Activation of Go-coupled Dopamine D2 Receptors Inhibits ERK1/ERK2 in Pituitary Cells
Bibliographic record
Abstract
In pituitary lactotrophs the prolactin gene is stimulated by neuropeptides and estrogen and is suppressed by dopamine via D2-type receptors. Stimulatory signals converge on activation of the mitogen-activated protein kinases ERK1/2, but dopamine regulation of this pathway is not well defined. Paradoxically, D2 agonists activate ERK1/2 in many cell types. Here we show that in prolactin-secreting GH4ZR7 cells and primary pituitary cells, dopamine treatment leads to a rapid, pronounced, and specific decrease in activated ERK1/2. The response is blocked by D2-specific antagonists and pertussis toxin. Interestingly, in stable lines expressing specific pertussis toxin-resistant Galpha subunits, toxin treatment blocks dopamine suppression of MAPK in Galpha(i2)- but not Galphao-expressing cells, demonstrating that G(o)-dependent pathways can effect the inhibitory MAPK response. At the nuclear level, the MEK1 inhibitor U0126 mimics the D2-agonist bromocryptine in suppressing levels of endogenous prolactin transcripts. Moreover, a good correlation is seen between the IC(50) values for inhibition of MEK1 and suppression of prolactin promoter function (PD184352 > U0126 > U0125). Both dopamine and U0126 enhance the nuclear localization of ERF, a MAPK-sensitive ETS repressor that inhibits prolactin promoter activity. In addition, U0126 suppression is transferred by tandem copies of the Pit-1-binding site, consistent with mapping experiments for dopamine responsiveness. Our data suggest that ERK1/2 suppression is an obligatory step in the dopaminergic control of prolactin gene transcription and that bidirectional control of ERK1/2 function in the pituitary may provide a key mechanism for endocrine gene control.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".