Carbohydrate sulfation as a mechanism for fine-tuning Siglec ligands
Bibliographic record
Abstract
Abstract The immunomodulatory family of Siglecs recognize sialic acid-containing glycans as ‘ self ’, which is exploited in cancer for immune-evasion. The biochemical nature of Siglec ligands remains incompletely understood with emerging evidence suggesting the importance of carbohydrate sulfation. Here, we investigate how specific sulfate modifications affect Siglec ligands by overexpressing eight carbohydrate sulfotransferases (CHSTs) in five cell lines. Overexpression of three CHSTs ( CHST1 , CHST2, or CHST4 ) significantly enhances the binding of numerous Siglecs. Unexpectedly, two other CHSTs ( Gal3ST2 and Gal3ST3 ) diminish Siglec binding, suggesting a new mode to modulate Siglec ligands via sulfation. Results are cell type dependent, indicating that the context in which sulfated glycans are presented is important. Moreover, pharmacological blockade of N - and O -glycan maturation reveals a cell type-specific pattern of importance for either class of glycan. Production of a highly homogenous CD33 (Siglec-3) fragment enabled a mass spectrometry-based binding assay to determine 10-fold and 3-fold enhanced affinity for Neu5Acα2-3(6- O -sulfo)Galβ1-4GlcNAc and Neu5Acα2-3Galβ1-4(6- O - sulfo)GlcNAc, respectively, over Neu5Acα2-3Galβ1-4GlcNAc. CD33 showed significant additivity in affinity (36-fold) for the disulfated ligand, Neu5Acα2-3(6- O -sulfo)Galβ1-4(6- O -sulfo)GlcNAc. Moreover, overexpression of both CHST1 and CHST2 in cells greatly enhanced the binding of several Siglecs, including CD33. Finally, we reveal that CHST1 is upregulated in numerous cancers, correlating with poorer survival rates and sodium chlorate sensitivity for the binding of Siglecs to cancer cell lines. These results provide new insights into carbohydrate sulfation as a modification that is a general mechanism for tuning Siglec ligands on cells, including in cancer.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".