The Joint Analysis of Multi-Omics Data Revealed the Methylation-Expression Regulations in Atrial Fibrillation

Area of Science:

• Cardiovascular Research
• Molecular Biology
• Genomics and Epigenetics

Background:

• Atrial fibrillation (AF) is a widespread cardiac arrhythmia with multifactorial causes, yet its underlying molecular mechanisms remain largely elusive.
• Established risk factors for AF include advanced age, male sex, diabetes, hypertension, and cardiac dysfunction, necessitating deeper molecular insights.

Purpose of the Study:

• To investigate the molecular underpinnings of atrial fibrillation (AF) by analyzing genome-wide DNA methylation and gene expression patterns.
• To identify key genes and regulatory networks associated with persistent and paroxysmal AF using a multi-omics approach.

Main Methods:

• Employed Methylation EPICBead Chip and RNA sequencing on blood samples from 10 persistent AF patients, 10 paroxysmal AF patients, and 10 healthy controls.
• Utilized the Boruta machine learning algorithm for feature selection to identify significant methylation and gene expression markers associated with AF.
• Analyzed interconnections between identified genes to elucidate regulatory pathways.

Main Results:

• Identified significant associations between DNA methylation patterns and gene expression profiles in patients with atrial fibrillation (AF).
• The study pinpointed KIF15 methylation as a potential regulator of PSMC3, TINAG, and NUDT6 gene expression.
• Discovered novel methylation-expression regulatory networks implicated in the pathogenesis of AF.

Conclusions:

• The identified methylation-expression regulations provide novel insights into the molecular mechanisms of atrial fibrillation (AF).
• These findings highlight the potential of multi-omics data in understanding complex cardiac conditions like AF.
• The study lays the groundwork for future research into AF's molecular basis and potential therapeutic interventions.