Multiple muscle wasting-related transcription factors are acetylated in dexamethasone-treated muscle cells
Bibliographic record
Abstract
Recent studies suggest that the expression and activity of the histone acetyltransferase p300 are upregulated in catabolic muscle allowing for acetylation of cellular proteins. The function of transcription factors is influenced by posttranslational modifications, including acetylation. It is not known if transcription factors involved in the regulation of muscle mass are acetylated in atrophying muscle. We determined cellular levels of acetylated C/EBPβ, C/EBPδ, FOXO1, FOXO3a, and NF-kB/p65 in dexamethasone-treated L6 muscle cells, a commonly used in vitro model of muscle wasting. The role of p300 in dexamethasone-induced transcription factor acetylation and myotube atrophy was examined by transfecting muscle cells with p300 siRNA. Treatment of L6 myotubes with dexamethasone resulted in increased cellular levels of acetylated C/EBPβ and δ, FOXO1 and 3a, and p65. Downregulation of p300 with p300 siRNA reduced acetylation of transcription factors and decreased dexamethasone-induced myotube atrophy and expression of the ubiquitin ligase MuRF1. The results suggest that several muscle wasting-related transcription factors are acetylated supporting the concept that posttranslational modifications of proteins regulating gene transcription may be involved in the loss of muscle mass. The results also suggest that acetylation of the transcription factors is at least in part regulated by p300 and plays a role in glucocorticoid-induced muscle atrophy. Targeting molecules that regulate acetylation of transcription factors may help reduce the impact of muscle wasting.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".