Atrial fibrillation rhythm is associated with marked changes in metabolic and myofibrillar protein expression in left atrial appendage.

Atrial fibrillation (AF) is strongly associated with risk of stroke and heart failure. AF promotes atrial remodeling that increases risk of stroke due to left atrial thrombogenesis, and increases energy demand to support high rate electrical activity and muscle contraction. While many transcriptomic studies have assessed AF-related changes in mRNA abundance, fewer studies have assessed proteomic changes. We performed a proteomic analysis on left atrial appendage (LAA) tissues from 12 patients with a history of AF undergoing elective surgery; atrial rhythm was documented at time of surgery. Proteomic analysis was performed using liquid chromatography with mass spectrometry (LC/MS-MS). Data-dependent analysis identified 3090 unique proteins, with 408 differentially expressed between sinus rhythm and AF. Ingenuity Pathway Analysis of differentially expressed proteins identified mitochondrial dysfunction, oxidative phosphorylation, and sirtuin signaling among the most affected pathways. Increased abundance of electron transport chain (ETC) proteins in AF was accompanied by decreased expression of ETC complex assembly factors, tricarboxylic acid cycle proteins, and other key metabolic modulators. Discordant changes were also evident in the contractile unit with both up and downregulation of key components. Similar pathways were affected in a comparison of patients with a history of persistent vs. paroxysmal AF, presenting for surgery in sinus rhythm. Together, these data suggest that while the LAA attempts to meet the energetic demands of AF, an uncoordinated response may reduce ATP availability, contribute to tissue contractile and electrophysiologic heterogeneity, and promote a progression of AF from paroxysmal episodes to development of a substrate amenable to persistent arrhythmia.

Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation.

Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.