MicroRNA‐146b‐5p promotes atrial fibrosis in atrial fibrillation by repressing TIMP4

Ye, Qing; Liu, Quan; Ma, Xiaolong; Bai, Shuyun; Chen, Pengfei; Zhao, Yichen; Bai, Caiquan; Liu, Yang; Liu, Kemin; Meng, Xia; Zeng, Caiwu; Zhao, Cheng; Yao, Yan; Wang, Jiangang

doi:10.1111/jcmm.16985

Cited by 23 publications

(19 citation statements)

References 29 publications

(42 reference statements)

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“…Regardless of this issue, we had sufficient miR (>42x10 6 ) and mRNA (49x10 6 ) reads detected in our RNAseq data, and could successfully detect differential expression of some of the previously identified miRs and genes. For example, miR-146b-5p − the most significantly upregulated miR in our miRseq (Table 1) -was previously found to be upregulated in AF, regulating structural [75,76] and electrical remodeling of atria [77]. Besides, miR-302 family was found deregulated in AF in some studies [78,79].…”

Section: Discussionmentioning

confidence: 62%

Integrated Analysis of microRNA-mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Human

Wang¹,

Bektik²,

Sakon³

et al. 2022

Preprint

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Atrial fibrillation (AF) is a form of sustained cardiac arrhythmia and microRNAs (miRs) play crucial roles in pathophysiology of AF. To identify novel miR-mRNA pairs, we performed RNAseq from atrial biopsies of AF and non-AF patients. Differentially expressed miRs (11-down and 9-up) and mRNAs (95-up and 82-down) were identified and hierarchically clustered in a heat-map. Subsequently, GO, KEGG, and GSEA analyses were run to identify deregulated pathways. Then, miR targets were predicted in miRDB database, and a regulatory network of negatively correlated miR-mRNA pairs was constructed using Cytoscape. To select potential candidate genes from GSEA analysis, top-50 enriched genes in GSEA were overlaid with predicted targets of differentially deregulated miRs. Besides, protein-protein-interaction (PPI) network of enriched genes in GSEA was constructed, and subsequently GO and canonical pathway analyses were run for genes in PPI network. Our analyses showed that TNF-α, p53, EMT, and SYDECAN1 signaling were among the highly affected pathways in AF samples. SDC-1 (syndecan-1) was the top-enriched gene in p53, EMT, and SYDECAN1 signaling. Consistently, SDC-1 mRNA and protein levels were significantly higher in atrial samples of AF patients. Among negatively correlated miRs, miR-302b-3p was experimentally validated to suppress SDC-1 transcript levels. Overall, our results suggested that miR-302b-3p/SDC-1 axis may involve in pathogenesis of AF.

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Section: Discussionmentioning

confidence: 62%

Integrated Analysis of microRNA-mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Human

Wang¹,

Bektik²,

Sakon³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Regardless of this issue, we had sufficient miR (> 42 × 10 6 ) and mRNA (49 × 10 6 ) reads detected in our RNA-seq data, and could successfully detect differential expression of some of the previously identified miRs and genes. For example, miR-146b-5p—the most significantly upregulated miR in our miR-seq ( Table 1 )—was previously found to be upregulated in AF, regulating structural [ 58 , 76 ] and electrical remodeling of atria [ 77 ]. Further, the miR-302 family was found deregulated in AF in some studies [ 78 , 79 ].…”

Section: Discussionmentioning

confidence: 97%

“…Interestingly, several members of miR-302 were among the highly downregulated miRs, miR-302b-3p being the top significantly downregulated miR ( Table 2 ). Among the upregulated miRs, miR-146b-5p was the top significantly upregulated miRs, which has been previously studied in the atrial fibrosis [ 58 ]. Additionally, some novel upregulated miRs were identified in our study including miR-549a-3p, miR-187-3p, miR-187-5p, miR-155-5p, miR-3690, and miR-592, which may have implications in AF through regulating their predicted target genes ( Table 4 ).…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis of the microRNA–mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Humans

Wang

Bektik

Sakon

et al. 2022

Cells

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Atrial fibrillation (AF) is a form of sustained cardiac arrhythmia and microRNAs (miRs) play crucial roles in the pathophysiology of AF. To identify novel miR–mRNA pairs, we performed RNA-seq from atrial biopsies of persistent AF patients and non-AF patients with normal sinus rhythm (SR). Differentially expressed miRs (11 down and 9 up) and mRNAs (95 up and 82 down) were identified and hierarchically clustered in a heat map. Subsequently, GO, KEGG, and GSEA analyses were run to identify deregulated pathways. Then, miR targets were predicted in the miRDB database, and a regulatory network of negatively correlated miR–mRNA pairs was constructed using Cytoscape. To select potential candidate genes from GSEA analysis, the top-50 enriched genes in GSEA were overlaid with predicted targets of differentially deregulated miRs. Further, the protein–protein interaction (PPI) network of enriched genes in GSEA was constructed, and subsequently, GO and canonical pathway analyses were run for genes in the PPI network. Our analyses showed that TNF-α, p53, EMT, and SYDECAN1 signaling were among the highly affected pathways in AF samples. SDC-1 (SYNDECAN-1) was the top-enriched gene in p53, EMT, and SYDECAN1 signaling. Consistently, SDC-1 mRNA and protein levels were significantly higher in atrial samples of AF patients. Among negatively correlated miRs, miR-302b-3p was experimentally validated to suppress SDC-1 transcript levels. Overall, our results suggested that the miR-302b-3p/SDC-1 axis may be involved in the pathogenesis of AF.

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“…Besides, in current study, we found that the CXCL12 expression could be regulated by a TF-miRNA-mRNA network. Previous study reported the role of miR-146b-5p in atrial fibrosis in AF by repressing TIMP-4 ( 38 , 39 ). Our result showed that miR-146b-5p might participate in local inflammation regulation in AF by negatively regulating NF-κB.…”

Section: Discussionmentioning

confidence: 98%

Hub Genes Identification, Small Molecule Compounds Prediction for Atrial Fibrillation and Diagnostic Model Construction Based on XGBoost Algorithm

Yang

Chen

Huang

2022

Front. Cardiovasc. Med.

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BackgroundAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia and engenders significant global health care burden. The underlying mechanisms of AF is remained to be revealed and current treatment options for AF have limitations. Besides, a detection system can help identify those at risk of developing AF and will enable personalized management.Materials and MethodsIn this study, we utilized the robust rank aggregation method to integrate six AF microarray datasets from the Gene Expression Omnibus database, and identified a set of differentially expressed genes between patients with AF and controls. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using weighted gene co-expression network analysis and protein–protein interaction network, respectively. The overlapped hub genes were further filtered. Subsequent analyses were performed to analyze the function, biological features, and regulatory networks. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes.ResultsA total of 156 upregulated and 34 downregulated genes were identified, some of which had not been previously investigated. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes, including CXCL12, LTBP1, LOXL1, and IGFBP3, were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Regulation of CXCL12 expression in AF was analyzed by constructing a transcription factor-miRNA-mRNA network. The Machine Learning-based diagnostic model generated in this study showed good efficacy and reliability.ConclusionKey genes involving in the pathogenesis of AF and potential therapeutic compounds for AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.

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MicroRNA‐146b‐5p promotes atrial fibrosis in atrial fibrillation by repressing TIMP4

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 23 publications

References 29 publications

Integrated Analysis of microRNA-mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Human

Integrated Analysis of microRNA-mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Human

Integrated Analysis of the microRNA–mRNA Network Predicts Potential Regulators of Atrial Fibrillation in Humans

Hub Genes Identification, Small Molecule Compounds Prediction for Atrial Fibrillation and Diagnostic Model Construction Based on XGBoost Algorithm

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