Frontiers in immunology · 2026
Integrating network pharmacology, molecular docking and experimental verification to explore the therapeutic effect of piceatannol on rheumatoid arthritis.
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Yang Mingyi, Ren Honghao, Xu Peng, Ren Xiaodong, Aihaiti Yirixiati, Wen Pengfei, Liu Lin, Xu Ke, Zhang Ming, Hou Weikun, Yang Zhi, Su Yani
Аннотация
OBJECTIVE: Piceatannol (PIC) exhibits antioxidant and anti-inflammatory activities. This study integrates network pharmacology and experimental validation to investigate its potential role in rheumatoid arthritis (RA).
METHODS: PIC targets were predicted using public databases. RA-related differentially expressed genes (DEGs) were identified from Gene Expression Omnibus (GEO) datasets (|logFC| ≥ 1 and P-value < 0.05). Intersection genes were analyzed via protein-protein interaction (PPI) network (hub gene selection), molecular docking (binding affinity < -5.0 kcal/mol as threshold), ConnectivityMap and molecular dynamics simulation. Experimental validation included CCK8, flow cytometry, real-time quantitative PCR (RT-qPCR), Western blotting, and an adjuvant-induced arthritis (AIA) rat model.
RESULTS: 35 intersecting genes were identified, from which 6 hub genes (SYK, CXCL8, TNF, NFKB1, PPARG, and CASP8) were selected. PIC showed stable binding to all hub genes (affinities: -5.6 to -7.8 kcal/mol). ConnectivityMap suggested a regulatory relationship between PIC and SYK. Molecular dynamics simulations demonstrate that the PIC-SYK complex maintains stable structural integrity. Experimental validation showed that PIC reduced MH7A cell viability, induced G2/M arrest and apoptosis, and downregulated mRNA levels of SYK, NFKB1, and CASP8, consistent with predictions. In vivo, PIC alleviated AIA severity.
CONCLUSION: These preliminary findings suggest that PIC exerts therapeutic effects in RA models, potentially via SYK/NFKB1/CASP8. The study provides a theoretical basis for further evaluation of PIC in RA, while acknowledging the exploratory nature of network pharmacology and preclinical models.
METHODS: PIC targets were predicted using public databases. RA-related differentially expressed genes (DEGs) were identified from Gene Expression Omnibus (GEO) datasets (|logFC| ≥ 1 and P-value < 0.05). Intersection genes were analyzed via protein-protein interaction (PPI) network (hub gene selection), molecular docking (binding affinity < -5.0 kcal/mol as threshold), ConnectivityMap and molecular dynamics simulation. Experimental validation included CCK8, flow cytometry, real-time quantitative PCR (RT-qPCR), Western blotting, and an adjuvant-induced arthritis (AIA) rat model.
RESULTS: 35 intersecting genes were identified, from which 6 hub genes (SYK, CXCL8, TNF, NFKB1, PPARG, and CASP8) were selected. PIC showed stable binding to all hub genes (affinities: -5.6 to -7.8 kcal/mol). ConnectivityMap suggested a regulatory relationship between PIC and SYK. Molecular dynamics simulations demonstrate that the PIC-SYK complex maintains stable structural integrity. Experimental validation showed that PIC reduced MH7A cell viability, induced G2/M arrest and apoptosis, and downregulated mRNA levels of SYK, NFKB1, and CASP8, consistent with predictions. In vivo, PIC alleviated AIA severity.
CONCLUSION: These preliminary findings suggest that PIC exerts therapeutic effects in RA models, potentially via SYK/NFKB1/CASP8. The study provides a theoretical basis for further evaluation of PIC in RA, while acknowledging the exploratory nature of network pharmacology and preclinical models.