Regulatory Mechanisms Underlying the Expression of Prolactin Receptor in Chicken Granulosa Cells
Bibliographic record
Abstract
Prolactin (PRL) has both pro- and anti-gonadal roles in the regulation of avian ovarian functions through its interaction with the receptor (PRLR). However, neither the pattern of expression of PRLR nor its regulatory mechanisms during follicle development have been clearly defined. The objective of the present study was to investigate mechanisms of PRLR expression in chicken granulosa cells. Levels of PRLR transcript were highest in the stroma and walls of follicles < 2 mm in diameter and progressively declined with the maturation of follicles. In preovulatory follicles, PRLR was expressed at higher levels in granulosa than theca layers. FSH exerted the greatest stimulatory effect on PRLR and StAR expression in cultured granulosa cells of the 6-8 mm follicles but this effect declined as follicles matured to F1. In contrast, LH did not alter the expression of PRLR in granulosa cells of all follicular classes but increased levels of StAR in F2 and F1 granulosa cells. Both non-glycosylated- (NG-) and glycosylated- (G-) PRL upregulated basal PRLR expression in granulosa cells of the 6-8 mm, F3 or F1 follicles but had little effect in F2 follicles. Furthermore, FSH-stimulated PRLR expression was reduced by the addition of either isoform of PRL especially in F2 granulosa cells. These results indicate that PRLR is differentially distributed and regulated by FSH or PRL variants independently or in combination in the follicular hierarchy. By using activators and inhibitors, we further demonstrated that multiple signaling pathways, including PKA, PKC, PI3K, mTOR and AMPK, are not only directly involved in, but they can also converge to modulate ERK2 activity to regulate FSH-mediated PRLR and StAR expression in undifferentiated granulosa cells. These data provide new insights into the regulatory mechanisms controlling the expression of PRLR in granulosa cells.
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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 | 0.001 | 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".