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Record W2986822291 · doi:10.7554/elife.17101.024

Author response: Hemi-methylated DNA regulates DNA methylation inheritance through allosteric activation of H3 ubiquitylation by UHRF1

2016· peer-review· en· W2986822291 on OpenAlexaff
Joseph S. Harrison, Evan M. Cornett, Dennis Goldfarb, Paul A. DaRosa, Zimeng M. Li, Feng Yan, Bradley M. Dickson, Angela H. Guo, Daniel V Cantu, Lilia Kaustov, Peter J. Brown, C.H. Arrowsmith, Dorothy A. Erie, Michael B. Major, Rachel E. Klevit, Krzysztof Krajewski, Brian Kuhlman, Brian D. Strahl, Scott B. Rothbart

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEpigeneticsDNA methylationChromatinBiologyGeneticsHistoneHistone methylationDNACell biologyMolecular biologyGeneGene expression

Abstract

fetched live from OpenAlex

Cells are able to regulate the activity of their genes in response to different cues. Genetic information is encoded in DNA and one way to regulate gene activity is to modify the DNA by attaching chemical “epigenetic” markers to it. When a cell divides, these epigenetic markers can be inherited by the daughter cells so that they share the same patterns of gene activity as the parent cell. When the DNA of the parent cell is copied prior to cell division, the epigenetic markers are also copied onto the new DNA. Mistakes in this process are linked to a wide range of diseases in humans, such as cancer and neurological disorders. One type of epigenetic marker is known as a methyl tag and it is added to DNA by certain enzymes in a process called DNA methylation. A protein called UHRF1 is required for human cells to inherit patterns of DNA methylation through cell division. This protein binds to newly copied DNA that lacks some methyl tags as well as to another protein associated with DNA called histone H3. UHRF1 modifies histone H3 by attaching a small protein molecule called ubiquitin to it. This helps to recruit a DNA methylation enzyme to place methyl tags on the newly copied DNA. However, it was not clear how the various properties of UHRF1 allow it to control how DNA methylation is inherited. Harrison et al. addressed this question by studying purified proteins and DNA fragments outside of living cells. The results show that UHRF1 binding to DNA and histone H3 work together to bring UHRF1 to the sites on DNA that require methylation. Further experiments revealed that the methylation pattern on newly copied DNA is able to activate the ability of UHRF1 to place ubiquitin on histone H3. The findings of Harrison et al. reveal a new mechanism by which dividing cells control how DNA methylation is inherited by their daughter cells. A future challenge will be to find out how attaching ubiquitin to histone H3 activates DNA methylation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.173
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.1730.053

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 head0.033
GPT teacher head0.332
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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