A multi-scale and multi-system approach to understand granuloma formation in TB

了解结核病肉芽肿形成的多尺度、多系统方法

• 批准号: 7659589
• 负责人: Steven Lynn Kunkel
• 金额: $ 22.57万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-15 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7659589
• 关键词: Address; Animals; Anti-Tumor Necrosis Factor Therapy; Antigens; Biological; Cells; Cessation of life; Collection; Computer Simulation; Data; Dendritic Cells; Development; Disease Outcome; Doctor of Philosophy; Effector Cell; Event; Goals; Grant; Granuloma; Immune; Immune response; Infection; Lead; Lung; Michigan; Modeling; Mus; Mycobacterium tuberculosis; Organ; Outcome; Role; Structure; System; T-Lymphocyte; TNF gene; Testing; Time; Tissues; Tuberculosis; Universities; Vaccine Therapy; World Health; abstracting; in vivo; lymph nodes; medical schools; molecular scale; nonhuman primate; pulmonary granuloma; tool; trafficking

DESCRIPTION (provided by applicant): Tuberculosis is responsible for 2 million deaths per year. The interplay between host and bacterial factors leads to different disease outcomes (latency, primary tuberculosis, reactivation tuberculosis). A key outcome is the formation of a collection of immune cells termed the granuloma. This structure acts not only as an immune microenvironment and a barrier to dissemination but also as a niche for long-term bacterial survival. The long- term goal of this project is to identify factors that contribute to different outcomes of M. tuberculosis infection. We hypothesize that these different infection outcomes are reflected locally at the level of the granuloma and that granuloma structure is the result of the interplay of events at organ, tissue, cellular, and molecular scales over the time course of minutes to years. Several models of granuloma formation in tuberculosis will be integrated: pulmonary granulomas induced by M. tuberculosis antigen (PPD) coated beads in vivo, M. tuberculosis infection in mice and non-human primates, and multi-scale in silico models. Our studies will include multiple spatial and temporal scales to address the following aims. Aim 1: Determine how specific immune cells and effector molecules in the lung influence the formation of different granuloma structures. Aim 2: Determine the role of dendritic cell and T cell trafficking between lung granuloma and draining lymph nodes in influencing granuloma development. Aim 3: Identify the mechanisms that determine TNF availability for the purpose of understanding how granulomas form as well as how treatment with anti-TNF-therapies leads to TB reactivation. Our interdisciplinary team's approach for integrating data and in silico models over the relevant biological and temporal scales will allow us to predict and test hypotheses regarding key factors that influence granuloma formation and structure. These factors are likely central to determining different disease outcomes following M. tuberculosis infection and will provide a new tool for testing therapies and vaccines against M. tuberculosis. Tuberculosis (TB) is a world health issue. The immune response to TB is unique, resulting in the formation of structures called granulomas in the lungs of infected people. We seek to understand the formation and function of these structures using integrated data generated from a variety of animal and computational models. (End of Abstract)

描述（由申请人提供）：结核病每年造成200万人死亡。宿主和细菌因素之间的相互作用导致不同的疾病结果（潜伏期、原发性结核病、再激活性结核病）。一个关键的结果是形成一组称为肉芽肿的免疫细胞。这种结构不仅作为免疫微环境和传播屏障，而且作为细菌长期生存的生态位。该项目的长期目标是确定导致结核分枝杆菌感染不同结果的因素。我们假设这些不同的感染结果在局部肉芽肿水平上反映出来，肉芽肿结构是器官、组织、细胞和分子尺度上的事件在几分钟到几年的时间过程中相互作用的结果。将整合结核病肉芽肿形成的几种模型：体内结核分枝杆菌抗原（PPD）包被珠诱导的肺肉芽肿，小鼠和非人灵长类动物的结核分枝杆菌感染，以及多尺度的硅模型。我们的研究将包括多个空间和时间尺度，以解决以下目标。目的1：确定肺内特异性免疫细胞和效应分子如何影响不同肉芽肿结构的形成。目的2：确定肺肉芽肿和引流淋巴结之间树突状细胞和T细胞转运在影响肉芽肿发展中的作用。目的3：确定确定肿瘤坏死因子可用性的机制，以了解肉芽肿如何形成以及抗肿瘤坏死因子治疗如何导致结核病再激活。我们的跨学科团队在相关的生物和时间尺度上整合数据和计算机模型的方法将使我们能够预测和测试有关影响肉芽肿形成和结构的关键因素的假设。这些因素可能是确定结核分枝杆菌感染后不同疾病结局的核心因素，并将为检测针对结核分枝杆菌的疗法和疫苗提供新的工具。结核病是一个世界卫生问题。对结核病的免疫反应是独特的，导致感染者肺部形成称为肉芽肿的结构。我们试图利用从各种动物和计算模型中产生的综合数据来理解这些结构的形成和功能。（摘要结束）