Title:Effective Analysis of Alzheimer's Disease and Mechanisms of Methyl-4- Hydroxybenzoate using Network Toxicology, Molecular Docking, and Machine Learning Strategies
Volume: 22
Issue: 6
Author(s): Jianren Wen, Jingxuan Hu, Xue Yang, Feifei Luo and Guohui Zou*
Affiliation:
- Jiangxi University of Traditional Chinese Medicine, Nanchang, China
- Affiliated Hospital of Jiangxi University of
Traditional Chinese Medicine, Nanchang, China
Keywords:
Methyl-4-hydroxybenzoate, environmental pollutants, Alzheimer's disease, network toxicology, molecular docking, machine learning.
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.
