Arrhythmogenic right ventricular cardiomyopathy

Evaluation of autoantibodies to desmoglein-2 in dogs with and without cardiac disease

Autoantibodies to desmoglein-2 have been associated with arrhythmogenic right ventricular cardiomyopathy (ARVC) in people. ARVC is a common disease in the Boxer dog. The role of anti-desmoglein-2 antibodies in Boxers with ARVC and correlation with disease status or severity is unknown. This prospective study is the first to evaluate dogs of various breeds and cardiac disease state for anti-desmoglein-2 antibodies. The sera of 46 dogs (10 ARVC Boxers, 9 healthy Boxers, 10 Doberman Pinschers with dilated cardiomyopathy, 10 dogs with myxomatous mitral valve disease, and 7 healthy non-Boxer dogs) were assessed for antibody presence and concentration via Western blotting and densitometry. Anti-desmoglein-2 antibodies were detected in all dogs. Autoantibody expression did not differ between study groups and there was no correlation with age or body weight. In dogs with cardiac disease, there was weak correlation with left ventricular dilation (r = 0.423, p = 0.020) but not left atrial size (r = 0.160, p = 0.407). In ARVC Boxers there was strong correlation with the complexity of ventricular arrhythmias (r = 0.841, p = 0.007) but not total number of ectopic beats (r = 0.383, p = 0.313). Anti-desmoglein-2 antibodies were not disease specific in the studied population of dogs. Correlation with some measures of disease severity requires further study with larger populations.

THE KEY-STONES OF RIGHT VENTRICULAR ARRHYTHMOGENIC CARDIOMYOPATHY-INDUCED MORPHOLOGICAL DISARRANGEMENT

Arrhythmogenic right ventricular cardiomyopathy is an urgent problem of modern cardiology. This myocardial remodeling manifests various desmosomopathies, channelopathies, and other mutations resulting in a violation of the coordinated heart work, particularly the myocardium. The incidence of this cardiomyopathy is not significant. Still, it is worth noting that athletes are at an increased risk of developing this disease, emphasizing the importance of studying this topic and its relevance from cardiologists and sports physicians. Moreover, the clinical pattern of this disease is heterogeneous. This pathology requires strengthening control and attention of medical personnel and constant improvement and optimization of diagnostic methods and treatment protocols. In this article, the pathophysiological mechanisms, molecular genetic aspects, and the dynamics of morphofunctional changes are represented in detail. Understanding the mechanisms of etiopathogenesis and the features of morphological changes observed in this cardiomyopathy and its more detailed study is fundamental in developing modern treatment methods to improve patients' quality and life expectancy.

Multi-assignment clustering: Machine learning from a biological perspective

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).