Formation of stress-specific p53 binding patterns is influenced by chromatin but not by modulation of p53 binding affinity to response elements †
Bibliographic record
Abstract
The p53 protein is crucial for adapting programs of gene expression in response to stress. Recently, we revealed that this occurs partly through the formation of stress-specific p53 binding patterns. However, the mechanisms that generate these binding patterns remain largely unknown. It is not established whether the selective binding of p53 is achieved through modulation of its binding affinity to certain response elements (REs) or via a chromatin-dependent mechanism. To shed light on this issue, we used a microsphere assay for protein-DNA binding to measure p53 binding patterns on naked DNA. In parallel, we measured p53 binding patterns within chromatin using chromatin immunoprecipitation and DNase I coupled to ligation-mediated polymerase chain reaction footprinting. Through this experimental approach, we revealed that UVB and Nutlin-3 doses, which lead to different cellular outcomes, induce similar p53 binding patterns on naked DNA. Conversely, the same treatments lead to stress-specific p53 binding patterns on chromatin. We show further that altering chromatin remodeling using an histone acetyltransferase inhibitor reduces p53 binding to REs. Altogether, our results reveal that the formation of p53 binding patterns is not due to the modulation of sequence-specific p53 binding affinity. Rather, we propose that chromatin and chromatin remodeling are required in this process.
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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 | 0.000 |
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".