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Record W2166039665 · doi:10.1038/ncomms4746

Maternal nutrition at conception modulates DNA methylation of human metastable epialleles

2014· article· en· W2166039665 on OpenAlexaff
Paula Domínguez-Salas, Sophie E. Moore, Maria S. Baker, Andrew W. Bergen, Sharon E. Cox, Roger Dyer, Anthony J. C. Fulford, Yongtao Guan, Eleonora Laritsky, Matt J. Silver, Gary E. Swan, Steven H. Zeisel, Sheila M. Innis, Robert A. Waterland, Andrew M. Prentice, Branwen J. Hennig

Bibliographic record

VenueNature Communications · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
FundersNational Institute of Environmental Health SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesMedical Research CouncilNational Institutes of HealthU.S. Department of AgricultureNational Institute on Drug AbuseWellcome Trust
KeywordsOffspringEpigeneticsDNA methylationPregnancyBiologyMethylationEndocrinologyMetastabilityGeneticsPhysiologyInternal medicineDNAMedicineGeneChemistryGene expression

Abstract

fetched live from OpenAlex

In experimental animals, maternal diet during the periconceptional period influences the establishment of DNA methylation at metastable epialleles in the offspring, with permanent phenotypic consequences. Pronounced naturally occurring seasonal differences in the diet of rural Gambian women allowed us to test this in humans. We show that significant seasonal variations in methyl-donor nutrient intake of mothers around the time of conception influence 13 relevant plasma biomarkers. The level of several of these maternal biomarkers predicts increased/decreased methylation at metastable epialleles in DNA extracted from lymphocytes and hair follicles in infants postnatally. Our results demonstrate that maternal nutritional status during early pregnancy causes persistent and systemic epigenetic changes at human metastable epialleles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.426

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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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 designBench or experimental
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

Citations506
Published2014
Admission routes1
Has abstractyes

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