A systems analysis of drug tolerance in Mycobacterium tuberculosis

项目摘要 该项目将解决对新的和有效的抗结核药物的迫切需求。我们的首要 目的是阐明结核分枝杆菌耐受 抗结核药物治疗。我们的动机假设是M。结核病耐药 诱导应激通过差异调节解毒酶，外排泵，代谢 活性、膜形成因子和细胞壁重塑系统。此外，我们假设， 靶向这些耐受性策略的一种或几种调节剂的第二药物将增强免疫耐受性。 主要药物治疗，并可能减少耐药性的出现。我们提出了一个 系统生物学方法产生药物诱导耐受的网络观点 机制以及它们如何由一个或几个监管机构协调， 使用组合治疗方案克服药物特异性耐受性。所以 我们提出的研究创新来自于整合药物的网络表征， 具体的耐受机制，以合理发现新的药物组合。在目标1中， 我们将对M进行转录分析十种药物治疗后的结核病 （主要药物）。利用我们实验室开发的技术， 将被映射到M.结核病推断 药物特异性耐受性子网络，并阐明关键的监管机构。我们还将确定 耐受性子网络，通过在存在的情况下生成全基因组适应性概况， 选择主要药物。药物相关的健康缺陷将揭示基因，是重要的 处理药物引起的压力，并假设聚集在一起，在药物特异性 容差子网络。在目标2中，我们将对约250种次要药物进行转录分析， 对所有主要和次要药物组合进行组合高通量筛选。 来自这些研究的数据将用于迭代地改进模型并开发机器 学习算法，以识别预测协同效应的基因和网络级特征 药物相互作用最后，协同药物组合的机制将被表征为： 选择性地扰动容差子网络的预测调节器。该项目将 推动系统生物学工具的发展，以准确预测新型协同药物 组合，从而指导实验评估和加速交付新的 治疗结核病感染者。

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.