Epigenetic Mechanisms in the Dopamine D2 Receptor-Dependent Inhibition of the Prolactin Gene
Bibliographic record
Abstract
Transcription of the prolactin gene is dynamically controlled by positive and negative hormone signals that target the regulatory promoter region. Based on the inducibility of prolactin gene expression by inhibitors of histone deacetylases (HDACs), we examined the role of histone acetylation at the genomic prolactin promoter as a late step in transcriptional regulation. Chromatin immunoprecipitation analysis of GH4 cells revealed elevated levels of acetylated histones in the promoter and enhancer regions of the gene, compared with downstream intron sequences. 17beta-Estradiol stimulated histone H4 acetylation in the promoter region by 2- to 3-fold within 30 min. Dopamine inhibited histone H4 acetylation by 2-fold in 30 min, an effect mimicked by the MAPK kinase (MEK1) inhibitor U0126. In contrast, the synthetic glucocorticoid dexamethasone, which inhibits prolactin transcription, failed to alter histone acetylation over the same time frame. Association of transcription activator Pit-1 with the prolactin promoter was unchanged by hormone treatment. However, in response to dopamine, histone deacetylase HDAC2 and corepressor mSin3A were rapidly recruited to the prolactin promoter, and association was sustained above basal levels over a 1-h period. Consistent with this corepressor function, depletion of endogenous mSin3A by small interfering RNA was sufficient to enhance prolactin gene expression by 70%, comparable to the induction by the HDAC inhibitor, trichostatin A. These studies demonstrate that dopamine D2 receptor activation and inhibition of MAPK (ERK1/2) signaling lead to rapid deacetylation of histones at the genomic prolactin promoter. Recruitment of specific HDAC/ corepressor complexes may be an important mechanism for repression of target gene transcription by Gi/o-coupled receptors.
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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".