Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, District of Columbia 20007, United States.
Information Science and Technology College, Dalian Maritime University, Dalian 116026, China.
J Proteome Res. 2024 Oct 4;23(10):4422-4432. doi: 10.1021/acs.jproteome.4c00388. Epub 2024 Sep 20.
O-Linked β--acetylglucosamine (O-GlcNAc) modification (i.e., O-GlcNAcylation) on proteins plays critical roles in the regulation of diverse biological processes. However, protein O-GlcNAcylation analysis, especially at a large scale, has been a challenge. So far, a number of enrichment materials and methods have been developed for site-specific O-GlcNAc proteomics in different biological settings. Despite the presence of multiple methods, their performance for the O-GlcNAc proteomics is largely unclear. In this work, by using the lysates of PANC-1 cells (a pancreatic cancer cell line), we provided a head-to-head comparison of three affinity enrichment methods and materials (i.e., antibody, lectin AANL6, and an OGA mutant) for site-specific O-GlcNAc proteomics. The enriched peptides were analyzed by HCD product-dependent EThcD (i.e., HCD-pd-EThcD) mass spectrometry. The resulting data files were processed by three different data analysis packages (i.e., Sequest HT, Byonic, and FragPipe). Our data suggest that each method captures a subpopulation of the O-GlcNAc proteins. Besides the enrichment methods, we also observe complementarity between the different data analysis tools. Thus, combining different approaches holds promise for enhanced coverage of O-GlcNAc proteomics.
O-连接β--乙酰氨基葡萄糖（O-GlcNAc）修饰（即 O-GlcNAcylation）在蛋白质中对调节多种生物过程起着至关重要的作用。然而，蛋白质 O-GlcNAcylation 分析，特别是在大规模水平上，一直是一个挑战。迄今为止，已经开发了许多用于不同生物环境中特定位置 O-GlcNAc 蛋白质组学的富集材料和方法。尽管存在多种方法，但它们在 O-GlcNAc 蛋白质组学中的性能在很大程度上仍不清楚。在这项工作中，我们使用 PANC-1 细胞（一种胰腺癌细胞系）的裂解物，对头对头比较了三种用于特定位置 O-GlcNAc 蛋白质组学的亲和富集方法和材料（即抗体、凝集素 AANL6 和 OGA 突变体）。通过 HCD 产物依赖的 EThcD（即 HCD-pd-EThcD）质谱法分析富集的肽段。通过三个不同的数据分析软件包（即 Sequest HT、Byonic 和 FragPipe）处理得到的数据文件。我们的数据表明，每种方法都捕获了 O-GlcNAc 蛋白质的一个亚群。除了富集方法，我们还观察到不同数据分析工具之间的互补性。因此，结合不同的方法有望提高 O-GlcNAc 蛋白质组学的覆盖度。
