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Functional interdependence of BRD4 and DOT1L in MLL leukemia

Nature Structural & Molecular Biology volume 23, pages 673–681 (2016)Cite this article

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Abstract

Targeted therapies against disruptor of telomeric silencing 1-like (DOT1L) and bromodomain-containing protein 4 (BRD4) are currently being evaluated in clinical trials. However, the mechanisms by which BRD4 and DOT1L regulate leukemogenic transcription programs remain unclear. Using quantitative proteomics, chemoproteomics and biochemical fractionation, we found that native BRD4 and DOT1L exist in separate protein complexes. Genetic disruption or small-molecule inhibition of BRD4 and DOT1L showed marked synergistic activity against MLL leukemia cell lines, primary human leukemia cells and mouse leukemia models. Mechanistically, we found a previously unrecognized functional collaboration between DOT1L and BRD4 that is especially important at highly transcribed genes in proximity to superenhancers. DOT1L, via dimethylated histone H3 K79, facilitates histone H4 acetylation, which in turn regulates the binding of BRD4 to chromatin. These data provide new insights into the regulation of transcription and specify a molecular framework for therapeutic intervention in this disease with poor prognosis.

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Figure 1: BRD4 and DOT1L are in separate protein complexes.
Figure 2: Inhibition of BRD4 and DOT1L leads to synergistic efficacy against MLL-FP leukemia cell lines and primary patient cells.
Figure 3: Inhibition of BRD4 and DOT1L by small-molecules and shRNA leads to synergistic efficacy against MLL-FP leukemia in vivo.
Figure 4: A subset of genes is responsive to inhibition of both BRD4 and DOT1L.
Figure 5: DOT1L regulates BRD4 binding at superenhancers via H3K79me2.
Figure 6: H3K79me2 regulates BRD4 binding via H4 acetylation.
Figure 7: H3K79me2 regulates BRD4 binding via EP300-mediated H4 acetylation.
Figure 8: Schematic model for the functional interdependency of DOT1L and BRD4 in MLL leukemia H3K79 is methylated by DOT1L, which results in a more open and permissive chromatin environment for the binding of transcription factors such as CREB1.

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Acknowledgements

A postdoctoral Fellowship awarded to O.G. from Leukaemia Foundation Australia partly supported this work. A mid-career Fellowship awarded to E.Y.N.L. from the Victoria Cancer Agency partly supported this work. A Senior Leukaemia Foundation Australia Fellowship and VESKI Innovation Fellowship currently support M.A.D. The National Health and Medical Research Council of Australia (1066545 (M.A.D.), 1085015 (M.A.D.), 1106444 (M.A.D.)) and Leukaemia Foundation Australia fund M.A.D.'s laboratory. This work was also funded in part by the Structural Genomics Consortium, a registered charity (no. 1097737) that received funds from AbbVie; Bayer; Boehringer Ingelheim; Genome Canada through the Ontario Genomics Institute (OGI-055); GlaxoSmithKline; Janssen; Lilly Canada; the Novartis Research Foundation; the Ontario Ministry of Economic Development and Innovation; Pfizer; Takeda; and the Wellcome Trust (092809/Z/10/Z).

Author information

Author notes

  1. Omer Gilan and Enid Y N Lam: These authors contributed equally to this work.

Authors and Affiliations

  1. Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Omer Gilan, Enid Y N Lam, Chun Yew Fong, Sarah Ftouni, Dean Tyler, Kym Stanley, Laura MacPherson, Chen-Fang Weng, Yih-Chih Chan, Margherita Ghisi, Ricky W Johnstone, Sarah-Jane Dawson & Mark A Dawson

  2. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia

    Omer Gilan, Enid Y N Lam, Chun Yew Fong, Dean Tyler, Ricky W Johnstone, Sarah-Jane Dawson & Mark A Dawson

  3. Cellzome GmbH, Molecular Discovery Research, GlaxoSmithKline, Heidelberg, Germany

    Isabelle Becher, Gerard Joberty, Aoife Ward, Gerard Drewes & Paola Grandi

  4. Epinova DPU, Immuno-Inflammation Therapy Area Unit, GlaxoSmithKline, Stevenage, UK

    Dave Lugo, Neil Garton & Rab K Prinjha

  5. The Gurdon Institute, University of Cambridge, Cambridge, UK

    Ester Cannizzaro, Meike Wiese, Andrew J Bannister & Tony Kouzarides

  6. Department of Haematology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Chun Yew Fong & Mark A Dawson

  7. Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada

    David Smil, Peter Brown & Cheryl H Arrowsmith

  8. Cancer Epigenetics DPU, GlaxoSmithKline, Collegeville, Pennsylvania, USA

    Christopher Carpenter

  9. The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Marnie E Blewitt

  10. Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia

    Marnie E Blewitt

  11. Department of Haematology, Cambridge Institute for Medical Research, Cambridge, UK

    Brian J P Huntly

  12. Cambridge Stem Cell Institute, Cambridge, UK

    Brian J P Huntly

  13. Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, Australia

    Sarah-Jane Dawson & Mark A Dawson

  14. Princess Margaret Cancer Centre, Toronto, Ontario, Canada

    Cheryl H Arrowsmith

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  1. Omer Gilan
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  2. Enid Y N Lam
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Contributions

O.G., E.Y.N.L. and M.A.D. designed the research, interpreted data and wrote the manuscript. O.G., E.Y.N.L., I.B., D.L., E.C., G.J., A.W., M.W., C.Y.F., S.F., D.T., K.S., L.M., C.-F.W., Y.-C.C. and N.G. performed experiments and analyzed data. M.G., D.S., C.C., P.B., M.E.B., A.J.B., T.K., B.J.P.H., R.W.J., G.D., S.-J.D., C.H.A., P.G. and R.K.P. provided critical reagents and aided in manuscript preparation.

Corresponding author

Correspondence to Mark A Dawson.

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Competing interests

I.B., D.L., G.J., A.W., C.C., N.G., G.D., P.G. and R.K.P. are employees of GlaxoSmithKline. D.S., P.B. and C.A. are employed by the Structural Genomics Consortium, a registered charity (no. 1097737) that received funds from AbbVie; Bayer; Boehringer Ingelheim; Genome Canada through the Ontario Genomics Institute (OGI-055); GlaxoSmithKline; Janssen; Lilly Canada; the Novartis Research Foundation; the Ontario Ministry of Economic Development and Innovation; Pfizer; Takeda; and the Wellcome Trust (092809/Z/10/Z).

Integrated supplementary information

Supplementary Figure 1 Characterization of SGC0946 and the BRD4- or DOT1L-containing protein complexes.

(a) Co-immunoprecipitation (Co-IP) of endogenous BRD4, DOT1L, MLLT1, and LEO1 in MOLM-13 cells. IgG was used as a control. Co-IP was followed by western blot analysis with antibodies against BRD4. (b) Size exclusion chromatography followed by western blot analysis of BRD4 and DOT1L, in MV4;11 cells. (c) Dose response to SGC0946 treatment in MV4;11 cells by western blot analysis using H3K79me2 antibodies, total H3 was used as control. (d) Time course of SGC0946 treatment in MV4;11 cells followed by western blot analysis for H3K79me2. (e) Western blot analysis of various histone lysine methylations (H3K79me/2/3, H3K36me3, and H3K4me3) following SGC0946 treatment in MV4;11 cells. (f) MV4;11 cells were treated with vehicle (DMSO), SGC0946, I-BET or combination followed by western blot analysis with antibodies against H3K79me2 and total H3.

Supplementary Figure 2 Chemical probes and proliferation assays.

(a) Chemical structure of the DOT1L inhibitors used in this study. (b) Chemical structure of DOT1L inhibitor compounds used for bead immobilization. (c) Pull downs with DOT1L inhibitor compounds immobilized on beads at 2 different coupling densities (CD) in HL60 nuclear extracts. 10µM FED1 (+) or DMSO (–) was used as soluble competitor. The eluates from the beads were analysed on Western blot using DOT1L antibodies. In vitro sub-therapeutic doses of (d) I-BET and (e) SGC0946 in MV4;11 cells. (f-h) Proliferation assay of the combination of low dose (LD, 100nM) I-BET plus low dose (LD, 1μM) SGC0946 in (f) SKM-1, (g) NB-4 (APML) and (h) HL-60 cells. Mean, error bars, s.d. (n = 2 cell culture replicates), representative graph from experiments done on 3 separate occasions.

Supplementary Figure 3 Phenotypic and pharmacokinetic analysis of single and combination therapies.

(a-b) Apoptotic response and cell cycle arrest in the combination treated cells. Annexin V staining of (a) MV4;11 and (b) K562 cells treated with either DMSO, SGC0946, I-BET, or combination. Mean, error bars, s.d. (n = 3 cell culture replicates), representative graph from experiments done on 3 separate occasions. (c-d) Cell cycle analysis by PI staining in (c) MV4;11 and (d) K562 cells treated with the inhibitors. These data show a cell cycle arrest in MV4;11 cells that is most prominent with combination therapy. Mean, error bars, s.d. (n = 3 cell culture replicates), representative graph from experiments done on 3 separate occasions. (e) Mouse pharmacokinetic studies comparing the blood concentration of SGC0946 administered via PO, SC or IP routes at 3 mg/kg (Samples taken post 2h below limit of quantification; 0.02 μM). Mean, error bars, s.d. (n = 3 mice per group). (f) Mouse pharmacokinetic study blood concentration-time profile of SGC0946 administered via surgically implanted mini-pump targeting 6 mg/kg/h for 72 h. Required exposure for in vivo model achieved. Mean, error bars, s.d. (n = 3 mice per group). (g) Western blot analysis of in vivo on-target activity of I-BET and SGC0946 on the bone marrows of three mice per group, using c-MYC, HSP60, H3K79me2 and total H3 antibodies.

Supplementary Figure 4 Inducible knockdown of BRD4 and DOT1L.

(a) Western blot and (b) qRT-PCR analysis of DOT1L expression in inducible RNAi samples. β-ACTIN was used as a loading control. (c) Western blot and (d) qRT-PCR analysis of BRD4 expression in inducible RNAi samples. HSP60 was used as a loading control. shDOT1L #7 and shBRD4 #498 were used in the competition assays (Figure 3c). (e) Schematic overview of the plasmids used. Flow plot examples showing cells transduced with single and combination inducible RNAi constructs expressing two different fluorophores. This strategy allowed for double knockdown within the same cell.

Supplementary Figure 5 Correlation of RNA-seq and ChIP–seq analysis of inhibitor-treated cells.

(a) GSEA using ROAST (Wu, D. et al. Bioinformatics 26, 2176–2182, 2010) of differentially expressed genes from RNA-Seq data of SGC0946 treated cells with previously published up- (red) and down-regulated (blue) genes from microarray analysis of MV4;11 cells treated with EPZ004777 (GSE29828). The shaded area in the centre of the plot shows genes ranked by log fold change in expression in SGC0946 compared with DMSO treated cells. Pink and blue shading represent significantly up- and down- regulated genes, respectively. (b) Heatmap of differential mRNA expression data from RNA-Seq of MV4;11 cells treated with I-BET, SGC0946, or combination in duplicate cell culture experiments. (c) geneGO analysis of the top ten diseases associated with the co-regulated genes. (d) Change in gene expression from RNA-Seq following BRD4 inhibition (I-BET), DOT1L inhibition (SGC0946) or a combination of BRD4 and DOT1L inhibition plotted against the changes in BRD4 binding and H3K79me2 levels in genes from ChIP-Seq. (e) Correlation between POL II and BRD4 binding or H3K79me2 levels at all genes in DMSO, I-BET and SGC0946 treated cells.

Supplementary Figure 6 ChIP–seq analysis of superenhancer-associated genes.

(a) Enhancers ranked by increasing BRD4 ChIP-Seq signal in reads per million (RPM) mapped reads. (b) ChIP-Seq profile of BRD4, H3K27ac, H3K79me2 and POL II at a typical enhancer and superenhancer indicated in Supplementary Fig 6a, in red and blue respectively. (c) Median H3K79me2, BRD4 and POL II coverage in RPKM across the co-regulated genes following I-BET, SGC0946 or combination treatment. (d) Heatmap of the expression of genes in closest proximity to superenhancers with I-BET and SGC0946 combination treatment. (e) qPCR analysis of H3K79me2 and BRD4 ChIP in MOLM-13 cells treated with SGC0946 using primers targeting CDK6, BCL2, MTHFD2 and Neg (negative control region). Mean, error bars, s.d. (n=3 technical replicates), representative graph from experiments done on 3 separate occasions

Supplementary Figure 7 SGC0946 washout experiment.

(a-b) Anti-H3K79me2 and anti-total H3 western blot. Samples are lysates from MV4;11 cells treated with DMSO or SGC0946 for 6, 8 and 10h and (a) harvested at end of treatment or (b) after washout of SGC0946, then harvested 72 hours later. (c) Proliferation Assay. Cell counts after short term treatment of MV4;11 cells with SGC0946 for 6 hours followed by wash-out or continuous treatment with SGC0946. (d-e) qRT-PCR of BCL2 expression from MV4;11 cells treated with DMSO or SGC0946 for 6, 8 and 10h and (d) harvested at end of treatment or (e) after washout of SGC0946, then harvested 72 hours later. ChIP-qPCR analysis for (f) BRD4 and (g) H3K79me2. MV4;11 cells were treated with I-BET for 6 hours, SGC0946 for 8 hours, SGC0946 for 72 hours continuously or SGC0946 for 8 hours followed by washout and assessed 72 hours later. Mean, error bars, s.d. (n=3 technical replicates), representative graph from experiments done on 3 separate occasions. (h), Median BRD4, H3K79me2 and POL II levels in RPKM across genes containing both BRD4 and H3K79me2 including 5kb upstream and downstream regions following short-term SGC0946 or SGC0946 washout treatment.

Supplementary Figure 8 Mechanism of BRD4 displacement by DOT1L inhibition.

(a) Left, Flow chart hierarchy of the numbers of expressed genes with H3K79me2 and BRD4 peaks that change their expression and have decreased H379me2 levels after SGC0946 treatment. Right, scatterplot of the log-fold change in BRD4 and H3K79me2 after SGC0946 treatment in the 175 SGC0946 regulated genes. (b) qRT-PCR of genes identified by RNA-seq as being regulated by both BRD4 depletion and DOT1L inhibition. Mean, error bars, s.d. (n = 3 experiments done on 3 separate occasions). (c) Quantitative proteomic analysis of BRD3 and BRD4 within HL-60 nuclear extracts that are bound to various biotinylated histone H3K79 peptides. BRD3 and BRD4 captured by the histone peptides were differentially quantified by isobaric tagging, as previously performed (Dawson, M.A. et al. Nature 478, 529-33, 2011). (d) ChIP-qPCR analysis of SGC0946 treated MOLM-13 cells using primers against CDK6, BCL2, MTHFD2 and Neg (Negative control region). Mean, error bars, s.d. (n=3 technical replicates), representative graph from 3 experiments done on 3 separate occasions. (e) Scatterplot of the log-fold change in H3K79me2 and H4K5ac levels after DOT1L inhibition in the 175 SGC0946 regulated genes. (f) Log-fold change in the levels of H4K5ac in the subsets of I-BET regulated, SGC0946 regulated and co-regulated genes following SGC0946 treatment. The upper limit, center and lower limit of boxplots denote the upper quartile, median and lower quartile of the data, respectively. Whiskers extend to 1.5 × interquartile range above and below the upper and lower quartiles, respectively. (g) Western blot of CREB1. Lysate from MV4;11 cells transduced with the two independent shRNAs used in Figure 7.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Note (PDF 9045 kb)

Supplementary Table 1

Comparison of I-BET and DOT1L matrix and competition with inhibitor (XLSX 1107 kb)

Supplementary Table 2

Genes co-regulated by I-BET and SGC0946 (XLSX 68 kb)

Supplementary Data Set 1

Uncropped western blots (PDF 344 kb)

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Gilan, O., Lam, E., Becher, I. et al. Functional interdependence of BRD4 and DOT1L in MLL leukemia. Nat Struct Mol Biol 23, 673–681 (2016). https://doi.org/10.1038/nsmb.3249

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