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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".