Beyond the Rhythm: In Silico Identification of Key Genes and Therapeutic Targets in Atrial Fibrillation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia worldwide and is characterized by a high risk of thromboembolism, ischemic stroke, and fatality. The precise molecular mechanisms of AF pathogenesis remain unclear. The purpose of this study was to use bioinformatics tools to identify novel key genes in AF, provide deeper insights into the molecular pathogenesis of AF, and uncover potential therapeutic targets. Four publicly available raw RNA-Seq datasets obtained through the ENA Browser, as well as proteomic analysis results, both derived from atrial tissues, were used in this analysis. Differential gene expression analysis was performed and cross-validated with proteomics results to identify common genes/proteins between them. A functional enrichment pathway analysis was performed. Cross-validation analysis revealed five differentially expressed genes, namely FGL2, IGFBP5, NNMT, PLA2G2A, and TNC, in patients with AF compared with those with sinus rhythm (SR). These genes play crucial roles in various cardiovascular functions and may be part of the molecular signature of AF. Furthermore, functional enrichment analysis revealed several pathways related to the extracellular matrix, inflammation, and structural remodeling. This study highlighted five key genes that constitute promising candidates for further experimental exploration as biomarkers as well as therapeutic targets for AF.

Therapeutic Drug Monitoring (TDM) Implementation in Public Hospitals in Greece in 2003 and 2021: A Comparative Analysis of TDM Evolution over the Years.

Therapeutic drug monitoring (TDM) is the clinical practice of measuring drug concentrations. TDM can be used to determine treatment efficacy and to prevent the occurrence or reduce the risk of drug-induced side effects, being, thus, a tool of personalized medicine. Drugs for which TDM is applied should have a narrow therapeutic range and exhibit both significant pharmacokinetic variability and a predefined target concentration range. The aim of our study was to assess the current status of TDM in Greek public hospitals and estimate its progress over the last 20 years. All Greek public hospitals were contacted to provide data and details on the clinical uptake of TDM in Greece for the years 2003 and 2021 through a structured questionnaire. Data from 113 out of 132 Greek hospitals were collected in 2003, whereas for 2021, we have collected data from 98 out of 122 hospitals. Among these, in 2003 and 2021, 64 and 51 hospitals, respectively, performed TDM. Antiepileptics and antibiotics were the most common drug categories monitored in both years. The total number of drug measurement assays decreased from 2003 to 2021 (153,313 ± 7794 vs. 90,065 ± 5698; p = 0.043). In direct comparisons between hospitals where TDM was performed both in 2003 and 2021 (n = 35), the mean number of measurements was found to decrease for most drugs, including carbamazepine (198.8 ± 46.6 vs. 46.6 ± 10.1, p < 0.001), phenytoin (253.6 ± 59 vs. 120 ± 34.3; p = 0.001), amikacin (147.3 ± 65.2 vs. 91.1 ± 71.4; p = 0.033), digoxin (783.2 ± 226.70 vs. 165.9 ± 28.9; p < 0.001), and theophylline (71.5 ± 28.7 vs. 11.9 ± 6.4; p = 0.004). Only for vancomycin, a significant increase in measurements was recorded (206.1 ± 96.1 vs. 789.1 ± 282.8; p = 0.012). In conclusion, our findings show that TDM clinical implementation is losing ground in Greek hospitals. Efforts and initiatives to reverse this trend are urgently needed.

Multi-omics integration to identify the genetic expression and protein signature of dilated and ischemic cardiomyopathy.

Introduction: Heart failure (HF) is a complex clinical syndrome leading to high morbidity. In this study, we aimed to identify the gene expression and protein signature of HF main causes, namely dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM). Methods: Omics data were accessed through GEO repository for transcriptomic and PRIDE repository for proteomic datasets. Sets of differentially expressed genes and proteins comprising DCM (DiSig) and ICM (IsSig) signatures were analyzed by a multilayered bioinformatics approach. Enrichment analysis via the Gene Ontology was performed through the Metascape platform to explore biological pathways. Protein-protein interaction networks were analyzed via STRING db and Network Analyst. Results: Intersection of transcriptomic and proteomic analysis showed 10 differentially expressed genes/proteins in DiSig (AEBP1, CA3, HBA2, HBB, HSPA2, MYH6, SERPINA3, SOD3, THBS4, UCHL1) and 15 differentially expressed genes/proteins in IsSig (AEBP1, APOA1, BGN, CA3, CFH, COL14A1, HBA2, HBB, HSPA2, LTBP2, LUM, MFAP4, SOD3, THBS4, UCHL1). Common and distinct biological pathways between DiSig and IsSig were retrieved, allowing for their molecular characterization. Extracellular matrix organization, cellular response to stress and transforming growth factor-beta were common between two subphenotypes. Muscle tissue development was dysregulated solely in DiSig, while immune cells activation and migration in IsSig. Discussion: Our bioinformatics approach sheds light on the molecular background of HF etiopathology showing molecular similarities as well as distinct expression differences between DCM and ICM. DiSig and IsSig encompass an array of "cross-validated" genes at both transcriptomic and proteomic level, which can serve as novel pharmacological targets and possible diagnostic biomarkers.