4.7 Article

An Integrated Strategy Reveals Complex Glycosylation of Erythropoietin Using Mass Spectrometry

Journal

JOURNAL OF PROTEOME RESEARCH
Volume 20, Issue 7, Pages 3654-3663

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.1c00221

Keywords

glycosylation; mass spectrometry; Python scripts; glycoinformatics; bottom-up; erythropoietin

Funding

  1. Horizon 2020 Marie Sklodowska-Curie Action ITN 2017 of the European Commission [765502-A4B]
  2. China Scholarship Council [201606220045, 201806780022]

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This study presents an in-depth analytical strategy for glycosylation of therapeutic glycoproteins, including N-glycan analysis, site-specific N-glycosylation analysis, and O-glycan identification. By using mass spectrometry and Python scripts, the study identified N-glycan compositions, revealed site-specific N-glycosylation, and analyzed different glycan modifications such as phosphorylation and O-acetylation. The integrated strategy proved to be effective in understanding the pharmacological properties of therapeutic glycoproteins and improving manufacturing processes.
The characterization of therapeutic glycoproteins is challenging due to the structural heterogeneity of the therapeutic protein glycosylation. This study presents an in-depth analytical strategy for glycosylation of first-generation erythropoietin (epoetin beta), including a developed mass spectrometric workflow for N-glycan analysis, bottom-up mass spectrometric methods for site-specific N-glycosylation, and a LC-MS approach for O-glycan identification. Permethylated N-glycans, peptides, and enriched glycopeptides of erythropoietin were analyzed by nanoLC-MS/MS, and de-N-glycosylated erythropoietin was measured by LC-MS, enabling the qualitative and quantitative analysis of glycosylation and different glycan modifications (e.g., phosphorylation and O-acetylation). The newly developed Python scripts enabled the identification of 140 N-glycan compositions (237 N-glycan structures) from erythropoietin, especially including 8 phosphorylated N-glycan species. The site-specificity of N-glycans was revealed at the glycopeptide level by pGlyco software using different proteases. In total, 114 N-glycan compositions were identified from glycopeptide analysis. Moreover, LC-MS analysis of deN-glycosylated erythropoietin species identified two O-glycan compositions based on the mass shifts between non-O-glycosylated and O-glycosylated species. Finally, this integrated strategy was proved to realize the in-depth glycosylation analysis of a therapeutic glycoprotein to understand its pharmacological properties and improving the manufacturing processes.

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