Maternal Stress Induces Epigenetic Signatures of Psychiatric and Neurological Diseases in the Offspring
Bibliographic record
Abstract
The gestational state is a period of particular vulnerability to diseases that affect maternal and fetal health. Stress during gestation may represent a powerful influence on maternal mental health and offspring brain plasticity and development. Here we show that the fetal transcriptome, through microRNA (miRNA) regulation, responds to prenatal stress in association with epigenetic signatures of psychiatric and neurological diseases. Pregnant Long-Evans rats were assigned to stress from gestational days 12 to 18 while others served as handled controls. Gestational stress in the dam disrupted parturient maternal behaviour and was accompanied by characteristic brain miRNA profiles in the mother and her offspring, and altered transcriptomic brain profiles in the offspring. In the offspring brains, prenatal stress upregulated miR-103, which is involved in brain pathologies, and downregulated its potential gene target Ptplb. Prenatal stress downregulated miR-145, a marker of multiple sclerosis in humans. Prenatal stress also upregulated miR-323 and miR-98, which may alter inflammatory responses in the brain. Furthermore, prenatal stress upregulated miR-219, which targets the gene Dazap1. Both miR-219 and Dazap1 are putative markers of schizophrenia and bipolar affective disorder in humans. Offspring transcriptomic changes included genes related to development, axonal guidance and neuropathology. These findings indicate that prenatal stress modifies epigenetic signatures linked to disease during critical periods of fetal brain development. These observations provide a new mechanistic association between environmental and genetic risk factors in psychiatric and neurological disease.
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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".