Prenatal Synthetic Glucocorticoid Treatment Changes DNA Methylation States in Male Organ Systems: Multigenerational Effects
Bibliographic record
Abstract
Prenatal synthetic glucocorticoids (sGC) are administered to pregnant women at risk of delivering preterm, approximately 10% of all pregnancies. Animal studies have demonstrated that offspring exposed to elevated glucocorticoids, either by administration of sGC or as a result of maternal stress, are at increased risk of developing behavioral, endocrine, and metabolic abnormalities. DNA methylation is a covalent modification of DNA that plays a critical role in long-lasting programming of gene expression. Here we tested the hypothesis that prenatal sGC treatment has both acute and long-term effects on DNA methylation states in the fetus and offspring and that these effects extend into a subsequent generation. Pregnant guinea pigs were treated with sGC in late gestation, and methylation analysis by luminometric methylation assay was undertaken in organs from fetuses and offspring across two generations. Expression of genes that modify the epigenetic state were measured by quantitative real-time PCR. Results indicate that there are organ-specific developmental trajectories of methylation in the fetus and newborn. Furthermore, these trajectories are substantially modified by intrauterine exposure to sGC. These sGC-induced changes in DNA methylation remain into adulthood and are evident in the next generation. Furthermore, prenatal sGC exposure alters the expression of several genes encoding proteins that modulate the epigenetic state. Several of these changes are long lasting and are also present in the next generation. These data support the hypothesis that prenatal sGC exposure leads to broad changes in critical components of the epigenetic machinery and that these effects can pass to the next generation.
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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".