Induction of Type 2 T Helper Cell Allergen Tolerance by IL-10–Differentiated Regulatory Dendritic Cells
Bibliographic record
Abstract
In mouse models of asthma, therapeutic use of allergen-presenting IL-10-differentiated dendritic cells (DCs) can abrogate airway hyperresponsiveness, and reduce other asthma-related responses to near background. Analogous human DCs can suppress human T cell responses in vitro, but the operative mechanisms are poorly defined. We investigated the ability of IL-10-treated human DCs to induce tolerance among autologous T cells of subjects with asthma and the mechanisms by which they do this. CD14(+) monocyte-derived DCs were differentiated in the presence of IL-10 (DC10) ex vivo from 11 donors with asthma and 4 control donors, and characterized for relevant markers. They were pulsed with specific or irrelevant allergen, and cultured with autologous peripheral blood CD4(+) T cells, either alone or together with autologous immunostimulatory DCs (DC-TNF), and the impact of this treatment on the T-cell responses was assessed for each donor. The DC10 expressed reduced levels of some relevant markers (CD40, CD80, human leukocyte antigen-DR) and stimulatory cytokines (IL-6 and IL-12), but augmented levels of Ig-like transcript-22/CD85j and IL-10 relative to DC-TNF. In cocultures, they dampened DC-TNF-driven T helper (Th) type 2 cell proliferation and cytokine (IL-4, -5, and -13) secretion. They also drove the development from atopic CD4(+)CD25(lo)Foxp3(lo) cells of a population of IL-10-secreting CD25(+)Foxp3(+)LAG-3(+)CTLA-4(+) regulatory T cells (Tregs). These Tregs suppressed stimulatory DC-induced autologous Th2 cell proliferation and cytokine secretion in a contact-dependent manner. Our data indicate that IL-10-treated human DCs induce Th2 cell allergen tolerance ex vivo by driving the differentiation of Tregs.
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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".