Current chemotherapy against
Mycobacterium tuberculosis( Mtb), an important human pathogen, requires a multidrug regimen lasting several months. While efforts have been made to optimize therapy by exploiting drug-drug synergies, testing new drug combinations in relevant host environments remains arduous. In particular, host environments profoundly affect the bacterial metabolic state and drug efficacy, limiting the accuracy of predictions based on in vitroassays alone. In this study, we utilize conditional Mtbknockdown mutants of essential genes as an experimentally-tractable surrogate for drug treatment, and probe the relationship between Mtbcarbon metabolism and chemical-genetic interactions (CGI). We examined the anti-tubercular drugs isoniazid, rifampicin and moxifloxacin, and found that CGI are differentially responsive to the metabolic state, defining both environment-independent and dependent interactions. Specifically, growth on the in vivo-relevant carbon source, cholesterol, reduced rifampicin efficacy by altering mycobacterial cell surface lipid composition. We report that a variety of perturbations in cell wall synthesis pathways restore rifampicin efficacy during growth on cholesterol, and that both environment-independent and cholesterol-dependent in vitroCGI could be leveraged to enhance bacterial clearance in the mouse infection model. Our findings present an atlas of novel chemical-genetic-environmental interactions that can be used to optimize drug-drug interactions as well as provide a framework for understanding in vitrocorrelates of in vivoefficacy. Significance
Efforts to improve tuberculosis therapy include optimizing multi-drug regimens to take advantage of drug-drug synergies. However, the complex host environment has a profound effect on bacterial metabolic state and drug activity, making predictions of optimal drug combinations difficult. In this study, we leverage a newly developed library of conditional knockdown
Mycobacterium tuberculosismutants in which genetic depletion of essential genes mimics the effect of drug therapy. This tractable system allowed us to assess the effect of growth condition on predicted drug-drug interactions. We found that these interactions can be differentially sensitive to the metabolic state and select in vitro-defined interactions can be leveraged to accelerate bacterial killing during infection. These findings suggest new strategies for optimizing tuberculosis therapy.