Measuring multiprotein assemblies that drive biological signals

测量驱动生物信号的多蛋白组装体

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要细胞通过与受体结合并传递细胞内信息来感知环境并做出反应通过信号转导级联。这个过程很大程度上是由结合的蛋白质网络控制的，解离并沿着生化途径推进信号进展。信号体可以是其中的一部分过程，当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同时控制多种信号通路的激活。正是这种模块化、有条件的蛋白质和通路之间的互连性主要负责提供逻辑电路信号传输所需的，合成来自多个离散细胞响应的指令信号输入。但是，尽管信号蛋白具有很高的生物学重要性，但对其的实证评估网络层面的综合体受到技术限制的严重限制，特别是在小规模的情况下提供少量生物材料用于评估的临床样本。我们建议推进一个新的 q-PiSCES 策略，可对信号复合物中可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 最初将针对 T 细胞中具有 55 个独特配对关联的 10 个蛋白质靶标集合进行开发已知对免疫反应具有强大控制作用的抗原受体 (TCR) 信号体（具体目标 1）。生物统计分析将进入生物信息学管道，重点关注以下三个特定参数：蛋白质复合物：蛋白质丰度、相同蛋白质的聚类和异型蛋白质共关联（具体目标 2）。我们将通过将 q-PiSCES 应用于人类蛋白质复合物的分析来对其进行现场测试与自身免疫性疾病斑秃相关（具体目标 3）。 q-PiSCES 共同致力于显着提高观察、测量和研究生理蛋白质网络模式的能力复合物。我们建议，源自患者的 q-PiSCES 数据将体现一种新的分析策略这些复合体，并说明其对许多研究领域和疾病类别的普遍适用性。