Multiple Layers of Complexity in O-Glycosylation Illustrated With the Urinary Glycoproteome

• Published in: Molecular & cellular proteomics 2022-12, Vol.21 (12), p.100439-100439, Article 100439
• Main Authors: Pap, Adam, Kiraly, Istvan Elod, Medzihradszky, Katalin F., Darula, Zsuzsanna
• Format: Article
• Language: English
• Subjects: EThcD; Glycopeptides - analysis; Glycosylation; Humans; mass spectrometry; misidentification; Proteome - chemistry; Reproducibility of Results; Search Engine; search engines; Software; urinary O-glycopeptides
• ISSN: 1535-9476; 1535-9484
• DOI: 10.1016/j.mcpro.2022.100439

While N-glycopeptides are relatively easy to characterize, O-glycosylation analysis is more complex. In this article, we illustrate the multiple layers of O-glycopeptide characterization that make this task so challenging. We believe our carefully curated dataset represents perhaps the largest intact human glycopeptide mixture derived from individuals, not from cell lines. The samples were collected from healthy individuals, patients with superficial or advanced bladder cancer (three of each group), and a single bladder inflammation patient. The data were scrutinized manually and interpreted using three different search engines: Byonic, Protein Prospector, and O-Pair, and the tool MS-Filter. Despite all the recent advances, reliable automatic O-glycopeptide assignment has not been solved yet. Our data reveal such diversity of site-specific O-glycosylation that has not been presented before. In addition to the potential biological implications, this dataset should be a valuable resource for software developers in the same way as some of our previously released data has been used in the development of O-Pair and O-Glycoproteome Analyzer. Based on the manual evaluation of the performance of the existing tools with our data, we lined up a series of recommendations that if implemented could significantly improve the reliability of glycopeptide assignments. [Display omitted] •Urinary O-glycopeptides were enriched using lectin affinity chromatography.•HCD and EThcD data were analyzed by four proteomic software packages.•High misidentification rate in spite of strict probability-based acceptance criteria.•Software development recommendations for more reliable O-glycopeptide analysis. In this study, mass spectrometric data acquired on urinary O-glycopeptides were analyzed by four software packages. The results were compared, and the rate of misidentification was assessed. The major factors leading to data misinterpretation were identified, and software development suggestions aiming more reliable automated data interpretation were made.