Abstract 1099: <i>In vitro</i> and <i>in cellulo</i> measurement of p53-binding activities reveals involvement of chromatin in p53-binding pattern formation
Bibliographic record
Abstract
Abstract p53 is a master transcriptional regulator that maintains genome integrity and cellular homeostasis in response to different stresses. Despite the relatively high number of research projects involving p53, the precise mechanism by which adaptable response is triggered remains unclear. Two theories are currently proposed. An initial model suggests that following stresses, pre-binding events (e.g. post-translational modifications, protein interactions, p53 concentration) direct p53 binding only to some specific effector genes. A second model proposes that p53 binds all the REs (response elements) and then post-binding events (e.g. post-translational modifications, presence of RNA pol II, etc.) operate as filters, thus resulting in the induction of specific genes. Recently, we revealed that pre- and post-binding events act together to regulate adapted stress responses. (Cancer Res. 2009 69:8463-71). The challenge now is to explore the mechanisms behind these stress-specific binding patterns. In order to do so, we used a novel approach that compares p53-binding activities to the p21 REs in vitro (on naked DNA) and in cellulo (on chromatinized DNA) in response to different UV-B and Nutlin-3 treatment doses. For in vitro experiments, human primary fibroblasts were treated, then p53-binding activities of nuclear extracts were assessed using a multiplex format microsphere assay of protein-DNA binding (MAPD, PLoS Genet. 2009 5:e1000462). These experiments showed that, following both UV-B and Nutlin-3 treatments, the −1354, −2242, −3969 and −11709 bp REs of p21 were bound by p53 to form similar binding patterns. On the other hand, the in cellulo approach based on DNase I footprinting revealed stress specific p53-binding patterns on the −1354, −2242 and −11709 bp REs only. Interestingly, these patterns appeared to be modulated by treatment doses. Finally, to investigate if a correlation exists between stress specific p53-binding patterns and cellular outcomes, we monitored apoptosis, cell cycle, cell proliferation and p21-mRNA level. We showed that when p53 bound the −1354 bp RE, it induced a high level of p21 mRNA correlated with permanent cell cycle arrest. Taken together, these data demonstrate the importance of chromatin on p53-binding pattern formation. Clearly p53 was not able to bind the −3969 bp RE in cellulo, indicating that accessibility to REs must be affected by the chromatin condensation state. Moreover, different treatments leading to similar p53 levels induce similar p53-binding activities in vitro. This underlines that the modulation of p53-binding activities to specific REs' DNA sequences is not involved in the formation of the binding patterns observed on p21’s REs. These data reflect the great versatility and complexity of the gene regulation achieved by p53 and help to understand the mechanisms behind adaptable responses to stress. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1099.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.005 | 0.002 |
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".