IL-27 promotes Tc1 polarization of human CD8 T lymphocytes and enhances their proliferation and effector functions (134.2)
Bibliographic record
Abstract
Abstract IL-27 has been shown to exhibit both pro- and anti-inflammatory properties. This cytokine favors naïve CD4 T cell differentiation into Th1 cells to the detriment of Th17 and Th2 skewing. Moreover, IL-27 promotes proliferation and cytotoxic functions of mouse CD8 T cells, but no data is available for human cells. We investigated the impact of IL-27 on human CD8 T cells. Surface IL-27 Receptor (IL-27R), composed of TCCR (also known as WSX-1) and gp130, is detectable on subsets of peripheral blood T cells. More CD8 than CD4 T cells are IL-27R positive and these proportions increase upon polyclonal activation. IL-27 activates rapid STAT1 and STAT3 signaling and transcription of SOCS1 and SOCS3 mRNA in CD8 T cells. Addition of IL-27 to anti-CD3 activated CD8 T cells leads to a significant augmentation of proliferation, and IFN-gamma and granzyme B production. Furthermore, purified naïve (CD45RA) but not memory (CD45RO) CD8 T cells are susceptible to those IL-27-enhancing effects. Moreover, anti-CD3 activated CD8 T cells express higher levels of T-bet upon IL-27 addition. Thus, our results support the capacity of IL-27 to drive naïve CD8 T cell polarization toward a Tc1 phenotype via STAT phosphorylation and induction of T-bet expression. Finally, the presence of IL-27 in the environmental milieu of naïve human CD8 T cells being activate through their TCR enhances the proliferation, IFN-gamma and granzyme B production thus contributes to generate more efficient effector cells.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".