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: 10367797
• 负责人: Nitin S Baliga
• 金额: $ 90.44万
• 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 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367797
• 关键词: Address; Adopted; Algorithms; Biochemical Pathway; Bioreactors; CRISPR interference; Cause of Death; Cells; Cessation of life; Characteristics; Clinical; Communicable Diseases; Complex; Cues; Development; Disease; Drug Combinations; Drug Targeting; Drug Tolerance; Drug resistance; Environment; Essential Genes; Evolution; Gene Expression Profile; Genetic; Genetic Transcription; Growth; Heterogeneity; Infection; Infectious Agent; Intervention; Italy; Machine Learning; Malignant Neoplasms; Metabolic; Modeling; Mycobacterium tuberculosis; Outcome; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physiologic tolerance; Physiological; Physiological Adaptation; Population; Population Heterogeneity; Predisposition; Progress Reports; Publications; Recurrent disease; Regimen; Reporting; Resistance; Resolution; Structure; System; Systems Analysis; Systems Biology; Techniques; Technology; Testing; Time; Tuberculosis; base; biomarker panel; chemical genetics; combinatorial; design; disease heterogeneity; emerging antimicrobial resistance; experimental study; macrophage; network models; new technology; new therapeutic target; novel; novel drug combination; pathogen; predictive test; promoter; response; support network; transcription factor; transcriptome; treatment duration; tuberculosis drugs; tuberculosis treatment

PROPOSAL SUMMARY This project will address the critical need for accelerated development of multidrug regimen to achieve fast and complete clearance of Mycobacterium tuberculosis (Mtb), thereby lowering the likelihood for the emergence of antimicrobial resistance. Mtb dynamically adapts to extra- and intracellular host environments by adopting heterogeneous physiologic states, with varied susceptibility profiles to frontline antitubercular drugs. In the first four years of the R01, we have made progress towards dissecting this capability of Mtb by developing technologies to (i) uncover regulatory mechanisms that drive the pathogen into dormant states in host-simulated environments (controlled bioreactors) and directly within host cells (Path-seq), (ii) sort and characterize at single cell resolution translationally-dormant persister-like subpopulations within isogenic cultures (PerSort), (iii) uncover and characterize context-specific vulnerabilities within regulatory and metabolic networks (EGRIN2 and PRIME), and (iv) rationally formulate novel synergistic drug combinations (DRonA and MLSynergy). Using these capabilities and their applications reported across sixteen publications, we discovered that heterogeneous drug tolerant subpopulations co-exist within an isogenic culture of Mtb, even in the absence of drug treatment. Furthermore, we discovered that stressful environments and treatments activate additional drug tolerance networks, which may potentiate the emergence of resistance. Based on these findings, we hypothesize that we can achieve fast and complete clearance of Mtb infection with a combination of drugs that target vulnerabilities across heterogeneous drug tolerant subpopulations that co-exist in varied combinations and proportions depending on host- and treatment-contexts. To test this hypothesis, we will mechanistically characterize how the heterogeneous population structure of Mtb changes dynamically in response to host-relevant environmental cues and drug treatments. We will then uncover and characterize vulnerabilities within regulatory and metabolic networks that support and drive transitions to drug tolerant states. Using machine-learning techniques, we will predict and validate synergistic drug combinations targeting multiple vulnerabilities to cripple heterogeneous environment- and drug-induced states of Mtb. By performing time kill curves, we will investigate whether validated combinatorial interventions accomplish complete and faster clearance of heterogeneous Mtb subpopulations in diverse contexts. Altogether, the proposed activities will identify novel drug targets, and novel drug combinations for fast and complete clearance of a heterogeneous Mtb population. Given that phenotypic heterogeneity as a means for tolerating and resisting drugs is a universal phenomenon, the systems biology framework developed in this project will be generalizable to the discovery of effective multidrug regimen for diverse infectious diseases and even cancers.

提案摘要 该项目将解决加速开发多药方案的迫切需要，以实现快速和 完全清除结核分枝杆菌（Mtb），从而降低出现 抗菌素耐药性结核杆菌通过采用新的分子结构来动态适应细胞外和细胞内宿主环境 不同的生理状态，对一线抗结核药物的敏感性不同。上 在R 01的四年中，我们通过开发 技术，以（i）揭示在宿主模拟环境中驱动病原体进入休眠状态的调控机制， （ii）在单一环境（受控生物反应器）和直接在宿主细胞内（Path-seq）进行分选和表征， 等基因培养物中细胞解析翻译休眠的持续存在样亚群（PerSort），（iii） 发现和描述监管和代谢网络中的特定环境漏洞（EGRIN 2和 PRIME），和（iv）合理配制新型协同药物组合（DronA和MLSynergy）。使用这些 在16篇出版物中报道了它们的能力和应用，我们发现， 耐受亚群共存于Mtb的同基因培养物内，即使在没有药物治疗的情况下。 此外，我们发现，压力环境和治疗激活额外的药物耐受性 网络，这可能会加剧耐药性的出现。基于这些发现，我们假设， 可以通过针对脆弱性的药物组合实现快速和完全清除结核病感染 在以不同组合和比例共存的异质耐药亚群中 取决于宿主和治疗环境。为了验证这一假设，我们将机械地描述 结核分枝杆菌异质性种群结构对宿主相关环境线索的动态响应 和药物治疗。然后，我们将发现和特点的漏洞，在监管和代谢 支持和推动向耐药状态过渡的网络。利用机器学习技术，我们将 预测和验证针对多种脆弱性的协同药物组合，以削弱异质 环境和药物诱导的结核分枝杆菌状态。通过执行时间杀死曲线，我们将调查是否 经验证的组合干预实现了异质性Mtb的完全和更快的清除 不同背景下的亚群。总之，所提出的活动将确定新的药物靶点， 用于快速和完全清除异质性Mtb群体的药物组合。鉴于表型 作为耐受和抵抗药物的手段的异质性是一种普遍现象，系统生物学 本项目开发的框架将推广到发现有效的多药治疗方案， 各种传染病甚至癌症。