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Liquid chromatography–tandem mass spectrometry glycoproteomic study of porcine IgG and detection of subtypes
Author(s) -
Battellino Taylor,
Bacala Raymond,
Gigolyk Baylie,
Ong Gideon,
Teraiya Milan V.,
Perreault Hélène
Publication year - 2021
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.9063
Subject(s) - chemistry , chromatography , mass spectrometry , tandem mass spectrometry , glycan , uniprot , selected reaction monitoring , glycosylation , electrospray ionization , sequence database , tandem mass tag , liquid chromatography–mass spectrometry , isobaric labeling , proteomics , protein mass spectrometry , biochemistry , glycoprotein , quantitative proteomics , gene
Rationale While high‐throughput proteomic methods have been widely applied to monoclonal antibodies and human immunoglobulin gamma (IgG) samples, less information is available on porcine IgG. As pigs are considered one of the most suitable species for xenotransplantation, it is important to characterize IgG amino acid sequences and glycosylation profiles, which is the focus of this study. Methods Three different purified porcine IgG samples, including wild‐type and knockout species, were digested with trypsin and enriched for glycopeptides. Digestion mixtures were spiked with a mixture of six standard peptides. Analysis was performed using electrospray ionization liquid chromatography–tandem mass spectrometry (MS/MS) in standard MS/MS data‐dependent acquisition mode on a hybrid triple quadrupole time‐of‐flight mass spectrometer. Results To facilitate the classification of subtypes detected experimentally, UniprotKB database entries were organized using comparative alignment scores. Sequences were grouped based on 11 different subtypes as translated from GenBank entries. Proteomic searches were accomplished automatically using specialized software, whereas glycoprotein searches were performed manually by monitoring the extracted chromatograms of diagnostic MS/MS glycan fragments and studying their corresponding mass spectra; 40–50 non‐glycosylated peptides and 4–5 glycosylated peptides were detected in each sample, with several glycoforms per sequence. Conclusions Proteomic analysis of porcine IgG is complicated by factors such as the presence of several subtypes, redundant heavy chain (HC) sequences in protein databases, and the lack of consistent cross‐referencing between databases. Aligning and comparing HC sequences were necessary to eliminate redundancy. This study highlights the complexity of pig IgG and shows the importance of MS in proteomics and glycoproteomics.