The p53 protein induces stable miRNAs that have the potential to modify subsequent p53 responses
Bibliographic record
Abstract
The p53 tumour suppressor is a transcription factor that can increase the expression of mRNAs and microRNAs (miRNAs). HT29-tsp53 cells expressing a temperature sensitive variant of p53 have provided a useful model to rapidly and reversibly control p53 activity. In this model, the majority of p53-responsive mRNAs were upregulated rapidly but they were short-lived leading to rapid decay of the p53 response at the restrictive temperature. Here we used oligonucleotide microarrays and reverse transcriptase PCR to show that p53-induced miRNAs exhibited a distinct temporal pattern of expression. Whereas p53-induced miRNAs like miR-143-3p, miR-145-5p, miR-34a-5p and miR-139-5p increased as fast as mRNAs, they were extremely stable persisting long after p53 induced mRNAs and even their corresponding primary miRNAs had decayed to baseline levels. Three p53-induced mRNAs (MDM2, BTG2 and CDKN1A) are experimentally verified targets of one or more of these specific miRNAs so we hypothesized that the sustained expression of p53-induced miRNAs could be explained by a post-transcriptional feedback loop. Activation of consecutive p53 responses separated by a period of recovery led to the selective attenuation of a subset of p53 regulated mRNAs corresponding to those targeted by one or more of the p53-responsive miRNAs. Our results indicate that the long term expression of p53 responsive miRNAs leads to an excess of miRNAs during the second response and this likely prevents the induction of MDM2, BTG2 and CDKN1A mRNA and/or protein. These observations are likely to have important implications for daily cancer therapies that activate p53 in normal tissues and/or tumour cells.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".