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Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease.

ABSTRACT: Inferring molecular networks can reveal how genetic perturbations interact with environmental factors to cause common complex diseases. We analyzed genetic and gene expression data from seven tissues relevant to coronary artery disease (CAD) and identified regulatory gene networks (RGNs) and their key drivers. By integrating data from genome-wide association studies, we identified 30 CAD-causal RGNs interconnected in vascular and metabolic tissues, and we validated them with corresponding data from the Hybrid Mouse Diversity Panel. As proof of concept, by targeting the key drivers AIP, DRAP1, POLR2I, and PQBP1 in a cross-species-validated, arterial-wall RGN involving RNA-processing genes, we re-identified this RGN in THP-1 foam cells and independent data from CAD macrophages and carotid lesions. This characterization of the molecular landscape in CAD will help better define the regulation of CAD candidate genes identified by genome-wide association studies and is a first step toward achieving the goals of precision medicine.

SUBMITTER: Talukdar HA

PROVIDER: S-EPMC4855300 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease.

Talukdar Husain A HA Foroughi Asl Hassan H Jain Rajeev K RK Ermel Raili R Ruusalepp Arno A Franzén Oscar O Kidd Brian A BA Readhead Ben B Giannarelli Chiara C Kovacic Jason C JC Ivert Torbjörn T Dudley Joel T JT Civelek Mete M Lusis Aldons J AJ Schadt Eric E EE Skogsberg Josefin J Michoel Tom T Björkegren Johan L M JL

Cell systems 20160303 3

Inferring molecular networks can reveal how genetic perturbations interact with environmental factors to cause common complex diseases. We analyzed genetic and gene expression data from seven tissues relevant to coronary artery disease (CAD) and identified regulatory gene networks (RGNs) and their key drivers. By integrating data from genome-wide association studies, we identified 30 CAD-causal RGNs interconnected in vascular and metabolic tissues, and we validated them with corresponding data f ...[more]

PMID: 27135365

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Project description:BackgroundGenetic variants currently known to affect coronary artery disease (CAD) risk explain less than one-quarter of disease heritability. The heritability contribution of gene regulatory networks (GRNs) in CAD, which are modulated by both genetic and environmental factors, is unknown.ObjectivesThis study sought to determine the heritability contributions of single nucleotide polymorphisms affecting gene expression (eSNPs) in GRNs causally linked to CAD.MethodsSeven vascular and metabolic tissues collected in 2 independent genetics-of-gene-expression studies of patients with CAD were used to identify eSNPs and to infer coexpression networks. To construct GRNs with causal relations to CAD, the prior information of eSNPs in the coexpression networks was used in a Bayesian algorithm. Narrow-sense CAD heritability conferred by the GRNs was calculated from individual-level genotype data from 9 European genome-wide association studies (GWAS) (13,612 cases, 13,758 control cases).ResultsThe authors identified and replicated 28 independent GRNs active in CAD. The genetic variation in these networks contributed to 10.0% of CAD heritability beyond the 22% attributable to risk loci identified by GWAS. GRNs in the atherosclerotic arterial wall (n = 7) and subcutaneous or visceral abdominal fat (n = 9) were most strongly implicated, jointly explaining 8.2% of CAD heritability. In all, these 28 GRNs (each contributing to >0.2% of CAD heritability) comprised 24 to 841 genes, whereof 1 to 28 genes had strong regulatory effects (key disease drivers) and harbored many relevant functions previously associated with CAD. The gene activity in these 28 GRNs also displayed strong associations with genetic and phenotypic cardiometabolic disease variations both in humans and mice, indicative of their pivotal roles as mediators of gene-environmental interactions in CAD.ConclusionsGRNs capture a major portion of genetic variance and contribute to heritability beyond that of genetic loci currently known to affect CAD risk. These networks provide a framework to identify novel risk genes/pathways and study molecular interactions within and across disease-relevant tissues leading to CAD.

Project description:Background and aimCoronary artery disease (CAD) poses a worldwide health threat. Compelling evidence shows that pericardial adipose tissue (PAT), a brown-like adipose adjacent to the external surface of the pericardium, is associated with CAD. However, the specific molecular mechanisms of PAT in CAD are elusive. This study aims to characterize human PAT and explore its association with CAD.MethodsWe acquired samples of PAT from 31 elective cardiac surgery patients (17 CAD patients and 14 controls). The transcriptome characteristics were assessed in 5 CAD patients and 4 controls via RNA-sequencing. Cluster profile R package, String database, Cytoscape were applied to analyze the potential pathways and PPI-network key to DEGS, whereas the hubgenes were predicted via Metascape, Cytohubba, and MCODE. We use Cibersort, ENCORI, and DGIDB to predict immunoinfiltration, mRNA-miRNA target gene network, and search potential drugs targeting key DEGs. The predictable hubgenes and infiltrating inflammatory cells were validated in 22 patients (12 CAD samples and 10 control samples) through RT-qPCR and immunohistochemistry.ResultsA total of 147 different genes (104 up-regulated genes and 43 down-regulated genes) were identified in CAD patients. These different genes were associated with immunity and inflammatory dysfunction. Cibersort analysis showed monocytes and macrophages were the most common subsets in immune cells, whereas immunohistochemical results revealed there were more macrophages and higher proportion of M1 subtype cells in PAT of CAD patients. The PPI network and module analysis uncovered several crucial genes, defined as candidate genes, including Jun, ATF3, CXCR4, FOSB, CCl4, which were validated through RT-qPCR. The miRNA-mRNA network implicated hsa-miR-185-5p as diagnostic targets and drug-gene network showed colchicine, fenofibrate as potential therapeutic drugs, respectively.ConclusionThis study demonstrates that PAT is mainly associated with the occurrence of CAD following the dysfunction of immune and inflammatory processes. The identified hubgenes, predicted drugs and miRNAs are promising biomarkers and therapeutic targets for CAD.

Project description:Coronary artery disease (CAD) is the leading cause of mortality and morbidity driven by both genetic and environmental risk factors. Meta-analyses of genome-wide association studies (GWAS) have identified multiple single nucleotide polymorphisms (SNPs) associated with CAD and myocardial infarction (MI) susceptibility in multi-ethnic populations. The majority of these variants reside in non-coding regulatory regions and are co-inherited with hundreds of candidate regulatory SNPs. Herein, we use integrative genomic, epigenomic, and transcriptomic fine-mapping in human coronary artery smooth muscle cells (HCASMC) and tissues to identify causal regulatory variation and mechanisms responsible for CAD associations. Using these genome-wide maps we prioritize 65 candidate variants and perform allele-specific binding and expression analyses on 7 top candidates. We validate our findings in two independent cohorts of diseased human arterial expression quantitative trait loci (eQTL), which together demonstrate fundamental links between CAD associations and regulatory function in the appropriate disease context. We performed ATAC-seq, ChIP-seq, and RNA-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). For ATAC-seq and RNA-seq we performed stimulations with growth factors (TGF-B1, PDGF-BB, PDGF-DD) versus serum-free control. We conducted two biological replicates for each condition using independent donors. For ATAC-seq experiments, sequencing was completed on an Illumina Hiseq 2500, paired-end 50bp reads. For ChIP-seq we performed immunoprecipitations using H3K27ac (Abcam ab4729). We conducted two biological replicates using HCASMC from independent donors, and also did an IgG control for these studies. For RNA-seq we also conducted two replicates using HCASMC from independent donors. For both ChIP-seq and RNA-seq experiments, sequencing was completed on an Illumina HiSeq 2500, paired-end 100bp reads. We also performed ex-vivo ATAC-seq on frozen tissues (isolated media) from normal and atherosclerotic human coronary arteries, using three independent donors for each. Sequencing was also completed on an Illumina HiSeq 2500, paired end 50bp reads.

Project description:BackgroundBTBD7_hsa_circ_0000563, which is located on chromosome 14, contains conserved binding sites with miR-155/130a and RNA-binding proteins according to bioinformatic prediction. We investigated the association of BTBD7_hsa_circ_0000563 expression in coronary artery segments with atherosclerotic stenosis and identified the proteome-wide BTBD7_hsa_circ_0000563-regulated proteins in human coronary artery.MethodsThe atherosclerotic grade and extent in coronary artery segments were determined by hematoxylin and eosin staining. BTBD7_hsa_circ_0000563 expression in eight coronary artery segments from one patient was quantified by RT-qPCR assay. A proteomic approach was adopted to reveal significant differences in protein expression between among four groups differing in their BTBD7_hsa_circ_0000563 expression levels.ResultsThe RT-qPCR assay revealed that coronary artery segments with severe atherosclerotic stenosis had significantly low BTBD7_hsa_circ_0000563 levels. The proteomic analysis identified 49 differentially expressed proteins among the segment groups with different BTBD7_hsa_circ_0000563 expression levels, of which 10 were downregulated and 39 were upregulated with increases in the BTBD7_hsa_circ_0000563 level. The 10 downregulated proteins were P61626 (LYSC_HUMAN), P02760 (AMBP_HUMAN), Q02985 (FHR3_HUMAN), P01701 (LV151_HUMAN), P06312(KV401_HUMAN), P01624 (KV315_HUMAN), P13671 (CO6_HUMAN), P01700(LV147_HUMAN), Q9Y287(ITM2B_HUMAN), and A0A075B6I0 (LV861_HUMAN). The top 10 upregulated proteins were Q92552 (RT27_HUMAN), Q9UJY1(HSPB8_HUMAN), Q9Y235(ABEC2_HUMAN), P19022 (CADH2_HUMAN), O43837(IDH3B_HUMAN), Q9H479(FN3K_HUMAN), Q9UM22(EPDR1_HUMAN), P48681(NEST_HUMAN), Q9NRP0(OSTC_HUMAN), and Q15628(TRADD_HUMAN).ConclusionBTBD7_hsa_circ_0000563 is involved in the atherosclerotic changes in human coronary artery segments. Verification, mechanistic, and function studies are needed to confirm whether patients with coronary artery disease would benefit from such personalized medicine in the future.

Dataset Information

Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease.

Publications

Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease.

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