PRDM16 binds MED1 and controls chromatin architecture to determine a brown fat transcriptional program.

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Abstract

PR (PRD1-BF1-RIZ1 homologous) domain-containing 16 (PRDM16) drives a brown fat differentiation program, but the mechanisms by which PRDM16 activates brown fat-selective genes have been unclear. Through chromatin immunoprecipitation (ChIP) followed by deep sequencing (ChIP-seq) analyses in brown adipose tissue (BAT), we reveal that PRDM16 binding is highly enriched at a broad set of brown fat-selective genes. Importantly, we found that PRDM16 physically binds to MED1, a component of the Mediator complex, and recruits it to superenhancers at brown fat-selective genes. PRDM16 deficiency in BAT reduces MED1 binding at PRDM16 target sites and causes a fundamental change in chromatin architecture at key brown fat-selective genes. Together, these data indicate that PRDM16 controls chromatin architecture and superenhancer activity in BAT.

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BEDTools: a flexible suite of utilities for comparing genomic features

Aaron Quinlan, Ira Hall (2010)

Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Da Huang, Brad T. Sherman, Richard A. Lempicki (2009)

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

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

Sven Heinz, Christopher Benner, Nathanael Spann … (2010)

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

Journal ID (iso-abbrev): Genes Dev.

Title: Genes & development

ISSN (Electronic): 1549-5477

ISSN (Print): 0890-9369

Publication date (Electronic): Feb 1 2015

Volume: 29

Issue: 3

Affiliations

[1 ] Institute for Diabetes, Obesity, and Metabolism, Department of Cell and Developmental Biology.

[2 ] Institute for Diabetes, Obesity, and Metabolism, Genetics Department, Smilow Center for Translational Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

[3 ] Genetics Department, Smilow Center for Translational Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA sealep@upenn.edu wonk@upenn.edu.

[4 ] Institute for Diabetes, Obesity, and Metabolism.

[5 ] Institute for Diabetes, Obesity, and Metabolism, Department of Cell and Developmental Biology, sealep@upenn.edu wonk@upenn.edu.