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.

Novel functional atrial fibrillation risk genes and pathways identified from coexpression analyses in human left atria.

BACKGROUND: Genomewide association studies have associated >100 genetic loci with atrial fibrillation (AF), but establishing causal genes contributing to AF remains challenging. OBJECTIVE: The purpose of this study was to determine candidate novel causal genes and mechanistic pathways associated with AF risk loci by incorporating gene expression and coexpression analyses and to provide a resource for functional studies and targeting of AF-associated genes. METHODS: Cis-expression quantitative trait loci were identified for candidate genes near AF risk variants in human left atrial tissues. Coexpression partners were identified for each candidate gene. Weighted gene coexpression network analysis (WGCNA) identified modules and modules with overrepresentation of candidate AF genes. Ingenuity pathway analysis (IPA) was applied to the coexpression partners of each candidate gene. IPA and gene set over representation analysis were applied to each WGCNA module. RESULTS: One hundred sixty-six AF-risk single nucleotide polymorphisms were located in 135 loci. Eighty-one novel genes not previously annotated as putative AF risk genes were identified. IPA identified mitochondrial dysfunction, oxidative stress, epithelial adherens junction signaling, and sirtuin signaling as the most frequent significant pathways. WGCNA characterized 64 modules (candidate AF genes overrepresented in 8), represented by cell injury, death, stress, developmental, metabolic/mitochondrial, transcription/translation, and immune activation/inflammation regulatory pathways. CONCLUSION: Candidate gene coexpression analyses suggest significant roles for cellular stress and remodeling in AF, supporting a dual risk model for AF: Genetic susceptibility to AF may not manifest until later in life, when cellular stressors overwhelm adaptive responses. These analyses also provide a novel resource to guide functional studies on potential causal AF genes.

New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation.

Objective: To identify radiomic and clinical features associated with post-ablation recurrence of AF, given that cardiac morphologic changes are associated with persistent atrial fibrillation (AF), and initiating triggers of AF often arise from the pulmonary veins which are targeted in ablation. Methods: Subjects with pre-ablation contrast CT scans prior to first-time catheter ablation for AF between 2014-2016 were retrospectively identified. A training dataset (D1) was constructed from left atrial and pulmonary vein morphometric features extracted from equal numbers of consecutively included subjects with and without AF recurrence determined at 1 year. The top-performing combination of feature selection and classifier methods based on C-statistic was evaluated on a validation dataset (D2), composed of subjects retrospectively identified between 2005-2010. Clinical models ([Formula: see text]) were similarly evaluated and compared to radiomic ([Formula: see text]) and radiomic-clinical models ([Formula: see text]), each independently validated on D2. Results: Of 150 subjects in D1, 108 received radiofrequency ablation and 42 received cryoballoon. Radiomic features of recurrence included greater right carina angle, reduced anterior-posterior atrial diameter, greater atrial volume normalized to height, and steeper right inferior pulmonary vein angle. Clinical features predicting recurrence included older age, greater BMI, hypertension, and warfarin use; apixaban use was associated with reduced recurrence. AF recurrence was predicted with radio-frequency ablation models on D2 subjects with C-statistics of 0.68, 0.63, and 0.70 for radiomic, clinical, and combined feature models, though these were not prognostic in patients treated with cryoballoon. Conclusions: Pulmonary vein morphology associated with increased likelihood of AF recurrence within 1 year of catheter ablation was identified on cardiac CT. Significance: Radiomic and clinical features-based predictive models may assist in identifying atrial fibrillation ablation candidates with greatest likelihood of successful outcome.

Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.

[Figure: see text].