The role of mRNA decay in p53-induced gene expression
Bibliographic record
Abstract
The p53 tumor suppressor is a DNA-damage-responsive sequence-specific transcriptional activator. The sustained activation of the p53 response is incompatible with cell growth and viability. To circumvent this issue, a variety of negative feedback loops exist to limit the duration of p53 activation. Despite our understanding of p53 regulation, very little is known about the effect of transient p53 activation on the long-term expression of p53 target genes. Here we used a temperature-sensitive variant of p53 and oligonucleotide microarrays to monitor gene expression during and following reversible p53 activation. The expression of most p53-induced transcripts was rapidly reversible, consistent with active mRNA decay. Representative 3' UTRs derived from short-lived transcripts (i.e., DDB2 and GDF15) conferred instability on a heterologous mRNA, while 3' UTRs derived from more stable transcripts (i.e., CRYAB and TP53I3) did not. The 3' UTRs derived from unstable p53-induced mRNAs were significantly longer than those derived from stable mRNAs. These 3' UTRs had high uridine and low cytosine content, leading to a higher density of U-, AU-, and GU-rich sequences. Remarkably, short-lived p53 targets were induced faster, reaching maximum transcript levels earlier than the stable p53 targets. Taken together, the evidence indicates that the p53 transcriptional response has evolved with primarily short-lived target mRNAs and that post-transcription processes play a prominent role in the p53 response.
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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 | 0.000 | 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) | 0.000 | 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".