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      BET inhibition disrupts transcription but retains enhancer-promoter contact

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          Abstract

          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.

          Abstract

          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.

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          Most cited references104

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                thomas.milne@imm.ox.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 January 2021
                11 January 2021
                2021
                : 12
                : 223
                Affiliations
                [1 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, , University of Oxford, ; Oxford, OX3 9DS UK
                [2 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, , University of Oxford, ; Oxford, OX3 9DS UK
                [3 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, , University of Oxford, ; Oxford, OX3 9DS UK
                [4 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Wolfson Imaging Centre Oxford, MRC Weatherall Institute of Molecular Medicine, , University of Oxford, ; Oxford, OX3 9DS UK
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Structural Genomics Consortium, Nuffield Department of Clinical Medicine, , University of Oxford, ; Oxford, OX3 7DQ UK
                [6 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Present Address: The Finsen Laboratory, Rigshospitalet, Faculty of Health Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                [7 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Present Address: Biotech Research and Innovation Centre (BRIC), Faculty of Health Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                Author information
                http://orcid.org/0000-0001-9610-6763
                http://orcid.org/0000-0001-8085-0731
                http://orcid.org/0000-0003-3774-5033
                http://orcid.org/0000-0002-4108-4357
                http://orcid.org/0000-0002-0413-4271
                Article
                20400
                10.1038/s41467-020-20400-z
                7801379
                33431820
                928df4e0-ea8a-48d9-843a-35d6915fc057
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 December 2019
                : 1 December 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Award ID: MR/N010051/1
                Award ID: MC_UU_12009/6
                Award ID: MC_UU_00016/6
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                chromatin analysis,epigenetics,chromatin structure,transcriptional regulatory elements

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