HGV&TB: a comprehensive online resource on human genes and genetic variants associated with tuberculosis.

Tuberculosis (TB) is an infectious disease caused by fastidious pathogen Mycobacterium tuberculosis. TB has emerged as one of the major causes of mortality in the developing world. Role of host genetic factors that modulate disease susceptibility have not been studied widely. Recent studies have reported few genetic loci that provide impetus to this area of research. The availability of tools has enabled genome-wide scans for disease susceptibility loci associated with infectious diseases. Till now, information on human genetic variations and their associated genes that modulate TB susceptibility have not been systematically compiled. In this work, we have created a resource: HGV&TB, which hosts genetic variations reported to be associated with TB susceptibility in humans. It currently houses information on 307 variations in 98 genes. In total, 101 of these variations are exonic, whereas 78 fall in intronic regions. We also analysed the pathogenicity of the genetic variations, their phenotypic consequences and ethnic origin. Using various computational analyses, 30 variations of the 101 exonic variations were predicted to be pathogenic. The resource is freely available at http://genome.igib.res.in/hgvtb/index.html. Using integrative analysis, we have shown that the disease associated variants are selectively enriched in the immune signalling pathways which are crucial in the pathophysiology of TB. Database URL: http://genome.igib.res.in/hgvtb/index.html

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.