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Record W3130960303 · doi:10.21203/rs.3.rs-263770/v1

Ezh2 as an epigenetic checkpoint regulator during monocyte differentiation: a potential target to improve cardiac repair after myocardial infarction

2021· preprint· en· W3130960303 on OpenAlexaff
Julie Rondeaux, Déborah Groussard, Sylvanie Renet, Virginie Tardif, Anaïs Dumesnil, Alphonse Chu, Jean‐Paul Henry, Zina Badji, Claire Vézier, Delphine Béziau, Dominique Guerrot, Marjorie Brand, Vincent Richard, Éric Durand, Ebba Bråkenhielm, Sylvain Fraineau

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsEZH2Myocardial infarctionEpigeneticsRegulatorMonocyteCardiologyMedicineInternal medicineCancer researchMaster regulatorBiologyTranscription factorGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Epigenetic regulation of histone H3K27 methylation has recently emerged as a key step during alternative M2-like macrophage polarization, essential for cardiac repair after Myocardial Infarction (MI). We hypothesized that EZH2, responsible for H3K27 methylation, could act as an epigenetic checkpoint regulator during this process. We demonstrate for the first-time an EZH2 ectopic, and putative inactive, cytoplasmic localization of the epigenetic enzyme, during monocyte differentiation in vitro as well as in M2 macrophages in vivo during post-MI cardiac inflammation. Moreover, we show that pharmacological EZH2 inhibition, with GSK-343, resolves H3K27 methylation at the promoter of bivalent genes, thus enhancing their expression to promote human monocyte repair functions. In line with this protective effect, GSK-343 treatment accelerated cardiac inflammatory resolution preventing infarct expansion and subsequent cardiac dysfunction after MI in vivo. In conclusion, our study reveals that epigenetic modulation of cardiac-infiltrating immune cells may hold promise to limit adverse cardiac remodeling after MI.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.001
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 head0.015
GPT teacher head0.315
Teacher spread0.300 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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