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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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.


  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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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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