重组流感糖蛋白特定位点糖基化图谱的变化。

IF 6.1 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Molecular & Cellular Proteomics Pub Date : 2024-09-01 Epub Date: 2024-08-10 DOI:10.1016/j.mcpro.2024.100827
Zachary C Goecker, Meghan C Burke, Concepcion A Remoroza, Yi Liu, Yuri A Mirokhin, Sergey L Sheetlin, Dmitrii V Tchekhovskoi, Xiaoyu Yang, Stephen E Stein
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引用次数: 0

摘要

这项工作详细测定了重组流感糖蛋白血凝素和神经氨酸酶的特定位点 N-糖分布。重组糖蛋白之间的糖基化差异无法预测,可能取决于生物制造过程的细节以及蛋白质结构的细节。本研究分析了来自四个不同供应商的八个菌株的重组流感蛋白质。其中包括五种血凝素蛋白和三种神经氨酸酶蛋白,每种蛋白都是由 HEK293 细胞系生产的。采用一系列复杂的多酶方法进行消化,目的是分离出含有单个 N-糖基化位点的糖肽。使用最近开发的一种方法生成了完整糖肽的特定位点糖基化图谱,并使用光谱相似性得分进行了比较。不同病毒株之间的聚糖丰度和分布差异最明显(相似度得分=383,满分999),而消化复制和注射复制的差异相对较小(相似度得分=957)。值得注意的是,流感糖蛋白变体同源区域的聚糖分布显示出较低的变异性。由于存在多种可能的变异来源,而且位点特异性聚糖测定存在固有的分析困难,因此对变异进行了多因素单独检查,包括供应商、生产批次、蛋白酶消化和重复测定的差异。在比较了所有糖基化分布后,可以为大多数位点确定四个可区分的类别。最后,我们尝试鉴定一个 HA 变体的相邻潜在 N-糖基化位点的糖基化分布。结果发现,只有第二个位点(NnST)被蛋白质组学中很少使用的两种蛋白酶--枯草蛋白酶(subtilisin)和埃斯佩尔酶(esperase)所占据,这两种蛋白酶都会选择性地裂解这些相邻位点。
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Variation of Site-Specific Glycosylation Profiles of Recombinant Influenza Glycoproteins.

This work presents a detailed determination of site-specific N-glycan distributions of the recombinant influenza glycoproteins hemagglutinin (HA) and neuraminidase. Variation in glycosylation among recombinant glycoproteins is not predictable and can depend on details of the biomanufacturing process as well as details of protein structure. In this study, recombinant influenza proteins were analyzed from eight strains of four different suppliers. These include five HA and three neuraminidase proteins, each produced from a HEK293 cell line. Digestion was conducted using a series of complex multienzymatic methods designed to isolate glycopeptides containing single N-glycosylated sites. Site-specific glycosylation profiles of intact glycopeptides were produced using a recently developed method and comparisons were made using spectral similarity scores. Variation in glycan abundances and distribution was most pronounced between different strains of virus (similarity score = 383 out of 999), whereas digestion replicates and injection replicates showed relatively little variation (similarity score = 957). Notably, glycan distributions for homologous regions of influenza glycoprotein variants showed low variability. Due to the multiple possible sources of variation and inherent analytical difficulties in site-specific glycan determinations, variations were individually examined for multiple factors, including differences in supplier, production batch, protease digestion, and replicate measurement. After comparing all glycosylation distributions, four distinguishable classes could be identified for the majority of sites. Finally, attempts to identify glycosylation distributions on adjacent potential N-glycosylated sites of one HA variant were made. Only the second site (NnST) was found to be occupied using two rarely used proteases in proteomics, subtilisin and esperase, both of which did selectively cleave these adjacent sites.

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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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