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Record W4407347962 · doi:10.1038/s41477-025-01924-y

Histone H3 lysine 4 methylation recruits DNA demethylases to enforce gene expression in Arabidopsis

2025· article· en· W4407347962 on OpenAlexaff
Ming Wang, Yan He, Zhenhui Zhong, Ashot Papikian, Shuya Wang, Jason Gardiner, Basudev Ghoshal, Suhua Feng, Yasaman Jami‐Alahmadi, James A. Wohlschlegel, Steven E. Jacobsen

Bibliographic record

VenueNature Plants · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsAgriculture and Agri-Food Canada
FundersBill and Melinda Gates FoundationHoward Hughes Medical Institute
KeywordsH3K4me3Histone methylationDNA methylationEpigenomicsBiologyDNA demethylationHistone methyltransferaseRNA-Directed DNA MethylationCancer epigeneticsHistoneHistone H3EpigeneticsMethylationGeneticsMolecular biologyDNAGeneGene expressionPromoter

Abstract

fetched live from OpenAlex

Patterning of DNA methylation in eukaryotic genomes is controlled by de novo methylation, maintenance mechanisms and demethylation pathways. In Arabidopsis thaliana, DNA demethylation enzymes are clearly important for shaping methylation patterns, but how they are regulated is poorly understood. Here we show that the targeting of histone H3 lysine four trimethylation (H3K4me3) with the catalytic domain of the SDG2 histone methyltransferase potently erased DNA methylation and gene silencing at FWA and also erased CG DNA methylation in many other regions of the Arabidopsis genome. This methylation erasure was completely blocked in the ros1 dml2 dml3 triple mutant lacking DNA demethylation enzymes, showing that H3K4me3 promotes the active removal of DNA methylation. Conversely, we found that the targeted removal of H3K4me3 increased the efficiency of targeted DNA methylation. These results highlight H3K4me3 as a potent anti-DNA methylation mark and also pave the way for development of more powerful epigenome engineering tools.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.309
Teacher spread0.288 · 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

Citations35
Published2025
Admission routes1
Has abstractyes

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