TRANSLATIONAL BIOINFORMATICS AND SYSTEMS BIOLOGY APPROACHES TO GENETIC AND TRANSCRIPTIONAL DATA INCOMPLEX HUMAN DISORDERS

Human complex diseases are caused by genetic and environmental factors. Genome-wide association studies (gwas) are aimed to identify common variants predisposing to those disorders. However, till date, the data generated from such studies have not been extensively explored to identify the molecular and functional framework hosting the susceptibility genes. We reconstructed the multiple sclerosis-MS genetic interactome and searched for their interactions with genes predisposing to either neurodegenerative or autoimmune diseases such as Parkinson's disease-PD, Alzheimer's disease-AD, multiple sclerosis-MS, rheumatoid arthritis-RA and Type 1 diabetes-T1D. It was observed that several genes predisposing to the other autoimmune or neurodegenerative disorders may come into contact with MS interactome, suggesting that susceptibility to distinct diseases may converge towards common molecular and biological networks. In order to test this hypothesis, we performed pathway enrichment analyses on each disease interactome independently. Several issues related to immune function and growth factor signaling pathways appeared in all autoimmune diseases. Further, the paired analyses of disease interactomes revealed significant molecular and functional relatedness among the diseases. Therefore, the shift from single genes to molecular frameworks via systems biology approach highlighted several known pathogenic processes, indicating that changes in these functions might be driven or sustained by the framework linked to genetic susceptibility. Notably, MS is a complex disease of the central nervous system (CNS), but many of the susceptibility genes play a role in immune system. Interestingly, the most widely used therapeutic drugs in MS are either immunosuppressive or immunomodulatory agents, indicating that targeting peripheral immune system is beneficial to patients with this CNS disorder. Next, we measured the global gene expression in peripheral blood mono nuclear cells (PBMCs) from MS and healthy subjects to discover disease genes, molecular biomarkers and drug targets. Extending the bioinformatics analysis of the transcriptome data to network-biology level enabled us to identify few crucial transcriptional regulators in MS. Further, as a first step towards translational research, studies were conducted in the animal model of MS, based on the outcomes of the bioinformatics analysis. Significant amelioration of disease activity was observed in diseased animals treated with drug targeting SP1 transcription factor, compared to the untreated group. Hence, disease transcriptomics combined with network-biology analysis provided a powerful platform for the identification of functional networks and molecular targets in MS.