Network-based transcriptomics identifies key hippocampal targets in Alzheimer’s disease and their modulation by Apigenin, Luteolin, and Berberine

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by cognitive decline and synaptic dysfunction, with the hippocampus being one of the most vulnerable brain regions. In this study, we performed a network-based transcriptomic analysis of a publicly available hippocampal microarray dataset to identify differentially expressed genes (DEGs) and disrupted biological pathways associated with AD pathology. Functional enrichment analyses revealed alterations in dopaminergic synapse signaling, calcium signaling, MAPK pathways, glial cell differentiation, and neuroinflammatory processes. Protein-protein interaction network analysis highlighted several highly connected genes, including DRD4, PTAFR, CAMK1G, RIMS2, and CACNG3, suggesting their central roles in hippocampal molecular networks. To explore potential structure-function relationships of these prioritized targets, we conducted molecular docking and molecular dynamics simulations with three natural flavonoids-Apigenin, Luteolin, and Berberine. Comparative analyses indicated that Luteolin displayed relatively more stable binding across multiple kinase targets, as evidenced by reduced structural fluctuations, compact conformations, and persistent intermolecular interactions throughout the simulation trajectories. These computational findings were interpreted qualitatively, acknowledging methodological limitations and the lack of experimental validation. Overall, this integrative approach links transcriptomic network perturbations to structural interaction patterns and provides hypothesis-generating insights into multi-target molecular interactions relevant to AD-associated signaling pathways. Our results underscore the value of combining network-based transcriptomics with in silico structural analyses, while emphasizing the need for future experimental studies to confirm the proposed mechanisms.