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中国精品科技期刊2020
丁其莹,刘鑫源,覃佳运,等. 基于药理学分析巴戟天治疗阿尔茨海默病的机制[J]. 食品工业科技,2024,45(16):36−46. doi: 10.13386/j.issn1002-0306.2023080130.
引用本文: 丁其莹,刘鑫源,覃佳运,等. 基于药理学分析巴戟天治疗阿尔茨海默病的机制[J]. 食品工业科技,2024,45(16):36−46. doi: 10.13386/j.issn1002-0306.2023080130.
DING Qiying, LIU Xinyuan, QIN Jiayun, et al. Pharmacological Analyses of the Mechanisms of Morinda officinalis How. in the Treatment of Alzheimer's Disease[J]. Science and Technology of Food Industry, 2024, 45(16): 36−46. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023080130.
Citation: DING Qiying, LIU Xinyuan, QIN Jiayun, et al. Pharmacological Analyses of the Mechanisms of Morinda officinalis How. in the Treatment of Alzheimer's Disease[J]. Science and Technology of Food Industry, 2024, 45(16): 36−46. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023080130.

基于药理学分析巴戟天治疗阿尔茨海默病的机制

Pharmacological Analyses of the Mechanisms of Morinda officinalis How. in the Treatment of Alzheimer's Disease

  • 摘要: 目的:基于网络药理学、分子对接和GEO数据分析巴戟天治疗阿尔茨海默病(Alzheimer’s disease,AD)的潜在靶点和作用机制。方法:利用中药系统药理学数据库与分析平台(TCMSP),获取巴戟天的主要活性成分,用SwissTargetPrediction获取巴戟天全部作用靶点。从DrugBank、PathCard、Chemogenomic Database和PubChem数据库获得AD相关靶点。使用韦恩图取交集,得到巴戟天治疗AD的共同作用靶点。利用Cytoscape 3.8.0构建靶点的“成分-靶点”网络图,并分析靶点的相互作用PPI网络图、基因本体论(GO)和KEGG信号通路等。使用Autodock对关键成分和靶点进行分子对接,并用Pymol和Discovery Studio展示对接结果。最后利用GEO数据库Alzdata分析关键靶点基因在AD中的表达。结果:预测得到巴戟天的50种主要活性成分,636个作用靶点,674个AD相关靶点,其中巴戟天治疗AD共同靶点124个。GO富集分析得到蛋白质磷酸化、磷酸化的正调控、细胞对氮化合物的反应、水解酶活性的调节、细胞对化学刺激的反应。KEGG富集分析显示阿尔茨海默病为最显著的通路。分子对接显示,巴戟天的5个核心成分2-羟基-1,5-二甲氧基-6-(甲氧基甲基)-9,10-蒽醌、1-羟基-3-甲氧基-9,10-蒽醌、大黄素-A、甲基异茜草素、甲基异茜草素-1-甲醚和3个核心靶点EGFR、PARP1、FTO的结合较好,并且Egfr在AD病人中显著上调表达,Parp1Fto在AD病人中显著下调表达。结论:巴戟天可能通过多个成分、多个靶点、多个通路,参与调控AD疾病进程。

     

    Abstract: Objective: To analyze the potential targets and mechanism of action underlying the therapeutic action of Morinda officinalis How. against Alzheimer’s disease (AD) based on network pharmacology, molecular docking, and gene expression omnibus (GEO) data. Methods: Using the traditional chinese medicine systematic pharmacology database and analysis platform (TCMSP), the main active components of Morinda officinalis were identified, and the targets of Morinda officinalis were obtained via SwissTargetPrediction. AD-related targets were obtained from DrugBank, PathCard, Chemogenomic Database, and PubChem databases. Then, Venn diagram was used to obtain the common targets of both Morinda officinalis and AD. Cytoscape 3.8.0 was used to construct “component-target” network diagrams of the targets. The protein-protein interaction (PPI) network diagrams, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways of the targets were analyzed. The molecular docking of key components and targets was performed using AutoDock, and the docking results were visualized using Pymol and Discovery Studio. Finally, the expressions of key AD-related target genes were analyzed using the GEO database from Alzdata. Results: Fifty main active components of Morinda officinalis were predicted. A total of 636 action targets and 674 AD-related targets were obtained, including 124 common targets related to AD treatment. GO enrichment analysis yielded protein phosphorylation, positive regulation of phosphorylation, cellular response to nitrogen compounds, regulation of hydrolase activity and cellular response to chemical stress. KEGG enrichment analysis showed that Alzheimer’s disease as the most significant pathway. Molecular docking revealed that the five core components of Morinda officinalis, including 2-hydroxy-1,5-dimethoxy-6-(methoxymethyl)-9,10-anthraquinone, 1-hydroxy-3-methoxy-9,10-anthraquinone, rhododendron-A, rubiadin and rubiadin-1-methyl ether, exhibited strong binding with the three core targets, EGFR, PARP1 and FTO. The expression of Egfr was significantly upregulated in AD patients, while Parp1 and Fto were significantly downregulated. Conclusion: Morinda officinalis might be useful in regulating AD progression via multiple components, targets and pathways.

     

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