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Atrial fibrillation (AF) is an increasingly prevalent arrhythmia with significant health and socioeconomic impact. The underlying mechanism of AF is still not well understood. In this study, we sought to identify hub genes involved in AF, and explored their functions and underlying mechanisms based on bioinformatics analysis. Five microarray datasets in GEO were used to identify the differentially expressed genes (DEGs) by Robust Rank Aggregation (RRA), and hub genes were screened out using protein–protein interaction (PPI) network. AF model was established using a mixture of acetylcholine and calcium chloride (Ach-CaCl 2 ) by tail vein injection. We totally got 35 robust DEGs that mainly involve in extracellular matrix formation, leukocyte transendothelial migration, and chemokine signaling pathway. Among these DEGs, we identified three hub genes involved in AF, of which CXCL12/CXCR4 axis significantly upregulated in AF patients stands out as one of the most potent targets for AF prevention, and its effect on AF pathogenesis and underlying mechanisms were investigated in vivo subsequently with the specific CXCR4 antagonist AMD3100 (6 mg/kg). Our results demonstrated an elevated transcription and translation of CXCL12/CXCR4 axis in AF patients and mice, accompanied with the anabatic atrial inflammation and fibrosis, thereby providing the substrate for AF maintenance. Blocking its signaling via AMD3100 administration in AF model mice reduced AF inducibility and duration, partly ascribed to decreased atrial inflammation and structural remodeling. Mechanistically, these effects were achieved by reducing the recruitment of CD3+ T lymphocytes and F4/80+ macrophages, and suppressing the hyperactivation of ERK1/2 and AKT/mTOR signaling in atria of AF model mice. In conclusion, this study provides new evidence that antagonizing CXCR4 prevents the development of AF, and suggests that CXCL12/CXCR4 axis may be a potential therapeutic target for AF.

CXCL12/CXCR4 axis as a key mediator in atrial fibrillation via bioinformatics analysis and functional identification