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Record W3167077475 · doi:10.1038/s41467-021-23775-9

HIRA stabilizes skeletal muscle lineage identity

2021· article· en· W3167077475 on OpenAlexaff
Joana Esteves de Lima, Reem Bou Akar, Léo Machado, Yuefeng Li, Bernadette Drayton‐Libotte, F. Jeffrey Dilworth, Frédéric Relaix

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersAgence Nationale de la Recherche
KeywordsLineage (genetic)Skeletal muscleIdentity (music)Computational biologyBioinformaticsEvolutionary biologyComputer scienceBiologyAnatomyGeneticsGenePhysics

Abstract

fetched live from OpenAlex

The epigenetic mechanisms coordinating the maintenance of adult cellular lineages and the inhibition of alternative cell fates remain poorly understood. Here we show that targeted ablation of the histone chaperone HIRA in myogenic cells leads to extensive transcriptional modifications, consistent with a role in maintaining skeletal muscle cellular identity. We demonstrate that conditional ablation of HIRA in muscle stem cells of adult mice compromises their capacity to regenerate and self-renew, leading to tissue repair failure. Chromatin analysis of Hira-deficient cells show a significant reduction of histone variant H3.3 deposition and H3K27ac modification at regulatory regions of muscle genes. Additionally, we find that genes from alternative lineages are ectopically expressed in Hira-mutant cells via MLL1/MLL2-mediated increase of H3K4me3 mark at silent promoter regions. Therefore, we conclude that HIRA sustains the chromatin landscape governing muscle cell lineage identity via incorporation of H3.3 at muscle gene regulatory regions, while preventing the expression of alternative lineage genes.

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 score0.002
Threshold uncertainty score0.007

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.310
Teacher spread0.297 · 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

Citations34
Published2021
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicMuscle Physiology and DisordersFrench-language works237,207