Omics for TB Disease Progression (OTB)

结核病进展组学 (OTB)

• 批准号: 8564003
• 负责人: ALAN A ADEREM
• 金额: $ 332.57万
• 依托单位: SEATTLE BIOMEDICAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-21 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8564003
• 关键词: Affect; African; Bacteria; Bacterial Infections; Behavior; Bone Marrow; Candidate Disease Gene; Cessation of life; Clinical; Cohort Effect; Complex; Containment; Data; Data Collection; Data Set; Development; Disease; Disease Progression; Equilibrium; Ethylnitrosourea; Evaluation; Foundations; Gene Expression Profile; Generations; Genes; Genetic Screening; Human; Immune response; Immunology; In Vitro; Infection; Lung; Modeling; Molecular Genetics; Mus; Mutant Strains Mice; Mutation; Mycobacterium tuberculosis; Natural Immunity; Outcome; Proteins; Proteomics; RNA Interference; Regulator Genes; Regulon; Research Infrastructure; Sampling; Symptoms; System; Systems Analysis; Systems Biology; Testing; Tuberculosis; adaptive immunity; cell type; combat; in vivo; macrophage; mutant; network models; novel; pathogen; repository; research study; response; tool; transcriptomics

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.

描述（由申请人提供）：从最初感染到症状发作，结核病（TB）是一种非常复杂的疾病。该提议测试了宿主和病原体的行为由交织的调控网络协调的概念，并且感染的结果（细菌遏制或活动性疾病）是许多空间和时间上变化的网络-网络相互作用的产物。如果是这样的话，那么扰动特定的网络既可以阐明更大网络的拓扑结构，也可以让我们定义对感染结果至关重要的步骤和组件。我们的两个项目和四个核心的联盟将测试这一假设，并揭示结核病进展的关键特征，在一个迭代周期：扰动精心选择的子网络 在MTB和主机内;收集匹配的组学数据集;使用新的模型进行建模、预测和验证 实验项目1利用大量突变小鼠库筛选来自南非独特临床队列的新型候选基因，以研究其对结核病进展的影响。项目2从一种新的体内遗传筛选开始，以确定影响肺部疾病进展的MTB调节因子。在每种情况下，一旦确定了关键调控因子，我们将定量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显示改变反应的特定点。对于这两个项目，我们利用我们广泛的初步数据缓存进行详细的系统分析的关键基因及其预测的调节子使用骨髓巨噬细胞感染离体。我们将从匹配的样本中收集宿主和MTB转录组以及全局蛋白水平变化。我们还将对来自感染的巨噬细胞内的关键MTB调节剂进行条件特异性ChIP-seq。这些数据将推动细菌和宿主反应网络的建模，预测将推动新一轮的突变评估，组学规模的数据收集和额外的建模。我们在该提案中的最终建模目标是一种新的整合的宿主/MTB网络模型，其人类相关性将在具有突变型MTB的原代人巨噬细胞中得到验证，并且相关宿主基因通过RNAi失调。 相关性：结核分枝杆菌每年导致约900万新发活动性疾病病例和140万例死亡，我们防治结核病的工具普遍过时和不匹配。该项目结合了系统生物学和网络建模的单独进展，以产生影响疾病进展的MTB调控网络的实验基础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）：Alan Aderem 描述（如申请人所提供）：项目1将应用系统方法来鉴定宿主调节基因（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ChlP-seq和蛋白质组学增强来自感染巨噬细胞的匹配样本内的关键宿主调节因子的一些概况。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。我们的最终建模目标：一个新的集成主机/MTB网络模型将使用 来自人类的样本，其中候选突变细菌和特定宿主基因都受到RNA干扰的调节。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。 相关性：结核分枝杆菌每年导致约900万新发活动性疾病病例和140万例死亡，我们防治结核病的工具普遍过时和不匹配。该项目结合了系统生物学和网络建模的单独进展，以产生影响结核病进展的宿主调控网络的实验基础和可验证的系统级模型。