Role of PKC in the regulation of gonadotropin subunit mRNA levels: interaction with two native forms of gonadotropin-releasing hormone
Bibliographic record
Abstract
Gonadotropin-releasing hormone (GnRH) is an important regulator of reproduction in all vertebrates through its actions on the production and secretion of pituitary gonadotropin hormones (GtHs). Most vertebrate species express at least two GnRHs, including one form, designated chicken (c)GnRH-II or type II GnRH, which has been well conserved throughout evolution. The goldfish brain and pituitary contain salmon GnRH and cGnRH-II. In goldfish, GnRH-induced luteinizing hormone (LH) secretion involves PKC; however, whether PKC mediates GnRH stimulation of GtH subunit mRNA levels is unknown. In this study, we used inhibitors and activators of PKC to examine its possible involvement in GnRH-induced increases in GtH-alpha, follicle-stimulating hormone (FSH)-beta and LH-beta mRNA levels in primary cultures of dispersed goldfish pituitary cells. Treatment with PKC inhibitors calphostin C and GF109203X unmasked a basal repression of GtH subunit mRNA levels by PKC; both inhibitors increased GtH subunit mRNA levels in a dose-dependent manner. PKC activators, 12-O-tetradecanoylphorbol 13-acetate (TPA), and 1,2-dioctanoyl-sn-glycerol, stimulated GtH subunit mRNA levels, whereas an inactive phorbol ester (4-alpha-TPA) was without effect. Thus, a dual, inhibitory and stimulatory, influence for PKC in the regulation of GtH subunit mRNA levels is suggested. In contrast, PKC inhibitor- and activator-induced effects were, for the most part, additive to those of GnRH, suggesting that conventional and novel PKCs are unlikely to be involved in GnRH-stimulated increases in GtH subunit mRNA levels. Our data illustrate major differences in the signal transduction of GnRH effects on GtH secretion and gene expression in the goldfish pituitary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".