BACKGROUND

Atrial structural and electrical remodeling are the pathophysiological mechanisms underlying atrial fibrillation (AF). Although previous studies have offered insights into these changes, the cellular interactions involved in atrial structural remodeling and the ion channel marker genes associated with electrical remodeling in AF remain insufficiently elucidated.

METHODS

We used single-cell RNA sequencing (scRNA-seq) to investigate the structural remodeling in AF at the cellular level. Raw data from atrial fibroblasts of AF patients and controls were pre-processed using Seurat (R package), with stringent quality control to filter out low-quality cells. Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. Cell trajectory analysis was carried out to explore the differentiation patterns of these fibroblasts using Monocle. Additionally, a cell-cell interaction analysis was performed using the CellChat package, and biological function and pathway enrichment analyses were done using GO, KEGG and GSEA pathways. Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.

RESULTS

In the structural remodeling investigation, single-cell analysis was employed to identify five distinct cell subtypes, including embryonic fibroblasts (EF), actively proliferating fibroblasts (APF), smooth muscle cells (SMC), endothelial cells (EC), and leukocytes (LBCs). These subtypes exhibited significantly different distributions between AF and SR. In the AF group, the proportions of EF, APF, and LBCs were increased, whereas the proportion of EC was decreased; by contrast, the SR group displayed a higher proportion of EC. Trajectory analysis suggested that EF cells in AF may differentiate into both APF and SMC subtypes. Cell-cell communication analysis revealed extensive signaling pathways (e.g., LAMININ and COLLAGEN) activated in EF cells under AF conditions, in addition to the specific activation of MK signaling in AF. It also uncovered a loss of certain EC signals (e.g., GRN-SORT1 and AGRN-DAG1) in AF and a marked reduction in NPPA-NPR1 signaling from SMC to EC. These findings indicate that such alterations may be crucial to the onset and maintenance of AF. In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. This approach ultimately identified and as the characteristic ion channel genes for AF. Both genes demonstrated strong discriminative power in distinguishing AF from SR. Finally, drug-targeting analyses suggested that phenytoin sodium-a known antiarrhythmic agent-may exert therapeutic effects by targeting critical EF subtypes in AF. Moreover, ionomycin and DIDS were found to be strongly associated with , whereas was linked to citalopram and topiramate.

CONCLUSION

This study underscores the critical roles of cell distribution, cell developmental trajectories, and intercellular interactions in the structural remodeling of AF, as well as the key ion channel biomarkers involved in AF-related electrical remodeling. In terms of structural remodeling, the proportions of EF, APF, and LBCs are elevated in AF, with EF cells potentially differentiating into APF or SMC. Moreover, active EF cell signaling and the loss of EC signals in AF may be crucial for the onset and maintenance of this arrhythmia. Regarding electrical remodeling, and have been identified as potential biomarker genes for AF. Notably, phenytoin sodium may exert therapeutic effects against AF by targeting EF subtypes. In addition, ionomycin, citalopram, and topiramate exhibit modulatory effects on ion channels, providing new potential treatment avenues. Such drug repurposing represents a rapid and efficient strategy for the discovery of novel AF therapies.