Dataset Information

Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci.

ABSTRACT: Recent 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.We 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.Our 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.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC5576868 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci.

Zhao Yuqi Y Chen Jing J Freudenberg Johannes M JM Meng Qingying Q Rajpal Deepak K DK Yang Xia X

Arteriosclerosis, thrombosis, and vascular biology 20160310 5

<h4>Objective</h4>Recent 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.<h4>A ...[more]

PMID: 26966275

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Project description: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.

Project description:BackgroundHundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets.MethodsWe aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task).ResultsWe identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks.ConclusionsWe identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

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.

Project description:ImportanceSpontaneous coronary artery dissection (SCAD), an idiopathic disorder that predominantly affects young to middle-aged women, has emerged as an important cause of acute coronary syndrome, myocardial infarction, and sudden cardiac death.ObjectiveTo identify common single-nucleotide variants (SNVs) associated with SCAD susceptibility.Design, setting, and participantsThis single-center genome-wide association study examined approximately 5 million genotyped and imputed SNVs and subsequent SNV-targeted replication analysis results in individuals enrolled in the Mayo Clinic SCAD registry from August 30, 2011, to August 2, 2018. Data analysis was performed from June 21, 2017, to December 30, 2019.Main outcomes and measuresGenetic loci and positional candidate genes associated with SCAD.ResultsThis study included 484 white women with SCAD (mean [SD] age, 46.6 [9.2] years) and 1477 white female controls in the discovery cohort (mean [SD] age, 64.0 [14.5] years) and 183 white women with SCAD (mean [SD] age, 47.1 [9.9] years) and 340 white female controls in the replication cohort (mean [SD] age, 51.0 [15.3] years). Associations with SCAD risk reached genome-wide significance at 3 loci (1q21.3 [OR, 1.78; 95% CI, 1.51-2.09; P = 2.63 × 10-12], 6p24.1 [OR, 1.77; 95% CI, 1.51-2.09; P = 7.09 × 10-12], and 12q13.3 [OR, 1.67; 95% CI, 1.42-1.97; P = 3.62 × 10-10]), and 7 loci had evidence suggestive of an association (1q24.2 [OR, 2.10; 95% CI, 1.58-2.79; P = 2.88 × 10-7], 3q22.3 [OR, 1.47; 95% CI, 1.26-1.71; P = 6.65 × 10-7], 4q34.3 [OR, 1.84; 95% CI, 1.44-2.35; P = 9.80 × 10-7], 8q24.3 [OR, 2.57; 95% CI, 1.76-3.75; P = 9.65 × 10-7], 15q21.1 [OR, 1.75; 95% CI, 1.40-2.18; P = 7.23 × 10-7], 16q24.1 [OR, 1.91; 95% CI, 1.49-2.44; P = 2.56 × 10-7], and 21q22.11 [OR, 2.11; 95% CI, 1.59-2.82; P = 3.12 × 10-7]) after adjusting for the top 5 principal components. Associations were validated for 5 of the 10 risk alleles in the replication cohort. In a meta-analysis of the discovery and replication cohorts, associations for the 5 SNVs were significant, with relatively large effect sizes (1q21.3 [OR, 1.77; 95% CI, 1.54-2.03; P = 3.26 × 10-16], 6p24.1 [OR, 1.71; 95% CI, 1.49-1.97; P = 4.59 × 10-14], 12q13.3 [OR, 1.69; 95% CI, 1.47-1.94; P = 1.42 × 10-13], 15q21.1 [OR, 1.79; 95% CI, 1.48-2.17; P = 2.12 × 10-9], and 21q22.11 [OR, 2.18; 95% CI, 1.70-2.81; P = 1.09 × 10-9]). Each index SNV was within or near a gene highly expressed in arterial tissue and previously linked to SCAD (PHACTR1) and/or other vascular disorders (LRP1, LINC00310, and FBN1).Conclusions and relevanceThis study revealed 5 replicated risk loci and positional candidate genes for SCAD, most of which are associated with extracoronary arteriopathies. Moreover, the alternate alleles of 3 SNVs have been previously associated with atherosclerotic coronary artery disease, further implicating allelic susceptibility to coronary artery atherosclerosis vs dissection.

Dataset Information

Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci.

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Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci.

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