Metachromatic Leukodystrophy: A Bioinformatics Approach through Protein-Protein Interaction Network Analysis

Abstract

System networks help in identifying new drug targets which in turn will generate more in-depth understanding of the underlying mechanism of diseases. Network based approach to study the pathogenesis of a disease is emerging as an important paradigm for analysis of biological systems. In this work the interaction network for Metachromatic Leukodystrophy (MLD) was built using Cytoscape and the analysis of network was carried out to find the drug targets. The most functional and highly interconnected sub-networks in the network were found which could help in understanding the mechanism of MLD. Metachromatic Leukodystrophy (MLD) is one of a group of genetic disorders called the leukodystrophies. These diseases impair the growth or development of the myelin sheath, the fatty covering that acts as an insulator around nerve fibers. MLD is caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lacking of complete knowledge of gene Co-expression network and pathogenesis for Metachromatic Leukodystrophy motivated us to develop an interaction network for this disorder. The in-silico prediction of potential interactions between nodes (genes) and target genes are of core importance for the identification of new drugs or novel targets for existing drugs. One of the aims of the study was to identify the functional and highly interconnected nodes in the network. Identifying the important sub-networks in the system could provide useful insights into the underlying molecular mechanism for Metachromatic Leukodystrophy. Another aim of the study was to identify candidate genes with high centrality score and perform their disease and pathway enrichment analysis to find out the disease classes in which these genes are involved and the pathways they are affecting. An additional aspect of study was chromosome enrichment analysis of candidate genes to calculate the distribution of genes across different chromosomes. Clique analysis was performed on the network and the highly interconnected clusters were found. SMAD9, PSAP, BMPR2, ARSA, UBE3A which may be the functional modules and can be identified as highly interconnected sub-graphs in the network. Most important potential drug targets found were TAF1, SMAD2, BRCA1, HNF4A, AR, SMAD9, CDC2, RB1, UBC, CDK2, UBB, PSAP, CDC23, MYC, MNAT1, CCNH, CDK7.