Evaluation of Toll-like Receptor 4 (TLR4) Involvement in Human Atrial Fibrillation: A Computational Study.
Paolo FagoneKatia ManganoMaria Sofia BasileJosé Francisco Munoz-ValleVincenzo PerciavalleFerdinando NicolettiKlaus BendtzenPublished in: Genes (2024)
In the present study, we have explored the involvement of Toll-like Receptor 4 (TLR4) in atrial fibrillation (AF), by using a meta-analysis of publicly available human transcriptomic data. The meta-analysis revealed 565 upregulated and 267 downregulated differentially expressed genes associated with AF. Pathway enrichment analysis highlighted a significant overrepresentation in immune-related pathways for the upregulated genes. A significant overlap between AF differentially expressed genes and TLR4-modulated genes was also identified, suggesting the potential role of TLR4 in AF-related transcriptional changes. Additionally, the analysis of other Toll-like receptors (TLRs) revealed a significant association with TLR2 and TLR3 in AF-related gene expression patterns. The examination of MYD88 and TICAM1, genes associated with TLR4 signalling pathways, indicated a significant yet nonspecific enrichment of AF differentially expressed genes. In summary, this study offers novel insights into the molecular aspects of AF, suggesting a pathophysiological role of TLR4 and other TLRs. By targeting these specific receptors, new treatments might be designed to better manage AF, offering hope for improved outcomes in affected patients.
Keyphrases
- toll like receptor
- atrial fibrillation
- inflammatory response
- nuclear factor
- immune response
- gene expression
- oral anticoagulants
- left atrial
- catheter ablation
- left atrial appendage
- heart failure
- genome wide
- systematic review
- endothelial cells
- direct oral anticoagulants
- percutaneous coronary intervention
- single cell
- dna methylation
- machine learning
- venous thromboembolism
- risk assessment
- coronary artery disease
- acute coronary syndrome
- mitral valve
- left ventricular
- artificial intelligence
- meta analyses
- rna seq