Measuring multiprotein assemblies that drive biological signals

项目总结/摘要 细胞通过与受体结合并传递细胞内信息来感知和响应环境 通过信号传导级联。这一过程在很大程度上是由蛋白质网络控制的， 解离，并沿沿着生物化学途径推进信号进程。信号体可能是其中的一部分 过程，形成时蛋白质作为网络枢纽协调与其他蛋白质节点的相互作用， 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相互连接，主要负责提供逻辑电路 所需的信号传输，合成指令的离散细胞反应，从多个 信号输入。但是，尽管它具有很高的生物学重要性， 复杂的网络水平受到技术限制的严重限制，特别是在小的情况下， 提供少量生物材料用于评估的临床样品。我们建议提出一个新的 策略，q-PiSCES，允许在信号复合物中检测到的蛋白质的分子定量 来自生理样品，例如来自人类临床患者或临床前小鼠模型的那些。Q-PiSCES 最初将开发用于T细胞中具有55个独特成对关联的10个蛋白质靶点的集合 抗原受体（TCR）信号体，已知其对免疫应答发挥强控制作用（特异性目的 1）。生物统计分析将输入生物信息学管道，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缔合 （具体目标2）。我们将通过将其应用于人类蛋白质复合物的分析来现场测试q-PiSCES 与自身免疫性疾病斑秃（特定目标3）相关。q-PiSCES将携手 显著提高观察、测量和研究生理蛋白质网络模式的能力 配合物我们认为，患者来源的q-PiSCES数据将成为一种新的分析策略， 这些复合物，并说明其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.