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通过网络药理学和分子对接来探索L.治疗腹主动脉瘤的潜在机制。

Through network pharmacology and molecular docking to explore the underlying mechanism of L. treating in abdominal aortic aneurysm.

作者信息

Jia Longyuan, Jing Yuchen, Wang Ding, Cheng Shuai, Fu Chen, Chu Xiangyu, Yang Chenye, Jiang Bo, Xin Shijie

机构信息

Department of Vascular Surgery, The First Affiliated Hospital of China Medical University, Shenyang, China.

Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China.

出版信息

Front Physiol. 2022 Oct 20;13:1034014. doi: 10.3389/fphys.2022.1034014. eCollection 2022.

DOI:10.3389/fphys.2022.1034014
PMID:36338468
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9634740/
Abstract

Abdominal aortic aneurysm (AAA) is a degenerative disease that causes health problems in humans. However, there are no effective drugs for the treatment of AAA. L. () is a traditional herbal that has been widely used in cardiovascular disease. Based on network pharmacology and molecular docking technology, this study predicted the practical components and potential mechanisms of inhibiting the occurrence and development of AAA. The main active ingredients and targets of were screened through the TCMSP database; the GeneCards, OMIM, PharmGkb, and TTD databases were used to search for the targeted genes of AAA and map them to the targets of the active ingredients to obtain the active ingredient therapy of . The targets of AAA were to construct a protein interaction network through the STRING platform. R software was used to carry out the enrichment analysis of GO and KEGG for relevant targets, and Cytoscape was used to construct the active ingredient-target network prediction model of . Finally, AutoDock Vina was used to verify the results of the active ingredients and critical targets. The main active ingredients obtained from for the treatment of AAA include quercetin, luteolin, kaempferol, isorhamnetin, and artemetin, as well as 117 effective targets, including RELA, MAPK14, CCND1, MAPK1, AKT1, MYC, MAPK8, TP53, ESR1, FOS, and JUN. The 11 targeted genes might play a key role in disease treatment. Enriched in 2115 GO biological processes, 159 molecular functions, 56 cellular components, and 156 KEGG pathways, inferred that its mechanism of action might be related to PI3K-Akt signaling pathway, fluid shear stress, atherosclerosis, and AGE-RAGE signaling pathway. Molecular docking results showed that the top five active components of had a good affinity for core disease targets and played a central role in treating AAA. The low binding energy molecular docking results provided valuable information for the development of drugs to treat AAA. Therefore, may have multiple components, multiple targets, and multiple signaling pathways to play a role in treating AAA. may have the potential to treat AAA.

摘要

腹主动脉瘤(AAA)是一种会给人类带来健康问题的退行性疾病。然而,目前尚无治疗AAA的有效药物。[植物名称]是一种传统草药,已广泛应用于心血管疾病治疗。基于网络药理学和分子对接技术,本研究预测了[植物名称]抑制AAA发生和发展的实际成分及潜在机制。通过中药系统药理学数据库与分析平台(TCMSP)筛选[植物名称]的主要活性成分和靶点;利用基因卡片(GeneCards)、在线人类孟德尔遗传数据库(OMIM)、药物基因组学知识库(PharmGkb)和治疗靶点数据库(TTD)搜索AAA的靶向基因,并将其映射到活性成分的靶点上,以获得[植物名称]的活性成分治疗作用。通过STRING平台构建AAA靶点的蛋白质相互作用网络。使用R软件对相关靶点进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析,并用Cytoscape构建[植物名称]的活性成分-靶点网络预测模型。最后,使用自动对接软件AutoDock Vina验证活性成分与关键靶点的结果。从[植物名称]中获得的治疗AAA的主要活性成分包括槲皮素、木犀草素、山奈酚、异鼠李素和青蒿素,以及117个有效靶点,包括RELA、丝裂原活化蛋白激酶14(MAPK14)、细胞周期蛋白D1(CCND1)、丝裂原活化蛋白激酶1(MAPK1)、蛋白激酶B(AKT1)、原癌基因c-Myc(MYC)、丝裂原活化蛋白激酶8(MAPK8)、肿瘤蛋白p53(TP53)、雌激素受体1(ESR1)、原癌基因c-Fos(FOS)和原癌基因c-Jun(JUN)基因。这11个靶向基因可能在疾病治疗中起关键作用。富集于2115个GO生物学过程、159个分子功能、56个细胞成分和156条KEGG信号通路,推测其作用机制可能与磷脂酰肌醇-3激酶-蛋白激酶B(PI3K-Akt)信号通路、流体剪切力、动脉粥样硬化和晚期糖基化终末产物-受体(AGE-RAGE)信号通路有关。分子对接结果表明,[植物名称]的前五种活性成分与核心疾病靶点具有良好的亲和力,在治疗AAA中起核心作用。低结合能分子对接结果为开发治疗AAA的药物提供了有价值的信息。因此,[植物名称]可能具有多种成分、多个靶点和多条信号通路在治疗AAA中发挥作用。[植物名称]可能具有治疗AAA的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/a5e1dd8bb9de/fphys-13-1034014-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/97c78274a9bf/fphys-13-1034014-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/8c7dd98391fc/fphys-13-1034014-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/f8536dbde0ab/fphys-13-1034014-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/0ccf9dcde860/fphys-13-1034014-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/f91ec65c60f8/fphys-13-1034014-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/a5e1dd8bb9de/fphys-13-1034014-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/97c78274a9bf/fphys-13-1034014-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/10df198e2a72/fphys-13-1034014-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/1f7cf95e40d3/fphys-13-1034014-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/8c7dd98391fc/fphys-13-1034014-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/f8536dbde0ab/fphys-13-1034014-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/0ccf9dcde860/fphys-13-1034014-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/f91ec65c60f8/fphys-13-1034014-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9340/9634740/a5e1dd8bb9de/fphys-13-1034014-g008.jpg

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