Defining how the spectrum of latency affects reactivation of TB

定义潜伏期范围如何影响结核病的重新激活

• 批准号: 8053154
• 负责人: JoAnne L. Flynn
• 金额: $ 67.5万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-17 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8053154
• 关键词: Address; Affect; Animal Model; Animals; Antigens; Area; Arts; Bacillus (bacterium); Biological; Biological Models; Cellular Structures; Characteristics; Clinical; Clinical Immunology; Collaborations; Complex; Computer Simulation; Computers; Data; Disease; Event; Factor Analysis; Goals; Granuloma; Human; Image; Imaging Techniques; Imaging technology; Immune response; Immunologic Factors; Immunologics; Immunologist; Immunology; Immunosuppressive Agents; Individual; Infection; Intervention; Lead; Lung; Lung diseases; Macaca; Methods; Modeling; Molecular Genetics; Monkeys; Mycobacterium tuberculosis; One-Step dentin bonding system; Organ; Outcome; PET/CT scan; Pathologic; Pathology; Peripheral; Persons; Phenotype; Population; Positioning Attribute; Risk; Sampling; Scientist; Structure of lymph node of thorax; System; Systems Biology; T cell response; Testing; Tissues; Tuberculosis; experience; high risk; human data; latent infection; lymph nodes; mathematical model; member; microbial; molecular scale; mycobacterial; next generation; nonhuman primate; pathogen; prevent; programs; public health relevance; reactivation from latency

DESCRIPTION (provided by applicant): Outcome of Mycobacterium tuberculosis infection in humans is clinically defined as "active" or "latent". These clinical definitions are inadequate to describe the continuum of M. tuberculosis infection. The actual presentation of "active" tuberculosis varies from mild to severe pulmonary disease, including cavitary tuberculosis, and to extrapulmonary or disseminated disease. Several lines of evidence support that latent infection is also a spectrum of infection outcomes, from subclinical disease to "dormant infection" to completely cleared infection. The concept of a latency spectrum has practical implications: we hypothesize that only a small percentage of latently infected persons is most likely to reactive TB, and identifying those persons, who we believe are "higher" on the latency spectrum, allows one to target interventions to those who most will benefit. In this proposal, we will explore the concept of the spectrum of latency and implications for reactivation using a systems biology approach. We propose to integrate data from humans, non-human primates, and computational systems to provide a comprehensive approach to latency and reactivation. We will use immunologic methods and state-of-the-art imaging technology to define the spectrum of latency in humans and non-human primates infected with M. tuberculosis. From non-human primates, we will go one step further and obtain granulomas for detailed study of the spectrum of latency, as well as during reactivation. These granulomas will be used in immunologic, microbiologic and pathologic studies to identify which are most likely to reactivate and the factors involved in maintaining a subclinical infection. In addition to vastly increasing our understanding of "latent" TB and the factors that contribute to reactivation, all of the human and non-human primate data will be incorporated into next generation multi-scale mathematical models of tuberculosis. This will provide the computational platform for sophisticated analysis of factors that contribute to the spectrum of latency and the risk of reactivation. Ultimately these models, informed by data from humans and a very relevant animal model, can be used to test our hypothesis that the position of an individual on the spectrum of latency influences the risk of reactivation. This project brings together an experienced team of immunologists, microbiologists, and computational scientists who have focused on the study of tuberculosis for many years. PUBLIC HEALTH RELEVANCE: Mycobacterium tuberculosis, the causative agent of tuberculosis, can cause clinically apparent disease (TB) or more commonly a clinically silent infection (latent TB) that can reactivate to cause TB as well. It is estimated that 1.7 billion people worldwide have latent TB infection. Here we integrate data from humans and animal models with computational and mathematical models in a comprehensive systems biology approach to a better understanding of latent TB and the factors that lead to reactivation.

描述（由申请人提供）：人类结核分枝杆菌感染的结果在临床上被定义为“活性”或“潜伏”。这些临床定义不足以描述结核分枝杆菌感染的连续过程。 “活动性”结核病的实际表现从轻度到重度肺部疾病（包括空洞性结核病）以及肺外或播散性疾病不等。多项证据支持潜伏感染也是一系列感染结果，从亚临床疾病到“休眠感染”再到完全清除的感染。潜伏期谱的概念具有实际意义：我们假设只有一小部分潜伏感染者最有可能感染反应性结核病，并且识别出那些我们认为潜伏期“较高”的人，可以有针对性地采取干预措施给那些最受益的人。在本提案中，我们将探讨延迟范围的概念以及使用系统生物学方法重新激活的影响。我们建议整合来自人类、非人类灵长类动物和计算系统的数据，以提供一种全面的延迟和重新激活方法。我们将使用免疫学方法和最先进的成像技术来定义感染结核分枝杆菌的人类和非人类灵长类动物的潜伏期。从非人类灵长类动物身上，我们将更进一步获得肉芽肿，以详细研究潜伏期以及重新激活过程。这些肉芽肿将用于免疫学、微生物学和病理学研究，以确定哪些最有可能重新激活以及维持亚临床感染所涉及的因素。除了大大增加我们对“潜伏”结核病和导致结核病重新激活的因素的了解之外，所有人类和非人类灵长类动物数据都将被纳入下一代结核病多尺度数学模型。这将为对导致延迟范围和重新激活风险的因素进行复杂分析提供计算平台。最终，这些模型根据来自人类和非常相关的动物模型的数据，可用于检验我们的假设，即个体在潜伏期范围内的位置会影响重新激活的风险。该项目汇集了经验丰富的免疫学家、微生物学家和计算科学家团队，他们多年来一直专注于结核病研究。 公共卫生相关性：结核分枝杆菌是结核病的病原体，可引起临床上明显的疾病 (TB)，或更常见的是临床上无症状的感染（潜伏性结核病），这种感染也可重新激活而引起结核病。据估计，全世界有 17 亿人患有潜伏性结核感染。在这里，我们通过综合系统生物学方法将人类和动物模型的数据与计算和数学模型相结合，以更好地了解潜在结核病和导致重新激活的因素。