Tissue-specific regulation of porcine prolactin receptor expression by estrogen, progesterone, and prolactin
Bibliographic record
Abstract
Prolactin (PRL) acts through its receptor (PRLR) via both endocrine and local paracrine/autocrine pathways to regulate biological processes including reproduction and lactation. We analyzed the tissue- and stage of gestation-specific regulation of PRL and PRLR expression in various tissues of pigs. Abundance of pPRLR-long form (LF) mRNA increased in the mammary gland and endometrium during gestation while in other tissues it remained constant. There was a parallel increase in the abundance of the pPRLR-LF protein in the mammary gland and endometrium during gestation. We determined the hormonal regulation of pPRLR-LF mRNA expression in various tissues from ovariectomized, hypoprolactinemic gilts given combinations of the replacement hormones estrogen (E(2)), progestin (P), and/or haloperidol-induced PRL. Abundance of pPRLR-LF mRNA in kidney and liver was unaffected by hormone treatments. Expression of uterine pPRLR-LF mRNA was induced by E(2) whereas the effect of E(2) was abolished by co-administering P. The expression of pPRLR-LF mRNA in the mammary gland stroma was induced by PRL, whereas E(2) induced its expression in the epithelium. In contrast to these changes in pPRLR expression, pPRL expression was relatively constant and low during gestation in all tissues except the pituitary. Taken together, these data reveal that specific combinations of E(2), P, and PRL differentially regulate pPRLR-LF expression in the endometrium and mammary glands, and that the action of PRL on its target tissues is dependent upon pPRLR-LF abundance more so than the local PRL expression.
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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) | 0.001 | 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 | 0.000 |
| 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".