Brett M Kroncke | Search PubMed

PubMed2026

Kv11.1 (hERG) Protein Interaction Networks Connect Endocytic Trafficking to Polygenic Influences on Cardiac Repolarization.

Polygenic scores (PGS) capture the combined effect of many common genetic variants on quantitative traits and disease risk, yet their functional consequences at the protein level remain poorly defined. Here, we integrated quantitative and interaction proteomics to resolve how polygenic liability for cardiac repolarization manifests in human cells. We studied human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from donors with extreme PGS for QT interval duration, a clinically relevant electrophysiologic trait associated with arrhythmia risk. Global quantitative proteomics revealed increased abundance of mitochondrial proteins in high-PGS cardiomyocytes. To define protein network-level effects on a key repolarizing ion channel, we performed multiplexed affinity purification-mass spectrometry (AP-MS) of Kv11.1. While mitochondrial changes did not directly explain Kv11.1-associated complexes, interactome analysis revealed increased association of Kv11.1 with myosin motor proteins and endosomal recycling machinery in high-PGS cells. These findings suggest altered channel trafficking dynamics of Kv11.1, distinct from the trafficking defects observed in monogenic Kv11.1 variants. Together, these data show that integrating global and interaction proteomics can resolve how polygenic variation reshapes protein networks. Future work using these methods could connect genomic risk to subcellular remodeling and our work provides a generalizable framework to probe the proteomic basis of complex traits. SIGNIFICANCE STATEMENT: Polygenic scores (PGS) predict disease risk, but how biological pathways are influenced by these common variants remains difficult to define. We generated human induced pluripotent stem cells from individuals with extreme high- and low- PGS for QT interval, a key electrocardiographic measure linked to arrhythmia risk. By combining global proteomics and interactomics for a common ion channel involved in regulating the QT interval (Kv11.1) we found potential mechanisms that are influenced by common genetic traits in patients. Our work provides an approach to connect polygenic scores to pathway-level molecular mechanisms in human cells and a general framework for uncovering how complex genetic architecture drives disease-relevant biology.

AP-MS↗

PubMed2025

Creating an atlas of variant effects to resolve variants of uncertain significance and guide cardiovascular medicine.

Cardiovascular diseases are leading global causes of death and disability, often presenting as interrelated phenotypes of atherosclerotic vascular disease, heart failure and arrhythmias. Cardiovascular diseases arise from interactions between environmental factors and predisposing genotypes and include common Mendelian lipid disorders, cardiomyopathies and arrhythmia syndromes. The identification of a pathogenic variant through genetic testing can inform disease diagnosis, risk prediction, treatment and family screening. However, a major roadblock in genomic medicine is that for many variants, especially missense variants, we lack sufficient evidence to enable a definitive classification, and therefore these variants are deemed as 'variants of uncertain significance'. In this Review, we describe how multiplexed assays of variant effects can enable the functional assessment of nearly all coding variants in a target sequence, potentially offering a proactive approach to identifying the functional significance of gene variants that are observed later in a patient. We discuss validation, including the role of in silico variant effect predictors, and how multiplexed experimental methods are informing cardiovascular disease biology and ultimately resolving the problem of variants of uncertain significance at scale.

Humans↗