Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment

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 framework—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.

抗生素需在体内多样化环境中保持抗菌活性。然而，病原体微环境可显著影响抗生素的效力。为对抗耐药性，药物联合疗法的应用日益增多，但目前尚未明确微环境对联合药物疗效的影响。为全面探究多样化微环境对联合药物的作用，本研究开发了一款基于代谢组与基因组的抗生素方案定制计算框架（Metabolism And GENomics-based Tailoring of Antibiotic regimens，缩写MAGENTA）。MAGENTA利用单药的化学基因组谱与代谢扰动数据，可预测不同微环境下的协同或拮抗药物相互作用。本研究通过筛选72种药物构成的2556种药物联合方案，发现了可在9种不同环境下对大肠杆菌（E. coli）和鲍曼不动杆菌（A. baumannii）均展现出稳定协同效应的抗生素联合方案。此外，MAGENTA还可准确预测甘油培养基中生长时，抑菌类与杀菌类药物联合疗法的疗效变化，该结果已在两种微生物中通过实验得到验证。本研究的系统生物学方法还鉴定出糖酵解与乙醛酸途径相关基因，分别为协同作用与拮抗作用的顶级预测因子。本研究的系统级方法可实现基于病原体微环境的抗生素治疗方案定制。