Prolactin decreases expression of Runx2, osteoprotegerin, and RANKL in primary osteoblasts derived from tibiae of adult female rats
Bibliographic record
Abstract
Hyperprolactinemia caused by physiological or pathological conditions, such as those occurring during lactation and prolactinoma, respectively, results in progressive osteopenia. The underlying mechanisms, however, are controversial. Prolactin (PRL) may directly attenuate the functions of osteoblasts, since these bone cells express PRL receptors. The present study therefore aimed to investigate the effects of PRL on the expression of genes related to the osteoblast functions by using quantitative real-time PCR technique. Herein, we used primary osteoblasts that were derived from the tibiae of adult rats and displayed characteristics of differentiated osteoblasts, including in vitro mineralization. Osteoblasts exposed for 48 h to 1000 ng/mL PRL, but not to 10 or 100 ng/mL PRL, showed decreases in the mRNA expression of Runx2, osteoprotegerin (OPG), and receptor activator of nuclear factor kappaBeta ligand (RANKL) by 60.49%, 72.74%, and 87.51%, respectively. Nevertheless, PRL did not change the RANKL/OPG ratio, since expression of OPG and RANKL were proportionally decreased. These concentrations of PRL had no effect on the mRNA expression of osteocalcin and osteopontin, nor on mineralization. High pathologic concentrations of PRL (1000 ng/mL) may downregulate expression of genes that are essential for osteoblast differentiation and functions. The present results explained the clinical findings of hyperprolactinemia-induced bone loss.
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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) | 0.000 | 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) | 0.001 | 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 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".