A Novel p53 Transcriptional Repressor Element (p53TRE) and the Asymmetrical Contribution of Two p53 Binding Sites Modulate the Response of the Placental Transforming Growth Factor-β Promoter to p53
Bibliographic record
Abstract
Previous studies in our laboratory and others identified placental transforming growth factor-beta (PTGF-beta) as an important downstream mediator of DNA damage signaling and a transcriptional target of p53. Here we show that accumulation of PTGF-beta mRNA in response to p53 overexpression is delayed relative to p21(WAF1), whereas the promoters of these genes respond to p53 with similar kinetics. Mutational analyses of two p53 binding sites within the PTGF-beta promoter revealed that site p53-1 (+29 bp) is responsible for as much as 80% of the transcriptional response to p53. This is consistent with electrophoretic mobility shift assays showing that site p53-1 binds p53 with a much higher affinity than site p53-2 (-850 bp). We also describe for the first time a novel 21-bp element (-222 to -242 bp) that acts to down-regulate the PTGF-beta promoter response to p53. Termed the p53 transcriptional repressor element (p53TRE), this sequence was shown to suppress p53 transactivation in a position- and promoter-independent fashion and to associate with a 28-kDa protein expressed in several tumor cell lines. A p53 suppressor element and asymmetric p53 binding sites may participate determining the activation thresholds of p53-responsive promoters in a cell- and context-specific manner.
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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 | 0.001 |
| 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".