A systems analysis of drug tolerance in Mycobacterium tuberculosis

结核分枝杆菌耐药性的系统分析

• 批准号: 9220609
• 负责人: Nitin S Baliga
• 金额: $ 87.16万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9220609
• 关键词: Address; Algorithms; Antitubercular Agents; Cell Wall; Cessation of life; Data; Defect; Drug Combinations; Drug Interactions; Drug Metabolic Detoxication; Drug Synergism; Drug Targeting; Drug Tolerance; Enzymes; Genes; Genetic Transcription; Genome; Goals; Infection; Knowledge; Laboratories; Machine Learning; Maps; Measures; Messenger RNA; Metabolic; Modeling; Mycobacterium tuberculosis; Patients; Pharmaceutical Preparations; Pharmacotherapy; Regulator Genes; Research; Resistance; Stress; Structure; System; Systems Analysis; Systems Biology; Systems Development; Techniques; Treatment Protocols; Tuberculosis; combinatorial; differential expression; drug discovery; efflux pump; fitness; genome-wide; high throughput screening; innovation; network models; novel; novel drug combination; response; tool; transcription factor; transcriptome; treatment response; tuberculosis treatment

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.

项目概要 该项目将满足对新型有效抗结核药物的迫切需求。我们的小学 目的是阐明结核分枝杆菌的耐受机制 抗结核药物治疗。我们的动机假设是结核分枝杆菌耐受药物 通过差异调节解毒酶、外排泵、代谢来诱导应激 活性、薄膜形成因子和细胞壁重塑系统。此外，我们假设 针对这些耐受策略的一个或几个调节因子的次要药物将增强 主要药物治疗，并可能减少耐药性的出现。我们提出一个 系统生物学方法产生药物诱导耐受的网络视角 机制以及如何由一个或几个可能针对的监管机构进行协调 使用组合治疗方案克服药物特异性耐受性。因此， 我们提出的研究的创新来自于药物网络特征的整合 将特定的耐受机制纳入新药物组合的合理发现中。在目标 1 中， 我们将在十种选定药物治疗后对结核分枝杆菌进行转录分析 （主要药物）。使用我们实验室开发的技术，差异表达基因 将被映射到结核分枝杆菌的系统规模基因调控网络模型上以推断 药物特异性耐受子网络并阐明关键监管机构。我们还将确定 通过在存在的情况下生成全基因组适应度概况来构建耐受子网络 选定的主要药物。与药物相关的健康缺陷将揭示对健康至关重要的基因 处理药物引起的压力，并假设在药物特异性中聚集在一起 容忍子网络。在目标 2 中，我们将转录分析约 250 种次要药物和 对所有主要和次要药物组合进行组合高通量筛选。 这些研究的数据将用于迭代完善模型并开发机器 学习算法来识别可预测协同作用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 有选择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，从而指导实验评估并加速新产品的交付 对结核感染患者进行治疗。