Abstract
N6-methyladenosine (m6A) is an abundant internal RNA modification1,2 that is catalysed predominantly by the METTL3–METTL14 methyltransferase complex3,4. The m6A methyltransferase METTL3 has been linked to the initiation and maintenance of acute myeloid leukaemia (AML), but the potential of therapeutic applications targeting this enzyme remains unknown5,6,7. Here we present the identification and characterization of STM2457, a highly potent and selective first-in-class catalytic inhibitor of METTL3, and a crystal structure of STM2457 in complex with METTL3–METTL14. Treatment of tumours with STM2457 leads to reduced AML growth and an increase in differentiation and apoptosis. These cellular effects are accompanied by selective reduction of m6A levels on known leukaemogenic mRNAs and a decrease in their expression consistent with a translational defect. We demonstrate that pharmacological inhibition of METTL3 in vivo leads to impaired engraftment and prolonged survival in various mouse models of AML, specifically targeting key stem cell subpopulations of AML. Collectively, these results reveal the inhibition of METTL3 as a potential therapeutic strategy against AML, and provide proof of concept that the targeting of RNA-modifying enzymes represents a promising avenue for anticancer therapy.
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 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The datasets related to STM2457 used in this study can be accessed from the European Nucleotide Archive under accession PRJEB41662. The previously published datasets from Barbieri et al. can be accessed from the Gene Expression Omnibus database with accession number GSE94613. The STM2457 structure has been deposited with the PDB as 7O2I. Additional details related to the chemical characterization of STM2457 can be found in the relevant published patent (WO2020201773) at World Intellectual Property Organization. Source data are provided with this paper.
References
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).
Liu, J. et al. A METTL3–METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014).
Wang, X. et al. Structural basis of N6-adenosine methylation by the METTL3–METTL14 complex. Nature 534, 575–578 (2016).
Tzelepis, K. et al. A CRISPR dropout screen identifies genetic vulnerabilities and therapeutic targets in acute myeloid leukemia. Cell Rep. 17, 1193–1205 (2016).
Barbieri, I. et al. Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control. Nature 552, 126–131 (2017).
Vu, L. P. et al. The N6-methyladenosine (m6A)-forming enzyme METTL3 controls myeloid differentiation of normal hematopoietic and leukemia cells. Nat. Med. 23, 1369–1376 (2017).
Schapira, M. Structural chemistry of human RNA methyltransferases. ACS Chem. Biol. 11, 575–582 (2016).
Li, F. et al. A radioactivity-based assay for screening human m6A-RNA methyltransferase, METTL3–METTL14 complex, and demethylase ALKBH5. J. Biomol. Screen. 21, 290–297 (2016).
Melnyk, S., Pogribna, M., Pogribny, I. P., Yi, P. & James, S. J. Measurement of plasma and intracellular S-adenosylmethionine and S-adenosylhomocysteine utilizing coulometric electrochemical detection: alterations with plasma homocysteine and pyridoxal 5′-phosphate concentrations. Clin. Chem. 46, 265–272 (2000).
Ripperger, T. et al. The heteromeric transcription factor GABP activates the ITGAM/CD11b promoter and induces myeloid differentiation. Biochim. Biophys. Acta 1849, 1145–1154 (2015).
O’Connor, L., Gilmour, J. & Bonifer, C. 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).
Choe, J. et al. mRNA circularization by METTL3–eIF3h enhances translation and promotes oncogenesis. Nature 561, 556–560 (2018).
Dawson, M. A. et al. Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia. Nature 478, 529–533 (2011).
Weng, H. et al. METTL14 inhibits hematopoietic stem/progenitor differentiation and promotes leukemogenesis via mRNA m6A modification. Cell Stem Cell 22, 191–205.e9 (2018).
Dominissini, D., Moshitch-Moshkovitz, S., Salmon-Divon, M., Amariglio, N. & Rechavi, G. Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing. Nat. Protoc. 8, 176–189 (2013).
Tzelepis, K., Rausch, O. & Kouzarides, T. RNA-modifying enzymes and their function in a chromatin context. Nat. Struct. Mol. Biol. 26, 858–862 (2019).
Alharbi, R. A., Pettengell, R., Pandha, H. S. & Morgan, R. The role of HOX genes in normal hematopoiesis and acute leukemia. Leukemia 27, 1000–1008 (2013).
Delgado, M. D. & León, J. Myc roles in hematopoiesis and leukemia. Genes Cancer 1, 605–616 (2010).
Liu, J. et al. N6-methyladenosine of chromosome-associated regulatory RNA regulates chromatin state and transcription. Science 367, 580–586 (2020).
Mikutis, S. et al. meCLICK-Seq, a substrate-hijacking and RNA degradation strategy for the study of RNA methylation. ACS Cent. Sci. 6, 2196–2208 (2020).
Xiang, Y. et al. RNA m6A methylation regulates the ultraviolet-induced DNA damage response. Nature 543, 573–576 (2017).
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).
Iwasaki, M., Liedtke, M., Gentles, A. J. & Cleary, M. L. CD93 marks a non-quiescent human leukemia stem cell population and is required for development of MLL-rearranged acute myeloid leukemia. Cell Stem Cell 17, 412–421 (2015).
Krivtsov, A. V. et al. Transformation from committed progenitor to leukaemia stem cell initiated by MLL–AF9. Nature 442, 818–822 (2006).
Ellison, R. R. et al. Arabinosyl cytosine: a useful agent in the treatment of acute leukemia in adults. Blood 32, 507–523 (1968).
Daver, N. et al. New directions for emerging therapies in acute myeloid leukemia: the next chapter. Blood Cancer J. 10, 107 (2020).
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).
Huang, Y. et al. Small-molecule targeting of oncogenic FTO demethylase in acute myeloid leukemia. Cancer Cell 35, 677–691.e10 (2019).
Moroz-Omori, E. V. et al. METTL3 inhibitors for epitranscriptomic modulation of cellular processes. Preprint at https://doi.org/10.1101/2020.09.25.311803 (2020).
Śledź, P. & Jinek, M. Structural insights into the molecular mechanism of the m6A writer complex. eLife 5, e18434 (2016).
Evans, P. R. & Murshudov, G. N. How good are my data and what is the resolution? Acta Crystallogr. D 69, 1204–1214 (2013).
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
Winn, M. D., Murshudov, G. N. & Papiz, M. Z. Macromolecular TLS refinement in REFMAC at moderate resolutions. Methods Enzymol. 374, 300–321 (2003).
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
Wlodek, S., Skillman, A. G. & Nicholls, A. Automated ligand placement and refinement with a combined force field and shape potential. Acta Crystallogr. D 62, 741–749 (2006).
Lee, B. H. 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).
Vick, B. et al. An advanced preclinical mouse model for acute myeloid leukemia using patients’ cells of various genetic subgroups and in vivo bioluminescence imaging. PLoS ONE 10, e0120925 (2015).
Panda, A. C., Martindale, J. L. & Gorospe, M. Polysome fractionation to analyze mRNA distribution profiles. Bio Protoc. 7, e2126 (2017).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Cui, X. et al. MeTDiff: A novel differential RNA methylation analysis for MeRIP-Seq data. IEEE/ACM Trans. Comput. Biol. Bioinformatics 15, 526–534 (2018).
Cui, X. et al. Guitar: An R/Bioconductor package for gene annotation guided transcriptomic analysis of rna-related genomic features. Biomed. Res. Int. 2016, 8367534 (2016).
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).
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).
Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Acknowledgements
T.K. was 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), Cancer Research UK (grant reference C6946/A14492) and Kay Kendall Leukaemia Fund project grant (grant reference RG88664). K.T. was funded by a Wellcome Trust Sir Henry Wellcome Fellowship (grant reference RG94424). K.T and M.E. were funded by Leukaemia UK (G108148). I.J. was funded by ERC (Consolidator Grant 681524). I.J. and K.T. were funded by Cambridge-LMU Strategic Partnership Award. G.S.V. was funded by a Cancer Research UK Senior Cancer Fellowship (C22324/A23015). We thank B. L. Ng, J. Graham, S. Thompson and C. Hall for help with flow cytometry and the Cambridge Blood and Stem Cell Biobank for human AML and cord blood sample processing; the staff of the Sanger Institute Core Sequencing facility for sequencing and the staff of the Sanger Institute Research Support Facility for help with mouse experiments; D. Hardick, B. Thomas, Y. Ofir-Rosenfeld and A. Sapetschnig at Storm Therapeutics for comments and advice; and K. Danker, M. Ridgill, C. Hoareau and their colleagues at Evotec for drug discovery services and lead optimization support.
Author information
Authors and Affiliations
Contributions
K.T., T.K. and O.R. conceived the study and designed the experiments; K.T., W.B., E.Y., M.A., E.S.P., D.A., J.R., E.D.B, M.G., D.L., A.G.H., B.A., B.V., N.A.W., R.F., P.G. and M.E. conducted chemical, biochemical and molecular experiments. K.T., E.Y., J.R., E.D.B. and M.G. performed mouse experiments. G.T. and J.M.L.D. performed bioinformatics analyses. E.S.P. performed X-ray crystallography, assisted by W.B. in data analysis and interpretation. E.Y. and D.A. performed polysome profiling with help and supervision from N.I. and K.T. A.J.B., I.J. and G.S.V. helped with data analysis, interpretation and direction. K.T., T.K., E.Y., M.E. and O.R. wrote the manuscript with help from all authors. All authors discussed the results and commented on the manuscript.
Corresponding authors
Ethics declarations
Competing interests
T.K. is a co-founder of Abcam Plc and Storm Therapeutics Ltd, Cambridge, UK and Scientific Advisor to Foghorn Therapeutics and EpiVario. E.Y. is funded by Storm Therapeutics Ltd, Cambridge, UK. W.B., M.A., G.T., D.L., B.A., R.F., A.G.H., N.A.W., P.G. and O.R. are employees of Storm Therapeutics Ltd, Cambridge, UK. E.S.P. is an employee of Evotec (UK) Ltd, Abingdon, UK. George S. Vassiliou is a consultant for Kymab, Cambridge, UK. Storm Therapeutics Ltd is the owner of a patent application (WO2020201773) covering the development of METTL3 RNA methyltrasferase inhibitors.
Additional information
Peer review information Nature thanks Richard Gregory and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 STM2457 is a specific small molecule inhibitor of METTL3 with no evidence of off-target effects.
a, Chemical structures of STM1760 and STM2120. b, Biochemical activity assay showing inhibition of the METTL3/METTL14 enzyme complex using a dose-range of STM1760. c, Surface plasmon resonance (SPR) sensorgram showing the binding of STM2457 to the METTL3/METTL14 protein complex. d, SPR sensorgram showing reduced binding of STM2457 to the METTL3/METTL14 protein complex in the presence of 50 μM SAM, illustrating that STM2457 is SAM competitive. e, SPR assay showing single-cycle binding kinetics of STM2457. f, Biochemical activity assay showing no inhibition of METTL16, NSUN1 and NSUN2 RNA methyltransferases using a dose-range of STM2457. g, Methyltransferase dendrograms showing that 10 μM STM2457 has selective inhibitory activity for METTL3/METTL14 over the indicated RNA and protein methyltransferases. h, Treatment with 10 μM STM2457 did not inhibit (that is, result in less than 50% control activity) any of the 468 kinases in the ScanMax (DiscoverX) kinase panel tested (marked by green dots).
Extended Data Fig. 2 Binding of STM2457 to the SAM pocket of METTL3.
a, Overlay of crystal structures of METTL3/METTL14 in complex with STM2457 (carbon atoms in cyan) and METTL3/14 in complex with SAM (carbon atoms in magenta, PDB code 7O2I). The position of K513 is shown in lines of the corresponding colour for each structure. b, Cellular thermal shift target engagement assay measuring binding affinity of STM2457 against human and mouse METTL3 proteins expressed in HeLa cells. The IC50 represents the concentration of STM2457 at which 50% of METTL3 is bound to STM2457. (mean +/− s.d., n = 3). c, Quantification of m6A levels on poly-A+-enriched RNA using RNA-mass spectrometry after 48 h of in vitro treatment of MOLM-13 with 1 μM of STM2457 or vehicle (DMSO). (mean +/− s.d., n = 3). d, Quantification of m6Am, m62A and m7G levels on poly-A+-enriched RNA 24 h of treatment of MOLM-13 with the indicated STM2457 concentrations (mean +/− s.d., n = 3). e, STM2457 in vivo pharmacokinetic profile in mouse blood and brain tissue using a dose of 50 mg/kg. f, STM2457 in vivo PK/PD relationship in non-tumour bearing animals from the PK study shown in e, demonstrating inhibition of m6A in spleen tissue over a range of STM2457 blood concentrations (n = 3). two-tailed Student’s t-test.
Extended Data Fig. 3 Treatment with STM2457 triggers colony forming deficiency and apoptosis in AML cells.
a, Colony-forming efficiency of CD34+ human cord blood cells (n = 3) in the presence of 1, 5, or 10 μM STM2457 (mean ± s.d., n = 3). These changes are not significant at the 95% confidence level according to one-way Anova on repeated measures. Error bars refer to variation across 3 different individuals (blue, brown and red square). b, Proliferation assay in MOLM-13 cells after treatment with the indicated doses of STM2457 and STM2120, illustrating no sensitivity to the latter at any tested dose (mean ± s.d., n = 3). c, Colony forming efficiency of primary murine MLL-ENL/Flt3ITD/+ and NPM1c/NRAS-G12D AML cells treated with 1 μM STM2457 showing decreased clonogenic potential compared with vehicle-treated (DMSO) controls (mean ± s.d., n = 3). d, Mac1 levels were used to assess differentiation of non-leukaemic haemopoietic cell line HPC7. Flow cytometry comparison on day 4 post-treatment between vehicle (DMSO) and 1 μM STM2457. e, Selective increased apoptosis in AML cells but not in non-leukaemic haematopoietic cells, following treatment with 1 μM of STM2457 at the presented time points (mean ± s.d., n = 3). f, Western blot for SP1 and ACTIN in MOLM-13 cells transduced with plasmids expressing SP1 cDNA or an empty control (n = 3). g, Dose–response curves of MOLM-13 cells to STM2457 after transduction with vectors expressing SP1 cDNA or an empty control, showing selective decrease of drug sensitivity upon ectopic expression the former. The dose–response curve of parental MOLM-13 (WT, light blue) shown in Fig. 2a is illustrated for comparison purposes. (mean ± s.d., n = 3). d, days; two-tailed Student’s t-test; *P < 0.05, **P < 0.01.
Extended Data Fig. 4 Differential expression analysis of AML cells after treatment with STM2457.
a, Volcano plot for MOLM13 cells treated with 1 μM STM2457 versus control samples after 48 h of treatment, showing significantly dysregulated genes in red (Padj ≤ 0.01). b, Extended GO analysis of the differentially expressed genes post-treatment with 1 μM STM2457 in MOLM-13 cells. c, Representative GO signatures of the differentially expressed genes post-treatment with STM2457 in MOLM-13 cells. LFC, Log Fold Change.
Extended Data Fig. 5 Pharmacological inhibition of METTL3 significantly reduces m6A on leukaemia-associated substrates.
a, Overlap between METTL3-dependent m6A poly-A+ RNAs in MOLM-13 cells either treated with 1 μM STM2457 or with genetic downregulation of METTL3 from Barbieri et al.7. b, Overlap between differential downregulated m6A peaks in MOLM-13 cells either treated with 1 μM STM2457 or with genetic downregulation of METTL3 from Barbieri et al.7. c, Genomic visualization of the m6A-meRIP normalized signal in MOLM13 cells following treatment with vehicle (DMSO) or 1 μM STM2457 for the METTL3-dependent m6A substrates BRD4 and HNRNPL (red stars indicate loss of m6A signal). d, m6A-meRIP-qPCR analysis of METTL3-dependent and METTL3-independent m6A substrates normalized to input in MOLM-13 cells treated for 24 or 48 h with either vehicle (DMSO) or 1 μM STM2457 (mean ± s.d., n = 3). e, GO analysis of differentially m6A-methylated mRNAs upon treatment with 1 μM STM2457. f, RT–qPCR quantification of METTL3 and DICER1 in total RNA samples from MOLM-13 cells treated with vehicle or STM2457 (mean +/− s.d., n = 3). g, Western blot for METTL3, METTL14, DDX3X, DICER1 and ACTIN in MOLM-13 cells treated with 10, 5 and 1 μM of STM2457 or vehicle (DMSO) for 72 h (n = 3). two-tailed Student’s t-test; n.s., not significant; KD, knockdown.
Extended Data Fig. 6 STM2457 shows high efficacy and strong target engagement in PDX models.
a, Quantification of luminescence for the animal experiment depicted in Fig. 4a (mean ± s.d., n = 5). b, Bioluminescence imaging of mice transplanted with AML PDX-3 (MLL-AF10) treated with vehicle or 50 mg/kg STM2457 (n = 5). c, Kaplan–Meier survival of AML PDX-3 (MLL-AF10) following 12 consecutive treatments with vehicle or 50 mg/kg STM2457 at indicated times (n = 5). d, Quantification of luminescence for the animal experiment depicted in Extended Data Fig. 4c (mean ± s.d., n = 5). e, Quantification of luminescence for the animal experiment depicted in Fig. 4c (mean ± s.d., n = 5). f, Body weight for the animal experiment depicted in Fig. 4a (n = 5). Statistical significance was determined by two-tailed Mann–Whitney U test and box plots showing median, IQR and extremes. g, Western blot for SP1, BRD4, HNRNPL, BCL2, METTL3 and ACTIN protein levels in AML PDX-3 (MLL-AF10) treated with vehicle or 50 mg/kg STM2457 (n = 4). h, RNA-mass spectrometry quantification of m6A levels on poly-A+-enriched RNA from bone marrow of AML PDX-3 (MLL-AF10) treated in vivo with vehicle, 30 mg/kg STM2457 or 50 mg/kg STM2457 (mean ± s.d., n = 4). D, day; n.s. not significant; two-tailed Student’s t-test; Log-rank (Mantel–Cox) test was used for survival comparisons.
Extended Data Fig. 7 STM2457 treatment is efficacious and targeted in primary murine AML.
a, Percentage of YFP+ MLL-AF9/Flt3Itd/+ cells in the bone marrow of mice treated with vehicle or 30 mg/kg STM2457 (mean ± s.d., n = 4). b, Spleen weight of MLL-AF9/Flt3Itd/+ murine AML models following treatment with vehicle or 30 mg/kg STM2457 (mean ± s.d., n = 4). c, Western blot showing SP1, BRD4, HNRNPL, BCL2, METTL3 and ACTIN protein levels in murine AML (MLL-AF9/Flt3Itd/+) models treated with either vehicle or 30 mg/kg STM2457 (n = 4). d, RNA-mass spectrometry quantification of m6A levels on poly-A+-enriched RNA in vivo from AML murine models (MLL-AF9/Flt3Itd/+) treated with vehicle, 30 mg/kg STM2457 or 50 mg/kg STM2457 (mean ± s.d., n = 4). e, Percentage of CD93+ cells in the bone marrow of MLL-AF9/Flt3Itd/+ murine models following treatment with either vehicle or 30 mg/kg STM2457 (mean ± s.d., n = 5). f, Percentage of L-GMP cells in the bone marrow of MLL-AF9/Flt3Itd/+ murine models following treatment with either vehicle or 30 mg/kg STM2457 (mean ± s.d., n = 5). g, CD48 levels of L-GMP cells in the bone marrow of MLL-AF9/Flt3Itd/+ murine models following treatment with either vehicle or 30 mg/kg STM2457 (mean ± s.d., n = 5). h. Kaplan–Meier survival after re-transplantation of cells isolated from primary transplanted animals with MLL-AF9/Flt3Itd/+ treated and treated with either vehicle or 30 mg/kg STM2457 (n = 5). i, Percentage of YFP+ cells in the peripheral blood 12 days after re-transplantation with MLL-AF9/Flt3Itd/+ (mean ± s.d., n = 5). D, day; BM, bone marrow; PBC, peripheral blood count; n.s. not significant; two-tailed Student’s t-test; Log-rank (Mantel–Cox) test was used for survival comparisons.
Extended Data Fig. 8 Pharmacological inhibition of METTL3 has no lasting effects on normal haematopoiesis.
a–c, Quantification of LSK (Lin-/Ska1+/c-Kit+) and HSC (LSK/CD150+/CD34-) compartments in bone marrow from WT C57BL/6J mice following 14 consecutive daily treatments with either vehicle or 50 mg/kg STM2457 (mean ± s.d., n = 5). d, Blood count results from animal experiments related to a–c. e, Body weight of mice from animal experiments related to a–d (n = 5). Statistical significance was determined by two-tailed Mann–Whitney U test and box plots showing median, IQR and extremes. f, RNA-mass spectrometry quantification of m6A levels on poly-A+-enriched RNA from healthy bone marrow related to the animal experiments in a–d, following 14 days of consecutive treatments with either vehicle or 50 mg/kg STM2457 (mean ± s.d., n = 5). HSC, haematopoietic stem cells; two-tailed Student’s t-test; n.s., not significant.
Supplementary information
Supplementary Information
This file contains Supplementary Figures 1-2 (the uncropped western blots and gating strategy for FACS experiments), and Supplementary Notes 1-2 (a description of the STM2457 X-ray crystallography results and chemical synthesis of STM2457).
Supplementary Table 1
Differential expression analysis using DESeq2 from RNAseq data.
Supplementary Table 2
GSEA of differential downregulated genes from RNAseq.
Supplementary Table 3
Differential analysis of m6A substrates using meRIP-seq data.
Supplementary Table 4
Overlap of differential downregulated m6A peaks between pharmacological and genetic inhibition of METTL3.
Supplementary Table 5
Gene Ontology (GO) enrichment of genes differentially m6A modified according to meRIP-seq.
Supplementary Table 6
Information for all primer sequences used.
Supplementary Table 7
Data collection and refinement statistics for X-ray crystallography.
Rights and permissions
About this article
Cite this article
Yankova, E., Blackaby, W., Albertella, M. et al. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia. Nature 593, 597–601 (2021). https://doi.org/10.1038/s41586-021-03536-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-021-03536-w
This article is cited by
-
METTL3 drives NSCLC metastasis by enhancing CYP19A1 translation and oestrogen synthesis
Cell & Bioscience (2024)
-
Regulation of inflammatory diseases via the control of mRNA decay
Inflammation and Regeneration (2024)
-
The role of the methyltransferase METTL3 in prostate cancer: a potential therapeutic target
BMC Cancer (2024)
-
A novel AML1-ETO/FTO positive feedback loop promotes leukemogenesis and Ara-C resistance via stabilizing IGFBP2 in t(8;21) acute myeloid leukemia
Experimental Hematology & Oncology (2024)
-
METTL3 promotes cellular senescence of colorectal cancer via modulation of CDKN2B transcription and mRNA stability
Oncogene (2024)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
