Down-regulation of Runx1 Expression by TCR Signal Involves an Autoregulatory Mechanism and Contributes to IL-2 Production
Bibliographic record
Abstract
Runx1 transcription factor plays multiple roles in T cell development, differentiation, and function. However, the regulatory mechanisms and functional significance of high Runx1 protein expression in resting peripheral CD4+ T cells is not well understood. Here, we demonstrate that T-cell receptor (TCR) activation down-regulates distal Runx1 transcription, resulting in a significant reduction of Runx1 protein. Interestingly, this down-regulation of distal Runx1 transcription appears to be mediated through a negative auto-regulatory mechanism, whereby Runx1 protein binds to a Runx consensus site in the distal promoter. Through the use of Runx1-overexpressing cells from transgenic mice, we demonstrate that interference with TCR-mediated Runx1 down-regulation inhibits IL-2 production and proliferation in activated CD4+ T cells. In contrast, using Runx1-deficient cells prepared from targeted mice, we show that the absence of Runx1 in unstimulated CD4+ T cells results in IL-2 derepression. In summary, we propose that high levels of Runx1 in resting CD4+ T cells functions negatively in the regulation of IL-2 transcription, and that TCR activation-mediated down-regulation of Runx1 involves negative auto-regulation of the distal Runx1 promoter and contributes to IL-2 production.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".