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Published on: June 29, 2022
Single-Cell Transcriptomics and Mendelian Randomization Analysis Reveal Key Genes in Atrial Fibrillation
Xiangpeng Chen1,2, Hongquan Chen3, Shiguang Xu1
1Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang City, China.
Insights
Atrial fibrillation (AF) involves increased SPP1-expressing macrophages driving disease progression. Key causal genes LRCH1, RSRC2, and VAMP2 offer new therapeutic targets for AF.
Area of Science:
- Cardiology
- Genetics
- Immunology
Background:
- Atrial fibrillation (AF) is a prevalent cardiac arrhythmia impacting quality of life and increasing stroke risk.
- Understanding AF pathogenesis requires identifying key cellular players and therapeutic targets.
Purpose of the Study:
- To identify critical cellular subtypes involved in atrial fibrillation (AF) pathogenesis.
- To screen for AF-related genes and assess their causal relationships with the condition.
Main Methods:
- Single-cell transcriptomics to analyze cellular composition in AF.
- High-dimensional weighted gene co-expression network analysis (hdWGCNA) and machine learning for gene screening.
- Mendelian randomization (MR) and colocalization analyses to determine causal gene effects.
Main Results:
- Single-cell analysis revealed a significant increase in macrophages, particularly SPP1-expressing macrophages, in AF.
- HdWGCNA identified gene modules associated with AF.
- LRCH1, RSRC2, and VAMP2 were identified as causally linked to AF through MR analysis.
Conclusions:
- SPP1-expressing macrophage accumulation may drive AF onset and progression.
- LRCH1, RSRC2, and VAMP2 represent key causal genes for AF, offering potential therapeutic avenues.
Background:
Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. It reduces quality of life and increases the risk of complications such as stroke. Although progress has been made in understanding its pathogenesis, the key cellular subtypes and therapeutic targets remain unclear.
Methods:
We applied single-cell transcriptomics to identify critical cellular subtypes in AF. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and machine learning were used to screen AF-related genes. Mendelian randomization (MR) and colocalization analyses were performed to assess causal relationships between these genes and AF.
Results:
Single-cell analysis showed a significant increase in macrophages in AF, especially SPP1-expressing macrophages, which may drive AF onset and progression. HdWGCNA identified AF-related gene modules. Three genes, LRCH1, RSRC2 and VAMP2, were found to be causally associated with AF. MR analysis confirmed their significant causal effects.
Conclusions:
The accumulation of SPP1-expressing macrophages may drive the onset and progression of AF. Furthermore, LRCH1, RSRC2, and VAMP2 were identified as key causal genes for AF, providing novel insights into its molecular mechanisms and potential therapeutic targets.
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