A pituitary gene network linking <i>vgll3</i> to regulators of sexual maturation in male Atlantic salmon
Bibliographic record
Abstract
Abstract Age at maturity is a key life history trait and a significant contributor to life history strategy variation. The maturation process is complex and influenced by genetic and environmental factors alike, but specific causes of variation in maturation timing remain elusive. In many species, the increase in the regulatory gonadotropin-releasing hormone 1 (GnRH1) marks the onset of puberty. Atlantic salmon, however, lack the gene encoding GnRH1, suggesting other regulatory factors are involved in the maturation process. Earlier research in Atlantic salmon has found a strong association between alternative alleles of vgll3 and maturation timing, making vgll3 a candidate reproductive axis gene regulator. Recently we reported strong induction of gonadotropin encoding genes ( fshb and lhb ) in the pituitary of male Atlantic salmon homozygous for the vgll3 allele linked with the early maturation allele ( E ). The induction of gonadotropins was accompanied by increased expression of their direct upstream regulators, c-jun and sf1 ( nr5a1b ) in the pituitary. In mammals, the transcriptional activation of c-jun and sf1 is also required for induction of fshb and lhb , however, GnRH1 is responsible for increased transcriptional activity of c-jun and sf1 . The absence of gnrh1 in salmon raises the possibility of the involvement of other regulators upstream of these factors. In this study, we investigated such a possibility through a stepwise approach for identifying a gene regulatory network (GRN) containing c-jun and sf1 and using the zebrafish coexpression database and transcription factor motif enrichment analysis. We found a GRN containing c-jun with predicted upstream regulators, e2f1, egr1, foxj1 and klf4 , which are also differentially expressed in the pituitary. Finally, we suggest a model for transcriptional regulation of c-jun and sf1 in the absence of gnrh1 in the pituitary, which may have broader implications across vertebrates.
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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".