Pathogen detection signaling network analysis of selectivity and sensitivity

病原体检测信号网络的选择性和灵敏度分析

• 批准号: 7677154
• 负责人: Roy Wollman
• 金额: $ 3.59万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7677154
• 关键词: Accounting; Affect; Asthma; Biochemical; Biological Models; Biological Process; Cell Communication; Cell Count; Cells; Cellular biology; Characteristics; Chronic Obstructive Airway Disease; Classification; Communication; Complex; Computer Simulation; Data; Data Sources; Detection; Development; Diffusion; Disease; Dose; Double-Stranded RNA; Environment; Exhibits; Flagellin; Fluorescence Microscopy; Gene Expression; Goals; Growth Factor; Health; Human; Hydrogels; Immune system; Indium; Individual; Infection; Inflammation; Inflammatory; Inhibitory Concentration 50; Knowledge; Measurement; Measures; Membrane; Microfluidic Microchips; Modeling; Molecular; Output; Paracrine Communication; Pathogen detection; Pathway Analysis; Pathway interactions; Population; Process; Property; Quantitative Microscopy; Reporter; Signal Pathway; Signal Transduction; Signaling Protein; Source; Structure; Surface; System; Testing; Time; Work; Wound Healing; base; design; disorder prevention; improved; insight; macrophage; mathematical model; novel; paracrine; pathogen; prevent; promoter; receptor; research study; response; therapy design; transcription factor

DESCRIPTION (provided by applicant): Macrophages' ability to recognize pathogens is crucial for the immune system response. Recognition is done by a set of membrane receptors and a signaling network that connects the receptors to activation of key transcription factors. High sensitivity to pathogen detection is crucial for disease prevention, whereas high selectivity is important for preventing unnecessary inflammation. The goal of this work is to quantitatively investigate how the structure of the signaling network affects sensitivity and selectivity toward pathogen detection. Specifically, I will focus on two elements in the network design: cross-talk between pathways within a single cell and cross-talk between cells via paracrine secretion. The first part of this work, investigates how sensitivity and selectivity are affected when macrophages are presented with multiple pathogen derived molecules. The main hypothesis is that by using cross talk between signaling pathways, macrophages are able to increase their sensitivity without hampering their response selectivity. To test this hypothesis I will perform systematic experiments to measure the transcriptional response using promoter reporter systems. These experiments would be augmented by computational modeling that will test how specific biochemical interactions in the signaling network results in increased sensitivity and selectivity towards pathogen detection. In the second part of my work, I will investigate the hypothesis that communication between cells via paracrine secretion increases the overall effective sensitivity of a population of cells when compared with the sensitivity of a single cell in that population. To test this hypothesis I will use hydrogels that create diffusion barriers and novel microfluidics devices that will enable me to control the number of communicating cells. In addition, computational modeling will test the affect of stochasticity in gene expression on pathogen recognition. The signaling network that is responsible for pathogen detection is important for human health and is related to several diseases. Aside for pathogen related infections, many inflammatory disorders (such as COPD, and Asthma) are associated with this signaling network. A better understanding of the functional significance of the signaling network structure both within and between cells, as well as predictive computational models of macrophages response are essential for long term progress and the rational therapy design.

描述（由申请人提供）：巨噬细胞识别病原体的能力对免疫系统反应至关重要。识别是通过一组膜受体和一个信号网络来完成的，该信号网络将受体与关键转录因子的激活相连接。对病原体检测的高灵敏度对于疾病预防至关重要，而高选择性对于预防不必要的炎症非常重要。这项工作的目标是定量研究信号网络的结构如何影响病原体检测的灵敏度和选择性。具体来说，我将集中在网络设计中的两个元素：单个细胞内通路之间的串扰和通过旁分泌的细胞之间的串扰。这项工作的第一部分，研究如何影响敏感性和选择性时，巨噬细胞与多种病原体衍生的分子。主要的假设是，通过使用信号通路之间的串扰，巨噬细胞能够增加它们的敏感性，而不妨碍它们的反应选择性。为了验证这一假设，我将进行系统的实验，以测量转录反应使用启动子报告系统。这些实验将通过计算建模来增强，该计算建模将测试信号网络中的特定生化相互作用如何导致对病原体检测的灵敏度和选择性增加。在我的工作的第二部分，我将调查的假设，通过旁分泌细胞之间的通信增加了整体有效的敏感性的细胞群体相比，一个单一的细胞群体中的敏感性。为了验证这一假设，我将使用能够产生扩散屏障的水凝胶和新型微流体设备，这些设备将使我能够控制通信细胞的数量。此外，计算建模将测试基因表达的随机性对病原体识别的影响。负责病原体检测的信号网络对人类健康很重要，并与多种疾病有关。除了病原体相关的感染外，许多炎症性疾病（如COPD和哮喘）与此信号网络有关。更好地理解细胞内和细胞间信号网络结构的功能意义以及巨噬细胞反应的预测计算模型对于长期进展和合理的治疗设计至关重要。