Gene network reveals , and are likely playing regulatory roles in multiple sclerosis.

INTRODUCTION

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 genomics, transcriptomics, proteomics, epigenomics, interactomics, and metabolomics database, it is now possible to construct sound systems biology models to extract full knowledge of the MS and recognize the pathway to uncover the personalized therapeutic tools.

METHODS

In this study, we used several 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 improve understanding of the complex molecular architecture underlying MS, distinguishing distinct metabolic pathways and providing a valuable foundation for the discovery of involved genes and possibly new treatments.

RESULTS

Results show that the , and genes were most likely playing a biological role in MS development. Results from qPCR showed a significant increase ( < 0.05) in and gene expression levels in MS patients compared to that in controls. However, a significant down regulation of gene was observed in the same comparison.

CONCLUSION

This study provides potential diagnostic and therapeutic biomarkers for enhanced understanding of gene regulation underlying MS.