Background: Alzheimer's disease (AD) as a neurodegenerative disease occupies 3/5-4/5 cases among patients with dementia, yet its pathogenetic mechanism remains unclear. Geraniol, on the other hand, is a well-known extract from essential oils of aromatic plants and has been proven that it has outstanding neuroprotective effects as well as ameliorating influence in memory impairment. Therefore, the present study aims to elucidate the potential of geraniol against AD by network pharmacology-based approach combined with molecular modeling study.
Materials and methods: Firstly, we evaluated the druggability of geraniol by ADME method. Then, we obtained the geraniol targets and AD-related targets from multiple open data sources. Afterward, we calculated the intersection through a Venn diagram to find common targets, and via Panther classification system to categorize them. In order to gain a macroscopic understanding of these common targets, we carried out GO terms and KEGG pathways enrichment analyses, according to which we constructed a compound-target-pathway-disease network. In addition, we built a preliminary PPI network which was further analyzed both functionally and topologically. Consequently, five hub targets were sorted out. Finally, we conducted molecular docking and molecular dynamic simulation to validate our findings.
Results: In the present study, the pharmacological properties of geraniol were assessed according to ADME and Lipinski's rule, which demonstrate promising druggability. Then, from 10,972 AD-related targets and 33 geraniol targets, 29 common targets were identified, among which 38.1% of them are metabolite interconversion enzymes, 23.8% are protein modifying enzymes, 33.3% are transmembrane receptors, and the rest are transporters. Enrichment analyses hint that geraniol is involved in cholinergic synapse, serotonergic synapse, and neuroactive ligand-receptor interaction. We also built a preliminary PPI network to investigate the interplay between these targets and their extensive interactions. Then, by functionally clustering the preliminary PPI network, we gained a cluster of proteins which formed a subnetwork with score of 8.476, and 22 nodes. Its results of GO terms and KEGG pathways enrichment analyses once again suggests that geraniol actively participates in cholinergic synapse, serotonergic synapse, and neuroactive ligand-receptor interaction, which are believed to be strongly associated with AD pathogenesis. Besides, topological analyses of the preliminary PPI network helped find 5 hub targets (i.e., CHRM3, PRKCA, PRKCD, JAK1, JAK2). To verify their interaction with geraniol molecule, we conducted molecular docking, and found that CHRM3 possesses the highest affinity in binding, indicating that geraniol molecules are closely bound to each hub target, and CHRM3 may serve as a key target of geraniol against AD. It was then further confirmed by molecular dynamic simulation, the result of which supports our hypothesis.
Conclusion: The present study shares a mechanistic insight of the potential of geraniol against AD, giving a reference to future experimental studies.
Keywords: Alzheimer’s disease; Data integration; Drug discovery; Geraniol; Network modeling.
© 2022. The Author(s).