School of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang 712046, China.
Department of Urological Surgery, Shaanxi University of Chinese Medicine Affiliated Hospital, Xianyang 712000, China.
Comb Chem High Throughput Screen. 2023;26(13):2321-2332. doi: 10.2174/1386207325666220930091758.
This study aimed to investigate the active components and mechanism of action of rosemary volatile oil for treating Alzheimer's disease (AD) using network pharmacology.
We obtained the constituents of the rosemary volatile oil by searching Chinese herbal systemic pharmacological databases and analytical platforms and constructed the constituent-target networks by predicting and screening the action targets of the rosemary volatile oil constituents using SwissTargetPrediction, metaTarFisher, and Pubchem. We obtained the AD-related targets using the Genecards, OMIM, and DisGeNET databases and constructed the protein-protein interaction networks (PPI) using the STRING database in Venny 2.1.0 graph to identify the cross-targets by screening the core-acting targets. Cytoscape 3.8.2 software was used to construct a componenttarget- pathway network to screen the potential active components of the rosemary volatile oil for the treatment of AD and predict the mechanism of action of the rosemary volatile oil for the treatment of AD in combination with existing pharmacological studies. We performed a gene ontology (GO) biological process and a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the targets of the rosemary volatile oil for the treatment of AD using R language and molecular docking using Discovery Studio 4.0 software to validate their biological activities.
A network constructed using gas chromatography-mass spectrometry (GC-MS) analysis identified 26 potentially active ingredients in the rosemary volatile oil. We retrieved a total of 10762 AD targets from Genecards and other databases. Our GO enrichment analysis yielded 39 entries (P < 0.05), including 14 entries for biological processes, five entries for cellular composition, and 20 entries for molecular function. A total of 14 entries (P < 0.05) were then enriched in the KEGG pathway that primarily involved the IL-17 signaling pathway and the AGE-RAGE pathway.
The active components of rosemary volatile oil had good inhibition of the inflammatory response. This study provides a reference and guidance for the in-depth study on rosemary volatile oil for the treatment of AD.
本研究采用网络药理学方法研究迷迭香挥发油治疗阿尔茨海默病(AD)的活性成分及作用机制。
通过检索中药系统药理学数据库与分析平台获取迷迭香挥发油的成分,并利用 SwissTargetPrediction、metaTarFisher 和 Pubchem 预测和筛选迷迭香挥发油成分的作用靶点,构建成分-靶点网络;通过 Genecards、OMIM 和 DisGeNET 数据库获取 AD 相关靶点,在 Venny 2.1.0 图形中利用 STRING 数据库构建蛋白质-蛋白质相互作用网络(PPI),通过筛选核心作用靶点确定交叉靶点;采用 Cytoscape 3.8.2 软件构建成分-靶点-通路网络,筛选迷迭香挥发油治疗 AD 的潜在活性成分,并结合现有药理学研究预测迷迭香挥发油治疗 AD 的作用机制。采用 R 语言对迷迭香挥发油治疗 AD 的靶点进行基因本体(GO)生物过程和京都基因与基因组百科全书(KEGG)富集分析,并用 Discovery Studio 4.0 软件进行分子对接验证其生物学活性。
采用气相色谱-质谱联用(GC-MS)分析构建的网络鉴定出迷迭香挥发油中 26 种潜在的活性成分。从 Genecards 和其他数据库共检索到 10762 个 AD 靶点。GO 富集分析得到 39 项(P<0.05),包括生物过程 14 项、细胞组成 5 项和分子功能 20 项;KEGG 通路富集分析得到 14 项(P<0.05),主要涉及白细胞介素-17 信号通路和晚期糖基化终末产物-受体(AGE-RAGE)通路。
迷迭香挥发油的活性成分对炎症反应有较好的抑制作用。本研究为深入研究迷迭香挥发油治疗 AD 提供了参考和指导。