Effects of Antenatal Synthetic Glucocorticoid on Glucocorticoid Receptor Binding, DNA Methylation, and Genome-Wide mRNA Levels in the Fetal Male Hippocampus
Bibliographic record
Abstract
The endogenous glucocorticoid (GC) surge in late gestation plays a vital role in maturation of several organ systems. For this reason, pregnant women at risk of preterm labor are administered synthetic glucocorticoids (sGCs) to promote fetal lung development. Animal studies have shown that fetal sGC exposure can cause life-long changes in endocrine and metabolic function. We have previously shown that antenatal sGC treatment is associated with alterations in global DNA methylation and modifications to the hippocampal methylome and acetylome. In this study, we hypothesized that: 1) there are changes in the transcriptional landscape of the fetal hippocampus in late gestation, associated with the endogenous cortisol surge; 2) fetal sGC exposure alters genome-wide transcription in the hippocampus; and 3) these changes in transcription are associated with modified glucocorticoid receptor (GR) DNA binding and DNA methylation. sGC was administered as 2 courses on gestational days (GD) 40, 41, 50, and 51, and the hippocampi of fetal guinea pigs were examined before (GD52) and after (GD65) the endogenous cortisol surge (Term ∼GD67). We also analyzed fetal hippocampi 24 hours and 14 days following maternal sGC injections (n = 3-4/group). Genome-wide modification of transcription and GR DNA binding occurred in late gestation, in parallel with the normal GC surge. Further, sGC exposure had a substantial impact on the hippocampal transcriptome, GR-DNA binding, and DNA methylation at 24 hours and 14 days following the final sGC treatment. These data support the hypothesis that GC exposure in late gestation plays a significant role in modifying the transcriptional and epigenetic landscape of the developing fetal hippocampus and that substantial effects are evident for at least 2 weeks after sGC exposure.
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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".