Transgenerational transmission of prenatal maternal stress across three generations of male progeny alters inflammatory stress markers in reproductive tissues
Bibliographic record
Abstract
Prenatal maternal stress may lead to adverse pregnancy outcomes such as preterm birth and low birth weight. Our team has demonstrated in multiple rat models that prenatal maternal stress modifies the expression of inflammatory and stress regulators in the uterus and that this is transgenerationally passed over multiple generations through the female progeny. In this study, we investigated if male progeny exposed to ancestral prenatal maternal stress could also transmit changes to cause fetal programming of reproductive organs, leading to adverse pregnancy outcomes. We created a paternal transgenerational prenatal stress rat model. Dams (F0) were exposed to chronic variable stress during pregnancy, and their F1 male offspring stressed in utero were bred with control females for two generations. Gestational lengths and litter sizes were unchanged. Elevated gene expression of pro-inflammatory molecules in the uteri of F2 and F3 offspring was observed. Uterine expression of stress markers in the F2 and F3 females also increased even though plasma corticosterone levels were unchanged. Changes in the testicular expression of inflammatory and stress markers were also transmitted through the paternal lineage. These changes, however, tended to bear anti-inflammatory and adaptive functions, indicating compensatory mechanisms at play. These results demonstrate that fetal programming of uterine and testicular gene expression patterns can be transmitted through male progeny exposed to prenatal maternal stress.
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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 | 0.001 | 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".