Delineating Common Cell Pathways that Influence Type 2 Diabetes and Neurodegenerative Diseases using a Network-based Approach

Abstract

Type 2 diabetes (T2D) is a chronic metabolic dysfunction characterized by resistance to insulin. T2D can cause acute and chronic damage to the vascular and immune systems which can increase the risk or severity of other diseases. A welldocumented group of diseases affected by T2D incidence are the neurodegenerative diseases (NDD). However, the interaction or influence of T2D on NDD is still poorly understood because the clinical complexity of NDD and T2D make conventional endocrinological methodologies render this very difficult. As an alternative approach, we used a strategy to discover cellular pathways common to NDD and T2D employing transcriptional analysis of affected tissues. We examined microarray transcript datasets from studies comparing control individuals with T2D patients, and likewise control and NDD sufferers. The latter included Alzheimers disease (AD), Parkinsons disease (PD), Huntingtons disease (HD), multiple sclerosis disease (MSD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FD), spinal muscular atrophy (SMA), Lewy body dementia (LBD) and epilepsy disorders (ED). Differentially expressed genes (DEG) for each selected pathologies were first identified then pairwise overlapping DEG between T2D and each NDD were identified by cross comparison. Gene set enrichment analysis (GSEA) was then undertaken for those common DEG using molecular pathway as well as gene ontology (GO) analysis. We thus uncovered new putative connections between pathological processes in T2D and NDD by identifying cell pathway commonalities. The findings were validated using Online Mendelian Inheritance in Man (OMIM) and dbGaP (gene SNP-disease association) databases for gold-standard benchmarking of their involvement in disease process. This methodology enables data-driven approaches to identify novel mechanisms affecting disease progressions and may enable prediction of disease co-morbidity development in a quantitative way.