Project 1: Mechanisms of Disease Progression

摘要-项目1 人类结核分枝杆菌感染导致各种各样的临床结果，从细菌根除到控制结核分枝杆菌感染， 和潜伏感染，进展和活动性疾病与一系列临床表型。我们最近 发现了一种血液转录特征，可预测结核病暴露个体长达18个月的结核病风险 在他们表现出临床症状之前，这是该领域的一个里程碑式的贡献。尽管如此， 结核病的进展仍然知之甚少，这在很大程度上是因为结核病患者体内的关键免疫反应， 不能容易地监测人肺。此外，结核病是一种高度异质性的疾病， 个体由于多种机制而进展为活动性疾病。在这个项目中，我们将进行一个 转录组、细胞因子、趋化因子和类花生酸谱的全面、多尺度整合， 肺和血液，以确定和模拟分子机制和途径， 决定感染的结果。首先，我们将使用多种实验策略来概括 小鼠中人Mtb感染的异质性。其中包括一种新型的“超低剂量”（ULD）感染 我们开创的模型中，小鼠感染1-3种细菌，随后表现出广泛的 从免疫控制到疾病进展。我们还将使用来自 协作交叉项目已证明极端结核表型和跨越一系列的结核分枝杆菌菌株 致病性。第二，我们将使用以下方法在这些小鼠模型中询问和建模宿主-Mtb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 在肺部先天性和适应性免疫细胞中。我们将测试关键调节分子的预测作用， 通过在体内对其进行遗传干扰并检查对Mtb感染控制的影响。我们还将应用 机器学习方法在小鼠中定义基于血液的多组学特征，以预测TB进展。 在我们的初步工作中，我们已经确定了一个早期的基于血液的签名，可以预测晚期细菌 ULD感染小鼠的负担。我们将把这个特征与免疫系统水平的测量相关联， 在肺中发挥作用，以揭示结核病控制机制。第三，我们将把这些发现转化为人类 疾病通过非洲健康研究所，我们将利用一个大规模的计划， 50，000人的基因组序列数据以及相关的流行病学和临床元数据 生活在结核病流行地区我们将进行候选基因遗传关联分析， 在小鼠中鉴定的调节分子，以确定人类直系同源物中的突变是否与 改变结核病的风险此外，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，并测试其预测TB的能力 人类的进步。

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.