Literature DB >> 31731197

Analysis of long non-coding RNA and mRNA profiles in epicardial adipose tissue of patients with atrial fibrillation.

Lei Zhao1, Zheng Ma1, Zongsheng Guo1, Meili Zheng1, Kuibao Li2, Xinchun Yang3.   

Abstract

Accumulating studies have suggested that epicardial adipose tissue (EAT) play an important role in the pathogenesis of atrial fibrillation (AF), but few have characterized the underlying mechanism between their interactions. Recent evidence suggested that bioactive molecules secreted from EAT, including exosomes carrying long non-coding RNAs (lncRNAs), may modulate atrial remodeling. LncRNAs are associated with cardiovascular disorders, including AF, but their roles in EAT remain elusive. The aim of the present study was to investigate the expression profile of lncRNAs in EAT with AF. Differentially expressed lncRNAs and nearby mRNAs interaction networks were constructed. Epicardial adipose samples were collected from patients with persistent non-valvular AF (n = 6) and sinus rhythm (SR) (n = 6), and the expression of lncRNAs and mRNAs were profiled using RNA-sequencing method. A total of 46,577 transcripts, including 35,552 protein-coding pattern, corresponding to 15,404 genes in EAT, among which, 655 mRNAs (265 upregulated and 390 downregulated) and 57 lncRNAs (17 upregulated and 40 downregulated) were differentially expressed between AF and SR (P < 0.05; fold change>1.5). GO enrichment, KEGG pathway analysis and interaction network construction showed that these differentially expressed lncRNAs were enriched in functional categories, including metabolism and stress response, which might contribute to the pathogenesis of AF. Our study demonstrated a differentially expressed lncRNA profile in EAT with AF, and provide a novel insight into the interactions between EAT and AF.
Copyright © 2019 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Epicardial adipose tissue; Long non-coding RNA

Mesh:

Substances:

Year:  2019        PMID: 31731197     DOI: 10.1016/j.biopha.2019.109634

Source DB:  PubMed          Journal:  Biomed Pharmacother        ISSN: 0753-3322            Impact factor:   6.529


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