Loss of the glucocorticoid receptor causes accelerated ovarian ageing in zebrafish
Bibliographic record
Abstract
Reproductive decline in mid-adult females is an established phenotype of the ageing process. Stress and the rise in glucocorticoids (GCs) accelerate reproductive ageing, but little is known about the mechanisms involved. During stress, GCs activate the glucocorticoid receptor (GR), a ubiquitously expressed, ligand-bound transcription factor, to elicit physiological changes for restoring homeostasis. Here, we tested the hypothesis that GC-GR signalling is essential for accelerating reproductive ageing. To test this, we used a ubiquitous GR knockout (GRKO) zebrafish, which is inherently hypercortisolemic, to delineate the role of high cortisol and GR signalling on reproductive ageing. The loss of GR led to premature ovarian ageing, including high frequency of typical and atypical follicular atresia in vitellogenic oocytes, yolk liquefaction and large inflammatory infiltrates. The reduction in oocyte quality was also associated with a decline in ovarian tert expression in the adult GRKO fish compared to the early adult GRKO and adult wild-type zebrafish. Accelerated ovarian ageing also impacted the progeny, including lower breeding success, fecundity, egg fertilization rate and delayed somitogenesis and embryo survival in the adult GRKO fish. We adduce that GR signalling is essential for prolonging the reproductive lifespan and improving the egg quality and embryo viability in zebrafish.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".