A systems biology approach to identifying genetic factors affected by aging, lifestyle factors, and type 2 diabetes that influences Parkinson's disease progression. (2020)

• Record Type: Journal Article
• Authors: Sakib, Najmus; Chowdhury, Utpala Nanda; Islam, M. Babul; Ahmad, Shamim; Moni, Mohammad Ali
• Is Part Of: Informatics in medicine unlocked. Volume 21(2020)
• Publication Date: 2020
• Subjects: Network-based -- Topological -- Parkinson's disease -- Aging -- Type II diabetes -- Alcohol consumption -- Smoking -- Lifestyle; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2020.100448
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: The underlying causes of Parkinson's disease (PD) remain unclear, making it difficult to determine how particular aspects of a patient's health may affect PD development and their treatment response. One alternative approach of investigating interactions between PD and co-morbidities (or other health factors) is to compare the dysregulated cellular or gene pathways they share with affected tissues. To achieve this, we developed a quantitative framework to reveal significant interactions between PD and other significant health factors that may be impacted. In this study, we analyzed gene expression microarray data from tissues affected by PD, type II diabetes (T2D), aging (AG), sedentary lifestyle (SL), high dietary fat (HFD), hypercholesterolemia (HC), high body fat (HBF), high red meat diet (RMD), high alcohol consumption (AC), smoking (SM) and control datasets. We have developed genetic associations of these factors with PD, based on the neighborhood-based benchmarking and multilayer network topology. We identified 1323 differentially expressed genes (DEGs) in the PD patients compared to healthy controls, 779 genes with down-regulated expression and 544 genes up-regulated. 69 dysregulated genes were common to PD and AC datasets; PD datasets also shared, respectively, 51, 45, 43 and 42 DEGs with the T2D, AG, HFD and HBF datasets. Ontological and pathway analyses identified significant gene ontology and molecular pathways with the potential to enhance our understanding of the fundamental molecular factors associated with PD progression. Moreover, we employed a validation gene expression dataset along with Mendelian Inheritance in Man (OMIM) and dbGaP as gold benchmark databases to validate the identified PD associated genes and molecular pathways. Our formulated methodologies used a network-based approach and identified a number of significant genes and pathways that may particularly affect PD, signifying how PD incidence and development may be influenced by the risk factors that may indicate avenues to new therapeutic approaches.