Rare and Common Genetic Variation Underlying Atrial Fibrillation Risk.

Importance: Atrial fibrillation (AF) has a substantial genetic component. The importance of polygenic risk is well established, while the contribution of rare variants to disease risk warrants characterization in large cohorts. Objective: To identify rare predicted loss-of-function (pLOF) variants associated with AF and elucidate their role in risk of AF, cardiomyopathy (CM), and heart failure (HF) in combination with a polygenic risk score (PRS). Design, Setting, and Participants: This was a genetic association and nested case-control study. The impact of rare pLOF variants was evaluated on the risk of incident AF. HF and CM were assessed in cause-specific Cox regressions. End of follow-up was July 1, 2022. Data were analyzed from January to October 2023. The UK Biobank enrolled 502 480 individuals aged 40 to 69 years at inclusion in the United Kingdom between March 13, 2006, and October 1, 2010. UK residents of European ancestry were included. Individuals with prior diagnosis of AF were excluded from analyses of incident AF. Exposures: Rare pLOF variants and an AF PRS. Main Outcomes and Measures: Risk of AF and incident HF or CM prior to and subsequent to AF diagnosis. Results: A total of 403 990 individuals (218 489 [54.1%] female) with a median (IQR) age of 58 (51-63) years were included; 24 447 were diagnosed with incident AF over a median (IQR) follow-up period of 13.3 (12.4-14.0) years. Rare pLOF variants in 6 genes (TTN, RPL3L, PKP2, CTNNA3, KDM5B, and C10orf71) were associated with AF. Of these, TTN, RPL3L, PKP2, CTNNA3, and KDM5B replicated in an external cohort. Combined with high PRS, rare pLOF variants conferred an odds ratio of 7.08 (95% CI, 6.03-8.28) for AF. Carriers with high PRS also had a substantial 10-year risk of AF (16% in female individuals and 24% in male individuals older than 60 years). Rare pLOF variants were associated with increased risk of CM both prior to AF (hazard ratio [HR], 3.13; 95% CI, 2.24-4.36) and subsequent to AF (HR, 2.98; 95% CI, 1.89-4.69). Conclusions and Relevance: Rare and common genetic variation were associated with an increased risk of AF. The findings provide insights into the genetic underpinnings of AF and may aid in future genetic risk stratification.

Proteomics couples electrical remodelling to inflammation in a murine model of heart failure with sinus node dysfunction.

AIMS: In patients with heart failure (HF), concomitant sinus node dysfunction (SND) is an important predictor of mortality, yet its molecular underpinnings are poorly understood. Using proteomics, this study aimed to dissect the protein and phosphorylation remodelling within the sinus node in an animal model of HF with concurrent SND. METHODS AND RESULTS: We acquired deep sinus node proteomes and phosphoproteomes in mice with heart failure and SND and report extensive remodelling. Intersecting the measured (phospho)proteome changes with human genomics pharmacovigilance data, highlighted downregulated proteins involved in electrical activity such as the pacemaker ion channel, Hcn4. We confirmed the importance of ion channel downregulation for sinus node physiology using computer modelling. Guided by the proteomics data, we hypothesized that an inflammatory response may drive the electrophysiological remodeling underlying SND in heart failure. In support of this, experimentally induced inflammation downregulated Hcn4 and slowed pacemaking in the isolated sinus node. From the proteomics data we identified proinflammatory cytokine-like protein galectin-3 as a potential target to mitigate the effect. Indeed, in vivo suppression of galectin-3 in the animal model of heart failure prevented SND. CONCLUSION: Collectively, we outline the protein and phosphorylation remodeling of SND in heart failure, we highlight a role for inflammation in electrophysiological remodelling of the sinus node, and we present galectin-3 signalling as a target to ameliorate SND in heart failure.

Outlining cardiac ion channel protein interactors and their signature in the human electrocardiogram.

Protein-protein interactions are essential for normal cellular processes and signaling events. Defining these interaction networks is therefore crucial for understanding complex cellular functions and interpretation of disease-associated gene variants. We need to build a comprehensive picture of the interactions, their affinities and interdependencies in the specific organ to decipher hitherto poorly understood signaling mechanisms through ion channels. Here we report the experimental identification of the ensemble of protein interactors for 13 types of ion channels in murine cardiac tissue. Of these, we validated the functional importance of ten interactors on cardiac electrophysiology through genetic knockouts in zebrafish, gene silencing in mice, super-resolution microscopy and patch clamp experiments. Furthermore, we establish a computational framework to reconstruct human cardiomyocyte ion channel networks from deep proteome mapping of human heart tissue and human heart single-cell gene expression data. Finally, we integrate the ion channel interactome with human population genetics data to identify proteins that influence the electrocardiogram (ECG). We demonstrate that the combined channel network is enriched for proteins influencing the ECG, with 44% of the network proteins significantly associated with an ECG phenotype. Altogether, we define interactomes of 13 major cardiac ion channels, contextualize their relevance to human electrophysiology and validate functional roles of ten interactors, including two regulators of the sodium current (epsin-2 and gelsolin). Overall, our data provide a roadmap for our understanding of the molecular machinery that regulates cardiac electrophysiology.

Beta-blocker/ACE inhibitor therapy differentially impacts the steady state signaling landscape of failing and non-failing hearts.

Heart failure is a multifactorial disease that affects an estimated 38 million people worldwide. Current pharmacotherapy of heart failure with reduced ejection fraction (HFrEF) includes combination therapy with angiotensin-converting enzyme inhibitors (ACEi) and ß-adrenergic receptor blockers (ß-AR blockers), a therapy also used as treatment for non-cardiac conditions. Our knowledge of the molecular changes accompanying treatment with ACEi and ß-AR blockers is limited. Here, we applied proteomics and phosphoproteomics approaches to profile the global changes in protein abundance and phosphorylation state in cardiac left ventricles consequent to combination therapy of ß-AR blocker and ACE inhibitor in HFrEF and control hearts. The phosphorylation changes induced by treatment were profoundly different for failing than for non-failing hearts. HFrEF was characterized by profound downregulation of mitochondrial proteins coupled with derangement of ß-adrenergic and pyruvate dehydrogenase signaling. Upon treatment, phosphorylation changes consequent to HFrEF were reversed. In control hearts, treatment mainly led to downregulation of canonical PKA signaling. The observation of divergent signaling outcomes depending on disease state underscores the importance of evaluating drug effects within the context of the specific conditions present in the recipient heart.

Cardiac fibroblast sub-types in vitro reflect pathological cardiac remodeling in vivo.

Many heart diseases are associated with fibrosis, but it is unclear whether different types of heart disease correlate with different subtypes of activated fibroblasts and to which extent such diversity is modeled during in vitro activation of primary cardiac fibroblasts. Analyzing the expression of 82 fibrosis related genes in 65 heart failure (HF) patients, we identified a panel of 12 genes clearly distinguishing HF patients better from healthy controls than measurement of the collagen-related hydroxyproline content. A subcluster enriched in ischemic HF was recognized, but not for diabetes, high BMI, or gender. Single-cell transcriptomic analysis of in vitro activated mouse cardiac fibroblasts distinguished 6 subpopulations, including a contractile Acta2high precursor population, which was predicted by time trajectory analysis to develop into Acta2low subpopulations with high production of extracellular matrix molecules. The 12 gene profile identified in HF patients showed highest similarity to the fibroblast subset with the strongest expression of extracellular matrix molecules. Population markers identified were furthermore able to clearly cluster different disease stages in a murine model for myocardial infarct. These data suggest that major features of cardiac fibroblast activation in heart failure patients, in murine heart disease models, and in cell culture of primary murine cardiac fibroblast are shared.

FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data.

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.