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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Gene Expression Omnibus
Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012)
Alarcón, C. R., Lee, H., Goodarzi, H., Halberg, N. & Tavazoie, S. F. N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482–485 (2015)
Patil, D. P. et al. m6A RNA methylation promotes XIST-mediated transcriptional repression. Nature 537, 369–373 (2016)
Liu, J. et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014)
Dunham, I. et al.; ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012)
Tzelepis, K. et al. A CRISPR dropout screen identifies genetic vulnerabilities and therapeutic targets in acute myeloid leukemia. Cell Rep. 17, 1193–1205 (2016)
Shi, J. et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015)
Pendleton, K. E. et al. The U6 snRNA m6A methyltransferase METTL16 regulates SAM synthetase intron retention. Cell 169, 824–835 (2017)
Batista, P. J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014)
Wang, Y. et al. N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014)
Meyer, K. D. et al. 5′ UTR m6A promotes cap-independent translation. Cell 163, 999–1010 (2015)
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)
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)
Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013)
Dawson, M. A. et al. JAK2 phosphorylates histone H3Y41 and excludes HP1α from chromatin. Nature 461, 819–822 (2009)
Ronchi, A. E., Bottardi, S., Mazzucchelli, C., Ottolenghi, S. & Santoro, C. 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)
Migliori, V. et al. Symmetric dimethylation of H3R2 is a newly identified histone mark that supports euchromatin maintenance. Nat. Struct. Mol. Biol. 19, 136–144 (2012)
Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015)
Slobodin, B. et al. Transcription impacts the efficiency of mRNA translation via co-transcriptional N6-adenosine methylation. Cell 169, 326–337 (2017)
Fustin, J. M. et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013)
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)
Geltinger, C., Hörtnagel, K. & Polack, A. TATA box and Sp1 sites mediate the activation of c-myc promoter P1 by immunoglobulin kappa enhancers. Gene Expr. 6, 113–127 (1996)
Knuckles, P. et al. RNA fate determination through cotranscriptional adenosine methylation and microprocessor binding. Nat. Struct. Mol. Biol. 24, 561–569 (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)
Wang, X. et al. N6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015)
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)
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014)
Castello, A. et al. Insights into RNA biology from an atlas of mammalian mRNA-binding proteins. Cell 149, 1393–1406 (2012)
Kwon, S. C. et al. The RNA-binding protein repertoire of embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1122–1130 (2013)
Gerstberger, S., Hafner, M. & Tuschl, T. A census of human RNA-binding proteins. Nat. Rev. Genet. 15, 829–845 (2014)
Baltz, A. G. et al. The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690 (2012)
Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014)
Fustin, J.-M. et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013)
Gundry, M. C. et al. Highly efficient genome editing of murine and human hematopoietic progenitor cells by CRISPR/Cas9. Cell Reports 17, 1453–1461 (2016)
Dawson, M. A. et al. Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia. Nature 478, 529–533 (2011)
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010)
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008)
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLOS Comput. Biol. 9, e1003118 (2013)
Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015)
Chen, H. & Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 35 (2011)
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)
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010)
Cui, X. et al. MeTDiff: a novel differential RNA methylation analysis for MeRIP-seq data. IEEE/ACM Trans. Comput. Biol. Bioinformat. https://doi.org/10.1109/TCBB.2015.2403355 (2015)
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. Protocols 8, 176–189 (2013)
Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009)
Xiao, Z., Zou, Q., Liu, Y. & Yang, X. Genome-wide assessment of differential translations with ribosome profiling data. Nat. Commun. 7, 11194 (2016)
Dunn, J. G. & Weissman, J. S. Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data. BMC Genomics 17, 958 (2016)
Panda, A. C., Martindale, J. L. & Gorospe, M. Polysome fractionation to analyze mRNA distribution profiles. Bio Protoc. 7, e2126 (2017)
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)
Luo, W ., Friedman, M. S ., Shedden, K ., Hankenson, K. D . & Woolf, P. J. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics 10, 161 (2009)
Wyspian´ska, B. S. et al. BET protein inhibition shows efficacy against JAK2V617F-driven neoplasms. Leukemia 28, 88–97 (2014)
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.
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.
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.
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Extended data figures and tables
Related to Fig. 1. a, Correlation between gene rankings from the two independent CRISPR–Cas9 screens. Goodness of fit is calculated as Pearson Correlation Coefficient. b, Average ratio of the percentage of GFP-positive RN2C cells between day 2 and day 10 after infection with lentiviral vectors expressing GFP and individual gRNAs against the indicated targets. The mean + s.e.m. depletion of three different gRNAs against the catalytic domain of the targets is shown. gRNA targeting the Rosa26 locus was a negative control. Rpa (replication protein A) is a positive control. c, Competitive co-culture assay showing negative selection of BFP+ MOLM13 or KMT2A–MLLT3 primary mouse cells upon targeting of METTL3 by CRISPR–Cas9. Cells were transduced with lentiviruses expressing four different gRNAs targeting the 5′ exons or the catalytic domain of METTL3 and the BFP-positive fraction was compared with the non-transduced population. Results were normalized to those at day 4 for each gRNA. The mean ± s.d. of two independent infections is shown. d, Colony formation assay of KMT2A–MLLT3 Flt3 ITD Cas9-expressing cells targeting Mettl3 (catalytic domain-specific) or control, showing decreased replating ability. CFU: colony-forming units; ***P < 0.001, t-test. The mean + s.d. of three independent experiments is shown. e, Average ratio of the percentage of GFP-positive NIH-3T3 mouse fibroblasts between day 2 and day 12 after infection with lentiviral vectors expressing GFP and individual gRNAs against the indicated targets. The mean ± s.e.m. depletion of three different gRNAs against the catalytic domain of the targets is shown. Rpa is a positive control. f, Colony formation assay of lineage-negative haematopoietic Cas9-expressing cells targeting Mettl3 (catalytic domain-specific; right) or control. ***P < 0.001; t-test. The mean + s.d. of three independent experiments is shown. g, Competitive co-culture assay showing negative selection of BFP+ AML cell lines upon targeting of METTL3, METTL1, METTL14 and METTL16 by CRISPR–Cas9 using two independent gRNAs for each target. Cells were transduced with lentiviruses expressing BFP and four different gRNAs targeting the 5′ exons or the catalytic domain of each target and the BFP-positive fraction was compared with the non-transduced population. Results were normalized to those at day 4 for each gRNA. The mean + s.d. of two independent infections is shown.
Related to Fig. 1. a, Competitive co-culture assay showing negative selection of BFP+ human cancer cell lines upon targeting of METTL3, METTL1, METTL14 and METTL16 by CRISPR–Cas9 using two independent gRNAs for each target. The experiment was performed as described above. b, Efficiency of genome editing for gRNAs targeting METTL3, METTL1, METTL14 and METTL16 was measured across the indicated 20 human cell lines through TIDE analysis. Efficiency of targeting was also measured in mouse primary cell lines for gRNAs targeting Mettl3. c, CD11b expression in METTL3 (catalytic domain-specific) targeted cells (THP1 human cell line) was measured by flow cytometry 6 days after infection. d, Haematoxylin and eosin staining of human and mouse AML cell lines infected with a control gRNA or gRNAs targeting the catalytic domain of METTL3. e, Time course quantification of luminescence from mice transplanted with luciferase-labelled MOLM13 cells targeting METTL3 using gRNAs specific for the catalytic domain or control (***P < 0.001).
Related to Fig. 1. a, METTL3 mRNA levels detected by RT–qPCR 4 days after shRNA induction with doxycycline in MOLM13 cells. The mean ± s.e.m. of four independent cultures is shown. b, Western blot showing METTL3 and H3 levels in MOLM13 cells infected with specific or control TET-inducible shRNAs 5 days after doxycycline treatment. For gel source data see Supplementary Information. c, METTL3 mRNA levels detected by RT–qPCR 4 days after shRNA induction with doxycyxline in THP1 cells (left). The mean ± s.e.m. of three independent cultures is shown. A proliferation assay of the cells was then performed with cell numbers measured between day 0 (4 d post doxycycline) and day 4 (8 d post doxycycline) (right). The mean ± s.d. of two independent replicates is shown. d, Western blot for METTL3 and actin in mouse AML cells. Npm1c/Flt3ltd/+/Rosa26Cas9/+ mouse AML cells were transduced with gRNAs targeting the catalytic domain of Mettl3 and plasmids expressing either wild-type METTL3 or a catalytically inactive mutant (DW/AA). For gel source data see Supplementary Information. e, Volcano plots for METTL3-KD versus control samples, showing the significance P value (log10) versus fold change (log2) of gene expression. Significantly upregulated and downregulated transcripts are shown in red (|logFC| > 1, P < 0.001, FDR < 0.01). f, Graphical representation of KEGG pathway regulation showing cell cycle downregulation (upper panel) and haematopoietic differentiation upregulation (lower panel) as obtained by comparing RNA-seq data from METTL3-KD and control MOLM13 cells (upregulated genes, red; downregulated genes, green).
Related to Figs 1, 2. a, METTL3 (top) and METTL14 (bottom) mRNA expression levels across cancer types from the TCGA database. b, Proliferation assay of human AML cell lines upon transduction with a vector expressing METTL3. Cell numbers were measured between day 1 and day 3 after electroporation. The mean + s.d. of three independent replicates is shown. c, Western blot for METTL3, METTL14, GAPDH and histone H3 on cytoplasmic, nucleoplasmic and chromatin fractions from MOLM13 cells. For gel source data see Supplementary Information. d, Genomic browser screenshot of METTL14 and H3K4me3 normalized ChIP–seq datasets on the human SP2 gene locus from MOLM13 cells. e, Pie charts of genomic regions associated with METTL14 (top) and METTL3 (bottom) ChIP–seq peaks. f, Distribution of METTL14 ChIP–seq reads centred on TSSs (upper) and histogram of ChIP–seq reads distribution relative to TSSs (lower). g, Top, Venn diagram showing the overlap between METTL3 and METTL14 peak datasets (statistical significance was evaluated by a χ2 test). Bottom, distribution of METTL3 and METTL14 ChIP–seq reads centred on METTL14 (left) or METTL3 (right) peaks.
Related to Fig. 2. a, ChIP–seq validation by ChIP–qPCR of METTL3 and METTL14 binding on the SP2 and RFX1 loci. The mean of six technical replicates ± s.d. is shown. The experiment was performed independently three times. b, METTL3 ChIP–seq validation by ChIP–qPCR on the indicated loci. The LMO2 promoter was used as a negative control. The mean of three technical replicates ± s.d. is shown. The experiment was performed independently three times. c, METTL3 ChIP–seq validation by ChIP–qPCR on the indicated TSSs using two independent METTL3 antibodies in MOLM13 cells. The mean of six technical replicates ± s.d. is shown. The experiment was performed independently three times. d, METTL3 ChIP–seq validation by ChIP–qPCR on the indicated TSS in control or METTL3-KD MOLM13 cells, showing a specific reduction of METTL3 binding in METTL3-KD cells. The mean of three technical replicates ± s.d. is shown. The experiment was performed independently three times.
Related to Fig. 2. a, Motif discovery analysis of the genomic sequences under METTL3 ChIP–seq peaks using HOMER. Significance was obtained using a hypergeometric test. b, Distribution of ChIP–seq reads for the indicated factors or histone modifications, centred on METTL3 (green) and METTL14 (blue) ChIP peaks. Statistical significance of the binary overlap was evaluated by a χ2 test. c, Venn diagram showing the overlap of H3R2me2s, WDR5, KLF9, NFYA and NFYB ChIP–seq peaks after filtering for H3K4me3 promoters. d, Venn diagram showing significant overlap between METTL3 peaks (but not METTL14 peaks) and the 447 loci carrying all five factors as in c. Statistical significance of the binary overlap was evaluated by a χ2 test.
Related to Fig. 2. a, Histogram representing the positive predictive power of the combined five factors compared with the predictive power of the ENCODE factors whose expression levels are tightly correlated with METTL3 expression. b, Correlation between CEBPZ and METTL3 mRNA expression levels in the Human Protein Atlas RNA-seq datasets, including non-transformed (blue) and cancer (pink) cell lines. (ρ, Spearmann correlation coefficient). c, Genomic plot of METTL3 and CEBPZ normalized ChIP–seq datasets on human SP1 and SP2 gene loci in MOLM13 and K562 cells, respectively. d, Distribution and heatmaps of normalized ChIP–seq reads for METTL3 centred on CEBPZ peaks. e, Distribution and heat maps of normalized ChIP–seq reads of METTL14 and CEBPZ centred on METTL14 (left) and CEBPZ (right) peaks. f, Competitive co-culture assay showing negative selection of BFP+ AML cell lines upon targeting of CEBPZ by CRISPR–Cas9 gRNAs. Cells were transduced with lentiviruses expressing a gRNA targeting the first exon of CEBPZ and the BFP-positive fraction was compared with the non-transduced population. Results were normalized to those at day 4. The mean + s.d. of two independent infections is shown. g, CEBPZ mRNA levels detected by RT–qPCR 4 days after shRNA induction with doxycycline in MOLM13 cells. The mean ± s.d. of three independent cultures is shown. h, Proliferation assay of control and CEBPZ-KD cells. Cell numbers were measured between day 0 (4 d post doxycycline) and day 4 (8 d post doxycycline). The mean ± s.d. of six independent replicates is shown. i, ChIP–qPCR of METTL3 binding on target TSSs in MOLM13 cells expressing a control shRNA or two independent shRNAs against CEBPZ, showing a specific reduction of METTL3 binding in CEBPZ-KD cells. The mean of three technical replicates + s.d. is shown. The experiment was performed independently three times. j, Box plot representing the expression levels of METTL3 targets upon METTL3-KD from the dataset shown in Extended Data Fig. 3e.
Related to Fig. 3. a, Motif analysis under the identified m6A immunoprecipitation peaks showing enrichment of the expected UGCAG and GGACU sequences and their central distribution throughout the m6A immunoprecipitation peaks, as obtained by MEME and CentriMo. b, Distribution of m6A immunoprecipitation reads throughout the mRNA metatranscript, showing the expected enrichment around the STOP codon in MOML13 cells. c, Scatter plots and density plot showing the general downregulation of m6A immunoprecipitation signal upon METTL3-KD in MOLM13 cells. d, Histogram showing METTL3-dependent m6A immunoprecipitation read coverage in mRNAs from METTL3-bound TSSs (ChIP), whole transcriptome (All) or the permutation of random sets of genes (Rand). e, m6A immunoprecipitation followed by qPCR for m6A peaks of HNRNPL, or GAPDH as a control. The plot shows the m6A immunoprecipitation signal over total input in MOLM13 cells expressing a control shRNA or shRNAs targeting CEBPZ. Mean ± s.d. of three technical replicates is shown; experiment was performed independently twice. f, SP1, SP2, HNRNPL and METTL3 mRNA levels detected by RT–qPCR 8 days after doxycycline induction in MOLM13 control or CEBPZ-KD cells. The mean ± s.d. of three independent cultures is shown. g, Histogram showing the enrichment of the [GAG]n motif within the transcript sequences of METTL3 ChIP-targets compared with random permutations of genes.
Related to Fig. 3. a, Distribution of ribosome profiling reads throughout the mRNA metatranscript from RNA inputs or ribosome-protected fragments (RPFs) showing absence of 3′UTR specifically in the RPF dataset. b, Reading frame analysis of ribosome profiling reads from RNA inputs and RPFs in MOLM13 cells showing enrichment of the 0 reading frame specifically in the RPF reads. c, Average read alignments to 5′ and 3′ ends of coding sequences in RNA inputs (upper) or RPFs (lower) showing triplet periodicity and accumulation of reads on the start site typical of cycloheximide pre-treatment. d, Principal component analysis of P-site codon distribution on mRNAs from METTL3-bound TSSs obtained by ribosome footprinting, 5 or 8 days after doxycycline administration, of METTL3-KD (KD5, KD8) or control (WT5, WT8) MOLM13 cells. e, Principal component analysis of P-site codon distribution on all mRNAs, as in d. f, Frequency of P-site occupancy of codons in METTL3-KD or control MOLM13 cells for either all coding genes or genes harbouring a METTL3 ChIP peak on their promoter (*P < 0.05; t-test). g, Frequency of codons within the coding sequence of METTL3 chromatin targets compared with the general frequency throughout the coding transcripts. The plot shows no significant overrepresentation of GAN codons in METTL3 chromatin targets.
Related to Fig. 4. a, RNA-seq normalized counts of SP1 and SP2 mRNAs from control or METTL3-KD MOLM13 cells at day 8 after doxycycline induction. Mean + s.d. of at least three biological replicates is shown. b, Western blot showing CEBPZ, SP1 and GAPDH levels in control and CEBPZ-KD cells. For gel source data, see Supplementary Information. c, Polysome fractionation analysis. Cell extracts from control or METTL3-KD cells were prepared and resolved in a 5–50% sucrose gradient. The absorbance at 254 was continuously measured. The peaks corresponding to free 40S and 60S subunits, 80S and polysomes are indicated. d, DICER1 and ACTB mRNAs in each ribosome fraction were quantified through qPCR and plotted as a percentage of the total. Data are from two independent polysome-profiling experiments. Mean ± s.e.m. is shown. e, Firefly luciferase activity in FADU cell line from UAS or scrambled (SCR) sequence carrying plasmid in the presence of GAL4 either alone or fused with METTL3 wild-type (CD) or inactive (CD DW/AA) catalytic domain (*P < 0.05; t-test). The mean + s.d. of three independent transfections is shown. f, Firefly luciferase mRNA from plasmids carrying UAS or scrambled sequence in the presence of GAL4 either alone or fused with METTL3 wild-type (CD) or inactive (CD DW/AA) catalytic domain, as evaluated by qPCR. The mean ± s.d. of three replicates is shown. g, Box plot showing transcriptional modulation of genes bound by SP1, SP2 or both between METTL3-KD and control MOLM13 cells (*P < 0.05; Wilcoxon test). h, Genomic browser screenshot of SP1 and SP2 normalized ChIP-seq dataset on the human MYC gene locus in K562 cells (from ENCODE). i, Western blot showing METTL3, SP1 and ACTIN protein levels in MOLM13 cells infected with METTL3-specific or control TET-inducible shRNAs and with an SP1 expression vector 5 days after doxycycline treatment. For gel source data see 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. (PDF 1124 kb)
This file contains gene scores, ranking and statistics of whole genome CRISPR-CAS9 Screen (Screen 1). (XLSX 1788 kb)
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). (XLSX 77 kb)
This file contains Gene Expression data from RNA-sequencing of WT and METTL3 knock-down MOLM-13 cells 8 days after doxycycline induction. (XLSX 1729 kb)
This file contains Gene Ontology analysis of KEGG Pathways differentially regulated upon METTL3 depletion (as in Supplementary Table 3) (XLSX 30 kb)
This file contains genomic coordinates and annotation of of METTL3 and METTL14 ChIP-sequencing peaks. (XLSX 78 kb)
This file contains m6A RNA-IP data of WT and METTL3 knock-down MOLM-13 cells 8 days after doxycycline induction. (XLSX 1274 kb)
This file contains ribosome profiling data of WT and METTL3 knock-down MOLM-13 cells. (XLSX 822 kb)
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Barbieri, I., Tzelepis, K., Pandolfini, L. et al. Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control. Nature 552, 126–131 (2017). https://doi.org/10.1038/nature24678
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