Host Determinants of TB Disease Progression

结核病进展的宿主决定因素

• 批准号: 8577272
• 负责人: ALAN A ADEREM
• 金额: $ 78.73万
• 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/8577272
• 关键词: Aerosols; Affect; Bacteria; Bacterial Infections; Behavior; Bone Marrow; Cells; Cessation of life; Containment; Cues; Data; Data Collection; Data Set; Dendritic Cell Pathway; Dendritic Cells; Dendritic cell activation; Development; Disease; Disease Progression; Equilibrium; Ethylnitrosourea; Evaluation; Foundations; Gene Expression; Gene Expression Profile; Gene Mutation; Generations; Genes; Human; Immune response; Immunology; In Vitro; Infection; Instruction; Lung; Modeling; Molecular Genetics; Mus; Mutant Strains Mice; Mutation; Mycobacterium tuberculosis; Natural Immunity; Nature; Outcome; Pathway interactions; Phenotype; Proteins; Proteomics; RNA Interference; Regulator Genes; Research Infrastructure; Sampling; Shapes; System; Systems Analysis; Systems Biology; T cell response; T-Lymphocyte; Testing; Time; Transgenic Organisms; Tuberculosis; Virulence; adaptive immunity; advanced system; base; cohort; combat; in vivo; macrophage; monocyte; mutant; network models; novel; pathogen; predictive modeling; repository; research study; residence; tool; transcriptomics

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

项目摘要（参见说明）： 项目 1 将应用系统方法来识别宿主调节基因 (HRG) 网络，从而确定无症状 MTB 感染与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。我们将收集宿主和 MTB 转录组、目标蛋白水平变化、条件特异性 ChlP-seq 以及 来自受感染巨噬细胞的匹配样本中关键宿主调节因子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，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：将使用人类样本测试新型集成宿主/MTB 网络模型，其中包括候选突变细菌和由 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，以产生基于实验和可验证的系统级