Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Shared genetic architecture and causal relationship between liver and heart disease

This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.

Association of an ADRB3 Variant with Coronary Artery Disease Within the Chinese Han Population: Construction of a Predictive Nomogram Model

Objective: Coronary artery disease (CAD) is a the most common type of heart disease, and is associated with the highest mortality rate. The role of the β₃-adrenergic receptor gene (ADRB3) in energy homeostasis and lipolysis suggests that it may be associated with obesity, insulin resistance, diabetes, and hypertension. Herein, we sought to examine the relationship between CAD and variants of the ADRB3 gene in individuals with Han and Uygur ethnicities in China. Methods: All 1022 participants were genotyped for two ADRB3 single nucleotide polymorphisms (SNPs; rs1892818 and rs9693898) using real-time polymerase chain reaction (TaqMan). Uygur (259 CAD patients, 161 control group) and Han (308 CAD patients, 294 control group) were included in two case-control studies. We subsequently developed a predictive model using ADRB3 genetic variation and clinical variables to predict risk of CAD. Results: The rs1892818 CT genotype (8.5% vs 3.9%, p = 0.019) and T allele (4.3% vs 1.9%, p = 0.021) were more frequently detected in the control subjects compared to CAD patients of the Han population but not in the Uygur population. The rs9693898 was not associated with CAD in either ethnic population. Logistic regression analysis further demonstrated that carriers of the rs1892818 CT genotype had a lower risk of CAD than did those with the CC genotype (CT vs CC, p = 0.044, odds ratio [OR] = 0.441, 95% confidence interval [CI]: 0.199-0.976). Using this data, we constructed a predictive nomogram model for CAD with an area under the curve (95% CI) of 0.722 (0.682, 0.761). Conclusions: Our results suggest that rs1892818 is associated with CAD in the Han population and that the CT genotype of rs1892818 may serve as a protective factor for CAD in Han individuals. The proposed nomograms can be used for the prediction of CAD in this population.

Signature pattern of gene expression and signaling pathway in premature diabetic patients uncover their correlation to early age coronary heart disease

BACKGROUND

Coronary Heart Disease (CHD) is the leading cause of death in industrialized countries. There is currently no direct relation between CHD and type 2 diabetes mellitus (T2D), one of the major modifiable risk factors for CHD. This study was carried out for genes expression profiling of T2D associated genes to identify related biological processes/es and modulated signaling pathway/s of male subjects with CHD.

METHOD

the subjects were divided into four groups based on their disease, including control, type 2 diabetes mellitus (T2D), CHD, and CHD + T2D groups. The RNA was extracted from their blood, and RT2 Profiler™ PCR Array was utilized to determine gene profiling between groups. Finally, the PCR Array results were validated by using Q-RT-PCR in a more extensive and independent population.

RESULT

PCR Array results revealed that the T2D and T2D + CHD groups shared 11 genes significantly up-regulated in both groups. Further analysis showed that the mRNA levels of AKT2, IL12B, IL6, IRS1, IRS2, MAPK14, and NFKB1 increased. Consequently, the mRNA levels of AQP2, FOXP3, G6PD, and PIK3R1 declined in the T2D + CHD group compared to the T2D group. Furthermore, in silico analysis indicated 36 Gene Ontology terms and 59 signaling pathways were significantly enriched in both groups, which may be a culprit in susceptibility of diabetic patients to CHD development.

CONCLUSION

Finally, the results revealed six genes as a hub gene in altering various biological processes and signaling pathways. The expression trend of these identified genes might be used as potential markers and diagnostic tools for the early identification of the vulnerability of T2D patients to develop premature CHD.

Thrombosis origin identification of cardioembolism and large artery atherosclerosis by distinct metabolites

BACKGROUND

The diagnosis of cerebral thrombosis origin is challenging and remains unclear. This study aims to identify thrombosis due to cardioembolism (CE) and large artery atherosclerosis (LAA) from a new perspective of distinct metabolites.

METHODS

Distinct metabolites between 26 CE and 22 LAA origin thrombi, which were extracted after successful mechanical thrombectomy in patients with acute ischemic stroke in the anterior circulation, were analyzed with a ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) system. Enriched metabolic pathways related to the metabolites were identified. Least absolute shrinkage selection operator regression analyses and a filtering method were used to select potential predictors. Furthermore, four machine learning classifiers, including decision tree, logistic regression, random forest (RF), and k means unsupervised classification model, were used to evaluate the predictive ability of the selected metabolites.

RESULTS

UPLC-QTOF-MS analysis revealed that levels of 88 and 55 metabolites were elevated in LAA and CE thrombi, respectively. Kyoto Encyclopedia of Genes and Genomes analysis revealed a significant difference between the pathways enriched in the two types of thrombi. Six metabolites (diglyceride (DG, 18:3/24:0), DG (22:0/24:0), phytosphingosine, galabiosylceramide (18:1/24:1), triglyceride (15:0/16:1/o-18:0), and glucosylceramide (18:1/24:0)) were finally selected to build a predictive model. The predictive RF model was confirmed to be the best, with a satisfactory stability and prediction capacity (area under the curve=0.889).

CONCLUSIONS

Six metabolites as potential predictors for distinguishing between cerebral thrombi of CE and LAA origin were identified. The results are useful for understanding the pathogenesis and for secondary stroke prevention.

The GEnetic Syntax Score: a genetic risk assessment implementation tool grading the complexity of coronary artery disease-rationale and design of the GESS study

Background

Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide and is associated with multiple inherited and environmental risk factors. This study is designed to identify, design, and develop a panel of genetic markers that combined with clinical and angiographic information, will facilitate the creation of a personalized risk prediction algorithm (GEnetic Syntax Score—GESS). GESS score could be a reliable tool for predicting cardiovascular risk for future adverse events and for guiding therapeutic strategies.

Methods

GESS (ClinicalTrials.gov Identifier: NCT03150680) is a prospective, non-interventional clinical study designed to enroll 1080 consecutive patients with no prior history of coronary revascularization procedure, who undergo scheduled or emergency coronary angiography in AHEPA, University General Hospital of Thessaloniki. Next generation sequencing (NGS) technology will be used to genotype specific single-nucleotide polymorphisms (SNPs) across the genome of study participants, which were identified as clinically relevant to CAD after extensive bioinformatic analysis of literature-based SNPs. Enrichment analyses of Gene Ontology-Molecular Function, Reactome Pathways and Disease Ontology terms were also performed to identify the top 15 statistically significant terms and pathways. Furthermore, the SYNTAX score will be calculated for the assessment of CAD severity of all patients based on their angiographic findings. All patients will be followed-up for one-year, in order to record any major adverse cardiovascular events.

Discussion

A group of 228 SNPs was identified through bioinformatic and pharmacogenomic analysis to be involved in CAD through a wide range of pathways and was correlated with various laboratory and clinical parameters, along with the patients' response to clopidogrel and statin therapy. The annotation of these SNPs revealed 127 genes being affected by the presence of one or more SNPs. The first patient was enrolled in the study in February 2019 and enrollment is expected to be completed until June 2021. Hence, GESS is the first trial to date aspiring to develop a novel risk prediction algorithm, the GEnetic Syntax Score, able to identify patients at high risk for complex CAD based on their molecular signature profile and ultimately promote pharmacogenomics and precision medicine in routine clinical settings.

Trial registration GESS trial registration: ClinicalTrials.gov Number: NCT03150680. Registered 12 May 2017- Prospectively registered, https://clinicaltrials.gov/ct2/show/NCT03150680.

Changes of protein levels in human urine reflect the dysregulation of signaling pathways of chronic kidney disease and its complications

The increasing prevalence of chronic kidney disease (CKD) seriously is threatening human health and overall quality of life. The discovery of biomarkers of pathogenesis of CKD and the associated complications are very important for CDK diagnosis and treatment. In this paper, urine protein biomarkers were investigated because urine sample collection is convenient and non-invasive. We analyzed the protein concentrations in the urine of CKD patients and extracted abnormal protein signals comparing with the healthy control groups. The enriched signaling pathways that may characterize CKD pathology were identified from these proteins. We applied surface-enhanced laser desorption and ionization time of flight mass spectrometry technology to detect different protein peaks in urine samples from patients with CKD and healthy controls. We searched the proteins corresponding to protein peaks through the UniProt database and identified the signaling pathways of CKD and its complications by using the NIH DAVID database. 42 low abundance proteins and 46 high abundance proteins in the urine samples from CKD patients were found by comparing with healthy controls. Seven KEGG pathways related to CKD and its complications were identified from the regulated proteins. These pathways included chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation, cardiac muscle contraction, Alzheimer's disease, Parkinson's disease, and salivary secretion. In CKD stages 2, 3, 4, and 5, five proteins showed significantly differential abundances. The differential protein signals and regulated signaling pathways will provide new insight for the pathogenesis of CKD and its complications. These altered proteins may also be used as novel biomarkers for the noninvasive and convenient diagnosis methods of CKD and its complications through urine testing in the future.

Non-alcoholic fatty liver disease and Atherosclerosis at a crossroad: The overlap of a theory of change and bioinformatics

Atherosclerosis (ATH) and non-alcoholic fatty liver disease (NAFLD) are medical conditions that straddle a communal epidemiology, underlying mechanism and a clinical syndrome that has protean manifestations, touching every organ in the body. These twin partners, ATH and NAFLD, are seemingly straightforward and relatively simple topics when considered alone, but their interdependence calls for more thought. The study of the mutual relationship of NAFLD and ATH should involve big data analytics approaches, given that they encompass a constellation of diseases and are related to several recognized risk factors and health determinants and calls to an explicit theory of change, to justify intervention. Research studies on the “association between aortic stiffness and liver steatosis in morbidly obese patients”, published recently, sparsely hypothesize new mechanisms of disease, claiming the “long shadow of NAFLD” as a risk factor, if not as a causative factor of arterial stiffness and ATH. This statement is probably overreaching the argument and harmful for the scientific credence of this area of medicine. Despite the verification that NAFLD and cardiovascular disease are strongly interrelated, current evidence is that NAFLD may be a useful indicator for flagging early arteriosclerosis, and not a likely causative factor. Greater sustainable contribution by precision medicine tools, by validated bioinformatics approaches, is needed for substantiating conjectures, assumptions and inferences related to the management of big data and addressed to intervention for behavioral changes within an explicit theory of change.

Exome-Wide Association Study Reveals Several Susceptibility Genes and Pathways Associated With Acute Coronary Syndromes in Han Chinese

Genome-wide association studies have identified more than 150 susceptibility loci for coronary artery disease (CAD); however, there is still a large proportion of missing heritability remaining to be investigated. This study sought to identify population-based genetic variation associated with acute coronary syndromes (ACS) in individuals of Chinese Han descent. We proposed a novel strategy integrating a well-developed risk prediction model into control selection in order to lower the potential misclassification bias and increase the statistical power. An exome-wide association analysis was performed for 1,669 ACS patients and 1,935 healthy controls. Promising variants were further replicated using the existing in silico dataset. Additionally, we performed gene- and pathway-based analyses to investigate the aggregate effect of multiple variants within the same genes or pathways. Although none of the association signals were consistent across studies after Bonferroni correction, one promising variant, rs10409124 at STRN4, showed potential impact on ACS in both European and East Asian populations. Gene-based analysis explored four genes (ANXA7, ZNF655, ZNF347, and ZNF750) that showed evidence for association with ACS after multiple test correction, and identification of ZNF655 was successfully replicated by another dataset. Pathway-based analysis revealed that 32 potential pathways might be involved in the pathogenesis of ACS. Our study identified several candidate genes and pathways associated with ACS. Future studies are needed to further validate these findings and explore these genes and pathways as potential therapeutic targets in ACS.