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）：从最初的感染到出现症状，结核病是一种非常复杂的疾病。这一提议验证了这样一个概念，即宿主和病原体的行为是由相互交织的调节网络协调的，感染的结果（细菌遏制或活动性疾病）是许多网络相互作用的产物，这些相互作用在空间和时间上都是不同的。如果是这样，那么干扰特定的网络将既阐明更大网络的拓扑结构，又使我们能够定义对感染结果至关重要的步骤和组成部分。我们由两个项目和四个核心组成的联盟将检验这一假设，并揭示结核病在迭代循环中进展的关键特征：干扰精心选择的子网