GENES & DEVELOPMENT 31:660–673(2017) 

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

Patrick Seale


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.EBF2 activates the expression of brown fat-selective genes in adipocytes, but whether EBF2 regulates these genes via direct binding was unknown. To address this question, we analyzed the genome-wide binding profile of EBF2 in BAT using ChIP followed by deep sequencing (ChIP-seq). We found that EBF2 binds to ∼28,000 sites in the genome (Supplemental Excel File 1), many of which are in regions near brown fat-specific genes that show characteristic enhancer marks in BAT but not white adipose tissue (WAT) (Fig. 1A; Harms et al. 2015). A de novo motif search within EBF2-binding sites identified EBF as the top enriched motif, which validates the specificity of our ChIP-seq results (Supplemental Fig. S1A). Genomic regions enrichment of annotations tool (GREAT) analysis of the top 5000 genes with nearby EBF2-binding sites revealed fatty acid metabolism, regulation of glucose metabolism, and brown fat cell differentiation as significantly enriched biological processes (Supplemental Fig. S1B.)To determine whether EBF2 is required for the activity of lineage-specific enhancers, we examined the levels of PPARγ, RNA polymerase II (Pol II), and H3K27ac at brown fat-specific genes in wild-type and Ebf2 knockout BAT. PPARγ binding was drastically reduced at enhancers of Ucp1 and Pparα in Ebf2 knockout relative to wild-type (control) BAT (Fig. 1A). The loss of Ebf2 also led to a striking reduction in the levels of the activating histone mark H3K27ac and RNA Pol II at many brown fat-selective genes, including Ucp1, Pparα, Cidea, Pgc1α, and Cox7a1 (Fig. 1A; Supplemental Fig. S1C–E). However, Ebf2 deletion did not affect the levels of these regulatory marks at common adipogenic genes such as Fabp4, which are not bound by EBF2 (Supplemental Fig. S1F). We performed genome-wide analysis anchoring on previously defined adipose depot-selective genes and regulatory regions (Supplemental Excel File 2; Harms et al. 2015). On a genome-wide scale, loss of Ebf2 reduced RNA Pol II gene body occupancy and H3K27ac levels at brown fat-selective regulatory regions and increased Pol II and H3K27ac levels at white fat genes and regulatory regions (Fig. 1B). We also performed an unbiased analysis of the relationship between EBF2 binding and global H3K27ac levels in wild-type and knockout tissue. In general, loss of EBF2 binding was highly correlated with loss of H3K27ac, suggesting that EBF2 predominantly acts as a transcriptional activator in BAT (Fig. 1C).Global analyses of gene expression by RNA-seq identified close to 1000 differentially expressed genes between wild-type and Ebf2 knockout BAT (Fig. 1D), with loss of EBF2 leading to an increase in muscle-related processes and a decrease in respiratory processes (Supplemental Fig. S1G). A refined gene ontology analysis of putative EBF2 target genes that are down-regulated in knockout BAT and have one or more EBF2-binding sites within a 50-kb window of the transcription start site revealed that Ebf2 deletion reduced the expression of genes associated with the respiratory electron transport chain and oxidative metabolism, which are critical processes for brown fat thermogenesis (Fig. 1E). Many of the down-regulated genes with proximal EBF2-binding sites have well-established roles in BAT and mitochondrial function (Supplemental Excel File 3). Furthermore, integration of gene expression and ChIP-seq data revealed that genes with a greater number of proximal EBF2-binding sites generally tended to be down-regulated in knockout tissue (Supplemental Fig. S1H). Taken together, these analyses reveal that EBF2 is an activator of the brown fat gene program through direct binding to lineage-specific cis-regulatory elements.Myf5Cre/+ C57BL/6 mice were obtained from the Jackson laboratory (stock 007893). Ebf2fl/fl conditional null mice were generated by our laboratory using standard gene targeting techniques to insert loxP sites flanking exon 3 of the Ebf2 gene (Cyagen) in the C57BL/6 strain. Ebf2 whole-body knockout animals were obtained from Randall Reed (Johns Hopkins University) and have been described previously (Wang et al. 2004). All animal work was approved by the University of Pennsylvania's Institutional Animal Care and Use Committee.We thank the University of Pennsylvania Diabetes Research Center for use of the Functional Genomics Core (P30-DK19525); the Institute of Diabetes, Obesity, and Metabolism for resources; and Dr. Silke Sperling, Dr. Huan Cui, Dr. Doug Epstein, and members of the Seale laboratory for advice and helpful discussion. We thank Rachel Stine and Chihiro Okada for technical assistance, and Anthony Angueira for assistance with statistical analyses. S.N.S. performed experiments and analyzed data. H.-W.L. and K.-J.W. performed computational analysis of ChIP-seq and RNA-seq data sets. A.P.S. and J.I. contributed new reagents and analytic tools. S.R. and M.J.H. generated sequencing libraries. S.N.S. and P.S. designed experiments and wrote the manuscript. This work was supported by National Institutes of Health grants 1F31DK108507-01 and 5T32GM008216-29 to S.N.S., 5R01DK106027-02 to K.J.W., and 5R01DK103008-02 to P.S.Supplemental material is available for this article.Article published online ahead of print. Article and publication date are online at
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