Modulation of Siglec-7 Signaling via <i>in situ</i> Created High-affinity <i>cis</i> -Ligands
Bibliographic record
Abstract
Abstract Sialic acid-binding immunoglobulin-like lectins, also known as Siglecs, have recently been designated as glyco-immune checkpoints. Through their interactions with sialylated glycan epitopes overexpressed on tumor cells, inhibitory Siglecs on innate and adaptive immune cells modulate signaling cascades to restrain anti-tumor immune responses. However, the mechanisms underlying these processes are just starting to be elucidated. We discover that when human natural killer (NK) cells attack tumor cells, glycan remodeling occurs on the target cells at the immunological synapse. This remodeling occurs through both transfer of sialylated glycans from NK cells to target tumor cells and accelerated de novo synthesis of sialosides on the tumor cell themselves. The functionalization of NK cells with a high-affinity ligand of Siglec-7 leads to multifaceted consequences in modulating Siglec-7-regulated NK-activation. At high levels, the added Siglec-7 ligand suppresses NK-cytotoxicity through the recruitment of Siglec-7, whereas at low levels the same ligand triggers the release of Siglec-7 from the cell surface into the culture medium, preventing Siglec-7-mediated inhibition of NK cytotoxicity. These results suggest that glycan engineering of NK cells may provide a means to boost NK effector functions.
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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) | 0.002 | 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".