A systems analysis of drug tolerance in Mycobacterium tuberculosis

项目总结 该项目将解决对新的、有效的抗结核药物的迫切需求。我们的初选 目的是阐明结核分枝杆菌耐受的机制。 抗结核药物治疗。我们的激励假设是结核分枝杆菌对药物耐受 差异调节解毒酶、外排泵、代谢诱导应激 活性、膜形成因子和细胞壁重塑系统。此外，我们假设一个 针对这些耐受策略中的一个或几个调节者的辅助药物将增强 初级药物治疗，并有可能减少耐药性的出现。我们提出了一个 用系统生物学方法生成药物耐受的网络视角 机制以及它们如何由一个或几个可能成为目标的监管机构协调 使用联合治疗方案克服药物特异性耐受。因此， 我们提出的研究的创新来自于整合药物的网络表征- 将特定的耐受机制转化为合理发现新的药物组合。在目标1中， 我们将转录描述用十种选定药物治疗后的结核杆菌。 (主要药物)。利用我们实验室开发的技术，差异表达的基因 将被映射到系统规模的结核分枝杆菌基因调控网络模型来推断 药物特异性耐受子网络，并阐明关键调控因素。我们还将确定 通过在存在的情况下生成基因组范围的适应度简档来生成耐受子网络 选定的主要药物。与药物相关的健康缺陷将揭示出对 处理药物引起的压力，并假设聚集在一起的药物特定 容忍子网络。在目标2中，我们将转录描述约250种二次药物和 对所有主要和次要药物组合进行高通量联合筛选。 这些研究的数据将被用于反复改进模型并开发一台机器 识别预测协同效应的基因和网络级特征的学习算法 药物相互作用。最后，对协同药物组合的作用机理进行了表征。 选择性地扰动容忍性子网络的预测调节器。这个项目将 推动系统生物学工具的发展，以准确预测新的增效药物 组合，从而指导实验评估并加快新产品的交付 结核病感染患者的治疗。

PROJECT SUMMARY This project will address the critical need for new and effective antitubercular drugs. Our primary objective is to elucidate the mechanisms by which Mycobacterium tuberculosis tolerates antitubercular drug treatment. Our motivating hypothesis is that M. tuberculosis tolerates drug induced stress by differentially regulating detoxification enzymes, efflux pumps, metabolic activity, pellicle-forming factors, and cell wall remodeling systems. Further, we postulate that a secondary drug targeting one or few regulators of these tolerance strategies will potentiate the primary drug-treatment, and potentially reduce the emergence of resistance. We propose a systems biology approach to generate a network perspective of drug-induced tolerance mechanisms and how they are coordinated by one or few regulators that could be targeted for overcoming drug-specific tolerance using combinatorial treatment regimens. Hence, the innovation of our proposed research emerges from integrating network characterization of drug- specific tolerance mechanisms into the rational discovery of novel drug combinations. In Aim 1, we will transcriptionally profile M. tuberculosis following treatment with ten selected drugs (primary drugs). Using techniques developed in our laboratory, differentially expressed genes will be mapped onto a systems-scale gene regulatory network model of M. tuberculosis to infer drug-specific tolerance sub-networks and elucidate key regulators. We will also identify tolerance sub-networks by generating genome-wide fitness profiles in the presence of the selected primary drugs. Drug-associated fitness defects will reveal genes that are important for dealing with drug-induced stress and are hypothesized to cluster together in drug-specific tolerance sub-networks. In Aim 2, we will transcriptionally profile ~250 secondary drugs and perform combination high-throughput screens of all primary and secondary drug combinations. Data from these studies will be used to iteratively refine the model and develop a machine learning algorithm to identify gene- and network-level features that are predictive of synergistic drug interactions. Finally, mechanism of synergistic drug combinations will be characterized by selectively perturbing the predicted regulators of the tolerance sub-networks. This project will propel the development of systems biology tools to accurately predict novel synergistic drug combinations, thereby guiding experimental assessment and accelerating the delivery of new treatments to patients with tuberculosis infection.