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Record W2908470212 · doi:10.1101/gad.320069.118

ZNF445 is a primary regulator of genomic imprinting

2019· article· en· W2908470212 on OpenAlexfundno aff
Nozomi Takahashi, Andrea Coluccio, Christian W. Thorball, Evarist Planet, Hui Shi, Sandra Offner, Priscilla Turelli, Michaël Imbeault, Anne C. Ferguson‐Smith, Didier Trono

Bibliographic record

VenueGenes & Development · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsnot available
FundersH2020 European Research CouncilMedical Research CouncilMedical Research Council CanadaEuropean CommissionWellcome TrustGebert Rüf StiftungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsBiologyGenomic imprintingImprinting (psychology)ReprogrammingEpigeneticsDNA methylationPenetranceGeneticsRegulatorRegulation of gene expressionZinc fingerGenePhenotypeGene expressionCell biologyTranscription factor

Abstract

fetched live from OpenAlex

Genomic imprinting is an epigenetic process regulated by germline-derived DNA methylation, causing parental origin-specific monoallelic gene expression. Zinc finger protein 57 (ZFP57) is critical for maintenance of this epigenetic memory during post-fertilization reprogramming, yet incomplete penetrance of ZFP57 mutations in humans and mice suggests additional effectors. We reveal that ZNF445/ZFP445, which we trace to the origins of imprinting, binds imprinting control regions (ICRs) in mice and humans. In mice, ZFP445 and ZFP57 act together, maintaining all but one ICR in vivo, whereas earlier embryonic expression of ZNF445 and its intolerance to loss-of-function mutations indicate greater importance in the maintenance of human imprints.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations224
Published2019
Admission routes1
Has abstractyes

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