Possible key microRNAs and corresponding molecular mechanisms for atrial fibrillation

Long Non-coding RNA Involved in the Pathophysiology of Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a prevalent and chronic cardiovascular disorder associated with various pathophysiological alterations, including atrial electrical and structural remodeling, disrupted calcium handling, autonomic nervous system dysfunction, aberrant energy metabolism, and immune dysregulation. Emerging evidence suggests that long non-coding RNAs (lncRNAs) play a significant role in the pathogenesis of AF.

OBJECTIVE

This discussion aims to elucidate the involvement of AF-related lncRNAs, with a specific focus on their role as miRNA sponges that modulate crucial signaling pathways, contributing to the progression of AF. We also address current limitations in AF-related lncRNA research and explore potential future directions in this field. Additionally, we summarize feasible strategies and promising delivery systems for targeting lncRNAs in AF therapy.

CONCLUSION

In conclusion, targeting AF-related lncRNAs holds substantial promise for future investigations and represents a potential therapeutic avenue for managing AF.

Cardiac microRNAs: diagnostic and therapeutic potential

MicroRNAs are small non-coding post-translational biomolecules which, when expressed, modify their target genes. It is estimated that microRNAs regulate production of approximately 60% of all human proteins and enzymes that are responsible for major physiological processes. In cardiovascular disease pathophysiology, there are several cells that produce microRNAs, including endothelial cells, vascular smooth muscle cells, macrophages, platelets, and cardiomyocytes. There is a constant crosstalk between microRNAs derived from various cell sources. Atherosclerosis initiation and progression are driven by many pro-inflammatory and pro-thrombotic microRNAs. Atherosclerotic plaque rupture is the leading cause of cardiovascular death resulting from acute coronary syndrome (ACS) and leads to cardiac remodeling and fibrosis following ACS. MicroRNAs are powerful modulators of plaque progression and transformation into a vulnerable state, which can eventually lead to plaque rupture. There is a growing body of evidence which demonstrates that following ACS, microRNAs might inhibit fibroblast proliferation and scarring, as well as harmful apoptosis of cardiomyocytes, and stimulate fibroblast reprogramming into induced cardiac progenitor cells. In this review, we focus on the role of cardiomyocyte-derived and cardiac fibroblast-derived microRNAs that are involved in the regulation of genes associated with cardiomyocyte and fibroblast function and in atherosclerosis-related cardiac ischemia. Understanding their mechanisms may lead to the development of microRNA cocktails that can potentially be used in regenerative cardiology.

Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis

Introduction

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, but the molecular mechanisms underlying AF are not known. We aimed to identify the pivotal genes and pathways involved in AF pathogenesis because they could become potential biomarkers and therapeutic targets of AF.

Methods

The microarray datasets of GSE31821 and GSE41177 were downloaded from the Gene Expression Omnibus database. After combining the two datasets, differentially expressed genes (DEGs) were screened by the Limma package. MicroRNAs (miRNAs) confirmed experimentally to have an interaction with AF were screened through the miRTarBase database. Target genes of miRNAs were predicted using the miRNet database, and the intersection between DEGs and target genes of miRNAs, which were defined as common genes (CGs), were analyzed. Functional and pathway-enrichment analyses of DEGs and CGs were performed using the databases DAVID and KOBAS. Protein-protein interaction (PPI) network, miRNA- messenger(m) RNA network, and drug-gene network was visualized. Finally, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the expression of hub genes in the miRNA-mRNA network.

Results

Thirty-three CGs were acquired from the intersection of 65 DEGs from the integrated dataset and 9777 target genes of miRNAs. Fifteen "hub" genes were selected from the PPI network, and the miRNA-mRNA network, including 82 miRNAs and 9 target mRNAs, was constructed. Furthermore, with the validation by RT-qPCR, macrophage migration inhibitory factor (MIF), MYC proto-oncogene, bHLH transcription factor (MYC), inhibitor of differentiation 1 (ID1), and C-X-C Motif Chemokine Receptor 4 (CXCR4) were upregulated and superoxide Dismutase 2 (SOD2) was downregulated in patients with AF compared with healthy controls. We also found MIF, MYC, and ID1 were enriched in the transforming growth factor (TGF)-β and Hippo signaling pathway.

Conclusion

We identified several pivotal genes and pathways involved in AF pathogenesis. MIF, MYC, and ID1 might participate in AF progression through the TGF-β and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.

Identification of Pivotal MicroRNAs and Target Genes Associated with Persistent Atrial Fibrillation Based on Bioinformatics Analysis

Atrial fibrillation (AF) is one of the most common supraventricular arrhythmias worldwide. However, the specific molecular mechanism underlying AF remains unclear. Our study is aimed at identifying pivotal microRNAs (miRNAs) and targeting genes associated with persistent AF (pAF) using bioinformatics analysis. Three gene expression array datasets (GSE31821, GSE41177, and GSE79768) and an miRNA expression array dataset (GSE68475) associated with pAF were downloaded. Differentially expressed genes (DEGs) were identified using the LIMMA package, and differentially expressed miRNAs (DEMs) were screened from GSE68475. Target genes for DEMs were predicted using the miRTarBase database, and intersections between these target genes and DEGs were selected for further analysis, including the generation of protein–protein interaction (PPI) network, miRNA–transcription factor–target regulatory network, and drug–gene network. A total of 264 DEGs and 40 DEMs were identified between the pAF and control groups. Functional and pathway enrichment analyses of up- and downregulated DEGs were performed. The common genes (CGs) were primarily enriched in the phosphoinositide 3-kinase- (PI3K-) protein kinase B (Akt) signaling pathway, negative regulation of cell division, and response to hypoxia. The PPI network, miRNA–transcription factor–target regulatory network, and drug–gene network were constructed using Cytoscape. The present study revealed several novel miRNAs and genes involved in pAF. We speculated that miR-4298, miR-3125, miR-4306, and miR-671-5p could represent significant miRNAs that act on the target gene superoxide dismutase 2 (SOD2) during the development of pAF and may serve as essential biomarkers for pAF diagnosis and treatment. Moreover, MYC might function in pAF pathogenesis through the PI3K–Akt signaling pathway.

Identifying the key microRNAs implicated in atrial fibrillation

OBJECTIVE

This study investigated the potential microRNAs (miRNAs) having a diagnostic value in atrial fibrillation (AF).

METHODS

The miRNA and mRNA expression profiles of atrial tissue from healthy individuals and patients with AF were downloaded from the Gene Expression Omnibus database. Differentially expressed miRNAs/mRNAs (DEMis/DEMs) were identified in patients with AF. Furthermore, an interaction network between DEMis and DMEs was constructed. The biological processes, molecular functions, and signaling pathways of DEMs were enriched. Then, the diagnostic values of candidate DECs among healthy individuals and patients with AF were preliminarily evaluated in the GSE101586, GSEE101684, and GSE112214 datasets.

RESULTS

Twenty DEMis were identified in patients with AF, including seven upregulated and 13 downregulated DEMis. Furthermore, 2,307 DEMs were identified in patients with AF. In the DEMi-DEM interaction network, downregulated miR-193b and upregulated miR-16 interacted with the most targeted DEMs, which interacted with 72 and 65 targeted DEMs, respectively. The targeted DEMs were significantly enriched in biological functions including apoptosis and the PI3K-Akt, mTOR, Hippo, HIF-1, and ErbB signaling pathways. Four of the 20 DEMis (i.e., miR-490-3p, miR-630, miR-146b-5p, and miR-367) had a potential value to distinguish patients with AF from healthy individuals in the GSE68475, GSE70887, and GSE28954 datasets. The area under the curve values for those four DEMis were 0.751, 0.719, 0.709, and 0.7, respectively.

CONCLUSION

DEMis might play key roles in AF progression through the mTOR and Hippo signaling pathways. miR-409-3p, miR-630, miR-146b-5p, and miR-367 had a potential diagnostic value to discriminate patients with AF from healthy controls in this study.