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Background: Antiretroviral therapy (ART) for HIV infection increases risk for coronary artery disease (CAD), presumably by causing dyslipidemia and increased atherosclerosis. We applied systems pharmacology to identify and validate specific regulatory gene networks through which ART drugs may promote CAD. Methods: Transcriptional responses of human cell lines to 15 ART drugs retrieved from the Library of Integrated Cellular Signatures (overall 1127 experiments) were used to establish consensus ART gene/transcriptional signatures. Next, enrichments of differentially expressed genes and gene-gene connectivity within these ART-consensus signatures were sought in 30 regulatory gene networks associated with CAD and CAD-related phenotypes in the Stockholm Atherosclerosis Gene Expression study. Results: Ten of 15 ART signatures were significantly enriched both for differential expression and connectivity in a specific atherosclerotic arterial wall regulatory gene network (AR-RGN) causal for CAD involving RNA processing genes. An atherosclerosis in vitro model of cholestryl ester-loaded foam cells was then used for experimental validation. Treatments of these foam cells with ritonavir, nelfinavir, and saquinavir at least doubled cholestryl ester accumulation ( P =0.02, 0.0009, and 0.02, respectively), whereas RNA silencing of the AR-RGN top key driver, PQBP1 (polyglutamine binding protein 1), significantly curbed cholestryl ester accumulation following treatment with any of these ART drugs by >37% ( P Conclusions: By applying a novel systems pharmacology data analysis framework, 3 commonly used ARTs (ritonavir, nelfinavir, and saquinavir) were found altering the activity of AR-RGN, a regulatory gene network promoting foam cell formation and risk of CAD. Targeting AR-RGN or its top key driver PQBP1 may help reduce CAD side effects of these ART drugs.

Genome-wide association studies (GWAS) have identified hundreds of cardiometabolic disease (CMD) risk loci. However, they contribute little to genetic variance, and most downstream gene-regulatory mechanisms are unknown. We genotyped and RNA-sequenced vascular and metabolic tissues from 600 coronary artery disease patients in the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET). Gene expression traits associated with CMD risk single-nucleotide polymorphism (SNPs) identified by GWAS were more extensively found in STARNET than in tissue- and disease-unspecific gene-tissue expression studies, indicating sharing of downstream cis-/trans-gene regulation across tissues and CMDs. In contrast, the regulatory effects of other GWAS risk SNPs were tissue-specific; abdominal fat emerged as an important gene-regulatory site for blood lipids, such as for the low-density lipoprotein cholesterol and coronary artery disease risk gene PCSK9 STARNET provides insights into gene-regulatory mechanisms for CMD risk loci, facilitating their translation into opportunities for diagnosis, therapy, and prevention.

Background Genetic 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. Objectives This study sought to determine the heritability contributions of single nucleotide polymorphisms affecting gene expression (eSNPs) in GRNs causally linked to CAD. Methods Seven 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). Results The 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. Conclusions GRNs 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.

Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks (“modules”). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene–protein interactions directly affected by genetic variance in CAD risk loci.

Background and aims Mitochondrial damage and augmented production of reactive oxygen species (ROS) may represent an intermediate step by which hypercholesterolemia exacerbates atherosclerotic lesion formation. Methods To test this hypothesis, in mice with severe but genetically reversible hypercholesterolemia (i.e. the so called Reversa mouse model), we performed time-resolved analyses of mitochondrial transcriptome in the aortic arch employing a systems-level network approach. Results During hypercholesterolemia, we observed a massive down-regulation (>28%) of mitochondrial genes, specifically at the time of rapid atherosclerotic lesion expansion and foam cell formation, i.e. between 30 and 40 weeks of age. Both phenomena - down-regulation of mitochondrial genes and lesion expansion - were largely reversible by genetically lowering plasma cholesterol (by >80%, from 427 to 54 ± 31 mg/L) at 30 weeks. Co-expression network analysis revealed that both mitochondrial signature genes were highly connected in two modules, negatively correlating with lesion size and supported as causal for coronary artery disease (CAD) in humans, as expression-associated single nucleotide polymorphisms (eSNPs) representing their genes overlapped markedly with established disease risk loci. Within these modules, we identified the transcription factor estrogen related receptor (ERR)-α and its co-factors PGC1-α and -β, i.e. two members of the peroxisome proliferator-activated receptor γ co-activator 1 family of transcription regulators, as key regulatory genes. Together, these factors are known as major orchestrators of mitochondrial biogenesis and antioxidant responses. Conclusions Using a network approach, we demonstrate how hypercholesterolemia could hamper mitochondrial activity during atherosclerosis progression and pinpoint potential therapeutic targets to counteract these processes.

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.

Plasma cholesterol lowering (PCL) slows and sometimes prevents progression of atherosclerosis and may even lead to regression. Little is known about how molecular processes in the atherosclerotic arterial wall respond to PCL and modify responses to atherosclerosis regression. We studied atherosclerosis regression and global gene expression responses to PCL (≥80%) and to atherosclerosis regression itself in early, mature, and advanced lesions. In atherosclerotic aortic wall from Ldlr−/−Apob 100/100 Mttp flox/floxMx1-Cre mice, atherosclerosis regressed after PCL regardless of lesion stage. However, near-complete regression was observed only in mice with early lesions; mice with mature and advanced lesions were left with regression-resistant, relatively unstable plaque remnants. Atherosclerosis genes responding to PCL before regression, unlike those responding to the regression itself, were enriched in inherited risk for coronary artery disease and myocardial infarction, indicating causality. Inference of transcription factor (TF) regulatory networks of these PCL-responsive gene sets revealed largely different networks in early, mature, and advanced lesions. In early lesions, PPARG was identified as a specific master regulator of the PCL-responsive atherosclerosis TF-regulatory network, whereas in mature and advanced lesions, the specific master regulators were MLL5 and SRSF10/XRN2, respectively. In a THP-1 foam cell model of atherosclerosis regression, siRNA targeting of these master regulators activated the time-point-specific TF-regulatory networks and altered the accumulation of cholesterol esters. We conclude that PCL leads to complete atherosclerosis regression only in mice with early lesions. Identified master regulators and related PCL-responsive TF-regulatory networks will be interesting targets to enhance PCL-mediated regression of mature and advanced atherosclerotic lesions., Author Summary The main underlying cause of heart attacks and strokes is atherosclerosis. One strategy to prevent these often deadly clinical events is therefore either to slow atherosclerosis progression or better, induce regression of atherosclerotic plaques making them more stable. Plasma cholesterol lowering (PCL) is the most efficient way to induce atherosclerosis regression but sometimes fails to do so. In our study, we used a mouse model with elevated LDL cholesterol levels, similar to humans who develop early atherosclerosis, and a genetic switch to lower plasma cholesterol at any time during atherosclerosis progression. In this model, we examined atherosclerosis gene expression and regression in response to PCL at three different stages of atherosclerosis progression. PCL led to complete regression in mice with early lesions but was incomplete in mice with mature and advanced lesions, indicating that early prevention with PCL in individuals with increased risk for heart attack or stroke would be particularly useful. In addition, by inferring PCL-responsive gene networks in early, mature and advanced atherosclerotic lesions, we identified key drivers specific for regression of early (PPARG), mature (MLL5) and advanced (SRSF10/XRN2) atherosclerosis. These key drivers should be interesting therapeutic targets to enhance PCL-mediated regression of atherosclerosis.