Developing novel technology for mapping dynamic oncoprotein interaction networks

描述（由申请人提供）：许多癌蛋白和肿瘤抑制蛋白在胞质溶胶和细胞核之间穿梭，因此与不同亚细胞区室中变化的蛋白质伴侣组相互作用。此外，越来越多的证据表明，这些蛋白质的致癌突变可以导致细胞定位的改变和癌蛋白相互作用网络的重新定位。因此，表征癌蛋白和肿瘤抑制蛋白的动态相互作用网络对于理解它们在肿瘤发生中的作用至关重要。在细胞内的生理条件下映射动态蛋白质相互作用网络的强大技术还不可用。我们建议开发一种新的方法来映射动态癌蛋白相互作用网络，使用内源性表位标记的蛋白质进行亲和纯化和定量蛋白质组学进行准确的网络识别。该申请建立在我们成功开发的通过同源重组介导的人癌细胞敲入将表位标签编码DNA引入内源基因座的创新方法以及我们在定量蛋白质组学分析方面的丰富经验的基础上。内源性标记的蛋白质在生理水平上表达，并为蛋白质复合物鉴定提供生理相关的环境和隔室，因此是绘制动态蛋白质网络的理想选择。这个应用程序的目标是双重的。首先，我们提出了一个详细的定量比较，我们的技术在区分突变体和野生型癌蛋白复合物对两种传统的方法研究蛋白质复合物。其次，我们测试了我们的新方法的潜力，用于研究响应细胞信号传导而发生的动态相互作用。该技术的成功开发将为理解致癌相关易位或突变导致的蛋白质相互作用网络的重构提供一个平台。随着癌症基因组图谱（TCGA）等大规模项目的开展，将会发现更多新的癌基因和抑癌基因;我们的技术通过绘制这些基因的动态相互作用网络，为在相互作用网络水平上了解这些基因的功能提供了重要的途径。

DESCRIPTION (provided by applicant): Many oncoproteins and tumor suppressive proteins shuttle between the cytosol and nucleus and thus interact with changing sets of protein partners in different sub-cellular compartments. Furthermore, increasing evidence indicates that oncogenic mutations of these proteins can result in alteration of cellular localization and rewirin of oncoprotein interaction networks. Characterization of the dynamic interaction network of oncoproteins and tumor suppressive proteins is therefore crucial to understand their roles in tumorigenesis. Robust technologies for mapping dynamic protein interaction networks under physiological conditions within the cell are not yet available. We propose to develop a novel approach for mapping dynamic oncoprotein interaction networks using endogenously epitope-tagged proteins for affinity- purification and quantitative proteomics for accurate network identification. This application builds upon our successful development of an innovative method to introduce epitope tag- encoding DNA into endogenous loci by homologous recombination-mediated knock-in in human cancer cells and our intensive experience in quantitative proteomics analysis. The endogenously tagged proteins are expressed at physiological levels and provide physiologically-relevant environments and compartments for protein complex identification and are therefore ideal for mapping dynamic protein networks. The goals of this application are twofold. First, we propose a detailed quantitative comparison of our technique in terms of distinguishing mutant and wild-type oncoprotein complexes against two conventional approaches for studying protein complexes. Second we test the potential of our new approach for studying the dynamic interactions that occur in response to cell signaling. Successful development of this technology will provide a platform for understanding the reconfigurations to protein interaction networks that result from oncogenic related translocation or mutation. As large-scale projects such as The Cancer Genome Atlas (TCGA) proceed, many more novel oncogenes and tumor suppressor genes will be discovered; our technology provides an important pipeline for understanding the function of these genes at the interaction network level by mapping their dynamic interaction networks.