IL-10 Has A Distinct Immunoregulatory Effect on Naive and Active T Cell Subsets
Why this work is in the frame
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Bibliographic record
Abstract
Interleukin-10 (IL-10) has been identified as a key immunomodulatory cytokine on T cells. However, both immunosuppressive and immunostimulatory effects of IL-10 on T cells also have been reported. The discrepancy between these in vitro effects of IL-10 may be due to the different T cells (naive vs. active or resting active T cells) used under various experimental conditions in these studies. Therefore, it is necessary to clearly define the IL-10 effect on T cell subsets in their different statuses. In this study, we used a molecularly defined T cell system, the ovalbumin (OVA)-specific CD4(+) and CD8(+) T cells from transgenic OT-I and OT-II mice expressing OVA-specific T cell receptor (TCR). We investigated the effect of IL-10 on these OVA-specific T cell subsets in their different statuses (i.e., naive and active T cells). Our data demonstrate that IL-10 has distinct immunoregulatory effects on naive and active T cell subsets. IL-10 inhibits active CD4(+) T cell proliferation, whereas it stimulates and suppresses active CD8(+) T cell proliferation and cytotoxicity, respectively. IL-10-treated dendritic cells (DCs) stimulate anergic cytotoxic T lymphocyte-associated molecule-4 (CTLA)-4-expressing CD4(+) T cell responses possibly through downregulation of major histocompetibility complex (MHC) class II and costimulatory molecule expression on DCs. The anergic CD4(+) T cells suppress T cell proliferation mainly through a CTLA-4-mediated pathway. The distinct role of IL-10 on T cell subsets may be useful in designing T cell-based immunotherapy of cancer and infectious diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it