Large-scale single-nuclei profiling identifies role for ATRNL1 in atrial fibrillation
Year: 2024
Type: article
Abstract: Atrial fibrillation (AF) is the most common sustained arrhythmia in humans, yet the molecular basis of AF remains incompletely understood. To determine the cell type-specific transcriptional changes underlying AF, we perform single-nucleus RNA-seq (snRNA-seq) on left atrial (LA) samples from patients with AF and controls. From more than 175,000 nuclei we find that only cardiomyocytes (CMs) and macrophages (MΦs) have a significant number of differentially expressed genes in patients with AF. Attractin Like 1 (ATRNL1) was overexpressed in CMs among patients with AF and localized to the intercalated disks. Further, in both knockdown and overexpression experiments we identify a potent role for ATRNL1 in cell stress response, and in the modulation of the cardiac action potential. Finally, we detect an unexpected expression pattern for a leading AF candidate gene, KCNN3. In sum, we uncover a role for ATRNL1 which may serve as potential therapeutic target for this common arrhythmia. Characterizing atrial fibrillation (AF) at the single cell level is challenging. Here, the authors perform snRNA-seq on 18 patients with AF to investigate the cell composition, and gene expression shifts associated with this common arrhythmia.
Source: Nature Communications
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $6,790
Funders: American Heart Association, Massachusetts General Hospital, European Commission, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of Neurological Disorders and Stroke, Common Fund
Awards: G1189786236, G1445764998, G2202424112, G3496720950, G3553811666, Genomics and Epigenetics of Atrial Fibrillation, G3897532131, G4405986302, G475755283, G5026478414, G5201430397, G5680026034, G6692319484, G6701400284, Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, G8616158874, G8767518440, Mechanisms of Drug Induced Atrial Fibrillation, Deep Learning to Identify Digital Biomarkers of Cardiovascular Disease, G9000166398, G9000175406, G9000214436, From variants to mechanisms for cardiac arrhythmias
OpenAlex ID: W4404501626
