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.

 描述(由申请人提供)：人类对结核分枝杆菌(Mtb)的保护性反应知之甚少，特别是那些可以防止感染或疾病进展的保护性反应。尽管约有30种疫苗处于不同的测试和临床试验阶段，但目前仍没有针对结核分枝杆菌的有效疫苗。迫切需要新的治疗和预防战略，以对结核病的传播、发病率和死亡率产生重大影响。在结核分枝杆菌感染过程中发生的宿主-病原体相互作用是复杂的，跨越了从细菌和细胞到器官再到整个宿主的范围。为了解决这种复杂的疾病，我们需要全面和综合的工具来产生可检验的假设，即什么是对结核分枝杆菌感染的有效免疫反应的特征。了解结核分枝杆菌的免疫反应需要一种系统生物学方法，特别是一种计算工具，可以整合关于结核病宿主-病原体相互作用的特定方面的大量不同类型的数据。这个项目代表了计算和实验方法之间的综合战略，以解决这一具有挑战性的问题。结核分枝杆菌感染的病理特征是肉芽肿，这是一组宿主细胞(如巨噬细胞和T细胞)，它们组织起来试图控制或消除感染。在一个宿主内，最初的感染会形成几个肉芽肿，这些肉芽肿具有不同的轨迹，使对这种感染的研究变得复杂。T细胞在预防结核病方面发挥着核心作用，最好的例证是艾滋病毒+人类对结核病的巨大易感性，即使在艾滋病毒感染的早期阶段也是如此。然而，T细胞有许多功能亚型。解开在清除感染中最有效的T细胞表型和功能的组合是一项艰巨的任务，这需要系统生物学的方法，结合相关的实验模型和多尺度和多间隔的计算模型。在这项拟议的工作中，我们将新数据纳入下一代多尺度和多隔室(肺-血-淋巴)计算模型，该模型具有主机规模的读数。我们与本文概述的以及正在进行的单独资助的研究中的人类和NHP研究配对，以校准和验证模型，并将它们用作迭代方式进行模型预测的测试基础，以实现三个关键目标：(1)通过实验表征结核分枝杆菌感染后早期NHP肉芽肿和淋巴结中T细胞的异质性、特异性和定位及其功能，并使用这些数据对我们的下一代计算模型进行参数化、细化和验证，从而增加目前缺乏的重要机制。(2)确定肉芽肿中促进和抗炎信号平衡的机制，区分限制肉芽肿T细胞功能的宿主和细菌因素的假说。(3)利用虚拟临床试验识别早期适应性反应，以防止感染或疾病的建立。我们15年来建立的合作将很好地服务于这项跨学科提案的目标。