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Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [Hi-C]

ABSTRACT: Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [Hi-C]

PROVIDER: PRJNA434487 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR6745451_1.fastq.gz	Fastqsanger.gz
	SRR6745451_2.fastq.gz	Fastqsanger.gz
	SRR6745452_1.fastq.gz	Fastqsanger.gz
	SRR6745452_2.fastq.gz	Fastqsanger.gz
	SRR6813820_1.fastq.gz	Fastqsanger.gz

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Similar Datasets

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks

Project description:Increased high density lipoprotein (HDL) cholesterol levels have been associated with mutations in the cholesterol efflux receptor gene SCARB1, contributing to coronary artery disease (CAD). Using chromatin capture techniques (4C-Seq and HiC), ChIP-Seq and RNA-Seq, we identified a potential mechanism between a non-coding SNP (rs10846744) within the first intron of SCARB1 and LAG3, a negative regulator of T cells, both on chromosome 12. We previously demonstrated how loss of LAG3 is associated with inflammation and CAD found in carriers of the rs10846744 risk allele (C) both in our hyperalphalipoproteinemia (HALP) participants and the Multi-Ethnic Study of Atherosclerosis (MESA), increasing the risk of a coronary event by 45% (PMID: 27777974). NR2F2 transcription factor binding, as detected by (yeast 1 hybrid assay, ChIP, and ChIP-Seq) to the rs10846744 risk (C) allele, bridges NR2F2, SCARB1, and LAG3 loci, altering gene networks associated with a CAD proinflammatory signature. This SuperSeries is composed of the SubSeries listed below.

2019-10-04 | GSE110761 | GEO

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks

Project description:Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks

| PRJNA434469 | ENA

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [Hi-C]

Project description:Purpose: Utilized Next-generation sequencing (NGS) to profile global chromatin interaction differences between two populations, a selected carrier (C) alele of SNP (rs10846744) associated with cardiovascular disease (CVD) and a selected non-carrier (G) allele. Methods: Chromatin was isolated from two subjects, one homozygous for the rs10846744 reference (G) allele and one homozygous for the rs10846744 risk (C) allele, processed for dilution HiC (Lieberman-Aiden, van Berkum et al., 2009) and then subjected next-gen sequencing. Results: HiC-Pro integrated pipeline (with default parameters) was used for generating the HiC contact probability matrices (Servant et al., 2015). The results were visualized using HiCPlotter (Akdemir and Chin, 2015) and HiTC (Servant et al., 2012) and the Washington University EpiGenome Browser (Zhou and Wang, 2013). Juicebox (Durand et al., 2016) was utilized to extract observed/expected KR (Knight-Ruiz) normalized contacts for chromosome 12. Conclusions: Our study represents the first detailed analysis of significant changes in gene expression by an altered chromatin interaction network from SCARB1 to NR2F2 and LAG3, contributing to CAD proinflammatory gene networks.

2019-10-04 | GSE110757 | GEO

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [4C-Seq]

Project description:Purpose: Isolating chromatin interactions from a specific viewpoint, we utilized Next-generation sequencing (NGS) to profile specific chromatin interaction differences between two populations, a selected carrier (C) alele of SNP (rs10846744) associated with cardiovascular disease (CVD) and a selected non-carrier (G) allele. Methods: Chromatin was isolated from two subjects, one homozygous for the rs10846744 reference (G) allele and one homozygous for the rs10846744 risk (C) allele, processed for 4C-Seq (van de Werken et al., 2012). Results: 4C-ker (Raviram et al, 2016) analyzed the chromatin interaction data from the specific viewpoint. The results were visualized using the Washington University EpiGenome Browser (Zhou and Wang, 2013). Conclusions: Our study represents the first detailed analysis of significant changes in gene expression by an altered chromatin interaction network from SCARB1 to NR2F2 and LAG3, contributing to CAD proinflammatory gene networks.

2019-10-04 | GSE110752 | GEO

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [RNA-Seq]

Project description:Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [RNA-Seq]

| PRJNA423020 | ENA

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [4C-Seq]

Project description:Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [4C-Seq]

| PRJNA434463 | ENA

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [ChIP-Seq]

Project description:Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [ChIP-Seq]

| PRJNA434466 | ENA

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [ChIP-Seq]

Project description:Purpose: Using a selected carrier (C) allele of rs10846744 SNP associated with cardiovascular disease (CVD) and a selected non-carrier (G) allele, chromatin immunoprecipitation of a protein of interest, NR2F2, overexpressed and bound to the rs10846744 region in the risk (CC) cells was performed in triplicate. Methods: Using NR2F2 as the target protein, chromatin was immunoprecipitated from each cell type and subjected to Next-gen sequencing. Input controls for each cell type were also sequenced. Results: MACS (Zhang et al., 2008) was utilized for analysis. The narrow peak results were visualized using the Washington University EpiGenome Browser (Zhou and Wang, 2013) and bedgraph files were visualized in the Integrative Genomics Viewer (Thorvaldsdóttir et al., 2013) as bigwig files. Conclusions: Our study represents the first detailed analysis of significant changes in gene expression by an altered chromatin interaction network from SCARB1 to NR2F2 and LAG3, contributing to CAD proinflammatory gene networks.

2019-10-04 | GSE110756 | GEO

Chromatin Capture Identifies SCARB1-LAG3 Proinflammatory Cardiovascular Disease Gene Networks [RNA-Seq]

Project description:Purpose: Utilized Next-generation sequencing (NGS) to profile transcriptional differences between two populations, a selected carrier (C) alele of SNP (rs10846744) associated with cardiovascular disease (CVD) and a selected non-carrier (G) allele. Methods: RNA was isolated from three subjects homozygous for the rs10846744 reference (G) allele and three subjects homozygous for the rs10846744 risk (C) allele and then subjected to full transcriptome sequencing. Triplicates of one sample from each group were also sequences to overlay with chromatin data from each group. Knockdown of NR2F2 using lentiviral particles assessed differential expression between the treated and untreated risk replicates. The Illumina NextSeq 500 next gen sequencing platform (UCONN CGI, Storrs, CT) was used for RNA-Seq. HiSeq 2000 was used for biological replicates at Perkin Elmer. RNA targets of interest were validated by qPCR using TaqMan assays and proteins were blotted using standard western methodologies. Results: We normalized the raw read count by FPKM and using Cufflinks, identified differential transcripts. RNA-seq data confirmed differential expression of NR2F2 and LAG3, validated by qPCR. Conclusions: Our study represents the first detailed analysis of significant changes in gene expression by an altered chromatin interaction network from SCARB1 to NR2F2 and LAG3, contributing to CAD proinflammatory gene networks.

2019-10-04 | GSE108274 | GEO

Transcription profiling of mouse CD4+ CD25+, CD4+ CD25- CD45RBlow LAG3+ and CD4+ CD25- CD45RBlow LAG3- T cells

Project description:Comparison of gene expression profile of CD4+ CD25+, CD4+ CD25- CD45RBlow LAG3+ and CD4+ CD25- CD45RBlow LAG3- T cells. Naive CD4+CD25-CD45RBhigh T cells were used as a reference for pair comparison with values from the three other subsets.

2009-08-19 | E-MEXP-1343 | biostudies-arrayexpress

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