STAT5-signaling cytokines regulate the expression of FOXP3 in CD4+CD25+ regulatory T cells and CD4+CD25− effector T cells
Bibliographic record
Abstract
Forkhead box P3 (FOXP3) is considered a specific marker for CD4(+)CD25(+) regulatory T (Treg) cells, but increasing evidence suggests that human CD4(+)CD25(-) effector T (Teff) cells can transiently express FOXP3 upon activation. We demonstrate that the signal transducer and activator of transcription 5 (STAT5)-signaling cytokines, IL-2, IL-15 and to a lesser extent IL-7, induce FOXP3 up-regulation in vitro in activated human Teff cells. In contrast, cytokines which do not activate STAT5, such as IL-4 or transforming growth factor-beta alone, do not directly induce FOXP3 expression in activated Teff cells. Moreover, expression of a constitutively active form of STAT5a is sufficient to induce FOXP3 expression in Teff cells. Expression of FOXP3 in activated Teff cells requires both TCR-mediated activation and endogenous IL-2, but is not dependent on cell division and does not induce suppressive function. The presence of STAT5-activating cytokines is also required to maintain high FOXP3 expression and suppressive activity of Treg cells in vitro. These data indicate that activation of STAT5 sustains FOXP3 expression in both Treg and Teff cells and contribute to our understanding of how cytokines affect the expression of FOXP3.
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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) | 0.002 | 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".