Secretoneurin is a potential paracrine factor from lactotrophs stimulating gonadotropin release in the goldfish pituitary
Bibliographic record
Abstract
Secretoneurin (SN) is a functional neuropeptide derived from the evolutionarily conserved part of precursor protein secretogranin II (SgII). In the time course study, SN (10 nM) stimulates luteinizing hormone (LH) production and secretion after 6 h of static incubation of goldfish pituitary cells. Due to the existence of SN-immunoreactivity (SN-IR) in goldfish lactotrophs, endogenous SN might exert a paracrine effect on LH in the pituitary. In an in vitro immunoneutralization experiment, coincubation with anti-SN antiserum reduces the stimulatory effect of salmon gonadotropin-releasing hormone (sGnRH) on LH release by 64%. Using Western blot analysis, we demonstrate that sGnRH significantly increases the expression of the major SgII-derived peptide (∼57 kDa, with SN-IR) and prolactin (PRL) after 12 h in the static culture of goldfish pituitary cells. Furthermore, there exists a significant correlation between the levels of these two proteins (R = 0.76, P = 0.004). Another ∼30 kDa SgII-derived peptide containing SN is only observed in sGnRH-treated pituitary cells. Consistent with the Western blot analysis results, real-time RT-PCR analysis shows that a 12-h treatment with sGnRH induced 1.6- and 1.7-fold increments in SgII and PRL mRNA levels, respectively. SgII gene expression was also associated with PRL gene expression (R = 0.66; P = 0.02). PRL cells loaded with the calcium-sensitive dye, fura 2/AM, respond to sGnRH treatment with increases in intracellular Ca(2+) concentration level, suggesting a potential mechanism of GnRH on PRL cells and thus SgII processing and SN secretion. Taken together, endogenous lactotroph-generated SN, under the control of hypothalamic GnRH, exerts a paracrine action on neighboring gonadotrophs to stimulate LH release.
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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".