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      EBF2 transcriptionally regulates brown adipogenesis via the histone reader DPF3 and the BAF chromatin remodeling complex

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          Abstract

          Shapira et al. show that EBF2 physically interacts with the chromatin remodeler BRG1 and the BAF chromatin remodeling complex in brown adipocytes. They identified the histone reader protein DPF3 as a brown fat-selective component of the BAF complex that was required for brown fat gene programming and mitochondrial function.

          Abstract

          The transcription factor early B-cell factor 2 (EBF2) is an essential mediator of brown adipocyte commitment and terminal differentiation. However, the mechanisms by which EBF2 regulates chromatin to activate brown fat-specific genes in adipocytes were unknown. ChIP-seq (chromatin immunoprecipitation [ChIP] followed by deep sequencing) analyses in brown adipose tissue showed that EBF2 binds and regulates the activity of lineage-specific enhancers. Mechanistically, EBF2 physically interacts with the chromatin remodeler BRG1 and the BAF chromatin remodeling complex in brown adipocytes. We identified the histone reader protein DPF3 as a brown fat-selective component of the BAF complex that was required for brown fat gene programming and mitochondrial function. Loss of DPF3 in brown adipocytes reduced chromatin accessibility at EBF2-bound enhancers and led to a decrease in basal and catecholamine-stimulated expression of brown fat-selective genes. Notably, Dpf3 is a direct transcriptional target of EBF2 in brown adipocytes, thereby establishing a regulatory module through which EBF2 activates and also recruits DPF3-anchored BAF complexes to chromatin. Together, these results reveal a novel mechanism by which EBF2 cooperates with a tissue-specific chromatin remodeling complex to activate brown fat identity genes.

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          Most cited references52

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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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              Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

              Genome-scale studies have revealed extensive, cell type-specific colocalization of transcription factors, but the mechanisms underlying this phenomenon remain poorly understood. Here, we demonstrate in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions. PU.1 binding initiates nucleosome remodeling, followed by H3K4 monomethylation at large numbers of genomic regions associated with both broadly and specifically expressed genes. These locations serve as beacons for additional factors, exemplified by liver X receptors, which drive both cell-specific gene expression and signal-dependent responses. Together with analyses of transcription factor binding and H3K4me1 patterns in other cell types, these studies suggest that simple combinations of lineage-determining transcription factors can specify the genomic sites ultimately responsible for both cell identity and cell type-specific responses to diverse signaling inputs. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Genes Dev
                Genes Dev
                genesdev
                genesdev
                GAD
                Genes & Development
                Cold Spring Harbor Laboratory Press
                0890-9369
                1549-5477
                1 April 2017
                : 31
                : 7
                : 660-673
                Affiliations
                [1 ]Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;
                [2 ]Department of Cell and Developmental Biology, Smilow Center for Translational Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;
                [3 ]Department of Genetics, Smilow Center for Translational Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
                Author notes
                Corresponding author: sealep@ 123456upenn.edu
                Article
                8711660
                10.1101/gad.294405.116
                5411707
                28428261
                820b068f-aade-4fb7-bc0a-924b661fa2e2
                © 2017 Shapira et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genesdev.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 30 November 2016
                : 23 March 2017
                Page count
                Pages: 14
                Funding
                Funded by: National Institutes of Health http://dx.doi.org/10.13039/100000002
                Award ID: 1F31DK108507-01
                Award ID: 5T32GM008216-29
                Award ID: 5R01DK106027-02
                Award ID: 5R01DK103008-02
                Categories
                Research Paper

                ebf2,dpf3,baf chromatin remodeling complex,brown adipogenesis,ucp1

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