Anna A Bakhtina, Helisa H Wippel, Juan D Chavez, James E Bruce
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引用次数: 0
摘要
在现代生物医学研究中,可培养的细胞系是不可或缺的工具,而选择表现出特定功能特征的细胞系往往是成功的关键。细胞功能通路是通过选择蛋白质分子内和分子间相互作用进化而来的,这些相互作用统称为相互作用组。在本研究中,研究人员利用体内定量蛋白质交联和质谱分析法来探究三种常用人类细胞系(即 HEK293、MCF-7 和 HeLa 细胞)之间大规模蛋白质相互作用组的差异。这些数据表明,定量相互作用组水平具有高度的可重复性,所有生物重复的 R2 值均大于 0.8。蛋白质组丰度水平也是通过与数据无关的获取定量蛋白质组学方法测量的。结合定量相互作用组和蛋白质组信息,可视化由蛋白质组水平适应和独立调控的相互作用组变化介导的细胞类型特异性相互作用组变化,从而更深入地了解这些变化的可能驱动因素。在蛋白质相互作用和构象中检测到的最大变化包括细胞骨架蛋白、RNA 结合蛋白、染色质重塑复合物、线粒体蛋白等的变化。总之,这些数据证明了定量交联在研究系统级相互作用组变化方面的实用性和可重复性。此外,这些结果还说明了结合定量相互作用组学和蛋白质组学如何为细胞功能图谱提供独特的见解。
Combining Quantitative Proteomics and Interactomics for a Deeper Insight into Molecular Differences between Human Cell Lines.
In modern biomedical research, cultivable cell lines are an indispensable tool, and the selection of cell lines that exhibit specific functional profiles is often critical to success. Cellular functional pathways have evolved through the selection of protein intra- and intermolecular interactions collectively referred to as the interactome. In the present work, quantitative in vivo protein cross-linking and mass spectrometry were used to probe large-scale protein interactome differences among three commonly employed human cell lines, namely, HEK293, MCF-7, and HeLa cells. These data illustrated highly reproducible quantitative interactome levels with R2 values larger than 0.8 for all biological replicates. Proteome abundance levels were also measured using data-independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteome information allowed the visualization of cell type-specific interactome changes mediated by proteome level adaptations and independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the largest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study system-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight into cellular functional landscapes.