Investigation of the underlying genes and mechanism of familial hypercholesterolemia through bioinformatics analysis

Integrated omics analysis of coronary artery calcifications and myocardial infarction: the Framingham Heart Study

Gene function can be described using various measures. We integrated association studies of three types of omics data to provide insights into the pathophysiology of subclinical coronary disease and myocardial infarction (MI). Using multivariable regression models, we associated: (1) single nucleotide polymorphism, (2) DNA methylation, and (3) gene expression with coronary artery calcification (CAC) scores and MI. Among 3106 participants of the Framingham Heart Study, 65 (2.1%) had prevalent MI and 60 (1.9%) had incident MI, median CAC value was 67.8 [IQR 10.8, 274.9], and 1403 (45.2%) had CAC scores > 0 (prevalent CAC). Prevalent CAC was associated with AHRR (linked to smoking) and EXOC3 (affecting platelet function and promoting hemostasis). CAC score was associated with VWA1 (extracellular matrix protein associated with cartilage structure in endomysium). For prevalent MI we identified FYTTD1 (down-regulated in familial hypercholesterolemia) and PINK1 (linked to cardiac tissue homeostasis and ischemia-reperfusion injury). Incident MI was associated with IRX3 (enhancing browning of white adipose tissue) and STXBP3 (controlling trafficking of glucose transporter type 4 to plasma). Using an integrative trans-omics approach, we identified both putatively novel and known candidate genes associated with CAC and MI. Replication of findings is warranted.

Reducing Premature Coronary Artery Disease in Malaysia by Early Identification of Familial Hypercholesterolemia Using the Familial Hypercholesterolemia Case Ascertainment Tool (FAMCAT): Protocol for a Mixed Methods Evaluation Study

BACKGROUND

Familial hypercholesterolemia (FH) is predominantly caused by mutations in the 4 FH candidate genes (FHCGs), namely, low-density lipoprotein receptor (LDLR), apolipoprotein B-100 (APOB-100), proprotein convertase subtilisin/kexin type 9 (PCSK9), and the LDL receptor adaptor protein 1 (LDLRAP1). It is characterized by elevated low-density lipoprotein cholesterol (LDL-c) levels leading to premature coronary artery disease. FH can be clinically diagnosed using established clinical criteria, namely, Simon Broome (SB) and Dutch Lipid Clinic Criteria (DLCC), and can be identified using the Familial Hypercholesterolemia Case Ascertainment Tool (FAMCAT), a primary care screening tool.

OBJECTIVE

This study aims to (1) compare the detection rate of genetically confirmed FH and diagnostic accuracy between the FAMCAT, SB, and DLCC in the Malaysian primary care setting; (2) identify the genetic mutation profiles, including novel variants, in individuals with suspected FH in primary care; (3) explore the experience, concern, and expectation of individuals with suspected FH who have undergone genetic testing in primary care; and (4) evaluate the clinical utility of a web-based FH Identification Tool that includes the FAMCAT, SB, and DLCC in the Malaysian primary care setting.

METHODS

This is a mixed methods evaluation study conducted in 11 Ministry of Health primary care clinics located at the central administrative region of Malaysia. In Work stream 1, the diagnostic accuracy study design is used to compare the detection rate and diagnostic accuracy of the FAMCAT, SB, and DLCC against molecular diagnosis as the gold standard. In Work stream 2, the targeted next-generation sequencing of the 4 FHCGs is used to identify the genetic mutation profiles among individuals with suspected FH. In Work stream 3a, a qualitative semistructured interview methodology is used to explore the experience, concern, and expectation of individuals with suspected FH who have undergone genetic testing. Lastly, in Work stream 3b, a qualitative real-time observation of primary care physicians using the "think-aloud" methodology is applied to evaluate the clinical utility of a web-based FH Identification Tool.

RESULTS

The recruitment for Work stream 1, and blood sampling and genetic analysis for Work stream 2 were completed in February 2023. Data collection for Work stream 3 was completed in March 2023. Data analysis for Work streams 1, 2, 3a, and 3b is projected to be completed by June 2023, with the results of this study anticipated to be published by December 2023.

CONCLUSIONS

This study will provide evidence on which clinical diagnostic criterion is the best to detect FH in the Malaysian primary care setting. The full spectrum of genetic mutations in the FHCGs including novel pathogenic variants will be identified. Patients' perspectives while undergoing genetic testing and the primary care physicians experience in utilizing the web-based tool will be established. These findings will have tremendous impact on the management of patients with FH in primary care and subsequently reduce their risk of premature coronary artery disease.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47911.

Dysfunctional network of hub genes in hypertrophic cardiomyopathy patients

BACKGROUND

Considering it is one of the major causes of sudden cardiac arrest, the proper management of hypertrophic cardiomyopathy (HCM) is essential. However, efficient treatment options for this disease are still lacking. The discovery of HCM-associated hub genes may help in diagnosis and offer a reliable tool for developing effective therapeutic strategies.

METHODS

We examined HCM-based gene expression datasets (GSE36961) from the Gene Expression Omnibus (GEO) database for the identification of differentially expressed genes (DEGs), PPI network development, module screening, and shortlisting of hub genes via GEOR2, STRING, and Cytoscape. Moreover, we also used another HCM-based gene expression dataset (GSE32453) for the expression validation of hub genes. Following this, we constructed the lncRNA-cricRNA-miRNA-mRNA regulatory network after retrieving information from the miRTarBase, miRDB, and MiRcode databases. Finally, we used DAVID to perform functional and pathway analysis of the hub genes.

RESULTS

From GSE36961, a total of the 262 most significant DEGs, including 162 down-regulated and 76 up-regulated, were identified between HCM patients and normal individuals. Among these DEGs, a total of 10 significantly down-regulated DEGs, including cluster of differentiation 14 (CD14), beta2 Integrin Gene (ITGB2), C1q subcomponent subunit B (C1QB), Cluster of Differentiation 163 (CD163), Hematopoietic Cell-Specific Lyn Substrate 1 (HCLS1), Arachidonate 5-Lipoxygenase Activating Protein (ALOX5AP), Pleckstrin (PLEK), Complement C1q C Chain (C1QC), Fc fragment Of IgE receptor Ig (FCER1G), and tyrosine kinase binding protein (TYROBP), were shortlisted as the hub genes. Pathway enrichment analysis showed that the identified hub genes were involved in the dysregulation of some diverse pathways in HCM patients. Such as, Pertussis, Complement and coagulation cascade, Legnionellosis, Asthma, Staphylococcus aureus infection, etc. Lastly, we also explored hub genes' regulatory 2 MicroRNAs (miRNAs, has-mir-7-5p and has-mir-27a-3p), one Long non-coding RNAs (lncRNA, OIP5-AS1-201), and one Circular RNA (cricRNA, CDR1as) via lncRNA-cricRNA-miRNA-mRNA regulatory network.

CONCLUSION

Our study revealed that ten hub genes (CD14, ITGB2, C1QB, CD163, HCLS1, ALOX5AP, PLEK, C1QC, FCER1G, and TYROBP) are involved in the development and progression of HCM. These genes can potentially be used as biomarkers and therapeutic targets for HCM patients.

Familial Hypercholesterolemia Genetic Variations and Long-Term Cardiovascular Outcomes in Patients with Hypercholesterolemia Who Underwent Coronary Angiography

Background: Familial hypercholesterolemia (FH) has been associated with early coronary artery disease (CAD) and increased risk of atherosclerotic cardiovascular disease. However, the prevalence of FH and its long-term outcomes in a CAD-high-risk cohort, defined as patients with hypercholesteremia who underwent coronary angiography, remains unknown. Besides, studies regarding the impact of genetic variations in FH on long-term cardiovascular (CV) outcomes are scarce. Methods and Results: In total, 285 patients hospitalized for coronary angiography with blood low-density lipoprotein cholesterol (LDL-C) levels ≥ 160 mg/dL were sequenced to detect FH genetic variations in LDL receptors apolipoprotein B and proprotein convertase subtilisin/kexin type 9. Risk factors associated with long-term CV outcomes were evaluated. The prevalence of FH was high (14.4%). CAD and early CAD were significantly more prevalent among FH variation carriers than non-carriers, despite comparable blood LDL-C levels. Moreover, the FH variation carriers also underwent more revascularization after a mean follow-up of 6.1 years. Multivariate logistic regression demonstrated that FH genetic variation was associated with increased incidence of cardiovascular disease and mortality (odds ratio = 3.17, p = 0.047). Two common FH variants, LDLR c.986G>A and LDLR c.268G>A, showed the most significant impacts on high blood LDL-C levels and early-onset CAD. Conclusions: Our results indicate that FH genetic variants may exhibit differential effects on early-onset CAD and revascularization risks in patients undergoing coronary angiography. FH genetic information might help identify high-risk patients with typical CAD symptoms for appropriate intervention.

Coronary Artery Disease Genetics Enlightened by Genome-Wide Association Studies

Many cardiovascular diseases are facilitated by strong inheritance. For example, large-scale genetic studies identified hundreds of genomic loci that affect the risk of coronary artery disease. At each of these loci, common variants are associated with disease risk with robust statistical evidence but individually small effect sizes. Only a minority of candidate genes found at these loci are involved in the pathophysiology of traditional risk factors, but experimental research is making progress in identifying novel, and, in part, unexpected mechanisms. Targets identified by genome-wide association studies have already led to the development of novel treatments, specifically in lipid metabolism. This review summarizes recent genetic and experimental findings in this field. In addition, the development and possible clinical usefulness of polygenic risk scores in risk prediction and individualization of treatment, particularly in lipid metabolism, are discussed.

Screening of Atherosclerotic Druggable Targets from the Proteome Network of Differentially Expressed Genes

Differently expressed genes of atherosclerotic sample analysis are helpful to sort the prominent genes that influence the plaque formation and progression. Scientific evidence-based protein-protein interaction network (PPIN) studies were used to find hub proteins in complex disease conditions. Druggable capacity is one of the important parameters to confirm as a successful drug target. Construction of protein interaction network and principal node analysis (PNA) on atherosclerotic data sets lead to screen the hub proteins. Furthermore, druggable property of protein pocket confirms the targetability of susceptible target candidates and for target selection. Differentially expressed genes are identified through GEO2R analyzer on data sets of various atherosclerotic samples. STRING database and Cytoscape are employed to construct PPIN. Targets were identified by PNA such as centrality measures and clustering algorithm. Gene Ontology enrichment also used as one of the screening parameters to filter the candidates related to atherosclerotic terms. Topological evaluation of target protein was successfully done by ITASSER and GROMACS, respectively. Grid-based principle of DoGSiteScorer is utilized for druggability analysis. Six proteins such as integrin alpha L (ITGAL), metallothionein 1F (MT1F), metallothionein 1X (MT1X), P-selectin glycoprotein ligand-1 (SELPLG), solute carrier family 30 A, zinc transporter protein (SLC30A1), and TNFSF13B are screened as potential biomarkers through network-based analysis. Among the six, ITGAL, SELPLG, SLC30A1, and TNSF13B are identified as better prioritized atherosclerotic targets through druggability efficiency.