ABSTRACT Chromatin regulation involves the selective recruitment of chromatin factors to facilitate DNA repair, replication and transcription. Here we demonstrate the utility of coupling

unbiased functional genomics with chromatin immunoprecipitation (CRISPR–ChIP) to identify the factors associated with active chromatin modifications in mammalian cells. Specifically, an

integrated reporter containing a _cis_-regulatory element of interest and a single guide RNA provide a chromatinized template for a direct readout for regulators of histone modifications

associated with actively transcribed genes such as H3K4me3 and H3K79me2. With CRISPR–ChIP, we identify all the nonredundant COMPASS complex members required for H3K4me3 and demonstrate that

endogenous DOT1L complex, where catalytic activity is directed by MLLT10. Overall, CRISPR–ChIP provides a powerful tool for the unbiased interrogation of the mechanisms underpinning

chromatin regulation. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access through your institution

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CONTENT BEING VIEWED BY OTHERS H3K27ME3-RICH GENOMIC REGIONS CAN FUNCTION AS SILENCERS TO REPRESS GENE EXPRESSION VIA CHROMATIN INTERACTIONS Article Open access 29 January 2021 ASXLS BINDING

TO THE PHD2/3 FINGERS OF MLL4 PROVIDES A MECHANISM FOR THE RECRUITMENT OF BAP1 TO ACTIVE ENHANCERS Article Open access 07 June 2024 AUTOMATED CUT&TAG PROFILING OF CHROMATIN

HETEROGENEITY IN MIXED-LINEAGE LEUKEMIA Article Open access 18 October 2021 DATA AVAILABILITY Sequencing data that support the findings of this study have been deposited into the sequence

read archive Gene Expression Omnibus, hosted by the National Center for Biotechnology Information. The accession number for the sequencing data reported in this paper is National Center for

Biotechnology Information sequence read archive GSE192562. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding

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DESeq2. _Genome Biol._ 15, 550 (2014). Article  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank the Flow Cytometry facility and Molecular Genomics Core

at the Peter MacCallum Cancer Centre and the ARAFlowcore Flow facility at the Australian Centre for Blood Diseases, Monash University. We thank the following funders for fellowship,

scholarship and grant support: VCA Mid-Career Research Fellowship (O.G.); Cancer Council Victoria Sir Edward Dunlop Research Fellowship, NHMRC Investigator Grant #1196749 and Howard Hughes

Medical Institute International Research Scholarship #55008729 (M.A.D.); and NHMRC Project Grants #1146192 (O.G.), #1085015 / #1106444 (M.A.D.) and #1128984 (M.A.D.). The funders had no role

in study design, data collection and analysis, decision to publish or preparation of the manuscript. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Peter MacCallum Cancer Centre, Melbourne,

Victoria, Australia Omer Gilan, Laure Talarmain, Charles C. Bell, Kathy Knezevic, Yih-Chih Chan, Enid Y. N. Lam & Mark A. Dawson * Sir Peter MacCallum Department of Oncology, University

of Melbourne, Melbourne, Victoria, Australia Omer Gilan, Laure Talarmain, Charles C. Bell, Yih-Chih Chan, Enid Y. N. Lam & Mark A. Dawson * Australian Centre for Blood Diseases, Monash

University, Melbourne, Victoria, Australia Omer Gilan, Daniel Neville & Daniel T. Ferguson * Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash

University, Clayton, Victoria, Australia Marion Boudes & Chen Davidovich * EMBL-Australia, Clayton, Victoria, Australia Chen Davidovich * Department of Clinical Haematology, Peter

MacCallum Cancer Centre & Royal Melbourne Hospital, Melbourne, Victoria, Australia Mark A. Dawson * Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, Australia

Mark A. Dawson Authors * Omer Gilan View author publications You can also search for this author inPubMed Google Scholar * Laure Talarmain View author publications You can also search for

this author inPubMed Google Scholar * Charles C. Bell View author publications You can also search for this author inPubMed Google Scholar * Daniel Neville View author publications You can

also search for this author inPubMed Google Scholar * Kathy Knezevic View author publications You can also search for this author inPubMed Google Scholar * Daniel T. Ferguson View author

publications You can also search for this author inPubMed Google Scholar * Marion Boudes View author publications You can also search for this author inPubMed Google Scholar * Yih-Chih Chan

View author publications You can also search for this author inPubMed Google Scholar * Chen Davidovich View author publications You can also search for this author inPubMed Google Scholar *

Enid Y. N. Lam View author publications You can also search for this author inPubMed Google Scholar * Mark A. Dawson View author publications You can also search for this author inPubMed 

Google Scholar CONTRIBUTIONS O.G. and M.A.D. conceived, designed and supervised the research and wrote the manuscript. O.G., C.C.B., D.N., D.T.F., K.K., M.B. and C.D. conducted experiments

and/or analyzed data. O.G., C.C.B. and K.K. performed the CRISPR–ChIP screens. E.Y.N.L. and L.T. led the analysis of the genomic data and CRISPR–ChIP screens, with contribution from Y.-C.C.

CORRESPONDING AUTHORS Correspondence to Omer Gilan or Mark A. Dawson. ETHICS DECLARATIONS COMPETING INTERESTS M.A.D. has been a member of advisory boards for GSK, CTX CRC, Storm

Therapeutics, Celgene and Cambridge Epigenetix. The Dawson Laboratory is a recipient of grant funding through the emerging science fund administered through Pfizer. The remaining authors

declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Structural & Molecular Biology_ thanks the anonymous reviewers for their contribution to the peer review of

this work. Primary Handling Editor: Dimitris Typas, in collaboration with the _Nature Structural & Molecular Biology_ team. Peer reviewer reports are available. ADDITIONAL INFORMATION

PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 CHROMATIN

REGULATION OF INTEGRATED REGULATORY ELEMENTS. A) ChIP-qPCR analysis of H3K27ac ChIP performed in parental K562 cells or K562 cells infected with the CRISPR-ChIP plasmid containing an _EF1a_

promoter. Primers for a negative control region (Neg Ctrl), endogenous EF1a or regions spanning the gRNA/EF1a (Primer 1), and EF1a/Puro (Primer 2) within the lentiviral CRISPR-ChIP vector

were used, data represents mean +/− SD from three independent biological replicates. B) ChIP-qPCR analysis of H3K27ac, H3K4me3, H3K9ac and RNA-Pol II ChIP at the endogenous _PGK_ promoter or

integrated _PGK_ promoter-Puro reporter, an intergenic region not enriched in active histone marks was used as a negative control (Neg control). Data represents mean +/− SD from three

independent biological replicates. C) ChIP-qPCR analysis of H3K27ac, H3K4me3, H3K79me2 and RNA-Pol II ChIP at the endogenous _MEIS1_ promoter or integrated _MEIS1_ promoter (~1.2Kb upstream

of TSS)-Puro reporter, an intergenic region not enriched in active histone marks was used as a negative control (Neg control). Data represents the mean from two independent biological

replicates. D) ChIP-qPCR analysis of H3K27ac, H3K4me3, and H3K9ac ChIP at the endogenous _MYC_ enhancer or integrated _MYC_ enhancer-mCMV-Puro sequence, an intergenic region not enriched in

active histone marks was used as a negative control (Neg control). Data represents the mean from two independent biological replicates. E) ChIP-qPCR analysis of H3K79me2 in K562 cells

infected with the CRISPR-ChIP plasmid and treated with DOT1L inhibitor (SGC0946) for 9 days. Primers for Negative control region (Neg Ctrl), Meis1, EF1a, and a region spanning the gRNA/EF1a

were used, data represents mean +/− SD from three independent biological replicates. F) qPCR analysis of H3K27ac ChIP in K562 cells infected with the CRISPR-ChIP vector and treated with

either DMSO or CBP/P300 inhibitor (A-485) for 24hrs. Primers for Negative control region (Neg Ctrl), Meis1, EF1a, and a region spanning the gRNA/EF1a were used, data represents the mean from

two independent biological replicates. G) NGS representation of control sgRNAs at day 2, day 10, ChIP input day 10, and H3K79me2 ChIP (Left panel). NGS representation of DOT1L sgRNAs at day

2, day 10, ChIP input day 10, and H3K79me2 ChIP (Right panel). Source data EXTENDED DATA FIG. 2 VALIDATION OF THE CRISPR-CHIP METHOD. A) The CRISPR-ChIP chromatin library was transduced

into Cas9 negative K562 cells. H3K27ac ChIP was performed, and guide representation was assessed by NGS in two sampled replicates of library control and two independent ChIP samples. Data

shown are correlation plots of library control Rep1 vs library control Rep2 (R=0.998), H3K27ac (50x10^6) Rep1 vs H3K27ac (50x10^6) Rep2 (R=0.993), and Library control Rep1 vs H3K27ac Rep1

(50x10^6) (R=0.985). B) Correlation plot of library guide counts between H3K27ac ChIP and a library control sample taken from Cas9 negative K562 cells. H3K27ac ChIP was performed from

different starting cell number, 5 million, 10 million, 25 million, 50 million and 100 million cells. C) Bubble plot of H3K27ac ChIP (50M cells) from Cas9 negative K562 cells, P values

calculated using the MAGECK algorithm and adjusted for multiple testing. D) Correlation plot of RBBP5 KO H3K4me3 LFC vs ASH2L KO H3K4me3 LFC. LFC=Log2FC. EXTENDED DATA FIG. 3 RATIONALE FOR

CRISPR-CHIP AFTER KNOCKOUT OF ESSENTIAL GENES. A) sgRNA negative selection competition assay in K562 Cas9 cells transduced with control sgRNA or two independent sgRNAs against RNA Pol II

(Pol II). Percentage of sgRNA positive cells remaining over time. Data represents the mean from n=2 experiments. B) FACS histogram of BFP expression in K562 Cas9 cells transduced with

control sgRNA or two independent sgRNAs against RNA Pol II. Analysis performed at day 5 post infection. C) FACS scatter plot of forward scatter (FSC) vs side scatter (SSC) in K562 Cas9 cells

transduced with control sgRNA or two independent sgRNAs against RNA Pol II. Analysis performed at day 5 post infection. D) Schematic of vector systems used for sgRNA expression and

destabilised GFP (GFP-PEST) expression. E) FACS histogram of GFP expression in K562 Cas9 EF1a-GFP-PEST cells transduced with control sgRNA or two independent sgRNAs against RNA Pol II.

Analysis performed at day 5 post infection. F) FACS scatter plot of GFP vs BFP expression in K562 Cas9 EF1a-GFP-PEST cells transduced with control sgRNA or two independent sgRNAs against the

RNA Pol II. Analysis performed at day 5 post infection. G) Correlation plot of normalised Pol II levels (rpm) vs normalised H3K4me3 levels (rpm). Source data EXTENDED DATA FIG. 4

TRANSCRIPTIONAL CONSEQUENCES OF DEPLETION OF THE DOT1L COMPLEX. A) Schematic of DOTCOM complex (DOT1L, ENL/MLLT1, MLLT6/AF17, MLLT10). B) Guide counts over time from CRISPR dropout screen in

MLL-AF9 cells for 2 MLLT10 and 2 DOT1L sgRNAs. C) PCA analysis of RNA-seq from MLLAF9 Cas9 cells transduced with control (non-targeting), DOT1L sg1, two independent ENL guides, two

independent MLLT10 sgRNAs and two independent MLLT6 sgRNAs. D) Heatmap of RNA-seq data described above, showing downregulated genes in the various knockouts as indicated. E) Correlation plot

of RNA-seq LFC between two independent MLLT6 sgRNAs and DOT1L KO for the top 100 downregulated genes in the DOT1L KO cells. LFC=Log2FC. EXTENDED DATA FIG. 5 ESTABLISHMENT OF A MODEL FOR THE

RAPID DEGRADATION OF MLLAF9. A) Kaplan-Meier curve of mice transplanted with MLLAF9FLAG-mAID cells or Nras + MLLAF9FLAG-mAID. B) Genome browser snapshot of MLLAF9 ChIP-seq replicates at the

_MEIS1_ locus. FLAG ChIP from non-FLAG tagged MLLAF9 cells used as a negative control and input shown. C) Genome browser snapshot of MLLAF9 ChIP-seq replicates at the _HOXA_ cluster. FLAG

ChIP from non-FLAG tagged MLLAF9 cells used as a negative control and input shown. D) Proliferation assay of MLLAF9FLAG-mAID Tir1 cells treated with DMSO or IAA (500uM) over 7 days. Eror

bars represent mean +/− SD from three independent biological replicates. E) FACS analysis of cell surface Gr1 levels in unstained, stained, DMSO or IAA treated MLLAF9FLAG-mAID Tir1 cells for

4 days. F) Profile plot of MLLAF9 FLAG ChIP-seq in MLLAF9FLAG-mAID cells treated with DMSO, IAA (2hrs and 4hrs). G) Heatmap of 4su-seq (nascent RNA-seq) in MLLAF9FLAG-mAID Tir1 cells

treated with DMSO or IAA for 2hrs, showing 260 downregulated genes. H) PCA plot of RNA-seq from MLLAF9FLAG-mAID Tir1 cells treated with DMSO or IAA (2hrs). I) Heatmap of RNA Pol II ser2

ChIP-seq at all genes in MLLAF9FLAG-mAID Tir1 cells treated with DMSO or IAA (2hrs). J) Genome browser snapshots of MLLAF9 targets, _EPHA7 and BAZ2B_ showing RNA-Pol IIS2ph and MLLAF9

ChIP-seq tracks after treatment with either DMSO or IAA (2hrs). Source data EXTENDED DATA FIG. 6 MENIN AND DOT1L INHIBITION EVICTS MLLAF9 AND MENIN FROM CHROMATIN. A) Heatmap of Menin

ChIP-seq in MLLAF9 cells treated with DMSO, SGC0946 (72hrs) or VTP50469 (48hrs) at all genes. B) MLLAF9 FLAG ChIP-seq in MLLAF9 cells treated with DMSO, IBET151 or IBET-VHL for 8hrs at all

genes. C) PCA analysis of RNA-seq data in MLLAF9 cells treated with DMSO, SGC0946 (72hrs), IAA (24hrs), VTP50469 (24hrs). D) Heatmap of H3K79me2 ChIP-seq analysis in MLLAF9-F3-mAID cells

treated with either DMSO, SGC0946 (6hrs), SGC0946 (48hrs), VTP50469 (6hrs), and VTP50469 (48hrs). E) Genome browser snapshot of the _MEIS1_ locus from the H3K79me2 ChIP-seq in (D). EXTENDED

DATA FIG. 7 H3K79ME2 IS SELECTIVELY LOST AFTER PROLONGED MENIN INHIBITION. A) Profile plot of MLLAF9 ChIP-seq in MLLAF9 cells treated with either DMSO, VTP50469 (6hrs), SGC0946 (48hrs) or

VTP50469 (48hrs) B) Heatmap of MLLAF9 and H3K79me2 ChIP-seq data in MLLAF9 cells after treatment with VTIP50469 for 48hrs at genes that show decreased H3K79me2 after VTP50469 treatment. C)

Genome browser snapshot of ChIP-seq tracks at canonical MLLAF9 target genes, _JMJD1C and RUNX2_, showing MLL-AF9 and H3K79me2 after treatment with VTP50469 or SGC0946 for 48hrs. EXTENDED

DATA FIG. 8 H3K79ME2 IS RETAINED AT MLLAF9 BOUND REGIONS IN MLLT10 KO CELLS. A) Average profile plot of H3K79me2 and input at all genes in MLL-AF9 cells infected with non-silencing guides

(Control), 2 independent DOT1L sgRNAs and 2 independent MLLT10 sgRNAs. B) Heatmap of H3K79me2 at direct MLL-AF9 target genes (from nascentRNA-seq after IAA treatment) for the indicated

samples. Genes are ranked from highest to lowest coverage of H3K79me2 in the control cells. C) Genome browser snapshot of a canonical MLLAF9 target, _HOXA9_, from H3K79me2 ChIP-seq in MLLAF9

cells transduced with control sgRNA or two independent targeting MLLT10 or DOT1L. D) Correlation plot of LFC H3K79me2 in MLLT10 depleted vs control cells for sgRNA 1 vs sgRNA 2 (left panel)

and sgRNA1 vs sgRNA 3 (right panel). E) Genome browser snapshots of exemplar direct and strongly bound MLL-AF9 target genes that show increased H3K79me2 following MLLT10 KO in MLL-AF9

cells, _Jmjd1c and Epha7_. F) Genome browser snapshot of exemplar direct MLLAF9 target genes (Meis1, Tsc22d2) in control or Cre deleted MLLT10 in murine MLLAF9 cells from Deshpande et al14.

G) Immunoblot analysis of gel filtration fractions (superose 6 column) from nuclear extracts of MV4;11 cells (human MLL-AF4) using antibodies against DOT1L, MLLT10, MLL1, and ENL. Fractions

from smallest to largest. Representative image from two independent experiments. H) Immunoblot analysis of gel filtration fractions (superose 6 column) from nuclear extracts of MOLM13 cells

(human MLL-AF9) using antibodies against DOT1L, MLLT10, MLL1 and ENL. Fractions from smallest to largest. Representative image from two independent experiments. Source data EXTENDED DATA

FIG. 9 MLLT10 DEPLETION SENSITISES CELLS TO MENIN INHIBITION. A) Heatmap of RNA-seq data from MLLAF9 Cas9 cells infected with control or MLLT10 sgRNAs and treated with either DMSO or

VTP50469 with 20 or 40nM. B) Proliferation assays using MLL-AF9 Cas9 cells transduced with either a control non-silencing sgRNA or two independent sgRNAs targeting MLLT10 (sg1 and sg2).

Cells were treated with either DMSO, VTP50469 25nM or 50nM. Plots represent n=3 biological replicates. Error bars represent mean +/− SD C) Proliferation assay in control and MLLT10 KO

MLLAF9FLAG-mAID Cas9 cells treated with either DMSO, VTP50469 (10nM) or SGC0946 (1uM) + VTP50469 (10nM). Cell counts shown at day 3 and day 7. Data represents mean +/− SD from n=3 biological

replicates. D) Profile plot of H3K79me2 ChIP-seq in MLLAF9 Cas9 cells infected with control or MLLT10 guides and treated with DMSO or VTP50469 (20nM) for 48hrs. Analysis performed across

MLLAF9 target genes. E) Proliferation assay in MOLM13 cells treated with either DMSO, SGC0946 (3uM), VTP50469 (100nM) or combination. Mean +/− SD from n=3 biological replicates. F)

Proliferation assay in MLLAF9FLAG-mAID Cas9 cells treated with either DMSO, SGC0946 (3uM), VTP50469 (50nM) or combination. Mean +/− SD from n=3 biological replicates. Source data

SUPPLEMENTARY INFORMATION REPORTING SUMMARY PEER REVIEW FILE SUPPLEMENTARY TABLES Supplementary Table 1: Human and mouse chromatin sgRNA library. Supplementary Table 2: CRISPR–ChIP H3K4me3

raw counts and MAGECK analysis. Supplementary Table 3: CRISPR–ChIP H3K79me2 raw counts and MAGECK analysis. Supplementary Table 4: Day 14–day 2 CRISPR screen dropout MAGECK analysis.

Supplementary Table 5: ChIP–qPCR and primary transcript qPCR primers. SOURCE DATA SOURCE DATA FIG. 1 Statistical source data. SOURCE DATA FIG. 1 Unprocessed western blots. SOURCE DATA FIG. 2

Unprocessed western blots. SOURCE DATA FIG. 3 Statistical source data. SOURCE DATA FIG. 3 Unprocessed western blots. SOURCE DATA FIG. 4 Unprocessed western blots. SOURCE DATA FIG. 5

Statistical source data. SOURCE DATA FIG. 7 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 1 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 3 Statistical source data.

SOURCE DATA EXTENDED DATA FIG. 5 Statistical source data. SOURCE DATA EXTENDED DATA FIG. 8 Unprocessed western blots. SOURCE DATA EXTENDED DATA FIG. 9 Statistical source data. RIGHTS AND

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ARTICLE CITE THIS ARTICLE Gilan, O., Talarmain, L., Bell, C.C. _et al._ CRISPR–ChIP reveals selective regulation of H3K79me2 by Menin in MLL leukemia. _Nat Struct Mol Biol_ 30, 1592–1606

(2023). https://doi.org/10.1038/s41594-023-01087-4 Download citation * Received: 03 January 2023 * Accepted: 03 August 2023 * Published: 07 September 2023 * Issue Date: October 2023 * DOI:

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