EXPRESSION-BASED NETWORK BIOLOGY IDENTIFIES HIGH SCORE SUB-NETWORKS AND THEIR ROLES IN RETT SYNDROME

Abstract

Rett syndrome (RTT) is a childhood neurodevelopmental disorder and one of the most common causes of mental retardation that primarily affects girls. Hence, there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signaling mechanisms that can lead to Rett syndrome and its associated complications. In this present work, we used Rett syndrome microarray data to identify the significant genes by gene expression data analysis and also constructed the protein-protein interaction (PPI) networks of genes/proteins involved in the pathophysiology of RTT. We identified three high score sub-networks from the large PPI networks and these three sub-networks have scale-freeness topology. The functional enrichment analysis for all the genes of these three sub-networks; we found genes from the third sub-network have biological processes such as neurological disorders and nervous system development and function related to RTT. An experimental research on this sub- network and their associated genes and pathways may further provide suitable drug target identification and better understanding of the pathophysiology of RTT.