Biology subjects

Kroncke, B.

Publications and source records attributed to Kroncke, B..

3 recordsLinked to original sources

bioRxiv2026

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

Polygenic scores (PGS) summarize the combined effects of common single-nucleotide polymorphisms and contribute to predictions of disease severity, but biological consequences linked to these common variants remain poorly defined. Here, we focused on polygenic liability for a measurable electrophysiologic trait (the QT interval). Prolonged QT interval, measured on patient electrocardiograms, is associated with an increased risk for cardiac arrhythmia. We investigated human induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) from donors with extreme PGS (i.e., high and low) related to QT interval duration. We paired global proteomics with multiplexed affinity purification mass spectrometry (AP-MS) centered on Kv11.1 (hERG), a major determinant of QT-interval repolarization. Global proteomics indicated increased mitochondrial protein abundance in high-PGS cardiomyocytes, but this did not explain the Kv11.1 interactome. In high-PGS cells, Kv11.1 showed increased associations with myosin motor proteins and endosomal recycling machinery, consistent with altered (and potentially increased) recycling/trafficking dynamics rather than trafficking deficiency observed with most pathogenic Kv11.1 variants. This proof-of-concept study underscores a framework for linking polygenic factors to tractable biological consequences by combining patient-specific hiPSCs, proteomics and affinity-purification. Linking polygenic scores to changes in protein networks provides testable mechanisms that can be applied across many diseases. Significance StatementPolygenic 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 mechanisms that are influenced by common genetic traits in patients. Our work connects polygenic scores to pathway-level molecular mechanisms in human cells and provides a general framework for uncovering how complex genetic architecture drives disease-relevant biology. ClassificationBiological Sciences; Medical Sciences

genomics↗

bioRxiv2023

Generation of human induced pluripotent stem cell (hiPSC) lines from patients with extreme high and low polygenic scores for QT interval

Long QT syndrome (LQTS) is an inherited cardiac arrhythmia syndrome with congenital and drug-induced presentations and known monogenic and polygenic contributions. LQTS represents a significant clinical challenge due to its complex genetic underpinning and propensity for fatal arrhythmias. In this study, we generated induced pluripotent stem cells (iPSCs) reprogrammed from peripheral blood mononuclear cells (PBMCs) of six patients with extreme polygenic scores for short and long corrected QT intervals. iPSC lines were rigorously validated for genomic integrity through karyotyping and targeted mutation analysis specific to a lengthened or shortened QT interval. Pluripotency was confirmed by expression of key markers TRA 1-60, TRA 1-81, SOX2, OCT4, NANOG, and REX1 via quantitative PCR and immunofluorescence. Subsequent cardiac induction successfully generated cardiomyocytes that were further characterized. This patient-specific approach will enable us to better understand variable expressivity and penetrance of LQTS. Rigorously validated iPSC lines serve as a vital resource for elucidating the molecular mechanisms underlying LQTS. Our study provides a robust and clinically relevant resource to facilitate our understanding the genetic and cellular complexity of LQTS.

genetics↗

bioRxiv2019

A Bayesian method using sparse data to estimate penetrance of disease-associated genetic variants

PurposeA major challenge in genomic medicine is how to best predict risk of disease from rare variants discovered in Mendelian disease genes but with limited phenotypic data. We have recently used Bayesian methods to show that in vitro functional measurements and computational pathogenicity classification of variants in the cardiac gene SCN5A correlate with rare arrhythmia penetrance. We hypothesized that similar predictors could be used to impute variant-specific penetrance prior probabilities.\n\nMethodsFrom a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A variant-specific penetrance priors for the heart arrhythmia Brugada syndrome (BrS).\n\nResultsThe resulting priors correlate with mean BrS penetrance posteriors (cross validated R2= 0.41). SCN5A variant function and structural context provide the most information predictive of BrS penetrance. The resulting priors are interpretable as equivalent to the observation of affected and unaffected carriers.\n\nConclusionsBayesian estimates of penetrance can efficiently integrate variant-specific data (e.g. functional, structural, and sequence) to accurately estimate disease risk attributable to individual variants. We suggest this formulation of penetrance is quantitative, probabilistic, and more precise than, but consistent with, discrete pathogenicity classification approaches.

genetics↗