Mapping and modeling host-pathogen interactions in TB latency and reactivation

描述（由申请人提供）：除非我们认识到对感染结果至关重要的宿主-病原体相互作用并采取行动，否则结核病无法得到成功控制。该提案的中心论点是，结核分枝杆菌感染在潜伏期和重新激活方面的结果取决于宿主和病原体信号网络、代谢途径和遗传程序之间的相互作用。拟议研究的目标是揭示并从机制上理解结核杆菌和肺巨噬细胞之间运作的细胞间网络如何在遗传程序和细胞代谢水平上控制潜伏期的转变。我们建议将 i) 统计路径分析和 ii) 自下而上和自上而下的建模策略结合起来，利用公开数据、参与实验室正在进行的研究提供的数据以及本计划中从人类原发性肺巨噬细胞体外感染结核分枝杆菌产生的数据。我们有三个具体目标。在目标 1 中，将通过统计途径分析来分析离体感染数据，以将巨噬细胞反应与供体感染状态（未感染、潜伏感染、活动性疾病）和感染杆菌的相对毒力（野生型与减毒型）相关联。这项工作应该揭示与宿主细胞中的潜伏和重新激活相关的过程，产生关于对任一结果至关重要的网络和节点的假设，或指导宿主和病原体之间遗传交叉调节的机械模型的开发。在目标 2 中，我们建议识别结核杆菌中的候选转换网络，并构建机械数学模型来确定休眠转换逻辑。特别是，我们将测试控制休眠过渡的网络是否是由复杂逻辑门耦合的多个互连主机引起的压力响应开关叠加的结果。这项工作应该能够预测生长停滞和休眠特异性基因表达特征的条件和机制。在目标 3 中，数学模型和实验测试将针对在脂质代谢水平上介导巨噬细胞与病原体相互作用的关键分子过程。具体来说，我们将寻求确定巨噬细胞和结核杆菌中发生的脂质代谢变化是否形成细胞间反馈回路。通过结合实验和理论方法开发的模型将允许进行计算机模拟。这些模拟将对离体感染方案进行额外的实验扰动，从而完善模型。了解结核杆菌与携带结核杆菌的巨噬细胞之间决定结果的相互作用将对结核疫苗研究、诊断和治疗产生深远影响。 公共卫生相关性：二十多年的结核病研究表明，除非我们认识到对感染结果至关重要的宿主与病原体之间的相互作用并采取行动，否则结核病无法得到成功控制。我们假设，结核杆菌感染的任何结果都可以被视为相互的、可能迭代的相互作用动力学的结果，其中宿主细胞和细菌在细胞和分子水平上相互改变。我们的项目建议通过结合实验、计算和建模方法来揭示和机械地理解控制这些动态的网络。我们的目标是通过针对关键网络节点的新疫苗和药物来颠覆这些网络，以获得宿主优势。

DESCRIPTION (provided by applicant): Tuberculosis cannot be successfully controlled unless we recognize - and act upon -- the host-pathogen interactions critical to infection outcome. The central thesis of this proposal is that the outcome of M. tuberculosis infection with respect to latency and reactivation depends on the reciprocal interplay between host and pathogen signaling networks, metabolic pathways, and genetic programs. The goal of the proposed research is to uncover and mechanistically understand how intercellular networks operating between tubercle bacillus and lung macrophage govern the transitions to/from latency at the level of genetic programs and cellular metabolism. We propose to combine i) statistical pathway analyses and ii) bottom-up and top-down modeling strategies utilizing publicly available data, data contributed by on-going research in participating laboratories, and data generated in the present program from ex vivo infection of human primary lung macrophages with M. tuberculosis. We have three specific aims. In Aim 1, ex vivo infection data will be analyzed by statistical pathway analysis to correlate macrophage response with donor infection state (uninfected, latently infected, active disease) and relative virulence of infecting bacilli (wild type vs. attenuated). This work should reveal processes associated with latency and reactivation in host cells, generate hypotheses concerning networks and nodes critical to either outcome, or guide development of a mechanistic model for genetic cross-regulation between host and pathogen. In Aim 2, we propose to identify candidate switch networks in the tubercle bacillus and construct mechanistic mathematical models to determine dormancy switch logic. In particular, we will test whether the network controlling the transition to dormancy results from the superposition of multiple interlinked host-induced stress-response switches coupled by complex logical gates. This work should result in predictions for conditions and mechanisms for growth arrest and dormancy- specific gene expression signatures. In Aim 3, mathematical modeling and experimental tests will target key molecular processes mediating reciprocal macrophage-pathogen interactions at the level of lipid metabolism. Specifically, we will seek to determine whether changes in lipid metabolism occurring in the macrophage and in the tubercle bacillus form an intercellular feedback loop. Models developed by combining experimental and theoretical approaches will allow in silico simulations. These simulations will direct additional experimental perturbations to the ex vivo infection protocol that will refine the models. Understanding outcome-determining interactions between tubercle bacilli and the macrophages that carry them will have far-reaching effects on tuberculosis vaccine research, diagnostics, and therapeutics. PUBLIC HEALTH RELEVANCE: More than two decades of intense effort in tuberculosis research have shown that tuberculosis cannot be successfully controlled unless we recognize - and act upon -- the host-pathogen interactions that are critical to infection outcome. We hypothesize that any outcome of infection with tubercle bacilli can be viewed as the result of reciprocal, likely iterative, interaction dynamics in which host cells and bacteria change each other at cellular and molecular levels. Our program proposes to uncover and mechanistically understand the networks controlling these dynamics by combining experimental, computational and modeling approaches. Our goal is to subvert these networks to the host advantage with new vaccines and drugs targeting critical network nodes.