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Record W4223622072 · doi:10.1101/gr.276383.121

H3K36 dimethylation shapes the epigenetic interaction landscape by directing repressive chromatin modifications in embryonic stem cells

2022· article· en· W4223622072 on OpenAlexafffund
Haifeng Chen, Bo Hu, Cynthia Horth, Eric Bareke, Phillip Rosenbaum, Sin Young Kwon, Jacinthe Sirois, Daniel N. Weinberg, F. Robison, Benjamin A. Garcia, Chao Lü, William A. Pastor, Jacek Majewski

Bibliographic record

VenueGenome Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill UniversityMcGill Genome Centre
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Cancer InstituteNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchSt. Jude Children's Research Hospital
KeywordsBiologyEpigenomeEpigeneticsChromatinDNA methylationEpigenomicsHistoneGeneticsHistone methyltransferaseCell biologyMethyltransferaseMethylationGeneGene expression

Abstract

fetched live from OpenAlex

Epigenetic modifications on the chromatin do not occur in isolation. Chromatin-associated proteins and their modification products form a highly interconnected network, and disturbing one component may rearrange the entire system. We see this increasingly clearly in epigenetically dysregulated cancers. It is important to understand the rules governing epigenetic interactions. Here, we use the mouse embryonic stem cell (mESC) model to describe in detail the relationships within the H3K27-H3K36-DNA methylation subnetwork. In particular, we focus on the major epigenetic reorganization caused by deletion of the histone 3 lysine 36 methyltransferase NSD1, which in mESCs deposits nearly all of the intergenic H3K36me2. Although disturbing the H3K27 and DNA methylation (DNAme) components also affects this network to a certain extent, the removal of H3K36me2 has the most drastic effect on the epigenetic landscape, resulting in full intergenic spread of H3K27me3 and a substantial decrease in DNAme. By profiling DNMT3A and CHH methylation (mCHH), we show that H3K36me2 loss upon Nsd1 -KO leads to a massive redistribution of DNMT3A and mCHH away from intergenic regions and toward active gene bodies, suggesting that DNAme reduction is at least in part caused by redistribution of de novo methylation. Additionally, we show that pervasive acetylation of H3K27 is regulated by the interplay of H3K36 and H3K27 methylation. Our analysis highlights the importance of H3K36me2 as a major determinant of the developmental epigenome and provides a framework for further consolidating our knowledge of epigenetic networks.

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)0.002
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 score0.096
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.048
GPT teacher head0.339
Teacher spread0.291 · 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

Citations49
Published2022
Admission routes2
Has abstractyes

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