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; 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.

项目摘要 该项目将满足对新的有效抗结核药物的关键需求。我们的主要 目的是阐明结核分枝杆菌耐受的机制 抗结核药物治疗。我们激励的假设是结核杆菌耐受药物 通过差异调节解毒酶，外排泵，代谢来诱导的应激 活性，组成因子和细胞壁重塑系统。此外，我们假设 针对这些容忍策略的一个或几个调节剂的次要药物将增强 原发性药物治疗，并有可能降低抗药性的出现。我们提出了一个 系统生物学方法来产生吸毒耐受性的网络视角 机制及其如何由一个或几个可以针对的监管机构协调 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整合药物的网络表征 新型药物组合的合理发现的特定耐受性机制。在AIM 1中， 我们将在用十种选定药物治疗后的转录特征。结核病。结核病 （初级药物）。使用在实验室中开发的技术，差异表达的基因 将映射到结核分枝杆菌的系统尺度基因调节网络模型以推断 药物特异性耐受性子网络和阐明关键调节剂。我们还将确定 耐受性子网络通过在存在的情况下产生全基因组健身谱。 选定的原发性药物。与药物相关的适应性缺陷将揭示对重要的基因 处理药物诱导的应激，并假设将药物特异性聚集在一起 耐受性子网络。在AIM 2中，我们将在转录上介绍〜250种二次药物，并且 执行所有初级和次级药物组合的高通量筛选组合。 这些研究的数据将用于迭代改进模型并开发机器 学习算法以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以 有选择地扰动公差子网络的预测调节剂。这个项目将 推动系统生物学工具的开发来准确预测新型的协同药物 组合，从而指导实验评估并加速了新的 对结核病感染患者的治疗。