Effect of Coronary Artery Disease risk SNPs on serum cytokine levels and cytokine imbalance in Premature Coronary Artery Disease

MRAS in coronary artery disease-Unchartered territory

Genome-wide association studies (GWAS) have identified coronary artery disease (CAD) susceptibility locus on chromosome 3q22.3. This locus contains a cluster of several genes that includes muscle rat sarcoma virus (MRAS). Common MRAS variants are also associated with CAD causing risk factors such as hypertension, dyslipidemia, obesity, and type II diabetes. The MRAS gene is an oncogene that encodes a membrane-bound small GTPase. It is involved in a variety of signaling pathways, regulating cell differentiation and cell survival (mitogen-activated protein kinase [MAPK]/extracellular signal-regulated kinase and phosphatidylinositol 3-kinase) as well as acute phase response signaling (tumor necrosis factor [TNF] and interleukin 6 [IL6] signaling). In this review, we will summarize the role of genetic MRAS variants in the etiology of CAD and its comorbidities with the focus on tissue distribution of MRAS isoforms, cell type/tissue specificity, and mode of action of single nucleotide variants in MRAS associated complex traits. Finally, we postulate that CAD risk variants in the MRAS locus are specific to smooth muscle cells and lead to higher levels of MRAS, particularly in arterial and cardiac tissue, resulting in MAPK-dependent tissue hypertrophy or hyperplasia.

Modifiable and Non-Modifiable Risk Factors for Premature Coronary Heart Disease (PCHD): Systematic Review and Meta-Analysis

AIM

We aimed to compare the prevalence of modifiable and non-modifiable coronary heart disease (CHD) risk factors among those with premature CHD and healthy individuals.

METHODS

PubMed, CINAHL, Embase, and Web of Science databases were searched (review protocol is registered in PROSPERO CRD42020173216). The quality of studies was assessed using the National Heart, Lung and Blood Institute tool for cross-sectional, cohort and case-control studies. Meta-analyses were performed using Review Manager 5.3. Effect sizes for categorical and continuous variables, odds ratio (OR) and mean differences (MD)/standardised mean differences (SMD) with 95% confidence intervals (CI) were reported.

RESULTS

A total of n=208 primary studies were included in this review. Individuals presenting with premature CHD (PCHD, age ≤65 years) had higher mean body mass index (MD 0.54 kg/m2, 95% CI 0.24, 0.83), total cholesterol (SMD 0.27, 95% CI 0.17, 0.38), triglycerides (SMD 0.50, 95% CI 0.41, 0.60) and lower high-density lipoprotein cholesterol (SMD 0.79, 95% CI: -0.91, -0.68) compared with healthy individuals. Individuals presenting with PCHD were more likely to be smokers (OR 2.88, 95% CI 2.51, 3.31), consumed excessive alcohol (OR 1.40, 95% CI 1.05, 1.86), had higher mean lipoprotein (a) levels (SMD 0.41, 95% CI 0.28, 0.54), and had a positive family history of CHD (OR 3.65, 95% CI 2.87, 4.66) compared with healthy individuals. Also, they were more likely to be obese (OR 1.59, 95% CI 1.32, 1.91), and to have had dyslipidaemia (OR 2.74, 95% CI 2.18, 3.45), hypertension (OR 2.80, 95% CI 2.28, 3.45), and type 2 diabetes mellitus (OR 2.93, 95% CI 2.50, 3.45) compared with healthy individuals.

CONCLUSION

This meta-analysis confirms current knowledge of risk factors for PCHD, and identifying these early may reduce CHD in young adults.

Haplotype of the Lipoprotein(a) Gene Variants rs10455872 and rs3798220 Is Associated with Parameters of Coagulation, Fibrinolysis, and Inflammation in Patients after Myocardial Infarction and Highly Elevated Lipoprotein(a) Values

Lipoprotein(a) (Lp(a)) is an independent risk factor for future coronary events. Variants rs10455872 and rs3798220 in the gene encoding Lp(a) are associated with an increased Lp(a) concentration and risk of coronary artery disease. We aimed to determine whether in high-risk coronary artery disease patients these two genetic variants and the kringle IV type 2 (KIV-2) repeats are associated with impairment of inflammatory and hemostatic parameters. Patients after myocardial infarction with elevated Lp(a) levels were included. Blood samples underwent biochemical and genetic analyses. In carriers of the AC haplotype, the concentrations of tumor necrosis factor (TNF)-α (4.46 vs. 3.91 ng/L, p = 0.046) and plasminogen activator inhibitor-1 (PAI-1) (p = 0.026) were significantly higher compared to non-carriers. The number of KIV-2 repeats was significantly associated with the concentration of high-sensitivity C-reactive protein (ρ = 0.251, p = 0.038) and overall fibrinolytic potential (r = -0.253, p = 0.038). In our patients, a direct association between the AC haplotype and both TNF-α and PAI-1 levels was observed. Our study shows that the number of KIV-2 repeats not only affects proatherosclerotic and proinflammatory effects of Lp(a) but is also associated with its antifibrinolytic properties.

Association between Genetic Variants of CELSR2-PSRC1-SORT1 and Cardiovascular Diseases: A Systematic Review and Meta-Analysis

A cluster of three genes CELSR2, PSRC1, and SORT1 has been associated with cardiovascular diseases. Thus, the aim of this study was (i) to perform a systematic review and updated meta-analysis of the association of three polymorphisms (rs646776, rs599839, and rs464218) of this cluster with cardiovascular diseases, and (ii) to explore by PheWAS signals of the three SNPs in cardiovascular diseases and to evaluate the effect of rs599839 with tissue expression by in silico tools. Three electronic databases were searched to identify eligible studies. The meta-analysis showed that the rs599839 (allelic OR 1.19, 95% CI 1.13–1.26, dominant OR 1.22, 95% CI 1.06–1.39, recessive OR 1.23, 95% CI 1.15–1.32), rs646776 (allelic OR 1.46, 95% CI 1.17–1.82) polymorphisms showed an increased risk for cardiovascular diseases. PheWas analysis showed associations with coronary artery disease and total cholesterol. Our results suggest a possible involvement of the CELSR2-PSRC1-SORT1 cluster variants in the risk association of cardiovascular diseases, particularly coronary artery disease.

A Deep Convolutional Neural Network for the Early Detection of Heart Disease

Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment.

GCN-GENE: A novel method for prediction of coronary heart disease-related genes

Coronary heart disease is the most common heart disease, it can induce myocardial infarction, and the cause of the disease has a lot to do with life and eating habits. The results of a large number of epidemiological studies at home and abroad show that the incidence of coronary heart disease has an obvious familial tendency. However, little is known about the genetic factors of coronary heart disease. Although genome-wide association analysis and gene knockout experiments have found some genes related to coronary heart disease, there are still a large number of genes potentially related to coronary heart disease that have not been discovered. If it is confirmed by biological experimental means, the time and money cost is too high. Therefore, it is urgent to identify genes related to coronary heart disease on a large scale by computational means, so as to conduct targeted biological experimental verification. This paper proposes a deep learning method based on biological networks for the identification of coronary heart disease-related genes. We constructed gene interaction networks and extracted gene expression levels in different tissues as features. Through the association information and expression characteristics between genes, we constructed a model of coronary heart disease-related genes. Through cross-validation, we found that our proposed GCN-GENE that has AUC as 0.75 and AUPR as 0.78, which is more accurate than other methods and is a reliable method for predicting coronary heart disease-related genes.

Translational Genomic Research: The Association between Genetic Profiles and Cognitive Functioning or Cardiac Function Among Breast Cancer Survivors Completing Chemotherapy

Introduction: Emerging evidence suggests that Chemotherapy (CT) treated breast cancer survivors (BCS) who have "risk variants" in genes may be more susceptible to cognitive impairment (CI) and/or poor cardiac phenotypes. The objective of this preliminary study was to examine whether there is a relationship between genetic variants and objective/subjective cognitive or cardiac phenotypes. Methods and Analysis: BCS were recruited from Moffitt Cancer Center, Morsani College of Medicine, AdventHealth Tampa and Sarasota Memorial Hospital. Genomic DNA were collected at baseline for genotyping analysis. A total of 16 single nucleotide polymorphisms (SNPs) from 14 genes involved in cognitive or cardiac function were evaluated. Three genetic models (additive, dominant, and recessive) were used to test correlation coefficients between genetic variants and objective/subjective measures of cognitive functioning and cardiac outcomes (heart rate, diastolic blood pressure, systolic blood pressure, respiration rate, and oxygen saturation). Results: BCS (207 participants) with a mean age of 56 enrolled in this study. The majority were non-Hispanic white (73.7%), married (63.1%), and received both CT and radiation treatment (77.3%). Three SNPs in genes related to cognitive functioning (rs429358 in APOE, rs1800497 in ANKK1, rs10119 in TOMM40) emerged with the most consistent significant relationship with cognitive outcomes. Among five candidate SNPs related to cardiac functioning, rs8055236 in CDH13 and rs1801133 in MTHER emerged with potential significant relationships with cardiac phenotype. Conclusions: These preliminary results provide initial targets to further examine whether BCS with specific genetic profiles may preferentially benefit from interventions designed to improve cognitive and cardiac functioning following CT.

Potential of immune-related genes as promising biomarkers for premature coronary heart disease through high throughput sequencing and integrated bioinformatics analysis

Background

Coronary heart disease (CHD) is the most common progressive disease that is difficult to diagnose and predict in the young asymptomatic period. Our study explored a mechanistic understanding of the genetic effects of premature CHD (PCHD) and provided potential biomarkers and treatment targets for further research through high throughput sequencing and integrated bioinformatics analysis.

Methods

High throughput sequencing was performed among recruited patients with PCHD and young healthy individuals, and CHD-related microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by using R software. Enrichment analysis and CIBERSORT were performed to explore the enriched pathways of DEGs and the characteristics of infiltrating immune cells. Hub genes identified by protein–protein interaction (PPI) networks were used to construct the competitive endogenous RNA (ceRNA) networks. Potential drugs were predicted by using the Drug Gene Interaction Database (DGIdb).

Results

A total of 35 DEGs were identified from the sequencing dataset and GEO database by the Venn Diagram. Enrichment analysis indicated that DEGs are mostly enriched in excessive immune activation pathways and signal transduction. CIBERSORT exhibited that resting memory CD4 T cells and neutrophils were more abundant, and M2 macrophages, CD8 T cells, and naïve CD4 T cells were relatively scarce in patients with PCHD. After the identification of 10 hub gens, three ceRNA networks of CD83, CXCL8, and NR4A2 were constructed by data retrieval and validation. In addition, CXCL8 might interact most with multiple chemical compounds mainly consisting of anti-inflammatory drugs.

Conclusions

The immune dysfunction mainly contributes to the pathogenesis of PCHD, and three ceRNA networks of CD83, CXCL8, and NR4A2 may be potential candidate biomarkers for early diagnosis and treatment targets of PCHD.

Association Study of Coronary Artery Disease-Associated Genome-Wide Significant SNPs with Coronary Stenosis in Pakistani Population

Genome-wide association studies (GWAS) of coronary artery disease (CAD) have revealed multiple genetic risk loci. We assessed the association of 47 genome-wide significant single-nucleotide polymorphisms (SNPs) at 43 CAD loci with coronary stenosis in a Pakistani sample comprising 663 clinically ascertained and angiographically confirmed cases. Genotypes were determined using the iPLEX Gold technology. All statistical analyses were performed using R software. Linkage disequilibrium (LD) between significant SNPs was determined using SNAP web portal, and functional annotation of SNPs was performed using the RegulomeDB and Genotype-Tissue Expression (GTEx) databases. Genotyping comparison was made between cases with severe stenosis (≥70%) and mild/minimal stenosis (<30%). Five SNPs demonstrated significant associations: three with additive genetic models PLG/rs4252120 (p = 0.0078), KIAA1462/rs2505083 (p = 0.005), and SLC22A3/rs2048327 (p = 0.045) and two with recessive models SORT1/rs602633 (p = 0.005) and UBE2Z/rs46522 (p = 0.03). PLG/rs4252120 was in LD with two functional PLG variants (rs4252126 and rs4252135), each with a RegulomeDB score of 1f. Likewise, KIAA1462/rs2505083 was in LD with a functional SNP, KIAA1462/rs3739998, having a RegulomeDB score of 2b. In the GTEx database, KIAA1462/rs2505083, SLC22A3/rs2048327, SORT1/rs602633, and UBE2Z/rs46522 SNPs were found to be expression quantitative trait loci (eQTLs) in CAD-associated tissues. In conclusion, five genome-wide significant SNPs previously reported in European GWAS were replicated in the Pakistani sample. Further association studies on larger non-European populations are needed to understand the worldwide genetic architecture of CAD.

The CXCL12 SNPs and their haplotypes are associated with serum lipid traits

The relationship among the single nucleotide polymorphisms (SNPs) of the C-X-C motif chemokine ligand 12 gene (CXCL12) and the serum lipid profiles in the Chinese population has rarely been described, especially in somewhat old-fashioned and isolated Maonan minority. The goal of the current study was to elucidate the connection among the CXCL12 rs501120 and rs1746048 SNPs, haplotypes, several environmental factors and serum lipid traits in the Maonan as well as Han populations. Genotyping of the two SNPs, gel electrophoresis and direct sequencing were accomplished in 1,494 distinct subjects (Maonan, 750 and Han, 744) using polymerase chain reaction and restriction fragment length polymorphism. The frequencies of genotypes as well as alleles of the two SNPs were not similar between the two ethnic groups. The rs501120 SNP was related with serum total cholesterol levels, while the rs1746048 SNP was related with serum apolipoprotein (Apo) B levels. Four haplotypes were identified, of which the rs501120A-rs1746048C haplotype was the most common. The haplotypes of rs501120A-rs1746048T increased and rs501120G-rs1746048C decreased the risk of hyperlipidemia (P < 0.001 for each), showing consistent association with the levels of serum triglyceride, ApoA1 and ApoB. These outcomes specify that the CXCL12 SNPs as well as their haplotypes are related to serum lipid levels. Different serum lipid levels between both populations may partially be related to the CXCL12 SNPs, their haplotypes along with several environmental factors.

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice – American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.

The Role of Cardiokines in Heart Diseases: Beneficial or Detrimental?

Cardiovascular disease remains the leading cause of morbidity and mortality, imposing a major disease burden worldwide. Therefore, there is an urgent need to identify new therapeutic targets. Recently, the concept that the heart acts as a secretory organ has attracted increasing attention. Proteins secreted by the heart are called cardiokines, and they play a critical physiological role in maintaining heart homeostasis or responding to myocardial damage and thereby influence the development of heart diseases. Given the critical role of cardiokines in heart disease, they might represent a promising therapeutic target. This review will focus on several cardiokines and discuss their roles in the pathogenesis of heart diseases and as potential therapeutics.