Bacterial Determinants of TB Progression

项目摘要（参见说明）： 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，这在该领域尚属首次。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 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，其中每个调节器的表达都受到干扰。我们将使用这些菌株来扰乱小鼠肺部气溶胶感染期间的每个 MTB 基因调控网络。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。然后，我们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。我们将从受感染巨噬细胞的匹配样本中收集宿主和 MTB 转录组、MTB 全局蛋白水平变化以及关键 MTB 调节因子的条件特异性 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。我们的最终建模目标是一种新型的集成宿主/MTB 网络模型，将使用人类样本进行测试，其中包括候选突变细菌和由 siRNA 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。我们还在 MTB 系统分析方面取得了良好的开端，基于大规模 ChlP-seq 和表达研究生成了预测基因调控网络。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方面的各自进展，以产生影响疾病进展的 MTB 调节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.