Chemical-genetic interaction mapping links carbon metabolism and cell wall structure to tuberculosis drug efficacy.
Chemical-genetic interaction mapping links carbon metabolism and cell wall structure to tuberculosis drug efficacy.
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DOI：
10.1073/pnas.2201632119
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发表时间：
2022-04-12
影响因子：
11.1
通讯作者：
Sassetti, Christopher M.
中科院分区：
文献类型：
--
作者：
Koh, Eun-Ik;Oluoch, Peter O.;Ruecker, Nadine;Proulx, Megan K.;Soni, Vijay;Murphy, Kenan C.;Papavinasasundaram, Kadamba;Reames, Charlotte J.;Trujillo, Carolina;Zaveri, Anisha;Zimmerman, Matthew D.;Aslebagh, Roshanak;Baker, Richard E.;Shaffer, Scott A.;Guinn, Kristine M.;Fitzgerald, Michael;Dartois, Veronique;Ehrt, Sabine;Hung, Deborah T.;Ioerger, Thomas R.;Rubin, Eric J.;Rhee, Kyu Y.;Schnappinger, Dirk;Sassetti, Christopher M.
改善结核病治疗的努力包括优化多药治疗方案，以利用药物间的协同作用。然而，复杂的宿主环境对细菌的代谢状态和药物活性有着深远的影响，使得预测最佳药物组合变得困难。在这项研究中，我们利用一个新开发的库的条件敲低结核分枝杆菌突变体中，遗传消耗的必需基因模仿药物治疗的效果。这个易于处理的系统使我们能够评估生长条件对预测的药物相互作用的影响。我们发现，这些相互作用可以对代谢状态有差异的敏感性，并且可以利用选择的体外定义的相互作用来加速感染期间的细菌杀灭。这些发现为优化结核病治疗提供了策略。目前针对结核分枝杆菌（Mtb）（一种重要的人类病原体）的化疗需要持续数月的多药方案。虽然已经努力通过利用药物-药物协同作用来优化治疗，但在相关宿主环境中测试新的药物组合仍然很困难。特别是，宿主环境深刻地影响细菌代谢状态和药物功效，限制了仅基于体外测定的预测的准确性。在这项研究中，我们利用必要基因的条件Mtb敲低突变体作为药物治疗的实验上易处理的替代品，并探测Mtb碳代谢和化学-遗传相互作用（CGIs）之间的关系。我们研究了抗结核药物异烟肼、利福平和氟诺沙星，发现CGIs对代谢状态有不同的反应，定义了环境独立和依赖的相互作用。具体而言，生长在体内相关的碳源，胆固醇，降低利福平的疗效，改变分枝杆菌细胞表面脂质组成。我们报告说，细胞壁合成途径的各种扰动恢复利福平的疗效在胆固醇的生长过程中，和环境无关的和胆固醇依赖的体外CGIs可以利用，以提高细菌清除在小鼠感染模型。我们的研究结果提供了一个化学-遗传-环境相互作用的图谱，可用于优化药物-药物相互作用，并为了解体内疗效的体外相关性提供了一个框架。
Efforts to improve tuberculosis therapy include optimizing multidrug 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 tuberculosis mutants 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 strategies for optimizing tuberculosis therapy. 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 vitro assays alone. In this study, we utilized conditional Mtb knockdown mutants of essential genes as an experimentally tractable surrogate for drug treatment and probe the relationship between Mtb carbon metabolism and chemical–genetic interactions (CGIs). We examined the antitubercular drugs isoniazid, rifampicin, and moxifloxacin and found that CGIs 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 vitro CGIs could be leveraged to enhance bacterial clearance in the mouse infection model. Our findings present an atlas of chemical–genetic–environmental interactions that can be used to optimize drug–drug interactions, as well as provide a framework for understanding in vitro correlates of in vivo efficacy.
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影响因子：
13.6
作者：
Cokol M;Kuru N;Bicak E;Larkins-Ford J;Aldridge BB
通讯作者：
Aldridge BB
影响因子：
64.8
作者：
Johnson, Eachan O.;LaVerriere, Emily;Hung, Deborah T.
通讯作者：
Hung, Deborah T.
DOI：
10.1073/pnas.0610634104
发表时间：
2007-03-20
影响因子：
11.1
作者：
Jain, Madhulika;Petzold, Christopher J.;Cox, Jeffery S.
通讯作者：
Cox, Jeffery S.
影响因子：
4.6
作者：
Baker, Jacob J.;Abramovitch, Robert B.
通讯作者：
Abramovitch, Robert B.
影响因子：
3.2
作者：
Gomez, JE;McKinney, JD
通讯作者：
McKinney, JD