BET inhibition disrupts transcription but retains enhancer-promoter contact

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.

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Thomas A Milne
Nov 10, 2020 Next-generation sequencing (Illumina). No software was used for data collection.
For ChIP-seq and ATAC-seq, quality control of FASTQ reads, genome alignment, PCR duplicate filtering, blacklisted region filtering and UCSC data hub generation was performed using the NGSeqBasic pipeline (https://github.com/Hughes-Genome-Group/NGseqBasic/ releases). Directories of sequence tags (reads) were generated from the sam files using the Homer (v4.8) tool makeTagDirectory. The makeBigWig.pl command was used to generate bigwig files for visualisation in UCSC, normalising tag counts to tags per 10 million. Peaks were called using the Homer tool findPeaks, with the input track provided for background correction, using the -style histone or -style factor options to call peaks in histone modification or transcription factor/ATAC datasets, respectively. For RNA-seq analysis, following QC analysis with fastQC v0.10.1 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) reads were aligned against the human genome assembly (hg19) using STAR (v2.4.2a). Duplicate reads were removed using the picard (v1.105) command MarkDuplicates.jar (http://broadinstitute.github.io/picard). Gene expression levels were quantified as read counts using the featureCounts function from the Subread package (v2.0.0) with default parameters. The read counts were used to identify differential gene expression between conditions and generate RPKM values using the edgeR package (v3.26.5). Capture-C analysis was performed using scripts available at https://github.com/Hughes-Genome-Group/CCseqBasicF/releases nature research | reporting summary

October 2018
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All newly-generated high throughput data have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE139437 (https:// www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse139437). Listed below are the datasets associated with each figure: Statistical methods were not used to assign sample size. Experiments were performed with 3-4 biological replicates as is common in the field; the observed biological effects of interest were consistent between replicates.
No data were excluded from this analysis ChIP-seq data represent a single biological replicate, with peaks and troughs of signal at specific loci confirmed by ChIP-qPCR. ChIP-qPCR experiments were conducted with multiple biological replicates to confirm any changes following drug treatment. Capture-C experiments were conducted in triplicate, with averaged data presented. Statistical difference between treatments were assessed by Holm-Bonferroni adjusted p-values from paired Mann-Whitney test. Nascent RNA-seq experiments were conducted in triplicate, with averaged data presented. Statistical difference between treatments were assessed using EdgeR. Where possible these differences were confirmed by qRT-PCR of total cellular RNA, however the short treatment times used mean that differences visible by nascent RNA-seq are not represented in mature mRNA levels. All attempts at replication were successful.
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