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新疆医科大学 药学院,乌鲁木齐 830011
Received:19 January 2022,
Published Online:01 April 2022,
Published:05 June 2022
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依木然·马瑞士 ,陈春丽,开沙尔江·买买提吐逊 等.基于网络药理学和实验验证探讨阿里红治疗阿尔茨海默病的作用机制[J].中国实验方剂学杂志,2022,28(11):173-183.
Emran·Maris ,CHEN Chun-li,Kaysar·Mamattursun ,et al.Mechanism of Dried Fruiting Bodies of Fomes officinalis Against Alzheimer's Disease: Based on Network Pharmacology and In Vitro Experimental Verification[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(11):173-183.
依木然·马瑞士 ,陈春丽,开沙尔江·买买提吐逊 等.基于网络药理学和实验验证探讨阿里红治疗阿尔茨海默病的作用机制[J].中国实验方剂学杂志,2022,28(11):173-183. DOI: 10.13422/j.cnki.syfjx.20220812.
Emran·Maris ,CHEN Chun-li,Kaysar·Mamattursun ,et al.Mechanism of Dried Fruiting Bodies of Fomes officinalis Against Alzheimer's Disease: Based on Network Pharmacology and In Vitro Experimental Verification[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(11):173-183. DOI: 10.13422/j.cnki.syfjx.20220812.
目的
2
利用网络药理学结合实验验证探讨阿里红治疗阿尔茨海默病(AD)可能的作用机制。
方法
2
借助中药分子机制的生物信息学分析工具(BATMAN-TCM)平台及公开报道的文献数据获得阿里红化学成分;利用PharmMapper药效团匹配平台和TargetNet数据库综合筛选出成分预测靶点;通过基因表达综合数据库(GEO)、DrugBank数据库等获取AD疾病靶点,取交集得出阿里红可能的作用靶点,并对其使用STRING数据库和Cytoscape 3.7.1网络可视化软件分别构建蛋白质-蛋白质相互作用(PPI)网络和成分-靶点网络,且对网络进行拓扑分析;进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析筛选出阿里红主要作用通路及相关靶点蛋白,并用分子对接和体外细胞实验验证其结果。
结果
2
共收集到阿里红24个候选成分和242个预测靶点,并得到与AD共同靶点96个,包括阿里红氨酸、3-酮基-去氢硫色多孔菌酸、齿孔酸等关键化合物和白蛋白(ALB)、乙酰胆碱酯酶(AChE)、雌激素受体
α
基因(ESR1)、胱天蛋白酶-3(Caspase-3)、淀粉样前体蛋白裂解酶1(BACE1)等潜在的作用靶点;GO富集分析获得392个条目,主要跟
β
-淀粉样蛋白代谢途径和胆碱酯酶活性有关,KEGG富集分析获得77个条目,主要涉及雌激素信号通路、胆碱能突触、AD等生物学过程及通路;分子对接结果显示,阿里红7个关键成分与淀粉样前体蛋白(APP),BACE1,AChE,Caspase-3之间表现出良好的自发结合能力。体外细胞验证实验显示,与模型组比较,不同剂量阿里红组细胞存活率均显著上升(
P
<
0.01)且具有浓度依赖性,同时可下调APP、BACE1、AChE、Caspase-3 mRNA和蛋白表达量(
P
<
0.05)。
结论
2
该研究结果首次揭示了阿里红对AD治疗具有多成分、多靶点、多途径相互作用的特点,为后续阿里红防治AD作用机制研究提供了参考依据。
Objective
2
To explore the possible mechanism of dried fruiting bodies of
Fomes officinalis
(FOA) against Alzheimer's disease (AD) based on network pharmacology and experimental verification.
Method
2
The effective components of FOA were retrieved from a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) and previous reports. The targets of the components were searched from PharmMapper and TargetNet, and the targets related to AD from Gene Expression Omnibus (GEO), DrugBank, among other databases. Thereby, the common targets of FOA and AD were obtained, and the protein-protein interaction (PPI) network and component-target network were established based on STRING and Cytoscape 3.7.1, followed by the topology analysis of the networks, and Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the common targets. The results were verified by the molecular docking and the
in vitro
cell experiment.
Result
2
A total of 24 candidate components and 242 predicted targets of FOA, and 96 common targets of FOA and AD were screened out. The key components included [2-(1-carboxyhexadecylamino)-2-aminosuccinic acid], 3-keto-dehydrosulfurenic acid, and eburicoic acid, and the active targets were albumin (ALB), acetylcholinesterase (AChE), estrogen receptor 1 (ESR1), cysteine aspartate-specific protease-3 (Caspase-3), and beta-secretase1 (BACE1). The common targets were involved in 392 GO terms, and the key terms were the
β
-amyloid metabolic process and cholinesterase activity. A total of 77 KEGG pathways were obtained, which mainly included estrogen signaling pathway, cholinergic synapse, and AD. The results of molecular docking showed that 7 components of FOA had high binding affinity to amyloid precursor protein (APP), BACE1, AChE, and Caspase-3. The cell survival rate rose (
P
<
0.01) and the mRNA and protein expression of APP, BACE1, AChE, and Caspase-3 reduced in FOA groups in a dose-dependent manner compared with those in the model group (
P
<
0.05).
Conclusion
2
This study reveals for the first time that FOA has multi-component, multi-target, and multi-pathway characteristics in the treatment of AD, which serves as a reference for further explaining the mechanism of FOA against
AD.
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