Adrenergic Regulation of the Distribution of Transducer of Regulated cAMP-Response Element-Binding Protein (TORC2) in Rat Pinealocytes
Bibliographic record
Abstract
Transducers of regulated cAMP-response element-binding protein (CREB) activity (TORC) are coactivators that can increase CREB transcriptional activity, suggesting that TORC may regulate the transcription of Aanat, a CREB-target gene. In the present study, we focused on the regulation of TORC2 and its role in Aanat transcription in the rat pineal gland. Although there was no endogenous Torc2 mRNA rhythm in the rat pineal gland and treatment of cultured pinealocytes with norepinephrine (NE) had no effect on the mRNA level of Torc2, the phosphorylation state and intracellular distribution of TORC2 protein were regulated by NE. Immunoblot analysis combined with cytosolic/nuclear fractionation or phosphatase treatment showed that TORC2 protein was rapidly dephosphorylated and translocated to the nucleus after NE stimulation in rat pinealocytes. Similar dephosphorylation of TORC2 also occurred nocturnally in the rat pineal gland. The NE-mediated TORC2 dephosphorylation was blocked by cotreatment with propranolol (a β-adrenergic antagonist) but not prazosin (an α(1)-adrenergic antagonist) and mimicked by dibutyryl cAMP, indicating the participation of the β-adrenergic receptor/cAMP pathway. Studies with protein phosphatase inhibitors showed that only okadaic acid and calyculin A were effective in blocking the NE-mediated TORC2 dephosphorylation, suggesting the involvement of protein phosphatase 2A in this dephosphorylation. Moreover, TORC2 overexpression had an enhancing effect on NE-stimulated Aanat transcription. Together, these results indicate that NE stimulation causes nuclear translocation of TORC2 by dephosphorylating the protein through a β-adrenoceptor/cAMP mechanism and that nuclear localization of TORC2 appears to regulate Aanat transcription by NE in the rat pineal gland.
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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".