Untangling Heparan Sulfate 3-<i>O</i>-Sulfation Using a Novel Offline Cationic-Peptide Affinity Enrichment, Followed by HILIC-cIM-MS
Bibliographic record
Abstract
Heparan sulfate (HS) is a linear polysaccharide that modifies proteoglycans. HS biosynthesis is regulated in a spatiotemporal manner, leading to structural diversity, including variable de- N -acetylation, N -sulfation, hexuronic acid C5 epimerization, and 2- O -, 6- O -, and 3- O -sulfation. Specific structural motifs within HS chains offer multiple specific binding sites for protein partners. The occurrence of HS 3- O -sulfation is relatively rare; however, there is accumulating evidence identifying the importance of this low-abundance modification in many different biological scenarios. Initially described as a key determinant for binding and activation of antithrombin, and more recently, as a coreceptor for viral infection, 3- O -sulfation has been associated with the progression of several neurological disorders. The analytical ability to study the biological roles of HS 3- O -sulfation is hindered by its low abundance within HS chains and the complex isomeric nature of highly sulfated HS, which places a burden on the tandem mass spectrometry step for assigning saccharide structures. In this context, we developed a specific cationic peptide-affinity method for 3- O -sulfation enrichment, followed by hydrophilic interaction liquid chromatography–cyclic ion mobility mass spectrometry analysis (HILIC-cIM-MS). We first demonstrated the high specificity of this approach to capture 3- O -sulfated HS oligosaccharides within complex mixtures. We next showed the influence of specific sulfate and epimerization patterns on HS binding selectivity. Finally, we used the enrichment strategy to analyze 3- O -sulfated HS oligosaccharides from heparin lyase III-digested HS from porcine intestinal mucosa (HSPIM). We concluded that this enrichment method was useful to guide new studies to reveal the biological roles of 3- O -sulfation and to elucidate new HS structural motifs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".