Extracellular signal‐regulated kinases (ERK) and protein kinase C (PKC) activities are involved in the modulation of <i>Nur77</i> and <i>Nor</i>‐1 expression by dopaminergic drugs
Bibliographic record
Abstract
The dopamine system is the main target of antipsychotic and psychostimulant drugs. These drugs induce intracellular events that culminate in the transcription of immediate early genes, such as c-fos. Another class of transcription factors, namely, the nuclear receptor subgroup called Nurs (Nur77, Nurr1 and Nor-1), has recently been associated with behavioral and biochemical effects mediated by dopamine. However, the signaling cascade leading to modulation of Nur mRNA levels in the brain has never been investigated. In the present study, we explore in vivo using specific kinase inhibitors the role of mitogen-associated and extracellular signal-regulated kinases (MEK) and protein kinase C (PKC) in the modulation of Nur expression induced by dopamine receptor drugs. Modulation of Nur77 expression by a dopamine D(2) receptor antagonist is associated with MEK and PKC activities, whereas only the PKC activity participates in the modulation of Nor-1 expression. Both MEK and PKC activities also participate in the modulation of Nur77 mRNA levels induced by dopamine receptor agonists, whereas a selective MEK activity is associated with the modulation of Nor-1 mRNA levels. Interestingly, modulation of dopamine drug-induced locomotor activities by kinase inhibitors is in accordance with the effects on Nur77, but not Nor-1, expression. Taken together, the results indicate that signaling events leading to modulation of Nur77 and Nor-1 expression following dopamine receptor interacting drugs are distinct. Considering that orphan nuclear receptors of the Nur subgroup display an important ligand-independent constitutive activity, characterization of the signaling cascades involved in the regulation of their expression represents an important step for understanding their role in dopamine system physiology and pathophysiology.
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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) | 0.001 | 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 | 0.001 |
| 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".