Lack of prolactin receptor signaling in mice results in lactotroph proliferation and prolactinomas by dopamine-dependent and -independent mechanisms
Bibliographic record
Abstract
Hypothalamic dopamine inhibits pituitary prolactin secretion and proliferation of prolactin-producing lactotroph cells by activating lactotroph dopamine D2 receptors (D2Rs). Conversely, prolactin (PRL) stimulates hypothalamic dopamine neurons via PRL receptors (PRLRs) in a short-loop feedback circuit. We used Drd2(-/-) and Prlr(-/-) mutant mice to bypass this feedback and investigate possible dopamine-independent effects of PRL on lactotroph function. The absence of either receptor induced hyperprolactinemia and large prolactinomas in females. Small macroadenomas developed in aged Prlr(-/-) males, but only microscopic adenomas were found in Drd2(-/-) male mice. Pharmacologic studies in Prlr(-/-) mice with D2R agonists and antagonists demonstrated a significant loss of endogenous dopamine tone, i.e., constitutive inhibitory signaling by the D2R, in the pituitary. However, Prlr(-/-) mice exhibited more profound hyperprolactinemia and larger tumors than did age-matched Drd2(-/-) mice, and there were additive effects in compound homozygous mutant male mice. In vitro, PRL treatment markedly inhibited the proliferation of wild-type female and male Drd2(-/-) lactotrophs, but had no effect on female Drd2(-/-) lactotrophs, suggesting a downregulation or desensitization of PRLR in response to chronic hyperprolactinemia. We conclude that PRL inhibits lactotrophs by two distinct mechanisms: (a) indirectly by activation of hypothalamic dopamine neurons and (b) directly within the pituitary in a dopamine-independent fashion.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".