AI Insight
Researchers developed a comprehensive mass spectrometry approach to map the complex sugar modifications (O-glycans) on osteopontin, a bone-building protein involved in various diseases. By combining native mass spectrometry, enzyme treatments, and computational modeling, they discovered that standard analysis methods significantly underestimate the extent of sialylation (a specific type of sugar modification) on this protein. The integrated workflow successfully reconciled measurements from intact protein and peptide-level analyses, providing the most detailed characterization to date of osteopontin's structural diversity.
Why it matters
This methodology framework can be applied to study other heavily modified proteins involved in bone diseases, cancer, and immune responses. Better understanding of osteopontin's modifications could lead to improved biomarkers for disease diagnosis and new therapeutic targets, as abnormal osteopontin has been implicated in bone disorders, cardiovascular disease, and tumor metastasis.
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⚠️ Preprint – Noch nicht peer-reviewed
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O-glycosylation is among the most abundant and structurally diverse post-translational modifications in eukaryotes, yet its heterogeneity renders O-glycoproteins exceptionally difficult to characterize. To overcome these challenges, we have developed an integrated mass spectrometry (MS) strategy to define the proteoform landscape of O-glycoproteins and applied it to human osteopontin (OPN). OPN is a disease-associated extracellular matrix protein subject to extensive modification. By combining native MS with serial exoglycosidase digestions, we directly resolved truncation, phosphorylation, sulfation, and O-glycosylation of OPN. Matched glycoproteomic analyses, using tailored (glyco)protease combinations, allowed us to quantify glycan heterogeneity inaccessible to conventional trypsin-based approaches or protein-centric methods. We integrated these datasets using forward compositional simulations to infer the intact OPN proteoform distribution and benchmarked the resulting models against an experimental intact-mass distribution obtained by proton-transfer charge-reduction MS. This comparison revealed that bottom-up O-glycoproteomics systematically underestimates the true extent of glycan sialylation, whereas assuming (near-)complete sialylation accurately reproduced the experimental intact OPN mass distribution. Together, these results provide a comprehensive, quantitative view of OPN compositional diversity and demonstrate how intact-protein and peptide-level measurements can be reconciled to resolve highly heterogeneous glycoform populations. The workflow establishes a broadly applicable framework for characterizing extensively O-glycosylated and multiply modified proteins.