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Record W3184150116 · doi:10.1080/15592294.2021.1955188

Comparing the whole genome methylation landscape of dairy calf blood cells revealed intergenerational inheritance of the maternal metabolism

2021· article· en· W3184150116 on OpenAlexafffund
Ying Zhang, Catherine Chaput, Éric Fournier, Julien Prunier, Marc‐André Sirard

Bibliographic record

VenueEpigenetics · 2021
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyDNA methylationInheritance (genetic algorithm)GenomeMethylationGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

This study evaluated the hypothesis that the maternal metabolic stressed status could be inherited to their F1 daughters via epigenetic mechanism. The maternal cow blood β-hydroxybutyric acid (BHB) level (≥0.9 mM/L) was used as an indicator of maternal metabolic stress. Eight newborn daughters' blood cells were used for methylation comparison and analysis. By Whole Genome Bisulphite Sequencing (WGBS), a total of 1,861 Differentially Methylated Regions (DMRs), including 944 differentially methylated cytosines (DMCs), were identified. Most DMRs were distributed in intronic and intergenic regions, and most of the DMR in promoter regions were hypermethylated. Differentially methylated genes (DMGs) with DMR methylation differences higher than 20% were mainly enriched in metabolism-related pathways. These results suggest that newborn calves' metabolic pathways were altered, with 64 DMGs being clustered with metabolic signalling by KEGG analysis. Our study revealed the whole epigenetic landscape of calf blood cells and suggested that the maternal metabolic status can affect the embryo's epigenetic status and metabolic-related pathways in offspring, providing further evidence for epigenetic intergenerational inheritance of metabolic stress in domestic animals. Besides, this study also contributed more evidence to support the Developmental Origins of Health and Disease (DOHAD) theory in large animals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.233

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 head0.029
GPT teacher head0.268
Teacher spread0.239 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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