Comparison of Induced versus Natural Regulatory T Cells of the Same TCR Specificity for Induction of Tolerance to an Environmental Antigen
Bibliographic record
Abstract
Recent evidence shows that natural CD25(+)Foxp3(+) regulatory T cells (nTreg) and induced CD25(+)Foxp3(+) regulatory T cells (iTreg) both contribute to tolerance in mouse models of colitis and asthma, but there is little evidence regarding their relative contributions to this tolerance. We compared the abilities of nTreg and iTreg, both from OVA-TCR-transgenic OTII mice, to mediate tolerance in OVA-asthmatic C57BL/6 mice. The iTreg were differentiated from Th2 effector T cells by exposure to IL-10-differentiated dendritic cells (DC10) in vitro or in vivo, whereas we purified nTreg from allergen-naive mice and exposed them to DC10 before use. Each Treg population was subsequently repurified and tested for its therapeutic efficacy in vitro and in vivo. DC10 engaged the nTreg in a cognate fashion in Forster (or fluorescence) resonance energy transfer assays, and these nTreg reduced in vitro OVA-asthmatic Th2 effector T cell responses by 41-56%, whereas the comparator iTreg reduced these responses by 72-86%. Neutralization of IL-10, but not TGF-β, eliminated the suppressive activities of iTreg but not nTreg. Delivery of 5 × 10(5) purified nTreg reduced allergen challenge-induced airway IL-4 (p ≤ 0.03) and IL-5 (p ≤ 0.001) responses of asthmatic recipients by ≤ 23% but did not affect airway hyperresponsiveness or IgE levels, whereas equal numbers of iTreg of identical TCR specificity reduced all airway responses to allergen challenge by 82-96% (p ≤ 0.001) and fully normalized airway hyperresponsiveness. These data confirm that allergen-specific iTreg and nTreg have active roles in asthma tolerance and that iTreg are substantially more tolerogenic in this setting.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".