Letter

Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control

  • Nature volume 552, pages 126131 (07 December 2017)
  • doi:10.1038/nature24678
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Received:
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Abstract

N6-methyladenosine (m6A) is an abundant internal RNA modification in both coding1 and non-coding RNAs2,3 that is catalysed by the METTL3–METTL14 methyltransferase complex4. However, the specific role of these enzymes in cancer is still largely unknown. Here we define a pathway that is specific for METTL3 and is implicated in the maintenance of a leukaemic state. We identify METTL3 as an essential gene for growth of acute myeloid leukaemia cells in two distinct genetic screens. Downregulation of METTL3 results in cell cycle arrest, differentiation of leukaemic cells and failure to establish leukaemia in immunodeficient mice. We show that METTL3, independently of METTL14, associates with chromatin and localizes to the transcriptional start sites of active genes. The vast majority of these genes have the CAATT-box binding protein CEBPZ present at the transcriptional start site5, and this is required for recruitment of METTL3 to chromatin. Promoter-bound METTL3 induces m6A modification within the coding region of the associated mRNA transcript, and enhances its translation by relieving ribosome stalling. We show that genes regulated by METTL3 in this way are necessary for acute myeloid leukaemia. Together, these data define METTL3 as a regulator of a chromatin-based pathway that is necessary for maintenance of the leukaemic state and identify this enzyme as a potential therapeutic target for acute myeloid leukaemia.

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References

  1. 1.

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

  2. 2.

    , , , & N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482–485 (2015)

  3. 3.

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

  4. 4.

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

  5. 5.

    et al.; ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012)

  6. 6.

    et al. A CRISPR dropout screen identifies genetic vulnerabilities and therapeutic targets in acute myeloid leukemia. Cell Rep. 17, 1193–1205 (2016)

  7. 7.

    et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015)

  8. 8.

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

  9. 9.

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

  10. 10.

    et al. N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014)

  11. 11.

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

  12. 12.

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

  13. 13.

    et al. The heteromeric transcription factor GABP activates the ITGAM/CD11b promoter and induces myeloid differentiation. Biochim. Biophys. Acta. 1849, 1145–1154 (2015)

  14. 14.

    et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013)

  15. 15.

    et al. JAK2 phosphorylates histone H3Y41 and excludes HP1α from chromatin. Nature 461, 819–822 (2009)

  16. 16.

    , , , & Differential binding of the NFE3 and CP1/NFY transcription factors to the human gamma- and epsilon-globin CCAAT boxes. J. Biol. Chem. 270, 21934–21941 (1995)

  17. 17.

    et al. Symmetric dimethylation of H3R2 is a newly identified histone mark that supports euchromatin maintenance. Nat. Struct. Mol. Biol. 19, 136–144 (2012)

  18. 18.

    et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015)

  19. 19.

    et al. Transcription impacts the efficiency of mRNA translation via co-transcriptional N6-adenosine methylation. Cell 169, 326–337 (2017)

  20. 20.

    et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013)

  21. 21.

    , & The role of the ubiquitously expressed transcription factor Sp1 in tissue-specific transcriptional regulation and in disease. Yale J. Biol. Med. 89, 513–525 (2016)

  22. 22.

    , & TATA box and Sp1 sites mediate the activation of c-myc promoter P1 by immunoglobulin kappa enhancers. Gene Expr. 6, 113–127 (1996)

  23. 23.

    et al. RNA fate determination through cotranscriptional adenosine methylation and microprocessor binding. Nat. Struct. Mol. Biol. 24, 561–569 (2017)

  24. 24.

    , , , & The m6A methyltransferase METTL3 promotes translation in human cancer cells. Mol. Cell 62, 335–345 (2016)

  25. 25.

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

  26. 26.

    et al. FLT3 mutations confer enhanced proliferation and survival properties to multipotent progenitors in a murine model of chronic myelomonocytic leukemia. Cancer Cell 12, 367–380 (2007)

  27. 27.

    et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014)

  28. 28.

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

  29. 29.

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

  30. 30.

    , & A census of human RNA-binding proteins. Nat. Rev. Genet. 15, 829–845 (2014)

  31. 31.

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

  32. 32.

    , , & Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014)

  33. 33.

    et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013)

  34. 34.

    et al. Highly efficient genome editing of murine and human hematopoietic progenitor cells by CRISPR/Cas9. Cell Reports 17, 1453–1461 (2016)

  35. 35.

    et al. Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia. Nature 478, 529–533 (2011)

  36. 36.

    & Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010)

  37. 37.

    et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

  38. 38.

    & BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)

  39. 39.

    et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008)

  40. 40.

    et al. Software for computing and annotating genomic ranges. PLOS Comput. Biol. 9, e1003118 (2013)

  41. 41.

    , & ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015)

  42. 42.

    & VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 35 (2011)

  43. 43.

    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)

  44. 44.

    et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010)

  45. 45.

    et al. MeTDiff: a novel differential RNA methylation analysis for MeRIP-seq data. IEEE/ACM Trans. Comput. Biol. Bioinformat. (2015)

  46. 46.

    , , , & Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing. Nat. Protocols 8, 176–189 (2013)

  47. 47.

    et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009)

  48. 48.

    , , & Genome-wide assessment of differential translations with ribosome profiling data. Nat. Commun. 7, 11194 (2016)

  49. 49.

    & Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data. BMC Genomics 17, 958 (2016)

  50. 50.

    , & Polysome fractionation to analyze mRNA distribution profiles. Bio Protoc. 7, e2126 (2017)

  51. 51.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

  52. 52.

    ., ., ., . & GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics 10, 161 (2009)

  53. 53.

    et al. BET protein inhibition shows efficacy against JAK2V617F-driven neoplasms. Leukemia 28, 88–97 (2014)

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Acknowledgements

We thank K. H. Che for help generating the RNA enzyme list. The Kouzarides laboratory is supported by grants from Cancer Research UK (grant reference RG17001) and ERC (project number 268569), in addition to benefiting from core support from the Wellcome Trust (Core Grant reference 092096) and Cancer Research UK (grant reference C6946/A14492). I.B. is funded by a Kay Kendall Leukaemia Fund project grant (grant reference RG88664). G.M.-Z. is funded by an EMBO fellowship (ALTF907-2014). G.S.V. was funded by a Wellcome Trust Senior Fellowship in Clinical Science (WT095663MA) and Cancer Research UK Senior Cancer Research Fellowship (C22324/A23015). The Vassiliou laboratory is supported by grants from the Kay Kendall Leukemia Fund and Bloodwise, as well as core funding from the Sanger Institute (WT098051). C.R.V. and J.S. are funded by a translational research grant from Northwell Health.

Author information

Author notes

    • Junwei Shi
    •  & Samuel C. Robson

    Present addresses: Department of Cancer Biology, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, 421 Curie Boulevard, Philadelphia, Pennsylvania 19104, USA (J.S.); School of Pharmacy & Biomedical Science, St Michael's Building, University of Portsmouth, White Swan Road, Portsmouth, UK (S.C.R.).

    • Isaia Barbieri
    • , Konstantinos Tzelepis
    •  & Luca Pandolfini

    These authors contributed equally to this work.

    • George S. Vassiliou
    •  & Tony Kouzarides

    These authors jointly supervised this work.

Affiliations

  1. The Gurdon Institute and Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, UK.

    • Isaia Barbieri
    • , Luca Pandolfini
    • , Gonzalo Millán-Zambrano
    • , Samuel C. Robson
    • , Valentina Migliori
    • , Andrew J. Bannister
    • , Namshik Han
    •  & Tony Kouzarides
  2. Haematological Cancer Genetics, Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK.

    • Konstantinos Tzelepis
    • , Demetrios Aspris
    • , Etienne De Braekeleer
    • , Hannes Ponstingl
    •  & George S. Vassiliou
  3. Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA.

    • Junwei Shi
    •  & Christopher R. Vakoc
  4. Storm Therapeutics Ltd, Moneta Building (B280), Babraham Research Campus, Cambridge CB22 3AT, UK.

    • Alan Hendrick
  5. Wellcome Trust–MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0XY, UK.

    • George S. Vassiliou
  6. Department of Haematology, Cambridge University Hospitals NHS Trust, Cambridge CB2 0QQ, UK.

    • George S. Vassiliou

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Contributions

I.B., K.T and J.S. designed, performed and validated the CRISPR screens. K.T. and E.D.B. performed the phenotypic analysis of human mouse targeted cells. H.P. performed bioinformatic analysis of genome-wide CRISPR screens. I.B. and L.P. generated the conditional KD cells, and performed and validated the RNA-seq, ChIP–seq, RNA–IP and riboprofiling experiments. L.P., S.C.R. and N.H. performed bioinformatic analyses of datasets. N.H. generated the expression profiles from the TCGA dataset G.M.-Z. performed and analysed the polysome fractionation experiments. I.B., L.P., K.T. and D.A. performed the rescue experiments and the luciferase assays. V.M., A.J.B. and A.H. took part in the validation of ChIP–seq and RNA–IP experiments. I.B., K.T. and L.P. designed experiments and interpreted results. C.R.V., G.S.V. and T.K. devised and supervised the project. A.J.B., G.S.V. and T.K. wrote the manuscript with contributions from all authors.

Competing interests

T.K. is a co-founder of Abcam Plc and Storm Therapeutics Ltd, Cambridge, UK. A.H. is an employee of Storm Therapeutics Ltd, Cambridge, UK.

Corresponding authors

Correspondence to George S. Vassiliou or Tony Kouzarides.

Reviewer Information Nature thanks K. Adelman, R. Agami, R. Levine and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

  2. 2.

    Supplementary Information

    This file contains full uncropped scans of Western blots used in Figure 4a and Extended Data Figures 3b, 3d, 4c, 10b and 10i. It also includes an example of the gating strategy used in flow cytometry experiments and the complete list of all the sequences of oligonucleotides employed.

Excel files

  1. 1.

    Supplementary Table 1

    This file contains gene scores, ranking and statistics of whole genome CRISPR-CAS9 Screen (Screen 1).

  2. 2.

    Supplementary Table 2

    This file contains a list of RNA Enzymes analysed for dropouts in Screen 1; gene scores, ranking and statistics of targeted CRISPR-CAS9 Screen (Screen 2).

  3. 3.

    Supplementary Table 3

    This file contains Gene Expression data from RNA-sequencing of WT and METTL3 knock-down MOLM-13 cells 8 days after doxycycline induction.

  4. 4.

    Supplementary Table 4

    This file contains Gene Ontology analysis of KEGG Pathways differentially regulated upon METTL3 depletion (as in Supplementary Table 3)

  5. 5.

    Supplementary Table 5

    This file contains genomic coordinates and annotation of of METTL3 and METTL14 ChIP-sequencing peaks.

  6. 6.

    Supplementary Table 6

    This file contains m6A RNA-IP data of WT and METTL3 knock-down MOLM-13 cells 8 days after doxycycline induction.

  7. 7.

    Supplementary Table 7

    This file contains ribosome profiling data of WT and METTL3 knock-down MOLM-13 cells.

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