Attenuation of Glucocorticoid Signaling through Targeted Degradation of p300 via the 26S Proteasome Pathway
Bibliographic record
Abstract
The effects of acetylation on gene expression are complex, with changes in chromatin accessibility intermingled with direct effects on transcriptional regulators. For the nuclear receptors, both positive and negative effects of acetylation on specific gene transcription have been observed. We report that p300 and steroid receptor coactivator 1 interact transiently with the glucocorticoid receptor and that the acetyltransferase activity of p300 makes an important contribution to glucocorticoid receptor-mediated transcription. Treatment of cells with the deacetylase inhibitor, sodium butyrate, inhibited steroid-induced transcription and altered the transient association of glucocorticoid receptor with p300 and steroid receptor coactivator 1. Additionally, sustained sodium butyrate treatment induced the degradation of p300 through the 26S proteasome pathway. Treatment with the proteasome inhibitor MG132 restored both the level of p300 protein and the transcriptional response to steroid over 20 h of treatment. These results reveal new levels for the regulatory control of gene expression by acetylation and suggest feedback control on p300 activity.
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) | 0.000 | 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) | 0.001 | 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".