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Record W3012096999 · doi:10.1242/dev.185629

Epigenetic changes occur at decidualisation genes as a function of reproductive ageing in mice

2020· article· en· W3012096999 on OpenAlexafffund
Laura Woods, Natasha P. Morgan, Xiang Zhao, Wendy Dean, Vicente Pérez-García, Myriam Hemberger

Bibliographic record

VenueDevelopment · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersBiotechnology and Biological Sciences Research CouncilMedical Research CouncilAlberta Children's Hospital FoundationRosetrees Trust
KeywordsBiologyEpigeneticsAgeingFunction (biology)GeneEpigenesisEcologyEvolutionary biologyGeneticsZoologyGene expressionDNA methylation

Abstract

fetched live from OpenAlex

Reproductive decline in older female mice can be attributed to a failure of the uterus to decidualise in response to steroid hormones. Here, we show that normal decidualisation is associated with significant epigenetic changes. Notably, we identify a cohort of differentially methylated regions (DMRs), most of which gain DNA methylation between the early and late stages of decidualisation. These DMRs are enriched at progesterone-responsive gene loci that are essential for reproductive function. In female mice nearing the end of their reproductive lifespan, DNA methylation fidelity is lost at a number of CpG islands (CGIs) resulting in CGI hypermethylation at key decidualisation genes. Importantly, this hypermethylated state correlates with the failure of the corresponding genes to become transcriptionally upregulated during the implantation window. Thus, age-associated DNA methylation changes may underlie the decidualisation defects that are a common occurrence in older females. Alterations to the epigenome of uterine cells may therefore contribute significantly to the reproductive decline associated with advanced maternal age.

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 score0.258
Threshold uncertainty score0.451

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 head0.035
GPT teacher head0.241
Teacher spread0.206 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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