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，尤其是那些能够防止感染或疾病进展的保护性反应。 尽管已有约 30 种疫苗，但仍然没有针对 Mtb 的有效疫苗。迫切需要新的治疗和预防策略，以对结核病的传播、发病率和死亡率产生重大影响。 结核分枝杆菌感染期间发生的宿主与病原体之间的相互作用是复杂的，而且是跨领域的。为了解决这种复杂的疾病，我们需要全面和综合的工具来生成可检验的假设，以了解对 Mtb 感染的有效免疫反应的特征，了解对 Mtb 的免疫反应需要采用系统生物学方法。 ，特别是一种计算工具，可以整合有关结核病宿主与病原体相互作用的特定方面的大量不同类型的数据，该项目代表了计算和实验方法之间的综合策略，以解决结核分枝杆菌感染的病理特征。是一个肉芽肿，宿主肉芽肿细胞（例如巨噬细胞和 T 细胞）的集合，它们组织起来试图消除感染或感染，在单个宿主内，响应初始感染而形成多个肉芽肿，并且这些肉芽肿具有不同的轨迹。使这种感染的研究变得复杂化，T细胞在预防结核病方面发挥着核心作用，艾滋病病毒感染者对结核病的高度易感性就是最好的例证，即使是在艾滋病毒的早期阶段。然而，T 细胞有许多功能亚型，对清除感染最有效的 T 细胞表型和功能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，将相关的实验模型与相关技术相结合。在拟议的工作中，我们将新数据纳入具有主机规模读数的下一代多尺度和多室（肺-血液-淋巴）计算模型。和本文概述了人类和 NHP 研究以及正在进行的单独资助的研究，以校准和验证模型，并将其用作以迭代方式进行模型预测的试验场，以实现 3 个关键目标：（1）通过实验表征异质性、特异性和定位T 细胞及其在 NHP 结核分枝杆菌感染后肉芽肿和早期淋巴结中的功能，并使用这些数据参数化、完善和验证我们的下一代计算模型，添加目前缺乏的重要机制 (2) 识别机制。平衡肉芽肿中的促炎信号和抗炎信号，并区分有关限制肉芽肿 T 细胞功能的宿主和细菌因素的假设。 (3) 使用我们超过 15 项的虚拟临床试验确定预防感染或疾病发生的早期适应性反应。几年将很好地实现这一跨学科提案的目标。