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

• Published in: Expert systems with applications 2017-11, Vol.85, p.249-260
• Main Authors: Park, Chihyun, Yoon, Youngmi, Oh, Min, Yu, Seok Jong, Ahn, Jaegyoon
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Datasets; Deoxyribonucleic acid; DNA; Gene expression; Genes; Genetic network construction; Integration; Intelligent systems; Methylation; Network analysis; Neurological diseases; Omics data integration
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.05.042

•We focus on revealing the mechanism of Alzheimer's disease (AD) by network analysis.•We present a novel method to construct a gene network by integrating omics data.•The gene network, maintaining the specificity of AD, was statistically optimized.•Potential genes and modules that can elucidate a mechanism of AD were identified.•We demonstrated the epigenetic factor and ribosomal process are associated with AD. Alzheimer's disease (AD) is a genetically complex neurodegenerative diseases and its pathological mechanism has not been fully discovered. The mechanism of AD can be inferred by elucidating how molecular entities are interacting on the pathway level and how some pathways collectively influence the occurrence of the disease. Such an analysis is considerably complex and cannot be manually performed by experts. It can be solved by integrating huge heterogeneous dataset and systematically building an intelligent system which model molecular network and analyze the causality. Here, we present a novel method to construct an optimized AD-specific differential gene network by integrating a high-confidence interactome and gene expression data. In order to consider an epigenetic factor, we identified differentially methylated genes in AD and the results were projected on the network for mechanism analysis. Through diverse topological analysis and functional enrichment tests, we experimentally demonstrated that the several potential genes and sub networks were significantly related with AD and they could be used to elucidate the molecular mechanism. Taken the experimental results and literature studies together, we newly discovered that ribosomal process-related genes and DNA methylation might play an important role in AD. The proposed system is applicable not only to AD but also to various complex genetic disease models that require new molecular mechanism analysis based on network.