Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory.

Abstract

Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive insilico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. For this purpose, we (a) investigated associated drugs and pathways for MetS, (b) employed eight different similarity measures to construct eight gene regulatory networks, (c) chose an optimal network, where a maximum number of drug targets were central, (d) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (e) employed these candidate genes to propose suitable drugs. Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.