Maternal Undernutrition Programs Tissue-Specific Epigenetic Changes in the Glucocorticoid Receptor in Adult Offspring
Bibliographic record
Abstract
Epidemiological data indicate that an adverse maternal environment during pregnancy predisposes offspring to metabolic syndrome with increased obesity, and type 2 diabetes. The mechanisms are still unclear although epigenetic modifications are implicated and the hypothalamus is a likely target. We hypothesized that maternal undernutrition (UN) around conception in sheep would lead to epigenetic changes in hypothalamic neurons regulating energy balance in the offspring, up to 5 years after the maternal insult. We found striking evidence of decreased glucocorticoid receptor (GR) promoter methylation, decreased histone lysine 27 trimethylation, and increased histone H3 lysine 9 acetylation in hypothalami from male and female adult offspring of UN mothers. These findings are entirely compatible with the increased GR mRNA and protein observed in the hypothalami. The increased GR predicted the decreased hypothalamic proopiomelanocortin expression and increased obesity that we observed in the 5-year-old adult males. The epigenetic and expression changes in GR were specific to the hypothalamus. Hippocampal GR mRNA and protein were decreased in UN offspring, whereas pituitary GR was altered in a sex-specific manner. In peripheral polymorphonuclear leukocytes there were no changes in GR methylation or protein, indicating that this epigenetic analysis did not predict changes in the brain. Overall, these results suggest that moderate changes in maternal nutrition, around the time of conception, signal life-long and tissue-specific epigenetic alterations in a key gene regulating energy balance in the hypothalamus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".