Bacterial Determinants of TB Progression

结核病进展的细菌决定因素

• 批准号: 8577274
• 负责人: DAVID R SHERMAN
• 金额: $ 58.47万
• 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/8577274
• 关键词: Aerosols; Affect; Bacteria; Bacterial Infections; Behavior; Bone Marrow; Cells; Cessation of life; Collection; Communities; Complex; Containment; DNA Binding; DNA-Protein Interaction; Data; Data Collection; Development; Disease; Disease Progression; Environmental Monitoring; Evaluation; Foundations; Gene Expression; Gene Expression Profile; Generations; Genes; Genetic; Human; Immune response; In Vitro; Infection; Information Systems; Instruction; Joints; Lung; Microbiology; Modeling; Molecular Genetics; Mus; Mycobacterium tuberculosis; Nature; Network-based; Outcome; Pathogenesis; Peripheral Blood Mononuclear Cell; Phenotype; Proteins; Proteomics; Public Health; Regulator Genes; Regulon; Relative (related person); Research; Research Infrastructure; Role; Sampling; Signal Transduction; Small Interfering RNA; System; Systems Analysis; Systems Biology; Testing; Time; Tuberculosis; Variant; base; cell type; combat; data modeling; experience; fitness; high throughput screening; improved; in vivo; innovation; interest; macrophage; mouse model; mutant; network models; novel; pathogen; response; screening; spatiotemporal; tool; transcription factor; transcriptomics

PROJECT SUMMARY (See instructions): Project 2 will apply systems approaches to dissect the complex problem of TB disease progression in vivo, a first for the field. We first describe an innovative screening strategy to identify the MTB genes critical for disease progression in the lung. Previously we built a DNA binding/gene expression model that allows us to predict a regulon for every MTB transcription factor, and assembled a unique collection of MTB strains in which expression of every regulator is perturbed. We will use these strains to perturb every MTB gene regulatory network during aerosol infection of mouse lungs. 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. We then perform detailed systems analysis of the key genes and their predicted regulons using bone marrow macrophages infected ex vivo. We will collect host and MTB transcriptomes, MTB global protein level changes and condition-specific ChlP-seq on key MTB 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 mutant 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 siRNA. 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 also made a strong start for systems analysis of MTB, producing predictive gene regulatory networks based on large-scale ChlP-seq and expression studies. This project combines separate advances in microbiology, transcriptomics, molecular genetics, ChlP-seq, proteomics and network modeling to produce an experimentally grounded and verifiable systems-level model of the MTB regulatory networks that affect disease progression.

项目总结（见说明）： 项目2将应用系统方法来剖析体内结核病进展的复杂问题，这是该领域的第一次。我们首先描述了一种创新的筛选策略，以确定肺疾病进展的关键MTB基因。之前，我们建立了一个DNA结合/基因表达模型，使我们能够预测每个MTB转录因子的调节子，并组装了一个独特的MTB菌株集合，其中每个调节子的表达都受到干扰。我们将使用这些菌株在小鼠肺的气溶胶感染期间扰乱每个MTB基因调控网络。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。然后，我们进行详细的系统分析的关键基因和预测的调节子使用骨髓巨噬细胞感染离体。我们将从感染的巨噬细胞的匹配样品内收集宿主和MTB转录组、MTB全局蛋白水平变化和关键MTB调节因子上的条件特异性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。我们的最终建模目标，一种新的集成主机/MTB网络模型将使用来自人类的样本进行测试，候选突变体细菌和特定的宿主基因的siRNA调制。 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。我们还为MTB的系统分析做了一个强有力的开始，基于大规模ChIP-seq和表达研究产生了预测性基因调控网络。该项目结合了微生物学，转录组学，分子遗传学，ChlP-seq，蛋白质组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。