Host Determinants of TB Disease Progression

项目总结（见说明）： 项目1将应用系统方法来确定宿主调节基因（HRG）网络，该网络决定无症状结核分枝杆菌感染和结核病进展之间的平衡。我们的策略集中在我们最近鉴定的预测人类活动性结核病（TB）进展的转录组特征上。通过整合我们的MTB疾病进展的人类转录组特征与巨噬细胞先天免疫的网络模型，我们已经鉴定了近200种MTB感染的候选HRG。 利用我们对大量和不断扩大的含有ENU诱导的偶然突变的小鼠库的访问，我们将筛选HRG小鼠突变体的体内MTB诱导的先天性和适应性免疫的改变。HRG突变改变结核病的进展将进行详细的机制分析。结核分枝杆菌调节的先天免疫网络，以及控制先天免疫和适应性免疫之间界面的网络将通过系统水平的分析在体外和体内进行详尽的表征。我们将收集宿主和MTB转录组、靶向蛋白水平变化、条件特异性ChIP-seq， 来自感染巨噬细胞的匹配样品内的关键宿主调节剂的蛋白质组增强体谱。这些数据将推动细菌和宿主反应网络的建模，预测将推动新一轮候选HRG评估，组学规模的数据收集和额外的建模。我们的终极建模目标：一种新的整合宿主/MTB网络模型将使用来自人类的样品进行测试，其中候选突变细菌和特定宿主基因都由RNAi调节。 近年来，我们为系统生物学所需的基础设施做出了巨大贡献，包括开发用于数据生成，分析和建模的关键工具。我们已经生成了一个广泛的先天调控网络的纲要，将作为MTB研究的基础。该项目结合了免疫学，转录组学，分子遗传学，ChlPseq，蛋白质组学和网络建模方面的单独进展，以产生一个基于实验和可验证的系统级

PROJECT SUMMARY (See instructions): Project 1 will apply systems approaches to identify Host Regulatory Gene (HRG) networks that determine the balance between asymptomatic MTB infection and TB disease progression. Our strategy is centered on our recent identification of transcriptomic signatures that predict progression to active tuberculosis (TB) in humans. By integrating our human transcriptomic signatures for MTB disease progression with network models of macrophage innate immunity, we have identified nearly 200 candidate HRGs of MTB infection. Leveraging our access to a vast and expanding repository of mice harboring ENU-induced incidental mutations, we will screen the HRG mouse mutants for altered MTB-induced innate and adaptive immunity in vivo. HRG mutants that alter TB disease progression will be advanced for detailed mechanistic analysis. MTB-regulated innate immunity networks, and networks governing the interface between innate and adaptive immunity will be exhaustively characterized in vitro and in vivo through systems-level profiling. We will collect host and MTB transcriptomes, targeted protein level changes, condition-specific ChlP-seq, and proteomic enhanceosome profiles of key host regulators from within matched samples of infected macrophages. These data will fuel modeling of both the bacterial and host response networks, predictions from which will drive a new round of candidate HRG evaluation, omics-scale data collection and additional modeling. Our ultimate modeling Aim: a novel integrated host/MTB network model will be tested using samples from humans, with both candidate mutant bacteria and specific host genes modulated by RNAi. In recent years, we have contributed substantially to the infrastructure needed for systems biology, including the development of key tools for data generation, analysis and modeling. We have generated an extensive compendium of innate regulatory networks that will serve as a foundation for the MTB studies proposed here. This project combines separate advances in immunology, transcriptomics, molecular genetics, ChlPseq, proteomics and network modeling to produce an experimentally grounded and verifiable systems-level