Bacterial Determinants of TB Progression

项目总结(见说明)： 项目2将应用系统方法来剖析结核病在体内发展的复杂问题，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，以确定对肺部疾病进展至关重要的结核分枝杆菌基因。之前，我们建立了一个DNA结合/基因表达模型，允许我们预测每个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部气溶胶感染期间扰乱每个结核分枝杆菌基因调控网络。一旦确定了关键调控因素，我们将对感染细胞类型的变化进行量化和表征，并确定疾病进展中特定突变体表现出改变反应的特定点。然后，我们利用体外感染的骨髓巨噬细胞对关键基因及其预测的调控进行详细的系统分析。我们将从匹配的受感染巨噬细胞样本中收集宿主和结核分枝杆菌的转录本、结核分枝杆菌全球蛋白水平的变化以及关键结核分枝杆菌调控因子的特定条件ChlP-seq。这些数据将推动细菌和宿主反应网络的建模，由此做出的预测将推动新一轮突变评估、组学规模的数据收集和额外的建模。我们的最终建模目标是，一个新的整合的宿主/结核分枝杆菌网络模型将使用来自人类的样本进行测试，候选突变细菌和特定的宿主基因都由siRNA调控。 近年来，我们为系统生物学所需的基础设施作出了重大贡献，包括开发用于数据生成、分析和建模的关键工具。我们也为结核分枝杆菌的系统分析开了一个好头，在大规模ChlP-seq和表达研究的基础上建立了预测性的基因调控网络。该项目结合了微生物学、转录学、分子遗传学、ChlP-seq、蛋白质组学和网络建模方面的不同进展，以产生影响疾病进展的结核分枝杆菌调控网络的实验基础和可验证的系统级模型。

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.