Network-based identification of genetic factors in ageing, lifestyle and type 2 diabetes that influence to the progression of Alzheimer's disease. (2020)

• Record Type: Journal Article
• Authors: Chowdhury, Utpala Nanda; Islam, M. Babul; Ahmad, Shamim; Moni, Mohammad Ali Moni
• Is Part Of: Informatics in medicine unlocked. Volume 19(2020)
• Publication Date: 2020
• Subjects: Network-based -- Topological -- Alzheimer's -- Type 2 diabetes -- Ageing -- Lifestyle; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2020.100309
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disease, the causes of which are poorly understood, although a number of strong risk factors for AD are known. Understanding how these risk factors affect cell pathways that are altered in AD could identify important causal pathways that could be targeted by therapeutics. We thus pro- posed network-based quantitative frameworks to investigate these risk factor-AD relation- ships. We analyzed gene expression microarray datasets from tissues affected by AD as well as by ageing, high alcohol consumption, type II diabetes (T2D), high dietary fat, obesity, high dietary red meat, sedentary lifestyle, and smoking. These datasets derived from studies that compared tissues affected by these factors with control tissues (not exposed to these factors) to identify differentially expressed genes (DEGs) specific to the risk factors. We employed these to develop gene association and diseasome networks based on neighborhood-based benchmarking and multilayer network topology. We identified 484 DEGs from AD brain tissue, of which 27 were also seen in the smoking DEGs gene set. AD data also showed 21 DEGs in common with T2D, and 12 with sedentary lifestyle datasets. AD shared less than ten DEGs with the other factors, but 3 (HLA-DRB4, IGH and IGHA2) were commonly up-regulated among the AD, T2D and high alcohol consumption datasets. IGHD and IGHG1 were up-regulated among AD, T2D, alcohol and sedentary lifestyle datasets. Protein-protein interaction networks identified 10 hub genes: CREBBP, PRKCB, ITGB1, GAD1, GNB5, PPP3CA, CABP1, SMARCA4, SNAP25 and GRIA1. Ontological and pathway analyses identified significant gene ontology and molecular pathways that could enhance our understanding of the mechanisms of AD progression by suggesting new therapeutic approaches to affect the development of AD. We verified genes from ontological and pathway analyses with gold benchmark gene-disease associations databases including Online Mendelian Inheritance in Man (OMIM) and dbGaP. This supports our identification of disease associations for the putative AD target genes. These outcomes provide further evidence that network-based approaches can generate new insights into AD progression.