Tolerance Induction by Transcutaneous Immunization through Ultraviolet-Irradiated Skin Is Transferable through CD4+CD25+ T Regulatory Cells and Is Dependent on Host-Derived IL-10
Bibliographic record
Abstract
UV radiation of the skin impairs immune responses to haptens and to tumor Ags. Transcutaneous immunization (TCI) is an effective method of inducing immune responses to protein and peptide Ag. We explore the effect of UV irradiation on TCI. The generation of Ag-specific CTL to OVA protein, but not class I MHC-restricted OVA peptide, is inhibited by TCI through UV-irradiated skin. Consequently, the induction of protein contact hypersensitivity and in vivo Ag-specific CTL activity following OVA protein immunization is prevented. Application of haptens to UV-exposed skin induces hapten-specific tolerance. We demonstrate that application of protein or class II MHC-restricted OVA peptide to UV-irradiated skin induces transferable Ag-specific tolerance. This tolerance is mediated by CD4(+)CD25(+) T regulatory (T(reg)) cells. These Ag-specific T(reg) cells inhibit the priming of CTL following protein immunization in the presence of CpG adjuvant. IL-10 deficiency is known to prevent hapten-specific tolerance induction. In this study, we demonstrate, using IL-10-deficient mice and adoptive T cell transfer, that IL-10 is required for the direct inhibition of CTL priming following immunization through UV-irradiated skin. However, IL-10 is not required for the induction of T(reg) cells through UV-irradiated skin as IL-10-deficient T(reg) cells are able to mediate tolerance. Rather, host-derived IL-10 is required for the function of UV-generated T(reg) cells. These experiments indicate that protein and peptide TCI through UV-irradiated skin may be used to induce robust Ag-specific tolerance to neo-Ags and that UV-induced T(reg) cells mediate their effects in part through the modulation of IL-10.
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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) | 0.001 | 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".