Omics for TB Disease Progression (OTB)

描述(申请人提供)：从最初的感染到出现症状，结核病(TB)是一种非常复杂的疾病。这一建议检验了这样的概念，即宿主和病原体的行为是由相互交织的调控网络协调的，并且感染(细菌遏制或活动性疾病)的结果是许多网络-网络相互作用的产物，这些网络在空间和时间上都不同。如果是这样，那么扰乱特定的网络既可以照亮更大网络的拓扑结构，又可以让我们定义对感染结果至关重要的步骤和组件。我们由两个项目和四个核心组成的联盟将测试这一假设，并在迭代周期中揭示结核病进展的关键特征：扰乱精心选择的子网络 在MTB和HOST内；收集匹配的组学数据集；使用新的 实验。项目1利用大量突变小鼠来筛选来自南非独特临床队列的新候选基因，以了解对结核病进展的影响。项目2以一种新的体内基因筛查开始，以确定影响肺部疾病进展的结核分枝杆菌调节因子。在每种情况下，一旦确定了关键调控因子，我们将量化和表征感染细胞类型的变化，并确定疾病进展中特定突变体表现出改变反应的特定点。对于这两个项目，我们利用我们广泛的初步数据缓存，使用体外感染的骨髓巨噬细胞对关键基因及其预测的调控进行详细的系统分析。我们将从匹配的样本中收集宿主和结核分枝杆菌的转录本以及全球蛋白质水平的变化。我们还将从受感染的巨噬细胞中对关键的结核分枝杆菌调节因子进行条件特异性ChlP-seq。这些数据将推动细菌和宿主反应网络的建模，由此做出的预测将推动新一轮突变评估、组学规模的数据收集和额外的建模。我们在这个建议中的最终建模目标是一个新的整合的主机/MTB网络模型，该模型将在原代人巨噬细胞中验证其与人类的相关性，其中突变的MTB和相关的宿主基因通过RNAi被破坏。 相关性：结核分枝杆菌每年导致约900万新的活动性疾病病例和140万人死亡，而我们抗击结核病(TB)疾病的工具普遍过时和过度匹配。该项目结合了系统生物学和网络建模方面的不同进展，以产生影响疾病进展的结核分枝杆菌调控网络的实验接地和可验证的系统级模型。 项目1：结核病进展的宿主决定因素 项目负责人(PL)：艾伦·阿德雷姆 描述(由申请人提供)：项目1将应用系统方法来确定宿主调控基因(HRG)网络，以确定无症状结核杆菌感染和结核病进展之间的平衡。我们的战略集中在我们最近识别的预测人类发展为活动性结核病(TB)的转录本签名。通过将我们对MTB疾病进展的人类转录信号与巨噬细胞天然免疫的网络模型相结合，我们已经确定了近200个候选的MTB感染HRGs。利用我们获得大量和不断扩大的含有ENU诱导的偶然突变的小鼠的信息库，我们将在体内筛选改变的MTB诱导的先天和获得性免疫的HRG小鼠突变体。改变结核病进展的HRG突变将被提出进行详细的机制分析。结核分枝杆菌调节的先天免疫网络，以及管理先天免疫和获得性免疫之间的接口的网络，将通过系统水平的描述在体外和体内详尽地描述。我们将从匹配的感染巨噬细胞样本中收集宿主和结核分枝杆菌的转录本、靶向蛋白水平的变化、特定条件的ChlP-seq，以及增强关键宿主调控因子的蛋白质组。这些数据将推动细菌和宿主反应网络的建模，由此做出的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。我们的最终建模目标：一种新型的集成主机/MTB网络模型将使用以下工具进行测试 来自人类的样本，候选突变细菌和特定宿主基因都受到RNAi的调控。近年来，我们为系统生物学所需的基础设施作出了重大贡献，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固有监管网络概要，将作为这里提出的MTB研究的基础。该项目结合了免疫学、转录学、分子遗传学、ChlPseq、蛋白质组学和网络建模方面的不同进展，以产生一个实验上扎根和可验证的系统级。 相关性：结核分枝杆菌每年导致约900万新的活动性疾病病例和140万人死亡，而我们抗击结核病(TB)疾病的工具普遍过时和过度匹配。该项目结合了系统生物学和网络建模方面的单独进展，以产生影响结核病进展的宿主调控网络的实验接地和可验证的系统级模型。

DESCRIPTION (as provided by applicant): From initial infection to the onset of symptoms, tuberculosis (TB) is a remarkably complex disease. This proposal tests the concept that behaviors of host and pathogen are coordinated by interwoven regulatory networks, and that the outcome of infection (bacterial containment or active disease) is the product of many network-network interactions that vary both spatially and temporally. If so, then perturbing specific networks will both illuminate the topology of the larger network and allow us to define the steps and components critical to infection outcome. Our consortium of two projects and four Cores will test this hypothesis and reveal key features of TB disease progression in an iterative cycle: perturb carefully chosen subnetworks within both MTB and host; collect matched omics data sets; model, predict, and validate with new experiments. Project 1 exploits a vast repository of mutant mice to screen novel candidate genes derived from a unique South African clinical cohort for effects on TB disease progression. Project 2 begins with a novel in vivo genetic screen to identify MTB regulators that affect disease progression in lungs. In each case, once key regulators are identified, we will quantitate and characterize the changes in infected cell types and determine the specific points in disease progression where particular mutants show altered responses. For both projects, we leverage our extensive cache of preliminary data to perform detailed systems analyses of key genes and their predicted regulons using bone marrow macrophages infected ex vivo. We will collect host and MTB transcriptomes and global protein level changes from matched samples. We will also perform condition-specific ChlP-seq on key MTB regulators from within infected macrophages. These data will fuel modeling of both the bacterial and host response networks, predictions from which will drive a new round of mutant evaluation, omics-scale data collection and additional modeling. Our ultimate modeling Aim in this proposal is a novel integrated host/MTB network model, human relevance of which will be validated in primary human macrophages with mutant MTB and relevant host genes dis-regulated via RNAi. RELEVANCE: Mycobacterium tuberculosis causes ~9 million new cases of active disease and 1.4 million deaths each year, and our tools to combat tuberculosis (TB) disease are universally outdated and overmatched. This project combines separate advances in systems biology and network modeling to produce an experimentally grounded and verifiable systems-level model of the MTB regulatory networks that affect disease progression. Project 1: Host Determinants of TB Disease Progression Project Leader (PL): Alan Aderem DESCRIPTION (as provided by applicant): 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 enhance some 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. RELEVANCE: Mycobacterium tuberculosis causes ~ 9 million new cases of active disease and 1.4 million deaths each year, and our tools to combat tuberculosis (TB) disease are universally outdated and overmatched. This project combines separate advances in systems biology and network modeling to produce an experimentally grounded and verifiable systems-level model of the host regulatory networks that affect TB progression.