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Mutational synergy during leukemia induction remodels chromatin accessibility, histone modifications and three-dimensional DNA topology to alter gene expression

Nature Genetics volume 53, pages 1443–1455 (2021)Cite this article

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Abstract

Altered transcription is a cardinal feature of acute myeloid leukemia (AML); however, exactly how mutations synergize to remodel the epigenetic landscape and rewire three-dimensional DNA topology is unknown. Here, we apply an integrated genomic approach to a murine allelic series that models the two most common mutations in AML: Flt3-ITD and Npm1c. We then deconvolute the contribution of each mutation to alterations of the epigenetic landscape and genome organization, and infer how mutations synergize in the induction of AML. Our studies demonstrate that Flt3-ITD signals to chromatin to alter the epigenetic environment and synergizes with mutations in Npm1c to alter gene expression and drive leukemia induction. These analyses also allow the identification of long-range cis-regulatory circuits, including a previously unknown superenhancer of Hoxa locus, as well as larger and more detailed gene-regulatory networks, driven by transcription factors including PU.1 and IRF8, whose importance we demonstrate through perturbation of network members.

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Fig. 1: Murine models show transcriptional synergy of AML mutations.
Fig. 2: Both FLT3-ITD and Npm1c alter chromatin accessibility.
Fig. 3: Flt3-ITD but not Npm1c remodels the enhancer landscape.
Fig. 4: Leukemia programs use new and existing 3D contacts.
Fig. 5: Integrated analyses identifies critical regulatory networks.
Fig. 6: Integrated analyses identifies critical transcriptional networks nodes.
Fig. 7: Network perturbation abrogates leukemia maintenance.

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Data availability

All sequencing raw data, normalized bigwig tracks for RNA-seq, ChIP-seq and ATAC-seq have been deposited in the GEO database under the series GSE146669 (subseries GSE146668 for RNA-seq, GSE146663 for ChIP-seq, GSE146613 for ATAC-seq and GSE146662 for pCHiC) and with no restrictions to access. All supporting data derived from the sequencing analysis to assist understanding of the results and discussions in the paper were provided in several supplementary tables. The studies have also reanalyzed multiple datasets that are publicly available: the ChIP-seq on BRD4 (ArrayExpress: ERR220396) and H3K27ac (GSM2716711) in OCI-AML3 cells; H3K27ac in Kasumi-1 cells (GSM2212053); H3K4me1 (GSM1816068) and CTCF (GSM651541) in human CD34+ HSPC; the DHS-seq in AML patients (GSM2893610, GSM2893614, GSM2893615 and GSM2893616); the ATAC-seq in human CD34+ HSPC (GSM1888536); the pCHiC from human CD34+ cells (ArrayExpress: ERR436027); H3K4me1 (GSM14412890), H3K27ac (GSM1441273) and ATAC-seq (GSM1463173) in mouse GMP cells. Source data are provided with this paper.

Code availability

All computational analysis is described in Methods with either the software default parameters and pipelines or custom code available at https://github.com/haiyang-yun/3D_chromatin_in_AML (archived also on Zenodo: https://doi.org/10.5281/zenodo.5009065)78.

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Acknowledgements

This study was carried out in the laboratory of B.J.P.H. with funding from Cancer Research UK (C18680/A25508), the European Research Council (647685), MRC (MR-R009708-1), the Kay Kendall Leukaemia Fund (KKL1243), the Wellcome Trust (205254/Z/16/Z) and the Cancer Research UK Cambridge Major Centre (C49940/A25117). This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), and was funded in part by the Wellcome Trust, who supported the Wellcome – MRC Cambridge Stem Cell Institute (203151/Z/16/Z) and Cambridge Institute for Medical Research (100140/Z/12/Z). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. D.L.-A. is Marie-Skłodowska-Curie International Fellow (886474). D.S. was a postdoctoral fellow of the Mildred-Scheel Organization, German Cancer Aid (111875). P.G. is funded by a Cancer Research UK Advanced Clinician Scientist Fellowship (C57799/A27964) and was previously funded by a Wellcome Trust fellowship (109967/Z/15/Z) and an ASH Global Research Award. G.S.V. is a CRUK Senior Cancer Research Fellow (C22/324/A23015) and work in his laboratory was supported by Blood Cancer UK (17006). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. We acknowledge M. Dawes in the Department of Hematology for interlab communications and organizational assistance, and thank M. Paramor in the NGS library facility at Cambridge Stem Cell Institute for the help with preparation of RNA-seq libraries and the Cancer Research UK (CRUK) Cambridge Institute Genomics Core for providing the NGS services.

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Author notes

  1. Annalisa Mupo

    Present address: Epigenetics Programme, The Babraham Institute, Cambridge, UK

Authors and Affiliations

  1. Wellcome - MRC Cambridge Stem Cell Institute, Cambridge, UK

    Haiyang Yun, Nisha Narayan, Shabana Vohra, George Giotopoulos, Annalisa Mupo, Pedro Madrigal, Daniel Sasca, David Lara-Astiaso, Sarah J. Horton, Shuchi Agrawal-Singh, Eshwar Meduri, Faisal Basheer, Ludovica Marando, Malgorzata Gozdecka, Oliver M. Dovey, Aracely Castillo-Venzor, Xiaonan Wang, Paolo Gallipoli, George S. Vassiliou & Brian J. P. Huntly

  2. Department of Haematology, University of Cambridge, Cambridge, UK

    Haiyang Yun, Nisha Narayan, Shabana Vohra, George Giotopoulos, Annalisa Mupo, Pedro Madrigal, Daniel Sasca, David Lara-Astiaso, Sarah J. Horton, Shuchi Agrawal-Singh, Eshwar Meduri, Faisal Basheer, Ludovica Marando, Malgorzata Gozdecka, Oliver M. Dovey, Xiaonan Wang, Paolo Gallipoli, George S. Vassiliou & Brian J. P. Huntly

  3. Department of Medicine V, Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany

    Haiyang Yun & Carsten Müller-Tidow

  4. Haematological Cancer Genetics, Wellcome Sanger Institute, Cambridge, UK

    Annalisa Mupo, Malgorzata Gozdecka, Oliver M. Dovey & George S. Vassiliou

  5. Department of Hematology, Oncology and Pneumology, University Medical Center Mainz, Mainz, Germany

    Daniel Sasca

  6. Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK

    Paolo Gallipoli

  7. Department of Medical and Molecular Genetics, King’s College London, London, UK

    Cameron S. Osborne

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  1. Haiyang Yun
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Contributions

H.Y. and B.J.P.H. conceived the study, designed the experiments and prepared the manuscript. H.Y. designed and conducted most of the experiments and analyzed the data. N.N. performed the CRISPR experiments in human leukemic cell lines. S.V. executed the NGS data alignment and helped with bioinformatic analysis. G.G. performed the flow cytometry experiments. A.M. and G.S.V. coordinated mouse tissues. P.M. performed the BaGFoot analysis. D.S., D.L.-A., S.J.H., S.A.-S., F.B., L.M., A.C.-V. and P.G. provided technical assistance. E.M. and X.W. helped with computational analysis. M.G. and O.M.D. provided the DM-Cas9 cells. C.M.-T. participated in the discussion of the project. C.S.O. provided expertise on promoter capture HiC assays. B.J.P.H supervised all the work and directed the project.

Corresponding author

Correspondence to Brian J. P. Huntly.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Genetics thanks Charles Mullighan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Transcriptional changes across WT and mutant HSPC.

a, Heatmap showing unsupervised clustering analysis of global gene expression in WT and mutant HSPC. b, Top 10 positively enriched gene ontology (GO) gene sets revealed by GSEA for each mutant (vs WT). c, Commonly enriched gene sets from GSEA analysis of differential gene expression by Npm1c and DM. d, GSEA enrichment plots showing gene set of Kong_E2F3_Targets in Npm1c or DM vs WT by GSEA analysis. e, Gene sets positively or negatively enriched in all three mutants vs WT.

Extended Data Fig. 2 Global chromatin accessibility across WT and mutant HSPC.

a, Heatmaps and profile plots of ATAC-seq enrichment across WT and mutant HSPC over regions with gain or loss of accessibility in the presence of Npm1c. Peaks were ranked by average enrichment across all samples. b, Chromatin accessibility at the Gata2 genes and its upstream enhancers in all four HSPC and wildtype neutrophils. Regions showing loss of accessibility in Npmc1 and DM HSPC are highlighted. c, Linking differential accessibility at gene promoters to their mRNA expression changes by each mutant. Up- or downregulation were defined by setting adjP (two-tailed and multiple testing corrected) < 0.05 and absolute FC ≥ 1.5. d-f, De novo motifs significantly enriched at genomic regions with altered accessibility by Npm1c (d) and Flt3-ITD (e) or open chromatin sites with static accessibility (f) where FDR of differential analysis > 0.2 in any mutant vs WT HSPC. HOMER outputs motifs with target coverage > 10% and ranked by p values (one-tailed, not multiple testing corrected). g and h, BaGFoot analysis illustrates TFs with differential footprint depth and accessibility in Npm1c (g) and Flt3-ITD (h) vs WT HSPC. Motifs outside the fence and with a p value (two-tailed, not multiple testing corrected) < 0.05 are statistically significant outliers; n.s., not significant.

Extended Data Fig. 3 Chromatin modifications at accessible enhancers across WT and mutant HSPC.

a, Distribution of all H3K4me3 peaks based on read counts across all four cell types. Active promoters were marked by high H3K4me3 with log2 CPM > 4. b, Genome distribution of ATAC-seq consensus peaks. c, Number of total accessible enhancer peaks defined in all four HSPC samples and percentage of dynamic enhancers which were defined by differential H3K4me1 in the presence of any mutations. d, Numbers of overlapping accessible enhancers with gain or loss of H3K4me1 in the presence of Flt3-ITD or DM. Hypergeometric test p values (one-tailed, not multiple testing corrected) are shown. e, Heatmaps of H3K4me1 enrichment over gained or lost enhancers during DM leukemia induction. Peaks were ranked by average enrichment across all samples. f, Percentage of accessible enhancers marked by both H3K4me1 and H3K27ac in total accessible enhancers across all four HSPC states. g, Percentage of enhancers showing dynamic H3K27ac modification in the presence of single or double mutations. h. Heatmaps and profile plots of H3K27ac enrichment over enhancers showing gain or loss of H3K27ac during DM leukemia induction. Peaks were ranked by average enrichment across all samples.

Extended Data Fig. 4 Immunophenotypic characterization of HSPCs isolated from bone marrow of wildtype and mutant mice.

a, Representative flow cytometry plots showing proportions of hematopoietic stem and progenitors within bone marrow compartment. Lin-, lineage negative; HPC, hematopoietic progenitors (Lin−Sca1−cKit+) containing all myeloid progenitors; LSK, Lin−Sca1+cKit+; CMP, common myeloid progenitors; GMP, granulocyte/macrophage progenitors; MEP, megakaryocyte/erythroid progenitors. b, Comparison of proportions of hematopoietic stem and progenitors in their parental populations in mutant mice (Npm1flox-cA/+;Mx1-Cre, n = 9; Flt3ITD/+, n = 8; Npm1flox-cA/+;Mx1-Cre;Flt3ITD/+, n = 9) compared with wildtype mice (WT, n = 7). Student’s unpaired t-tests (two-sided) were performed for the comparisons; data are presented as mean values ± standard deviation; Only the p values < 0.05 were shown.

Extended Data Fig. 5 3D chromatin interaction profiles across WT and mutant HSPC.

a, Numbers of total and high-confidence pCHiC interactions in all four HSPC states. High-confidence interactions were defined as significant interactions (with CHiCAGO score ≥ 5) overlapping in both replicates of each cell type. b, Numbers of pCHiC interactions captured by individual promoters. c, Distance of interacting regions from their target promoters. Median distance was shown. d, Illustration of chromatin compartments A/B levels at a DM-induced ‘B to A’ flipped region (highlighted) containing the Igf1 oncogene. e. Illustration of chromatin compartments A/B levels at a DM-induced ‘B to A’ flipped region (highlighted) containing an Ifi (interferon-inducible) gene cluster (in red). f and g, Significant interactions, represented by arcs, associated with the Gfi1b (f) or Irf8 (g) promoters in different cell types.

Extended Data Fig. 6 Integrative analysis with Seurat-guided clustering of chromatin profiles across WT and mutant HSPC.

a, Profile plots of chromatin marks and accessibility at accessible regions in each of the 10 clusters (except for Cluster-6 and -10). b, De novo motifs significantly enriched at Clusters 7–9 chromatin regions. HOMER outputs motifs with target coverage > 10% and ranked by p values (one-tailed, not multiple testing corrected). c, GO analysis of genes that are linked to the chromatin regions in Cluster-10 using pCHiC profiles and downregulated during leukemia induction. d, Volcano plot showing differential expression of Cluster-10 linked genes in DM vs WT HSPC. Up- or downregulation were defined by setting adjP (two-tailed and multiple testing corrected) < 0.05 and absolute FC ≥ 1.5. e, Percentage of Cluster-6 genes with human overlap showing their expression in DM vs WT HSPC. Differential expression was defined by setting adjP (two-tailed and multiple testing corrected) < 0.05 and absolute FC ≥ 1.5.

Extended Data Fig. 7 Correlating enhancer alterations with differential expression of target genes or DNA topology changes.

a, Percentage of dynamic enhancers interacting with gene promoters and the percent of up- or downregulation of the genes they linked to. Dynamic enhancers were marked by either differential H3K4me1 only, or together with differential H3K27ac and accessibility. b, Percentage of genes involving flipped chromatin compartments and expressed in DM vs WT HSPC. Differential expression was defined by setting adjP (two-tailed and multiple testing corrected) < 0.05 and absolute FC ≥ 1.5. c, Percentage of enhancers with dynamic interactions showing their H3K4me1 read counts in DM vs WT HSPC. Differential H3K4m1 levels were defined by setting FDR (two-tailed and multiple testing corrected) < 0.05 and FC ≥ 1.5. d, Example genomic region demonstrating correlation of H3K4me1 changes with ‘rewired’ interactions.

Extended Data Fig. 8 Differential Super-enhancer usage across WT and mutant HSPC.

a, Definition of super-enhancers (SEs) by ranking H3K27ac peaks that were overlapped in both replicates of each cell type based on normalised H3K27 counts. The top-ranked 801 regions were considered as SEs across four HSPC. b, Number of super-enhancers (SE) with increase (UP) or decrease (DOWN) in H3K27ac modification in mutant vs WT HSPC. c, Heatmaps and profile plots of H3K27ac enrichment in WT and mutant HSPC over SEs showing gain or loss of H3K27ac during DM leukemia induction. d, Linking differential activity of SEs to altered mRNA expression of their target genes (determined by pCHiC interactions) in mutant vs WT HSPC. Upregulated genes connecting to SEs with H3K27ac gain or downregulated genes connecting to SEs with H3K27ac loss were determined by setting adjP (two-tailed and multiple testing corrected) < 0.05 and FC ≥ 1.5 for expression, or FDR (two-tailed and multiple testing corrected) < 0.05 and FC ≥ 1.5 for H3K27ac. Several Hoxa genes were indicated in Npm1c vs WT. e, Significantly enriched GO terms for genes linked to gained SEs and upregulated in DM vs WT (upper panel) or for genes linked to lost SEs and downregulated (lower panel). f and g, Promoter-contact plots showing the read counts of promoter bait to target pairs (bait-‘other end’) for Spi1 (f) and Hoxa9/Hoxa10 (g). Regions highlighted yellow show rewired interactions.

Extended Data Fig. 9 Perturbation of critical cis-regulatory hubs and their target genes abrogates leukemia maintenance in mouse DM leukemia.

a, mRNA expression of AP-1 components (Jun, Fos) detected by RT-qPCR in DM cells expressing shRNAs targeting Jun or Fos relative to control shRNA (n = 3 independent experiments). b, CFU assays of DM cells expressing shRNAs targeting Jun or Fos or control gRNAs (n = 3 independent experiments). c, mRNA expression of Hoxa9, Hoxa10, and Spi1 in DM cells expressing shRNAs targeting them specifically relative to control shRNA (n = 3 independent experiments). d and e, CFU assays (d) and ex vivo cell proliferation (e) of DM cells expressing shRNAs targeting Hoxa9, Hoxa10, Spi1 or control shRNA (n = 3 independent experiments). f and i, Gel electrophoresis of PCR products on genomic DNA of DM-Cas9 cells expressing control gRNAs, Spi1-URE (f) or Hoxa-LRSE (i) gRNAs. PCR experiment was performed once on three independent gRNA transductions (marked with #). g and j, Sanger sequencing to confirm the deletion of Spi1-URE (g) or Hoxa-LRSE (j) in DM-Cas9 cells expressing Spi1-URE gRNAs. h and k, Ex vivo cell growth of DM-Cas9 cells expressing Spi1-URE gRNAs (h) or Hoxa-LRSE gRNAs (k) in comparison to cells expressing control gRNAs (n = 3 independent experiments). Statistical analyses in a-d, h and k were performed by running Student’s unpaired t-tests and p values (two-sided, not multiple testing corrected) were shown; error bars represent mean and standard error of mean.

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Extended Data Fig. 10 Perturbation of critical target genes impairs human leukemia cells.

a and b, Relative mRNA expression of target gene expression upon CRISPR-mediated knockdown in OCI-AML3 (a) and MOLM-13 (b) leukemia cell lines (n = 3 independent experiments). c-e, Cell proliferation of MOLM-13 cells expressing human gRNAs targeting SPI1 (c), IRF8 (d) and IGF1 (e) relative to control gRNAs (n = 3, 4, 3 independent experiments, respectively). # indicates two independent gRNAs. f, CFU assays of MOLM-13 cells expressing human gRNAs targeting SPI1, IRF8 and IGF1 relative to control gRNAs (n = 3 independent experiments). Statistical analyses in a-f were performed by running Student’s unpaired t-tests and p values (two-sided, not multiple testing corrected) were shown; error bars represent mean and standard error of mean.

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Supplementary Note and Figs. 1–3.

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Raw gel electrophoresis image for Extended Data Fig. 9f,i.

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Yun, H., Narayan, N., Vohra, S. et al. Mutational synergy during leukemia induction remodels chromatin accessibility, histone modifications and three-dimensional DNA topology to alter gene expression. Nat Genet 53, 1443–1455 (2021). https://doi.org/10.1038/s41588-021-00925-9

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