Unravelling the impact of RNA methylation genetic and epigenetic machinery in the treatment of cardiomyopathy.

Cardiomyopathy (CM) represents a heterogeneous group of diseases primarily affecting cardiac structure and function, with genetic and epigenetic dysregulation playing a pivotal role in its pathogenesis. Emerging evidence from the burgeoning field of epitranscriptomics has brought to light the significant impact of various RNA modifications, notably N6-methyladenosine (m6A), 5-methylcytosine (m5C), N7-methylguanosine (m7G), N1-methyladenosine (m1A), 2'-O-methylation (Nm), and 6,2'-O-dimethyladenosine (m6Am), on cardiomyocyte function and the broader processes of cardiac and vascular remodelling. These modifications have been shown to influence key pathological mechanisms including mitochondrial dysfunction, oxidative stress, cardiomyocyte apoptosis, inflammation, immune response, and myocardial fibrosis. Importantly, aberrations in the RNA methylation machinery have been observed in human CM cases and animal models, highlighting the critical role of RNA methylating enzymes and their potential as therapeutic targets or biomarkers for CM. This review underscores the necessity for a deeper understanding of RNA methylation processes in the context of CM, to illuminate novel therapeutic avenues and diagnostic tools, thereby addressing a significant gap in the current management strategies for this complex disease.

Deciphering the role of SMARCA4 in cardiac disorders: Insights from single-cell studies on dilated cardiomyopathy and coronary heart disease.

BACKGROUND: Dilated cardiomyopathy (DCM) and coronary heart disease (CHD) stand as two of the foremost causes of mortality. However, the comprehensive comprehension of the regulatory mechanisms governing DCM and CHD remains limited, particularly from the vantage point of single-cell transcriptional analysis. METHOD: We used the GSE121893 dataset from the GEO database, analyzing single-cell expressions with tools like DropletUtils, Seurat, and Monocle. We also utilized the GSVA package for comparing gene roles in DCM and CHD, Finally, we conducted qRT-PCR and Western blot analyses to measure the expression levels of SMARCA4, Col1A1, Col3A1 and α-SMA, and the role of SMARCA4 on fibroblasts were explored by EdU and Transwell assay. RESULTS: Our analysis identified six cell types in heart tissue, with fibroblasts showing the most interaction with other cells. DEGs in fibroblasts were linked to muscle development and morphogenesis. Pseudotime analysis revealed the dynamics of fibroblast changes in both the normal and disease groups and many transcription factors (TFs) potentially involved in this process. Among these TFs, SMARCA4 which was translated into protein BRG1, showed the most significantly difference. In vivo experiments have demonstrated that SMARCA4 indeed promoted fibroblasts proliferation and migration. CONCLUSION: This study provides a clearer understanding of cell-type dynamics in heart diseases, emphasizing the role of fibroblasts and the significance of SMARCA4 in their function. Our results offer insights into the cellular mechanisms underlying DCM and CHD, potentially guiding future therapeutic strategies.

Online public opinion evaluation through the functional resonance analysis method and deep analysis.

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.