Neurosteroids and Female Reproduction: Estrogen Increases 3β-HSD mRNA and Activity in Rat Hypothalamus
Bibliographic record
Abstract
A central event in mammalian reproduction is the LH surge that induces ovulation and corpus luteum formation. Typically, the LH surge is initiated in ovariectomized rats by sequential treatment with estrogen and progesterone (PROG). The traditional explanation for this paradigm is that estrogen induces PROG receptors (PR) that are activated by exogenous PROG. Recent evidence suggests that whereas exogenous estrogen is necessary, exogenous PROG is not. In ovariectomized-adrenalectomized rats, estrogen treatment increases hypothalamic PROG levels before an LH surge. This estrogen-induced LH surge was blocked by an inhibitor of 3beta-hydroxysteroid dehydrogenase/delta5-delta4 isomerase (3beta-HSD), the proximal enzyme for PROG synthesis. These data indicate that estrogen induces de novo synthesis of PROG from cholesterol in the hypothalamus, which initiates the LH surge. The mechanism(s) by which estrogen up-regulates neuro-PROG is unknown. We investigated whether estrogen increases 1) mRNA levels for several proteins involved in PROG synthesis and/or 2) activity of 3beta-HSD in the hypothalamus. In ovariectomized-adrenalectomized rats, estrogen treatment increased 3beta-HSD mRNA in the hypothalamus, as measured by relative quantitative RT-PCR. The mRNAs for other proteins involved in steroid synthesis (sterol carrier protein 2, steroidogenic acute regulatory protein, and P450 side chain cleavage) were detectable in hypothalamus but not affected by estrogen. In a biochemical assay, estrogen treatment also increased 3beta-HSD activity. These data support the hypothesis that PROG is a neurosteroid, produced locally in the hypothalamus from cholesterol, which functions in the estrogen positive-feedback mechanism driving the LH surge.
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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 | 0.001 | 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) | 0.003 | 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".