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Molecular cell

Complex-dependent histone acetyltransferase activity of KAT8 determines its role in transcription and cellular homeostasis.

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Article Details

Authors

Aliaksandra Radzisheuskaya, Pavel V Shliaha, Vasily V Grinev, Daria Shlyueva, Helene Damhofer, Richard Koche, Vladimir Gorshkov, Sergey Kovalchuk, Yingqian Zhan, Keli L Rodriguez, Andrea L Johnstone, Michael-C Keogh, Ronald C Hendrickson, Ole N Jensen, Kristian Helin

Journal

Molecular cell

PM Id

33657400

DOI

10.1016/j.molcel.2021.02.012

Table of Contents

Complex-Dependent Histo

Highlights

Article

Complex-Dependent Histone Acetyltransferase

Aliaksandra Radzisheuskaya,1,2,3 Pavel V. Shliaha,4,5,10 Vasily V. Grinev,6,10 Daria Shlyueva,1,2,3 Helene Damhofer,1,2,3 Richard Koche,7 Vladimir Gorshkov,4 Sergey Kovalchuk,4,9 Yingqian Zhan,7 Keli L. Rodriguez,8 Andrea L. Johnstone,8 Michael-C Keogh,8 Ronald C. Hendrickson,5 Ole N. Jensen,4 And Kristian Helin1,2,3,11,*

SUMMARY

INTRODUCTION

RESULTS

Limitations

KEY RESOURCES TABLE

Continued

RESOURCE AVAILABILITY

Lead Contact

Materials Availability

Data And Code Availability

EXPERIMENTAL MODEL AND SUBJECT DETAILS

METHOD DETAILS

Plasmid And SgRNA Cloning

Flow Cytometry

RNA-Seq

RNA-Seq Data Analysis

Enrichment Tests

Chromatin Immunoprecipitation (ChIP) And Sequencing

MNase Assay

ATAC-Seq

Histone PTM Analysis

NSL Complex Interactomics Analysis

Global Proteomics Profiling

Luminex

Cell Cycle Analysis

HiC Analysis

QUANTIFICATION AND STATISTICAL ANALYSIS

Article

Complex-dependent histo

ne acetyltransferase activity of KAT8 determines its role in transcription and cellular homeostasis Graphical abstract MSL1 MSL2 MSL3 KAT8 KAT8 NSL complex KAT8 KANSL1 KANSL2 KANSL3MCRS1 PHF20 WDR5 PHF20L1 HCFC1 MSL complex TSS H4K5ac H4K8ac Pol II Initiation factors (e.g. TAF1,TAF3) NSL complex A B HiC 10 MB H4K16ac MSL complex

Highlights

d The NSL but not the MSL complex is essential for the proliferation of human cells d The NSL complex places H4K5ac and H4K8ac at TSSs, promoting transcription initiation d H4K16ac is a highly abundant modification placed by KAT8 as part of the MSL complex d H4K16ac marks open chromatin but does not affect global chromatin accessibility Radzisheuskaya et al., 2021, Molecular Cell 81, 1749–1765 April 15, 2021 ª 2021 Elsevier Inc. https://doi.org/10.1016/j.molcel.2021.02.012 Authors Aliaksandra Radzisheuskaya, Pavel V. Shliaha, Vasily V. Grinev, ..., Ronald C. Hendrickson, Ole N. Jensen, Kristian Helin Correspondence helink@mskcc.org In brief Radzisheuskaya et al. report that histone acetyltransferase KAT8 switches functions depending on associated proteins. It catalyzes promoter- associated H4K5 and H4K8 acetylation as part of the NSL complex, which is required for the activation of essential genes. As part of the MSL complex, it places H4K16 acetylation marking all open chromatin. ll ll

Article

Complex-dependent histone acetyltransferase

activity of KAT8 determines its role in transcription and cellular homeostasis

1Cell Biology Program and Center for Epigenetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA 2Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen N 2200, Denmark 3The Novo Nordisk Foundation Center for Stem Cell Biology (Danstem), University of Copenhagen, Copenhagen N 2200, Denmark 4Department of Biochemistry and Molecular Biology, VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense 5230, Denmark 5Microchemistry and Proteomics Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA 6Department of Genetics, Faculty of Biology, Belarusian State University, Minsk 220030, Belarus 7Center for Epigenetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA 8EpiCypher Inc., Durham, NC 27709, USA 9Laboratory of Bioinformatic Methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russian Federation 10These authors contributed equally 11Lead contact *Correspondence: helink@mskcc.org https://doi.org/10.1016/j.molcel.2021.02.012

SUMMARY

Acetylation of lysine 16 on histone H4 (H4K16ac) is catalyzed by histone acetyltransferase KAT8 and can prevent chromatin compaction in vitro. Although extensively studied in Drosophila, the functions of H4K16ac and two KAT8-containing protein complexes (NSL and MSL) are not well understood in mammals. Here, we demonstrate a surprising complex-dependent activity of KAT8: it catalyzes H4K5ac and H4K8ac as part of the NSL complex, whereas it catalyzes the bulk of H4K16ac as part of the MSL complex. Furthermore, we show thatMSL complex proteins andH4K16ac are not required for cell proliferation and chromatin accessibility, whereas the NSL complex is essential for cell survival, as it stimulates transcription initiation at the promoters of housekeeping genes. In summary, we show that KAT8 switches catalytic activity and function depending on its associated proteins and that, when in the NSL complex, it catalyzes H4K5ac and H4K8ac required for the expression of essential genes.

INTRODUCTION

Histone acetylation is well known for its association with transcriptionally active chromatin. It is assumed to contribute to open chromatin directly, by introducing structural barriers to compaction, and indirectly, by providing a platform for binding of proteins with specific reader domains. Most evidence for direct regulation of chromatin compaction stems from research on lysine 16 acetylation of histone H4. Specifically, H4K16ac was shown to prevent nucleosome array assembly in vitro (Allahverdi et al., 2011; Liu et al., 2011; Robinson et al., 2008; ShogrenKnaak et al., 2006), potentially by increasing disorder of the H4 tail basic patch, which, in turn, decreases H4K16 interactions with adjacent nucleosomes (Zhang et al., 2017; Zhou et al., 2012). Despite strong in vitro evidence, the role of H4K16ac in Mole regulating chromatin accessibility in vivo has not been demonstrated. H4K16ac is catalyzed by theMYST-family histone acetyltransferase KAT8, also known as MOF or MYST1 (Akhtar and Becker, 2000; Hilfiker et al., 1997). KAT8 serves as a catalytic subunit of two independent protein complexes conserved from Drosophila to mammals: MSL (male-specific lethal) and NSL (non-specific lethal). In Drosophila, the MSL complex is required for sex chromosome dosage compensation in males and, thereby, their viability (Belote and Lucchesi, 1980; Hilfiker et al., 1997; Tanaka et al., 1976). It places widespread H4K16ac on the male X chromosome, which increases expression of its genes 2-fold (Larschan et al., 2011; Lucchesi and Kuroda, 2015). In contrast, depletion of the NSL complex is lethal in both Drosophila sexes (Mendjan et al., 2006). The NSL complex was found to bind to cular Cell 81, 1749–1765, April 15, 2021 ª 2021 Elsevier Inc. 1749 0 1.5 KANSL3 ChIP WT KANSL3 ChIP KANSL3 KD TSS TES Av er ag e no rm al iz ed si gn al B C D | 5022000 | 5027000 | 5032000 | 5037000chr16 | 69748000 | 69757000 | 69766000 | 69775000chr16 NAGPA NAGPA-AS1 NOB1 WWP2 KANSL3 ChIP WT KANSL3 ChIP KANSL3 KD H3K4me3 ChIP WT KANSL3 ChIP WT KANSL3 ChIP KANSL3 KD H3K4me3 ChIP WT 0 5 0 5 0 80 0 7 0 7 0 80 E KA NS L1 KA NS L2 KA NS L3 MC RS 1 PH F2 0 PH F2 0L 1 W DR 5 HC FC 1 KA T8 MS L1 MS L2 MS L3 MC M2 Ne gC trl 0.0 0.2 0.4 0.6 0.8 1.0 1.2 % B FP no rm al is ed to D ay 3 an d ne ga tiv e co nt ro l Day 3 Day 7 Day 10 Day 13 NSL complex KO sgRNAs Control sgRNAs MSL complex KO sgRNAs KANSL1 KANSL2 KANSL3 MCRS1 PHF20L1 PHF20 HCFC1 WDR5 3xFLAGKANSL2 (8) 3xFLAGKANSL3 (11) 8 0 3 0 OGT HSPA5 PPM1G 0 4 KAT8 MSL1 MSL2 MSL3 3xFLAGKAT8 (12) log2FC > 2, q value <= 0.05, # peptides > 2 0 −2 −1 0 K A N S L1 D ep en de nc y sc or e K A N S L3 K A N S L2 M C R S 1 P H F2 0 P H F2 0L 1 W D R 5 H C FC 1 K A T8 M S L1 M S L2 M S L3 NSL complex members members MSL complex Stu ffe r c on tro l 3x FL AG -K AN SL 2 3x FL AG -K AN SL 3 0.000 0.002 0.004 0.006 0.008 0.010 0.012 R el at iv e ab un da nc e KAT8 * * G A F MSL1 MSL2 MSL3 KAT8 KAT8 NSL complex MSL complex KAT8 KANSL1 KANSL2 KANSL3MCRS1 PHF20 WDR5 PHF20L1 HCFC1 2500 bp 40 2500 bp KA N SL 3 pe ak s (n =3 73 ) KANSL3 ChIP KANSL3 KD KANSL3 ChIP WT KANSL3 peak summit KANSL3 peak summit q va lu e 400 300 200 100 0N um be ro fK AN SL 3 pe ak s Distance to the nearest H3K4me3 peak, Mb 43210 H (legend on next page) 1750 Molecular Cell 81, 1749–1765, April 15, 2021 promoters of housekeeping genes in Drosophila cells and to stimulate expression of a subset of these genes (Feller et al., 2012; Lam et al., 2012; Raja et al., 2010). Particularly, NSL complex depletion was shown to reduce RNA polymerase II (Pol II) (Gaub et al., 2020; Lam et al., 2012), TBP (Lam et al., 2012), TFIIB (Lam et al., 2012), and BRD4 (Gaub et al., 2020) binding and to induce loss of nucleosome-free regions at target promoters (Lam et al., 2019). Although it is currently unknown how the NSL complex directly contributes to this regulation, many attribute these phenotypes to H4K16 acetylation, as the main catalytic activity of KAT8 (Gaub et al., 2020; Sheikh et al., 2019). Studies in mammalian cells have shown that loss of KAT8 and consequent global loss of H4K16ac lead to a number of phenotypes, including a deficient DNA damage response (Sharma et al., 2010), reduced fatty acid oxidation and mitochondrial respiration (Khoa et al., 2020), cell death and apoptosis (Li et al., 2012; Sheikh et al., 2016; Yin et al., 2017), and autophagy (F€ullgrabe et al., 2013). Although Kat8 / mouse embryos exhibit a global loss of H4K16ac and die at implantation (Gupta et al., 2008; Thomas et al., 2008), the precise regulation of these processes by different KAT8-associated complexes is not well understood. Previous results in mouse embryonic stem cells (ESCs) show that, similarly to Drosophila, the NSL and MSL complexes have different genomic localizations and affect different sets of genes: the NSL complex is enriched at promoter regions, while the MSL complex is associated with gene bodies and intergenic regions (Ravens et al., 2014). Additionally, the bulk of H4K16ac in mammalian cells is produced by the MSL complex, but depletion of the NSL complex has a stronger effect on ESC proliferation (Chelmicki et al., 2014; Ravens et al., 2014). If both KAT8-associated complexes function through H4K16ac, a critical question is how one specific modification exerts different cellular effects depending on genome localization. It could be that members of the NSL and MSL complexes have additional, non-overlapping functions, for instance recruiting different interacting partners. Alternatively, KAT8-containing complexes could have distinct, previously uncharacterized, substrate specificities. In fact, in vitro histone acetylation studies have shown that KAT8 can catalyze H4K16ac as part of both complexes, while the NSL complex can also acetylate H4K5 and H4K8 (Cai et al., 2010; Zhao et al., 2013). However, evidence that the NSL complex catalyzes those modifications in vivo is thus far lacking (Karoutas et al., 2019; Ravens et al., 2014). In this study, we investigated the chromatin functions of KAT8 and provide insights into how they are specified by the associated protein complexes in mammalian cells.

RESULTS

The NSL but not the MSL complex is essential for proliferation of human cells To determine the functions of KAT8 and its associated complexes in human cells, we used the human acute myeloid leukemia cell line THP-1 as a main model because of the ability to study chromatin changes in both steady state and during cell differentiation into macrophages. To confirm the existence of two distinct KAT8-containing chromatin complexes, we generated THP-1 cells expressing either 3xFLAG-tagged mammalian NSL complex members KANSL2 and KANSL3 or KAT8 and performed immunoprecipitation followed by mass spectrometrybased identification of associated proteins. We identified all the reported human NSL complex subunits (KANSL1, KANSL2, KANSL3, KAT8, MCRS1, PHF20, PHF20L1, HCFC1, and WDR5) (Figures 1A and 1B; Table S1). Moreover, KAT8 also co-purified with all the reported subunits of the human MSL complex (MSL1, MSL2, and MSL3) (Figures 1A and 1B; Table S1). We also identified three unique interactors of KANSL3 (OGT, HSPA5, and PPM1G), suggesting it has functions outside the NSL complex (Figure 1A). To determine whether the two KAT8-containing complexes are required for growth of human cells, we performed CRISPRmediated knockout (KO) of each subunit followed by a competition assay with wild-type cells. THP-1 cells expressing wild-type Cas9 were transduced with a lentivirus expressing BFP (blue fluorescent protein) and a single guide RNA (sgRNA) against the gene of interest. The percentage of BFP-positive (sgRNA-expressing) cells was then monitored over time. Depletion of six of eight subunits of the NSL complex led to cell death (KANSL1, KANSL2, KANSL3, MCRS1, WDR5, and HCFC1), while the MSL complex depletion did not (Figure 1C). We also compared essentiality scores for all complex subunits in the DepMap CRISPR database (https://depmap.org/portal/) and observed similar growth dependency patterns in a set of 739 human cell lines (Figure 1D). As PHF20 and PHF20L1 are the only nonessential subunits of the NSL complex and are close orthologs, we knocked out both genes to test redundancy. As shown in Figures S1A and S1B, both genes are dispensable for proliferation. ntly localizes to transcription start sites KANSL2 KD KANSL3 KD KAT8 KD NegCtrl A C Ne gC trl KA NS L2 KA NS L3 KA T8 0.0 0.5 1.0 1.5 2.0 KANSL2 Ex pr es sio n re la tiv e to R PL P0 an d no rm al iz ed to th e st an da rd cu rv e 0.0 0.5 1.0 1.5 2.0 KANSL3 0.0 0.5 1.0 1.5 KAT8 B MR PL 9 ER AL 1 LA RS 2 PO P4 TT C2 7 TA RS 2 MC M2 Ne gC trl 0.0 0.2 0.4 0.6 0.8 1.0 1.2 % B FP no rm al is ed to D ay 3 Day 3 Day 6 Day 10 Day 14 Control sgRNAs I G 146 121727 Downregulated genes with KANSL3 binding at TSS (173) Essential genes according to DepMap (1244) -0.8 -0.6 -0.4 -0.2 0.0 NES = -2.2 p = 0.0 q = 0.0 -1.0 -0.5 0.0 0.5 1.0 0 2000 4000 6000 Rank in ordered gene list lo g 2 F C H its H Top downregulated category: Mitochondrial translation elongation KANSL2 KD NegCtrl KANSL3 target mRNA expression KANSL2 KD/ NegCtrl KANSL3 target mRNA expression KANSL3 KD/ NegCtrl KANSL3 target mRNA expression KAT8 KD/ NegCtrl log2FC log2FC log2FC 8 6 4 2 0 -3 -2 -1 0 1 2 3 8 6 4 2 0 -3 -2 -1 0 1 2 3 8 6 4 2 0 -3 -2 -1 0 1 2 3 Ne gC trl KA NS L2 KD KA NS L3 KD KA T8 KD KANSL2 GAPDH Ne gC trl KA NS L2 KD KA NS L3 KD KA T8 KD Ne gC trl KA NS L2 KD KA NS L3 KD KA T8 KD KANSL3 GAPDH KAT8 VINCULIN Global transcriptomics profilingF J Global proteomics profiling 48.2% down, 5.8% up 49.5% down, 4.5% up 31.3% down, 9.1% up 60 30 0 -30 -60 Pr in ci pa lc om po ne nt 2 Principal component 1 60300-30-60 cluster #1 cluster #3 cluster #2 100 50 0 -50 -100 -150 Pr in ci pa lc om po ne nt 2 Principal component 1 10050-50-100-150 cluster #1 cluster #3 cluster #2 0 KANSL2 KD KANSL3 KD KAT8 KD NegCtrl KD Ne gC trl KA NS L2 KA NS L3 KA T8 KD Ne gC trl KA NS L2 KA NS L3 KA T8 KD -lo g 1 0 ( q va lu es ) -lo g 1 0 ( q va lu es ) -lo g 1 0 ( q va lu es ) KO K En ric hm en t s co re 0 10 20 30 40 50 60 70 80 90 100 % of c el ls EdU labeling G1 S G2/M Sub-G1 0 20 40 60 80 100 % of c el ls KANSL2 KD KANSL3 KD KAT8 KD NegCtrl Ne gC trl KA NS L2 KD KA NS L3 KD KA T8 KD live An ne xin V- , DA PI- tot al DA PI+ tot al An ne xin V+ ea rly ap op tot ic An ne xin V+ , DA PIlat e a po pto tic An ne xin V+ , DA PI+ n ec rot ic An ne xin V, DA PI+ D E 5 6 7 8 9 10 11 12 13 0.1 1 10 100 1000 cu m ul at iv e ce ll nu m be r [ e6 ] days after transduction KAT8 KD NegCtrl KANSL2 KD KANSL3 KD (legend on next page) 1752 Molecular Cell 81, 1749–1765, April 15, 2021 In summary, we confirmed that KAT8 exists in two distinct protein complexes in human cells, NSL and MSL, and found that only the function of the NSL complex is essential for cell survival. The NSL complex is required for expression of a subset of essential genes in human cells To determine genomic occupancy of the NSL complex in human cells, we performed KANSL3 chromatin immunoprecipitation sequencing (ChIP-seq) in wild-type THP-1 cells or cells with CRISPR interference (CRISPRi)-mediated KANSL3 knockdown (KD) 5 days after sgRNA transduction (Figure S1C). We chose CRISPRi over the CRISPR KO approach to avoid truncated or in-frame-modified protein products, which might be recognized by the anti-KANSL3 antibody. KANSL3 ChIP-seq identified 373 KANSL3-specific peaks (Figure 1E; Table S1), the majority of which (77.2%) co-localized with H3K4me3 (Figures 1F and 1G) andwere positioned at transcription start sites (TSSs) (Figure 1H; Figure S1D). This localization is consistent with previous reports in Drosophila (Feller et al., 2012; Lam et al., 2012; Raja et al., 2010) and mouse ESCs (Chelmicki et al., 2014; Ravens et al., 2014). KANSL3 peaks were frequently found with ETS family transcription factor motifs (Figure S1E), potentially suggesting a role for these factors in NSL complex recruitment. To identify genes regulated by the NSL complex, we performed global transcriptomics (RNA sequencing [RNA-seq]) and proteomics profiling following CRISPRi KD of KANSL2, KANSL3, and KAT8 (Figures 2A and 2B). We used the milder CRISPRi assay over CRISPR KO because of the essentiality of KANSL2, KANSL3, and KAT8. CRISPRi-mediated KD of the three genes induced growth defects (Figure 2C) due to cell-cycle arrest in G1 and G2/M phases (Figure 2D) and apoptosis (Figure 2E) starting about 7 days after sgRNA transduction. Thus, we chose a time point of 3 days after transduction to capture changes in gene expression, prior to the onset of cell death. Principal-component analysis of both global transcriptomic and proteomic data revealed that KANSL2- and KANSL3-KD samples clustered together and clustered apart from KAT8-KD (Figures 2F and 2G; Figures S2A and S2B; Table S2), indicating additional KAT8 functions, consistent with its also being a part of the MSL complex. Interestingly, KANSL2 KD led to decreased protein but not transcript levels of KANSL1, KANSL3,MCRS1, and PHF20L1 (Figure S2C), indicating its importance for complex integrity. Of the genes with KANSL3 enrichment at their promoters, 48.2% and 49.5% are downregulated by KD of KANSL2 and KANSL3, respectively, whereas relatively few genes had Figure 2. The NSL complex is required for expression of a subset of e (A and B) qRT-PCR (A) and western blotting (B) analysis of KANSL2, KANSL3, and KRAB cells with corresponding sgRNAs. In (A), values are normalized to RPLP0 (C) Growth curves of THP-1/cdCas9-KRAB cells transduced with a NegCtrl sgR (D and E) Cell cycle (E) and apoptosis (F) analysis 7 days after KANSL2, KANSL3 (F and G) Principal-component and non-hierarchical clustering analysis of the RN (H) Volcano plots describing expression changes for all genes with a nearby KAN expressed genes are indicated in light blue (q < 0.05). (I) Venn diagram indicating overlap between downregulated KANSL3 target gene (J) The gene set enrichment analysis (GSEA) enrichment plot for GO:0070125 ge expression after KANSL2 KD. (K) CRISPR KO competition assays for the selected NSL complex target genes. See also Figure S2 and Table S2. increased expression (5.8% and 4.5%, respectively) (Figure 2H; Figure S2D). These data suggest that the NSL complex facilitates gene expression in human cells, similar to what has been observed in Drosophila (Feller et al., 2012; Lam et al., 2012) and mouse ESCs (Chelmicki et al., 2014). Unexpectedly, KD of KAT8 led to fewer changes in NSL target gene expression than KANSL2 and KANSL3 KD, at both the mRNA and protein levels (Figure 2H; Figures S2D–S2F), and induced a weaker effect on cell proliferation (Figures 2C–2E). This is likely due to incomplete KD of KAT8 and is discussed in detail below. To understand the essential nature of the NSL complex, we compared the genes directly activated by the NSL complex with the genes identified as pan-essential in the DepMap project. This identified 27 essential genes downregulated by KD of KANSL2 and KANSL3 (Figure 2I). Several of these encode proteins involved in mitochondrial translation and other aspects of mitochondrion biology (LARS2, TTC27, POP4, TARS2, MRPL9, MRPS31, QRSL1, SLC25A26, PMPCB, MALSU1, and TEFM). Consistently, Gene Ontology analysis of the downregulated proteins after KANSL2 KD uncovered mitochondrial translation as the top category (Figure 2J; Figure S2G). In addition, essential proteins involved in transcription and RNA splicing (RPAP1, GTF3C3, DDX21, ISY1, and MAGOH), translation (RRP15), telomere elongation (RTEL1), and vacuole homeostasis (TMEM199) were among the direct targets of the NSL complex. To ensure that the identified genes are essential in THP-1 cells, several of these were independently validated (Figure 2K; Figure S2H). In summary, the NSL complex binds to several essential genes and is required for maintaining their expression and, therefore, for cell survival. Depletion of KAT8, but not KANSL2 and KANSL3, abrogates global H4K16ac levels As KAT8 is the main acetyltransferase for H4K16ac, it is widely believed that both KAT8-containing complexes, MSL and NSL, exert their activity through H4K16ac (Ravens et al., 2014; Sheikh et al., 2019; Yang et al., 2014). Therefore, we tested whether H4K16ac is involved in maintaining NSL complex target gene expression in mammalian cells. First, we validated the specificity of a commercial H4K16ac antibody by ELISA with an in-house library of 158 histone peptides (Figure S3A; Table S3) and by a Luminex-based approach with a panel of 24 designer nucleosomes (Figure S3B; Table S3). Next, we defined the optimal antibody/ chromatin ratio (Figure S3C) and performed H4K16ac ChIPseq in six human cell lines (THP-1, K562, MOLM13, U2OS, ssential genes in human cells KAT8 expression 3 days after transduction of THP-1/catalytic dead (cd) Cas9and shown as mean ± SD. NA or sgRNAs against KANSL2, KANSL3, or KAT8. , or KAT8 KD. The values are shown as mean ± SD. A-seq (F) and proteomics (G) expression data from NSL complex KD series. SL3 ChIP-seq peak (KANSL3 targets) in NSL complex KD series. Differentially s and the list of pan-essential genes from the DepMap project. ne set (mitochondrial translation elongation) on the basis of differential protein Molecular Cell 81, 1749–1765, April 15, 2021 1753 0 15 0 15 0 15 0 15 0 15 0 15 0 15 0 15 ZNF501 KIAA1143 KIF15 TMEM42 TGM4 ZDHHC3 | 44740000 | 44810000 | 44880000 | 44950000chr3 125803 bp 0 5 0 5 -1 0 1 Ac tiv e pr om ot er W ea k pr om ot er In ac tiv e pr om ot er St ro ng e nh an ce r W ea k en ha nc er In su la to r Tx n tra ns iti on Tx n el on ga tio n W ea k Tx n R ep re ss ed H et er oc hr om at in R ep et iti ve Max Min lo g 1 0 ( un its ) H4K16ac - NSL target genes H4K16ac - all genes TSS TES TSS TES NegCtrl KANSL2 KANSL3 KAT8 H 4K 16 ac g en e si gn al in N eg C trl H 4K 16 ac al l g en es NegCtrl KANSL2 KD KANSL3 KD KAT8 KD H4K16ac B TSS TES DC GF 0 10 20 30 % In pu t H4K16ac ChIP-qPCR KANSL2 KD KANSL3 KD KAT8 KD NegCtrl targets not targets, expressed not targets, not expressed NA GP A T SS SU MF 1 T SS RP L1 0 T SS OT X1 TS S LG R5 TS S ME IS1 TS S NA GP A G B SU MF 1 G B Ge ne de ser t (op en ch rom atin ) Ge ne de ser t (clo sed ch rom atin ) MYO1H KCTD10 UBE3B MMAB MVK FAM222A |109420000 |109510000 |109600000|109690000chr12 170385 bp 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 NegCtrl KANSL2 KD KANSL3 KD KAT8 KD NegCtrl KANSL2 KD KANSL3 KD KAT8 KD NegCtrl Input KANSL2 KD Input KANSL3 KD Input KAT8 KD Input NegCtrl Input KANSL2 KD Input KANSL3 KD Input KAT8 KD Input 40 0.0 0.2 0.4 0.6 K1 2a c K1 2a cK 16 ac K1 6a c K5 ac K5 ac K1 2a c K5 ac K1 2a cK 16 ac K5 ac K1 6a c K5 ac K8 ac K5 ac K8 ac K1 2a c K5 ac K8 ac K1 2a cK 16 ac K5 ac K8 ac K1 6a c K8 ac K8 ac K1 2a c K8 ac K1 2a cK 16 ac K8 ac K1 6a c un mo dif ied Ab un da nc e ESCs THP-1 E H 4K 16 ac si gn al Q 3 HEK293HeLaK562 MOLM13THP-1 U2OSA TSS TES TSS TES TSS TES TSS TES TSS TES TSS TES 10 H 4K 16 ac g en e si gn al in e ac h ce ll lin e al l g en es Threshold of expression H 5.0 4.0 3.0 2.0 1.0 0.0 Av er ag e no rm al iz ed re ad c ou nt H 4K 16 ac n or m al iz ed re ad c ou nt H4K16ac H3 Ne gC trl KA T8 KA NS L3 KA NS L2 I Histone H4 (4-17 aa) TSS TES TSS TES TSS TES KD KD (legend on next page) 1754 Molecular Cell 81, 1749–1765, April 15, 2021 HeLa, and HEK293). We found that H4K16ac is an abundant histone modification with extensive presence throughout the genome (Figure 3A). H4K16ac was particularly enriched at gene bodies, positively correlating with gene expression, and depleted at TSSs in all cell lines tested (Figures 3A–3C; Figure S3D). This was consistent with ChIP results using another anti-H4K16ac antibody (Figure S3E). Furthermore, we confirmed the high abundance of H4K16ac (about 30% of all histone H4) in THP-1 and mouse ESCs using mass spectrometry (Figure 3D). H4K16ac was previously reported to be enriched at gene promoters and enhancers in mouse ESCs (Taylor et al., 2013). Because this disagrees with our mapping data in human cells, we performed H4K16ac ChIP-seq in mouse ESCs and mouse MLL-AF9-rearranged acute myeloid leukemia cells. Both mouse cell types demonstrated widespread H4K16ac with enrichment at gene bodies (Figure S3F), but H4K16ac was present at TSSs only in mouse ESCs (Figure S3F), suggesting a specialized regulation in this cell type. To analyze changes in H4K16ac upon downregulation of the NSL complex, we performed H4K16ac ChIP-seq following CRISPRi KD of KANSL2, KANSL3, and KAT8 in THP-1 cells. KAT8 KD led to a widespread genome-wide loss of H4K16ac, with the exception of TSSs, where a low signal was mostly maintained (Figures 3E–3G). This remaining signal could potentially represent ‘‘phantom’’ ChIP-seq peaks commonly occurring at promoter regions (Jain et al., 2015). Alternatively, it could represent the activity of TIP60, also known to acetylate H4K16 (Kimura and Horikoshi, 1998). Surprisingly, KANSL2 and KANSL3 KD did not lead to changes in H4K16ac at their target genes or globally (Figures 3E–3H). Similar results were observed by measuring global levels of H4K16ac using western blotting (Figure 3I). Furthermore, detailed analysis of the abundances of 125 histone PTM combinations using mass spectrometry showed that whereas KANSL2 and KANSL3 KD had no discernible impact, KAT8 KD led to a significant decrease in the abundance of multiple H4K16ac-containing peptides (H4K16ac, H4K8acK16ac, H4K5acK16ac, H4K12acK16ac, H4K5acK12acK16ac, and H4K8acK12acK16ac) (Figure S3G; Table S4). Taken together, these results demonstrate that KAT8 catalyzes widespread and abundant H4K16ac outside TSS regions in the cell lines tested (with the exception of mouse ESCs, where Figure 3. Depletion of KAT8 but not KANSL2 and KANSL3 abrogates g (A) Heatmaps of H4K16ac ChIP-seq signal at all genes in six human cell lines (ge TSS, transcription start site; TES, transcription end site. (B) Density plot of normalized THP-1 H4K16ac ChIP-seq signal across different ch Similar results were obtained using K562H4K16ac ChIP-seq data. Txn, transcript to maximum within one lane. The amount of H4K16ac is shown on the y axis. (C) Boxplots demonstrating statistical summary of H4K16ac ChIP-seq signal in ge THP-1 cells. All groups are significantly different (p < 2.2e-16, two-sided Wilcoxo (D) Bar plot indicating abundances of individual H4 peptides (amino acids 4–17) w measured using mass spectrometry. (E) Heatmaps of H4K16ac ChIP-seq signal at all genes in NSL complex KD se methods). (F) Average normalized H4K16ac ChIP-seq profiles across NSL target genes or a (G) qRT-PCR quantitation of H4K16ac ChIP signal at selected loci after KDs of KA are shown as mean ± SD. (H) Example H4K16ac ChIP-seq tracks in NSL complex KD series. (I) Western blotting analysis of H4K16ac and H3 levels after KANSL2, KANSL3, o See also Figure S3 and Tables S3 and S4. H4K16ac is also present at TSSs). Importantly, KD of KANSL2 and KANSL3 did not affect H4K16ac levels, suggesting that the NSL complex uses alternative mechanisms for regulating gene expression. The NSL complex catalyzes H4K5ac and H4K8ac and is required for transcription initiation Previous reports demonstrated that, in addition to catalyzing H4K16ac, the NSL complex can catalyze H4K5 and H4K8 acetylation when incubated with nucleosomes in vitro (Cai et al., 2010; Zhao et al., 2013). These modifications can also be catalyzed in vitro by histone acetyltransferases from other families (GNAT and p300/CBP) (Anamika et al., 2010), but the contribution of individual enzymes to the in vivo patterns of these marks is unknown. H4K5ac and H4K8ac were shown to localize to gene promoters in human cells (Zhang et al., 2020) though their involvement in transcriptional regulation is unclear. To test whether the NSL complex catalyzes these modifications in vivo, we profiled H4K5 and H4K8 acetylation using ChIP-seq after depletion of NSL complex members. It proved challenging to identify a specific H4K5ac antibody, because of high similarity of the epitope with H2AK5ac (Figure S4A). From a set of five commercial antibodies, we chose the two showing the best results in histone peptide array ELISA and designer nucleosomes assays (Figures S4B–S4E). We also confirmed high specificity of the selected H4K8ac antibody in both antibody specificity assays (Figures S4F–S4H). Both H4K5ac and H4K8ac demonstrated specific enrichment at TSSs, and their global levels were not affected by KD of KANSL2, KANSL3, or KAT8 (Figures 4A and 4B; Figures S4I–S4K). Importantly, KANSL2, KANSL3, or KAT8 KD specifically reduced both modifications at promoters of NSL target genes (Figures 4B–4D; Figures S5A– S5D). These results were verified by ChIP experiments with another H4K5ac antibody (Figures S5E–S5F). This suggests that the NSL complex regulates transcription by placing H4K5ac and H4K8ac at TSSs of target genes. Consistently, RNA Pol II binding is lost at NSL target promoters upon KANSL2 or KANSL3 KD (Figure S5G). Similar to the modest target gene expression changes after KAT8 KD (Figure 2H), it also had milder effect on H4K5ac and H4K8ac in comparison with KANSL2 or KANSL3 KD. lobal H4K16ac levels ne length is normalized). The signal is normalized to the total number of reads. romatin states defined by the Broad Institute ChromHMMproject in K562 cells. ion. Color in the density plot conveys shape of the signal distribution normalized ne bodies of genes binned according to quantile (Q) distribution of expression in n rank-sum test). ith different acetylation combinations in mouse ESCs and human THP-1 cells ries. The signal is normalized to the number of Drosophila reads (see STAR ll genes in NSL complex KD series. NSL2, KANSL3, or KAT8. TSS, transcription start site; GB, gene body. Values r KAT8 KDs. Molecular Cell 81, 1749–1765, April 15, 2021 1755 H 4K 8a c pr om ot er s ig na l al l g en es 0 8 NegCtrl KANSL2 KD KANSL3 KD KAT8 KD 0 8 H4K5ac - NSL target promoters H4K5ac - promoters of all genes - 2.5kb - 2.5kb2.5kb 2.5kbTSS TSS H4K8ac - NSL target promoters H4K8ac - promoters of all genes 0 8 0 8 NegCtrl KANSL2 KD KANSL3 KD KAT8 KD TSS TES THP-1 H 4K 5a c pr om ot er s ig na l THP-1 H 4K 8a c H 4K 5a c TSS TES A al l g en es 0 4 B C D 0 10 0 10 0 10 0 10 NAGPA NAGPA-AS1 | 5027000 | 5032000 | 5037000chr16 7467 bp NegCtrl KANSL2 KD KANSL3 KD KAT8 KD 0 9 0 9 0 9 0 9 NAGPA NAGPA-AS1 | 5024000 | 5030000 | 5036000chr16 9892 bp NegCtrl KANSL2 KD KANSL3 KD KAT8 KD 0 2 4 6 8 % In pu t H4K5ac SU MF 1 T SS Ge ne des ert (clo sed chr om atin ) NA GP A T SS GA TB TS S Ge ne des ert (op en chr om atin ) 0 5 10 15 20 % In pu t H4K8ac Av er ag e no rm al iz ed re ad c ou nt - 2.5kb - 2.5kb2.5kb 2.5kbTSS TSSA ve ra ge n or m al iz ed re ad c ou nt H4K5ac H4K8ac E 0 1 10-5 2 10-5 3 10-5 4 10-5 5 10-5 Ex pr es si on re la tiv e to R pl p0 nascent Sumf1 0 2 10-5 4 10-5 6 10-5 8 10-5 1 10-4 nascent Dmap1 0.0 5.0 10-4 1.0 10-3 1.5 10-3 Ex pr es si on re la tiv e to R pl p0 Sumf1 0 2 10-5 4 10-5 6 10-5 8 10-5 Nagpa 0 1 10-3 2 10-3 3 10-3 4 10-3 5 10-3 Dmap1 0 1 10-5 2 10-5 3 10-5 4 10-5 5 10-5 nascent Nagpa F Na gp a T SS Su mf 1 T SS Dm ap 1 T SS Ge ne de se rt 1 Ge ne de se rt 2 Ge ne de se rt 3 0.0 0.2 0.4 0.6 % In pu t HA ChIP 0 h 2 h dTAG-13 +dTAG-13 KANSL3 HA GAPDH 0 20 40 60 H4K5ac % In pu t 0 10 20 30 H4K8ac % In pu t H I J K dTAG-13 + dT AG -1 3 0 6 0 6 0 10 0 10 Sumf1 Itpr1 | 108110000 | 108150000 | 108190000chr6 62882 bp H4K5ac 0h H4K5ac 2h dTAG-13 H4K8ac 0h H4K8ac 2h dTAG-13 no dTAG-13 KANSL3 FKBPv HA HA KANSL3 FKBPV HA HA KANSL3 FKBPV HA HA + dTAG-13 CRBN-E3 dTAG-13 0 2 4 8 12 24 h N C K8K3K2 N C K8K3K2 N C K8K3K2 N C K8K3K2 N C K8K3K2 N C K8K3K2 N C K8K3K2 N C K8K3K2 N C K8K3K2 N C K8K3K2 SU MF 1 T SS Ge ne des ert (clo sed chr om atin ) NA GP A T SS GA TB TS S Ge ne des ert (op en chr om atin ) 0 2 4 8 12 24 0 2 4 8 12 24 0 2 4 8 12 24 +dTAG-13 0 2 4 8 12 24 0 2 4 8 12 24 0 2 4 8 12 24 hhh hhh 0 2 4 8 12 24 h + dT AG -1 302 4 8 12 24 h Su mf 1T SS Na gp aT SS Dm ap 1T SS Ga pd hT SS (no n-t arg et) Ge ne de ser t 2Ge ne de ser t 1 Su mf 1 T SS Na gp a T SS Dm ap 1 T SS Ga pd h T SS (no n-t arg et) Ge ne de ser t 2Ge ne de ser t 1 +dTAG-13 +dTAG-13 +dTAG-13 +dTAG-13 0 25 50 75 100 125 150 175 200 fo ld c el l e xp an si on 0 5 G DM SO dT AG -13 0 20 40 60 80 100 % Tr yp an B lu e po si tiv e DM SO dT AG -13 (legend on next page) 1756 Molecular Cell 81, 1749–1765, April 15, 2021 To test whether H4K5ac and H4K8ac are directly regulated by the NSL complex, we generated a mouse ESC line with a biallelic FKBPF36V degradation tag (dTAG) knockin (Nabet et al., 2018) at theKansl3 locus. In this system, addition of dTAG-13 ligand to culture medium targets KANSL3-FKBPF36V fusion protein for CRBNmediated protein degradation (Figures 4E and 4F). Consistent with an essential role of the NSL complex in human cells (Figures 1C and 1D), dTAG-13 treatment of degron-knockin ESCs induced cell-cycle arrest and cell death (Figure 4G; Figures S5H and S5I). Because the NSL complex is common essential (and, therefore, presumably binds to the same genes across cell lines), we selected several ubiquitously expressed genes previously identified as targets of the NSL complex in THP-1 cells and showed that KANSL3 is also bound to their promoters in ESCs and that KANSL3 binding was completely lost after dTAG-13 treatment (Figure 4H). Excitingly, total and nascent expression of KANSL3 target genes was markedly reduced just 2 h after dTAG-13 treatment, suggesting that the NSL complex directly regulates their expression (Figure 4I; Figure S5J). Importantly, dTAG-13 treatment also led to the loss of promoter-associated H4K5ac and H4K8ac (Figures 4J and 4K; Figure S5K) as well as RNA Pol II binding (Figure S5L) at NSL complex target genes concomitantly with downregulation of nascent mRNA. Altogether, these results strongly suggest that the NSL complex catalyzes H4K5ac and H4K8ac at its target genes, which is required for recruitment of RNA Pol II and transcriptional initiation. BET bromodomain proteins, particularly BRD4, are known to have high affinity to H4K5ac and H4K8ac in vitro (Filippakopoulos et al., 2012; Zaware and Zhou, 2019). However, BRD4 recruitment cannot provide a downstreammechanism for transcription activation by these modifications, as it facilitates formation of a functional elongation complex, downstream of Pol II recruitment (Muhar et al., 2018;Winter et al., 2017). Among the Pol II initiation factors, TAF1 has been shown to bind acetylated H4 tails through its bromodomains in vitro (Jacobson et al., 2000). Strikingly, TAF1 binding was decreased at NSL target genes after KANSL2 or KANSL3 KD (Figure S5M), highlighting a potential mechanism for regulation of transcription initiation by H4K5ac and H4K8ac. To test whether the essential function of the NSL complex depends on the catalytic activity of KAT8, we expressed wild-type KAT8 (wtKAT8) or catalytic dead (E350Q) KAT8 (cdKAT8) in THP1 cells. In contrast to wtKAT8, it was not possible to achieve high levels of cdKAT8 overexpression (Figures S6A and S6B), which may suggest a dominant-negative effect on cell proliferation. Moreover, cdKAT8 expression did not rescue cell survival after KAT8 KD (Figure S6C), suggesting that the catalytic activity of KAT8 is essential for its function. We hypothesized that the milder effect of KAT8 KD could be due to incomplete KAT8 loss and that the remaining amount of KAT8 (Figure 2B) could be associated with the NSL complex and sufficient to partially preserve its function, despite loss of the bulk of H4K16ac. Consistent with this hypothesis, the effects of KANSL2 and KAT8 CRISPR KO were comparable (Figures S6D–S6G). Moreover, we observed higher enrichment of KAT8 at KANSL3 binding sites than at other KAT8 binding sites, and the overlapping KANSL3 and KAT8 sites were less sensitive to KAT8 loss after KAT8 KD (Figures S6H–S6J; Table S5), further suggesting a high affinity of KAT8 for the NSL complex. Interestingly, we did not observe KAT8 binding in gene body regions (Figures S6I and S6J), where it catalyzes broad patterns of H4K16ac. This suggests that KAT8 is more transiently associated with chromatin when not in the NSL complex. Taken together, we show that the catalytic activity of KAT8 is required for its function and that the NSL complex facilitates transcriptional initiation of its target genes potentially through acetylation of H4K5 and H4K8 at their TSSs. H4K16ac broadly correlates with open chromatin and does not regulate chromatin accessibility in vivo As global levels of H4K16ac were affected by the loss of KAT8 but not the NSL complex, we tested whether the bulk of H4K16ac is established by the MSL complex. We performed CRISPR KO of individual MSL complex members and found that loss of MSL1 and MSL3, but not MSL2, causes global decrease in H4K16ac, withMSL1 KO having the strongest effect (Figure 5A; Figure S7A). H4K16ac ChIP-seq inMSL1-KO cells revealed dramatic signal loss throughout the genome, similar to KAT8-KD cells (Figure 5B; Figure S7B). Consistent with our competition assays (Figure 1B), MSL1-KO cells did not exhibit any proliferation defects (Figure 5C) and underwent efficient monocyte-to-macrophage differentiation (data not shown). is required for transcription initiation 1758 Molecular Cell 81, 1749–1765, April 15, 2021 0 1 2 3 USP30GATB NAGPA SUMF1 ATG14 NSL complex target gene expression NA GP A GB SU MF 1 GB GA TB G B NA GP A T SS SU MF 1 T SS Ge ne de se rt 0 10 20 30 H4K16ac ChIP SU MF 1 T SS GA TB TS S SU MF 1 G B GA TB G B Ge ne de se rt 0 5 10 15 H4K5ac ChIP THP-1 SKMEL5 SKHEP1 Caki1 SU MF 1 T SS GA TB TS S SU MF 1 GB GA TB G B Ge ne de se rt 0 5 10 15 20 25 H4K8ac ChIP ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 4 5 6 7 −1.50 −1.25 −1.00 −0.75 −0.50 Dependency K A T8 lo g2 (T PM +1 ) Dependency ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● Breast (r = 0.438) CNS (r = 0.537) Liver (r = 0.688) Ovary (r = 0.519) Dependency KAT8 KO Pearson’s r = 0.156 p = 1.82e-05 KAT8 KD KAT8 KD - individual lineages D E J A B C H I KAT8 H4K16ac beta-Actin THP -1 Cak i1 SKH EP1 SKM EL5 SKM EL2 8 0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 R el at iv e pr ot ei n am ou nt 0.0000 0.0001 0.0002 0.0003 0.0004 F 0.00 0.05 0.10 0.15 0.20 % In pu t KANSL3 ChIP SKMEL5 SKHEP1 Caki1 3 4 5 6 7 K A T8 lo g2 (T PM +1 ) 3 4 5 6 7 −1.50 −1.00 −0.50 0.0 −1.00 -0.50 K A T8 lo g2 (T PM +1 ) Pearson’s r 1.00.50.0-0.5 10 8 6 4 2 0 -lo g 1 0 (q va lu es ) log2 FC KAT8high/KAT8low 210-1-2 10 8 6 4 2 0 -lo g 1 0 (q v a lu es ) log2 FC KAT8high/KAT8low 210-1-2 100 80 60 40 20 0 -lo g 1 0 (q va lu es ) log2 FC KAT8high/KAT8low 210-1-2 NSL target genes All non-target genesNSL target genes All non-target genes K L Pearson’s r = 0.337 p = 2.2e-16 THP -1 SKM EL5 SKH EP1 Cak i1 SU MF 1 T SS NA GP A T SS GA TB TS S AT G1 4 T SS US P3 0 T SS Ge ne des ert Breast carcinoma Colon adenocarcinoma Glioblastoma Renal cell carcinoma Acute myeloid leukemia Stomach adenocarcinoma Ovarian serous cystadenocarcinoma TCGA - all samples Breast carcinoma Colon adenocarcinoma Glioblastoma KANSL1 KANSL2 KANSL3 HCFC1 MCRS1 PHF20 PHF20L1 WDR5 KAT8 MSL1 MSL2 MSL3Breast carcinoma Colon adenocarcinoma Glioblastoma Renal cell carcinoma Acute myeloid leukemia Ovarian serous cystadenocarcinoma Stomach adenocarcinoma G 8 6 4 2 TH P1 Ca ki1 SK HE P1 SK ME L2 8 SK ME L5 TH P1 Ca ki1 SK HE P1 SK ME L2 8 SK ME L5 KAT8/beta-Actin H4K16ac/beta-Actin Ex pr es si on re la tiv e to R PL P0 an d no rm al iz ed to th e st an da rd c ur ve % In pu t % In pu t % In pu t THP-1 SKMEL5 SKHEP1 Caki1 THP-1 SKMEL5 SKHEP1 Caki1 Ex pr es si on , l og 2( TP M ) (legend on next page) Molecular Cell 81, 1749–1765, April 15, 2021 1759 As in vitro experiments suggest that H4K16ac facilitates chromatin openness, we tested whether MSL1- KO cells exhibit altered chromatin accessibility patterns. MNase digestion of extracted nuclei did not reveal any difference in chromatin accessibility betweenMSL1-KO and wild-type cells (Figure 5D). Assay for transposase-accessible chromatin sequencing (ATAC-seq) revealed some accessibility changes induced by MSL1 KO (161 significantly changed peaks, 1.5-fold change, q < 0.05) but no global effect on chromatin accessibility (Figure 5E; Figure S7C; Table S6). Consistently, we did not find changes in global levels of any of the 125 histone PTM combinations in KAT8-KD cells assessed using mass spectrometry, except for those containing H4K16ac (Figure S3G; Table S4). Interestingly, we observed that H4K16ac forms large megabase-scale domains conserved between different cell lines (Figure S7D). These domains largely coincided with genomic compartment A, defined by HiC data in THP-1 cells and representing open chromatin with high transcriptional activity and high density of ATAC-seq peaks (Figures 5F and 5G; Figure S7E). Thus, although not regulating chromatin compaction globally, H4K16ac represents a hallmark of open chromatin and transcription. KAT8-low cancers retain NSL complex function but lose H4K16ac globally Multiple studies reported decreased levels of KAT8 and H4K16ac in different types of cancer cells (Cai et al., 2015; Cao et al., 2014; Fraga et al., 2005; Liu et al., 2013; Pfister et al., 2008; Zhang et al., 2014), but the significance of this observation is unknown. As we found that the NSL complex is essential for cell survival, and that KAT8 is strongly associated with it, we hypothesized that KAT8-low cancers would have decreased MSL, but not NSL, complex activity. To test this, we selected several cancer cell lines with low KAT8 expression (SKMEL5, SKHEP1, and Caki1) on the basis of the DepMap project data (Figure 6A). In those cells, we confirmed several NSL complex target genes identified in THP-1 cells (Figure 6B) and found comparable expression of these genes between the KAT8-high (THP-1) and KAT8-low (SKMEL5, SKHEP1, Caki1) cell lines (Figure 6C), as well as comparable levels of H4K5ac and H4K8ac at their promoters (Figures 6D and 6E). Excitingly, KAT8-low cancer cells had lower H4K16ac at gene body regions (Figure 6F). Next, we analyzed a large RNA-seq dataset from The Cancer Genome Atlas (n = 10,433) and found that both NSL and MSL complexes are highly expressed in different cancer subtypes (Figure 6G). At the same time, in these cancer samples, we observed either very weak or no correlation between the levels of KAT8 expression and expression levels of NSL complex target genes defined in THP-1 cells (Figures 6H and 6I). Taken together, these results indicate that KAT8-low cancer cells exhibit mostly intact activity of the NSL complex, while their H4K16ac-associated MSL complex function is markedly decreased. This could suggest that KAT8-low cancers are more susceptible to further KAT8 loss, as their remaining KAT8 is associated with the essential NSL complex. On the basis of the data from the DepMap project, there is weak correlation between KAT8 expression and cell sensitivity to KAT8 KO (Figure 6J), suggesting that complete KAT8 loss is detrimental independently of the starting KAT8 expression level. At the same time, there was a correlation between KAT8 expression and cell sensitivity to KAT8 RNAi-mediated KD (Figure 6K), which was even more pronounced for certain cancer lineages (Figure 6L). DISCUSSION One of our main findings is that KAT8 catalyzes H4K5 and H5K8 acetylation as part of the NSL complex, while it catalyzes H4K16 acetylation as part of the MSL complex. Although the mechanism leading to this difference in activity remains to be determined, the NSL complex and, therefore, potentially H4K5 and H4K8 acetylation are essential for cell survival, whereas the MSL complex and H4K16 acetylation are not (Figure 7). Investigation of how the NSL complex regulates transcription revealed that it stimulates RNA Pol II recruitment and, thereby, transcriptional initiation of a subset of essential genes, potentially through acetylation of H4K5 and H4K8. Acetylated H4 is known to be recognized by TAF1, a subunit of TFIID transcription initiation complex (Jacobson et al., 2000). Consistently, TAF1 binding at NSL complex target genes was reduced after KANSL2 or KANSL3 KD. Previous studies suggest that H3K4me3 recognition by TAF3, another TFIID subunit, aids pre-initiation complex binding and stabilization (Lauberth et al., 2013). Thus, TAF1 recognition of promoter-associated H4K5ac and H4K8ac could represent an additional mechanism 16ac globally As part of the NSL complex, KAT8 binds to a subset of active gene promoters, where it stimulates TAF1 binding, through H4K5 and H4K8 acetylation. This facilitates Pol II recruitment and transcription initiation. As part of the MSL complex, KAT8 catalyzes the bulk of H4K16ac in open chromatin regions and, especially, in gene bodies. K1, KANSL1; K2, KANSL2; K3, KANSL3; K8, KAT8; M1, MSL1; M2, MSL2; M3, MSL3. facilitating transcription initiation. The dual H4K5ac/H4K8ac mark is also known to be recognized by the BET bromodomain family members in vitro (Filippakopoulos et al., 2012; Zaware and Zhou, 2019). In fact, BRD4 was recently reported to colocalize with the NSL complex in Drosophila (Gaub et al., 2020). However, BRD4 functions after RNA Pol II recruitment (Muhar et al., 2018; Winter et al., 2017) and is unlikely to act downstream of the NSL complex. Indeed, although Nsl1 KD in Drosophila led to RNA Pol II loss at promoter regions, BRD4 inhibition decreased RNA Pol II occupancy only at gene body regions (Gaub et al., 2020). Interestingly, depletion of the NSL complex does not lead to complete removal of H4K5ac and H4K8ac at target promoters or complete gene repression, which may suggest that other histone acetyltransferases can deposit these modifications and sustain basal transcription levels. We found that H4K16ac is catalyzed by theMSL complex and is highly abundant (i.e., present on about 30% of all histone H4). It forms megabase-scale genomic domains, which highly correlate with transcriptionally active genome compartment A. This indicates that H4K16ac is not a mere consequence of transcription but rather a hallmark of all euchromatin in cells. Despite this strong association with open chromatin, depletion of H4K16ac did not lead to cell proliferation and differentiation phenotypes or changes in chromatin accessibility patterns. This is particularly surprising, as the strong phenotypes of KAT8 loss observed in different cell types are widely believed to be caused by a global loss of H4K16ac. Our study decouples the two and allows a more direct assessment ofH4K16ac function. Interestingly, two recent reports (Armstrong et al., 2018; Copur et al., 2018) demonstrated that female Drosophila flies in which H4K16 was substituted with arginine are viable. Another report (Taylor et al., 2013) demonstrated that changes in H4K16ac levels did not lead to significant differences in distances between fluorescence in situ hybridization (FISH) probes at several genomic loci. Collectively, these studies support our observation that H4K16ac does not have a key role regulating global chromatin structure. Chromatin compaction is known to be regulated by several other mechanisms (e.g., nucleosome spacing and binding of linker histone H1 and HMG proteins) (Gibson et al., 2019; Postnikov and Bustin, 2016). Thus, further studies are warranted to determine if there is a redundancy between these mechanisms and H4K16ac. Our results also suggest that KAT8 is more stable when associated with the NSL complex, which may have an impact in cancer biology. Specifically, we showed that KAT8-low cancer cells have diminished MSL complex function but retained activity of the NSL complex. These results may suggest that there is a ‘‘therapeutic window’’ for inhibition of KAT8 activity in KAT8low tumors, as the remaining KAT8 in these cells is associated with the essential NSL complex. In summary, this study uncovers a dichotomy of KAT8 functions depending on the associated proteins. As part of the NSL complex, KAT8 binds to a subset of active gene promoters, where it stimulates initiation of transcription, potentially via H4K5ac and H4K8ac. As part of the MSL complex, KAT8 catalyzes the majority of H4K16ac at euchromatic regions, but loss of this function does not affect cell proliferation or chromatin accessibility in vivo. Molecular Cell 81, 1749–1765, April 15, 2021 1761

Limitations

Our results suggest that the NSL complex is essential because of its role in activating a set of housekeeping genes. However, it should be noted that the complex also has been reported to regulate mitochondrial respiration (Chatterjee et al., 2016), nuclear lamina integrity (Karoutas et al., 2019), and p53 activity (Li et al., 2009). Thus, the phenotypic effects induced by NSL complex loss may to some degree be attributed to defects in non-transcriptional NSL complex functions. We also propose that H4K16ac does not regulate chromatin compaction in vivo. It should be noted, that although MSL1 KO causes 70%–80% loss of H4K16ac, we cannot exclude that remaining H4K16ac is sufficient to maintain wild-type patterns of chromatin accessibility. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d KEY RESOURCES TABLE d RESOURCE AVAILABILITY 176 B Lead contact B Materials availability B Data and code availability d EXPERIMENTAL MODEL AND SUBJECT DETAILS d METHOD DETAILS B Plasmid and sgRNA cloning B sgRNA design B Generation of the KANSL3 degron knockin cell line B Flow cytometry B Virus production and lentiviral transduction B RNA extraction, cDNA synthesis and qRT-PCR analysis B Growth curves and competition assays B RNA-seq B RNA-seq data analysis B Enrichment tests B Chromatin immunoprecipitation (ChIP) and sequencing B MNase assay B ATAC-seq B ATAC- and ChIP-seq data analysis B Histone PTM analysis B NSL complex interactomics analysis B Global proteomics profiling B Checking antibody specificity by ELISA using the his- tone peptide array library B Luminex B Annexin V staining B Cell cycle analysis B HiC analysis d QUANTIFICATION AND STATISTICAL ANALYSIS SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j. molcel.2021.02.012. 2 Molecular Cell 81, 1749–1765, April 15, 2021 ACKNOWLEDGMENTS We thank members of the Helin laboratory for discussions; Robin Armstrong for critical reading of the manuscript; Sarah Teed, Abbas Nazir, Eugenia Lorenzini, Charlotte Holck, and Zhifan Yang for technical assistance; Zuo-Fei Yuan for epiProfile software support; Jedd Wolchok’s lab for SKMEL28 cells; Joshua Brickman’s lab for E14 mouse ESCs; and Xuejun Jiang’s lab for HeLa and U2OS cells. A.R. and D.S. were in part funded by the European Union’s Horizon 2020 research and innovation program under Marie Sk1odowska-Curie grant agreements 659171 and 749362, respectively. The work in the Helin laboratory was supported by the Danish Cancer Society (R167-A10877), through a center grant from the Novo Nordisk Foundation (NNF) to the NNF Center for Stem Cell Biology (NNF17CC0027852), and through the Memorial Sloan Kettering Cancer Center Support Grant (NIH P30 CA008748). Studies in EpiCypher were funded by the NIH (R44 CA212733, R44 GM136172, R44 HG008907, and R44 GM116584). Experimental and computational proteomics work at the University of Southern Denmark (SDU) (P.V.S., V.G., S.K., and O.N.J.) was supported by the research infrastructure provided by the Danish National Mass Spectrometry Platform for Functional Proteomics (PRO-MS; 5072-00007B) and the VILLUM Center for Bioanalytical Sciences (7292). P.V.S. was supported by a postdoctoral fellowship from the Lundbeck Foundation (R231-2016-3093). S.K. was supported by a research grant from Independent Research Fund Denmark (to O.N.J., 4181-00172B). Research in the V.V.G. laboratory was supported in part by the Ministry of Education of the Republic of Belarus (grant 3.08.3 [469/54]). AUTHOR CONTRIBUTIONS Conceptualization, A.R. and K.H.; Methodology, A.R. and P.V.S.; Investigation, A.R., P.V.S., V.V.G., D.S., H.D., R.K., V.G., S.K., Y.Z., K.L.R., A.L.J., andM.-C.K.; Writing – Original Draft, A.R. and K.H.; Writing – Review & Editing, all authors; Visualization, A.R. and V.V.G.; Funding Acquisition, A.R., P.V.S., O.N.J., and K.H.; Supervision, R.C.H., O.N.J., and K.H. DECLARATION OF INTERESTS EpiCypher is a commercial developer and supplier of reagents (recombinant semi-synthetic modified nucleosomes) and the antibody characterization platforms used in this study. K.H. is a consultant for Inthera Bioscience AG and a scientific advisor for Hannibal Health Innovation. Received: October 13, 2020 Revised: February 1, 2021 Accepted: February 4, 2021 Published: March 2, 2021 REFERENCES Akhtar, A., and Becker, P.B. (2000). Activation of transcription through histone H4 acetylation by MOF, an acetyltransferase essential for dosage compensation in Drosophila. Mol. Cell 5, 367–375. Allahverdi, A., Yang, R., Korolev, N., Fan, Y., Davey, C.A., Liu, C.-F., and Nordenskiöld, L. (2011). The effects of histone H4 tail acetylations on cationinduced chromatin folding and self-association. Nucleic Acids Res. 39, 1680–1691. Anamika, K., Krebs, A.R., Thompson, J., Poch, O., Devys, D., and Tora, L. (2010). Lessons from genome-wide studies: an integrated definition of the coactivator function of histone acetyl transferases. Epigenetics Chromatin 3, 18, 10. Anders, S., McCarthy, D.J., Chen, Y., Okoniewski, M., Smyth, G.K., Huber, W., and Robinson, M.D. (2013). 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KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Anti-KANSL2 Sigma Cat# HPA038497; RRID: AB_10674685 Anti-KANSL3 Sigma Cat# HPA035018; RRID: AB_10601763 Anti-KAT8 Abcam Cat# ab200660 Anti-H4K5ac (#1) Abcam Cat# ab51997; RRID: AB_2264109 Anti-H4K5ac (#2) Abcam Cat# ab114146 Anti-H4K5ac (#3) Diagenode Cat# C1541005 Anti-H4K16ac (#1) Abcam Cat# ab109463; RRID: AB_10858987 Anti-H4K16ac (#2) Millipore Cat# 07-329; RRID: AB_310525 Anti-H4K8ac Abcam Cat# ab45166; RRID: AB_732937 Anti-Pol II Cell Signaling Cat# 14958; RRID: AB_2687876 Anti-H3K4me3 Cell Signaling Cat# 9751; RRID: AB_2616028 Anti-H3 Abcam Cat# ab24834; RRID: AB_470335 Anti-H4 Abcam Cat# ab17036; RRID: AB_1209245 Anti-HA Cell Signaling Cat# 3724S; RRID: AB_1549585 Anti-FLAG Sigma Cat# F1804; RRID: AB_262044 Anti-Vinculin Sigma Cat# SAB4200080; RRID: AB_10604160 Anti-GAPDH Abcam Cat# ab181602; RRID: AB_2630358 Anti-b-Actin Abcam Cat# ab6276; RRID: AB_2223210 Anti-MSL2 Cell Signaling Cat# 44006; RRID: AB_2799256 Anti-TAF1 Cell Signaling Cat# 12781; RRID: AB_2798025 Goat Anti-Rabbit IgG Antibody (H+L), Peroxidase Vector Laboratories Cat# PI-1000; RRID: AB_2336198 Horse Anti-Mouse IgG Antibody (H+L), Peroxidase Vector Laboratories Cat# PI-2000; RRID: AB_2336177 IRDye 800CW Goat anti-Rabbit IgG LI-COR Biosciences Cat# 925-32211; RRID: AB_2651127 IRDye 680RD Goat anti-Mouse IgG LI-COR Biosciences Cat# 926-68070; RRID: AB_10956588 Bacterial and virus strains DH5alpha Chemically Competent Cells Lab prep N/A Stbl3 Chemically Competent Cells Lab prep N/A Chemicals, peptides, and recombinant proteins Agencourt Ampure XP beads Beckman Coulter A63881 LIF Lab prep N/A Polybrene Fisher NC9200896 RetroNectin Fisher 50444032 Protein G Sepharose 4 Fast Flow GE Healthcare 17061802 Protein G Dynabeads Thermo Fisher Scientific 10004D ANTI-FLAG M2 Affinity Gel Sigma A2220 TMTpro 16plex Label Reagent Set Thermo Fisher Scientific A44520 TMT10plex Isobaric Label Reagent Set plus TMT11-131C Label Reagent Thermo Fisher Scientific A34808 EdU (5-ethynyl-20-deoxyuridine) Lumiprobe 10540 Sulfo-Cyanine5 azide Lumiprobe A3330 FITC Annexin V BD 556420 (Continued on next page) e1 Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021

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REAGENT or RESOURCE SOURCE IDENTIFIER Digitonin Promega G9441 Critical commercial assays SNAP-ChIP K-AcylStat panel EpiCypher 193001 Illumina Tagment DNA Enzyme and Buffer Illumina 20034197 NEBNext Ultra II DNA Library Prep Kit for Illumina NEB E7645L Illumina TruSeq v2 kit Illumina RS1222001 Deposited data Raw and analyzed RNA-seq data GEO GEO: GSE158521 Raw and analyzed ChIP-seq data GEO GEO: GSE158736 Raw and analyzed ATAC-seq data GEO GEO: GSE157583 Uncropped western blots Mendeley Data https://doi.org/10.17632/v7mbmfxxy5.1 Raw and analyzed proteomics data ProteomeXchange ProteomeXchange: PXD022267, PXD021943, PXD023865 Experimental models: cell lines HEK293FT Kristian Helin lab RRID: CVCL_6911 HEK293 Kristian Helin lab RRID: CVCL_0045 HeLa Xuejun Jiang Lab RRID: CVCL_0030 U2OS Xuejun Jiang Lab RRID: CVCL_0042 THP-1 Kristian Helin lab RRID: CVCL_0006 K562 Kristian Helin lab RRID: CVCL_0004 MOLM13 Kristian Helin lab RRID: CVCL_2119 Caki1 MSKCC cell line core RRID: CVCL_0234 SKHEP1 MSKCC cell line core RRID: CVCL_0525 SKMEL28 Jedd Wolchok lab RRID: CVCL_0526 SKMEL5 MSKCC cell line core RRID: CVCL_0527 Mouse MLL-AF9 secondary leukemia cells Lab prep N/A E14 mouse embryonic stem cells Joshua Brickman lab RRID: CVCL_C320 See Table S7 for primer and sgRNA sequences N/A N/A Recombinant DNA pLentiCas9-blast Addgene 60955 pU6-sgRNA-EF1a-puro-T2A-BFP Addgene 52962 pHR-SFFV-KRAB-dCas9-2A-CHERRY Addgene 60954 pLenti-PGK-Hygro dest Addgene 19066 U6-sgRNA-SFFV-puro-P2A-EGFP This study N/A eSpCas9(1.1)-T2A-mCherry Ian Chambers lab N/A pCRIS-PITChv2-BSD-dTAG (BRD4) Addgene 91792 pUC19_FKBPF36V-KANSL3-targeting This study N/A Software and algorithms sgRNA Designer: CRISPRko https://portals.broadinstitute.org/gpp/ public/analysis-tools/sgrna-design N/A CRISPRi sgRNA design Radzisheuskaya et al., 2016 N/A Image Studio Lite software LI-COR Biosciences N/A R/Bioconductor package ShortRead v.1.36.1 Morgan et al., 2009 N/A twoBitToFa utility Speir et al., 2016 N/A ( on next page) Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021 e2

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REAGENT or RESOURCE SOURCE IDENTIFIER R/Bioconductor package Rsubread v.1.32.2 Liao et al., 2019 N/A R/Bioconductor package Rsamtools v.2.3.3 Morgan et al., 2016 N/A R/Bioconductor package DESeq2 v.1.20.0 Love et al., 2018 N/A R/Bioconductor package limma v.3.42.2 Ritchie et al., 2015 N/A R/Bioconductor package edgeR v.3.28.0 Robinson et al., 2010 N/A bowtie2 http://bowtie-bio.sourceforge.net/bowtie2/ index.shtml N/A MACS2 https://github.com/macs3-project/MACS N/A BEDTools suite https://bedtools.readthedocs.io N/A EaSeq Lerdrup et al., 2016 N/A Homer v4.5 http://homer.ucsd.edu N/A HiC-Pro version 2.11.0 Servant et al., 2015 N/A HiCExplorer 3.4.3 Ramı́rez et al., 2018 N/A R/Bioconductor package circlize v.0.4.8 Krzywinski et al., 2009 N/A

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Kristian Helin (helink@mskcc.org).

Materials availability

Plasmids and cell lines generated in this study will be made available upon request.

Data and code availability

Original next-generation sequencing data have been deposited to GEO and can be accessed as follows: ATAC-sequencing (GEO: GSE157583), RNA-sequencing (GEO: GSE158521), ChIP-sequencing (GEO: GSE158736). Original mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository (Vizcaı́no et al., 2013) with the dataset identifiers: ProteomeXchange: PXD022267, PXD021943, PXD023865. Uncropped western blots have been deposited at Mendeley Data: https://doi.org/10.17632/v7mbmfxxy5.1. GitHub project with the RNA-sequencing analysis code is available at: https://github.com/VGrinev/TranscriptomicFeatures.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

HEK293FT packaging cells (female, RRID: CVCL_6911), HEK293 (female, RRID: CVCL_0045), HeLa (female, RRID: CVCL_0030) and U2OS (female, RRID: CVCL_0042) cells were cultured in DMEM, high glucose, GlutaMAX supplement, sodium pyruvate medium (Thermo Fisher SCIENTIFIC) containing 10% heat-inactivated FBS (HyClone) and 1x Penicillin/Streptomycin (Thermo Fisher SCIENTIFIC). THP-1 (male, RRID: CVCL_0006), K562 (female, RRID: CVCL_0004), MOLM13 (male, RRID: CVCL_2119), Caki1 (male, RRID: CVCL_0234), SKHEP1 (male, RRID: CVCL_0525), SKMEL28 (male, RRID: CVCL_0526) and SKMEL5 (female, RRID: CVCL_0527) cells were cultured in RPMI 1640, GlutaMAX supplement, sodium pyruvate medium (Thermo Fisher SCIENTIFIC) containing 10% heat-inactivated FBS (HyClone) and 1x Penicillin/Streptomycin (Thermo Fisher SCIENTIFIC). Mouse MLL-AF9 secondary leukemia cells (female) were cultured in RPMI 1640, GlutaMAX supplement, sodium puryvate medium (Thermo Fisher SCIENTIFIC) containing 20% heat-inactivated FBS (HyClone), 1x Penicillin/Streptomycin (Thermo Fisher SCIENTIFIC) and 20% of culture media supernatant from the IL-3-secreting cell line (homemade). E14 mouse ESCs (male, RRID: CVCL_C320) were cultured in the serum/LIF medium containing Glasgow Minimum Essential Media (Sigma), 10% heat-inactivated FBS (HyClone), 1x Penicillin/Streptomycin (Thermo Fisher SCIENTIFIC), 2 mM GlutaMAX (Thermo Fisher SCIENTIFIC), 100 mM b-Mercaptoethanol (Thermo Fisher SCIENTIFIC), 1 mM sodium pyruvate (Thermo Fisher SCIENTIFIC), 0.1 mMMEMNon-Essential Amino Acids Solution (Thermo Fisher SCIENTIFIC) and Leukemia Inhibitory Factor (homemade). Where indicated the cells were selected using the following antibiotic concentrations: puromycin 2 ug/ml, hygromycin 200 ug/ml, blasticidin 10ug/ml. dTAG-13 was used at a final concentration of 500 nM. PMAwas used at a final concentration of 100 nM. All the cell lines were cultured at 37 C. All the cell lines were routinely tested for mycoplasma, but were not authenticated. e3 Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021

METHOD DETAILS

Plasmid and sgRNA cloning

The U6-sgRNA-SFFV-puro-P2A-EGFP vector was generated by substituting Cas9 open reading frame with a puromycin resistance cassette in the pL-CRISPR.SFFV.GFP plasmid (Addgene cat. no 57827). For sgRNA cloning into pU6-sgRNA-EF1a-puro-T2A-BFP, oligos were annealed in annealing buffer (200 mM potassium acetate, 60 mM HEPES-KOH pH 7.4, 4 mM magnesium acetate) and ligated into BstXI+BlpI (NEB) digested pU6-sgRNA-EF1a-puro-T2A-BFP. For sgRNA cloning into U6-sgRNA-SFFV-puro-P2A-EGFP, the oligos were phosphorylated by T4 PNK (NEB) and annealed in the T4 ligation buffer (NEB). The oligos and plasmid mixture was then subjected to digestion by BsmBI (NEB) and ligation by T4 ligase (NEB) (4 cycles of 42 C – 5 min and 16 C – 5 min, inactivation 55 C – 15min). 3xFLAG-KANSL2, 3x-FLAG-KANSL3 and 3xFLAG-KAT8 open reading frames were expressed using the pLenti PGK Hygro plasmid. The FKBPF36V cassette was amplified from pCRIS-PITChv2-BSD-dTAG (BRD4) plasmid (Addgene cat. no 91792). The eSpCas9(1.1)-T2A-mCherry plasmid used for degron knockin generation. sgRNA design CRISPRko sgRNAs were designed using the sgRNA Designer: CRISPRko – Broad Institute (https://portals.broadinstitute.org/gpp/ public/analysis-tools/sgrna-design). CRISPRi sgRNAs were designed as previously (Radzisheuskaya et al., 2016). Table S7 contains sequences of all the sgRNAs used in this study. Generation of the KANSL3 degron knockin cell line The targeting construct was assembled from the PCR products or synthetic DNA blocks (IDT) using the In-Fusion cloning kit (Takara) and contained the following elements: 500 bp left homology arm, blasticidin resistance gene, P2A, 2x HA tags, FKBPF36V, 3x GGGGS linkers, 500 bp right homology arm. The targeting construct and the eSpCas9(1.1)-T2A-mCherry plasmid with the sgRNA targeting Kansl3 start codon were co-transfected into the wild-type ESCs using Lipofectamine 3000 (Thermo Fisher SCIENTIFIC) according to the manufacturer’s protocol. The transfected cells were selected with blasticidin, single cell sorted to obtain clonal cell lines and screened for correct bi-allelic integration. The sequence of the sgRNAs used for knockin generation can be found in Table S7.

Flow cytometry

was performed using a BD LSR II flow cytometer, BD FACSAria III Cell Sorter and Beckman Coulter CytoFlex. Virus production and lentiviral transduction HEK293FT cells were co-transfected with the construct of interest and pAX8 and pCMV-VSV using a standard calcium phosphate protocol. The viral supernatant was collected 72 hours after HEK293FT transfection and used for transduction. Leukemia cells were transduced using a RetroNectin Bound Virus Infection Method (TaKaRa) according to the manufacturer’s instructions. Transduction was performed in presence of polybrene at 8 mg/ml. Antibiotic selection was added 24 hours after transduction. RNA extraction, cDNA synthesis and qRT-PCR analysis Total RNAwas extracted using RNeasy PlusMini Kit (QIAGEN) in accordancewith themanufacturer’s protocol. Onemicrogram of total RNAwas subjected to reverse transcription using TranscriptorUniversal cDNAMaster (Roche).QuantitativePCRwith reverse transcription (qRT-PCR) reactions were set up in triplicate using LightCycler 480 SYBRGreen I Master (Roche) or PowerUp SYBRGreenMaster Mix (Thermo Fisher SCIENTIFIC) and primers listed in Table S7. qRT-PCRexperimentswere performed on a LightCycler 480 Instrument II (Roche) or Quant Studio 6 FLEX Real-Time PCR (Thermo Fisher Scientific) system. Relative quantitation was performed to a housekeeping gene either using a standard curve or a delta delta CT method, as indicated in the corresponding figure legends. Growth curves and competition assays For the growth curve experiments, cells were plated at 300,000 per well in a 6-well plate. The cells were counted every 72 hours and 300,000 cells used for replating. For competition assays, cells were collected 2 days after the addition of puromycin (3 days posttransduction) and mixed with untransduced cells at approximately 8:1 [transduced to untransduced cells]. The percentage of BFP+ or GFP+ (transduced) cells was then recorded on a flow cytometer at the specified time points.

RNA-seq

RNAwas extracted using a QIAGEN RNeasy Kit. Libraries were prepared using an Illumina TruSeq v2 kit and samples sequenced on a NextSeq 500.

RNA-seq data analysis

Weused 11 samples for RNA-seq analysis (3 replicates of cells transducedwith the non-targeting sgRNA or sgRNAs againstKANSL3 and KAT8 and 2 replicates with the sgRNA against KANSL2, the third KANSL2 replicate was a strong outlier in the set and was excluded from the analysis). Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021 e4 A comprehensive RNA-seq data quality assessment and pre-processing of the raw data was performed using the R/Bioconductor package ShortRead v.1.36.1 (Morgan et al., 2009) and the standard R infrastructure. GRCh38/hg38 reference assembly of the human genome was downloaded as a twoBit file from the FTP server of the UCSC Genome Browser. It was then converted to a standard FASTA format with twoBitToFa utility (Speir et al., 2016). A hash table for the reference genome was built with the function buildindex from the R/Bioconductor package Rsubread v.1.32.2 (Liao et al., 2019). At this step, 16-mers subreads were extracted in every 3 bases from the reference genome and the threshold 24 was used to exclude highly repetitive subreads from the created hash table. A global alignment of RNA-seq reads against the reference genome was carried out with the function subjunc from the R/Bioconductor package Rsubread v.1.32.2 (Liao et al., 2019) and the above created hash table. This function implements a seed-andvote mapping paradigm for a fast and accurate alignment (Liao et al., 2013). At this step, we used the default settings of the function subjunc and collected only uniquely mapped reads with the maximum of 3 mismatched bases in the alignment. The resulting BAM files were sorted with the function sortBam and indexed with the function indexBam, both from the R/Bioconductor package Rsamtools v.2.3.3 (Morgan et al., 2016). For each gene, the genomic coordinates of exons were retrieved from Ensembl annotations (Ensembl release 85, GRCh38.p7 reference assembly of human genome) (Zerbino et al., 2018). These coordinates were intersected and joined into overlapping groups called exon clusters. The exon clusters shorter than 10 nucleotides were removed, and the final list of genomic intervals was sorted, assigned with gene information and converted into an object of a class GRanges. We used the function featureCounts from the R/ Bioconductor package Rsubread v.1.32.2 (Liao et al., 2013; 2019; 2014) to assign the mapped RNA-seq reads to the exon clusters and summarize to the genomicmeta-features (genes). Each readwas counted in an unstrandedmodewith theminimum1 base overlapping an exon cluster, and only uniquely mapped reads were collected for subsequence calculations. To identify differentially expressed genes with DESeq2, the mapped RNA-seq reads were assigned to the genomic meta-features (genes) as described above. Next, the resulting count matrix was subjected to filtering against too low sequencing depth and it was wrapped (together with the sample information) into a DESeq DataSet object (Love et al., 2018). Then, differentially expressed genes were identified using the functions DESeq and results from the R/Bioconductor package DESeq2 v.1.20.0 (Love et al., 2018). These functions were run in a default mode and according to the standard DESeq2 pipeline (Anders et al., 2013; Love et al., 2018). Finally, the results were parsed, and genes with the q-value below 0.01 were annotated as differentially expressed. To identify differentially expressed genes with edgeR/limma, themapped RNA-seq reads were assigned to the genomic meta-features as described above. Second, the resulting count matrix was subjected to filtering against too low sequencing depth and it was wrapped (together with the sample information) into a DGEList object (Robinson et al., 2010). Third, to calculate an effective size of each RNA-seq library, the scaling factors were estimated using the ‘‘trimmedmean ofM-values’’ method (Ritchie et al., 2015). Fourth, by applying the calculated scaling factors, the count data were converted into counts per million (CPM) and logarithmically transformed, the mean-variance relationship was estimated, and the appropriate observational-level weights were calculated using the voom algorithm (Law et al., 2014). Fifth, the multiple simple linear models were fitted to the normalized count matrix by least-squares method using the function lmFit from the R/Bioconductor package limma v.3.42.2 (Ritchie et al., 2015). Sixth, fold changes (log2 FC) and empirical Bayes statistics were calculated using respective functions from R/Bioconductor package edgeR v.3.28.0 (Robinson et al., 2010) and limma v.3.42.2 (Ritchie et al., 2015). Finally, results were parsed and genes with the q-value below 0.01 were annotated as differentially expressed. To perform RNA-seq clustering analysis, the mapped RNA-seq reads were summarized as described above. Then, the resulting count matrix was normalized, logarithmically transformed and standardized (by scaling and centering of data). The optimal number of sample clusters was determined by comparing the outcomes from the with-in-sum-of-squares, ‘‘silhouette’’ and gap statistics methods. Non-hierarchical clustering of samples was performed using the method of k-means. For better visualization of cluster composition and proximity, the conventional principal component analysis was used.

Enrichment tests

We used up-to-date ODO and GAF files from the Gene Ontology Consortium (Ashburner et al., 2000) to develop a comprehensive list of the reference functional gene sets. As above, from this list, we selected the gene sets containing ten or more members for downstream analysis. Next, two-sided Fisher’s exact test was used to identify the under- and/or over-represented query gene set(-s) among the reference gene sets. Query results were parsed and under- or over-represented gene sets that passed members size > = 10 and FDR adjusted p-value threshold of 0.05 were collected. If indicated, differential gene expression was determined before gene enrichment analysis. According to the sign of log2 FC, all differentially expressed genes were grouped into up- and downregulated and significantly enriched gene sets were inferred by enrichment test separately for each group. Finally, Cytoscape plug-in EnrichmentMap19was used to handle gene-set redundancy and hierarchical visualization of the enrichment results. In enrichmentmap, nodes represent significantly enriched gene sets, node size is proportional to the number of members in a gene set and color intensity depends on the q-value. Edges indicate gene overlap between the nodes and the thickness of edges is equivalent to the degree of gene overlap between the nodes. Functionally related gene sets were clustered and named.

Chromatin immunoprecipitation (ChIP) and sequencing

The cells were fixed with 1% formaldehyde for 10 min, after which the reaction was quenched by the addition of glycine to the final concentration of 0.125 M. The fixed cells were washed with PBS and resuspended in SDS buffer (100 mM NaCl, 50 mM Tris-HCl pH e5 Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021 8.0, 5 mM EDTA, 0.5% SDS, 1x protease inhibitor cocktail from Roche). The resulting nuclei were spun down, resuspended in the immunoprecipitation buffer at 1 mL per 16 million cells (SDS buffer and Triton Dilution buffer (100 mM NaCl, 100 mM TrisHCl pH 8.0, 5 mM EDTA, 5% Triton X-100) mixed in 2:1 ratio with the addition of 1x protease inhibitor cocktail from Roche) and processed on a Bioruptor Plus Sonicator (Diagenode) to achieve an average fragment length of 200-300 bps. Chromatin concentrations were estimated using Bradford (Bio-Rad) or BCA assays (Thermo Fisher SCIENTIFIC) according to manufacturer’s protocols. The immunoprecipitation reactions were set up in 1 mL of the immunoprecipitation buffer as indicated below and incubated overnight at 4 C. The next day, BSA-blocked Fast-flow Protein G Sepharose beads (GE Healthcare) or Protein G Dynabeads (Thermo Fisher SCIENTIFIC) were added to the reactions and incubated for 3 hours at 4 C. The beads were then washed three times with low-salt washing buffer (150 mM NaCl, 1% Triton X-100, 0.1% SDS, 2 mM EDTA, 20 mM TrisHCl pH8.0) and two times with high-salt washing buffer (500 mM NaCl, 1% Triton X-100, 0.1% SDS, 2 mM EDTA, 20 mM TrisHCl pH8.0). The samples were then reverse-crosslinked overnight in the elution buffer (1% SDS, 0.1 M NaHCO3) and purified using QIAQuick PCR purification kit (QIAGEN). The following chromatin/antibody amounts were used: KANSL3 ChIP – 250ug/5ug, H3K4me3 ChIP – 5ug/2ul, H4K16ac ChIP – 10ug/2ug, H4K5ac ChIP – 20ug/2ug, H4K8ac ChIP – 20ug/2ug, PolII ChIP – 300ug/2ul, KAT8 ChIP – 700ug/3ug, HA ChIP – 300ug/2ul, TAF1 ChIP – 100ug/ 1ug. Inhibitors used in the ChIP immunoprecipitation buffer where specified in the figure legend: sodium butyrate (5 mM) and trichostatin A (250 nM). ChIP-seq libraries were prepared using NEBNext Ultra II DNA Library Prep Kit for Illumina according to manufacturer’s protocol and samples were sequenced on a NextSeq 500. All the primers used for ChIP-qPCR are in Table S7. For histone acetylation ChIP-seq, 5% of the crosslinked Drosophila chromatin (homemade) was added prior to immunoprecipitation step. For all H4K16acChIP-seq reactions, normalization factors were calculated as follows: a) the number of the unique human readswas divided by the number of all the Drosophila reads for each sample including inputs; b) the obtained ratios for each sample were then divided by the corresponding ratios of the inputs. The resulting normalization factors are available at the GEO database together with the raw and processed ChIP-seq data (see Data Availability). Since no global changes in levels of H4K5ac and H4K8ac were observed after depletion of NSL complex members, spike-in normalization was not used for analysis of these modifications.

MNase assay

30 million cells were washed with PBS and resuspended in 3 mL of buffer containing PBS, 1x protease inhibitor cocktail (Roche) and 0.2% Triton X-100 and divided into the required number of aliquots. The samples were rotated for 10 min at 4 C and spun down for 5 min at 4 C and 3300 g. The resulting pellets were resuspended in 50 ul of digestion buffer (10 mM HEPES pH 7.6, 100 mM NaCl, 1.5 mMMgCl2, 0.5 mM EDTA, 10% v/v glycerol, 1 mM DTT, 2 mMCaCl2, 100 ug/ml BSA, 0.2 ul MNase from NEB) and incubated at 37 C for various times (30 s, 1 min, 2 min, 5 min, 15 min, 30 min). Reactions were stopped by the addition of EDTA to the final concentration of 10 mM. The samples were purified using QIAQuick PCR purification kit (QIAGEN) and run on an agarose gel.

ATAC-seq

was performed using the Omni-ATAC protocol (Corces et al., 2017). 50,000 cells per condition were washed in PBS, resuspended in 50 ul of the ice-cold ATAC-RSB buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2) with the addition of 0.1% NP-40, 0.1% Tween-20 and 0.01% Digitonin and incubated on ice for 3 min. Then, 1 mL of ice-cold ATAC-RSB buffer with the addition of 0.1% Tween-20 was added to each sample and gently mixed by inverting the tubes. The nuclei were then pelleted at 500 g for 10 min at 4 C, resuspended in 50 ul of the transposition mix (25ul 2x TD buffer from Illumina, 2.5 ul Tn5 from Illumina, 16.5 ul PBS, 0.5 ul 1%Digitonin, 0.5 ul 10% Tween-20, 5 ul water) and incubated for 30 min at 37 Cwith shaking. DNA fragments were isolated using QIAQuick PCR purification kit (QIAGEN), subjected to PCR amplification, size selection and sequencing using NextSeq 500. ATAC- and ChIP-seq data analysis ATAC and ChIP sequencing reads were 30 trimmed and filtered for quality and adaptor content using TrimGalore (v0.4.5), with a quality setting of 15, and running version 1.15 of cutadapt and version 0.11.5 of FastQC. Reads were aligned to human assembly hg38 or mouse assembly mm10 with version 2.3.4.1 of bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) and were deduplicated using MarkDuplicates in Picard Tools (v2.16.0). For KANSL3 and KAT8 ChIP-seq analysis, to ascertain enriched regions, MACS2 (https://github.com/macs3-project/MACS) was used with a p-value setting of 0.001 with KANSL3 KD cells as background controls or input, respectively. The BEDTools suite (https://bedtools.readthedocs.io) was used to create normalized read density profiles, using a read extension of 200 bp for ChIP samples and 0 bp for ATAC samples. Subsequent ChIP-seq data visualization and analysis were performed using EaSeq software (Lerdrup et al., 2016). To analyze H4K16ac distribution across different chromatin states, Chromatin State Segmentation by HMM data for K562 cells from ENCODE/Broad (Ernst and Kellis, 2010; Ernst et al., 2011) was downloaded from the UCSC Genome Browser and converted to the GRCh38/hg38 genome assembly using the liftOver tool available through the UCSC Genome Browser. Motif signatures were obtained using Homer v4.5 (http://homer.ucsd.edu). For ATAC-seq, a global peak atlas was created by first removing blacklisted regions (http://mitra.stanford.edu/kundaje/akundaje/ release/blacklists/hg38-human/hg38.blacklist.bed.gz) then merging all peaks within 500 bp and counting reads with version 1.6.1 of featureCounts (http://subread.sourceforge.net). Comparison of intra versus inter-group clustering in principle component analysis was used to determine normalization strategy, using either the median ratio method of DESeq2 or a sequencing depth-based factor normalized to ten million uniquely mapped fragments. Differential enrichment was scored using DESeq2 for all pairwise group contrasts. Peak-gene associations were created using linear genomic distance to transcription start site. Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021 e6

Histone PTM analysis

Histones were extracted from 4 million cells as described (Sidoli et al., 2016). TCA precipitation was substituted with desalting using disposable SEC PD-10 columns (GE healthcare, 17085101), and the samples were subsequently concentrated by speedvac to 100 ul. Histoneswere quantified byBCA assay (each sample produced 20-30 ug), and 10 ugwere digested and derivatized in 30 ul of buffer, as previously described (Maile et al., 2015). Histonesweredesalted using 3mgofHLBmaterial (Waters), as recommended by themanufacturer. The samples were analyzed using DIA strategy with high resolution orbitrapMS1 and low resolution ion trapMS2 scans (Sidoli et al., 2015) on Orbitrap Velos coupled to nanoAcquity UPLC system (Waters). Peptides (600 ng per injection) were directly loaded on 30 cm long columnwith 150 uM inner diameter packedwith intertsil 2 um sorbent using flashPackmethod (Kovalchuk et al., 2019). The flow rate was kept at 1 ul/min and column temperature was 30 C during the analysis. The aqueous mobile phase A was 0.1% formic acid, the organic mobile phase B was acetonitrile with 0.1% formic acid. The gradient was 4%–25% B in 49 min, 25%–40% in 13 minutes, 40%–80% in 4minutes. Peptideswere electrosprayed through etched emitters (Kelly et al., 2006).MS1 scanswere performed at 15K resolution in Orbitrap analyzer, MS2 peptides were isolated in ten 80 Da windows and fragmented using CID with 35NCE. To improve peak definitions, every scan was acquired with two microscans. The results were analyzed by epiProfile (Yuan et al., 2015), the code was modified to specify phenyl isocyanate (119.037114) as N-terminal modification.

NSL complex interactomics analysis

For each of the four replicates, 10million cells were lysed in 50mMEPPS, pH 7.4, 150mMNaCl, 1 mMEDTA, 1% Triton X-100, complete protease inhibitors and 25 U/mL of Benzonase. The lysate was kept on ice for 5 minutes to allow DNA digestion and cleared by centrifugation at 20.000 g for 5 minutes and filtering through acropep advance 96-well 2 ml, 1 mm glass fiber filter plate. Immunopreciptation was performed for 1 hour at 4 C with ANTI-FLAG M2 Affinity Gel (Sigma, A2220) in Eppendorf Deepwell Plates 96 with shaking at 1300 rpm. After immunoprecipitation, the beads were washed five times in wash buffer (50 mM EPPS, pH 7.4, 150 mM NaCl) and subjected to trypsin digestion for two hours at 37 C with 15 ul of 10 ng/ml LysC and 20 ng/ul Trypsin in 10 mM EPPS pH 8.5 on Orochem OF1100 plates. The digest was labeled with 4 ul 20 g/L TMTPro tags, as recommended by the manufacturer, and thematerial was fractionated by Pierce High pH Reversed-Phase Peptide Fractionation Kit concatenating 2 fractions into a superfraction (e.g., 1 and 5). After fractionation, the samples were evaporated using vacuum centrifuge, resuspended in 20 ul 0.1% TFA and 4.5 ul was analyzed byWaters nanoAcquityMClass UPLC on 2 mmparticle size, 75 mmx 500mmeasyspray column in direct injection mode. The samples were separated using the following gradient of buffer A (0.1% formic acid in water) and buffer B (0.1% formic acid in acetonitrile): 0%–7% in 5 min, 7%–30% in 90 min, 30%–50% in 20 min. Eluting peptides were analyzed on Orbitrap Fusion Lumos instrument using MS3 SPSmethod with the settings recommended by the instrument manufacturer with the following modifications: 1. CID NCE for MS2 was set at 32; 2. HCD NCE for MS3 was set at 45; 3. C series exclusion was disabled, since TMTPro reagent was not enabled in C-series exclusion. Data were analyzed in Proteome Discoverer 2.4 software. A database search was performedwith Sequest HT search engine usingHomo sapiensUniProt database containing only reviewed entries and canonical isoforms (retrieved on 14/06/2019). Oxidation (M) was set as a variable modification, while TMTPro was set as fixed modification. A maximum of two missed cleavages were permitted. The precursor and fragment mass tolerances were 10 ppm and 0.6 Da, respectively. PSMs were validated by percolator with a 0.01 posterior error probability (PEP) threshold. Only PSMs with isolation interference < 25% and at least 5 MS2 fragments selected for MS3 matched to peptide sequence were considered. The quantification results of peptide spectrum matches were combined into protein-level quantitation using MSstatsTMT R package (Choi et al., 2014) with at least 2 peptides per protein.

Global proteomics profiling

Data acquisition and analysis were performed as previously described (Radzisheuskaya et al., 2019) with sample pre-fractionated into 30 high pH fractions. We used TMT11-plex for sample multiplexing (3 replicates of the cells transduced with NegCtrl sgRNA, 3 replicates with sgRNAs against KANSL2 and KAT8 and 2 replicates with the sgRNA against KANSL3, total 11 samples). Checking antibody specificity by ELISA using the histone peptide array library Streptavidin-coated plates (Thermo Fisher SCIENTIFIC) were rinsed three times with wash buffer (0.1% BSA, 0.05% Tween-20 in PBS), and 50 ul containing 20 ng of biotinylated histone tail peptide (Table S3, tab 1) was added to individual wells and incubated for two hours at room temperature with shaking (1000 rpm). Each well was washed three times with wash buffer, and 50 ul of primary antibody (1: 10,000 in wash buffer) added and incubated for one hour at room temperature with shaking (1000 rpm). Wells were washed three times with wash buffer, and 50 ul of peroxidase-labeled anti-rabbit IgG secondary antibody (Vector Laboratories; 1: 5,000 in wash buffer) was added to each well and incubated for 30 min at room temperature with shaking (1000 rpm). Wells were washed three times with wash buffer and once with water. 50 ul of the TMB ELISA substrate was added per well, left for 10- 30 min (until bright color was observed) and 50 ul of H2SO4 added to each well to stop the reaction. Results were read using a spectrophotometer at 450 nm.

Luminex

To examine antibody specificity in a nucleosome context by , we assembled a diversity set to mirror the related DNA-barcoded SNAP-ChIP K-AcylStat panel (EpiCypher #19-3001) (Table S3, tab 2). In brief, designer nucleosomes [a PTM-defined human e7 Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021 histone octamer assembled on 147 bp 50 biotinylated 601 DNA; EpiCypher] were individually coupled to saturation on different MagPlex avidin bead regions (Luminex), multiplexed at equivalence (1000 beads/region/well) in flat black-bottom 96-well plates (GreinerBio; VWR 89089-582), and used to query the binding specificity of each antibody at three different concentrations (1:250, 1:1000, 1:4000). 50 ul of each antibody dilution in TB_NaCl (25mM Tris pH 7.5, 0.1% Tween 20; 0.1% BSA, 200mM NaCl) was added per well, the plate covered in a dark seal, and incubated for one hour at room temperature with shaking (800 rpm). Beads were then captured on a plate-base magnet, the liquid in each well discarded, and the magnet removed. 100 ul of ChIP buffer #2 (25mM Tris pH 7.5, 5mM MgCl2, 300mM KCl, 10% Glycerol, 0.1% NP-40) was added per well, the plate covered in a dark seal, and incubated for two minutes at room temperature with shaking (800 rpm). Beads were then captured and successively washed as above with ChIP buffer #3 (25mM Tris pH 7.5, 240mM LiCl, 1mM EDTA, 0.5% sodium deoxycholate, 0.5% NP-40), ChIP buffer #1 (25mM Tris pH 7.5, 5mMMgCl2, 100mM KCl, 10%Glycerol, 0.1% NP-40) and TB-NaCl. Beads were then resuspended in 100 ul anti-rabbit IgG*PE (1:1000 in TB-NaCl), the plate covered in a dark seal, incubated for 30 minutes at room temperature with shaking (800 rpm), and successively washed three times with TB (25mMTris pH 7.5, 0.1% Tween 20; 0.1%BSA). Beads were then resuspended in 50 ul TE, read on a FlexMap-3D (Luminex), and data presented as % on-target signal for each antibody. Annexin V staining To assess cell death, 150,000 cells per replicate were washed in AnnexinV binding buffer (BD) and incubated for 30 min with FITCAnnexin V dye (BD). After two more washing steps, stained cells were resuspended in Annexin V binding buffer containing 10 ng/ml DAPI and analyzed using flow cytometry. Early and late apoptotic cells were determined by single Annexin V positivity or double positivity with AnnexinV and DAPI, respectively.

Cell cycle analysis

20,000 ES cells were seeded in 6 well plates and treated for 4 days with 500 nM dTAG-13 or DMSO. To label cells undergoing DNA synthesis, 20 uM EdU was added for 10 min before harvest. 500,000 sgRNA-expressing THP-1/cdCas9-KRAB cells were seeded in triplicates the day before and labeled with 10 uM EdU for 60 min before harvest. For both ES and THP-1 cells, 250,000 cells per replicate were fixed for 15 min with Cytofix/Cytoperm solution (BD), followed by washes with PBS and Perm/Wash buffer (BD). EdU labeling reaction was performed for 30 min in the dark in 150 ul reaction buffer (2 mMCuSO4, 20mg/ml L-ascorbic acid, 8 uMCy5-azide in PBS). After 3 washes, cells were resuspended in Perm/wash buffer containing 1ug/ml DAPI and incubated for 1 hour before acquisition on a flow cytometer.

HiC analysis

HiC data for THP-1 cells was generated as a part of another project (unpublished). HiC was performed using the Arima HiC Kit according to manufacturer’s protocol. The samples were sequenced using Novaseq. The HiC data were processed with HiC-Pro version 2.11.0 (Servant et al., 2015). Replicates were pooled to obtain 431 million validate interactions in wild-type THP-1 cells. Iterative correction (ICE) (Imakaev et al., 2012) was applied to the pooled validate interactions using HiC-Pro with default settings. Then the samples were further normalized together to the smallest read using HiCExplorer (3.4.3) (Ramı́rez et al., 2018). The final HiC matrices were binned at 25 kb and the median counts per bin is 2700 in the range of 2600 – 2850. A/B compartments were then computed using hicPCA function provided by HiCExplorer (3.4.3) with ‘lieberman’ algorithm (Lieberman-Aiden et al., 2009) and the signs were corrected by the gene density. For the integrated representation of the complex ChIP-Seq and HiC data, an implementation of Circos plot in R/Bioconductor package circlize v.0.4.8 was used (Krzywinski et al., 2009).

QUANTIFICATION AND STATISTICAL ANALYSIS

The statistical details of the experiments can be found in the figure legends and the method details. Boxplot summary: outliers (points), minimum (lower whisker), first quartile (lower bound of box), median (horizontal line inside box), third quartile (upper bound of box), interquartile range (box), and maximum (upper whisker). Molecular Cell 81, 1749–1765.e1–e8, April 15, 2021 e8

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