α2-3 Sialic Acids-Decorated Allergens Exert a Tolerogenic Effect on CD4+ T Cells from Der p 2-Allergic Patients
Bibliographic record
Abstract
INTRODUCTION: Allergen-specific immunotherapy is so far the only disease-modifying therapy for allergy, resulting in a long-lasting tolerance. However, the existing safety concerns and the need for more efficacious alternatives that shorten the duration of treatment have stimulated research into the development of novel alternatives. Some of these novel alternatives involve modifying allergens with molecules that target innate immunomodulatory receptors to suppress the immune activity of immune cells. METHODS: Freshly prepared monocyte-derived dendritic cells (moDCs) from mite-allergic and non-atopic volunteers were treated with α2-3 sialic acid-conjugated recombinant Der p 2 (sia-Der p 2) and unconjugated Der p 2 in culture and matured with toll-like receptor 1/2 (Pam3CSK4) (Pam3) and 2/4 (lipopolysaccharide [LPS]) agonists, followed by coculture with autologous CD4+ T cells. Secretion of cytokines in supernatants was measured by ELISA, and expression of cell surface and intracellular markers was measured by flow cytometry. RESULTS: Sia-Der p 2 unlike Der p 2 modulated moDCs from mite-allergic volunteers by reducing expression of CD83 and CXCR5. We also observed that sia-Der p 2-treated moDCs in the presence of Pam3 and LPS significantly suppressed the proportion of CD25+, Ki67+, IL-13+, and IFNγ+ CD4+ T cells of mite-allergic volunteers, while Der p 2-treated moDCs did not. Sia-Der p 2-treated moDC did not alter these CD4+ T-cell populations in non-atopic volunteers. CONCLUSION: Our data suggest that Der p 2 conjugated with α2-3 sialic acids modifies moDCs and promotes the differentiation of allergen-specific CD4+ T cells toward a regulatory profile.
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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) | 0.003 | 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".