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Record W4406236026 · doi:10.1038/s42003-024-07444-3

Prenatal maternal stress in rats alters the epigenetic and transcriptomic landscape of the maternal-fetal interface across four generations

2025· article· en· W4406236026 on OpenAlexafffund
Stephanie E. King, Nicola A. Schatz, Olena Babenko, Yaroslav Ilnytskyy, Igor Kovalchuk, Gerlinde A. S. Metz

Bibliographic record

VenueCommunications Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of Lethbridge
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsEpigeneticsPrenatal stressFetusTranscriptomePregnancyBiologyInterface (matter)Stress (linguistics)Transgenerational epigeneticsPhysiologyMedicineBioinformaticsGeneticsGeneGestationGene expression

Abstract

fetched live from OpenAlex

Prenatal maternal stress (PNMS) determines lifetime mental and physical health. Here, we show in rats that PNMS has consequences for placental function and fetal brain development across four generations (F0-F3). Using a systems biology approach, comprehensive DNA methylation (DNAm), miRNA, and mRNA profiling revealed a moderate impact of PNMS in the F1 generation, but drastic changes in F2 and F3 generations, suggesting compounding effects of PNMS with each successive generation. Both maternal and placental miRNA gene targets included de novo DNA methyltransferases, indicating robust PNMS-induced disruption in the complex epigenetic regulatory network between miRNAs and DNAm. Transgenerational programming mainly involved genes and biological pathways associated with neurological and psychiatric diseases which were linked to maternal-fetal crosstalk facilitated by the placenta. The highly correlated placenta-brain profiles support the use of placenta as a noninvasive biomarker resource to predict pathological changes in the neonatal brain. The transgenerational persistence of critical DNAm, miRNA and mRNA signatures may explain familial non-genetic disease risks.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.343
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2025
Admission routes2
Has abstractyes

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