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Kv11.1 (hERG) Protein Interaction Networks Connect Endocytic Trafficking to Polygenic Influences on Cardiac
Christian L Egly1, Lea Barny2,3, Suah Woo1
1Vanderbilt Center for Arrhythmia Research and Therapeutics, Department of Medicine, Vanderbilt University Medical Center.
Biorxiv : the Preprint Server for Biology
|February 9, 2026
Summary
Polygenic scores link common genetic variants to cellular mechanisms. This study used patient-derived cells to uncover how genetic liability for prolonged QT interval affects protein networks, revealing new disease pathways.
Area of Science:
- Genetics
- Cardiology
- Molecular Biology
Background:
- Polygenic scores (PGS) predict disease risk but their biological underpinnings are unclear.
- The QT interval, a measure of cardiac repolarization, is linked to arrhythmia risk.
- Understanding how common genetic variants influence complex traits like QT interval is crucial for disease prediction and intervention.
Purpose of the Study:
- To investigate the biological consequences of polygenic liability for QT interval duration.
- To link polygenic scores to specific molecular mechanisms in human cells.
- To establish a framework for connecting complex genetic architecture to disease-relevant biology.
Main Methods:
- Generated human induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) from individuals with extreme high and low PGS for QT interval.
- Employed global proteomics and affinity purification mass spectrometry (AP-MS) targeting the Kv11.1 (hERG) channel.
- Analyzed protein-protein interactions and abundance changes in high-PGS versus low-PGS hiPSC-CMs.
Main Results:
- High-PGS cardiomyocytes showed increased mitochondrial protein abundance, but this did not explain Kv11.1 interactome changes.
- Kv11.1 in high-PGS cells exhibited altered associations with myosin motor proteins and endosomal recycling machinery.
- These findings suggest modified recycling/trafficking dynamics in high-PGS cells, distinct from typical pathogenic variants.
Conclusions:
- This study provides a proof-of-concept for linking polygenic scores to molecular mechanisms using patient-specific hiPSCs, proteomics, and interactomics.
- Connecting polygenic scores to protein networks offers testable hypotheses for disease mechanisms.
- The developed framework can be applied to various diseases influenced by complex genetic factors.
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