Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment
Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment
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DOI：
10.1371/journal.pcbi.1006677
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发表时间：
2018-12-01
影响因子：
4.3
通讯作者：
Chandrasekaran, Sriram
中科院分区：
文献类型：
--
作者：
Cokol, Murat;Li, Chen;Chandrasekaran, Sriram
抗生素需要在不同的体内环境中有效。然而，病原体微环境可能对抗生素效力产生重大影响。此外，抗生素越来越多地组合使用以对抗耐药性，然而，微环境对药物组合功效的影响尚不清楚。为了彻底探索不同微环境对药物组合的影响，在这里，我们开发了一个计算框架-基于代谢和基因组学的抗生素方案定制（洋红）。洋红使用单个药物的化学基因组学特征和代谢扰动来预测不同微环境中的协同或拮抗药物相互作用。我们发现抗生素组合在9种不同的环境中对E. coli和A.通过对72种药物的2556种药物组合的检索，洋红还准确预测了在甘油培养基中生长期间抑菌和杀菌药物组合的功效变化，我们在两种微生物中进行了实验证实。我们的方法将糖酵解和乙醛酸途径中的基因分别确定为协同作用和拮抗作用的最佳预测因子。我们的系统方法可以根据病原体微环境定制抗生素疗法。
Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational frame-work-Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.