MicroRNA-7a2 Regulates Prolactin in Developing Lactotrophs and Prolactinoma Cells
Bibliographic record
Abstract
Prolactin production is controlled by a complex and temporally dynamic network of factors. Despite this tightly coordinated system, pathological hyperprolactinemia is a common endocrine disorder that is often not understood, thereby highlighting the need to expand our molecular understanding of lactotroph cell regulation. MicroRNA-7 (miR-7) is the most highly expressed miRNA family in the pituitary gland and the loss of the miR-7 family member, miR-7a2, is sufficient to reduce prolactin gene expression in mice. Here, we used conditional loss-of-function and gain-of-function mouse models to characterize the function of miR-7a2 in lactotroph cells. We found that pituitary miR-7a2 expression undergoes developmental and sex hormone-dependent regulation. Unexpectedly, the loss of mir-7a2 induces a premature increase in prolactin expression and lactotroph abundance during embryonic development, followed by a gradual loss of prolactin into adulthood. On the other hand, lactotroph development is delayed in mice overexpressing miR-7a2. This regulation of lactotroph function by miR-7a2 involves complementary mechanisms in multiple cell populations. In mouse pituitary and rat prolactinoma cells, miR-7a2 represses its target Raf1, which promotes prolactin gene expression. These findings shed light on the complex regulation of prolactin production and may have implications for the physiological and pathological mechanisms underlying hyperprolactinemia.
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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 | 0.001 |
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".