Elucidating the Possible Molecular Targeting Mechanism of Triptolide in Parkinson's Disease Using Single-Cell RNA-Seq Analysis and Network Pharmacology
Year: 2026
Type: article
Abstract: Introduction: Parkinson's disease (PD) is a prevalent neurodegenerative condition. This study aims to elucidate potential targets of triptolide (TP) in PD using an integrated approach that combines single-cell RNA sequencing (scRNA-seq) and network pharmacology. Methods: We gathered compounds and targets of TP from the Traditional Chinese Medicine System Pharmacology Database (TCMSP). Using the Gene Expression Omnibus (GEO) database (GSE20292), we identified differentially expressed genes in PD. Weighted gene co-expression network analysis (WGCNA) was applied to identify hub modules significantly associated with PD. Single- cell RNA sequencing data were processed to cluster cells and identify major cell populations. Molecular docking was used to evaluate the binding affinity of TP to key targets. Results: We identified 67 TP-related PD target genes. Functional enrichment analyses (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) revealed that TP-PD target genes predominantly influence AMPK pathway-related metabolic signaling. Further exploration using scRNA-seq analysis identified four major cell types in PD: astrocytes, macrophages, smooth muscle cells, and T cells. From differential cell clusters, eight TP-PD target genes were identified. Notably, HMGCR and PPARGC1A emerged as potential PD biomarkers. Molecular docking suggested potential binding of TP to HMGCR and PPARGC1A, indicating a role in astrocytes. Conclusion: Our analysis identifies TP's PD targets, highlighting AMPK pathway modulation and astrocyte-localized biomarkers that help elucidate its therapeutic mechanisms.
Source: Current Neuropharmacology
Authors: Shijun Peng, Jia Ouyang, Guangyong Wu, Ruen Liu
Institution: Peking University People's Hospital
Language: English
Subfield: Complementary and alternative medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7151447017
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