Gene Networks Reveal LASP1, TUBA1C, and S100A6 as Likely Drivers of Multiple Sclerosis (MS)

Abstract

Background: Multiple sclerosis (MS), a non-contagious and chronic disease of the central nervous system, is an unpredictable and indirectly inherited disease affecting different people in different ways.Using Omics platforms data i.e. genomics, transcriptomics, proteomics, epigenomics, interactomics, and metabolomics it is now possible to construct sound systems biology models to extract full knowledge of the MS and path the way to likely construct personalized and therapeutic tools.Methods: In this study, we learned many Bayesian Networks in order to find the transcriptional gene regulation networks that drive MS disease. We used a set of BN algorithms using the R add-on package bnlearn. The BN results underwent further downstream analysis and were validated using a wide range of Cytoscape algorithms, web based computational tools and qPCR amplification of blood samples from 56 MS patients and 44 healthy controls. The results were semantically integrated to come up with improved understanding of the complex molecular architecture underlying MS, distinguishing distinct metabolic pathways and providing a valuable foundation for the discovery of deriven genes and possibly new treatments. Results: Results show that the LASP1, TUBA1C, and S100A6 genes were most significant and likely playing a biological role in MS development. Results from qPCR showed a significant increase (P <0.05) in LASP1 and S100A6 gene expression levels in MS patients compared to controls. However, a significant down regulation of TUBA1C was observed in the same comparison. Conclusion: This study provides potential diagnostic and therapeutic biomarkers for increasing understanding of gene regulation underlying MS.