Measuring multiprotein assemblies that drive biological signals

测量驱动生物信号的多蛋白组装体

• 批准号: 10408708
• 负责人: Adam G. Schrum
• 金额: $ 40.69万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10408708
• 关键词: Affect; Affinity; Alopecia Areata; Autoimmune Diseases; Binding; Binding Proteins; Biochemical; Biochemical Pathway; Biochemical Process; Biocompatible Materials; Bioinformatics; Biological; Biological Assay; Biometry; Biopsy; Calibration; Cells; Chimeric Proteins; Clinical; Co-Immunoprecipitations; Collection; Complex; Custom; Data; Data Analyses; Detection; Disease; Drug Targeting; Environment; Enzyme-Linked Immunosorbent Assay; Epitopes; Fab Immunoglobulins; Flow Cytometry; Fluorescence; Glycine decarboxylase; Goals; Health; Homo; Human; Immune response; Immunoglobulin G; Immunoprecipitation; Instruction; Knowledge; Logic; Maps; Measurement; Measures; Mediating; Methods; Microspheres; Modeling; Mole the mammal; Molecular; Monoclonal Antibodies; Mouse Protein; Mus; Oranges; Pathway interactions; Patients; Pattern; Pharmacology; Physiological; Process; Protein Analysis; Proteins; Reagent; Recombinant Proteins; Sampling; Signal Pathway; Signal Transduction; Signaling Protein; Source; Surface; Systems Analysis; T-Cell Receptor; T-Lymphocyte; Techniques; Therapeutic; Tissues; Visualization; Yeasts; base; bioinformatics pipeline; biosignature; data pipeline; design; field study; mouse model; novel; pre-clinical; protein complex; protein protein interaction; receptor; response; single molecule; styrofoam; tool; transmission process; yeast two hybrid system

Project Summary/Abstract Cells perceive and respond to their environment by engaging receptors and transmitting intracellular messages via signal transduction cascades. This process is largely controlled by networks of proteins that bind, dissociate, and advance signal progression along biochemical pathways. Signalosomes can be part of this process, formed when proteins acting as network hubs orchestrate interactions with other protein nodes to control activation of various signaling pathways simultaneously. It is this modular, conditional interconnectivity between proteins and pathways that is largely responsible for providing the logic circuits required for signal transmission, synthesizing instructions for discrete cellular responses from multiple signaling inputs. But despite its high biological importance, the empirical assessment of signaling protein complexes at the network level is severely restricted by technological limitations, especially in the case of small clinical samples that provide low amounts of biomaterial for assessment. We propose to advance a new strategy, q-PiSCES, to allow molecular quantification of proteins that can be detected in signaling complexes from physiologic samples, such as those from human clinical patients or pre-clinical mouse models. Q-PiSCES will initially be developed for a collection of 10 protein targets with 55 unique pairwise associations in the T cell antigen receptor (TCR) signalosome that is known to exert strong control of immune responses (Specific Aim 1). Biostatistical analysis will feed into a Bioinformatics pipeline to focus on three specific parameters of protein complexes: protein abundance, clustering of identical proteins, and heterotypic protein co-associations (Specific Aim 2). We will field-test q-PiSCES by applying it to the analysis of human protein complexes associated with the autoimmune disease, Alopecia Areata (Specific Aim 3). Together, q-PiSCES stands to dramatically increase the ability to observe, measure, and study network patterns of physiologic protein complexes. We propose that the patient-derived q-PiSCES data will exemplify a new strategy for analyzing these complexes, and illustrate its general applicability to many fields of study and classes of disease.

项目摘要/摘要 细胞通过参与受体和传播细胞内消息来感知并响应其环境 通过信号转导级联。这个过程在很大程度上由结合的蛋白质网络控制 沿生化途径分离并提高信号进程。信号体可以成为其中的一部分 过程，当蛋白质充当网络枢纽的蛋白质协调与其他蛋白质节点的相互作用时形成的过程 同时控制各种信号通路的激活。这是一个模块化的条件 蛋白质和途径之间的互连性很大程度上负责提供逻辑电路 信号传输所需 信号输入。但是尽管具有很高的生物学意义，但信号蛋白的经验评估 网络水平的复合物受到技术限制的严重限制，尤其是在小的情况下 提供低量的生物材料进行评估的临床样品。我们建议推进一个新的 策略，Q-pisces，允许对信号复合物中可以检测到的蛋白质进行分子定量 来自生理样本，例如来自人类临床患者或临床前小鼠模型的样本。 q-pisces 最初，将开发用于在T细胞中具有55个独特成对关联的10个蛋白质靶标的集合 已知可以强烈控制免疫反应的抗原受体（TCR）信号体（特定目标 1）。生物统计分析将以生物信息学管道为例，以关注三个特定参数 蛋白质复合物：蛋白质丰度，相同蛋白质的聚类和异型蛋白共同促成 （特定目标2）。我们将通过将其应用于人类蛋白质复合物的分析来进行现场测试Q-pisces 与自身免疫性疾病有关，脱发Areata（特定目标3）。一起，Q-pisces代表 显着提高观察，测量和研究生理蛋白网络模式的能力 复合物。我们建议患者来源的Q-pisces数据将体现出一种新的分析策略 这些复合物，并说明了其对许多研究领域和疾病类别的一般适用性。