Sudden death in mild hypertrophic cardiomyopathy with compound DSG2/DSC2/MYH6 mutations: Revisiting phenotype after genetic assessment in a master runner athlete

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

OBJECTIVES

Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.

METHODS

Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy.

RESULTS

Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM.

CONCLUSIONS

RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.

Using whole genome sequence findings to assess gene-disease causality in cardiomyopathy and arrhythmia patients

Aim: The genetic etiologies of cardiomyopathies and arrhythmias have not been fully elucidated. Materials & methods: Research findings from genome analyses in a cardiomyopathy and arrhythmia cohort were gathered. Gene-disease relationships from two databases were compared with patient phenotypes. A literature review was conducted for genes with limited evidence. Results: Of 43 genes with candidate findings from 18 cases, 23.3% of genes had never been curated, 15.0% were curated for cardiomyopathies, 16.7% for arrhythmias and 31.3% for other conditions. 25.5% of candidate findings were curated for the patient's specific phenotype with 11.8% having definitive evidence. MYH6 and TPCN1 were flagged for recuration. Conclusion: Findings from genome sequencing in disease cohorts may be useful to guide gene-curation efforts.

Significance of α-Myosin Heavy Chain (MYH6) Variants in Hypoplastic Left Heart Syndrome and Related Cardiovascular Diseases

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart disease (CHD) with complex genetic inheritance. HLHS segregates with other left ventricular outflow tract (LVOT) malformations in families, and can present as either an isolated phenotype or as a feature of a larger genetic disorder. The multifactorial etiology of HLHS makes it difficult to interpret the clinical significance of genetic variants. Specific genes have been implicated in HLHS, including rare, predicted damaging MYH6 variants that are present in >10% of HLHS patients, and which have been shown to be associated with decreased transplant-free survival in our previous studies. MYH6 (α-myosin heavy chain, α-MHC) variants have been reported in HLHS and numerous other CHDs, including LVOT malformations, and may provide a genetic link to these disorders. In this paper, we outline the MYH6 variants that have been identified, discuss how bioinformatic and functional studies can inform clinical decision making, and highlight the importance of genetic testing in HLHS.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Bioinformatic analysis of membrane and associated proteins in murine cardiomyocytes and human myocardium

In the current study we examined several proteomic- and RNA-Seq-based datasets of cardiac-enriched, cell-surface and membrane-associated proteins in human fetal and mouse neonatal ventricular cardiomyocytes. By integrating available microarray and tissue expression profiles with MGI phenotypic analysis, we identified 173 membrane-associated proteins that are cardiac-enriched, conserved amongst eukaryotic species, and have not yet been linked to a 'cardiac' Phenotype-Ontology. To highlight the utility of this dataset, we selected several proteins to investigate more carefully, including FAM162A, MCT1, and COX20, to show cardiac enrichment, subcellular distribution and expression patterns in disease. We performed three-dimensional confocal imaging analysis to validate subcellular localization and expression in adult mouse ventricular cardiomyocytes. FAM162A, MCT1, and COX20 were expressed differentially at the transcriptomic and proteomic levels in multiple models of mouse and human heart diseases and may represent potential diagnostic and therapeutic targets for human dilated and ischemic cardiomyopathies. Altogether, we believe this comprehensive cardiomyocyte membrane proteome dataset will prove instrumental to future investigations aimed at characterizing heart disease markers and/or therapeutic targets for heart failure.

Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database

BACKGROUND

Ischemic cardiomyopathy (ICM) is one of the most usual causes of death worldwide. This study aimed to find the candidate gene for ICM.

METHODS

We studied differentially expressed genes (DEGs) in ICM compared to healthy control. According to these DEGs, we carried out the functional annotation, protein-protein interaction (PPI) network and transcriptional regulatory network constructions. The expression of selected candidate genes were confirmed using a published dataset and Quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS

From three Gene Expression Omnibus (GEO) datasets, we acquired 1081 DEGs (578 up-regulated and 503 down-regulated genes) between ICM and healthy control. The functional annotation analysis revealed that cardiac muscle contraction, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in ICM. SNRPB, BLM, RRS1, CDK2, BCL6, BCL2L1, FKBP5, IPO7, TUBB4B and ATP1A1 were considered the hub proteins. PALLD, THBS4, ATP1A1, NFASC, FKBP5, ECM2 and BCL2L1 were top six transcription factors (TFs) with the most downstream genes. The expression of 6 DEGs (MYH6, THBS4, BCL6, BLM, IPO7 and SERPINA3) were consistent with our integration analysis and GSE116250 validation results.

CONCLUSIONS

The candidate DEGs and TFs may be related to the ICM process. This study provided novel perspective for understanding mechanism and exploiting new therapeutic means for ICM.

ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations

A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be.

Reevaluating the Mutation Classification in Genetic Studies of Bradycardia Using ACMG/AMP Variant Classification Framework

PURPOSE

Next-generation sequencing (NGS) has become more accessible, leading to an increasing number of genetic studies of familial bradycardia being reported. However, most of the variants lack full evaluation. The relationship between genetic factors and bradycardia should be summarized and reevaluated.

METHODS

We summarized genetic studies published in the PubMed database from 2008/1/1 to 2019/9/1 and used the ACMG/AMP classification framework to analyze related sequence variants.

RESULTS

We identified 88 articles, 99 sequence variants, and 34 genes after searching the PubMed database and classified ABCC9, ACTN2, CACNA1C, DES, HCN4, KCNQ1, KCNH2, LMNA, MECP2, LAMP2, NPPA, SCN5A, and TRPM4 as high-priority genes causing familial bradycardia. Most mutated genes have been reported as having multiple clinical manifestations.

CONCLUSIONS

For patients with familial CCD, 13 high-priority genes are recommended for evaluation. For genetic studies, variants should be carefully evaluated using the ACMG/AMP variant classification framework before publication.