Systems biological approach on neurological disorders: a novel molecular connectivity to aging and psychiatric diseases
1 Department of Biotechnology, School of Bioengineering, SRM University, Kattankulathur -603 203, Tamil Nadu, India
2 Computational Neuroscience Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai -600 036, Tamil Nadu, India
3 Department of Bioinformatics, School of Bioengineering, SRM University, Kattankulathur -603 203, Tamil Nadu, India
4 Department of Neurology, SRM Medical College Hospital and Research Centre, Kattankulathur -603 203, Tamil Nadu, India
BMC Systems Biology 2011, 5:6 doi:10.1186/1752-0509-5-6Published: 12 January 2011
Systems biological approach of molecular connectivity map has reached to a great interest to understand the gene functional similarities between the diseases. In this study, we developed a computational framework to build molecular connectivity maps by integrating mutated and differentially expressed genes of neurological and psychiatric diseases to determine its relationship with aging.
The systematic large-scale analyses of 124 human diseases create three classes of molecular connectivity maps. First, molecular interaction of disease protein network generates 3632 proteins with 6172 interactions, which determines the common genes/proteins between diseases. Second, Disease-disease network includes 4845 positively scored disease-disease relationships. The comparison of these disease-disease pairs with Medical Subject Headings (MeSH) classification tree suggests 25% of the disease-disease pairs were in same disease area. The remaining can be a novel disease-disease relationship based on gene/protein similarity. Inclusion of aging genes set showed 79 neurological and 20 psychiatric diseases have the strong association with aging. Third and lastly, a curated disease biomarker network was created by relating the proteins/genes in specific disease contexts, such analysis showed 73 markers for 24 diseases. Further, the overall quality of the results was achieved by a series of statistical methods, to avoid insignificant data in biological networks.
This study improves the understanding of the complex interactions that occur between neurological and psychiatric diseases with aging, which lead to determine the diagnostic markers. Also, the disease-disease association results could be helpful to determine the symptom relationships between neurological and psychiatric diseases. Together, our study presents many research opportunities in post-genomic biomarkers development.