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Record W2526126992 · doi:10.1017/s0954579416000833

Early life adversity alters normal sex-dependent developmental dynamics of DNA methylation

2016· article· en· W2526126992 on OpenAlexafffund
Renaud Massart, Zsófia Nemoda, Matthew Suderman, Sheila Sutti, Angela Ruggiero, Amanda M. Dettmer, Stephen J. Suomi, Moshe Szyf

Bibliographic record

VenueDevelopment and Psychopathology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill University
FundersEconomic and Social Research CouncilCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsDNA methylationEpigeneticsMaternal deprivationMethylationWeaningPhenotypeGenePsychologyBiologyPhysiologyGeneticsDevelopmental psychologyGene expressionEndocrinology

Abstract

fetched live from OpenAlex

Studies in rodents, nonhuman primates, and humans suggest that epigenetic processes mediate between early life experiences and adult phenotype. However, the normal evolution of epigenetic programs during child development, the effect of sex, and the impact of early life adversity on these trajectories are not well understood. This study mapped the genome-wide DNA methylation changes in CD3+ T lymphocytes from rhesus monkeys from postnatal day 14 through 2 years of age in both males and females and determined the impact of maternal deprivation on the DNA methylation profile. We show here that DNA methylation profiles evolve from birth to adolescence and are sex dependent. DNA methylation changes accompany imposed weaning, attenuating the difference between males and females. Maternal separation at birth alters the normal evolution of DNA methylation profiles and targets genes that are also affected by a later stage maternal separation, that is, weaning. Our results suggest that early life events dynamically interfere with the normal developmental evolution of the DNA methylation profile and that these changes are highly effected by sex.

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 score0.193
Threshold uncertainty score0.483

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations40
Published2016
Admission routes2
Has abstractyes

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