Dataset Information

Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.

ABSTRACT: Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.

SUBMITTER: Nair J

PROVIDER: S-EPMC3988072 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.

Nair Jiny J Ghatge Madankumar M Kakkar Vijay V VV Shanker Jayashree J

PloS one 20140415 4

Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting ...[more]

PMID: 24736319

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Project description:ObjectiveRecent genome-wide association studies of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic loci. However, the molecular mechanisms through which they contribute to CAD and the clinical implications of these findings remain largely unknown. We aim to retrieve gene subnetworks of the 206 CAD loci and identify and prioritize candidate regulators to better understand the biological mechanisms underlying the genetic associations.Approach and resultsWe devised a new integrative genomics approach that incorporated (1) candidate genes from the top CAD loci, (2) the complete genetic association results from the 1000 genomes-based CAD genome-wide association studies from the Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus the Coronary Artery Disease consortium, (3) tissue-specific gene regulatory networks that depict the potential relationship and interactions between genes, and (4) tissue-specific gene expression patterns between CAD patients and controls. The networks and top-ranked regulators according to these data-driven criteria were further queried against literature, experimental evidence, and drug information to evaluate their disease relevance and potential as drug targets. Our analysis uncovered several potential novel regulators of CAD such as LUM and STAT3, which possess properties suitable as drug targets. We also revealed molecular relations and potential mechanisms through which the top CAD loci operate. Furthermore, we found that multiple CAD-relevant biological processes such as extracellular matrix, inflammatory and immune pathways, complement and coagulation cascades, and lipid metabolism interact in the CAD networks.ConclusionsOur data-driven integrative genomics framework unraveled tissue-specific relations among the candidate genes of the CAD genome-wide association studies loci and prioritized novel network regulatory genes orchestrating biological processes relevant to CAD.

Project description:Coronary artery disease (CAD) is a major cause of end-stage cardiac disease. Although profound efforts have been made to illuminate the pathogenesis, the molecular mechanisms of CAD remain to be analyzed. To identify the candidate genes in the advancement of CAD, microarray dataset GSE23766 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and pathway and gene ontology (GO) enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Biological General Repository for Interaction Datasets (BioGRID) and Cytoscape. Additionally, target genes-miRNA regulatory network and target genes-TF regulatory network were constructed and analyzed. There were 894 DEGs between male human CAD samples and female human CAD samples, including 456 up regulated genes and 438 down regulated genes. Pathway enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the superpathway of steroid hormone biosynthesis, ABC transporters, oxidative ethanol degradation III and Complement and coagulation cascades. Similarly, geneontology enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the forebrain neuron differentiation, filopodium membrane, platelet degranulation and blood microparticle. In the PPI network and modules (up and down regulated), MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74 and VHL were hub genes. In the target genes-miRNA regulatory network and target genes-TF regulatory network (up and down regulated), TAOK1, KHSRP, HSD17B11 and PAH were target genes. In conclusion, the pathway and GO ontology enriched by DEGs may reveal the molecular mechanism of CAD. Its hub and target genes, MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74, VHL, TAOK1, KHSRP, HSD17B11 and PAH were expected to be new targets for CAD. Our finding provided clues for exploring molecular mechanism and developing new prognostics, diagnostic and therapeutic strategies for CAD.

Project description:Recent meta-analyses of genome wide association studies (GWAS) have identified approximately 150 loci that are associated with coronary artery disease (CAD). To link the causal genes in these loci to functional transcriptional networks, we have used chromatin immunoprecipitation sequencing (ChIP-Seq) with human coronary artery smooth muscle cells (HCASMC) to investigate the cellular and molecular program downstream of one CAD associated transcription factor, TCF21. Analysis of the TCF21 downstream target genes for enrichment of molecular and cellular annotation terms identified processes relevant to CAD pathophysiology, including growth factor binding (PDGF, VEGF), matrix interactions (“integrin binding,” “cell adhesion”), and smooth muscle contraction (“actin filament-based processes,” “actin cytoskeleton”). Motif searches of peak sequences confirmed the canonical E-box CAGCTG as the likely binding sequence for TCF21, but also identified bZip motifs that coordinate binding of AP1 family transcription factors. Follow-up ChIP-Seq studies verified enriched binding of bZip factors JUN and JUND to TCF21 target loci. Importantly, analysis of the representation of CAD genes among TCF21 target loci showed highly significant enrichment. Further, expression quantitative trait variation mapped to target genes of TCF21 were significantly enriched among variants with low P-values in the GWAS analyses, and single nucleotide polymorphisms within TCF21 peaks were shown to be in linkage disequilibrium with CAD-associated SNPs, suggesting a functional interaction between TCF21 binding and causal variants in other CAD disease loci. Thus, data and analyses presented here provide evidence for a transcriptional regulatory network that links TCF21 function in smooth muscle cells with a number of other CAD-associated genes, and suggest that study of GWAS transcription factors may be a highly useful approach to identifying disease gene interactions and thus pathways that may be relevant to complex disease etiology. We did ChIP-Seq on human coronary artery smooth muscle cells grown in SmGM-2 Smooth Muscle Growth Medium-2 including hEGF, insulin, hFGF-B and FBS, but without antibiotics (Lonza, #CC-3182. We did immunoprecipitations with two antibodies (Sigma HPA013189 and Abcam 49475). We conducted two biological replicates for each antibody, and also did an IgG control for these studies. For these experiments, sequencing was done on an Illumina GAII, single end reads. In separate set of experiments we performed ChIP-Seq for JUN (Ab sc-1694) and JUND (Ab sc-74) with the same HCASMC cells under the same culture conditions. For these studies we did one replicate for each antibody, and also included an IgG control. Sequencing for the JUN and JUND studies was paired ends, on an Illumina HiSeq machine.

Dataset Information

Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.

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Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease.

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