Proteasomal Proteolysis in the Adrenergic Induction of Arylalkylamine-N-Acetyltransferase in Rat Pinealocytes
Bibliographic record
Abstract
In this study, we investigated the effect of proteasomal inhibition on the induction of arylalkylamine-N-acetyltransferase (AA-NAT) enzyme in cultured rat pinealocytes, using two proteasome inhibitors, MG132 and clastolactacystin beta-lactone (c-lact). Addition of c-lact or MG132 3 h after norepinephrine (NE) stimulation produced a significant increase in AA-NAT protein level and enzyme activity. However, when the proteasome inhibitors were added before or together with NE, significant reductions of the NE-induced aa-nat mRNA, protein, and enzyme activity were observed. A similar inhibitory effect of MG132 on aa-nat transcription was observed when cells were stimulated by dibutyryl cAMP, indicating an effect distal to a post-cAMP step. The inhibitory effect of MG132 on adrenergic-induced aa-nat transcription was long lasting because it remained effective after 14 h of washout and appeared specific for aa-nat because the induction of another adrenergic-regulated gene, MAPK phosphatase-1, by NE was not affected. Time-profile studies revealed that the inhibitory effect of MG132 on NE-stimulated aa-nat induction was detected after 1 h, suggesting accumulation of a protein repressor as a possible mechanism of action. This possibility was also supported by the finding that the inhibitory effect of c-lact on NE-induced aa-nat induction was markedly reduced by cycloheximide, a protein synthesis inhibitor. Together, these results support an important role of proteasomal proteolysis in the adrenergic-mediated induction of aa-nat transcription through its effect on a protein repressor.
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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".