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Record W4299125525 · doi:10.17615/2cks-1p06

Multivalent histone engagement by the linked tandem Tudor and PHD domains of UHRF1 is required for the epigenetic inheritance of DNA methylation

2020· article· en· W4299125525 on OpenAlexfundno aff
M. S. Ong, B. M. Dickson, D. B. Kireev, B. D. Strahl, C.H. Arrowsmith, S. Houliston, S. B. Rothbart, K. Krajewski

Bibliographic record

VenueUNC Libraries · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of North Carolina at Chapel HillNational Institutes of Health
KeywordsEpigeneticsDNA methylationGeneticsInheritance (genetic algorithm)BiologyHistoneDNAComputational biologyGeneGene expression

Abstract

fetched live from OpenAlex

Histone post-translational modifications regulate chromatin structure and function largely through interactions with effector proteins that often contain multiple histone-binding domains. While significant progress has been made characterizing individual effector domains, the role of paired domains and how they function in a combinatorial fashion within chromatin are poorly defined. Here we show that the linked tandem Tudor and plant homeodomain (PHD) of UHRF1 (ubiquitin-like PHD and RING finger domain-containing protein 1) operates as a functional unit in cells, providing a defined combinatorial readout of a heterochromatin signature within a single histone H3 tail that is essential for UHRF1-directed epigenetic inheritance of DNA methylation. These findings provide critical support for the “histone code” hypothesis, demonstrating that multivalent histone engagement plays a key role in driving a fundamental downstream biological event in chromatin.

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 score0.005

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 head0.034
GPT teacher head0.266
Teacher spread0.231 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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