Additional Candidate Genes for Human Atherosclerotic Disease Identified Through Annotation Based on Chromatin Organization

Atherosclerotic Plaque Epigenetic Age Acceleration Predicts a Poor Prognosis and Is Associated With Endothelial-to-Mesenchymal Transition in Humans

BACKGROUND

Epigenetic age estimators (clocks) are predictive of human mortality risk. However, it is not yet known whether the epigenetic age of atherosclerotic plaques is predictive for the risk of cardiovascular events.

METHODS

Whole-genome DNA methylation of human carotid atherosclerotic plaques (n=485) and of blood (n=93) from the Athero-Express endarterectomy cohort was used to calculate epigenetic age acceleration (EAA). EAA was linked to clinical characteristics, plaque histology, and future cardiovascular events (n=136). We studied whole-genome DNA methylation and bulk and single-cell transcriptomics to uncover molecular mechanisms of plaque EAA. We experimentally confirmed our in silico findings using in vitro experiments in primary human coronary endothelial cells.

RESULTS

Male and female patients with severe atherosclerosis had a median chronological age of 69 years. The median epigenetic age was 65 years in females (median EAA, -2.2 [interquartile range, -4.3 to 2.2] years) and 68 years in males (median EAA, -0.3 [interquartile range, -2.9 to 3.8] years). Patients with diabetes and a high body mass index had higher plaque EAA. Increased EAA of plaque predicted future events in a 3-year follow-up in a Cox regression model (univariate hazard ratio, 1.7; P=0.0034) and adjusted multivariate model (hazard ratio, 1.56; P=0.02). Plaque EAA predicted outcome independent of blood EAA (hazard ratio, 1.3; P=0.018) and of plaque hemorrhage (hazard ratio, 1.7; P=0.02). Single-cell RNA sequencing in plaque samples from 46 patients in the same cohort revealed smooth muscle and endothelial cells as important cell types in plaque EAA. Endothelial-to-mesenchymal transition was associated with EAA, which was experimentally confirmed by TGFβ-triggered endothelial-to-mesenchymal transition inducing rapid epigenetic aging in coronary endothelial cells.

CONCLUSIONS

Plaque EAA is a strong and independent marker of poor outcome in patients with severe atherosclerosis. Plaque EAA was linked to mesenchymal endothelial and smooth muscle cells. Endothelial-to-mesenchymal transition was associated with EAA, which was experimentally validated. Epigenetic aging mechanisms may provide new targets for treatments that reduce atherosclerosis complications.

Advances and Challenges for GWAS Analysis in Cardiac Diseases: A Focus on Coronary Artery Disease (CAD)

The achievement of genome-wide association studies (GWAS) has rapidly progressed our understanding of the etiology of coronary artery disease (CAD). It unlocks new strategies to strengthen the stalling of CAD drug development. In this review, we highlighted the recent drawbacks, mainly pointing out those involved in identifying causal genes and interpreting the connections between disease pathology and risk variants. We also benchmark the novel insights into the biological mechanism behind the disease primarily based on outcomes of GWAS. Furthermore, we also shed light on the successful discovery of novel treatment targets by introducing various layers of "omics" data and applying systems genetics strategies. Lastly, we discuss in-depth the significance of precision medicine that is helpful to improve through GWAS analysis in cardiovascular research.

Chromatin Conformation in Development and Disease

Chromatin domains and loops are important elements of chromatin structure and dynamics, but much remains to be learned about their exact biological role and nature. Topological associated domains and functional loops are key to gene expression and hold the answer to many questions regarding developmental decisions and diseases. Here, we discuss new findings, which have linked chromatin conformation with development, differentiation and diseases and hypothesized on various models while integrating all recent findings on how chromatin architecture affects gene expression during development, evolution and disease.

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Family history and polygenic risk of cardiovascular disease: Independent factors associated with secondary cardiovascular events in patients undergoing carotid endarterectomy

BACKGROUND AND AIMS

Family history (FHx) of cardiovascular disease (CVD) is a risk factor for CVD and a proxy for cardiovascular heritability. Polygenic risk scores (PRS) summarizing >1 million variants for coronary artery disease (CAD) are associated with incident and recurrent CAD events. However, little is known about the influence of FHx or PRS on secondary cardiovascular events (sCVE) in patients undergoing carotid endarterectomy (CEA).

METHODS

We included 1788 CEA patients from the Athero-Express Biobank. A weighted PRS for CAD including 1.7 million variants was calculated (MetaGRS). The composite endpoint of sCVE during three years of follow-up included coronary, cerebrovascular and peripheral events and cardiovascular death. We assessed the impact of FHx and MetaGRS on sCVE and carotid plaque composition.

RESULTS

Positive FHx was associated with a higher 3-year risk of sCVE independent of cardiovascular risk factors and MetaGRS (adjusted HR 1.40, 95%CI 1.07-1.82, p = 0.013). Patients in the highest MetaGRS quintile had a higher 3-year risk of sCVE compared to the rest of the cohort independent of cardiovascular risk factors including FHx (adjusted HR 1.35, 95%CI 1.01-1.79, p = 0.043), and their atherosclerotic plaques contained more fat (adjusted OR 1.59, 95%CI, 1.11-2.29, p = 0.013) and more macrophages (OR 1.49, 95%CI 1.12-1.99, p = 0.006).

CONCLUSIONS

In CEA patients, both positive FHx and higher MetaGRS were independently associated with increased risk of sCVE. Moreover, higher MetaGRS was associated with vulnerable plaque characteristics. Future studies should unravel underlying mechanisms and focus on the added value of PRS and FHx in individual risk prediction for sCVE.

QTL mapping of rat blood pressure loci on RNO1 within a homologous region linked to human hypertension on HSA15

Fine-mapping of regions linked to the inheritance of hypertension is accomplished by genetic dissection of blood pressure quantitative trait loci (BP QTLs) in rats. The goal of the current study was to further fine-map two genomic regions on rat chromosome 1 with opposing blood pressure effects (BP QTL1b1 and BP QTL1b1a), the homologous region of which on human chromosome 15 harbors BP QTLs. Two new substrains were constructed and studied from the previously reported BP QTL1b1, one having significantly lower systolic BP by 17 mmHg than that of the salt-sensitive (S) rat (P = 0.007). The new limits of BP QTL1b1 were between 134.09 Mb and 135.40 Mb with a 43% improvement from the previous 2.31 Mb to the current 1.31 Mb interval containing 4 protein-coding genes (Rgma, Chd2, Fam174b, and St8sia2), 2 predicted miRNAs, and 4 lncRNAs. One new substrain was constructed and studied from the previously reported BPQTL1b1a having a significantly higher systolic BP by 22 mmHg (P = 0.006) than that of the S rat. The new limits of BPQTL1b1a were between 133.53 Mb and 134.52 Mb with a 32% improvement from the previous1.45 Mb to the current 990.21 Kb interval containing 1 protein-coding gene, Mctp2, and a lncRNA. The congenic segments of these two BP QTLs overlapped between 134.09 Mb and 134.52 Mb. No exonic variants were detected in any of the genes. These findings reiterate complexity of genetic regulation of BP within QTL regions, where elements beyond protein-coding sequences could be factors in controlling BP.

A decade of genome-wide association studies for coronary artery disease: the challenges ahead

In this review, we summarize current knowledge on the genetics of coronary artery disease, based on 10 years of genome-wide association studies. The discoveries began with individual studies using 200K single nucleotide polymorphism arrays and progressed to large-scale collaborative efforts, involving more than a 100 000 people and up to 40 Mio genetic variants. We discuss the challenges ahead, including those involved in identifying causal genes and deciphering the links between risk variants and disease pathology. We also describe novel insights into disease biology based on the findings of genome-wide association studies. Moreover, we discuss the potential for discovery of novel treatment targets through the integration of different layers of 'omics' data and the application of systems genetics approaches. Finally, we provide a brief outlook on the potential for precision medicine to be enhanced by genome-wide association study findings in the cardiovascular field.

Challenges and opportunities in stroke genetics

Stroke, ischaemic stroke and subtypes of ischaemic stroke display substantial heritability. When compared with related vascular conditions, the number of established risk loci reaching genome-wide significance for association with stroke is still in the lower range, particularly for aetiological stroke subtypes such as large artery atherosclerotic stroke or small vessel stroke. Nevertheless, for individual loci substantial progress has been made in determining the specific mechanisms mediating stroke risk. In this review, we present a roadmap for functional follow-up of common risk variants associated with stroke. First, we discuss in silico strategies for characterizing signals in non-coding regions and highlight databases providing information on quantitative trait loci for mRNA and protein expression, as well as methylation, focussing on those with presumed relevance for stroke. Next, we discuss experimental strategies for following up on non-coding risk variants and regions such as massively parallel reporter assays, proteome-wide association studies, and chromatin conformation capture (3C) assays. These and other approaches are relevant for gaining insight into the specific variants and mechanisms mediating genetic stroke risk. Finally, we discuss how genetic findings could influence clinical practice by adding to diagnostic algorithms and eventually improve treatment options for stroke.

Relevance of Multi-Omics Studies in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of death around the world. Despite the larger number of genes and loci identified, the precise mechanisms by which these genes influence risk of cardiovascular disease is not well understood. Recent advances in the development and optimization of high-throughput technologies for the generation of “omics data” have provided a deeper understanding of the processes and dynamic interactions involved in human diseases. However, the integrative analysis of “omics” data is not straightforward and represents several logistic and computational challenges. In spite of these difficulties, several studies have successfully applied integrative genomics approaches for the investigation of novel mechanisms and plasma biomarkers involved in cardiovascular diseases. In this review, we summarized recent studies aimed to understand the molecular framework of these diseases using multi-omics data from mice and humans. We discuss examples of omics studies for cardiovascular diseases focused on the integration of genomics, epigenomics, transcriptomics, and proteomics. This review also describes current gaps in the study of complex diseases using systems genetics approaches as well as potential limitations and future directions of this emerging field.

Understanding the 3D genome: Emerging impacts on human disease

Recent burst of new technologies that allow for quantitatively delineating chromatin structure has greatly expanded our understanding of how the genome is organized in the three-dimensional (3D) space of the nucleus. It is now clear that the hierarchical organization of the eukaryotic genome critically impacts nuclear activities such as transcription, replication, as well as cellular and developmental events such as cell cycle, cell fate decision and embryonic development. In this review, we discuss new insights into how the structural features of the 3D genome hierarchy are established and maintained, how this hierarchy undergoes dynamic rearrangement during normal development and how its perturbation will lead to human disease, highlighting the accumulating evidence that links the diverse 3D genome architecture components to a multitude of human diseases and the emerging mechanisms by which 3D genome derangement causes disease phenotypes.

Non-coding DNA in IBD: from sequence variation in DNA regulatory elements to novel therapeutic potential

Genome-wide association studies have identified over 200 loci associated with IBD. We and others have recently shown that, in addition to variants in protein-coding genes, the majority of the associated loci are related to DNA regulatory elements (DREs). These findings add a dimension to the already complex genetic background of IBD. In this review we summarise the existing evidence on the role of DREs in IBD. We discuss how epigenetic research can be used in candidate gene approaches that take non-coding variants into account and can help to pinpoint the essential pathways and cell types in the pathogenesis of IBD. Despite the increased level of genetic complexity, these findings can contribute to novel therapeutic options that target transcription factor binding and enhancer activity. Finally, we summarise the future directions and challenges of this emerging field.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

Translating GWAS Findings to Novel Therapeutic Targets for Coronary Artery Disease

The success of genome-wide association studies (GWAS) has significantly advanced our understanding of the etiology of coronary artery disease (CAD) and opens new opportunities to reinvigorate the stalling CAD drug development. However, there exists remarkable disconnection between the CAD GWAS findings and commercialized drugs. While this could implicate major untapped translational and therapeutic potentials in CAD GWAS, it also brings forward extensive technical challenges. In this review we summarize the motivation to leverage GWAS for drug discovery, outline the critical bottlenecks in the field, and highlight several promising strategies such as functional genomics and network-based approaches to enhance the translational value of CAD GWAS findings in driving novel therapeutics

Systems analysis of dilated cardiomyopathy in the next generation sequencing era

Dilated cardiomyopathy (DCM) is a form of severe failure of cardiac muscle caused by a long list of etiologies ranging from myocardial infarction, DNA mutations in cardiac genes, to toxics. Systems analysis integrating next-generation sequencing (NGS)-based omics approaches, such as the sequencing of DNA, RNA, and chromatin, provide valuable insights into DCM mechanisms. The outcome and interpretation of NGS methods can be affected by the localization of cardiac biopsy, level of tissue degradation, and variable ratios of different cell populations, especially in the presence of fibrosis. Heart tissue composition may even differ between sexes, or siblings carrying the same disease causing mutation. Therefore, before planning any experiments, it is important to fully appreciate the complexities of DCM, and the selection of samples suitable for given research question should be an interdisciplinary effort involving clinicians and biologists. The list of NGS omics datasets in DCM to date is short. More studies have to be performed to contribute to public data repositories and facilitate systems analysis. In addition, proper data integration is a difficult task requiring complex computational approaches. Despite these complications, there are multiple promising implications of systems analysis in DCM. By combining various types of datasets, for example, RNA-seq, ChIP-seq, or 4C, deep insights into cardiac biology, and possible biomarkers and treatment targets, can be gained. Systems analysis can also facilitate the annotation of noncoding mutations in cardiac-specific DNA regulatory regions that play a substantial role in maintaining the tissue- and cell-specific transcriptional programs in the heart. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods Laboratory Methods and Technologies > RNA Methods.