Project 1: Mechanisms of Disease Progression

项目1：疾病进展机制

• 批准号: 10339373
• 负责人: ALAN A ADEREM
• 金额: $ 95.12万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-12 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10339373
• 关键词: Africa; Bacillus; Bacteria; Blood; Candidate Disease Gene; Cell Nucleus; Cells; Clinical; Clinical Trials; Data; Data Set; Disease; Disease Progression; Dose; Early treatment; Eicosanoids; Epidemiology; Exhibits; Gene Expression Profile; Genetic; Genomics; Health; Heterogeneity; Human; Immune; Immune response; Individual; Infection; Inflammation Mediators; Inflammatory Response; Inhalation; Link; Lung; Machine Learning; Measurement; Mediating; Metadata; Modeling; Molecular; Monitor; Mouse Strains; Mus; Mutation; Mycobacterium tuberculosis; Orthologous Gene; Outcome; Pathogenicity; Pathway interactions; Phenotype; Physiological; Population; Process; Publishing; Research Institute; Risk; Role; Shapes; Site; Symptoms; System; Systems Biology; Testing; Time; Translating; Tuberculosis; Vaccines; Water; Work; aerosolized; base; chemokine; clinical phenotype; cytokine; disease heterogeneity; genetic association; genetic manipulation; human disease; immune function; improved; in vivo; insight; latent infection; molecular modeling; mouse model; multiple omics; nonhuman primate; novel; predictive signature; predictive test; prevent; programs; response; transcriptomics

Abstract – Project 1 Human Mtb infection results in a large variety of clinical outcomes, ranging from bacterial eradication, to control and latent infection, to progression and active disease with a range of clinical phenotypes. We recently discovered a blood transcriptional signature that predicts TB risk in Mtb-exposed individuals up to 18 months before they exhibit clinical symptoms, a landmark contribution to the field. Still, the mechanisms that underlie TB disease progression remain poorly understood, in large part because the key immune responses within the human lung cannot be readily monitored. Furthermore, TB is a highly heterogeneous disease in which individuals progress to active disease due to a variety of mechanisms. In this project, we will conduct a comprehensive, multi-scale integration of transcriptomic, cytokine, chemokine and eicosanoid profiles from lung and blood during Mtb infection in order to identify and model molecular mechanisms and pathways that determine the outcome of infection. First, we will use multiple experimental strategies to recapitulate the heterogeneity of human Mtb infection in the mouse. These include a novel “ultra low dose” (ULD) infection model that we have pioneered in which mice are infected with 1-3 bacteria and subsequently exhibit a broad range of outcomes, ranging from immune control to progression. We will also employ mice from the Collaborative Cross project that have demonstrated extreme TB phenotypes and Mtb strains that span a range of pathogenicity. Second, we will interrogate and model the host-Mtb interaction in these mouse models using a variety of systems biology approaches in order to uncover the molecular regulators, pathways, and networks in pulmonary innate and adaptive immune cells. We will test the predicted role of critical regulatory molecules by genetically perturbing them in vivo and examining the impact on control of Mtb infection. We will also apply machine-learning approaches to define multi-omic blood based signatures in mice that predict TB progression. In our preliminary work, we have defined an early blood-based signature that predicts the late-time bacterial burdens in ULD-infected mice. We will correlate this signature with systems-level measurements of immune function in the lung to uncover mechanisms of Mtb control. Third, we will translate these findings to human disease. Through the Africa Health Research Institute, we will leverage a large-scale program that will obtain genomic sequence data as well as associated epidemiological and clinical metadata on 50,000 individuals living in a TB-endemic region. We will conduct a candidate gene genetic association analysis to validate regulatory molecules identified in mice to determine whether mutations in human orthologs are associated with altered risk of TB. In addition, we will use several existing non-human primate and human datasets to refine the blood based multi-omic progression signatures defined in mice and test their ability to predict TB progression in humans.

摘要 – 项目 1 人类结核分枝杆菌感染会导致多种临床结果，从细菌根除到控制 和潜伏感染，到进展和具有一系列临床表型的活动性疾病。 发现了一种血液转录特征，可以预测 Mtb 暴露个体长达 18 个月的结核病风险 在它们表现出临床症状之前，这仍然是该领域的里程碑式贡献。 结核病的进展仍然知之甚少，很大程度上是因为体内的关键免疫反应 此外，结核病是一种高度异质性的疾病，其中。 由于多种机制，个体会发展为活动性疾病。在这个项目中，我们将进行一项研究。 转录组、细胞因子、趋化因子和类二十烷酸谱的全面、多尺度整合 Mtb 感染期间的肺和血液，以便识别和模拟分子机制和途径 首先，我们将使用多种实验策略来概括感染的结果。 小鼠中人类结核分枝杆菌感染的异质性包括一种新型“超低剂量”（ULD）感染。 我们首创的模型中，小鼠感染 1-3 种细菌，随后表现出广泛的 我们还将使用来自免疫控制和进展的小鼠。 协作交叉项目已证明极端结核病表型和跨越一系列的结核分枝杆菌菌株 其次，我们将使用这些小鼠模型来探究和模拟宿主与 Mtb 的相互作用。 各种系统生物学方法，以揭示分子调节因子、途径和网络 我们将测试关键调节分子的预测作用。 通过在体内对它们进行基因干扰并检查对控制结核分枝杆菌感染的影响，我们也将应用该方法。 机器学习方法定义小鼠的多组学血液特征，预测结核病的进展。 在我们的初步工作中，我们定义了一种基于早期血液的特征，可以预测晚期细菌 我们将把这个特征与免疫系统水平的测量联系起来。 第三，我们将把这些发现应用到人类身上。 通过非洲健康研究所，我们将利用一项大规模计划，该计划将获得 50,000 人的基因组序列数据以及相关流行病学和临床元数据 我们将进行候选基因遗传关联分析来验证。 在小鼠中鉴定的调节分子以确定人类直向同源物的突变是否与 此外，我们将使用几个现有的非人类灵长类动物和人类数据集来完善。 在小鼠中定义的基于血液的多组学进展特征并测试它们预测结核病的能力 人类的进步。