. The m6A methyltransferase METTL3 promotes translation in human cancer cells. Mol. Cell 62, 335–345 (2016).

    Article  CAS  Google Scholar 

  15. Li, Z. et al. FTO plays an oncogenic role in acute myeloid leukemia as a N6-methyladenosine RNA demethylase. Cancer Cell 31, 127–141 (2017).

    Article  Google Scholar 

  16. Batista, P.J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).

    Article  CAS  Google Scholar 

  17. Zhao, B.S. et al. m6A-dependent maternal mRNA clearance facilitates zebrafish maternal-to-zygotic transition. Nature 542, 475–478 (2017).

    Article  CAS  Google Scholar 

  18. Geula, S. et al. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 347, 1002–1006 (2015).

    Article  CAS  Google Scholar 

  19. Wang, X. et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

    Article  Google Scholar 

  20. Meyer, K.D. et al. 5′ UTR m6A promotes cap-independent translation. Cell 163, 999–1010 (2015).

    Article  CAS  Google Scholar 

  21. Zhou, J. et al. Dynamic m6A mRNA methylation directs translational control of heat shock response. Nature 526, 591–594 (2015).

    Article  CAS  Google Scholar 

  22. Wang, X. et al. N6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).

    Article  CAS  Google Scholar 

  23. Alarcón, C.R., Lee, H., Goodarzi, H., Halberg, N. & Tavazoie, S.F. N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482–485 (2015).

    Article  Google Scholar 

  24. Xiao, W. et al. Nuclear m6A reader YTHDC1 regulates mRNA splicing. Mol. Cell 61, 507–519 (2016).

    Article  CAS  Google Scholar 

  25. Patil, D.P. et al. m6A RNA methylation promotes XIST-mediated transcriptional repression. Nature 537, 369–373 (2016).

    Article  CAS  Google Scholar 

  26. Cao, G., Li, H.B., Yin, Z. & Flavell, R.A. Recent advances in dynamic m6A RNA modification. Open Biol. 6, 160003 (2016).

    Article  Google Scholar 

  27. Shi, H. et al. YTHDF3 facilitates translation and decay of N6-methyladenosine-modified RNA. Cell Res. 27, 315–328 (2017).

    Article  CAS  Google Scholar 

  28. Alarcón, C.R. et al. HNRNPA2B1 is a mediator of m6A-dependent nuclear RNA processing events. Cell 162, 1299–1308 (2015).

    Article  Google Scholar 

  29. Liu, N. et al. N6-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560–564 (2015).

    Article  CAS  Google Scholar 

  30. Liu, N. et al. N6-methyladenosine alters RNA structure to regulate binding of a low-complexity protein. Nucleic Acids Res. 45, 6051–6063 (2017).

    Article  CAS  Google Scholar 

  31. Spruijt, C.G. & Vermeulen, M. DNA methylation: old dog, new tricks? Nat. Struct. Mol. Biol. 21, 949–954 (2014).

    Article  CAS  Google Scholar 

  32. Penagarikano, O., Mulle, J.G. & Warren, S.T. The pathophysiology of fragile X syndrome. Annu. Rev. Genomics Hum. Genet. 8, 109–129 (2007).

    Article  CAS  Google Scholar 

  33. Matsuki, H. et al. Both G3BP1 and G3BP2 contribute to stress granule formation. Genes Cells 18, 135–146 (2013).

    Article  CAS  Google Scholar 

  34. Tourrière, H. et al. The RasGAP-associated endoribonuclease G3BP assembles stress granules. J. Cell Biol. 160, 823–831 (2003).

    Article  Google Scholar 

  35. Kedersha, N. et al. G3BP-Caprin1-USP10 complexes mediate stress granule condensation and associate with 40S subunits. J. Cell Biol. 212, 845–860 (2016).

    Article  CAS  Google Scholar 

  36. Pendleton, K.E. et al. The U6 snRNA m6A methyltransferase METTL16 regulates SAM synthetase intron retention. Cell 169, 824–835 (2017).

    Article  CAS  Google Scholar 

  37. Castello, A. et al. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell 149, 1393–1406 (2012).

    Article  CAS  Google Scholar 

  38. Baltz, A.G. et al. The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690 (2012).

    Article  CAS  Google Scholar 

  39. Kwon, S.C. et al. The RNA-binding protein repertoire of embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1122–1130 (2013).

    Article  CAS  Google Scholar 

  40. Irvine, K., Stirling, R., Hume, D. & Kennedy, D. Rasputin, more promiscuous than ever: a review of G3BP. Int. J. Dev. Biol. 48, 1065–1077 (2004).

    Article  CAS  Google Scholar 

  41. Wei, C.M. & Moss, B. Nucleotide sequences at the N6-methyladenosine sites of HeLa cell messenger ribonucleic acid. Biochemistry 16, 1672–1676 (1977).

    Article  CAS  Google Scholar 

  42. Ke, S. et al. A majority of m6A residues are in the last exons, allowing the potential for 3′ UTR regulation. Genes Dev. 29, 2037–2053 (2015).

    Article  CAS  Google Scholar 

  43. Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).

    Article  CAS  Google Scholar 

  44. Tani, H. et al. Genome-wide determination of RNA stability reveals hundreds of short-lived noncoding transcripts in mammals. Genome Res. 22, 947–956 (2012).

    Article  CAS  Google Scholar 

  45. Myrick, L.K., Hashimoto, H., Cheng, X. & Warren, S.T. Human FMRP contains an integral tandem Agenet (Tudor) and KH motif in the amino terminal domain. Hum. Mol. Genet. 24, 1733–1740 (2015).

    Article  CAS  Google Scholar 

  46. Brown, V. et al. Microarray identification of FMRP-associated brain mRNAs and altered mRNA translational profiles in fragile X syndrome. Cell 107, 477–487 (2001).

    Article  CAS  Google Scholar 

  47. Darnell, J.C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    Article  CAS  Google Scholar 

  48. Ascano, M. Jr. et al. FMRP targets distinct mRNA sequence elements to regulate protein expression. Nature 492, 382–386 (2012).

    Article  CAS  Google Scholar 

  49. Schwanhäusser, B., Gossen, M., Dittmar, G. & Selbach, M. Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9, 205–209 (2009).

    Article  Google Scholar 

  50. Visscher, M. et al. Proteome-wide changes in protein turnover rates in C. elegans models of longevity and age-related disease. Cell Rep. 16, 3041–3051 (2016).

    Article  CAS  Google Scholar 

  51. Chen, C.A. & Shyu, A.B. Emerging themes in regulation of global mRNA turnover in cis. Trends Biochem. Sci. 42, 16–27 (2017).

    Article  CAS  Google Scholar 

  52. Costa, M., Ochem, A., Staub, A. & Falaschi, A. Human DNA helicase VIII: a DNA and RNA helicase corresponding to the G3BP protein, an element of the ras transduction pathway. Nucleic Acids Res. 27, 817–821 (1999).

    Article  CAS  Google Scholar 

  53. Anderson, P. & Kedersha, N. Stress granules: the Tao of RNA triage. Trends Biochem. Sci. 33, 141–150 (2008).

    Article  CAS  Google Scholar 

  54. Scadden, A.D. Inosine-containing dsRNA binds a stress-granule-like complex and downregulates gene expression in trans. Mol. Cell 28, 491–500 (2007).

    Article  CAS  Google Scholar 

  55. Kim, S.H., Dong, W.K., Weiler, I.J. & Greenough, W.T. Fragile X mental retardation protein shifts between polyribosomes and stress granules after neuronal injury by arsenite stress or in vivo hippocampal electrode insertion. J. Neurosci. 26, 2413–2418 (2006).

    Article  CAS  Google Scholar 

  56. Spruijt, C.G. et al. Dynamic readers for 5-(hydroxy)methylcytosine and its oxidized derivatives. Cell 152, 1146–1159 (2013).

    Article  CAS  Google Scholar 

  57. Qian, K. et al. A simple and efficient system for regulating gene expression in human pluripotent stem cells and derivatives. Stem Cells 32, 1230–1238 (2014).

    Article  CAS  Google Scholar 

  58. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Article  CAS  Google Scholar 

  59. Boersema, P.J., Raijmakers, R., Lemeer, S., Mohammed, S. & Heck, A.J. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4, 484–494 (2009).

    Article  CAS  Google Scholar 

  60. Pearson, W.R., Wood, T., Zhang, Z. & Miller, W. Comparison of DNA sequences with protein sequences. Genomics 46, 24–36 (1997).

    Article  CAS  Google Scholar 

  61. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  Google Scholar 

  62. Corcoran, D.L. et al. PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol. 12, R79 (2011).

    Article  CAS  Google Scholar 

  63. 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).

    Article  CAS  Google Scholar 

  64. Hinrichs, A.S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006).

    Article  CAS  Google Scholar 

  65. Huang, W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  Google Scholar 

  66. Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).

    Article  CAS  Google Scholar 

  67. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  68. Bray, N.L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  Google Scholar 

  69. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  Google Scholar 

  70. Wiśniewski, J.R., Zougman, A. & Mann, M. Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome. J. Proteome Res. 8, 5674–5678 (2009).

    Article  Google Scholar 

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Acknowledgements

We thank members of the Vermeulen lab for fruitful discussions. Work in the Vermeulen lab is supported by the NWO Gravitation program Cancer Genomics Netherlands. Work in the He lab is supported by NHGRI, NIH (HG008688). C.H. is an Investigator of the Howard Hughes Medical Institute. Work in the Carell lab is supported by Deutsche Forschungsgemeinschaft (grants SFB749, SFB1032 and SPP1784) and Bundesministerium für Bildung und Forschung (EXC114).

Author information

Author notes

  1. Raghu R Edupuganti, Simon Geiger, Rik G H Lindeboom and Hailing Shi: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands

    Raghu R Edupuganti, Rik G H Lindeboom, Shuang-Yin Wang, Marijke P A Baltissen, Pascal W T C Jansen, Hendrik G Stunnenberg & Michiel Vermeulen

  2. Center for Integrated Protein Science at the Fakultät für Chemie und Pharmazie, Ludwig-Maximilians-Universität München, Munich, Germany

    Simon Geiger, Martin Rossa, Markus Müller & Thomas Carell

  3. Department of Chemistry, Department of Biochemistry and Molecular Biology, and Institute for Biophysical Dynamics, Howard Hughes Medical Institute, The University of Chicago, Chicago, Illinois, USA

    Hailing Shi, Phillip J Hsu, Zhike Lu & Chuan He

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  1. Raghu R Edupuganti
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Contributions

R.R.E. and M.V. conceived the project and wrote the manuscript, with input from all other authors. R.R.E. performed most wet-lab experiments. S.G. and M.R. prepared the m6A probes. R.G.H.L. analyzed RNA-seq and whole cell proteome data. H.S. and P.J.H. performed CLIP and PAR-CLIP experiments. Z.L. analyzed PAR-CLIP data. S.-Y.W. performed bioinformatics analysis of FMR1 PAR-CLIP data. M.P.A.B. provided technical support. P.W.T.C.J. measured mass spectrometry samples. M.M. and H.G.S. provided scientific input. C.H., T.C. and M.V. supervised the project.

Corresponding authors

Correspondence to Chuan He, Thomas Carell or Michiel Vermeulen.

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Integrated supplementary information

Supplementary Figure 1 A global m6A interactome.

(a) Schematic representation of the workflow used in this study (see detailed description of the approach and analysis in Online Methods). (b) Structure of the biotin molecule and linker used in the m6A probes. The RNA strand is attached to the phosphate group that is shown. (c) Mass spectra showing control and m6A probes. Calculated and observed molecular weights of the probes are indicated. The observed single peaks illustrate the purity of the probes. (d) Agarose gel showing depletion of biotin tagged probes after incubation with streptavidin beads. (e-f) Scatter plots of SILAC-based m6A pull-downs in mouse ESC nuclear extract I and mouse ESC cytoplasmic extract (f). (g-h) Scatter plots of di-methyl based m6A pull-downs in mNPC nuclear extract (g) and mNPC cytoplasmic extract (h). (i) Scatter plots of m6A pull-downs in mouse 3T3 nuclear lysates. (j-k) Scatter plots of SILAC based m6A pull downs in mESC whole cell extract using degenerate sequence probe 1 (j) and degenerate sequence probe 2 (k) The sequence context of the probes is depicted at the bottom. (l) Protein domain distribution of m6A repelled proteins in humans. (related to Fig. 1).

Supplementary Figure 2 G3BP1 and G3BP2 PAR-CLIP analysis.

(a) Scatter plot of a SILAC-based m6A pull down in mESC cytoplasmic extract in a GAm6ACU sequence context. Note the presence of G3BP1 in the background cloud. (b) The summary of PAR-CLIP sequencing reads (c) The genomic distribution of G3BP1 and G3BP2 binding sites on mRNA. (d) Enriched Most GO-terms for G3BP2 target genes. (e-f) Distribution of G3BP1 (e) and G3BP2 (f) peaks relative to YTHDF2. (g-h) Distribution of G3BP1 (g) and G3BP2 (h) peaks relative to YTHDC1 across the transcriptome. (related to Fig. 2).

Supplementary Figure 3 RNA-seq validation.

(a) Correlation between YTHDF2 mRNA binding and mRNA stability after the effect of m6A on mRNA stability was regressed out (see Online Methods). (b) Q-PCR validation showing the effect of G3BP1 KD on mRNA stability (Data are shown as range; n = 2 independent biological experiments). (c) Q-PCR validation showing the effect of G3BP1 OE on mRNA stability (Data are shown as means ± data range; n = 2 independent experiments) (related to Fig. 3).

Supplementary Figure 4 In vitro and in vivo data regarding FMR1 and m6A.

(a) FMR1 domain structure and overview of different GST-tagged FMR1 deletion constructs (b) GST western blot analysis of GST-FMR1 deletion constructs binding to unmodified and m6A-containing RNA probes (GGACU context). For all constructs, the correct size band is indicated with a red arrow. (c) Western blots showing expression of FLAG-HA, FLAG-HA-FMR1 and FLAG-HA-FMR1 I304N mutant (FMR1-m) in HEK293 cells. (d) Representative LC/MS based quantification showing levels of m6A in FLAG-HA-FMR1 and FLAG-HA-FMR1 mutant enriched fraction, prior to RiboMinus treatment. Data are shown as range; n = 2 independent biological experiments. (e) Venn diagram showing the overlap between FMR1 (mouse brain) and YTHDF1 (HEK cells) target transcripts. (f) GO-term enrichment analysis of common target transcripts of FMR1 (mouse brain) and YTHDF1 (HEK cells). GO terms related to neurogenesis are indicated in red. (g) Box-plots depicting the global changes in protein half-lives in the indicated samples. (related to Fig. 4).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 1022 kb)

Life Sciences Reporting Summary (PDF 159 kb)

Supplementary Table 1

RNA probes and oligos (XLSX 12 kb)

Supplementary Data Set 1

Methods and uncropped images. (PDF 8355 kb)

Supplementary Data Set 2

Mass spectrometry data from RNA pulldowns. (XLSX 6909 kb)

Supplementary Data Set 3

G3BP1 and G3BP2 PAR-CLIP data. (XLSX 3279 kb)

Supplementary Data Set 4

mRNA half-life estimation RNA-seq data. (XLSX 623 kb)

Supplementary Data Set 5

Pulse-SILAC data. (XLSX 725 kb)

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Edupuganti, R., Geiger, S., Lindeboom, R. et al. N6-methyladenosine (m6A) recruits and repels proteins to regulate mRNA homeostasis. Nat Struct Mol Biol 24, 870–878 (2017). https://doi.org/10.1038/nsmb.3462

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