Contribution of Gene Regulatory Networks to Heritability of Coronary Artery Disease

Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis

BACKGROUND

While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remains poorly understood.

METHODS

Single-cell RNA sequencing data generated with SmartSeq2 (≈8000 genes/cell) in 16 588 single cells isolated during atherosclerosis progression in Ldlr-/-Apob100/100 mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes. For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease patients in the STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) study.

RESULTS

Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by 3 smooth muscle cells (SMCs), and 3 macrophage subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type proinflammatory/Trem2-high lipid-associated (macrophage) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of 3 arterial wall GRNs: GRN33 (macrophage), GRN39 (SMC), and GRN122 (macrophage) with major contributions to coronary artery disease heritability and strong associations with clinical scores of coronary atherosclerosis severity. The presence and pathophysiological relevance of GRN39 were verified in 5 independent RNAseq data sets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM in cultured human coronary artery SMCs.

CONCLUSIONS

By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a coronary artery disease framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced, symptomatic atherosclerosis.

Gene Regulatory Networks in Coronary Artery Disease

PURPOSE OF REVIEW

Coronary artery disease is a complex disorder and the leading cause of mortality worldwide. As technologies for the generation of high-throughput multiomics data have advanced, gene regulatory network modeling has become an increasingly powerful tool in understanding coronary artery disease. This review summarizes recent and novel gene regulatory network tools for bulk tissue and single cell data, existing databases for network construction, and applications of gene regulatory networks in coronary artery disease.

RECENT FINDINGS

New gene regulatory network tools can integrate multiomics data to elucidate complex disease mechanisms at unprecedented cellular and spatial resolutions. At the same time, updates to coronary artery disease expression data in existing databases have enabled researchers to build gene regulatory networks to study novel disease mechanisms. Gene regulatory networks have proven extremely useful in understanding CAD heritability beyond what is explained by GWAS loci and in identifying mechanisms and key driver genes underlying disease onset and progression. Gene regulatory networks can holistically and comprehensively address the complex nature of coronary artery disease. In this review, we discuss key algorithmic approaches to construct gene regulatory networks and highlight state-of-the-art methods that model specific modes of gene regulation. We also explore recent applications of these tools in coronary artery disease patient data repositories to understand disease heritability and shared and distinct disease mechanisms and key driver genes across tissues, between sexes, and between species.

Sex differences in the genetic and molecular mechanisms of coronary artery disease

Sex differences in coronary artery disease (CAD) presentation, risk factors and prognosis have been widely studied. Similarly, studies on atherosclerosis have shown prominent sex differences in plaque biology. Our understanding of the underlying genetic and molecular mechanisms that drive these differences remains fragmented and largely understudied. Through reviewing genetic and epigenetic studies, we identified more than 40 sex-differential candidate genes (13 within known CAD loci) that may explain, at least in part, sex differences in vascular remodeling, lipid metabolism and endothelial dysfunction. Studies with transcriptomic and single-cell RNA sequencing data from atherosclerotic plaques highlight potential sex differences in smooth muscle cell and endothelial cell biology. Especially, phenotypic switching of smooth muscle cells seems to play a crucial role in female atherosclerosis. This matches the known sex differences in atherosclerotic phenotypes, with men being more prone to lipid-rich plaques, while women are more likely to develop fibrous plaques with endothelial dysfunction. To unravel the complex mechanisms that drive sex differences in CAD, increased statistical power and adjustments to study designs and analysis strategies are required. This entails increasing inclusion rates of women, performing well-defined sex-stratified analyses and the integration of multi-omics data.

Impact of gallstone disease on the risk of stroke and coronary artery disease: evidence from prospective observational studies and genetic analyses

BACKGROUND

Despite epidemiological evidence associating gallstone disease (GSD) with cardiovascular disease (CVD), a dilemma remains on the role of cholecystectomy in modifying the risk of CVD. We aimed to characterize the phenotypic and genetic relationships between GSD and two CVD events - stroke and coronary artery disease (CAD).

METHODS

We first performed a meta-analysis of cohort studies to quantify an overall phenotypic association between GSD and CVD. We then investigated the genetic relationship leveraging the largest genome-wide genetic summary statistics. We finally examined the phenotypic association using the comprehensive data from UK Biobank (UKB).

RESULTS

An overall significant effect of GSD on CVD was found in meta-analysis (relative risk [RR] = 1.26, 95% confidence interval [CI] = 1.19-1.34). Genetically, a positive shared genetic basis was observed for GSD with stroke ([Formula: see text]=0.16, P = 6.00 × 10-4) and CAD ([Formula: see text]=0.27, P = 2.27 × 10-15), corroborated by local signals. The shared genetic architecture was largely explained by the multiple pleiotropic loci identified in cross-phenotype association study and the shared gene-tissue pairs detected by transcriptome-wide association study, but not a causal relationship (GSD to CVD) examined through Mendelian randomization (MR) (GSD-stroke: odds ratio [OR] = 1.00, 95%CI = 0.97-1.03; GSD-CAD: OR = 1.01, 95%CI = 0.98-1.04). After a careful adjustment of confounders or considering lag time using UKB data, no significant phenotypic effect of GSD on CVD was detected (GSD-stroke: hazard ratio [HR] = 0.95, 95%CI = 0.83-1.09; GSD-CAD: HR = 0.98, 95%CI = 0.91-1.06), further supporting MR findings.

CONCLUSIONS

Our work demonstrates a phenotypic and genetic relationship between GSD and CVD, highlighting a shared biological mechanism rather than a direct causal effect. These findings may provide insight into clinical and public health applications.

The Potential of Single Nucleotide Polymorphisms (SNPs) as Biomarkers and Their Association with the Increased Risk of Coronary Heart Disease: A Systematic Review

Human genetic analyses and epidemiological studies showed a potential association between several types of gene polymorphism and the development of coronary heart disease (CHD). Many studies on this pertinent topic need to be investigated further to reach an evidence-based conclusion. Therefore, in this current review, we describe several types of gene polymorphisms that are potentially linked to CHD. A systematic review using the databases EBSCO, PubMed, and ScienceDirect databases was searched until October of 2022 to find relevant studies on the topic of gene polymorphisms on risk factors for CHD, especially for the factors associated with single nucleotide polymorphisms (SNPs). The risk of bias and quality assessment was evaluated by Joanna Briggs Institute (JBI) guidelines. From keyword search results, a total of 6243 articles were identified, which were subsequently narrowed to 14 articles using prespecified inclusion criteria. The results suggested that there were 33 single nucleotide polymorphisms (SNPs) that can potentially increase the risk factors and clinical symptoms of CHD. This study also indicated that gene polymorphisms had a potential role in increasing CHD risk factors that were causally associated with atherosclerosis, increased homocysteine, immune/inflammatory response, Low-Density Lipoprotein (LDL), arterial lesions, and reduction of therapeutic effectiveness. In conclusion, the findings of this study indicate that SNPs may increase risk factors for CHD and SNPs show different effects between individuals. This demonstrates that knowledge of SNPs on CHD risk factors can be used to develop biomarkers for diagnostics and therapeutic response prediction to decide successful therapy and become the basis for defining personalized medicine in future.

Cardiovascular prevention: sometimes dreams can come true

Cardiovascular disease (CVD) is a chronic condition driven by the complex interaction of different risk factors including genetics, lifestyle, environment, etc. which, differently from other pathologies, can be prevented. Treatment of CVD has been inconceivably successful but now it seems that it has reached a plateau suggesting that prevention is the way forward. However, the COVID-19 pandemic has spotted all the limits of the actual health system regarding territorial and, particularly, of preventive medicine. To this end, recently, the SCORE2 risk prediction algorithms, a contemporary model to estimate 10 years risk of CVD in Europe and the new guidelines on prevention have been released. The present review article describes a dream: how prevention of CVD should be addressed in the future. New concepts and paradigms like early genetically personalized and imaging driven risk factors, cardiac risk cartography, measurements of the exposome, estimation of costs of a delayed outcome vs. healthy lifespan, are all addressed. We highlight the importance of technologies and the concept of being engaged in a 'healthy' and not just 'sick' system as it is today. The concept of 'clearing house' with a 'care health team' instead of a 'heart team' is described. Finally, we articulate the four points necessary for the dream to come true.

Sudden death in ischemic heart disease: looking for new predictors: polygenic risk

The phenomenon of sudden death (SD) occurs, in 70% of cases, in people who do not fall within the indications of the guidelines relating to the implantation of the defibrillator. There is a way of inheriting the risk condition by genetic means, the polygenic one, in which mutations are not found, but an increase in alleles of common variations called polymorphisms. The PRE-DETERMINE cohort study has the primary objective of determining whether biological markers, and electrocardiogram can be used to identify individuals more likely to experience SD. Within the study, we investigated the utility of the genome-wide polygenic score for coronary artery disease (GPSCAD) for SD risk stratification in an intermediate-risk population with stable coronary artery disease without severe systolic dysfunction and/or indication for an implantable cardioverter defibrillator in primary prevention. Over a mean follow-up period of 8.0 years, patients in the top decile of GPSCAD were at higher absolute (8.0% vs. 4.8%; P < 0.005) and relative (29% vs. 16%; P < 0.0003) risk of SD compared to the rest of the cohort. No association was found between the highest decile of GPSCAD and other forms of death, cardiac, and non-cardiac. The data on the increase in absolute and relative terms of SD can be used, at this stage, only for a theoretical estimate on the possible efficacy of the defibrillator in the population with chronic coronary artery disease and moderately depressed left ventricular function as number needed to treat and possible reduction of mortality in high-risk patients (those included in the top decile of GPSCAD).

Implications of CRISPR-Cas9 Genome Editing Methods in Atherosclerotic Cardiovascular Diseases

Today, new methods have been developed to treat or modify the natural course of cardiovascular diseases (CVDs), including atherosclerosis, by the clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 (CRISPR-Cas9) system. Genome-editing tools are CRISPR-related palindromic short iteration systems such as CRISPR-Cas9, a valuable technology for achieving somatic and germinal genomic manipulation in model cells and organisms for various applications, including the creation of deletion alleles. Mutations in genomic deoxyribonucleic acid and new genes' placement have emerged. Based on World Health Organization fact sheets, 17.9 million people die from CVDs each year, an estimated 32% of all deaths worldwide. 85% of all CVD deaths are due to acute coronary events and strokes. This review discusses the applications of CRISPR-Cas9 technology throughout atherosclerotic disease research and the prospects for future in vivo genome editing therapies. We also describe several limitations that must be considered to achieve the full scientific and therapeutic potential of cardiovascular genome editing in the treatment of atherosclerosis.

Loss of soluble guanylyl cyclase in platelets contributes to atherosclerotic plaque formation and vascular inflammation

Variants in genes encoding the soluble guanylyl cyclase (sGC) in platelets are associated with coronary artery disease (CAD) risk. Here, by using histology, flow cytometry and intravital microscopy, we show that functional loss of sGC in platelets of atherosclerosis-prone Ldlr^-/- mice contributes to atherosclerotic plaque formation, particularly via increasing in vivo leukocyte adhesion to atherosclerotic lesions. In vitro experiments revealed that supernatant from activated platelets lacking sGC promotes leukocyte adhesion to endothelial cells (ECs) by activating ECs. Profiling of platelet-released cytokines indicated that reduced platelet angiopoietin-1 release by sGC-depleted platelets, which was validated in isolated human platelets from carriers of GUCY1A1 risk alleles, enhances leukocyte adhesion to ECs. I mp or ta ntly, p ha rm ac ol ogical sGC stimulation increased platelet angiopoietin-1 release in vitro and reduced leukocyte recruitment and atherosclerotic plaque formation in atherosclerosis-prone Ldlr^-/- mice. Therefore, pharmacological sGC stimulation might represent a potential therapeutic strategy to prevent and treat CAD.

Cardiovascular prevention: sometimes dreams can come true

Cardiovascular disease (CVD) is a chronic condition driven by the complex interaction of different risk factors including genetics, lifestyle, environment, etc. which, differently from other pathologies, can be prevented. Treatment of CVD has been inconceivably successful but now it seems that it has reached a plateau suggesting that prevention is the way forward. However, the COVID-19 pandemic has spotted all the limits of the actual health system regarding territorial and, particularly, of preventive medicine. To this end, recently, the SCORE2 risk prediction algorithms, a contemporary model to estimate 10-years risk of CVD in Europe and the new guidelines on prevention have been released. The present review article describes a dream: how prevention of CVD should be addressed in the future. New concepts and paradigms like early genetically personalized and imaging driven risk factors, cardiac risk cartography, measurements of the exposome, estimation of costs of a delayed outcome vs. healthy lifespan, are all addressed. We highlight the importance of technologies and the concept of being engaged in a ‘healthy’ and not just ‘sick’ system as it is today. The concept of ‘clearing house’ with a ‘healthcare team’ instead of a ‘heart team’ is described. Finally, we articulate the four points necessary for the dream to come true.

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The ‘gold standard’ method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Cis-epistasis at the LPA locus and risk of cardiovascular diseases

AIMS

Coronary artery disease (CAD) has a strong genetic predisposition. However, despite substantial discoveries made by genome-wide association studies (GWAS), a large proportion of heritability awaits identification. Non-additive genetic effects might be responsible for part of the unaccounted genetic variance. Here, we attempted a proof-of-concept study to identify non-additive genetic effects, namely epistatic interactions, associated with CAD.

METHODS AND RESULTS

We tested for epistatic interactions in 10 CAD case-control studies and UK Biobank with focus on 8068 SNPs at 56 loci with known associations with CAD risk. We identified a SNP pair located in cis at the LPA locus, rs1800769 and rs9458001, to be jointly associated with risk for CAD [odds ratio (OR) = 1.37, P = 1.07 × 10-11], peripheral arterial disease (OR = 1.22, P = 2.32 × 10-4), aortic stenosis (OR = 1.47, P = 6.95 × 10-7), hepatic lipoprotein(a) (Lp(a)) transcript levels (beta = 0.39, P = 1.41 × 10-8), and Lp(a) serum levels (beta = 0.58, P = 8.7 × 10-32), while individual SNPs displayed no association. Further exploration of the LPA locus revealed a strong dependency of these associations on a rare variant, rs140570886, that was previously associated with Lp(a) levels. We confirmed increased CAD risk for heterozygous (relative OR = 1.46, P = 9.97 × 10-32) and individuals homozygous for the minor allele (relative OR = 1.77, P = 0.09) of rs140570886. Using forward model selection, we also show that epistatic interactions between rs140570886, rs9458001, and rs1800769 modulate the effects of the rs140570886 risk allele.

CONCLUSIONS

These results demonstrate the feasibility of a large-scale knowledge-based epistasis scan and provide rare evidence of an epistatic interaction in a complex human disease. We were directed to a variant (rs140570886) influencing risk through additive genetic as well as epistatic effects. In summary, this study provides deeper insights into the genetic architecture of a locus important for cardiovascular diseases.

Integrative Prioritization of Causal Genes for Coronary Artery Disease

BACKGROUND

Hundreds 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.

METHODS

We 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).

RESULTS

We 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.

CONCLUSIONS

We 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.

A mechanistic framework for cardiometabolic and coronary artery diseases

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with (n = 600) and without (n = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver (n = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.

Air, Land, and Sea: Gene-Environment Interaction in Chronic Disease

Each of us reflects a unique convergence of DNA and the environment. Over the past 2 decades, huge biobanks linked to electronic medical records have positioned the clinical and scientific communities to understand the complex genetic architecture underlying many common diseases. Although these efforts are producing increasingly accurate gene-based risk prediction algorithms for use in routine clinical care, the algorithms often fail to include environmental factors. This review explores the concept of heritability (genetic vs nongenetic determinants of disease), with emphasis on the role of environmental factors as risk determinants for common complex diseases influenced by air and water quality. Efforts to define patient exposure to specific toxicants in practice-based data sets will deepen our understanding of diseases with low heritability, and improved land management practices will reduce the burden of disease.

Genetics of coronary artery disease in the post-GWAS era

During the past decade, genome-wide association studies (GWAS) have transformed our understanding of many heritable traits. Three recent large-scale GWAS meta-analyses now further markedly expand the knowledge on coronary artery disease (CAD) genetics in doubling the number of loci with genome-wide significant signals. Here, we review the unprecedented discoveries of CAD GWAS on low-frequency variants, underrepresented populations, sex differences and integrated polygenic risk. We present the milestones of CAD GWAS and post-GWAS studies from 2007 to 2021, and the trend in identification of variants with smaller odds ratio by year due to the increasing sample size. We compile the 321 CAD loci discovered thus far and classify candidate genes as well as distinct functional pathways on the road to indepth biological investigation and identification of novel treatment targets. We draw attention to systems genetics in integrating these loci into gene regulatory networks within and across tissues. We review the traits, biomarkers and diseases scrutinized by Mendelian randomization studies for CAD. Finally, we discuss the potentials and concerns of polygenic scores in predicting CAD risk in patient care as well as future directions of GWAS and post-GWAS studies in the field of precision medicine.

Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis

Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.

The "Common Soil Hypothesis" Revisited-Risk Factors for Type 2 Diabetes and Cardiovascular Disease

The prevalence and the incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance, as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic variants in interaction with lifestyle and environmental factors. Cardiovascular disease (CVD) is the major cause of morbidity and mortality. Detailed understanding of molecular mechanisms underlying in CVD events is still largely missing. Several risk factors are shared between T2D and CVD, including obesity, insulin resistance, dyslipidemia, and hyperglycemia. CVD can precede the development of T2D, and T2D is a major risk factor for CVD, suggesting that both conditions have common genetic and environmental antecedents and that they share “common soil”. We analyzed the relationship between the risk factors for T2D and CVD based on genetics and population-based studies with emphasis on Mendelian randomization studies.

New prevention scenarios: polygenic risk

Although coronary heart disease is a highly preventable disease, it is still the leading cause of morbidity and mortality in developed countries. This is also due to the fact that the risk models used in clinical practice have proved ineffective in identifying people at risk: up to 30% of cases of myocardial infarction do not have traditional risk factors used in risk estimation models. Although the genetic component of myocardial infarction has been known for many years, with an inheritance rate of between 40% and 60%, it is not yet used as a risk factor in primary prevention models such as the Heart Card or the European SCORE. Recent advances in genomics and the use of clinical big data have allowed the development of genetic risk scores called Polygenic Risk Score (PRS), capable of identifying populations with average LDL-C levels, but with the same risk of heart attack of carriers of hypercholesterolaemia. The clinical usefulness of the PRS lies precisely in identifying high-risk individuals who are invisible to traditional models. The clinical applications of PRS for coronary artery disease are discussed in this report.

Patient Endothelial Colony-Forming Cells to Model Coronary Artery Disease Susceptibility and Unravel the Role of Dysregulated Mitochondrial Redox Signalling

Mechanisms involved in the individual susceptibility to atherosclerotic coronary artery disease (CAD) beyond traditional risk factors are poorly understood. Here, we describe the utility of cultured patient-derived endothelial colony-forming cells (ECFCs) in examining novel mechanisms of CAD susceptibility, particularly the role of dysregulated redox signalling. ECFCs were selectively cultured from peripheral blood mononuclear cells from 828 patients from the BioHEART-CT cohort, each with corresponding demographic, clinical and CT coronary angiographic imaging data. Spontaneous growth occurred in 178 (21.5%) patients and was more common in patients with hypertension (OR 1.45 (95% CI 1.03-2.02), p = 0.031), and less likely in patients with obesity (OR 0.62 [95% CI 0.40-0.95], p = 0.027) or obstructive CAD (stenosis > 50%) (OR 0.60 [95% CI 0.38-0.95], p = 0.027). ECFCs from patients with CAD had higher mitochondrial production of superoxide (O₂^--MitoSOX assay). The latter was strongly correlated with the severity of CAD as measured by either coronary artery calcium score (R² = 0.46; p = 0.0051) or Gensini Score (R² = 0.67; p = 0.0002). Patient-derived ECFCs were successfully cultured in 3D culture pulsatile mini-vessels. Patient-derived ECFCs can provide a novel resource for discovering mechanisms of CAD disease susceptibility, particularly in relation to mitochondrial redox signalling.

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study

Aims

Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalized risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilize a homogenous set of features and require the presence of a physician. The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments.

Methods and results

Across 466 052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data.

Conclusion

We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilized in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples.

Precision Medicine Approaches to Vascular Disease: JACC Focus Seminar 2/5

In this second of a 5-part Focus Seminar series, we focus on precision medicine in the context of vascular disease. The most common vascular disease worldwide is atherosclerosis, which is the primary cause of coronary artery disease, peripheral vascular disease, and a large proportion of strokes and other disorders. Atherosclerosis is a complex genetic disease that likely involves many hundreds to thousands of single nucleotide polymorphisms, each with a relatively modest effect for causing disease. Conversely, although less prevalent, there are many vascular disorders that typically involve only a single genetic change, but these changes can often have a profound effect that is sufficient to cause disease. These are termed "Mendelian vascular diseases," which include Marfan and Loeys-Dietz syndromes. Given the very different genetic basis of atherosclerosis versus Mendelian vascular diseases, this article was divided into 2 parts to cover the most promising precision medicine approaches for these disease types.

Current progress in clinical, molecular, and genetic aspects of adult fibromuscular dysplasia

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disease that may involve medium-sized muscular arteries throughout the body. The majority of FMD patients are women. Although a variety of genetic, mechanical, and hormonal factors play a role in the pathogenesis of FMD, overall, its cause remains poorly understood. It is probable that the pathogenesis of FMD is linked to a combination of genetic and environmental factors. Extensive studies have correlated the arterial lesions of FMD to histopathological findings of arterial fibrosis, cellular hyperplasia, and distortion of the abnormal architecture of the arterial wall. More recently, the vascular phenotype of lesions associated with FMD has been expanded to include arterial aneurysms, dissections, and tortuosity. However, in the absence of a string of beads or focal stenosis, these lesions do not suffice to establish the diagnosis. While FMD most commonly involves renal and cerebrovascular arteries, involvement of most arteries throughout the body has been reported. Increasing evidence highlights that FMD is a systemic arterial disease and that subclinical alterations can be found in non-affected arterial segments. Recent significant progress in FMD-related research which has led to improved understandings of the disease's clinical manifestations, natural history, epidemiology, and genetics. Ongoing work continues to focus on FMD genetics and proteomics, physiological effects of FMD on cardiovascular structure and function, and novel imaging modalities and blood-based biomarkers that can be used to identify subclinical FMD. It is also hoped that the next decade will bring the development of multi-centred and potentially international clinical trials to provide comparative effectiveness data to inform the optimal management of patients with FMD.

Inflammation-Related Risk Loci in Genome-Wide Association Studies of Coronary Artery Disease

Although the importance of inflammation in atherosclerosis is now well established, the exact molecular processes linking inflammation to the development and course of the disease are not sufficiently understood. In this context, modern genetics—as applied by genome-wide association studies (GWAS)—can serve as a comprehensive and unbiased tool for the screening of potentially involved pathways. Indeed, a considerable proportion of loci discovered by GWAS is assumed to affect inflammatory processes. Despite many well-replicated association findings, however, translating genomic hits to specific molecular mechanisms remains challenging. This review provides an overview of the currently most relevant inflammation-related GWAS findings in coronary artery disease and explores their potential clinical perspectives.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

Detecting Rare Variants and Heteroplasmy of Mitochondrial DNA from High-Throughput Sequencing in Patients with Coronary Artery Disease

Background

Although mutations and dysfunction of mitochondrial DNA (mtDNA) are related to a variety of diseases, few studies have focused on the relationship between mtDNA and coronary artery disease (CAD), especially the relationship between rare variants and CAD.

Material/Methods

Two-stage high-throughput sequencing was performed to detect mtDNA variants or heteroplasmy and the relationship between them and CAD phenotypes. In the discovery stage, mtDNA was analyzed by high-throughput sequencing of long-range PCR products generated from the peripheral blood of 85 CAD patients and 80 demographically matched controls. In the validation stage, high-throughput sequencing for mtDNA target regions captured by GenCap Kit was performed on 100 CAD samples and 100 controls. Finally, tRNA fine mapping was performed between our study and the reported Chinese CAD study.

Results

Among the tRNA genes, we confirmed a highly conserved rare variant, A5592G, previously reported in the Chinese CAD study, and 2 novel rare mutations that reached Bonferroni’s correction significance in the combined analysis were found (P=7.39×10⁻⁴ for T5628C in tRNA^Ala and P=1.01×10⁻⁵ for T681C in 12S rRNA) in the CAD study. Both of them were predicted to be pathological, with T5628C disrupting an extremely conservative base-pairing at the AC stem of tRNA^Ala. Furthermore, we confirmed the controversial issue that the number of non-synonymous heteroplasmic sites per sample was significantly higher in CAD patients.

Conclusions

In conclusion, our study confirmed the contribution of rare variants in CAD and showed that CAD patients had more non-synonymous heterogeneity mutations, which may be helpful in identifying the genetic and molecular basis of CAD.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Multiple independent mechanisms link gene polymorphisms in the region of ZEB2 with risk of coronary artery disease

BACKGROUND AND AIMS

Coronary artery disease (CAD) arises from the interaction of genetic and environmental factors. Although genome-wide association studies (GWAS) have identified multiple risk loci and single nucleotide polymorphisms (SNPs) associated with risk of CAD, they are predominantly located in non-coding or intergenic regions and their mechanisms of effect are largely unknown. Accordingly, our objective was to develop a data-driven informatics pipeline to understand complex CAD risk loci, and to apply this to a poorly understood cluster of SNPs in the vicinity of ZEB2.

METHODS

We developed a unique informatics pipeline leveraging a multi-tissue CAD genetics-of-gene-expression dataset, GWAS datasets, and other resources. The pipeline first dissected SNP locations and their linkage disequilibrium relationships, and progressed through analyses of tissue-specific expression quantitative trait loci, and then gene-gene, gene-phenotype, SNP-phenotype relationships. The pipeline concluded by exploring CAD-relevant gene regulatory networks (GRNs).

RESULTS

We identified three independent CAD risk SNPs in close proximity to the ZEB2 coding region (rs6740731, rs17678683 and rs2252641/rs1830321). Our pipeline determined that these SNPs likely act in concert via the atherosclerotic arterial wall and adipose tissues, by governing metabolic and lipid functions. In addition, ZEB2 is the top key driver of a liver-specific GRN that is related to lipid levels, metabolic and anthropometric measures, and CAD severity.

CONCLUSIONS

Using a novel informatics pipeline, we disclosed the multi-faceted mechanisms of action of the ZEB2-associated CAD risk SNPs. This pipeline can serve as a roadmap to dissect complex SNP-gene-tissue-phenotype relationships and to reveal targets for tissue- and gene-specific therapeutic interventions.

Novel Approaches to Fine-Tune Therapeutic Targeting of Platelets in Atherosclerosis: A Critical Appraisal

The pathogenesis of atherosclerotic vascular disease is driven by a multitude of risk factors intertwining metabolic and inflammatory pathways. Increasing knowledge about platelet biology sheds light on how platelets take part in these processes from early to later stages of plaque development. Recent insights from experimental studies and mouse models substantiate platelets as initiators and amplifiers in atherogenic leukocyte recruitment. These studies are complemented by results from genetics studies shedding light on novel molecular mechanisms which provide an interesting prospect as novel targets. For instance, experimental studies provide further details how platelet-decorated von Willebrand factor tethered to activated endothelial cells plays a role in atherogenic monocyte recruitment. Novel aspects of platelets as atherogenic inductors of neutrophil extracellular traps and particularities in signaling pathways such as cyclic guanosine monophosphate and the inhibitory adaptor molecule SHB23/LNK associating platelets with atherogenesis are shared. In summary, it was our intention to balance insights from recent experimental data that support a plausible role for platelets in atherogenesis against a paucity of clinical evidence needed to validate this concept in humans.

Model-based clustering of multi-tissue gene expression data

MOTIVATION

Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues.

RESULTS

We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals.

AVAILABILITY AND IMPLEMENTATION

Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review

A new cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for early and sustainable, evidence-based therapeutic targeting to promote cardiometabolic health and mitigate the development and ravages of cardiovascular disease. In the first part of this JACC State-of-the-Art Review, a framework is presented for CMBCD, focusing on 3 primary drivers (genetics, environment, and behavior) and 2 metabolic drivers (adiposity and dysglycemia) with applications to 3 cardiovascular endpoints (coronary heart disease, heart failure, and atrial fibrillation). Specific mechanistic pathways are presented configuring early primary drivers with subsequent adiposity, insulin resistance, β-cell dysfunction, and metabolic syndrome, leading to cardiovascular disease. The context for building this CMBCD model is to expose actionable targets for prevention to achieve optimal cardiovascular outcomes. The tactical implementation of this CMBCD model is the subject of second part of this JACC State-of-the-Art Review.

Genomic Strategies Toward Identification of Novel Therapeutic Targets

Coronary artery disease, myocardial infarction, and secondary damages of the myocardium in the form of ischemic heart disease remain major causes of death in Western countries. Beyond traditional risk factors such as smoking, hypertension, dyslipidemia, or diabetes, a positive family history is known to increase risk. The genetic factors underlying this observation remained unknown for decades until genetic studies were able to identify multiple genomic loci contributing to the heritability of the trait. Knowledge of the affected genes and the resulting molecular and cellular mechanisms leads to improved understanding of the pathophysiology leading to coronary atherosclerosis. Major goals are also to improve prevention and therapy of coronary artery disease and its sequelae via improved risk prediction tools and pharmacological targets. In this chapter, we recapitulate recent major findings. We focus on established novel targets and discuss possible further targets which are currently explored in translational studies.

Prediction and management of CAD risk based on genetic stratification

Discovery of genetic risk variants for CAD and their assembly on a computerized microarray enables a genetic risk score (GRS) to be expressed as a single number. Utilizing this array, genetic risk stratification has been performed in over 1 million cases and controls. The genetic score based on one's DNA can be determined anytime from birth on and is independent of age and conventional risk factors. Utilizing the GRS, one can select those at highest risk and would benefit most from primary prevention. Clinical trials have shown that modifying lifestyle or using statin therapy reduces the risk for CAD by approximately 50%. The use of the GRS for primary prevention will have a transformative effect on preventing the spread of CAD.