Evolutionary and Functional Significance of Novel Mutations in MDR-XDR TB

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DESCRIPTION (provided by applicant): Last year over 9 million cases of active TB and 1 million deaths due to TB were reported. The development of drug-resistant Mtb is increasing and threatens TB control efforts. Global Consortium for Drug-resistant TB and Diagnostics (GCDD) has identified over 100 (out of 419) resistant Mtb isolates without known resistance-causing mutations. Additionally, GCDD has identified distinct regional genotypic-phenotypic relationships at its four study sites (India, Moldova, the Philippines, and South Africa) for seven first and second line drugs, suggesting that Mtb is taking a different evolutionary route to resistance at each site. This could also mean that Mtb is using different mechanisms for producing resistance at these study sites and therefore could soon require new set of anti-TB drugs for each evolutionary path/study site. While the emergence of the distinct evolutionary paths are probably due to variable availability of anti-TB drugs, the emergence of this distinctiveness is alarming and if continued, Mtb can evolve into different states of total drug resistance (TDR- TB) causing an epidemic that requires region-specific treatment and public health strategy. This project aims to uncover previously-unknown mechanisms of drug resistance through in silico functional characterization of newly identified gene and regulatory elements associated with drug resistance. This will explain the unexplained cases of resistance. The functional characterization will allow the identification of the mechanism of resistance for each drug involving these novel mutations, identification of "keystone" mutations that can cause resistance to multiple drugs, and identify central loci in the functional interaction network that can serve as new drug targets. This project will also perform a phylogenetic analysis of all 400+ GCDD Mtb genomes. The combination of functional characterization of novel and known mutations and the evolutionary analysis of drug resistance will answer whether distinct evolutionary paths and mechanisms of resistance are a reality or not. If so, the model can be extended to predict future regional evolutionary trends. Experimental recombinant technology will be used to confirm the significant the keystone mutations responsible for resistance in the laboratory.