Modulation of Nur77 expression by dopaminergic drug is altered by a MEK pathway inhibitor in vivo
Bibliographic record
Abstract
Transcription factors of the Nurs family (Nurr‐1, Nur77 and Nor‐1) are orphan receptors that have been recently associated to dopaminergic neurotransmission. Previous studies have shown that dopaminergic agonists and antagonists (antipsychotic) administration up‐regulated Nur77 mRNA levels in dynorphin‐ and enkephalin‐bearing striatal neurons, respectively. But, the intracellular cascade involved in Nur77 expression remains unexplored in the CNS. To this aim, we evaluated the role of a MEK (MAP kinase or ERK Kinase) inhibitor (SL327) and a PKC (Protein Kinase C) inhibitor (NPC‐15437), that cross the brain blood barrier, in these effects. Groups of mice received acute injections of vehicle, SL327, NPC‐15437, eticlopride (D2 antagonist), a combination or D1 and D2 agonists (SKF82958 and quinpirole) and combination of these dopaminergic agents with SL327 or NPC‐15437. Nur77 mRNA levels were measured by in situ hybridization. SL327 potentialized D2 antagonist‐induced Nur77 mRNA expression in the striatum. On the other hand, combination of SKF82958 + quinpirole with SL327 and NPC‐15437 reduce up‐regulated Nur77 mRNA levels in the striatum. These results show that the MEK and PKC pathways are involved in the signaling cascade leading to modulation of Nur77 expression by dopaminergic drug in vivo and that these pathways distinctly modulates Nur77 expression in the two striatal cell populations. Interestingly the MEK and PKC pathways did not modulate Nur77 mRNA level in substantia nigra neurons expressing the dopamine D2 autoreceptor. The fact that NPC‐15437 has no effect in enkephalin‐positive neurons suggest that the effects of D2 antagonist act downstream of PKC. Support provided by the Canadian Institutes for Health Research (CIHR).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".