Acetylation of histones by lysine acetyltransferases (KATs) is essential for chromatin organization and function1. Among the genes coding for the MYST family of KATs (KAT5–KAT8) are the oncogenes KAT6A (also known as MOZ) and KAT6B (also known as MORF and QKF)2,3. KAT6A has essential roles in normal haematopoietic stem cells4,5,6 and is the target of recurrent chromosomal translocations, causing acute myeloid leukaemia7,8. Similarly, chromosomal translocations in KAT6B have been identified in diverse cancers8. KAT6A suppresses cellular senescence through the regulation of suppressors of the CDKN2A locus9,10, a function that requires its KAT activity10. Loss of one allele of KAT6A extends the median survival of mice with MYC-induced lymphoma from 105 to 413 days11. These findings suggest that inhibition of KAT6A and KAT6B may provide a therapeutic benefit in cancer. Here we present highly potent, selective inhibitors of KAT6A and KAT6B, denoted WM-8014 and WM-1119. Biochemical and structural studies demonstrate that these compounds are reversible competitors of acetyl coenzyme A and inhibit MYST-catalysed histone acetylation. WM-8014 and WM-1119 induce cell cycle exit and cellular senescence without causing DNA damage. Senescence is INK4A/ARF-dependent and is accompanied by changes in gene expression that are typical of loss of KAT6A function. WM-8014 potentiates oncogene-induced senescence in vitro and in a zebrafish model of hepatocellular carcinoma. WM-1119, which has increased bioavailability, arrests the progression of lymphoma in mice. We anticipate that this class of inhibitors will help to accelerate the development of therapeutics that target gene transcription regulated by histone acetylation.

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  1. 1.

    Lee, K. K. & Workman, J. L. Histone acetyltransferase complexes: one size doesn’t fit all. Nat. Rev. Mol. Cell Biol. 8, 284–295 (2007).

  2. 2.

    Allis, C. D. et al. New nomenclature for chromatin-modifying enzymes. Cell 131, 633–636 (2007).

  3. 3.

    Voss, A. K. & Thomas, T. MYST family histone acetyltransferases take center stage in stem cells and development. BioEssays 31, 1050–1061 (2009).

  4. 4.

    Katsumoto, T. et al. MOZ is essential for maintenance of hematopoietic stem cells. Genes Dev. 20, 1321–1330 (2006).

  5. 5.

    Thomas, T. et al. Monocytic leukemia zinc finger protein is essential for the development of long-term reconstituting hematopoietic stem cells. Genes Dev. 20, 1175–1186 (2006).

  6. 6.

    Sheikh, B. N. et al. MOZ (KAT6A) is essential for the maintenance of classically defined adult hematopoietic stem cells. Blood 128, 2307–2318 (2016).

  7. 7.

    Borrow, J. et al. The translocation t(8;16)(p11;p13) of acute myeloid leukaemia fuses a putative acetyltransferase to the CREB-binding protein. Nat. Genet. 14, 33–41 (1996).

  8. 8.

    Huang, F., Abmayr, S. M. & Workman, J. L. Regulation of KAT6 acetyltransferases and their roles in cell cycle progression, stem cell maintenance, and human disease. Mol. Cell. Biol. 36, 1900–1907 (2016).

  9. 9.

    Sheikh, B. N. et al. MOZ (MYST3, KAT6A) inhibits senescence via the INK4A–ARF pathway. Oncogene 34, 5807–5820 (2015).

  10. 10.

    Perez-Campo, F. M. et al. MOZ-mediated repression of p16 INK4a is critical for the self-renewal of neural and hematopoietic stem cells. Stem Cells 32, 1591–1601 (2014).

  11. 11.

    Sheikh, B. N. et al. MOZ regulates B-cell progenitors and, consequently, Moz haploinsufficiency dramatically retards MYC-induced lymphoma development. Blood 125, 1910–1921 (2015).

  12. 12.

    Falk, H. et al. An efficient high-throughput screening method for MYST family acetyltransferases, a new class of epigenetic drug targets. J. Biomol. Screen. 16, 1196–1205 (2011).

  13. 13.

    Holbert, M. A. et al. The human monocytic leukemia zinc finger histone acetyltransferase domain contains DNA-binding activity implicated in chromatin targeting. J. Biol. Chem. 282, 36603–36613 (2007).

  14. 14.

    Sakaue-Sawano, A. et al. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132, 487–498 (2008).

  15. 15.

    Yan, Z. et al. Cdc6 is regulated by E2F and is essential for DNA replication in mammalian cells. Proc. Natl Acad. Sci. USA 95, 3603–3608 (1998).

  16. 16.

    Kueh, A. J., Dixon, M. P., Voss, A. K. & Thomas, T. HBO1 is required for H3K14 acetylation and normal transcriptional activity during embryonic development. Mol. Cell. Biol. 31, 845–860 (2011).

  17. 17.

    Voss, A. K., Collin, C., Dixon, M. P. & Thomas, T. Moz and retinoic acid coordinately regulate H3K9 acetylation, Hox gene expression, and segment identity. Dev. Cell 17, 674–686 (2009).

  18. 18.

    Voss, A. K. et al. MOZ regulates the Tbx1 locus, and Moz mutation partially phenocopies DiGeorge syndrome. Dev. Cell 23, 652–663 (2012).

  19. 19.

    Chew, T. W. et al. Crosstalk of Ras and Rho: activation of RhoA abates Kras-induced liver tumorigenesis in transgenic zebrafish models. Oncogene 33, 2717–2727 (2014).

  20. 20.

    Adams, J. M. et al. The c-myc oncogene driven by immunoglobulin enhancers induces lymphoid malignancy in transgenic mice. Nature 318, 533–538 (1985).

  21. 21.

    Wallace, A. C., Laskowski, R. A. & Thornton, J. M. LIGPLOT: a program to generate schematic diagrams of protein–ligand interactions. Protein Eng. 8, 127–134 (1995).

  22. 22.

    Yuan, H. et al. MYST protein acetyltransferase activity requires active site lysine autoacetylation. EMBO J. 31, 58–70 (2012).

  23. 23.

    Chang, H. Y. et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2, e7 (2004).

  24. 24.

    Kong, L. J., Chang, J. T., Bild, A. H. & Nevins, J. R. Compensation and specificity of function within the E2F family. Oncogene 26, 321–327 (2007).

  25. 25.

    Tang, X., Milyavsky, M., Goldfinger, N. & Rotter, V. Amyloid-β precursor-like protein APLP1 is a novel p53 transcriptional target gene that augments neuroblastoma cell death upon genotoxic stress. Oncogene 26, 7302–7312 (2007).

  26. 26.

    Lujambio, A. et al. Non-cell-autonomous tumor suppression by p53. Cell 153, 449–460 (2013).

  27. 27.

    Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

  28. 28.

    Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

  29. 29.

    Serrano, M., Lin, A. W., McCurrach, M. E., Beach, D. & Lowe, S. W. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a. Cell 88, 593–602 (1997).

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We thank F. Dabrowski, C. D’Alessandro, WEHI Bioservices, the WEHI FACS laboratory, the MX2 beamline staff at the Australian Synchrotron for their expert help and Z. Gong for the two transgenic zebrafish lines. This work was funded by the Australian Government through NHMRC project grants 1030704, 1080146, Research Fellowships (T.T., A.K.V., G.K.S., J.K.H., M.W.P. and J.B.), the NHMRC IRIISS and the Cancer Therapeutics Cooperative Research Centre. The Victorian State Government OIS Grants to WEHI, Monash and St Vincent’s Institute are gratefully acknowledged.

Reviewer information

Nature thanks P. Adams, R. Marmorstein and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors jointly supervised this work: Anne K. Voss, Tim Thomas


  1. Medicinal Chemistry Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia

    • Jonathan B. Baell
    • , David J. Leaver
    • , Nghi Nguyen
    • , Ben Cleary
    •  & H. Rachel Lagiakos
  2. School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing, China

    • Jonathan B. Baell
  3. ACRF Rational Drug Discovery Centre, St Vincent’s Institute of Medical Research, Fitzroy, Victoria, Australia

    • Stefan J. Hermans
    • , Matthew C. Chung
    •  & Michael W. Parker
  4. The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria, Australia

    • Gemma L. Kelly
    • , Margs S. Brennan
    • , Natalie L. Downer
    • , Johannes Wichmann
    • , Helen M. McRae
    • , Yuqing Yang
    • , Stephen Mieruszynski
    • , Guido Pacini
    • , Hannah K. Vanyai
    • , Maria I. Bergamasco
    • , Rose E. May
    • , Bethany K. Davey
    • , Kimberly J. Morgan
    • , Andrew J. Sealey
    • , Beinan Wang
    • , Natasha Zamudio
    • , Stephen Wilcox
    • , Alexandra L. Garnham
    • , Bilal N. Sheikh
    • , Brandon J. Aubrey
    • , Karen Doggett
    • , Melanie de Silva
    • , Hendrik Falk
    • , Jai Rautela
    • , Edwin D. Hawkins
    • , Nicholas D. Huntington
    • , Joan K. Heath
    • , Andreas Strasser
    • , Gordon K. Smyth
    • , Ian P. Street
    • , Brendon J. Monahan
    • , Anne K. Voss
    •  & Tim Thomas
  5. Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia

    • Gemma L. Kelly
    • , Margs S. Brennan
    • , Johannes Wichmann
    • , Helen M. McRae
    • , Yuqing Yang
    • , Stephen Mieruszynski
    • , Hannah K. Vanyai
    • , Maria I. Bergamasco
    • , Kimberly J. Morgan
    • , Andrew J. Sealey
    • , Beinan Wang
    • , Natasha Zamudio
    • , Stephen Wilcox
    • , Alexandra L. Garnham
    • , Bilal N. Sheikh
    • , Brandon J. Aubrey
    • , Karen Doggett
    • , Hendrik Falk
    • , Jai Rautela
    • , Edwin D. Hawkins
    • , Nicholas D. Huntington
    • , Joan K. Heath
    • , Andreas Strasser
    • , Ian P. Street
    • , Brendon J. Monahan
    • , Anne K. Voss
    •  & Tim Thomas
  6. Cancer Therapeutics CRC, Parkville, Victoria, Australia

    • H. Rachel Lagiakos
    • , Bethany K. Davey
    • , Melanie de Silva
    • , Hendrik Falk
    • , Ian P. Street
    •  & Brendon J. Monahan
  7. School of Pharmaceutical Sciences, Tsinghua University, Beijing, China

    • Beinan Wang
  8. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Biomedical Program, Parkville, Victoria, Australia

    • John Bentley
    • , Pat Pilling
    • , Meghan Hattarki
    • , Olan Dolezal
    • , Matthew L. Dennis
    • , Bin Ren
    •  & Thomas S. Peat
  9. Centre for Drug Candidate Optimisation, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia

    • Susan A. Charman
    •  & Karen L. White
  10. The Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    • Andrea Newbold
    •  & Ricky W. Johnstone
  11. Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia

    • Michael W. Parker
  12. Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia

    • Gordon K. Smyth


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T.T. was responsible for initiating the project. T.T. and A.K.V. supervised the project, performed experiments and wrote the manuscript. Medicinal chemistry: supervised by J.B.B., team: D.J.L., N.N., B.C. and H.R.L. Structural biology: S.J.H., M.C.C., B.R., T.S.P. and M.W.P. Chemical screening, protein biochemistry and assays: M.d.S., J.B., P.P., M.H., O.D., M.L.D., H.F., I.P.S. and B.J.M. Pharmacology: S.A.C. and K.L.W. Bioinformatics: G.P., A.L.G. and G.K.S. Cell-based assays, molecular biology and biochemistry: N.L.D., J.W., H.M.M., Y.Y., H.K.V., M.I.B., R.E.M., B.K.D., B.W., N.Z., S.W., B.N.S. and B.J.A. Zebrafish model: S.M., K.J.M., A.J.S., K.D. and J.K.H. Mouse cancer models: G.L.K., M.S.B., J.R., A.N., E.D.H., R.W.J., N.D.H. and A.S.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Jonathan B. Baell or Anne K. Voss or Tim Thomas.

Extended data figures and tables

  1. Extended Data Fig. 1 Binding characteristics of the MYST domain–WM-8014 protein–ligand interaction and comparison of MYST family histone acetyltransferase domains.

    a, SPR binding data for the interaction of WM-8014 with immobilized KAT6A and KAT7 MYST domains. Injected concentrations of WM-8014 are indicated. Binding responses (data; black sensorgrams) are overlaid, fitted curves of a 1:1 kinetic interaction model that included mass transport component (coloured lines), as well as derived kinetic rate constants (ka, kd) and equilibrium dissociation (KD) constant. One of at least two experiments is shown. b, WM-8014 bound to MYSTCryst, with the WM-8014 OMIT electron density map contoured to 3σ shown in green. c, Acetyl-CoA bound to MYSTCryst, with the acetyl-CoA OMIT map contoured to 3σ shown in green. d, Ribbon diagram showing WM-8014 and acetyl-CoA superimposed. e, Protein–ligand interactions (LIGPLOT)21 between WM-8014 and amino acids within the acetyl-CoA-binding pocket of the MYST domain. The amino acids that differ between MYST family members are indicated. Data collection and refinement statistics of the WM-8014 and acetyl-CoA co-crystal structures can be found in Supplementary Table 4. The overall structure of WM-8014 bound to MYSTCryst is nearly identical to the MYSTCryst–acetyl-CoA complex. The pantothenate arm of acetyl-CoA adopts an identical position to published MYST HAT domain structures; as observed previously, there are differing positions for the 3′-phosphate ADP13. Autoacetylation of K604 was observed, as expected22. Gol denotes glycerol. f, Comparison of the conserved MYST domain between MYST family proteins. MYSTCryst is a MYST domain modified to improve solubility and used in crystallization studies. Numbering as in KAT6A sequence, NP_006757.2; amino acids interacting with WM-8014 (depicted in the LIGPLOT) are shown in red.

  2. Extended Data Fig. 2 Time course of MEF growth inhibition upon treatment with WM-8014, and requirement for INK4A/ARF and p53 for WM-8014-induced cell cycle arrest.

    a, MEF proliferation after treatment with three high concentrations of WM-8014. MEFs were treated either continuously for 15 days, or treatment was discontinued after 1, 2, 4 or 8 days to determine whether cells could re-enter the cell cycle. b, Phase-contrast images of MEFs after 15 days of treatment with 10 µM WM-8014 or 10 µM WM-2474. Note cells with senescence morphology; that is, large nuclei indicating endoreplication without cell division and extensive cytoplasm (WM-8014 panel). c, Flow-cytometry gating strategy for the cell cycle analysis using incorporation of the nucleotide analogue BrdU to mark cells in S phase and 7-aminoactinomycin D (7-AAD) to determine 2N (G0/G1) and 4N (G2/M) DNA content. d, Flow-cytometry gating strategy for the cell cycle analysis of transgenic Fucci cells that express Azami Green in mid-S phase, G2 and M, Kusabira Orange in mid–late G1, are double-positive yellow in early S phase and double-negative in early G1. e, Cell cycle analysis of Cdkn2a null (Ink4a−/−Arf−/−) and littermate control cells after treatment for 8 days with WM-8014, vehicle and the inactive compound WM-2474. MEFs were exposed to BrdU for 1 h before flow-cytometry analysis of BrdU incorporation during DNA synthesis (S phase) and DNA content of 2N (G0/G1) compared with 4N (G2/M) using 7-AAD. f, Senescence-associated β-galactosidase activity in Cdkn2a−/− and control MEFs after treatment for 15 days with 10 μM WM-8014, 10 µM WM-2474 or DMSO vehicle control. g, Cell cycle analysis of Trp53 null MEFs (Trp53−/−) and littermate control cells after treatment with WM-8014, vehicle and inactive compound WM-2474, as in c. n = 3 MEF isolates per genotype (ae). Data are mean ± s.e.m., and were analysed by two-way ANOVA within duration of treatment with concentration and days of culture as the independent factors (a), or by one-way ANOVA followed by Bonferroni post hoc test (eg). Source Data

  3. Extended Data Fig. 3 The effect of WM-8014 on cell proliferation is mediated through the cell cycle regulators p16INK4A and p19ARF.

    a, RT–qPCR analysis of expression levels of cell cycle regulators Ink4a and Arf (alternative splice products of the Cdkn2a locus), Ink4b (also known as Cdkn2b) and Cdkn1a (encoding p21WAF1/CIP1) mRNA in MEFs treated for 4 days and 10 days with 10 µM WM-8014 or 10 µM control WM-2474. b, Dose–response plots of WM-8014 induction of Ink4a mRNA expression in MEFs. c, RT–qPCR analysis of expression changes in the KAT6A target gene detected by RNA-seq. MEFs were treated for 4 days and 10 days with 10 µM WM-8014, 10 µM control WM-2474 or DMSO. d, Dose–response plots of WM-8014-dependent reduction in E2f2 and Cdc6 mRNA levels in MEFs. e, Levels of mRNA coding for MYST-family proteins after treatment of MEFs for 4 days or 10 days with WM-8014, vehicle or the inactive compound WM-2474. n = 3 MEF isolates treated with WM-8014, WM-2474 or vehicle (a–e). Data are mean ± s.e.m. and are analysed by one-way ANOVA followed by Bonferroni post hoc test (ac, e) and by regression analysis (d). mRNA levels normalized to housekeeping genes (HK) were regressed on the log(concentration) of WM-8014 (d). Source Data

  4. Extended Data Fig. 4 WM-8014 causes cell cycle exit and senescence in MEFs, but not DNA damage or cell death.

    a, Assessment of DNA damage using flow cytometry to detect γH2A.X. Top, exposure of MEFs to ultraviolet light (positive control). Bottom, experimental samples. Quantification is displayed in the bar graph. b, Flow-cytometry gating strategy for cell death analysis and representative experimental samples. Negative and positive controls (untreated and ultraviolet-light-irradiated cells, respectively) are shown in the left panels. Annexin V marks phosphatidylserine externalization on cells undergoing apoptosis, propidium iodine (PI) uptake marks cells undergoing other forms of cell death, annexin V/PI double-positive staining marks cells in late-stage apoptosis. n = 3 cultures (a, b). Data are mean ± s.e.m. and were analysed by one-way ANOVA with treatment as the independent factor. Source Data

  5. Extended Data Fig. 5 WM-8014 treatment induces a gene signature of cellular senescence.

    a, Multidimensional scaling plot (log2 fold changes) showing clustering of MEF expression profiles after treatment with WM-8014 or control WM-2474. MEFs were isolated from 3 different embryos, numbered 5, 6 and 7 and treated for 4 days (96 h, red) or 10 days (240 h, green). b, Scatter plot showing gene-wise t-statistics for differentially expressed (DE) genes (FDR < 0.05) between the compounds at day 4 and day 10. Most genes were equally affected by 4 days or 10 days of treatment (green). Genes differentially expressed at day 10 only are highlighted blue, those differentially expressed at day 4 only are highlighted red. c, Mean-difference plot of treatment, log2 fold changes versus average log2 expression. Treatment effects at 4 days and 10 days have been averaged. Differentially expressed genes are highlighted in red or blue as indicated (FDR < 0.05). d, Number of differentially expressed genes for MEFs treated with WM-8014 versus WM-2474 (FDR < 0.05). e, Mean-difference plot of treatment log2 fold changes versus average log2 expression comparing WM-2474 to vehicle DMSO. The four differentially expressed genes (FDR < 0.05) are marked in red. f, Mean-difference plot of log2 fold changes versus average log2 expression comparing Kat6a−/− MEFs with Kat6a+/+ control MEFs. Differentially expressed genes are highlighted in red or blue as indicated (FDR < 0.05). g, Genes typical of cycling cells23 and E2F3 target genes24 are downregulated in MEFs treated with WM-8014 versus WM-2474 (combined day 4 and day 10 treatment; ROAST gene set tests, P = 0.0001). h, Genes downregulated during p53-induced cellular senescence25 were downregulated in MEFs treated with WM-8014 versus WM-2474 (combined day 4 and day 10 treatment; ROAST P = 0.0001). Differentially expressed genes in cellular senescence26 are strongly correlated with differentially expressed genes in MEFs treated with WM-8014 versus WM-2474 (ROAST P = 0.0039). i, DAVID27 was used to test for functional enrichment in genes downregulated after treatment with WM-8014 versus WM-2474, with FDR < 0.05. Cell cycle regulation was the top enriched pathway (FDR = 1.58 × 10−16), with 85% of the genes downregulated after 10 days of treatment with WM-8014. Schematic drawing is based on mmu04110: cell cycle28. Downregulated genes are shaded blue; unchanged, green; upregulated genes (Ink4a, Arf, Ink4b and p21) are shaded red. Data were collected from n = 3 MEFs isolates from 3 different embryos per treatment group, WM-8014 or WM-2474 treatment, for 96 h or 240 h.

  6. Extended Data Fig. 6 WM-8014 potentiates oncogene-induced senescence.

    a, Growth curves of MEFs expressing empty vector control (pBABE) or oncogenic29 HRASG12V treated with increasing concentrations of WM-8014 as indicated or DMSO vehicle control. All experiments were performed in 3% O2. b, The effects of WM-8014 treatment in a zebrafish model of hepatocellular carcinoma19. Doxycycline-inducible, liver-specific expression of a GFP-krasG12V transgene leads to the accumulation of a constitutively active, GFP-tagged form of KRAS in hepatocytes. TO-krasG12V transgenic embryos were treated with doxycycline at 2 days post fertilization (dpf) and 5 dpf to initiate KRASG12V-driven hepatocyte proliferation. The size of the liver was measured by two-photon microscopy. Representative three-dimensional reconstructions of whole livers from image stacks after treatment of transgenic zebrafish Tg(TO-krasG12V) expressing KRASGV12 and GFP (green) in the liver or transgenic zebrafish Tg(lfabp10:RFP;elaA:eGFP) expressing only RFP (red). c, Quantification of liver volume. d, Incorporation of the nucleotide analogue 5-ethynyl-2′-deoxyuridine (EdU) after treatment of transgenic zebrafish expressing KRASG12V or control zebrafish with WM-8014 or control compound WM-2474. e, RT–qPCR determination of Cdkn2a (Ink4a) and Cdkn1a (encoding p21WAF1/CIP1) mRNA levels in transgenic zebrafish Tg(TO-krasG12V) treated as described in b. n = 6 independent cultures (a), 20 zebrafish (b, c), 10–12 zebrafish (d) and 4–5 zebrafish (e). Data are mean ± s.e.m. and were analysed by two-way ANOVA (a) or one-way ANOVA (d, e) followed by Bonferroni post hoc test with treatment and with or without treatment duration as the independent factors or by linear regression analysis regressing liver volume on WM-8014 concentration (c). Source Data

  7. Extended Data Fig. 7 Medicinal chemistry optimization of WM-8014, designed to reduce plasma-protein binding, resulted in compound WM-1119.

    a, SPR binding data for the interaction of WM-1119 with immobilized KAT6A, KAT7 and KAT5, compared with the interaction of WM-8014. b, Crystal structure of WM-1119 bound to the MYST lysine acetyltransferase domain (MYSTCryst). Ribbon diagram of MYSTCryst (blue) showing WM-1119 (yellow, with element colouring) bound to the acetyl-CoA-binding site. Data collection and refinement statistics of the WM-1119 co-crystal structures (2.13 Å resolution) are listed in Supplementary Table 5. PDB: 6CT2. c, Space-filling model showing WM-1119 in the acetyl-CoA-binding pocket of MYSTCryst. d, WM-1119 bound to MYSTCryst with the OMIT electron density map contoured to 3σ shown in green. e, Ribbon diagram of MYSTCryst showing key amino acids interacting with WM-1119, in stick fashion with element colouring. Hydrogen bonds are shown as dashed lines. f, Schematic diagram of protein–ligand interactions (LIGPLOT)21 showing interactions between the compound WM-1119 and amino acids within the acetyl-CoA-binding pocket of the MYST domain derived from the crystal structure.

  8. Extended Data Fig. 8 WM-1119 causes retention of cells in the G1 phase of the cell cycle.

    a, WM-1119 causes cell cycle arrest in MEFs grown in 3% O2. Epifluorescence phase contrast images of Fucci MEFs after 8 days of treatment with 10 µM WM-1119 compared to 10 µM control WM-2474-treated cells. b, WM-1119 was tested at concentrations from 1 to 10 µM, compared to DMSO or 10 µM inactive compound WM-2474. The cell number under each condition was assessed at passage. c, Flow-cytometry analysis of Azami Green (mAG1; mid-S, G2, M), Kusabira Orange (mKO2; mid–late G1), double-positive yellow (early S) and double-negative (DN, early G1). Dot plots are shown for DMSO and 10 µM WM-2474 control treatment groups, and after treatment with 1 µM and 2.5 µM active compound WM1119. d, Percentage of cells in each phase of the cell cycle, quantified for all treatment groups. A higher proportion of WM-1119-treated cells is in mid–late G1. n = 3 independent MEF isolates. Data are mean ± s.e.m. and were analysed by two-way ANOVA (b) or one-way ANOVA followed by Bonferroni post hoc test (d) with treatment and with or without time as the independent factors. Source Data

  9. Extended Data Fig. 9 Characterization of WM-1119 and lymphoma cell line EMRK1184.

    a, Pharmacokinetic parameters for WM-1119 in mice following intraperitoneal injection. Note that the plasma concentration falls below 1 μM after 4 h. Data of n = 2 mice are shown. b, Characterization of the Eµ-Myc lymphoma cell line EMRK1184. Left, Western blot of p53 and p19ARF. The negative control cell line EMRK1263 lacks the ARF (p19ARF) band. Upregulation of p53 protein levels in positive control cell line EMRK1172 indicates non-functional p53 (commonly mutations in the DNA-binding domain). Right, EMRK1184 cells were sensitive to nutlin-3a-induced cell death, indicating intact p53. By contrast, EMRK1172 cells are insensitive to nutlin-3a. p53 exon sequencing of EMRK1184 using the MiSeq system (Illumina) confirmed wild-type p53 exon sequences. c, Multidimensional scaling plot showing two-dimensional clustering of the EMRK1184 lymphoma cell culture expression profiles. EMRK1184 lymphoma cells were treated for either 3 days or 6 days, in triplicate, with WM-1119 or vehicle before RNA-seq. Distances on the plot corresponding to leading log2 fold change between gene expression profiles. d, Mean-difference plot of treatment log2 fold changes versus average log2 expression for gene expression changes in the EMRK1184 lymphoma cell line after treatment for 3 days and 6 days with WM-1119 or DMSO vehicle control. Differentially expressed genes are highlighted (FDR < 0.05). e, mRNA levels assessed by RNA-seq of EMRK1184 cells treated with WM-1119 or vehicle. mRNA levels for Cdkn2a (coding for p16INK4A/p19ARF), Cdkn2b and Cdkn1a are shown. f, Western blot and densitometry analysis showing p16INK4A and p19ARF protein in EMRK1184 treated with WM-1119 or vehicle for 3 days. Each lane represents one independent culture, a total of 6 lanes (= 6 cultures) are shown. Data are mean ± confidence interval (a) or ± s.e.m. (e, f). Data in b were derived from three (EMRK1172) and two (EMRK1184) independent cell culture experiments, reflected by the individual data points. Data in ce were derived from three independent cultures per treatment group and analysed as described under RNA-seq in the Supplementary Methods. Data in f were analysed by one-way ANOVA followed by Bonferroni post hoc test. Source Data

  10. Extended Data Fig. 10 WM-1119 is effective in inhibiting tumour progression.

    a, Tumour development monitored by luciferase activity and bioluminescence imaging. Lateral images of mice treated four times per day with either vehicle or WM-1119 between day 7 and day 14 after injection with tumour cells. Baseline tumour burden is shown at higher sensitivity setting for day 3 (before treatment) in Fig. 4. Here, images at days 7, 10, 12 and 14 after tumour cell transplant are shown on the same, less-sensitive scale. Mice are imaged in the same order. Red boxes indicate the area used for quantification. b, Mouse body weights are not affected by treatment three or four times per day. c, Concentration of WM-1119 in peripheral blood and spleen 6 h after the final injection (four times per day; n = 6 mice per treatment group). d, Flow-cytometry analysis of total spleen cells from vehicle- or WM-1119-treated groups (four times per day; analysis of spleens assayed in a to identify tumour cells independently of luciferase expression). The lymphoma cell line EMRK1184 has a cell surface phenotype of CD19+IgMIgD. Flow cytometry was used to quantify the CD19+IgD population; this can be distinguished from normal splenic B cell populations, which are CD19+IgD+. e, Intracellular flow-cytometry analysis of H3K9ac in tumour cells. Left, the histogram shows H3K9ac levels in the remaining tumour cells (CD19+IgM) in spleens of the WM-1119-treated mice (red profile) compared to the vehicle-treated mice (blue profile). The shift in the red (WM-1119-treated) profile compared to the blue (vehicle-treated) profile indicates a reduction in signal. Right, the median fluorescence intensity (mean ± s.e.m) is shown in the bar graph. f, Peripheral blood analysis of vehicle- or WM-1119-treated mice. The cohort of mice that was treated three times per day is compared to the cohort that was treated four times per day. Images representative of n = 9 mice per treatment group in the four-times-per-day treatment regime (a). n = 3 mice per treatment group (b, df) and n = 6 mice per treatment group in (c). Data are mean ± s.e.m. and were analysed by one-way ANOVA with treatment as the independent factor followed by Bonferroni post hoc test (b), or two sided t-test (c, d, f) or one-sided t-test (e). Source Data

Supplementary information

  1. Supplementary Information

    This file contains full images of all uncropped western blot gels.

  2. Reporting Summary

  3. Supplementary Information

    This file contains supplementary methods; which includes supplementary tables 1-7.

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