Omics for TB Disease Progression (OTB)

从最初感染到出现症状，结核病是一种非常复杂的疾病。这 该提案验证了宿主和病原体的行为是通过相互交织的调节机制来协调的这一概念。 网络，感染的结果（细菌遏制或活动性疾病）是许多因素的产物。 网络与网络之间的互动在空间和时间上都是变化的。如果是这样，那么干扰特定的网络 将阐明大型网络的拓扑结构，并允许我们定义步骤和组件 对感染结果至关重要。我们的两个项目和四个核心的财团将测试这一假设， 揭示结核病在迭代循环中进展的关键特征：扰乱精心选择的子网络 在MTB和主机内;收集匹配的组学数据集;使用新的 实验 项目1利用大量的突变小鼠库来筛选来自一种独特的 南非临床队列研究对结核病进展的影响。项目2从一部小说开始， 基因筛选，以确定影响肺部疾病进展的MTB调节因子。在每种情况下， 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改变的反应的特定点。 对于这两个项目，我们利用大量的初步数据缓存来执行详细的系统分析 关键基因及其预测的调节子使用骨髓巨噬细胞感染离体。我们将收集 宿主和MTB转录组以及来自匹配样品的总体蛋白水平变化。我们还将表演 条件特异性ChIP-seq对来自感染的巨噬细胞内的关键MTB调节剂的作用。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 突变体评估、组学规模数据收集和额外建模。我们的终极建模目标 建议是一种新的集成主机/MTB网络模型，其中的人类相关性将在 具有突变型MTB的原代人巨噬细胞和通过RNAi失调的相关宿主基因。

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.