The functional genome of agronomically important plant species remains largely unexplored, yet presents a virtually untapped resource for targeted crop improvement. Functional elements of regulatory DNA revealed through profiles of chromatin accessibility can be harnessed for fine-tuning gene expression to optimal phenotypes in specific environments.Zea mays) genome during early reproductive development of pollen- and grain-bearing inflorescences. Using an assay for differential sensitivity of chromatin to micrococcal nuclease (MNase) digestion, we profile accessible chromatin and nucleosome occupancy in these largely undifferentiated tissues and classify at least 1.6% of the genome as accessible, with the majority of MNase hypersensitive sites marking proximal promoters, but also 3′ ends of maize genes. This approach maps regulatory elements to footprint-level resolution. Integration of complementary transcriptome profiles and transcription factor occupancy data are used to annotate regulatory factors, such as combinatorial transcription factor binding motifs and long non-coding RNAs, that potentially contribute to organogenesis, including tissue-specific regulation between male and female inflorescence structures. Finally, genome-wide association studies for inflorescence architecture traits based solely on functional regions delineated by MNase hypersensitivity reveals new SNP-trait associations in known regulators of inflorescence development as well as new candidates.Here, we investigate the non-coding regulatory space in the maize (cis-regulatory landscape during inflorescence differentiation in a major cereal crop, which ultimately shapes architecture and influences yield potential.These analyses provide a comprehensive look into the  Genetic variation in an enhancer region ~ 60 kb upstream of tb1 modulates its expression through two distinct transposable elements (TEs) [gt1 controls its expression [Growth and development of all organisms depends on coordinated regulation of gene expression in time and space. In order for this to occur, chromatin conformation must be remodeled accordingly so that specific sequences in the DNA are accessible to regulatory transcription factors (TFs) . Eukaryol manner –4. Genetl manner , 6. Althl manner –10, our l manner . Variousl manner –15 and tl manner –20. Overl manner , 22. Sinl manner . These rng range . Functiong range –25. Asidng range . In maizts (TEs) , 28. Simpression . Genome-pression , 30, 31,pression , 33.Our analysis of tassel and ear specifically examines primordia, which are enriched for meristematic cells ideal for mapping of HS signatures to footprint-level resolution. These accessible chromatin maps were integrated with complementary genomics datasets from comparable tissues, e.g., transcriptome profiles and TF occupancy maps, to validate functional regions in the maize genome. We also annotated a suite of long non-coding (lnc) RNAs associated with signatures of chromatin accessibility in the young maize inflorescences, which showed spatiotemporal specificity. Finally, we identified novel trait-SNP associations for key inflorescence architecture traits by using the functional genome to guide GWAS analyses. Results from this work provide a foundation for deeper explorations of the regulatory mechanisms underlying early developmental transitions during maize tassel and ear morphogenesis, which can be leveraged for crop improvement through molecular breeding and/or precision engineering.To determine the regulatory landscape of the maize genome during early inflorescence development, we generated genome-wide, tissue-specific maps of accessible chromatin from immature tassels and ears. We used a method that defines differential sensitivity of chromatin using different concentrations of micrococcal nuclease (MNase), which was previously demonstrated in maize for vegetative tissue types , 19, 34.Mapped reads from light and heavy digests were compared to reveal regions of differential nuclease sensitivity (DNS), including MNase sensitive (accessible chromatin) and MNase resistant (closed chromatin) signatures ) , as previously shown for other tissue types [p value < 0.001, permutations n = 1000; Additional file We asked whether the increased tassel-specific accessibility in the intergenic space was associated with transposable elements (TEs). Approximately 85% of the maize genome is made up of TEs, and 50% of the transposon component resides within gene-rich regions of the genome , 40. Basue types , 42. NotPrevious work comparing chromatin accessibility in different maize tissues showed that HS in proximal promoters of genes tended to correlate with gene expression , 19. HerWe expect chromatin accessibility to be highly dynamic during meristem development and organogenesis. Genes that control meristem determinacy are largely expressed in both tassel and ear, but it is the precise regulation of these genes that fine-tune developmental processes that lead to subtle morphological differences. By defining differential promoter accessibility and/or usage coupled to gene expression changes, we hope to resolve tissue-specific regulatory differences that underlie variation in branching, or meristem determinacy, between tassel and ear. We asked whether differential degrees of HS in proximal promoters of genes between tassel and ear translated to differential expression. Based on a set of criteria described in the Methods, a differential DNS analysis was performed for promoters of PCGs (500 bp directly upstream of the TSS). This revealed 1925 genes with differential accessibility in their immediate proximal promoters; 877 showed increased HS in tassel and 1048 in ear , GRF (q = 1.19e-02), GRAS (q = 4.19e-02), ERF (q = 5.67e-02) and TCP  families. The latter included five differentially regulated family members including tb1 (AC233950.1_FG002), a class II TCP that is well known for its function in repressing shoot branching in maize [tb1 in repressing branching in ears, there was increased accessibility in its promoter and much higher expression in ear primordia  were 112 TFs, including multiple members of TF families implicated in meristem development and organogenesis  , ramosa 3 (ra3) [thick tassel dwarf (td1) [We performed functional enrichment analysis on differentially regulated genes using Gene Ontologies (GO) based on maize-Gamer  annotati 3 (ra3)  and thicrf (td1)  , protein synthesis and turnover, and sugar metabolism   homeobox locus was accessible only in inflorescence tissue , a homeodomain TF that regulates maintenance of all plant meristems , and FAStb1 Fig. c. Anothesue Fig. c.Fig. 3p = 1.3e-183). Density plots of LCS showed high enrichment centered around FEA4 motifs in both tassel and ear  showed overrepresentation of GO terms related to ABA-related signaling, e.g., negative regulation of abscisic acid-activated signaling pathway  and seed dormancy process , and other hormone pathways such as ethylene activated signaling  compared to genes flanking a random set (n = 1039) of FEA4 motifs , which constituted less than 0.01% of the maize genome. Alignment with FEA4 binding sites was statistically significant (http://jaspar.genereg.net/). Genes that showed increased promoter accessibility in tassel were highly enriched for ERF and ARF TF motifs, while those that were more accessible in ear were enriched for WRKY, C2H2 zinc finger, TCP and MYC elements  was specifically expressed in a particular meristem type or developmental stage  investigate their association with intergenic HS chromatin signatures and ii) to extend our regulatory maps for tassel and ear development. We used a pipeline described by De Quattro et al. 2017)  to annot7  to annLncRNAs were classified as genic or intergenic. Based on overlap with known gene features, genic lncRNAs were further divided into exonic-, intronic- and exon-intron-lncRNAs Fig. b. IntergTo test whether any of these early inflorescence-expressed lncRNAs were associated with genomic enhancer-like features, we leveraged a published set of ~ 1500 candidate enhancer loci from maize leaf (V5) and husk tissues that were identified based on combined enrichment of histone 3 lysine 9 acetylation (H3K9ac) marks, high chromatin accessibility and low DNA methylation . We expecis-regulatory relationships between inflorescence-expressed lncRNAs and nearby PCGs, we analyzed correlation of their expression profiles across the eight spatiotemporal stages of immature tassel and ear development. Expression trajectories of PCGs were aligned with those of lncRNAs that were classified either as intronic or intergenic, and we prioritized lncRNA-PCG regulatory pairs based on congruence of expression patterns and their proximity to one another in the genome  and other homeobox family members, as well as hormone synthesis and signaling components, e.g., ethylene insensitive 2 , gibberellin 20-oxidase 2  and nine-cis-epoxycarotenoid dioxygenase 3 .1To explore potential sbp3; GRMZM2G101499) and ga20ox2.Based on the assumption that transcripts with similar expression trajectories might act in common pathways, we determined relationships between co-expressed lncRNA-PCG pairs (modules) based on hierarchical clustering  and ERN (n = 49)  whole genome v3 SNPs and ii) SNPs within accessible chromatin regions, a stepwise model selection procedure was used . Using t49) Fig. a. In addULTRAPETALA1 gene from Arabidopsis implicated in floral meristem determinacy [Gibberellic acid (GA) 20-oxidase 5 (GRMZM2G049418), which we showed to be differentially regulated between tassel and ear , a classical maize gene that plays a major role in tassel branching [, harbored a SNP association for TBN. Another gene that regulates inflorescence branching in maize, ramosa enhancer locus 2  [sbp3 (GRMZM2G101499) using only MNase SNPs  , was iden = 71,024), new False Discovery Rate (FDR)-adjusted P-values were calculated using the Benjamini and Hochberg [p-values compared to the genome-wide analysis , and larger, more productive ears . Since tramosa (ra) genes [tb1, however little is known about the mechanisms or what other factors they interact with. A number of other transcriptional regulators were preferentially expressed in the ear during early development, and there was tissue-specific chromatin accessibility associated with them. Among the most differentially regulated genes were five TCP TFs, including tb1 and four others that have not yet been functionally characterized. We also found that TCP binding motifs were enriched in promoters that were more accessible in ear. TCPs are among the bHLH (basic Helix-Loop-Helix) family of TFs, and are further grouped into class I or class II TCPs. Aside from tb1, the other four TCPs that were differentially regulated in ear are class I.Our analysis of differentially regulated genes between tassel and ear was consistent with a determinacy program imposed on BMs in the ear, allowing for kernels born on short branches in organized rows. We have some understanding of the key players that suppress branching in the inflorescence such as the a) genes , 71 and tb1 [wavy auricle on blade 1/ branch angle defective 1 (wab1/ bad1) [HaCYC2 [LIPOXYGENASE 2 (LOX2), which encodes a key enzyme in JA biosynthesis [There are several examples of class II TCPs regulating plant form in crops; suppression of axillary meristem or tiller outgrowth in grasses by tb1 , 74, lat1/ bad1) , 76, and [HaCYC2 . Recentl [HaCYC2 . Class I [HaCYC2 , 80, and [HaCYC2 , and TCPynthesis . In our ynthesis , 84. Claynthesis , which cynthesis . We obsetb1 gene was associated with any functional data in maize. This highlights the utility of genomics-enabled research for gene discovery, particularly when viewed in a context-specific manner. There are approximately 2700 TFs in the maize genome and we only have experimental evidence on how a small fraction of them function [in planta methods such as ChIP-seq [in planta TF binding data with MNase HS in the same tissue type, suggesting that accessible regions can be viewed as a proxy for TF binding. We also observed increased accessibility in promoters of genes bound by two TFs based on experimental data, indicating that degree of accessibility scaled with number of binding factors. Therefore, these tissue-specific accessibility maps can help drive computational-based predictions of TF binding and refine results from in vitro methods such as DNA Affinity and Purification sequencing (DAP-seq) [Of the five TCP TFs that we identified as being differentially regulated in the ear, only the well-characterized function , 86. Confunction , 87. GenChIP-seq . We showDAP-seq) , 89. Alocis and trans, several studies have highlighted control of the local regulatory “neighborhood” by lncRNAs [APOLO, is auxin-dependent and destabilizes a chromatin loop to the promoter of PINOID (PID), a key regulator of polar auxin transport [There is increasing evidence for lncRNA-mediated regulation of genome structure in both plants and animals, e.g., through nucleosome positioning, interaction with chromatin remodelers and chromatin looping , 55, 90. lncRNAs –93. Vari lncRNAs , as well lncRNAs , 96. In ransport , allowinsbp3, was co-expressed with a lncRNA immediately downstream. SBP TFs play key roles in developmental processes, however sbp3 has not been functionally characterized. Further, its association with a GWAS SNP for TBN and its enhanced expression in ear, suggest that sbp3 and the co-expressed lncRNA are potential candidates for regulating branching . Notabld to ERN . ExperimIn summary, the functional maps described here provide a foundation for pinpointing key regulatory signatures that underlie growth, development, and morphological diversity in reproductive structures of maize, the most important cereal crop. The MNase-seq profiles offer genome-wide views of the regulatory space, including both TF binding sites as well as nucleosome occupancy, which can be leveraged for making informed predictions on functional DNA, and as a complement to many other genomics analyses. One timely application is guiding gene editing strategies by determining accessibility of target regions. Our integrated analyses of gene regulation during early inflorescence organogenesis defined targets for manipulating architecture, and provided insights into tassel- and ear-specific developmental programs that can be harnessed for crop improvement.cis-regulatory landscape underlying developmental transitions that occur during differentiation of male and female inflorecences of maize. Chromatin accessibility and nucleosome occupancy maps in these young, meristematic tissues revealed regulatory signatures specific to tassel and ear development including differentially regulated members of certain TF families, and predicted TF binding sites to footprint level resolution. On a genome-wide scale, differences in accessibility were observed with tassel-specific HS signatures enriched in intergenic regions while ear-specific HS were more prominent in gene body features. Several intergenic accessible sites coincided with predicted promoters of spatiotemporally expressed lncRNAs, combinatorial TF binding, and trait-SNP associations from GWAS, prioritizing them as potential long-range regulatory elements. These integrated functional maps and associated analyses can be directly leveraged for targeted crop improvement in maize and are readily translatable to other economically important cereal crops.Our ability to predict and achieve desired crop phenotypes through alteration of genetic elements is enhanced by context-specific regulatory maps targeted to traits of interest. The systems-level analyses presented here explore the Zea mays) B73 plants were grown in a greenhouse environment (27 °C/23 °C day/night) at the DDPSC Integrated Plant Growth Facility. Three seedlings per 2 gal pot (in Pro-Mix BRK-20 soil) were harvested 4–5 weeks and 6–7 weeks after sowing to collect tassel and ear primordia, respectively. For each tissue type, approximately 170 inflorescence primordia (between 1 and 5 mm in size) were hand-dissected and pooled into each of two biological replicates. Primordia were immediately flash frozen in liquid nitrogen, and stored at − 80 °C.Maize . Manual inspection in a genome browser revealed several cases where MNase HS was visible in the two biological replicates, but only called as a peak in one likely due to coverage threshold  was calculated using the function sum. The midpoint of the MNase HS sites were computed in R, converted to a GRanges object, and intersected with respective genomic regions using the function findOverlaps with the options type = within, ignore.strand = F. Intergenic MNase HS sites falling within the reference repeats annotation file (AGPv3.31) were excluded from the analysis.To map distributions of MNase HS sites across genomic features, we used the Bioconductor packages Features . A transGenomicRanges . The enrichment analysis was conducted using the Bioconductor package regioneR [n = 1000). A randomization strategy to compare intergenic genomic regions was performed using the function circularRandomizeRegions.The maize TE annotation file (AGPv4) was downloaded from Gramene release 61 . TEs annregioneR  based on--novel-splicesite-outfile, −-dta’ and without a reference gene annotation file. Based on information from this first alignment, a database of unique splice junctions was created to maximize the number of mapped reads in the second mapping iteration using the option ‘--novel-splicesite-infile’. Then for each library we used the reference gene models (AGPv3.31) to guide the transcriptome reconstruction (assembly) and merged (using option --merge) all assemblies to create a non-redundant reference for this dataset. The transcript sequences (FASTA) and the gene models (GFF) were extracted using the gffread utility distributed with the program Cufflinks [Publicly available RNA-seq data from developing maize tassel and ear primordia were downloaded from the NCBI Sequence Read Archive (SRA) database, BioProject PRJNA219741   and Striufflinks .travis (https://github.com/dvera/travis), the normalized coverage (RPM) was calculated for light digests, heavy digest and the differential (light -heavy) at the midpoint for 25 bp genomic windows.MNase HS and expression data for each PCG were correlated using the mean normalized depth (reads per million) for the heavy and light MNase digests and the StringTie v1.3.5 TPM values for 1–2 mm tassel and ear data. Using the R package Differential accessibility within 500 bp upstream of the TSS of PCGs was calculated between tassel and ear was by computing the MNase HS value (tassel DHS - ear DHS) in 25 bp windows. Based on these values, changes of > 1 or < − 1 were defined as differentially accessible in tassel or ear, respectively. Genes characterized by differential accessibility in their promoter were filtered to include those with > 100 read coverage selected based on the RNA-seq data. A fold change of |1.5| between the two tissues was used to exclude genes with minimal expression changes .ChIP-seq data for FEA4 and KN1 are publicly available in the NCBI Gene Expression Omnibus . Genome-wide binding sites were determined for FEA4 using MACS v2  with parwww.meme-suite.org/; [n = 2163). Random 200 bp maize genomic DNA fragments were used as background. The resulting FEA4 PWM was located within FEA4 ChIP-seq peaks using Find Individual Motif Occurrences  in MEME. The custom R package genmat (https://github.com/dvera/genmat) was used to generate density plots of MNase HS around FEA4 binding. HS regions overlapping FEA4 binding sites called by iSeg (bc 2.0) in tassel and ear were used to scan for enriched proximal motifs using two de novo motif finding tools: MEME-ChIP and Hypergeometric Optimization of Motif EnRichment . MEME-ChIP was used with settings ‘-ccut 0’ and inputting a random set of genomic sequences as a control. A parallel analysis used a random set of iSeg-called maize sequences as the input compared to random genomic controls. Input and control sequences were repeat-masked. The Tomtom function in MEME was used to assign resulting motifs to best matches in the JASPAR core plant PWMs . We used the findmotifsgenome.pl function in HOMER with setting ‘-size given’ to report best matches to known plant motifs within the HOMER database. Resulting PWMs from HOMER were also matched to the JASPAR core plant PWMs.The MEME suite v5.0.5  was useDe-novo motif discovery using these footprints was performed using Discriminative Regular Expression Motif Elicitation (DREME) in the MEME suite using default settings. Random background sequences of equal size from the same promoters were used as control. Tomtom was used to find the best matches to motifs in the JASPAR plant database. The resulting PWMs were located within footprints using FIMO (threshold = 0.005).To resolve locations of putative TF footprints we mapped the midpoints of small (< 131 bp) fragments reads generated from light MNase digests. To find the fragment center hot spots, we converted the single-base fragment center data using a sliding window average, where the total number of fragment centers in a 21 bp window was assigned to the central 5 bp in that 21 bp window, followed by repeating a 5 bp step size. To aid visualization of the fragment center populations on a genome browser, this procedure was also performed for the 5′ and 3′ ends of the light digest MNase fragments (0–130 bp). The resulting fragment center values showed local peaks that were segmented using the iSeg peak-calling program. n = 393,418)  entirely within exons, ii) entirely within introns, or iii) spanning exon-intron boundaries. LncRNAs overlapping a PCG model in the antisense orientation were classified as antisense. Expression profiles of lncRNAs were determined by calculating TPM values across the eight stages of maize inflorescence development using Salmon v0.8.2 in mapping-based mode [tximport [BioQC [High-confidence lncRNAs were classified based on their relative genomic location to protein coding genes. We defined overlaps between lncRNAs and PCGs using the Bioconductor package, sed mode  and the tximport . Meristet [BioQC  and the rlog in the Bioconductor package DESeq2 [bicor from the Bioconductor package WGCNA [To correlate expression levels of the lncRNAs with their closest PCGs, pairwise co-expression analyses were performed across each lncRNA - PCG pair independently for each of these four classes of lncRNAs: intronic-lncRNAs and intergenic lncRNAs < 2 kb, < 10 kb, and > 10 kb. First, expression values of PCGs and lncRNAs were normalized by log transformation using the function e DESeq2 . For eacge WGCNA . The lncPublic raw data from whole-genome bisulfite sequencing  of B73 immature ears (~ 5 mm in length) were downloaded from the NCBI Sequence Read Archive using the run ID SRR2079442 . Raw datdoi.org/10.7946/P2S62P) and used as input for the gene set enrichment analysis. The function enricher from the Bioconductor package clusterProfiler [https://www.ebi.ac.uk/QuickGO/). Maize TF annotations were downloaded from Grassius [Maize-GAMER  AGPv3 agProfiler  was usedGrassius  and PlanGrassius . The twoThe dependent variable for each GWAS model was the residuals from a joint linkage analysis model fitted in  containiZbrowse, an interactive GWAS viewer (www.baxterlab.org) [For each set of markers, a stepwise model selection procedure implemented in the software TASSEL v5.0  was usedlab.org) .p-values for SNPs within tassel MNase HS regions and 1000 random subsets  using the Benjamini and Hochberg procedure  for TBocedure  and Tables (S1-S6).Additional file 2: Dataset S1. Coordinates of all MNase HS peaks .Additional file 3: Dataset S2. Coordinates of all MNase HS peaks .Additional file 4: Dataset S3. High confidence MNase HS peaks .Additional file 5: Dataset S4. Coordinates of all MNase HS peaks  mapped to the maize reference genome RefGen_v5.Additional file 6: Dataset S5. Unique MNase HS peaks (DNS) in tassel and ear primordia (iSeg bc 2.0).Additional file 7 Dataset S6. Normalized expression levels (TPM) of PCGs, binned by expression group.Additional file 8 Dataset S7. Functional annotations, differential accessibility and expression data for differentially regulated PCGs in tassel vs. ear.Additional file 9 Dataset S8. Genes bound by FEA4 and/or KN1 TFs in MNase HS regions of their proximal promoters, and functional enrichment using GO.Additional file 10 Dataset S9. Distance matrix between ABI3 motifs and FEA4 binding sites, closest gene models and annotations associated with these combinatorial cis-element modules, and functional enrichment analysis using maize-GAMER GO for genes within 1 kb of FEA4-ABI3 cis-element modules.Additional file 11: Dataset S10. Tassel and ear footprints at the iSeg threshold (iSeg bc 4.0).Additional file 12: Dataset S11. Annotated high-confidence lncRNAs from inflorescence primordia .Additional file 13: Dataset S12. High-confidence lncRNAs supported by Sanger sequencing.Additional file 14: Dataset S13. Classification of inflorescence-expressed high-confidence lncRNAs.Additional file 15: Dataset S14. SNP-trait associations and closest gene annotations from GWAS analyses of TBN and ERN using all HapMap V3 SNPs and a subset within MNase HS regions only.Additional file 16: Dataset S15. Output from stepwise GWAS analysis of TBN and ERN using both HapMapv3 SNPs and markers subset based on MNase HS regions.Additional file 17. Review history.


Epileptic encephalopathies (EE) are severe epilepsy syndromes characterized by multiple seizure types, developmental delay and even regression. This class of disorders are increasingly being identified as resulting from de novo genetic mutations including many identified mutations in the family of chromodomain helicase DNA binding (CHD) proteins. In particular, several de novo pathogenic mutations have been identified in the gene encoding chromodomain helicase DNA binding protein 2 (CHD2), a member of the sucrose nonfermenting (SNF-2) protein family of epigenetic regulators. These mutations in the CHD2 gene are causative of early onset epileptic encephalopathy, abnormal brain function, and intellectual disability. Our understanding of the mechanisms by which modification or loss of CHD2 cause this condition remains poorly understood. Here, we review what is known and still to be elucidated as regards the structure and function of CHD2 and how its dysregulation leads to a highly variable range of phenotypic presentations. Chromatin arrangement is a major functional aspect of transcriptional control and thus of the complex co-ordination of all body functions and systems in eukaryotes . ChromatIn 1993 Delmas et al. reported the discovery of a DNA-binding protein that contains both a chromo (chromatin organization modifier) domain and a SWI/SNF helicase domain that is present in most mammals. They named this protein chromodomain-helicase-DNA-binding protein (CHD-1) . FurtherThe array of auxiliary functional domains within each of nine human CHD family protein members underlies their division into three subfamilies ,9. NotabThe structures shown in While the highlighted structural similarities and differences between the CHD subfamilies and individual protein motifs are well documented, some uncharacterized domains still remain, whose functions are yet to be established ,40.The canonical function of CHD proteins is the spatial and temporal regulation of gene expression via reversible structural and chemical changes to DNA to modulate accessibility of the transcriptional machinery ,41,42,43Histone H3 lysine 4 di- and tri-methylation (H3K4me2/3) are epigenetic markings associated with transcriptionally active euchromatin ,45. WhilDrosophila melanogaster also take place at sites of active transcription and are similarly facilitated by their chromodomains, but H3K4me sites are not necessary [The C-terminal DNA binding domains (DBD) that are conserved in CHD1 and CHD2, bind AT-rich regions of DNA in vitro, but DNA binding in vivo actually occurs via the tandem chromodomains at the recognized H3K4me2/3 sites ,46,48. Tecessary ,50. In fecessary . As suggecessary ,51, and ecessary ,53. CHD5ecessary ,54. Miceecessary . CHD6 isecessary . This isecessary , as oppoecessary . CHD2, Cecessary ,57.coloboma, congenital heart defects, choanal atresia, retardation of growth and development, genital hypoplasia, and ear anomalies associated with deafness) syndrome  CHD7 is associated with many but not all instances of the disease [Drosophila kismet protein in interactions with RNAPII [−/− (knockout) mice are viable and develop normally [In CHARGE  was published in which it was noted that mutations within this region result in variable combinations of intrauterine growth retardation (IUGR), microcephaly, abnormal face and ears, micrognathia, highly arched palate, renal abnormalities, lung hypoplasia, failure to thrive, and developmental delay/mental retardation . Since tCHD2 [Further reports of 15q26.1 deletions followed and in 2009 Veredice and co-authors published one such report detailing a de novo microdeletion at 15q26.1 in which the patient suffered from refractory myoclonic epilepsy along with minor physical anomalies . SubsequCHD2 ,73,76,80CHD2 disruption in the majority of cases involving deletions and/or mutation in this narrow chromosomal region, when seizures are present [These studies and subsequent follow up analyses firmly established the role of  present ,21,74,75CHD2 mutation leading to haploinsufficiency causes mild to severe neurodevelopmental disruption in a phenotypically diverse “syndrome” that most commonly results in the presentation of myoclonic epileptic encephalopathy with onset of seizures in the first few years of life, in combination with varying degrees of other cognitive and growth deficits, and dysmorphic features characteristic of disruptions in proximate genetic loci. While this is the case, the mechanism by which epilepsy develops in patients affected by these mutations is still largely unknown, as is a comprehensive and definitive description of the role of CHD2, as distinct from the numerous extant related proteins [Thus, by 2017 the body of evidence convincingly supported that hemizygous proteins ,84.CHD2 mutation is a driver of cancer, particularly lymphoma [CHD2 is ubiquitously expressed in all tissue types in humans  with lowlymphoma ,107,108.Chd2 is expressed prominently in regions undergoing neurogenesis in mouse neural progenitor cells. Specifically, Chd2 is highly expressed in radial glial (RG) progenitors and is rarely expressed in intermediate progenitors (IPs), which are the primary precursors of neurons, during the development of the cerebral cortex [Chd2 is expressed throughout the brain and is particularly strongly expressed in the olfactory bulb, neocortex, hippocampus, and cerebellum. With regard to specific subpopulations of neuronal cells, they found that Chd2 is expressed in all mature neurons, GABAergic interneurons, and oligodendrocytes but not GFAP-positive astrocytes [CHD2 expression increased during cIN differentiation [Recent findings have demonstrated that l cortex . In yountrocytes . Similarntiation .Thus, it has been demonstrated that CHD2 is expressed throughout brain tissues but that differences in expression occur in various cellular subtypes at different stages of development.CHD1 and CHD2 recruitment is directly coupled to transcription at transcription start sites (TSS) indicating that their regulatory roles are linked to regions of active chromatin . CHD1 anEarly characterization work showed that tissue specific CHD2 splice variants exist , implyinChd2 has indicated that the long non-coding RNA (lncRNA) LINC01578 or CHD2 Adjacent Suppressive Regulatory RNA (Chaserr), represses Chd2 gene expression solely in cis, and that the phenotypic consequences of Chaserr loss are rescued when Chd2 is also perturbed [In addition, further investigation into the regulation of erturbed . Little erturbed . Prelimierturbed .These possible additional intricacies in the gene regulation interactions that govern CHD2 expression and function may account to some extent for the variable phenotypes seen in CHD2 encephalopathies.Due to differences in the chromodomain, CHD2 has a lower affinity for H3K4me than CHD1. It has been found that when CHD2 interacts with sites of active chromatin, it is initially recruited by an RNA polymerase after which it deposits the H3.3 histone variant leading to altered target gene expression levels ,112. In The epigenetic control mediated by CHD2 was uncovered in a study aimed at elucidating how muscle cell differentiation-regulating factors are themselves regulated. Harada and colleagues identified CHD2 as a regulator of MyoD—a transcription factor that determines cell fate. They found that CHD2 incorporates the histone variant H3.3 into genes in cells destined to differentiate into muscle cells. Although such functionality lies outside of the CNS, it provides further insight into a consolidated mechanism of CHD2 activity in development .CHD2 deletions in epileptic disorders in humans and developmental defects in both humans and mice, investigated the biochemical activity of CHD2 at the molecular level via a range of biochemical assays. They noted the affinity of CHD2 for chromatin and its capacity for chromatin remodeling, highlighting many of the interdomain control mechanisms discussed in the preceding section. Additionally, the authors sought to determine whether CHD2 plays a role in nucleosome assembly. They found that CHD2 assembles regularly spaced nucleosome arrays from purified components in the presence of ATP. They thus concluded that CHD2 functions not only as a chromatin remodeler in regulation, but also as a chromatin assembly factor [In 2015, Liu and colleagues, prompted by the implication of y factor .Recently, CHD2 has been found to play a critical role in DNA repair mechanisms. Luijsterburg and colleagues found that at sites of DNA double stranded breaks (DSBs), poly(ADP-ribose) polymerase 1 (PARP1) recruits CHD2 to rapidly expand chromatin and deposit H3.3 variants to initiate non-homologous end joining repair (NHEJ) . It was REST) gene ). They found that when CHD2 is silenced, REST expression is decreased. Conversely, when CHD2 was overexpressed REST expression increased. They demonstrated that regulation of neural differentiation mediated by REST is promoted by CHD2 expression, and importantly, that this occurs via direct association of the REST gene and CHD2 protein, a process not mediated by H3K4me. This implies that cell stress caused by CHD2 knockdown can be rescued by REST overexpression [CHD2 has been found to interact with other important developmental transcriptional regulators. Shen et al. demonstrated that CHD2 binds directly to the repressor element 1-silencing transcription factor , showed that CHD2 is necessary for cellular differentiation. Indeed, CHD2 deficiency impaired the development of cINs from hESCs and altered electrophysiological characteristics of the cINs. This study further revealed that CHD2 regulates cIN development via interaction with the medial ganglionic eminence (MGE) associated transcription factor NKX2-1 .Chd2 heterozygous mice have reduced GABAergic progenitor proliferation compared to WT mice. Conversely, in glutamatergic populations transcriptional changes resulted in heightened synaptic activity [activity . Surprisactivity .Danio rerio, for the study of developmental epileptic encephalopathies [chd2 knockdown model was generated using targeted morpholino antisense oligomers [Pioneering work from the Baraban lab has demonstrated the suitability of Zebrafish, opathies ,127,128.ligomers . Upon religomers .Chd2 knockdown zebrafish larvae displayed markedly increased photosensitivity  as compared to WT controls [Chd2 knockdown zebrafish studies strongly support the implication of de novo CHD2 mutations in epilepsy in humans.Following this work, Galizia and co-authors used this Chd2 knockdown zebrafish and altered the field potential recording methodology to compare light and dark recording periods. controls . Since pChd2 mutation was generated in mice in 2006 by Marfella and colleagues [Chd2 locus. The induced mutation resulted in truncated CHD2 protein that lacked the C-terminal DNA binding domain. They demonstrated that homozygous mutants were non-viable, displaying perinatal lethality and developmental abnormalities prior to death. They also found that heterozygous mutation severely affects development, causes gross kidney abnormalities, and severely decreases longevity/survival [The first animal model of lleagues . The autsurvival .Chd2 led to embryonic and perinatal lethality. Further, they found that expression of Chd2 is ubiquitous, but that regional expression of Chd2 in cardiac, forebrain, facial and dorsal tissues was developmental stage specific in mouse embryos. The heterozygous (+/Chd2-) mice displayed growth retardation and morphological abnormalities including lordokyphosis, lowered body fat, and postnatal runting [Kulkarni and co-authors later reported the generation of another CHD2 deficient mouse line, also using a gene-trap system. In line with the findings of Marfella and colleagues they found that disturbances in  runting .Chd2 heterozygous mutant mouse line was again generated by gene-trap cassette in 2009. The induced mutation was found to affect the region downstream of the DNA binding domain, causing disruption of the C-terminal end of the protein. The heterozygous mutants showed extramedullary haematopoiesis and increased susceptibility to lymphomas. The +/-Chd2 mice displayed aberrant DNA damage repair and genomic integrity [A ntegrity .+/-Chd2) mouse line was generated by crossing transgenic mice containing loxP-flanked exon 3 of Chd2 with a b-actin Cre line [+/-Chd2 mice displayed divergent expression of a variety of genes responsible for chromatin regulation, neurogenesis, and synaptic transmission [More recently, a heterozygous deletion  lines modelling loss of function (LOF) of CHD2 have been developed. These lines were generated by combining the steps of somatic cell reprogramming to iPSCs and the induction of LOF mutations using CRISPR/Cas9 guide RNA (gRNA) delivered via episomal vectors [CHD2 variant patients (or the development of a system for the efficient generation of specific mutations in iPSCs or their precursors) in order to investigate specific disease-causing mutations in vitro. Advances are rapidly being made in the field of directed mutagenesis, including highly efficient base editing systems [Although so much important information has been acquired by the use of the abovementioned  vectors . The tec vectors . The gen vectors ,130. Thu systems  which wiCHD2 variants are, at present, difficult to predict. While some progress has been made, our understanding of how mutations in CHD2 drives the development of hyperexcitable neuronal circuits is still poorly developed.The molecular consequences of CHD2 mutations. There are many rapidly developing technologies which will, in time, undoubtedly facilitate the creation of human specific models of CHD2 variation. Most prominent of these, are the iPSC based models of the disease (including the development of brain organoids). When coupled to technologies involving directed mutagenesis using “base editing” or Crispr/Cas9 based approaches, such technologies promise to soon make accessible essential information relating to specific CHD2 variants. In addition, extant in silico methods like gene target network analysis and next generation sequencing (NGS) techniques and analysis tools, can be applied to the information that will be generated, providing the potential for delineating the complexity of regulatory systems governing CHD2-mediated transcriptional control.A more comprehensive understanding of how CHD2 regulates both neuronal development and brain function may provide key insights into the variable phenotype observed in patients with known Beyond this, such information may also inform novel treatment options for patients, who are often extremely drug refractory and have uncontrolled seizures, aggression and cognitive impairment. Once a definitive set of CHD2 targets has been identified and characterized, the modulation of a specific pathway or specific individual genes could be investigated for the alteration of expression of key proteins in the CNS phenotype. The possibility that the CHD2 “phenotype” is influenced by auxiliary molecules such as the regulatory lncRNA Chaserr must also be considered and investigated. Indeed, targeting of such molecules using antisense oligonucleotide approaches may offer the possibility to correct or enhance CHD2 expression and/or function .CHD2 mutations.Recent advances in these technologies should spur further research in these areas with the ultimate aim of mitigating the widespread effects of


Enhancers are DNA sequences that enable complex temporal and tissue-specific regulation of genes in higher eukaryotes. Although it is not entirely clear how enhancer-promoter interactions can increase gene expression, this proximity has been observed in multiple systems at multiple loci and is thought to be essential for the maintenance of gene expression. Bromodomain and Extra-Terminal domain (BET) and Mediator proteins have been shown capable of forming phase condensates and are thought to be essential for super-enhancer function. Here, we show that targeting of cells with inhibitors of BET proteins or pharmacological degradation of BET protein Bromodomain-containing protein 4 (BRD4) has a strong impact on transcription but very little impact on enhancer-promoter interactions. Dissolving phase condensates reduces BRD4 and Mediator binding at enhancers and can also strongly affect gene transcription, without disrupting enhancer-promoter interactions. These results suggest that activation of transcription and maintenance of enhancer-promoter interactions are separable events. Our findings further indicate that enhancer-promoter interactions are not dependent on high levels of BRD4 and Mediator, and are likely maintained by a complex set of factors including additional activator complexes and, at some sites, CTCF and cohesin. The role of BRD4 and Mediator in regulating enhancer-promoter interactions is poorly understood. Here the authors find that treatment with BET inhibitors or pharmacological degradation of BRD4 disrupts transcription while having very little effect on enhancer-promoter interactions. Despite the importance of enhancers, very little is known about exactly how they function, although they have been proposed to act mainly as binding platforms for the assembly of protein complexes that can promote gene activation3. A key aspect to this assembly is the binding of sequence-specific DNA binding factors such as transcription factors (TFs). Enhancers can be situated far away from the genes they regulate4. Although not always the case6, at many gene loci proximity between enhancers and promoters is thought to be essential for enhancer function and gene activation8. How these enhancer–promoter interactions are initiated and maintained is not clearly understood.In higher eukaryotes, enhancers are DNA sequences that allow for the complex regulation of genes in different tissues and at different times10. It is not entirely clear how these higher-order structures impact enhancer function, but generally speaking functional enhancer–promoter interactions are limited to genes within or at the edges of domains. The genome-wide existence of these more localized enhancer–promoter looping structures has been demonstrated by global chromosome conformation capture (3 C) techniques12. However, unless each sample is sequenced extremely deeply13 , Hi-C is not able to delineate enhancer–promoter interactions at high resolution, so it is difficult to study these structures in detail genome-wide. The high complexity of Hi-C libraries and the high cost per sample and difficulty of analyzing such large datasets has meant that there have been multiple attempts to develop high-throughput methods to provide more information specifically about enhancer–promoter interactions15, but the highest resolution studies focus on individual genes/enhancers using next-generation techniques, such as 4 C16, UMI-4C seq17, Next Generation Capture-C18, Tri-C19, and tiled Capture-C20.Emerging work suggests that enhancers function within larger domains, the boundaries of which are defined by the combined effects of CTCF-marked boundary regions and cohesin looping, through a process known as loop extrusion23 and protein/RNA complexes bound to both the enhancer and promoter that interact with one another26. Binding of these complexes is likely initiated by key sequence-specific TFs. The presence of specific histone modifications at the enhancer is thought to contribute to one or all of these models, mainly by stabilizing the presence of specific protein complexes, such as cohesin binding to H3K4me1 or BRD4 (Bromodomain-containing protein 4) binding to H3K27ac29. Recent work from our lab also suggests that at H3K79me2/3-dependent enhancer elements (KEEs), the presence of H3K79me2/3 can help maintain open chromatin regions to facilitate the binding of sequence-specific transcription factors, and is required for enhancer–promoter interaction30. This could constitute a more general principle where histone modifications help regulate DNA accessibility and TF binding, and ultimately the formation of enhancer–promoter loops.While chromatin looping mediated by cohesin and bounded by CTCF binding is the most common explanation for controlling large-scale chromatin structure, less is known about what stabilizes more localized enhancer–promoter contacts. Possible models include cohesin-stabilized enhancer–promoter interactions32. In cancer cells, important oncogenes are often associated with super-enhancers34. Recent work has shown that many enhancer-associated factors, such as Mediator  and BRD4, assemble into phase-separated activation complexes, and these interactions are proposed to be integral to their ability to activate transcription39, but a direct requirement for phase condensate formation in transcription has not been established40. Since these coactivator clusters, which assemble at enhancers (particularly super-enhancers), are also proposed to incorporate promoter-bound RNA polymerase into the condensate39, it is possible that they act as a bridge between these distal DNA elements, and may have a role in initiating and/or maintaining enhancer–promoter interactions42.Super-enhancers are enhancers with increased enrichment for binding of TFs and coactivator complexes such as BRD4 and Mediator, and are also associated with high levels of transcriptional activity21. However, this study used Hi-C (binned at 5 kb) and promoter capture Hi-C, which are relatively low resolution and low sensitivity techniques, so it is possible that perturbations in specific enhancer–promoter contacts may have been missed. It therefore remains unclear whether BRD4 or Mediator play any role in organizing chromatin structure and maintaining enhancer–promoter interactions at active genes.Taken together, these various strands of evidence suggest the following model: (a) loop extrusion mediated by cohesin generates higher-order chromatin structures bounded by CTCF; (b) TFs bound to enhancers and promoters assemble phase condensates made up of chromatin proteins such as BRD4 and coactivators such as Mediator; (c) histone modifications maintain accessibility for the binding of TFs and create additional affinities to further stabilize complexes; (d) the CTCF/cohesin-delimited structures create a smaller DNA compartment, increasing the frequency of random interactions between complexes bound at enhancers and promoters; and (e) the phase-separated condensates containing BRD4 and Mediator anchored at the enhancer and promoter act as a bridge to stabilize these enhancer–promoter interactions. Thus, the model posits that formation of phase condensates is a key requirement for at least a subset enhancer–promoter looping. Recent work testing this model indicated no loss of enhancer–promoter contact following degron-mediated loss of MED14, suggesting that Mediator is not responsible for these interactions18 in cells treated with BET inhibitors, a BRD4-degrading compound (AT1) and 1,6-hexanediol. This technique provides the greatest resolution and sensitivity of all the available 3 C methods for higher eukaryotic cells43. Data are generated using four-cutter restriction enzymes and are of sufficient sequencing depth that they can be reported at single restriction fragment resolution. In addition, the method is highly sensitive and reproducible, meaning that changes in interaction frequency can be analyzed quantitatively under different conditions at many genes simultaneously.In this paper, we directly test the role of BRD4 and Mediator in enhancer–promoter interactions by performing high resolution Next Generation Capture-CWe find that reduction of BRD4 and Mediator binding at enhancers has a dramatic and rapid effect on gene expression, but enhancer–promoter looping structures remain stably intact. This suggests that the function of these activation complexes at enhancers does not involve stabilization of the enhancer–promoter interaction. Instead, we see evidence of CTCF and cohesin binding at many enhancers, indicating that these complexes can stabilize and maintain looping structures even in the presence of reduced transcription and activation complexes at the enhancer. Finally, our results demonstrate that stabilization of enhancer–promoter interactions and promotion of transcription are separable events, and that the presence of an enhancer–promoter loop is not sufficient for the maintenance of transcription.34. We analyzed levels of BET-domain  proteins and Mediator subunits at ATAC peaks genome-wide in the leukemia cell line SEM, with peaks ranked by the relative levels of H3K4me3 and H3K4me1, thereby separating promoter (top of heatmap) and enhancer (bottom of heatmap) loci  region, composed of two major domains, located ~1.7 Mb away. This long-distance interaction has also been observed and characterized in several other cell types51. Reciprocal Capture-C from the more proximal of the two enhancer regions demonstrated contact with the MYC promoter, avoiding intervening regions, as well as a relatively weak interaction with the more distal enhancer domain , which disrupts binding of BET protein bromodomains to acetyllysine residues. IBET is known to disrupt transcription, so we wanted to use a short treatment time to limit secondary effects from regulatory events downstream of initial transcriptional changes. We used qRT-PCR to assess how quickly IBET treatment affects gene expression. However, the stability of mature transcripts means that there is often a delay between decreased transcription and changes in mRNA levels  bifunctional molecule AT1. This consists of the BET inhibitor JQ1 conjugated to a von Hippel-Lindau (VHL) ligand, resulting in targeted degradation of BRD4ted Fig.  and ChIPted Fig. , consistted Fig. . Notablyted Fig. , the dowted Fig. , suggest42, and this clustering is proposed to be important for the function of super-enhancers, potentially by mediating interactions with target gene promoters. In previously published work using high resolution imaging, treatment of cells with the small molecule inhibitor JQ1, which, like IBET-151, disrupts binding of the BRD4 BET domain to acetyllysine residues, prevented clustering of Mediator35, indicating that association with chromatin is integral to phase condensation of these proteins. We therefore asked whether inhibition of BRD4 binding would disrupt enhancer–promoter interactions, as this might explain the strong effect of IBET treatment on transcription.BRD4 and MED1 have recently been shown to be present in phase condensates in the nucleusMYC enhancer, the major regions of contact with the promoter remained virtually unchanged by 90 min IBET treatment, with only small differences in interaction frequency , which is not marked with BET proteins or other enhancer features  targeted to telomeric sequences appear to bring loci together42, and dissolution of phase condensates prevents the estrogen-induced colocalization of enhancers76. However, it is unclear how closely these observations represent physiological enhancer–promoter interactions, or whether these results are representative of mechanisms functioning generally at most enhancers. Our data demonstrate that these low-affinity interactions are not necessary for the maintenance of enhancer–promoter contacts, as loss of BRD4 chromatin binding had no effect on promoter interaction profiles. A similar lack of effects was recently observed, albeit at lower resolution by Hi-C, in Mediator mutant mouse embryonic stem cells (mESCs)21. Importantly, the high resolution and sensitivity of Capture-C confirms the lack of even subtle changes in enhancer–promoter contacts in our experiments, for example localized to BRD4 binding sites.Recent models have proposed that the low-affinity interactions that drive phase condensation may be sufficient for both enhancer–promoter colocalization as well as promoting transcriptional activation21, suggesting that it may be a genuine consequence of Mediator loss. It is possible that these structural changes are an indirect effect of the transcriptional disruption following BRD4/Mediator loss, as has been observed before78, although there was no correlation with transcriptional response.We note that, although our drug treatments reduced BRD4 and Mediator binding, some signal was still detectable by ChIP. This raises the possibility that high levels of BET/Mediator are needed for transcription, but low levels of BET/Mediator binding may be sufficient to maintain enhancer–promoter interactions Fig. . Arguing73. Depletion of cohesin or its loader NIPBL disrupts interactions between some promoters and distal enhancers80, although in some cases this may be an indirect effect through loss of TAD boundaries rather than physical contact with enhancers. As has been reported59, in our analysis we observed an enrichment for CTCF/RAD21 binding at promoter-interacting loci, consistent with a direct role for loop extrusion in mediating enhancer–promoter interactions. Indeed, loss of Rad21 results in decreased enhancer–promoter contacts and transcriptional downregulation at Sik1 and Elf3 in mESCs79. Similarly, enhancer–promoter interactions at MYC are dependent on CTCF23. However, it is possible that this gene is unusual, as long-distance enhancer–promoter interactions over 1 Mb are not common. Strikingly, the majority of genes rapidly downregulated following CTCF degradation in mESCs show CTCF binding at the promoter, although this effect was proposed to be a result of a looping-independent function of CTCF in transcription81.What, then, is responsible for establishing and maintaining enhancer–promoter interactions? We favor a role for cohesin and CTCF at the subset of genes where they are bound, potentially in combination with low levels of BRD4 and Mediator Fig.  and othe83, although cohesin may be important for inducible gene expression84. This suggests, assuming that the majority of enhancer–promoter interactions are productive, that cohesin is not essential for most enhancer function. However, given that our current understanding of enhancer function remains incomplete, this point alone is insufficient to rule out a role for loop extrusion in linking at least a subset of enhancers to promoters.One complicating aspect of the role for cohesin in enhancer–promoter contacts is the fact that disruption of loop extrusion, either by loss of cohesin itself, its loader NIPBL or CTCF, does not have widespread effects on gene expression59. For example, deletion of the sole CTCF site in the Sox2 super-enhancer in mESCs reduces, but does not abolish, contact with the promoter60. Indeed, in our analysis, while many of the promoter interaction sites overlap with correctly oriented CTCF sites, there are also many sites of interaction that lack an obvious peak of CTCF binding . We also observe broad regions of interaction that are bookended by peaks of CTCF/RAD21 , which suggests that, while CTCF and cohesin may define the borders of interaction, additional factors may play a role in more local contacts with the promoter. H3K4me1, a mark of enhancers, has itself been found to interact with cohesin29. Transcription factors are another plausible anchor, with a number of mechanisms proposed85. For example, degradation of Oct4 (Pou5f1), but not Nanog, in mESCs results in a loss of Rad21 association with TF binding sites79, arguing that specific TFs may be able to recruit or stabilize cohesin, potentially directing enhancer–promoter interactions. Mediator itself has been suggested to physically interact with cohesin86. Additional structural proteins may also be important, for example YY124 and WIZ87.It is likely that multiple mechanisms exist to facilitate enhancer–promoter interactions at different genes, and may function at least partly redundantly. Recent work using Promoter Capture Hi-C has shown that many, but not all, promoter interactions are unaffected by cohesin or CTCF depletion, indicating that loop extrusion likely has a role in only a subset of these contacts92. Notably, while mRNA has been shown to direct the formation of phase condensate compartments in the cytoplasm93 and eRNAs have been proposed to play a similar role in the formation of enhancer–promoter complexes26 our results following dissolution of phase condensates with hexanediol treatment argue that these interactions are not sufficient for maintaining contact at many genes. However, RNA may play other roles in directing enhancer–promoter interactions, for example recruitment of cohesin90, or the process of enhancer transcription itself may be important91.There may also be a role for noncoding RNA in enhancer–promoter interactionsOur results show that BRD4 and Mediator play a key role in the transcription of many genes, but they achieve this mainly via a functional rather than structural role. The high levels of these proteins at enhancers relative to promoters argues that contact between these loci is likely important for expression of many genes, but that this interaction functions primarily to enrich the local concentration of enhancer-bound factors at the promoter. Similarly, while the presence of phase condensates appears to be important for the transcription of super-enhancer-associated genes, this is likely a mechanism to concentrate key transcription-related proteins at the enhancer–promoter complex, and inhibition of this clustering has little or no effect on enhancer–promoter looping. Physical contact between promoter and enhancer is not, per se, sufficient for transcription, and is not dependent on high levels of BRD4 or Mediator.94 cells were purchased from DSMZ (www.cell-lines.de) and cultured in IMDM with 10% FBS and Glutamax. For drug treatments cells were diluted to 5 × 105 cells/ml. IBET-151 (Tocris) was used at a final concentration of 1 µM, AT1 (Tocris) at 1 µM, (+)-JQ1 (Tocris) at 1 µM and 1,6-hexanediol (Merck) at 1.5% (w/v).SEM (an MLL-AF4 B-ALL cell line)7 cells using a Covaris  following the manufacturer’s recommendations. Antibodies used for ChIP are detailed in Supplementary Table Briefly, double-fixed samples (2 mM disuccinimidyl glutarate (Sigma) for 30 min followed by 1% formaldehyde (Sigma) for 30 min) were sonicated in batches of 1095. Briefly, QC was checked with fastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), then reads were mapped against the human genome assembly (hg19) using Bowtie96. Unmapped reads were trimmed with trim_galore  and remapped. Short unmapped reads from this step were combined using Flash and mapped again. PCR duplicates were removed with samtools rmdup97, and any reads mapping to Duke blacklisted regions (UCSC) were removed with bedtools. Sequence tag (read) directories were generated from the sam files with the Homer tool makeTagDirectory98. The command makeBigWig.pl was used to generate bigwig files for visualization in UCSC, normalizing tag counts to tags per 107. Peaks were called using the Homer tool findPeaks, with the input track provided for background correction, using -style histone or -style factor options to call peaks in histone modification or transcription factor datasets, respectively. Super-enhancers were identified using findPeaks with the options -style super -minDist 12500 -L 1, providing tag directories for H3K27ac, H3K4me1, BRD4, and MED1. Typical enhancers were defined as H3K27ac peaks that overlap with H3K4me1 peaks, are >1 kb from an annotated TSS and do not overlap with a super-enhancer. Genes were annotated by assigning enhancers to the nearest TSS. Metagene profiles were generated using the Homer tool annotatePeaks.pl. Heatmaps were drawn using the R package heatmap3. CTCF motif orientations were assigned using the FIMO function of the MEME Suite99.Quality control of FASTQ reads, alignments, PCR duplicate filtering, blacklisted region filtering and UCSC data hub generation were performed using the NGseqBasic pipeline100 was achieved by adding fixed Drosophila melanogaster S2 cells to fixed SEM cells at the ChIP lysis step prior to sonication, in a 1:4 ratio, and the ChIP protocol was followed as normal. After sequencing, input and IP reads were mapped to both hg19 and dm3 genome builds, and hg19 read counts were adjusted based on the ratio of dm3:hg19 reads in input and IP control and treatment samples.Reference normalization6 cell pellets using the RNeasy Mini kit (Qiagen). RNA was reverse-transcribed using SuperScript III (ThermoFisher Scientific) with random hexamer primers, then quantified using SyBr Green or TaqMan qPCR  for the final 1 h (IBET treatments) or 30 min (hexanediol treatment) of the drug treatment time . Pelleted cells were lysed with Trizol (ThermoFisher Scientific) and total RNA was precipitated and DNase I-treated. 4-SU-incorporated RNA was biotinylated with EZ-link Biotin-HPDP (ThermoFisher Scientific) and purified with Streptavidin bead pull-down (Miltenyi). DNA libraries were generated from RNA using the NEBNext Ultra Directional RNA Library Preparation kit (NEB). Samples were sequenced by 75 bp paired-end sequencing using a NextSeq 500 (Illumina).10http://www.bioinformatics.babraham.ac.uk/projects/fastqc) reads were aligned against the human genome assembly (hg19) using STAR101. Duplicate reads were removed using the picard command MarkDuplicates.jar (http://broadinstitute.github.io/picard). Gene expression levels were quantified as read counts using the featureCounts function from the Subread package with default parameters102. The read counts were used to identify differential gene expression between conditions and generate RPKM values using the edgeR package103. Genes were considered differentially expressed if they had an adjusted p-value (FDR) of less than 0.05. Strand-specific RNA-seq was visualized on UCSC using the bam file as input for Homer98 commands makeTagDirectory (with options -flip and -sspe) and makeMultiWigHub.pl (with option -strand separate).Following QC analysis with fastQC  were added, using Herculase II (Agilent) to amplify the DNA. Indexing was performed in duplicate to maintain library complexity, with libraries pooled after indexing. Previously-designed Capture-C probes30 targeting promoters or enhancers  and those on trans chromosomes were removed from the analysis. Peaks within 10 kb of the Capture-C probe hybridization site were also removed. Holm–Bonferroni adjusted p-values for each peak were calculated by comparing all of the normalized read counts for each DpnII fragment and all replicates within a peak using a paired Mann–Whitney test for the two treatment conditions.Next-generation Capture-C was performed as described6 SEM cells by incubating cells in a high-salt lysis buffer , and protein levels were analyzed by western blotting104. Antibodies used are detailed in Supplementary Table Salt-soluble proteins were extracted from 1 × 10Statistical analyses used and sample sizes are indicated in figure legends; n numbers refer to independent experiments. All tests were conducted two-tailed, all correlation analyses were conducted using the Pearson method.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary