Understanding the Glycosylation Pathways Involved in the Biosynthesis of the Sulfated Glycan Ligands for Siglecs
Bibliographic record
Abstract
Carbohydrate sulfation plays a pivotal role in modulating the strength of Siglec–glycan interactions. Recently, new aspects of Siglec binding to sulfated cell surface carbohydrates have been discovered, but the class of glycan presenting these sulfated Siglec ligands has not been fully elucidated. In this study, the contribution of different classes of glycans to cis and trans Siglec ligands was investigated within cells expressing the carbohydrate sulfotransferase 1 (CHST1) or CHST2. For some Siglecs, the glycan class mediating binding was clear, such as O -glycans for Siglec-7 and N -glycans for Siglec-2 and Siglec-9. Both N -glycans and mucin-type O -glycans contributed to ligands for Siglec-3, -5, -8, and -15. However, significant levels of Siglec-3 and -8 ligands remained in CHST1-expressing cells lacking complex N -glycans and mucin-type O -glycans. A combination of genetic, pharmacological, and enzymatic treatment strategies ruled out heparan sulfates and glycoRNA as contributors, although Siglec-8 did exhibit some binding to glycolipids. Genetic disruption of O -mannose glycans within CHST1-expressing cells had a small but significant impact on Siglec-3 and -8 binding, demonstrating that this class of glycans can present sulfated Siglec ligands. We also investigated the ability of sulfated cis ligands to mask Siglec-3 and Siglec-7. For Siglec-7, cis ligands were again found to be mucin-type O -glycans. While N -glycans were the major sulfated trans ligands for Siglec-3, disruption of complex mucin-type O -glycans had the largest impact on Siglec-3 masking. Overall, this study enhances our knowledge of the types of sulfated glycans that can serve as Siglec ligands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".