System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Abstract

Alzheimer’s disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease

“…Gene set enrichment analysis identifies DEGs from a large set of genes linked to disease symptoms using a number of statistical approaches. 29 , 30 Figure 1 visualizes the steps involved in data acquisition and identifying DEGs also shows the analytical approaches in a nutshell. GSE64810 and GSE95343 shared a total of 162 common genes, whereas 106 genes were up-regulated and 56 genes were down-regulated.…”

Identification of the shared gene signatures and pathways between sarcopenia and type 2 diabetes mellitus

“…Finding the functions of protein is the primary step in systems biology and drug discovery [ 23 ]. Protein-protein interaction (PPI) and the obtained network are crucial in the field of biological processes at many levels of cellular structure and function, including basic metabolism and cell differentiation [ 24 , 25 ]. Similar DEGs in sarcopenia and T2DM were identified through the NetworkAnalyst platform ( https://www.networkanalyst.ca/ ) and organized into a visual PPIs network [ 26 ].…”