基于生物信息与网络药理学探究飞龙掌血治疗骨关节炎的作用机制

doi:10.5246/jcps.2025.09.063

中国药学(英文版) ›› 2025, Vol. 34 ›› Issue (9): 860-872.DOI: 10.5246/jcps.2025.09.063

基于生物信息与网络药理学探究飞龙掌血治疗骨关节炎的作用机制

邓茜¹^,^#, 彭紫凝¹^,^#, 毛丹宁¹^,^#, 黄元波¹, 刘念¹^,^*(), 晏蔚田²^,^*(), 彭江云²^,^*()

^1. 云南中医药大学第一临床医学院, 云南昆明 650500
^2. 云南中医药大学第一附属医院/云南省中医医院风湿科, 云南昆明 650032

收稿日期:2025-04-08 修回日期:2025-05-11 接受日期:2025-06-03 出版日期:2025-10-02 发布日期:2025-10-02
通讯作者: 刘念, 晏蔚田, 彭江云

Exploring the mechanism of Toddalia asiatica (L.) Lam. in the treatment of osteoarthritis through bioinformatics and network pharmacology

Qian Deng¹^,^#, Zining Peng¹^,^#, Danning Mao¹^,^#, Yuanbo Huang¹, Nian Liu¹^,^*(), Weitian Yan²^,^*(), Jiangyun Peng²^,^*()

¹ First School of Clinical Medicine, Yunnan University of Chinese Medicine, Kunming 650500, Yunnan, China
² Department of Rheumatology, No. 1 Affiliated Hospital of Yunnan University of Chinese Medicine/ Yunnan Provincial Hospital of Traditional Chinese Medicine, Kunming 650032, Yunnan China

Received:2025-04-08 Revised:2025-05-11 Accepted:2025-06-03 Online:2025-10-02 Published:2025-10-02
Contact: Nian Liu, Weitian Yan, Jiangyun Peng
About author:
^# Qian Deng, Zining Peng, and Danning Mao are co-first authors.
Supported by:
National Natural Science Foundations of China (Grant No. 81960870), Key Research and Development Program of Yunnan Science and Technology Department (Grant No. 2024-03AC100019), Key Research and Development Program of Yunnan Science and Technology Department (Grant No. 202303AC100326), Expert Workstation of zhangxuan in Yunnan Province (Grant No. 202305AF150175), and Education Department of Yunnan Province (Grant No. 2023Y0463 and 2024Y380).

摘要/Abstract

摘要：

本研究以生物信息学和网络药理学相结合的综合策略为基础, 探究飞龙掌血干预骨关节炎(OA)的可能活性成分与作用机制。首先从文献中检索飞龙掌血的化学成分, 通过SwissADME评价有效成分, 再利用SwissTargetPrediction数据库预测有效成分对应的靶点, 然后通过Cytoscape (v 3.8.0)构建药物-成分-靶点网络, 根据网络的度值确定药物的主要活性成分。同时, 通过GEO数据综合分析OA相关的差异表达基因。再将飞龙掌血的靶点与OA相关靶标取交集, 对交集基因进行GO和KEGG富集分析的同时, 通过String平台构建交集靶点蛋白-蛋白互作(PPI)网络。再利用Cytoscape(v 3.8.0)对拓扑网络进行可视化, Cytohubba插件识别核心基因。最终预测出54个飞龙掌血的有效成分及694个对应靶标。根据药物-成分-靶点网络度值, 最终确定了22个飞龙掌血的主要活性成分。从GEO数据库中获得493个与OA相关的靶点。飞龙掌血与OA共有46个交集靶标, 这些交集基因的富集分析表明, 飞龙掌血可以通过多成分、多靶点、多途径发挥抗OA的作用。PPI分析得到MCL1这一核心靶点。此外, 通过OA相关外部验证集, 确定了该基因诊断效能。通过分子对接, 明确了飞龙掌血的22个主要活性成分与MCL1的结合能力。本研究增加了飞龙掌血及其有效成分抗OA药理作用的新认识, 也为后续研究提供思路与依据。

关键词: 飞龙掌血, 骨关节炎, 生物信息学, 网络药理学, 分子对接, 机制

Abstract:

This study employed an integrated approach combining bioinformatics and network pharmacology to elucidate the potential active compounds and mechanisms of action of Toddalia asiatica (L.) Lam. (T. asiatica) in treating osteoarthritis (OA). Initially, the components of T. asiatica were extracted from the literature, and their ADMF properties were assessed using SwissADME. Subsequently, the corresponding targets for the effective components were predicted through the SwissTargetPrediction database. A drug-component-target network was then constructed using Cytoscape, which facilitated the identification of the primary active components based on their degree values within the network. In parallel, differentially expressed genes related to OA were comprehensively analyzed utilizing GEO data. The intersection of the targets from T. asiatica and those associated with OA was determined, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, as well as the construction of a protein-protein interaction (PPI) network through the String platform. The topological network was visualized using Cytoscape, and core genes were identified via the Cytohubba plugin. In total, 54 effective components and 694 corresponding targets of T. asiatica were predicted. Based on the degree values from the drug-component-target network, 22 principal active components were ultimately identified. From the GEO database, 493 OA-related targets were retrieved, revealing 46 shared targets between T. asiatica and OA. The enrichment analysis of these intersecting genes suggested that T. asiatica may exert anti-OA effects through multiple components, targets, and pathways. Notably, the PPI network analysis highlighted MCL1 as a core target. The diagnostic potential of this gene was further evaluated using an external validation set for OA. Additionally, the binding affinity of the 22 main active components of T. asiatica to MCL1 was elucidated through molecular docking studies. This research enhanced our understanding of the pharmacological effects of T. asiatica against OA and its active components, providing valuable insights and evidence for future studies.

Key words: Toddalia asiatica (L.) Lam., Osteoarthritis, Bioinformatics, Network pharmacology, Molecular docking, Mechanism

Supporting:

邓茜, 彭紫凝, 毛丹宁, 黄元波, 刘念, 晏蔚田, 彭江云. 基于生物信息与网络药理学探究飞龙掌血治疗骨关节炎的作用机制[J]. 中国药学(英文版), 2025, 34(9): 860-872.

Qian Deng, Zining Peng, Danning Mao, Yuanbo Huang, Nian Liu, Weitian Yan, Jiangyun Peng. Exploring the mechanism of Toddalia asiatica (L.) Lam. in the treatment of osteoarthritis through bioinformatics and network pharmacology[J]. Journal of Chinese Pharmaceutical Sciences, 2025, 34(9): 860-872.

图/表 9

Figure 1. Flowchart of the research process.

Figure 2. Diagram of the drug-component-target network.

Table 1. The 22 core components of T. asiatica.

Figure 3. (A) OA-related GSE55235, GSE206848, GSE82107, GSE55457, and GSE1919 gene chips before and after batch correction. (B) Heatmap of 50 upregulated and 50 downregulated DEGs in OA. (C) Volcano map of DEGs in OA.

Table 2. Fundamental information and classification of the GEO data set.

Figure 4. Venn diagram of the intersection of DEGs and targets of T. asiatica.

Figure 5. (A) GO enrichment analysis circle plot of intersection targets. (B) Bubble plots representing the top 10 terms of CC, MF, and BP. (C) The top five terms of the functional enrichment analysis are displayed by a bubble diagram to observe the relationship between the genes and terms. (D) Barplot for KEGG enrichment analysis.

Figure 6. (A) PPI network diagram of intersection genes between DEGs and T. asiatica. (B) Visualization of the PPI network by Cytoscape. (C) MCL1 ranked in the top 10 using each of the 10 methods. (D) ROC curves of MCL1 in the GSE169077 validation set.

Figure 7. (A) Petal figure of T. asiatica 22 core active components and MCL1 molecular docking results. The outer circle number corresponds to the serial number of each element in Table 1. The second circle of numbers indicates the molecular docking results (kcal/mol). (B) Visualization of the top three best molecular docking results, which involve dihydrochelerythrine, 8-acetonyldihydrochelerythrine, and oxychelerythrine docking with MCL1.

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基于生物信息与网络药理学探究飞龙掌血治疗骨关节炎的作用机制

Exploring the mechanism of Toddalia asiatica (L.) Lam. in the treatment of osteoarthritis through bioinformatics and network pharmacology

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