Machine learning analysis of ARVC informed by sodium channel protein-based interactome networks

Machine learning analysis of ARVC informed by sodium channel protein-based interactome networks

BackgroundArrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiac disorder characterized by sodium channel dysfunction. However, the clinical management of ARVC remains challenging. Identifying novel compounds for the treatment of ARVC is crucial for advancing drug development...

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Main Authors: Yanan Zhu, Hui Zhang, Xuan Zhao, Xin Wang, Lina Xing, Sijie Yao, Xiao Jin, Tingting Li, Ting Dai, Xinyue Ding, Zhen Qi, Zongjun Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Pharmacology
Subjects:
ARVC
sodium channel protein
machine learning
protein-protein interaction
binding affinity
cardiac organoids
Online Access:https://www.frontiersin.org/articles/10.3389/fphar.2025.1611342/full
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Summary:BackgroundArrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiac disorder characterized by sodium channel dysfunction. However, the clinical management of ARVC remains challenging. Identifying novel compounds for the treatment of ARVC is crucial for advancing drug development.PurposeIn this study, we aim to identify novel compounds for treating ARVC.MethodsMachine learning (ML) models were constructed using proteins analyzed from the scRNA-seq data of ARVC rats and their corresponding protein-protein interaction (PPI) network to predict binding affinity (BA). To validate these predictions, a series of experiments in cardiac organoids were conducted, including Western blotting, ELISA, MEA, and Masson staining to assess the effects of these compounds.ResultsWe first discovered and identified SCN5A as the most significantly affected sodium channel protein in ARVC. ML models predicted that Kaempferol binds to SCN5A with high affinity. In vitro experiments further confirmed that Kaempferol exerted therapeutic effects in ARVC.ConclusionThis study presents a novel approach for identifying potential compounds to treat ARVC. By integrating ML modeling with organoid validation, our platform provides valuable support in addressing the public health challenges posed by ARVC, with broad application prospects. Kaempferol shows promise as a lead compound for ARVC treatment.
ISSN:1663-9812

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