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研究绘制出乳腺癌的蛋白质相互作用图谱
作者:小柯机器人 发布时间:2021/10/9 16:11:40

美国加州大学Nevan J. Krogan、Trey Ideker等研究人员合作绘制出乳腺癌的蛋白质相互作用图谱。相关论文于2021年10月1日发表在《科学》杂志上。

癌症与一系列不同的基因组改变有关。为了帮助从机理上理解乳腺浸润性癌的这种改变,研究人员应用亲和纯化-质谱法为40种经常改变的乳腺癌(BC)蛋白划定了全面的生物物理相互作用网络,无论是否有相关突变,并横跨三个人类乳腺细胞系。这些网络确定了癌症特定的蛋白质-蛋白质相互作用(PPI),它们相互关联并富含常见和罕见的癌症突变,这些突变因引入关键的BC突变而被大幅重构。分析确定了BPIFA1和SCGB2A1是PIK3CA的相互作用蛋白,它们抑制PI3K-AKT信号,并发现了USP28和UBE2N是BRCA1的功能相关的相互作用者。

结果还表明,蛋白磷酸酶1调节亚基spinophilin与BRCA1相互作用并调节其去磷酸化从而促进DNA双链断裂修复。因此,PPI图谱为机制性地解释疾病基因组数据提供了一个强大的框架,并能确定有价值的治疗靶标。

附:英文原文

Title: A protein interaction landscape of breast cancer

Author: Minkyu Kim, Jisoo Park, Mehdi Bouhaddou, Kyumin Kim, Ajda Rojc, Maya Modak, Margaret Soucheray, Michael J. McGregor, Patrick O’Leary, Denise Wolf, Erica Stevenson, Tzeh Keong Foo, Dominique Mitchell, Kari A. Herrington, Denise P. Muoz, Beril Tutuncuoglu, Kuei-Ho Chen, Fan Zheng, Jason F. Kreisberg, Morgan E. Diolaiti, John D. Gordan, Jean-Philippe Coppé, Danielle L. Swaney, Bing Xia, Laura van ’t Veer, Alan Ashworth, Trey Ideker, Nevan J. Krogan

Issue&Volume: 2021-10-01

Abstract: Cancers have been associated with a diverse array of genomic alterations. To help mechanistically understand such alterations in breast-invasive carcinoma, we applied affinity purification–mass spectrometry to delineate comprehensive biophysical interaction networks for 40 frequently altered breast cancer (BC) proteins, with and without relevant mutations, across three human breast cell lines. These networks identify cancer-specific protein-protein interactions (PPIs), interconnected and enriched for common and rare cancer mutations, that are substantially rewired by the introduction of key BC mutations. Our analysis identified BPIFA1 and SCGB2A1 as PIK3CA-interacting proteins, which repress PI3K-AKT signaling, and uncovered USP28 and UBE2N as functionally relevant interactors of BRCA1. We also show that the protein phosphatase 1 regulatory subunit spinophilin interacts with and regulates dephosphorylation of BRCA1 to promote DNA double-strand break repair. Thus, PPI landscapes provide a powerful framework for mechanistically interpreting disease genomic data and can identify valuable therapeutic targets.

DOI: abf3066

Source: https://www.science.org/doi/10.1126/science.abf3066

 

期刊信息
Science:《科学》,创刊于1880年。隶属于美国科学促进会,最新IF:41.037