Peptide immunotherapy in allergic asthma generates IL-10–dependent immunological tolerance associated with linked epitope suppression
Bibliographic record
Abstract
Treatment of patients with allergic asthma using low doses of peptides containing T cell epitopes from Fel d 1, the major cat allergen, reduces allergic sensitization and improves surrogate markers of disease. Here, we demonstrate a key immunological mechanism, linked epitope suppression, associated with this therapeutic effect. Treatment with selected epitopes from a single allergen resulted in suppression of responses to other ("linked") epitopes within the same molecule. This phenomenon was induced after peptide immunotherapy in human asthmatic subjects and in a novel HLA-DR1 transgenic mouse model of asthma. Tracking of allergen-specific T cells using DR1 tetramers determined that suppression was associated with the induction of interleukin (IL)-10(+) T cells that were more abundant than T cells specific for the single-treatment peptide and was reversed by anti-IL-10 receptor administration. Resolution of airway pathophysiology in this model was associated with reduced recruitment, proliferation, and effector function of allergen-specific Th2 cells. Our results provide, for the first time, in vivo evidence of linked epitope suppression and IL-10 induction in both human allergic disease and a mouse model designed to closely mimic peptide therapy in humans.
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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 | 0.001 |
| 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".