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Record W4387151870 · doi:10.1093/emph/eoad031

Mother’s warmth from maternal genes: genomic imprinting of brown adipose tissue

2023· article· en· W4387151870 on OpenAlexaff
Lynn Ayache, Aiden Bushell, Jessica Lee, Iiro Salminen, Bernard J. Crespi

Bibliographic record

VenueEvolution Medicine and Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGenomic imprintingBiologyImprinting (psychology)GeneOffspringGeneticsCandidate geneGene expressionDNA methylationPregnancy

Abstract

fetched live from OpenAlex

Abstract Background and objectives Brown adipose tissue (BAT) plays key roles in mammalian physiology, most notably with regard to thermoregulation in infants and juveniles. Previous studies have suggested that intragenomic conflict, in the form of genomic imprinting, mediates BAT thermogenesis, because it represents a public good for groups of siblings, or a mother with her offspring, who huddle together to conserve warmth. By this hypothesis, maternally expressed imprinted genes should promote BAT, while paternally expressed genes should repress it. Methodology We systematically searched the literature using two curated lists of genes imprinted in humans and/or mice, in association with evidence regarding effects of perturbation to imprinted gene expression on BAT development or activity. Results Overall, enhanced BAT was associated with relatively higher expression of maternally expressed imprinted genes, and relatively lower expression of paternally expressed imprinted genes; this pattern was found for 16 of the 19 genes with sufficient information for robust ascertainment (Binomial test, P < 0.005, 2-tailed). Conclusions and implications These results support the kinship theory of imprinting and indicate that future studies of BAT, and its roles in human health and disease, may usefully focus on effects of imprinted genes and associated genomic conflicts.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.054
GPT teacher head0.333
Teacher spread0.278 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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