Multiomics Analysis of Genetics and Epigenetics Reveals Pathogenesis and Therapeutic Targets for Atrial Fibrillation

• Published in: BioMed research international 2021, Vol.2021 (1), p.6644827-6644827
• Main Authors: Liu, Li, Huang, Jianjun, Wei, Baomin, Mo, Jianjiao, Wei, Qinjiang, Chen, Chengcai, Yan, Wei, Huang, Xiannan, He, Fengzhen, Qin, Lingling, Huang, Hehua, Li, Xue, Pan, Xingshou
• Format: Article
• Language: English
• Subjects: Actin; Aged; Analysis; Atrial fibrillation; Atrial Fibrillation - drug therapy; Atrial Fibrillation - genetics; Biomarkers; Cadherins - genetics; Cadherins - metabolism; Cardiac arrhythmia; Care and treatment; CCR2 protein; Cell activation; Cytoskeleton; Datasets; Deoxyribonucleic acid; Development and progression; DNA; DNA methylation; DNA Methylation - genetics; Epigenesis, Genetic; Epigenetic inheritance; Epigenetics; Exome Sequencing; Female; Fibrillation; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Genes; Genetic aspects; Genetics; Genomes; Genomics; Humans; Identification and classification; Leukocytes (neutrophilic); Male; Methods; Molecular biology; Molecular modelling; Molecular Targeted Therapy; Monocyte chemoattractant protein 1; Mutation; Mutation - genetics; Network analysis; Nucleotides; Pathogenesis; Polymorphism; Reproducibility of Results; RhoA protein; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Statistical analysis; Statistical methods; Therapeutic applications; Therapeutic targets
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2021/6644827

Objective. This study is aimed at understanding the molecular mechanisms and exploring potential therapeutic targets for atrial fibrillation (AF) by multiomics analysis. Methods. Transcriptomics and methylation data of AF patients were retrieved from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially methylated sites between AF and normal samples were screened. Then, highly expressed and hypomethylated and lowly expressed and hypermethylated genes were identified for AF. Weighted gene coexpression network analysis (WGCNA) was presented to construct AF-related coexpression networks. 52 AF blood samples were used for whole exome sequence. The mutation was visualized by the maftools package in R. Key genes were validated in AF using independent datasets. Results. DEGs were identified between AF and controls, which were enriched in neutrophil activation and regulation of actin cytoskeleton. RHOA, CCR2, CASP8, and SYNPO2L exhibited abnormal expression and methylation, which have been confirmed to be related to AF. PCDHA family genes had high methylation and low expression in AF. We constructed two AF-related coexpression modules. Single-nucleotide polymorphism (SNP) was the most common mutation type in AF, especially T>C. MUC4 was the most frequent mutation gene, followed by PHLDA1, AHNAK2, and MAML3. There was no statistical difference in expression of AHNAK2 and MAML3, for AF. PHLDA1 and MUC4 were confirmed to be abnormally expressed in AF. Conclusion. Our findings identified DEGs related to DNA methylation and mutation for AF, which may offer possible therapeutic targets and a new insight into the pathogenesis of AF from a multiomics perspective.