Bioinformatics prediction of potential mechanisms and biomarkers underlying dilated cardiomyopathy

Novel pathogenic variant in MED12 causing non-syndromic dilated cardiomyopathy

BACKGROUND

Dilated cardiomyopathy (DCM) is a major cause of sudden cardiac death and heart failure. Up to 50% of all DCM cases have a genetic background, with variants in over 250 genes reported in association with DCM. Whole-exome sequencing (WES) is a powerful tool to identify variants underlying genetic cardiomyopathies. Via WES, we sought to identify DCM causes in a family with 2 affected patients.

METHODS

WES was performed on the affected members of an Iranian family to identify the genetic etiology of DCM. The candidate variant was segregated via polymerase chain reaction and Sanger sequencing. Computational modeling and protein-protein docking were performed to survey the impact of the variant on the structure and function of the protein.

RESULTS

A novel single-nucleotide substitution (G > A) in exon 9 of MED12, c.1249G > A: p.Val417Ile, NM_005120.3, was identified. The c.1249G > A variant was validated in the family. Bioinformatic analysis and computational modeling confirmed that c.1249G > A was the pathogenic variant responsible for the DCM phenotype.

CONCLUSION

We detected a novel DCM-causing variant in MED12 using WES. The variant in MED12 may decrease binding to cyclin-dependent kinase 8 (CDK8), affect its activation, and cause alterations in calcium-handling gene expression in the heart, leading to DCM.

CCND1 Overexpression in Idiopathic Dilated Cardiomyopathy: A Promising Biomarker?

Cardiomyopathy, a disorder of electrical or heart muscle function, represents a type of cardiac muscle failure and culminates in severe heart conditions. The prevalence of dilated cardiomyopathy (DCM) is higher than that of other types (hypertrophic cardiomyopathy and restrictive cardiomyopathy) and causes many deaths. Idiopathic dilated cardiomyopathy (IDCM) is a type of DCM with an unknown underlying cause. This study aims to analyze the gene network of IDCM patients to identify disease biomarkers. Data were first extracted from the Gene Expression Omnibus (GEO) dataset and normalized based on the RMA algorithm (Bioconductor package), and differentially expressed genes were identified. The gene network was mapped on the STRING website, and the data were transferred to Cytoscape software to determine the top 100 genes. In the following, several genes, including VEGFA, IGF1, APP, STAT1, CCND1, MYH10, and MYH11, were selected for clinical studies. Peripheral blood samples were taken from 14 identified IDCM patients and 14 controls. The RT-PCR results revealed no significant differences in the expression of the genes APP, MYH10, and MYH11 between the two groups. By contrast, the STAT1, IGF1, CCND1, and VEGFA genes were overexpressed in patients more than in controls. The highest expression was found for VEGFA, followed by CCND1 (p < 0.001). Overexpression of these genes may contribute to disease progression in patients with IDCM. However, more patients and genes need to be analyzed in order to achieve more robust results.

Metabolomic profiling in dogs with dilated cardiomyopathy eating non-traditional or traditional diets and in healthy controls

Dilated cardiomyopathy (DCM), caused by genetic and environmental factors, usually progresses to heart failure, a major cause of death in elderly people. A diet-associated form of DCM was recently identified in pet dogs eating non-traditional (NT) diets. To identify potential dietary causes, we analyzed metabolomic signatures and gene set/pathway enrichment in (1) all dogs based on disease, diet, and their interactions and (2) dogs with DCM based on diet. Metabolomic analysis was performed in 38 dogs with DCM eating NT diets (DCM-NT), 8 dogs with DCM eating traditional diets, 12 healthy controls eating NT diets, and 17 healthy controls eating traditional diets. Overall, 153 and 63 metabolites differed significantly between dogs with DCM versus healthy controls and dogs eating NT versus traditional diets, respectively, with 12 metabolites overlapping both analyses. Protein-protein interaction networks and gene set enrichment analysis identified 105 significant pathways and gene sets including aging-related pathways (e.g., nuclear factor-kappa B, oxidative damage, inflammation). Seventeen metabolites differed significantly in dogs with DCM eating NT versus traditional diets (e.g., fatty acids, amino acids, legume biomarkers), suggesting different mechanisms for primary versus diet-associated DCM. Our multifaceted metabolomic assessment of DCM in dogs highlighted diet's role in some forms of DCM.