Ligation of Siglec-9 inhibits FcɛRI-dependent mediator release from human mast cells
Bibliographic record
Abstract
Abstract Mast cell activation is critical for the development of allergic diseases. Ligation of Sialic acid-binding immunoglobin-like lectins (Siglecs) such as Siglec-7 and 8 has been shown to inhibit mast cell activation and is considered one of the main approaches to reduce mast cell contribution to allergic disease. Recent high throughput studies provided evidence that human mast cells express Siglec-9, an inhibitory receptor that is mainly expressed by innate immune cells such as neutrophils, monocytes and eosinophils. Based on this evidence, we aimed to examine Siglec-9 expression and function in human mast cells. Flow cytometry analysis showed that Siglec-9 is expressed in the human mast cell lines LAD2, LUVA, and HMC-1, and in peripheral blood-derived cultured human mast cells (PBCMCs). The expression of Siglec-9 in PBCMCs peaks at week 5 of culture and correlates positively with the expression of the high affinity receptor for IgE (FcɛRI). Pre-treatment of human mast cells with the Siglec-9 agonists glycophorin A or high molecular weight hyaluronic acid (HMW-HA) followed by FcɛRI-dependent stimulation has an inhibitory effect on mast cell degranulation. SIGLEC9 gene disruption by CRISPR/Cas9 editing resulted in a significant reduction in Siglec-9 expression in LAD2 cells that also became impervious to inhibition by Siglec-9 agonists. Together, our data shows that human mast cells express Siglec-9 and that engaging this inhibitory receptor can reduce mast cell degranulation.
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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".