Epigenetic Programming of Stress Responses through Variations in Maternal Care
Bibliographic record
Abstract
Early life experiences shape an individual's physical and mental health across the lifespan. Not surprisingly, an upbringing that is associated with adversity can produce detrimental effects on health. A central theme that arises from studies in human and nonhuman species is that the effects of adversity are mediated by the interactions between a mother and her young. In this review we describe some of the long-term effects of maternal care on the offspring and we focus on the impact of naturally occurring variations in the behavior of female rats. Of particular interest are mothers that engage in high or low amounts of licking/grooming (LG) and arched-back nursing (ABN) of their pups, but do so within the normal range for this species. Such variations in LG-ABN can alter the function of the hypothalamic-pituitary-adrenal (HPA) axis, and cognitive and emotional development by directly affecting the underlying neural mechanisms. At the heart of these mechanisms is gene expression. By studying the hippocampal glucocorticoid receptor gene, we have identified that maternal care regulates its expression by changing two processes: the acetylation of histones H3-K9, and the methylation of the NGFI-A consensus sequence on the exon 1(7) promoter. Sustained "maternal effects" appear elsewhere in biology, including plants, insects, and lizards, and may have evolved to program advantages in the environments that the offspring will likely face as adults. Given the importance of early life and parent-child interactions to later behavior, prevention and intervention programs should target this critical phase of development.
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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".