Effective Analysis of Alzheimer's Disease and Mechanisms of Methyl-4- Hydroxybenzoate using Network Toxicology, Molecular Docking, and Machine Learning Strategies.

Jianren Wen, Jingxuan Hu, Xue Yang, Feifei Luo, Guohui Zou
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Abstract

Introduction: Nowadays, the large increase in environmental pollutants has led to the occurrence and development of an increasing number of diseases. Studies have shown that exposure to environmental pollutants, such as methyl-4-hydroxybenzoate (MEP) may lead to Alzheimer's disease (AD). Therefore, the purpose of this study was to elucidate the complex effects and potential molecular mechanisms of environmental pollutants MEP on AD.

Methods: Through exhaustive exploration of databases, such as ChEMBL, STITCH, SwissTarget- Prediction, and Gene Expression Omnibus DataSets (GEO DataSets), we have identified a comprehensive list of 46 potential targets closely related to MEP and AD. After rigorous screening using the STRING platform and Cytoscape software, we narrowed the list to nine candidate targets and ultimately identified six hub targets using three proven machine learning methods (LASSO, RF, and SVM): CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5. The "clusterProfiler" R package was used to conduct GO and KEGG enrichment analysis. At the same time, we also constructed disease prediction models for core genes. At last, six hub targets were executed molecular docking.

Results: We derived 46 key target genes related to MEP and AD and conducted gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. MEP might play a role in AD by affecting the pathways of neuroactive ligand-receptor interaction. Nine genes were screened as pivotal targets, followed by machine learning methods to identify six hub targets. Molecular docking analysis showed a good binding ability between MEP and CREBBP, BCL6, CXCR4, GRIN1, GOT2 and ITGA5. In addition, changes in the immune microenvironment revealed a significant impact of immune status on AD.

Discussions: This study revealed that MEP may induce AD through multiple mechanisms, such as oxidative stress, neurotoxicity, and immune regulation, and identified six core targets (CREBBP, BCL6, etc.) and found that they are related to changes in the immune microenvironment, such as T cells and B cells, providing new molecular targets for AD intervention.

Conclusion: Overall, CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5 have been identified as the crucial targets correlating with AD. Our findings provide a theoretical framework for understanding the complex molecular mechanisms underlying the effects of MEP on AD and provide insights for the development of prevention and treatment of AD caused by exposure to MEP.

使用网络毒理学、分子对接和机器学习策略有效分析4-羟苯甲酸甲酯的阿尔茨海默病和机制。
导读:如今,环境污染物的大量增加导致了越来越多疾病的发生和发展。研究表明,接触环境污染物,如甲基-4-羟基苯甲酸酯(MEP)可能导致阿尔茨海默病(AD)。因此,本研究的目的是阐明环境污染物MEP对AD的复杂影响及其可能的分子机制。方法:通过对ChEMBL、STITCH、SwissTarget- Prediction和Gene Expression Omnibus DataSets (GEO DataSets)等数据库的全面研究,我们确定了46个与MEP和AD密切相关的潜在靶点。在使用STRING平台和Cytoscape软件进行严格筛选后,我们将列表缩小到9个候选靶点,并最终使用三种经过验证的机器学习方法(LASSO, RF和SVM)确定了6个枢纽靶点:CREBBP, BCL6, CXCR4, GRIN1, GOT2和ITGA5。使用“clusterProfiler”R包进行GO和KEGG富集分析。同时,我们还构建了核心基因的疾病预测模型。最后对6个枢纽靶点进行分子对接。结果:我们获得了46个与MEP和AD相关的关键靶基因,并进行了基因本体(GO)和京都基因与基因组百科全书(KEGG)通路分析。MEP可能通过影响神经活性配体-受体相互作用的途径在AD中发挥作用。筛选9个基因作为关键靶点,然后通过机器学习方法确定6个枢纽靶点。分子对接分析表明,MEP与CREBBP、BCL6、CXCR4、GRIN1、GOT2和ITGA5具有良好的结合能力。此外,免疫微环境的变化揭示了免疫状态对AD的显著影响。讨论:本研究揭示了MEP可能通过氧化应激、神经毒性、免疫调节等多种机制诱发AD,并确定了6个核心靶点(CREBBP、BCL6等),发现它们与免疫微环境(如T细胞、B细胞)的改变有关,为AD干预提供了新的分子靶点。结论:总的来说,CREBBP、BCL6、CXCR4、GRIN1、GOT2和ITGA5被确定为与AD相关的关键靶点。我们的研究结果为理解MEP对AD影响的复杂分子机制提供了理论框架,并为MEP暴露引起的AD的预防和治疗的发展提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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