Genome-wide mapping of Sox6 binding sites in skeletal muscle reveals both direct and indirect regulation of muscle terminal differentiation by Sox6

Division of Cardiovascular Medicine, Department of Internal Medicine, University of California, Davis, One Shields Avenue, Davis, California 95616, USA.
BMC Developmental Biology (Impact Factor: 2.75). 10/2011; 11:59. DOI: 10.1186/1471-213X-11-59
Source: PubMed

ABSTRACT Sox6 is a multi-faceted transcription factor involved in the terminal differentiation of many different cell types in vertebrates. It has been suggested that in mice as well as in zebrafish Sox6 plays a role in the terminal differentiation of skeletal muscle by suppressing transcription of slow fiber specific genes. In order to understand how Sox6 coordinately regulates the transcription of multiple fiber type specific genes during muscle development, we have performed ChIP-seq analyses to identify Sox6 target genes in mouse fetal myotubes and generated muscle-specific Sox6 knockout (KO) mice to determine the Sox6 null muscle phenotype in adult mice.
We have identified 1,066 Sox6 binding sites using mouse fetal myotubes. The Sox6 binding sites were found to be associated with slow fiber-specific, cardiac, and embryonic isoform genes that are expressed in the sarcomere as well as transcription factor genes known to play roles in muscle development. The concurrently performed RNA polymerase II (Pol II) ChIP-seq analysis revealed that 84% of the Sox6 peak-associated genes exhibited little to no binding of Pol II, suggesting that the majority of the Sox6 target genes are transcriptionally inactive. These results indicate that Sox6 directly regulates terminal differentiation of muscle by affecting the expression of sarcomere protein genes as well as indirectly through influencing the expression of transcription factors relevant to muscle development. Gene expression profiling of Sox6 KO skeletal and cardiac muscle revealed a significant increase in the expression of the genes associated with Sox6 binding. In the absence of the Sox6 gene, there was dramatic upregulation of slow fiber-specific, cardiac, and embryonic isoform gene expression in Sox6 KO skeletal muscle and fetal isoform gene expression in Sox6 KO cardiac muscle, thus confirming the role Sox6 plays as a transcriptional suppressor in muscle development.
Our present data indicate that during development, Sox6 functions as a transcriptional suppressor of fiber type-specific and developmental isoform genes to promote functional specification of muscle which is critical for optimum muscle performance and health.

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    • "The slow-fiber type program appears to be partially promoted by PGC-1 (Rasbach, Gupta et al. 2010, Summermatter, Thurnheer et al. 2012), PPARγ (Luquet, Lopez-Soriano et al. 2003) and MEF2 (Wu, Naya et al. 2000). At the transcriptional level the slow program can be enhanced by Pdrm1a which represses the transcriptional factor Sox 6 (von Hofsten, Elworthy et al. 2008, Wang, Ono et al. 2011), a transcriptional repressor of the slow-fiber type program (Hagiwara, Ma et al. 2005, Quiat, Voelker et al. 2011). The fast-fiber type program is dependent upon the six transcriptional complex (STC), where elimination of Six1, Six4 and the cofactor Eya1 can prevent fast-twitch muscle fiber formation (Grifone, Laclef et al. 2004, Niro, Demignon et al. 2010, Richard, Demignon et al. 2011). "
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    ABSTRACT: Satellite cells derived from fast and slow muscles have been shown to adopt contractile and metabolic properties of their parent muscle. Mouse muscle shows less distinctive fiber-type profiles than rat or rabbit muscle. Therefore, in this study we sought to determine whether three-dimensional muscle constructs engineered from slow soleus (SOL) and fast tibialis anterior (TA) from mice would adopt the contractile and metabolic properties of their parent muscle. Time-to-peak tension (TPT) and half-relaxation time (1/2RT) was significantly slower in SOL constructs. In agreement with TPT, TA constructs contained significantly higher levels of fast myosin heavy chain (MHC) and fast troponin C, I, and T isoforms. Fast SERCA protein, both slow and fast calsequestrin isoforms and parvalbumin were found at higher levels in TA constructs. SOL constructs were more fatigue resistant and contained higher levels of the mitochondrial proteins SDH and ATP synthase and the fatty acid transporter CPT-1. SOL constructs contained lower levels of the glycolytic enzyme phosphofructokinase but higher levels of the β-oxidation enzymes LCAD and VLCAD suggesting greater fat oxidation. Despite no changes in PGC-1α protein, SOL constructs contained higher levels of SIRT1 and PRC. TA constructs contained higher levels of the slow-fiber program repressor SOX6 and the six transcriptional complex (STC) proteins Eya1and Six4 which may underlie the higher in fast-fiber and lower slow-fiber program proteins. Overall, we have found that muscles engineered from predominantly slow and fast mouse muscle retain contractile and metabolic properties of their native muscle. J. Cell. Physiol. © 2014 Wiley Periodicals, Inc.
    Journal of Cellular Physiology 10/2014; 230(8). DOI:10.1002/jcp.24848 · 3.87 Impact Factor
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    • "Different studies have demonstrated that Sox6-null muscle has increased levels of I/β slowMyHC and a general switch towards a slower phenotype [79–81]. It has been shown that Sox6 exerts its function by direct binding to the I/β slowMyHC promoter [79, 80, 82]. Moreover, the group of Olson identified a microRNA (miR)-mediated transcriptional regulatory network. "
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    ABSTRACT: Skeletal myogenesis has been and is currently under extensive study in both mammals and teleosts, with the latter providing a good model for skeletal myogenesis because of their flexible and conserved genome. Parallel investigations of muscle studies using both these models have strongly accelerated the advances in the field. However, when transferring the knowledge from one model to the other, it is important to take into account both their similarities and differences. The main difficulties in comparing mammals and teleosts arise from their different temporal development. Conserved aspects can be seen for muscle developmental origin and segmentation, and for the presence of multiple myogenic waves. Among the divergences, many fish have an indeterminate growth capacity throughout their entire life span, which is absent in mammals, thus implying different post-natal growth mechanisms. This review covers the current state of the art on myogenesis, with a focus on the most conserved and divergent aspects between mammals and teleosts.
    Cellular and Molecular Life Sciences CMLS 03/2014; 71(16). DOI:10.1007/s00018-014-1604-5 · 5.86 Impact Factor
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    • "The final collection of datasets contained 191 GEO series containing a total of 917 ChIP-seq and 292 control libraries. Except for a limited number of cases in which a GEO series was associated with multiple publications, two or three GEO series were associated with the same publication, or a GEO series has not yet been used in a publication, and there is a one-to-one relationship between GEO series and published articles in the literature (Robertson et al. 2007; Chen et al. 2008; Marson et al. 2008; Bilodeau et al. 2009; Cheng et al. 2009; De Santa et al. 2009; Lister et al. 2009; Nishiyama et al. 2009; Visel et al. 2009; Welboren et al. 2009; Wilson et al. 2009; Yu et al. 2009; Yuan et al. 2009; Barish et al. 2010; Blow et al. 2010; Blow et al. 2010; Cao et al. 2010; Chi et al. 2010; Chia et al. 2010; Chicas et al. 2010; Corbo et al. 2010; Cuddapah et al. 2009; Durant et al. 2010; Fortschegger et al. 2010; Gotea et al. 2010; Gu et al. 2010; Han et al. 2010; Heinz et al. 2010; Heng et al. 2010; Ho et al. 2009; Hollenhorst et al. 2009; Hu et al. 2010; Johannes et al. 2010; Jung et al. 2010; Kagey et al. 2010; Kassouf et al. 2010; Kim et al. 2010; Kong et al. 2010; Kouwenhoven et al. 2010; Krebs et al. 2010; Kunarso et al. 2010; Kwon et al. 2009; Law et al. 2010; Lee et al. 2010; Lefterova et al. 2010; Li et al. 2010; Lin et al. 2010; Liu et al. 2010; Ma et al. 2010; MacIsaac et al. 2010; Mahony et al. 2010; Martinez et al. 2010; Palii et al. 2010; Qi et al. 2010; Rada-Iglesias et al. 2010; Rahl et al. 2010; Ramagopalan et al. 2010; Ramos et al. 2010; Schlesinger et al. 2010; Schnetz et al. 2010; Sehat et al. 2010; Steger et al. 2010; Tallack et al. 2010; Tang et al. 2010; Vermeulen et al. 2010; Verzi et al. 2010; Vivar et al. 2010; Wei et al. 2010; Woodfield et al. 2010; Yang et al. 2010; Yao et al. 2010; Yu et al. 2010; An et al. 2011; Ang et al. 2011; Bergsland et al. 2011; Bernt et al. 2011; Botcheva et al. 2011; Brown et al. 2011; Bugge et al. 2011; Ceol et al. 2011; Ceschin et al. 2011; Costessi et al. 2011; Ebert et al. 2011; Fang et al. 2011; Handoko et al. 2011; He et al. 2011; Heikkinen et al. 2011; Holmstrom et al. 2011; Horiuchi et al. 2011; Hu et al. 2011; Joseph et al. 2010; Kim et al. 2011; Klisch et al. 2011; Koeppel et al. 2011; Kong et al. 2011; Little et al. 2011; Liu et al. 2011; Lo et al. 2011; Marban et al. 2011; Mazzoni et al. 2011; McManus et al. 2011; Mendoza-Parra et al. 2011; Meyer et al. 2012; Miyazaki et al. 2011; Mullen et al. 2011; Mullican et al. 2011; Nakayamada et al. 2011; Nitzsche et al. 2011; Norton et al. 2011; Novershtern et al. 2011; Quenneville et al. 2011; Rao et al. 2011; Rey et al. 2011; Sahu et al. 2011; Schmitz et al. 2011; Seitz et al. 2011; Shen et al. 2011; Shukla et al. 2011; Siersbæk et al. 2011; Smeenk et al. 2011; Smith et al. 2011; Soccio et al. 2011; Stadler et al. 2011; Sun et al. 2011; Tan et al. 2011a; Tan et al. 2011b; Teo et al. 2011; Tijssen et al. 2011; Tiwari et al. 2011a; Tiwari et al. 2011b; Trompouki et al. 2011; van Heeringen et al. 2011; Verzi et al. 2011; Wang et al. 2011a; Wang et al. 2011b; Wei et al. 2011; Whyte et al. 2011; Wu et al. 2011a; Wu et al. 2011b; Xu et al. 2011; Yang et al. 2011; Yildirim et al. 2011; Yoon et al. 2011; Zhang et al. 2011; Zhao et al. 2011a; Zhao et al. 2011b; Avvakumov et al. 2012; Barish et al. 2012; Boergesen et al. 2012; Bugge et al. 2012; Canella et al. 2012; Cardamone et al. 2012; Cheng et al. 2012; Chlon et al. 2012; Cho et al. 2012; Doré et al. 2012; Fan et al. 2012; Feng et al. 2011; Fong et al. 2012; Gao et al. 2012; Gowher et al. 2012; Hunkapiller et al. 2012; Hutchins et al. 2012; Li et al. 2012; Lu et al. 2012; Miller et al. 2011; Ntziachristos et al. 2012; Pehkonen et al. 2012; Ptasinska et al. 2012; Remeseiro et al. 2012; Sadasivam et al. 2012; Sakabe et al. 2012; Schödel et al. 2012; Trowbridge et al. 2012; Vilagos et al. 2012; Wu et al. 2012; Xiao et al. 2012; Yu et al. 2012; unpublished at the time of completion of this manuscript are the following GEO accession numbers: GSE33346, GSE33850, GSE36561, GSE30919, GSE33128, GSE35109, GSE25426, GSE31951, GSE26711, GSE23581, GSE26136, GSE26680, GSE15844, GSE21916, GSE22303, and GSE29180; direct links to all GEO series can be found in Table S1). "
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    ABSTRACT: ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique in PubMed as of December 2012. Individually and in aggregate these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia Of DNA Elements (ENCODE) project, developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1st 2012. The majority (55%) of datasets scored as highly successful, but a substantial minority (20%) were of apparently poor quality, and another ~25% were of intermediate quality. We discuss how different uses of ChIP-Seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e. no-immunoprecipitation and mock-immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.
    G3-Genes Genomes Genetics 12/2013; 4(2). DOI:10.1534/g3.113.008680 · 2.51 Impact Factor
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