Network-based identification of genetic factors in Ageing, lifestyle and Type 2 Diabetes that Influence in the progression of Alzheimer’s disease

Alzheimer’s disease (AD) is an incurable disease, and the causative risk factors, especially the modifiable ones, are still poorly understood which impedes the effective prevention and remedy strategies. We proposed a network-based quantitative framework to reveal the complex relationship of various biasing genetic factors for the AD. We analyzed gene expression microarray data from the AD, ageing, severe alcohol consumption, type II diabetes, high body fat, high dietary fat, obesity, dietary red meat, sedentary lifestyle, smoking, and control datasets. We have developed genetic associations and diseasome network of various factors with the AD based on the neighborhood-based benchmarking and multilayer network topology approaches. The study identified 484 differentially expressed genes of the AD. Among them, 27 genes were highly expressed in both for the AD and smoking whereas the number of genes is 21 for the AD and type II diabetes, and 12 for the AD and sedentary lifestyle. However, AD shared less than ten significant biomarkers with other factors. Notably, 3 significant genes, HLA-DRB4, IGH and IGHA2 are commonly up-regulated among the AD, type II diabetes and alcohol consumption; 2 significant genes IGHD and IGHG1 are commonly up-regulated among the AD, type II diabetes, alcohol consumption and sedentary lifestyle. Protein-protein interaction network identified 10 hub genes: CREBBP, PRKCB, ITGB1, GAD1, GNB5, PPP3CA, CABP1, SMARCA4, SNAP25 and GRIA1. Ontological and pathway analyses have identified significant gene ontology and molecular pathways that enhance our understanding of the fundamental molecular procedure of AD progression. Therapeutic targets of the AD could be developed using these identified target genes, ontologies and pathways. Online Mendelian Inheritance in Man (OMIM) and dbGaP databases were used for gold benchmark gene-disease associations to validate the significance of these identified target genes in AD progression. Our formulated methodologies demonstrate a network-based approach to understand the disease mechanism and the causative reason for the AD, and the identification of therapeutic targets for the AD.

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