bioRxivGenomicsPreprint
An epigenomic investigation of atrial fibrillation in a matched left and right atrial human cohort
Alejandro Rodríguez-GonzáleziD, Stephanie FrostiD, Alison ThomasiD, Diego M. Fernández‐ArocaiD, Jishan Choudhury, Georgios Tikkas, Andrew TinkeriD, Patricia B. MunroeiD, Christopher G. BelliD, Diego VillariD
Abstract
As the most prevalent cardiac arrythmia, atrial fibrillation is an important contributor to cardiovascular morbidity and mortality. Human population findings increasingly support its complex genetic architecture, with most genetic association signals for atrial fibrillation found in the non-coding genome. In this study, we integrated genome-wide histone modification, gene expression and DNA methylation levels in a paired left and right atrial cohort comprising permanent atrial fibrillation patients and sinus rhythm controls. First, we first identified epigenomic regions enriched in histone H3 lysine 27 acetylation (H3K27ac) across left and right atria from patients and controls, and associated them with differentially expressed genes to derive a set of dysregulated candidate loci - including NPPB and SCX. Second, by incorporating an independent replication cohort, we were able to validate gene expression and epigenomic differences for a subset of these candidate loci. Third, we profiled base-resolution DNA methylation levels and identified differentially-methylated regions (DMRs) between atrial fibrillation and sinus rhythm samples. Integration of these data with histone modification levels and gene expression allowed us to propose epigenetic mechanisms underlying transcriptomic and epigenomic changes across dysregulated loci, such as disruption of transcription factor binding by DNA methylation at the LRRC4B locus. The data and analyses we report constitute a systematic investigation of gene regulatory alterations across the left and right atria in permanent atrial fibrillation.
- organism: human
- assay: DNA methylation
- assay: bulk RNA-seq
- assay: ChIP-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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