Acetylation of Histone H3 and Adrenergic-Regulated Gene Transcription in Rat Pinealocytes
Bibliographic record
Abstract
In this study we investigated the effect of histone acetylation on the transcription of adrenergic-induced genes in rat pinealocytes. We found that treatment of pinealocytes with trichostatin A (TSA), a histone deacetylase inhibitor, caused hyperacetylation of histone H3 (H3) Lys14 at nanomolar concentrations. Hyperacetylation of H3 was also observed after treatment with scriptaid, a structurally unrelated histone deacetylase inhibitor. The effects of TSA and scriptaid were inhibitory on the adrenergic induction of arylalkylamine-n-acetyltransferase (aa-nat) mRNA, protein, and enzyme activity, and on melatonin production. TSA at higher concentrations also inhibited the adrenergic induction of mapk phosphatase-1 (mkp-1) and inducible cAMP early repressor mRNAs. In contrast, the effect of TSA on the norepinephrine induction of the c-fos mRNA was stimulatory. Moreover, the effect of TSA on adrenergic-induced gene transcription was dependent on the time of its addition; its effect was only observed during the active phase of transcription. Chromatin immunoprecipitation with antibodies against acetylated Lys14 of H3 showed an increase in DNA recovery of the promoter regions of aa-nat, mkp-1, and c-fos after treatment with TSA. Together, our results demonstrate that histone acetylation differentially influences the transcription of adrenergic-induced genes, an enhancing effect for c-fos but inhibitory for aa-nat, mkp-1, and inducible cAMP early repressor. Moreover, both inhibitory and enhancing effects appear to be mediated through specific modification of promoter-bound histones during gene transcription.
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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".