Predicting protective T-cell responses in Tuberculosis using a systems biology approach

使用系统生物学方法预测结核病中的保护性 T 细胞反应

• 批准号: 9072491
• 负责人: JoAnne L. Flynn
• 金额: $ 76.77万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9072491
• 关键词: Address; Anti-Inflammatory Agents; Anti-inflammatory; Antigens; Blood; Cause of Death; Cell physiology; Cells; Clinical Trials; Collaborations; Collection; Communicable Diseases; Complex; Computer Simulation; Data; Disease; Environment; Equilibrium; Event; Experimental Models; Funding; Granuloma; Growth; HIV; HIV Infections; Heterogeneity; Human; Immune; Immune response; Immunity; Infection; Infection Control; Infection prevention; Inflammation; Inflammatory; Lead; Lung; Lymph; Macaca; Modeling; Morbidity - disease rate; Mycobacterium tuberculosis; Organ; Outcome; Pathologic; Phenotype; Play; Predisposition; Prevention strategy; Publishing; Risk; Role; Signal Transduction; Specificity; Staging; Systems Biology; T cell response; T-Lymphocyte; T-Lymphocyte Subsets; Techniques; Testing; Tissue Sample; Tissues; Tuberculosis; Vaccines; Work; computerized tools; cytokine; design; disorder prevention; killings; latent infection; lymph nodes; macrophage; mathematical model; mortality; next generation; novel; pathogen; prevent; public health relevance; response; tool; transmission process; treatment strategy; vaccine development; vaccine trial; virtual

 DESCRIPTION (provided by applicant): Protective responses against M. tuberculosis (Mtb) in humans are poorly understood, especially those that would prevent establishment of infection or progression to disease. There is still no efficacious vaccine against Mtb, although ~30 vaccines are in various stages of testing and clinical trials. New treatment and prevention strategies are desperately needed to make a major impact on transmission, morbidity and mortality of TB. The host-pathogen interactions occurring during Mtb infection are complex and span across scales, from bacterial and cellular to organ to an entire host. To address this complex disease we need comprehensive and integrative tools to generate testable hypotheses about what characterizes an effective immune response to Mtb infection. Understanding the immune response to Mtb requires a systems biology approach, particularly a computational tool that can integrate large amounts and different types of data regarding specific aspects of the host-pathogen interaction in TB. This project represents an integrated strategy between computational and experimental approaches to tackle this challenging problem. The pathologic hallmark of Mtb infection is a granuloma, a collection of host cells (e.g. macrophages and T cells) that organize in an attempt to contain or eliminate the infection. Within a single host, several granulomas form in response to initial infection, and these granulomas are heterogeneous with variable trajectories, complicating the study of this infection. T cells play a central role in protection against TB, as best exemplified by the dramatic susceptibility of HIV+ humans to TB, even in the early stages of HIV infection. However, T cells come in many functional sub-types. De-convoluting the combination of T cell phenotypes and function that are most efficacious in clearing infection is a herculean task, one that requires a systems biology approach, marrying relevant experimental models and multi-scale and multi-compartment computational models. In the proposed work, we incorporate new data into a next-generation multi-scale and multi-compartment (lung-blood-lymph) computational model that has a host-scale readout. We pair with human and NHP studies both outlined herein and in ongoing separately funded studies to calibrate and validate the models and use them as a testing ground for model predictions in an iterative fashion in 3 key aims: (1) Characterize experimentally the heterogeneity, specificity, and localization of T cells and their function in granulomas and lymph nodes early post-Mtb infection in NHPs and use these data to parameterize, refine and validate our next-generation computational model adding important mechanisms that are currently lacking. (2) Identify mechanisms that balance pro and anti-inflammatory signals in granulomas and distinguish hypotheses regarding host and bacterial factors that limit granuloma T-cell function. (3) Identify early adaptive responses that prevent establishment of infection or disease using virtual clinical trials. Our established collaboration over 15 years will serve well the aims of this interdisciplinary proposal.

 性状（由申请方提供）：对M.人类对结核病（Mtb）的了解甚少，特别是那些可以防止感染或疾病进展的药物。目前仍没有有效的抗结核疫苗，尽管约有30种疫苗处于不同的测试和临床试验阶段。迫切需要新的治疗和预防战略，以便对结核病的传播、发病率和死亡率产生重大影响。在结核分枝杆菌感染期间发生的宿主-病原体相互作用是复杂的，并且跨越从细菌和细胞到器官到整个宿主的尺度。为了解决这一复杂的疾病，我们需要全面和综合的工具来产生可检验的假设，什么是对结核分枝杆菌感染的有效免疫反应的特征。了解对结核分枝杆菌的免疫反应需要系统生物学方法，特别是可以整合有关结核病宿主-病原体相互作用特定方面的大量不同类型数据的计算工具。该项目代表了计算和实验方法之间的综合策略，以解决这一具有挑战性的问题。Mtb感染的病理标志是肉芽肿，即组织化以试图包含或消除感染的宿主细胞（例如巨噬细胞和T细胞）的集合。在一个单一的主机，几个肉芽肿的形成，在响应初始感染，这些肉芽肿是异质性的可变轨迹，这种感染的研究复杂化。T细胞在预防结核病方面发挥着核心作用，最好的例证是HIV+人群对结核病的巨大易感性，即使在HIV感染的早期阶段。然而，T细胞有许多功能亚型。对在清除感染中最有效的T细胞表型和功能的组合进行去卷积是一项艰巨的任务，需要采用系统生物学方法，将相关实验模型与多尺度和多隔室计算模型结合起来。在拟议的工作中，我们将新的数据纳入下一代多尺度和多隔室（肺-血液-淋巴）计算模型，该模型具有宿主尺度读数。我们与本文概述的人类和NHP研究以及正在进行的单独资助的研究配对，以校准和验证模型，并将其作为模型预测的测试基础，以迭代方式实现3个关键目标：（1）实验性地表征NHP中Mtb感染后早期T细胞的异质性、特异性和定位及其在肉芽肿和淋巴结中的功能，并使用这些数据来参数化，完善和验证我们的下一代计算模型，增加目前缺乏的重要机制。(2)确定机制，平衡促炎和抗炎信号在肉芽肿和区分假说宿主和细菌因素限制肉芽肿T细胞功能。(3)使用虚拟临床试验确定早期适应性反应，以防止感染或疾病的建立。我们15年的合作将很好地服务于这个跨学科提案的目标。