A systems analysis of drug tolerance in Mycobacterium tuberculosis

建议书摘要 该项目将解决加速开发多种药物方案的迫切需要，以实现快速和 彻底清除结核分枝杆菌，从而降低出现 抗菌素耐药性。MTB动态适应胞外和胞内主机环境，采用 不同的生理状态，对一线抗结核药物的敏感性不同。在第一个 在R01的四年时间里，我们通过开发 技术：(I)揭示在模拟寄主环境中驱动病原体进入休眠状态的调节机制 环境(受控生物反应器)和直接在宿主细胞内(路径序列)，(Ii)在单个 细胞分辨率(PerSort)，(Iii)等基因培养中翻译休眠的持久姐妹样亚群 发现并描述监管和代谢网络中特定于环境的漏洞(EGRIN2和 (4)合理配制新的增效药物组合(Drona和MLSynergy)。使用这些 通过16篇文献报道的能力及其应用，我们发现异质药物 即使在没有药物治疗的情况下，耐受亚群也存在于结核分枝杆菌的同基因培养中。 此外，我们还发现，紧张的环境和治疗会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，我们假设我们 可以通过针对脆弱性的药物组合实现快速完全清除结核分枝杆菌感染 跨以不同组合和比例共存的异质耐药亚群 取决于宿主和治疗环境。为了检验这一假设，我们将机械地描述 结核分枝杆菌异质性种群结构随寄主相关环境线索的动态变化 和药物治疗。然后，我们将发现并描述监管和代谢方面的漏洞 支持和推动向耐药状态过渡的网络。使用机器学习技术，我们将 预测和验证针对多个漏洞的协同药物组合，以削弱异质性 环境和药物诱导的结核分枝杆菌状态。通过绘制时间杀死曲线，我们将调查 有效的组合干预措施实现了对异质性结核分枝杆菌的全面、快速清除 不同背景下的亚群。总之，拟议的活动将确定新的药物靶点，以及新的 用于快速和完全清除异质结核杆菌种群的药物组合。考虑到这种表型 异质性作为一种耐受和抵抗药物的手段是一种普遍现象，系统生物学 该项目开发的框架将可推广到发现有效的多药方案 各种传染病，甚至癌症。

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.