Intergenerational response of steroidogenesis-related genes to maternal malnutrition
Bibliographic record
Abstract
We sought to examine whether rat maternal food restriction (MFR) affects the expression of steroidogenesis-related genes Cyp19, Cyp17a1, Insl3 and Gdf-9 in the ovaries of offspring from the first (FRG1) and second (FRG2) generations at pre-pubertal age (week 4) and during adulthood (week 8). At week 4, MFR significantly increased the expression of RNAs for all analyzed genes in both FRG1 and FRG2 females, which may indicate that MFR affects the onset of the reproductive lifespan, by inducing early pubertal onset. At week 8, the Cyp19 gene was still upregulated in MRF-subjected animals (Cyp19: P=0.0049 and P=0.0508 in FRG1 and FRG2, respectively), but MFR induced a significant decrease in Cyp17 and Gdf-9 gene expression in the offspring of both FRG1 and FRG2 females when compared with the controls (Cyp17: P=0.0018 and P=0.0016, respectively; Gdf-9: P=0.0047 and P=0.0023, respectively). This suggests that females at week 8, which should normally be in their optimal reproductive capacity, experience premature ovarian aging. At week 4, the activation of Cyp19 and Cyp17 was higher in the FRG1 ovaries than in the FRG2 ovaries, whereas the extent of Insl3 and Gdf-9 activation was lower in the FRG1 ovaries. This may indicate that FRG2 females were more vulnerable to MFR than their mothers (FRG1) and grandmothers, which is consistent with the 'predictive adaptive response' hypothesis. Our findings reveal that MFR may induce intergenerational ovarian changes as an adaptive response to ensure reproductive success before death.
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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) | 0.001 | 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".