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      Transcriptome analysis of dominant-negative Brd4 mutants identifies Brd4-specific target genes of small molecule inhibitor JQ1

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          Abstract

          The bromodomain protein Brd4 is an epigenetic reader and plays a critical role in the development and maintenance of leukemia. Brd4 binds to acetylated histone tails and activates transcription by recruiting the positive elongation factor P-TEFb. Small molecule inhibitor JQ1 competitively binds the bromodomains of Brd4 and displaces the protein from acetylated histones. However, it remains unclear whether genes targeted by JQ1 are mainly regulated by Brd4 or by other bromodomain proteins such as Brd2 and Brd3. Here, we describe anti-proliferative dominant-negative Brd4 mutants that compete with the function of distinct Brd4 domains. We used these Brd4 mutants to compare the Brd4-specific transcriptome with the transcriptome of JQ1-treated cells. We found that most JQ1-regulated genes are also regulated by dominant-negative Brd4 mutants, including the mutant that competes with the P-TEFb recruitment function of Brd4. Importantly, JQ1 and dominant-negative Brd4 mutants regulated the same set of target genes of c-Myc, a key regulator of the JQ1 response in leukemia cells. Our results suggest that Brd4 mediates most of the anti-cancer effects of JQ1 and that the major function of Brd4 in this process is the recruitment of P-TEFb. In summary, our studies define the molecular targets of JQ1 in more detail.

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          Most cited references26

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

            , , (2013)
            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

              The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
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                Author and article information

                Contributors
                eick@helmholtz-muenchen.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 May 2017
                10 May 2017
                2017
                : 7
                : 1684
                Affiliations
                [1 ]GRID grid.452329.b, Department of Molecular Epigenetics, , Helmholtz Center Munich and Center for Integrated Protein Science Munich (CIPSM), ; Marchioninistrasse 25, 81377 Munich, Germany
                [2 ]ISNI 0000 0004 1936 973X, GRID grid.5252.0, Institute for Informatics, , Ludwig-Maximilians-Universität München, ; Amalienstr. 17, Munich, 80333 Germany
                [3 ]ISNI 0000 0004 1936 973X, GRID grid.5252.0, Laboratory for Functional Genome Analysis (LAFUGA) at the Gene Center, , Ludwig-Maximilians-Universität München, ; Munich, Germany
                Article
                1943
                10.1038/s41598-017-01943-6
                5431861
                28490802
                26e0eac8-08b5-4178-986c-66e24b2196f7
                © The Author(s) 2017

                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
                : 9 January 2017
                : 5 April 2017
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