Life at the Interface Between a Dynamic Environment and a Fixed Genome: Epigenetic Programming of Stress Responses by Maternal Behavior
Bibliographic record
Abstract
In evolutionary developmental biology, a common theme across all life forms is the ability to vary phenotype in response to environmental conditions, a process termed phenotypic plasticity. In humans and non-human primates, the nature of mother-infant interactions in early postnatal life has a profound role in mediating variation in offspring phenotype, including emotional and cognitive development, which is endured through life. Similarly, maternal behavior in rodents is associated with long-term programming of individual differences in behavioral and hypothalamic-pituitary-adrenal (HPA) responses to stress in the offspring. One critical question is how is such “environmental programming” established and sustained in the offspring? In this chapter, we discuss a novel mechanism to explain how maternal licking/grooming behavior in the rat can alter the hippocampal glucocorticoid receptor (GR) expression in the offspring, which concomitantly alters the HPA axis and the stress responsiveness of these animals. Both in vivo and in vitro studies show that maternal behavior increases GR expression in the offspring via increased hippocampal serotonergic tone accompanied by increased histone acetylase transferase activity, histone acetylation and DNA demethylation mediated by the transcription factor NGFI-A. In summary, this research demonstrates that an epigenetic state of a gene can be established through early in life experience, and is potentially reversible in adulthood. We suggest that epigenetic modifications of specific genomic regions in response to variations in environmental conditions might serve as a major source of variation in biological and behavioral phenotypes. Accordingly, we consider the relevance of our findings in relation to other known epigenetic mechanisms in neuroscience and discuss the clinical implications of these observations for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".