Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Chromatin immunoprecipitation to identify binding sites for Gata4, Mef2a, Nkx2.5, Srf, p300, Pol_II, H3ac, H3K4me1 in mouse cardiomyocyte cell line, HL1

ABSTRACT: The aim of the experiment was to identify genome wide binding sites for Gata4, Mef2a, Nkx2.5, Srf, p300, Pol_II, H3ac, H3K4me1 by using Chromatin Immunoprecipitation followed by microarray analysis (ChIP-chip) in HL1 cells.

ORGANISM(S): Mus musculus

SUBMITTER: Silke Sperling

PROVIDER: E-TABM-378 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

The cardiac transcription network modulated by Gata4, Mef2a, Nkx2.5, Srf, histone modifications, and microRNAs.

Schlesinger Jenny J Schueler Markus M Grunert Marcel M Fischer Jenny J JJ Zhang Qin Q Krueger Tammo T Lange Martin M Tönjes Martje M Dunkel Ilona I Sperling Silke R SR

PLoS genetics 20110217 2

The transcriptome, as the pool of all transcribed elements in a given cell, is regulated by the interaction between different molecular levels, involving epigenetic, transcriptional, and post-transcriptional mechanisms. However, many previous studies investigated each of these levels individually, and little is known about their interdependency. We present a systems biology study integrating mRNA profiles with DNA-binding events of key cardiac transcription factors (Gata4, Mef2a, Nkx2.5, and Srf ...[more]

PMID: 21379568

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Project description:Identifying tissue and condition-specific gene regulatory elements and the mechanisms by which they associate with their target genes in human cells remains a significant challenge, largely due to the vast amount of non-protein-coding sequence in the human genome. Despite increasing evidence of physical interactions between distant regulatory regions and gene promoters in a variety of mammalian cell types, many searches for regulatory regions still consider only genomic regions proximal to the gene promoter. We identify a set of putative cis-regulatory modules (CRMs) of human skeletal muscle differentiation by combining new ChIP-chip data measuring the binding of myogenic TFs MyoG, MyoD, and SRF over the timecourse of differentiation with previously generated histone modification data. A substantial proportion of these putative CRMs are located distant (> 20 kb) from muscle gene promoters, and these distantly located CRMs are more likely than proximal promoter regions to show differentiation-specific changes in myogenic TF binding. Examination of two distant CRMs, which were previously shown to activate transcription in differentiating muscle cells, revealed that they interact physically with their upstream or downstream gene promoters (PDLIM3 and ACTA1) specifically upon myogenic differentiation. Our results highlight the importance of considering CRMs located distant from their target genes in investigations of mammalian gene regulation and further support the hypothesis that enhancer-promoter looping contacts are a general mechanism of regulation by distant CRMs. ChIP-chip experiments were performed using a custom Agilent 1x244K oligonucleotide array format (AMADID # 015037) to achieve 25-bp resolution tiling of 100 kb surrounding each of 104 selected human genes as well as positive and negative control regions. Biological and technical replicates of ChIP-chip experiments were performed using antibodies for MyoG and SRF in primary human skeletal muscle myoblasts crosslinked at 0 and 48 h relative to the removal of serum to induce differentiation. Additional technical replicate ChIP-chip experiments were performed for MyoD at 0 and 48 hours post-differentiation and p300 at 48 h post-differentiation. Total Input DNA for each experiment was labeled with Cy3 and used to create log-ratios with the Cy5 labeled IP data. Negative control no antibody mock-IP ChIP experiments were performed at 0 and 48 h post-differentiation.

Project description:Genome-wide occupancy analysis of TBX5, NKX2-5 and GATA4 in differentiating WT, Nkx2-5KO (NKO), Tbx5KO (TKO) and Nkx2-5;Tbx5KO (DKO) cells at the cardiac precursor (CP) and cardiomyocyte (CM) differentiation stages. Analysis of TBX5, NKX2-5 and GATA4 occupancy a and gene expression in WT, Tbx5KO, Nkx2-5KO and DoubleKO precursor (CP) and mature (CM) in vitro differentiated cardiomyocytes. We performed ChIP-exo experiments for NKX2-5, TBX5 and GATA4 in differentiating WT, Nkx2-5KO (NKO), Tbx5KO (TKO) and Nkx2-5;Tbx5KO (DKO) cells at the CP and CM stages. ChIP-exo was performed according to methods published previously (Boyer et al., 2005; Serandour et al., 2013; Wamstad et al., 2012) using specific antisera to TBX5 (sc-17866 XS, lot no. B1213), NKX2-5 (sc-8697 XS, lot no. B1213), and GATA4 (sc-1237 XS, lot no. B1213) on chromatin isolated from 10 mill. cells. Re-ChIP-exo was performed from 40 mill cells, combining methods published previously (Serandour et al., 2013; Shankaranarayanan et al., 2011). ChIP-exo Analysis: Barcoded libraries were single-end 50bp sequenced on an Illumina HiSeq 2500 instrument. Reads were trimmed using the fastq-mcf program (Aronesty, 2011), and then aligned to the mm9 mouse genome assembly using bowtie2 (ChIP-seq, ChIP-exo) (Langmead and Salzberg, 2012). Samtools was then used to retain reads that had a mapq score of 30 or greater (Li et al., 2009), thereby ensuring that each mapped uniquely to the genome assembly. The 5' -most position of reads that mapped to the reference strand and the 3Ã¢??-most position of reads that mapped to the non-reference strand were identified for each read as the actual edges of each exonuclease-treated fragment. The genome was divided into 20bp genomic bins and a normalized tag density was calculated for each genomic bin 'i' as follows: tag density(i)=([#tags within 75bp of i] X [total# genomic bins])/(total #of tags) To identify broad regions of binding, bins with tag densities of greater than 100 were merged to generate a peak list for each sample. Within 1kb of each region, strand-specific single-base-resolution tag densities were calculated for each dataset by dividing each region into 1bp bins, then counting the number of tags within 5bp of each bin. For each region of binding, the footprint for each bound region was defined as the span from the peak position of '+' strand binding to the peak position of '-' strand binding as seen from the high-resolution tag densities. For subsequent analyses, any overlapping footprint for replica/factor/stage/genotype was merged to generate an average footprint list. Only average footprints present in at least two replicas of the same factor, stage and genotype were considered (Final Average Footprints).

Dataset Information

Chromatin immunoprecipitation to identify binding sites for Gata4, Mef2a, Nkx2.5, Srf, p300, Pol_II, H3ac, H3K4me1 in mouse cardiomyocyte cell line, HL1

Publications

The cardiac transcription network modulated by Gata4, Mef2a, Nkx2.5, Srf, histone modifications, and microRNAs.

Similar Datasets

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