Causal mitochondrial signatures in atrial fibrillation: Insights from integrated multi-omics and multi-faceted...

Mitochondrial dysfunction is increasingly recognized as a key factor in the development and progression of atrial fibrillation (AF).

This study aimed too systematically assess the causal relationship between mitochondrial genes and AF.

The study used mitochondrial genes from MitoCarta version 3.0 to extract corresponding methylation data from GoDMC, gene expression data from eQTLGen and GTEx, and protein abundance data from UKB-PPP and deCODE to identify quantitative trait loci (QTLs). These were used as instrumental variables for Mendelian Randomization, summary-based Mendelian Randomization, and colocalization analysis, with the Benjamini-Hochberg method employed to control for false positives, to investigate associations with AF (from FinnGen). Based on the analysis results, multi-omics evidence was categorized. Subsequently, single-cell RNA sequencing was employed to investigate the cellular mechanisms of key genes, and predictive models were constructed to assess their potential as biomarkers.

Through the integration of multi-omics data, 16 genes were identified via MR and SMR analyses. Among these, the NME4 gene demonstrated consistent causal associations with AF across all 3 levels; specifically, methylation at the cg08183303 site within the NME4 gene, as well as high gene expression and elevated protein levels, were associated with an increased risk of AF. Analysis at the cellular level revealed that the key gene is highly expressed in fibroblasts, with expression levels dynamically changing as cells evolve. Machine learning models incorporating these key genes demonstrated good performance in disease prediction.

The study identified 16 mitochondrial genes associated with AF and demonstrated the potential of mitochondrial genes as biomarkers.