Suppr超能文献

揭示[具体药物或疗法名称未给出]在阿尔茨海默病中的潜在治疗机制:一种计算生物学方法。

Revealing the potential therapeutic mechanism of in Alzheimer's disease: a computational biology approach.

作者信息

Xiang Qin, Xiang Yu, Liu Yao, Chen Yongjun, He Qi, Chen Taolin, Tang Liang, He Binsheng, Li Jianming

机构信息

Hunan Provincial University Key Laboratory of the Fundamental and Clinical Research on Neurodegenerative Diseases, Changsha Medical University, Changsha, China.

Hunan Provincial University Key Laboratory of the Fundamental and Clinical Research on Functional Nucleic Acid, Changsha Medical University, Changsha, China.

出版信息

Front Med (Lausanne). 2024 Nov 13;11:1468561. doi: 10.3389/fmed.2024.1468561. eCollection 2024.

Abstract

BACKGROUND

Alzheimer's disease (AD) is a degenerative brain disease without a cure. (LJF), a traditional Chinese herbal medicine, possesses a neuroprotective effect, but its mechanisms for AD are not well understood. This study aimed to investigate potential targets and constituents of LJF against AD.

METHODS

Network pharmacology and bioinformatics analyses were performed to screen potential compounds and targets. Gene Expression Omnibus (GEO) datasets related to AD patients were used to screen core targets of differential expression. Gene expression profiling interactive analysis (GEPIA) was used to validate the correlation between core target genes and major causative genes of AD. The receiver operating characteristic (ROC) analysis was used to evaluate the predictive efficacy of core targets based on GEO datasets. Molecular docking and dynamics simulation were conducted to analyze the binding affinities of effective compounds with core targets.

RESULTS

Network pharmacology analysis showed that 112 intersection targets were identified. Bioinformatics analysis displayed that 32 putative core targets were identified from 112 intersection targets. Only eight core targets were differentially expressed based on GEO datasets. Finally, six core targets of MAPK8, CTNNB1, NFKB1, EGFR, BCL2, and NFE2L2 were related to AD progression and had good predictive ability based on correlation and ROC analyses. Molecular docking and dynamics simulation analyses elucidated that the component of lignan interacted with EGFR, the component of β-carotene interacted with CTNNB1 and BCL2, the component of β-sitosterol interacted with BCL2, the component of hederagenin interacted with NFKB1, the component of berberine interacted with EGFR and BCL2, and the component of baicalein interacted with NFKB1, EGFR and BCL2.

CONCLUSION

Through a comprehensive analysis, this study revealed that six core targets (MAPK8, CTNNB1, NFKB1, EGFR, BCL2, and NFE2L2) and six practical components (lignan, β-carotene, β-sitosterol, hederagenin, berberine, and baicalein) were involved in the mechanism of action of LJF against AD. Our work demonstrated that LJF effectively treats AD through its multi-component and multi-target properties. The findings of this study will establish a theoretical basis for the expanded application of LJF in AD treatment and, hopefully, can guide more advanced experimental research in the future.

摘要

背景

阿尔茨海默病(AD)是一种无法治愈的退行性脑病。连花煎剂(LJF)是一种传统中药,具有神经保护作用,但其治疗AD的机制尚不清楚。本研究旨在探讨LJF抗AD的潜在靶点和成分。

方法

采用网络药理学和生物信息学分析筛选潜在化合物和靶点。使用与AD患者相关的基因表达综合数据库(GEO)数据集筛选差异表达的核心靶点。利用基因表达谱交互分析(GEPIA)验证核心靶基因与AD主要致病基因之间的相关性。采用受试者工作特征(ROC)分析基于GEO数据集评估核心靶点的预测效能。进行分子对接和动力学模拟分析有效化合物与核心靶点的结合亲和力。

结果

网络药理学分析显示共鉴定出112个交集靶点。生物信息学分析表明从112个交集靶点中鉴定出32个潜在核心靶点。基于GEO数据集仅8个核心靶点差异表达。最后,丝裂原活化蛋白激酶8(MAPK8)、β-连环蛋白1(CTNNB1)、核因子κB1(NFKB1)、表皮生长因子受体(EGFR)、B细胞淋巴瘤2(BCL2)和核因子E2相关因子2(NFE2L2)这6个核心靶点与AD进展相关,且基于相关性和ROC分析具有良好的预测能力。分子对接和动力学模拟分析表明木脂素成分与EGFR相互作用,β-胡萝卜素成分与CTNNB1和BCL2相互作用,β-谷甾醇成分与BCL2相互作用,常春藤皂苷元成分与NFKB1相互作用,小檗碱成分与EGFR和BCL2相互作用,黄芩苷成分与NFKB1、EGFR和BCL2相互作用。

结论

通过综合分析,本研究揭示6个核心靶点(MAPK8、CTNNB1、NFKB1、EGFR、BCL2和NFE2L2)和6种实际成分(木脂素、β-胡萝卜素、β-谷甾醇、常春藤皂苷元、小檗碱和黄芩苷)参与LJF抗AD的作用机制。我们的研究表明LJF通过其多成分、多靶点特性有效治疗AD。本研究结果将为LJF在AD治疗中的拓展应用奠定理论基础,并有望在未来指导更深入的实验研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c36/11598349/965de3aeeaaa/fmed-11-1468561-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验