Insights into amyloid precursor protein target through PPI network analysis

Alzheimer's disease (AD) is the leading cause of dementia worldwide with therapeutic lacunae till date. The beta-amyloid (Aβ) accumulation triggers AD pathogenesis, though clinical trials lowering Aβ have not altered disease outcomes suggesting other interacting factors to be identified for drug design of AD. Therefore, it is of interest to identify potential hub proteins interlinked with disease-driving pathways using a network-based approach for AD therapeutic designing. Literature mining was done to identify proteins implicated in AD etiology. Protein-protein interactions (PPIs) were retrieved from the STRING database and merged into a single network using Cytoscape 3.10.1. The hub proteins involved in AD etiology were predicted based on the topological algorithms of CytoHubba. Six major proteins, with STRING database identifiers - APP, BACE1, PSEN1, MAPT, APOE4 and TREM2, were identified to be involved in AD pathogenesis. The merged network of PPIs of these proteins contained 51 nodes and 211 edges, as predicted by Analyzer module of Cytoscape. The Amyloid precursor protein (APP) emerged as the highest-scoring hub protein across multiple centrality measures and topological algorithms. Thus, current data provides evidence to support the ongoing investigation of APP's multifaceted functions and therapeutic potential for AD.