A New Bihormonal Model for the Brain Regulation of Gonadotropins in Teleosts
Bibliographic record
Abstract
BACKGROUND: The brain-pituitary-gonadal tissues play a key role in the neuroendocrine regulation of reproduction in vertebrates. Brain hormones, especially gonadotropin-releasing hormone (GnRH) is considered an important stimulant of gonadotropins (luteinizing hormone and follicle-stimulating hormone) released from the anterior pituitary. The current concept proposes a single brain hormone (GnRH) stimulating the release of both gonadotropins in fish and mammals. However, two articles published in 2024 proposed a dual-hormone concept in the brain regulation of gonadotropins in female medaka and zebrafish. SUMMARY: The emerging concept proposes GnRH as the LH releasing hormone (LH-RH), and a second hormone, cholecystokinin (Cck), as the FSH-releasing hormone (FSH-RH) in these species. The two studies discussed here found that Cck is a potent FSH-RH. The line of evidence from the first study to support this notion includes the abundance of Cck receptors in the anterior pituitary Fsh-producing gonadotrophs, and severe reproductive defects in female medaka that genetically lacks Cck receptor 2. The second study used zebrafish, and found hypothalamic expression of Cck, anterior pituitary abundance of Cck receptors, and an all-male phenotype when Cck receptor 2 was knocked out. In both studies, Cck was found to be a more potent stimulant of intracellular Ca2+, when compared to GnRH effects. KEY MESSAGES: These evidence from two independent studies indicate that Cck is a potent FSH-RH, and GnRH is the LH-RH, and supports a bihormonal model for the regulation of gonadotropin secretion from teleost pituitary. However, whether Cck elicits FSH-RH effects in other fish species remains unknown. In addition, the role of other hormones in the diverse endocrine milieu that regulate reproduction in modulating the phenotype seen in Cck receptor deficient fish warrants further consideration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".