Molecular basis of Siglec-10 ligand recognition and antibody blockade
Bibliographic record
Abstract
ABSTRACT Siglec-10 is a sialic acid-binding immunoglobulin-like lectin implicated in immune regulation, yet the molecular basis for ligand recognition and how this is functionally linked to immune modulation remains poorly defined. Herein, we present a multidisciplinary study encompassing structural, biochemical, and cellular approaches to elucidate Siglec-10-carbohydrate interactions and their functional consequences. The crystal structure of the extracellular domain of Siglec-10 in complex with α2-6 sialyllactose revealed the presence of two key arginine residues within the Siglec-10 binding site that interact with the carboxyl group of sialic acid, the canonical R119 and R127, suggesting potential dual contributions to ligand engagement. Saturation Transfer Difference (STD)-Nuclear Magnetic Resonance (NMR) confirmed that R119 is essential for sialoglycan binding in solution, whereas R127 appears dispensable for interactions with glycans under these conditions. In contrast, cell-based binding assays using primary human T cells and engineered monocytic lines demonstrated that both arginine residues (R119 and R127) are critical for cellular recognition, revealing a context-dependent interaction. By obtaining direct images at a molecular resolution of 6-7 nm, super-resolution microscopy further revealed glycan-independent dimerization of the Siglec-10 receptor on the surface of human monocytes. Ligand blockade mediated by anti-Siglec-10 mAb (clone S10A) restores CAR-T cell cytotoxicity in vitro , supporting its role as an immune checkpoint receptor. Finally, although CD24 was not identified as a Siglec-10 ligand on T cells, proximity labeling and mass spectrometry uncovered other sialylated glycoproteins that may mediate this interaction. Together, these results identify Siglec-10 as a modulatory receptor with structural and functional features distinct from other Siglec family members and highlight its potential for therapeutic targeting in cancer immunotherapy.
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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".