Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs

N.K.D. and N.B. contributed equally to this article. ABSTRACT IdentifyingMycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the du-ration of tuberculosis (TB) treatment. We developed computational algorithms that predictM. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughputM. tuberculosismutant library screen data, mycobac-terial global transcriptional profiles in mice andmacrophages, and functional interaction networks. We selected 57 unique, ge-netically definedmutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set ofM. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Ex-perimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes. IMPORTANCE Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and eluci-date mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15 % of the ~4,000M. tuberculosis genes have been tested experimentally. In this paper, a