Omics for TB Disease Progression (OTB)

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 提案检验了寄主和病原体的行为通过相互交织的调控来协调的概念 网络，感染的结果(细菌遏制或活动性疾病)是许多 在空间和时间上变化的网络-网络交互。如果是这样，那么扰乱特定网络 既能说明更大网络的拓扑结构，又能让我们定义步骤和组件 对感染结果至关重要。我们由两个项目和四个核心组成的联盟将测试这一假设，并 在迭代周期中揭示结核病进展的关键特征：扰乱精心选择的子网络 在MTB和HOST内；收集匹配的组学数据集；使用新的 实验。 项目1利用大量突变小鼠来筛选来自独特基因的新候选基因 南非临床队列研究对结核病进展的影响。项目2以一部活体内的小说开始 基因筛查，以确定影响肺部疾病进展的结核分枝杆菌调节因子。在每种情况下，一次密钥 我们将对感染细胞类型的变化进行量化和表征，并确定 疾病进展中的特定点，特定的突变体表现出不同的反应。 对于这两个项目，我们利用我们广泛的初步数据缓存来执行详细的系统分析 利用体外感染的骨髓巨噬细胞研究关键基因及其预测的调控。我们会收集 宿主和结核分枝杆菌的转录本和来自匹配样本的全球蛋白质水平的变化。我们还将表演 受感染巨噬细胞内关键结核分枝杆菌调控因子的条件特异性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。我们在这方面的最终建模目标 建议是一种新的集成主机/MTB网络模型，其人力相关性将在 原代MTB突变的人巨噬细胞和相关宿主基因通过RNAi失控。

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.