Modeling of Lung HIV Cytokine/Chemokine Networks

肺 HIV 细胞因子/趋化因子网络的建模

• 批准号: 6804027
• 负责人: Paul R Skolnik
• 金额: $ 43.37万
• 依托单位: BOSTON MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-26 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6804027
• 关键词: AIDS; AIDS therapy; HIV infections; alveolar macrophages; antiAIDS agent; biological signal transduction; blood tests; bronchoscopy; cell line; chemokine; clinical research; cytokine; human subject; human tissue; immunoregulation; lung; mathematical model; messenger RNA; mitogen activated protein kinase; model design /development; protein biosynthesis; transcription factor; tumor necrosis factor alpha

DESCRIPTION (provided by applicant): Alterations in host innate immune function within the lung may predispose to opportunistic infections that cause great morbidity and mortality during HIV infection. Mathematical models are needed to help understand the complex interactions of cytokines, cytokine antagonists, and chemokines that affect the innate host immune response within the lung during HIV infection. To date, analyses have been limited to descriptions of how HIV affects single or small sets of these molecules, instead of the larger network that acts in a coordinated manner to affect host immunity. Our group has developed "reverse engineering" methods to model networks of interacting proteins and mRNA. We will use these reverse engineering methods to determine the structure of the cytokine/chemokine networks in the lung, how these are altered during HIV infection, and after treatment with highly active antiretroviral therapy (HAART). The reverse engineering algorithms suggest the most efficient way to resolve the network structure through an iterative process in which analysis of data from one experiment informs the design of the next experiment. Specifically, we will create mathematical models of cytokine/chemokine networks, using reverse engineering methods, in cell lines (U1 and U937) and in AMs from HIV+ subjects (with or without HAART) and HIV- subjects. This will be accomplished by performing a series of experiments in which we perturb different components of the cytokine/chemokine networks, measure changes in the components of the network, and apply the reverse engineering algorithms to determine the next most informative perturbations to resolve the network structure. The components of the network that will be both measured and perturbed include selected cytokines and chemokines, signaling pathway molecules, transcription factors, and mRNA for these moieties. Identification of the most central molecules in these linked systems may suggest new therapeutic or vaccine approaches to bolster the host immune response to HIV.

描述（由申请方提供）：肺内宿主先天免疫功能的改变可能易发生机会性感染，在HIV感染期间导致高发病率和死亡率。需要数学模型来帮助理解细胞因子、细胞因子拮抗剂和趋化因子在HIV感染期间影响肺内的先天宿主免疫应答的复杂相互作用。迄今为止，分析仅限于描述HIV如何影响这些分子的单个或小集合，而不是以协调方式影响宿主免疫力的更大网络。我们的团队已经开发了“逆向工程”方法来模拟相互作用的蛋白质和mRNA的网络。我们将使用这些逆向工程方法来确定肺中细胞因子/趋化因子网络的结构，这些网络在HIV感染期间以及在用高效抗逆转录病毒疗法（HAART）治疗后如何改变。逆向工程算法提出了通过迭代过程来解析网络结构的最有效方法，在该过程中，对一个实验的数据分析为下一个实验的设计提供了信息。具体来说，我们将使用逆向工程方法，在细胞系（U1和U937）和来自HIV+受试者（有或没有HAART）和HIV-受试者的AM中创建细胞因子/趋化因子网络的数学模型。这将通过进行一系列实验来实现，在这些实验中，我们扰动细胞因子/趋化因子网络的不同组件，测量网络组件的变化，并应用逆向工程算法来确定下一个最具信息性的扰动来解析网络结构。将被测量和扰动的网络的组分包括选定的细胞因子和趋化因子、信号传导途径分子、转录因子和这些部分的mRNA。在这些相互关联的系统中识别最核心的分子可能会提出新的治疗或疫苗方法，以加强宿主对HIV的免疫反应。