The Effects of GABA Agonists on Glutamic Acid Decarboxylase, GABA‐Transaminase, Activin, Salmon Gonadotrophin‐Releasing Hormone and Tyrosine Hydroxylase mRNA in the Goldfish (<i>Carassius auratus</i>) Neuroendocrine Brain
Bibliographic record
Abstract
GABA plays a pivotal role in reproduction by regulating luteinising hormone (LH) release from the anterior pituitary. Current evidence indicates that there is a prominent stimulatory effect of GABA on LH release in teleost fish which results from enhanced gonadotrophin-releasing hormone (GnRH) release and decreased dopamine turnover in the brain and pituitary. We hypothesised that there may be additional mechanisms underlying LH release in goldfish and investigated the relative mRNA levels of GABA synthesising enzymes (GAD65 and GAD67), degrading enzyme (GABA-T), activin betaa and betab, salmon GnRH (sGnRH), and tyrosine hydroxylase (TH) with the real-time reverse transcriptase-polymerase chain reaction after GABA agonist treatment. Sexually regressed female goldfish were i.p. injected with either the GABA(A) agonist muscimol (1 microg/g body weight) or the GABA(B) agonist baclofen (10 microg/g body weight). Both agonists significantly increased serum LH after 6 h. Muscimol decreased GAD65 (approximately ten-fold), GABA-T (approximately 15-fold) and TH (approximately three-fold) mRNA in the telencephalon. Baclofen significantly reduced GAD67 (approximately two-fold) and GABA-T (approximately two-fold) mRNA levels in the hypothalamus. Activin betaa, but not activin betab, steady-state mRNA was increased approximately three- to four-fold in both the hypothalamus and telencephalon after baclofen treatment. There was no change in sGnRH mRNA levels in either tissue after GABA agonist treatment. We show that the GABA(A) and GABA(B) receptor agonists have differing and rapid effects on gene transcription in the goldfish neuroendocrine brain and, by affecting specific targets, we identify putative genomic mechanisms underlying GABA-stimulated LH release in fish.
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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".