IL-10- and retinoic acid-induced regulatory dendritic cells are therapeutically equivalent in mouse models of asthma and food allergy
Bibliographic record
Abstract
IL-10-induced DC (DC10) secrete IL-10, thereby promoting aeroallergen tolerance by activation of CD25+Foxp3+ Treg, while retinoic acid-induced DC (DC-RA) foster food allergen tolerance via IL-27-dependent induction of Foxp3− Treg. In some respects, these outcomes reflect those seen with aero- or food allergen-presenting lung and intestinal DC, respectively. Herein we asked whether these DCreg would also be functional in their reciprocal settings. DC-RA expressed lower levels of CCR5, CCR9, and CD103 than DC10, but higher levels of CD40, CD86, MHC II, TGF-β, IL-27 and Aldh1A2. DC-RA were also more effective in suppressing OVA-specific T cell proliferation in vitro (p ≤ 0.05). Co-culture of DC10 or DC-RA with OVA-specific T cells activated a regulatory phenotype therein, with the DC-10-induced Treg being modestly more suppressive in vitro than the DC-RA-induced Treg. We previously reported on lung trafficking of DC10, but DC-RA also traffic through the lungs and mediastinal lymph nodes before accumulating in the mesenteric lymph nodes. Both DCreg populations equally reduced airway hyperresponsiveness, Th2 responses (Th2 cytokines, eosinophilia) to airway allergen challenge, and plasma IgE/IgG1 levels in an OVA-asthma model. Similarly, they were equally effective in our OVA-food allergy model, where they reduced clinical scores (p ≤ 0.001), mast cell activation (p ≤ 0.05) and Th2 cytokine responses to allergen challenge, as well as OVA-specific IgE/IgG1 levels (p ≤ 0.01). Taken together, this data indicates that while DC10 and DC-RA employ distinct operative mechanisms, they were both able to induce tolerance in lung- and gut-associated allergic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".