Glucocorticoid-stimulated preadipocyte differentiation is mediated through acetylation of C/EBPβ by GCN5
Bibliographic record
Abstract
Preadipocyte differentiation in culture is driven by an insulin and cAMP dependant transcriptional cascade which induces the bzip transcription factors C/EBPbeta and C/EBPdelta. We have previously shown that glucocorticoid treatment, which strongly potentiates this differentiation pathway, stimulates the titration of the corepressor histone deacetylase 1 (HDAC1) from C/EBPbeta. This results in a dramatic enhancement of C/EBPbeta-dependent transcription from the C/EBPalpha promoter, concomitant with potentiation of preadipocyte differentiation. Here, we show that C/EBPbeta is acetylated by GCN5 and PCAF within a cluster of lysine residues between amino acids 98-102 and that this acetylation is strongly induced by glucocorticoid treatment. Arginine substitution of the lysine residues within the acetylation motif of C/EBPbeta prevented acetylation and blocked the ability of glucocorticoids to enhance C/EBPbeta-directed transcription and to potentiate C/EBPbeta-dependent preadipocyte differentiation. Moreover, acetylation of C/EBPbeta appeared to directly interfere with the interaction of HDAC1 with C/EBPbeta, suggesting that PCAF/GCN5-dependent acetylation of C/EBPbeta serves as an important molecular switch in determining the transcriptional regulatory potential of this transcription factor.
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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) | 0.001 | 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".