Accelerating early anti-TB drug discovery by creating mycobacterial indicator strains that predict mode of action [Mycobacterium tuberculosis]

Due to the rise of drug resistant forms of tuberculosis there is an urgent need for novel antibiotics to effectively combat these cases and to shorten treatment regimens. Recently, drug screens using whole cell analyses have shown to be successful. However, current high throughput screens focus mostly on stricto sensu life-death screening that give little qualitative information and often require the lengthy process of target and mode of action (MoA) identification. In doing so, promising compound scaffolds or non-optimized compounds that fail to reach inhibitory concentrations are missed. To accelerate early TB drug discovery, we performed RNA sequencing on Mycobacterium tuberculosis and Mycobacterium marinum to map the stress responses that follow upon exposure to sub-inhibitory concentrations of antibiotics with known targets: ciprofloxacin, ethambutol, isoniazid, streptomycin and rifampicin. The resulting dataset comprises the first overview of transcriptional stress responses of mycobacteria to different antibiotics. We show that antibiotics can be distinguished based on their specific transcriptional stress fingerprint i.e. DNA damage for ciprofloxacin and ribosomal stress for streptomycin. Notably, this fingerprint was more distinctive in M. marinum and we decided to use this to our advantage and continue with this model organism. A selection of diverse antibiotic stress genes was used to construct stress reporters. In total, three functional reporters were constructed for DNA damage, cell wall damage and ribosomal inhibition. Subsequently, these reporter strains were used to screen a small anti-TB compound library to predict the mode of action. In doing so we could identify the putative mode of action for three novel compounds, which confirms our approach. RNA expression profiles of Mycobacterium tuberculosis and Mycobacterium marinum was generated upon exposure to sub-inhibitory concentrations of antibiotics with known targets: ciprofloxacin, ethambutol, isoniazid, streptomycin and rifampicin for 4 hrs and 24hrs duration and compared with controls without any drug exposure

由于耐药性结核分枝杆菌菌株的出现，开发新型抗生素以有效对抗此类感染并缩短治疗方案的需求愈发迫切。近年来，基于全细胞分析的药物筛选已被证实颇具成效，但当前的高通量筛选大多聚焦于严格意义上的生死筛选（stricto sensu life-death screening），这类筛选仅能提供极少的定性信息，且往往需要耗时冗长的靶点与作用模式（Mode of Action, MoA）鉴定流程。在此过程中，未达到抑制浓度的有潜力的化合物骨架或未优化化合物常会被遗漏。为加速结核病药物发现的早期阶段，我们对结核分枝杆菌（Mycobacterium tuberculosis）与海分枝杆菌（Mycobacterium marinum）进行了RNA测序，以绘制暴露于已知靶点抗生素的亚抑制浓度后的应激反应图谱，所涉抗生素包括环丙沙星（ciprofloxacin）、乙胺丁醇（ethambutol）、异烟肼（isoniazid）、链霉素（streptomycin）与利福平（rifampicin）。本数据集首次全面呈现了分枝杆菌针对不同抗生素的转录应激反应概况。研究表明，可依据抗生素特异性的转录应激指纹对其进行区分：例如环丙沙星引发DNA损伤，链霉素则引发核糖体应激。值得注意的是，海分枝杆菌的该应激指纹更为显著，我们遂借此优势选择该模式生物开展后续研究。我们选取了多样化的抗生素应激基因以构建应激报告基因系统，最终成功构建了针对DNA损伤、细胞壁损伤与核糖体抑制的三类功能性报告菌株。随后，利用这些报告菌株筛选小型抗结核化合物库，以预测其作用模式。借此，我们成功鉴定出三种新型化合物的推定作用模式，验证了本研究方法的有效性。本研究生成了结核分枝杆菌与海分枝杆菌在暴露于上述已知靶点抗生素的亚抑制浓度下，分别培养4小时与24小时后的RNA表达谱，并与未施加任何药物的对照组进行了对比。