Exploring the pharmacological mechanism of Bu-Wang San on Alzheimer's disease through multiple GEO datasets of the human hippocampus, network pharmacology, and metabolomics based on GC-MS and UPLC-Q/TOF-MS.

ETHNOPHARMACOLOGICAL RELEVANCE: Bu-Wang San (BWS) is a prominent traditional Chinese medicine known for calming the mind and promoting intelligence. It has been reported to improve learning and memory, enhance memory ability, and promote synaptic plasticity. However, the complexity of the material basis and the diversity of therapeutic targets of BWS on Alzheimer's disease (AD) have not been elucidated.

AIM OF THE STUDY: This study aimed to investigate the therapeutic material basis and the mechanism of BWS in AD treatment by comprehensively analyzing multiple GEO datasets of the human hippocampus, network pharmacology, and multi-platform metabolomics validation.

MATERIALS AND METHODS: Three GEO datasets of the human hippocampus were utilized to identify AD-associated targets using weighted gene co-expression network analysis (WGCNA) and differential analysis. Network pharmacology analyses were performed to investigate BWS's therapeutic material basis and predict the therapeutic targets of BWS on AD. A rat model was induced through the concurrent administration of AlCl3 and D-galactose to validate BWS's therapeutic potential and underlying mechanisms in AD. To validate the results of GEO data mining and network pharmacology, a comprehensive metabolomics approach integrating gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was conducted on rat serum samples to uncover potential metabolic alterations and their associated pathways.

RESULTS: A total of 6367 genes were selected as AD drug targets through WGCNA analysis and enrichment analysis of disease-associated gene expression profiles in the GEO database. Network pharmacology was performed in this study for the identification of potential interactions between the components of BWS and its targets, TP53, STAT3, EGFR, MAOA, NOS3, PPARG, PRKCA, MAPK8, AChE, ARG1, among others, which were among the top 25 highest probable targets of BWS acting on AD. The multi-platform metabolomics indicated that amino sugar and nucleotide sugar metabolism, glycine, serine and threonine metabolism pathways, and other pathways may be associated with the AD model based on AlCl3 and D-galactose. The comparison of differential metabolites between the AD model group and the BWS intervention group revealed that 66 of the 97 differential metabolites exhibited a pullback trend, indicating a potential therapeutic effect of BWS on these metabolites.

CONCLUSION: This study builds a systematic strategy combining GEO datasets, network pharmacology, and multi-platform metabolomics and provides valuable insights into the pharmacological mechanism of BWS on AD. The results suggest that BWS may exert its therapeutic effects on AD by modulating the amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, glycine, serine and threonine metabolism pathway and acting on the drug targets of ARG1, MAOA, AChE, XDH, GAD2 et al. This strategy provides a deep understanding of the molecular mechanisms of herbal medicine in treating AD at a systematic level.