Systematic identification of differential gene network to elucidate Alzheimer's disease

Abstract

Alzheimer’s disease (AD) is a genetically complex neurodegenerative diseases and its pathological mechanismhas not been fully discovered. The mechanism of AD can be inferred by elucidating how molecularentities are interacting on the pathway level and how some pathways collectively influence the occurrenceof the disease. Such an analysis is considerably complex and cannot be manually performed byexperts. It can be solved by integrating huge heterogeneous dataset and systematically building an intelligentsystem which model molecular network and analyze the causality. Here, we present a novel methodto construct an optimized AD-specific differential gene network by integrating a high-confidence interactomeand gene expression data. In order to consider an epigenetic factor, we identified differentiallymethylated genes in AD and the results were projected on the network for mechanism analysis. Throughdiverse topological analysis and functional enrichment tests, we experimentally demonstrated that theseveral potential genes and sub networks were significantly related with AD and they could be used toelucidate the molecular mechanism. Taken the experimental results and literature studies together, wenewly discovered that ribosomal process-related genes and DNA methylation might play an importantrole in AD. The proposed system is applicable not only to AD but also to various complex genetic diseasemodels that require new molecular mechanism analysis based on network