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Tuberculosis (Edinb). 2011 Sep;91(5):407-13. doi: 10.1016/j.tube.2011.03.006. Epub 2011 Apr 21.

Molecular epidemiology of tuberculosis in India: moving forward with a systems biology approach.

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  • 1Pathogen Biology Laboratory, School of Life Sciences, University of Hyderabad, Hyderabad, India. niyazSL@uohyd.ernet.in

Abstract

Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to be the leading source of mortality and morbidity across the world with India fast emerging as the TB capital of the world. In order to develop effective intervention strategies it is equally important to focus not only on a system of information and efficient methods for localizing sources of infection, but also highlight tools that enable enhanced understanding of the dynamics of spreading of disease. Accurate identification of the underlying strains in an epidemiological setting is therefore of paramount significance. There is no scientific evidence to explain that some strains of the TB bacilli spread faster and transmit more aggressively than others although strains such as M. tuberculosis Beijing/W have been widely reported to cause large scale and fatal outbreaks perhaps linked to their postulated propensity to transmit faster. We provide an overview of the present scenario of molecular epidemiology and dissemination dynamics of M. tuberculosis and discuss how systematic, genome sequence based methods allow decipherment of the population genetic structure of M. tuberculosis in India which was not achievable with traditional fingerprinting methods. We discuss the prevalence of ancestral genotypes in India which perhaps represent less disseminating and more controllable lineages that infect a majority of TB patients in this high burden country. Further, we suggest 'functional molecular infection epidemiology' as a new discipline to guide investigation of the impact of pathogen diversity (as juxtaposed to the host response) on the disease phenotype. We also propose systems biology to be a powerful new science to holistically analyze the epidemic through integration of high-throughput multi-omics data to understand the dynamic interactions that occur at the level of host-pathogen cross-talks and to identify potentially novel drivers of the future control strategies.

Copyright © 2011 Elsevier Ltd. All rights reserved.

PMID:
21514230
[PubMed - indexed for MEDLINE]
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