Measuring multiprotein assemblies that drive biological signals

项目摘要/摘要 细胞通过接触受体和传递细胞内信息来感知和响应环境 通过信号转导级联。这个过程在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程。信号小体可能是其中的一部分 过程，当蛋白质作为网络枢纽协调与其他蛋白质节点的相互作用时形成的 同时控制各种信号通路的激活。正是这种模块化的、有条件的 蛋白质和通路之间的互连，它主要负责提供逻辑电路 信号传输所需的，合成来自多个 信号输入。但尽管它具有很高的生物学重要性，信号蛋白的经验评估 网络级别的复合体受到技术限制的严重限制，特别是在小型 提供少量用于评估的生物材料的临床样本。我们建议提出一项新的 Q-双鱼策略，允许对可以在信号复合体中检测到的蛋白质进行分子量化 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-双鱼座 最初将为T细胞中55个独特的成对关联的10个蛋白质靶点的集合而开发 抗原受体(TCR)信号小体，已知对免疫反应施加强有力的控制(特定目的 1)。生物统计分析将提供给生物信息学管道，以关注以下三个特定参数 蛋白质复合体：蛋白质丰度、相同蛋白质的聚集性和异型蛋白质相互关联 (具体目标2)。我们将现场测试Q-PISCES，将其应用于人类蛋白质复合体的分析 与自身免疫性疾病有关的斑秃(特定目标3)。总而言之，Q-双鱼座 显著提高观察、测量和研究生理蛋白质网络模式的能力 复合体。我们认为，患者得出的Q-PISCES数据将例证一种新的分析策略 这些复合体，并说明了它对许多研究领域和疾病类别的普遍适用性。

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.