Omics for TB Disease Progression (OTB)

结核病进展组学 (OTB)

• 批准号: 8686744
• 负责人: ALAN A ADEREM
• 金额: $ 407.54万
• 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/8686744
• 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）：从最初感染到出现症状，结核病 (TB) 是一种非常复杂的疾病。该提案测试了这样的概念：宿主和病原体的行为是通过交织的调节网络来协调的，并且感染的结果（细菌遏制或活动性疾病）是许多在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，那么扰动特定网络将既阐明更大网络的拓扑结构，又允许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，并揭示迭代周期中结核病进展的关键特征：扰乱精心选择的子网络 在 MTB 和主机内；收集匹配的组学数据集；使用新的模型、预测和验证 实验。项目 1 利用大量突变小鼠来筛选来自独特的南非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 从一种新颖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，一旦确定了关键调节因子，我们将定量和表征受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反应改变的特定点。对于这两个项目，我们利用大量的初步数据，使用离体感染的骨髓巨噬细胞对关键基因及其预测的调节子进行详细的系统分析。我们将从匹配的样本中收集宿主和 MTB 转录组以及整体蛋白质水平变化。我们还将对受感染巨噬细胞内的关键 MTB 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，从中进行的预测将推动新一轮的突变体评估、组学规模的数据收集和其他建模。我们在本提案中的最终建模目标是一种新颖的集成宿主/MTB 网络模型，其与人类的相关性将在具有突变 MTB 的原代人类巨噬细胞和通过 RNAi 解除调节的相关宿主基因中得到验证。 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，而我们对抗结核病 (TB) 的工具普遍已经过时且过度匹配。该项目结合了系统生物学和网络建模方面的各自进展，产生了一个基于实验且可验证的影响疾病进展的 MTB 调控网络的系统级模型。 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（由申请人提供）：项目 1 将应用系统方法来识别宿主调节基因 (HRG) 网络，以确定无症状 MTB 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转录组特征上，这些特征可以预测人类活动性结核病 (TB) 的进展。通过将 MTB 疾病进展的人类转录组特征与巨噬细胞先天免疫网络模型相结合，我们已经鉴定出近 200 个 MTB 感染的候选 HRG。利用我们对含有 ENU 诱导的偶然突变的巨大且不断扩大的小鼠资源库的访问权，我们将筛选 HRG 小鼠突变体，以改变 MTB 诱导的体内先天和适应性免疫。改变结核病进展的 HRG 突变体将被推进进行详细的机制分析。 MTB 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级分析在体外和体内进行详尽的表征。我们将从受感染巨噬细胞的匹配样本中收集宿主和 MTB 转录组、目标蛋白质水平变化、条件特异性 ChlP-seq 和蛋白质组增强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，从中进行的预测将推动新一轮候选 HRG 评估、组学规模的数据收集和其他建模。我们的最终建模目标：将使用新的集成主机/MTB 网络模型进行测试 来自人类的样本，其中包含候选突变细菌和受 RNAi 调节的特定宿主基因。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节网络概要，它将作为此处提出的 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，以产生基于实验的、可验证的系统水平。 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，而我们对抗结核病 (TB) 的工具普遍已经过时且过度匹配。该项目结合了系统生物学和网络建模方面的各自进展，以产生影响结核病进展的宿主调节网络的实验基础和可验证的系统级模型。