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Record W2966258113 · doi:10.1016/j.celrep.2019.06.097

Histone Recycling by FACT and Spt6 during Transcription Prevents the Scrambling of Histone Modifications

2019· article· en· W2966258113 on OpenAlexafffund
Celia Jerónimo, Christian Poitras, François Robert

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
FundersFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéCompute Canada
KeywordsEpigenomicsHistoneHistone methylationHistone codeEpigenomeNucleosomeTranscription (linguistics)BiologyCell biologyHistone H2AGeneticsChemistryDNA methylationDNAGene expressionGene

Abstract

fetched live from OpenAlex

Genomic DNA is framed by additional layers of information, referred to as the epigenome. Epigenomic marks such as DNA methylation, histone modifications, and histone variants are concentrated on specific genomic sites, where they can both instruct and reflect gene expression. How this information is maintained, notably in the face of transcription, is not completely understood. Specifically, the extent to which modified histones themselves are retained through RNA polymerase II passage is unclear. Here, we show that several histone modifications are mislocalized when the transcription-coupled histone chaperones FACT or Spt6 are disrupted in Saccharomyces cerevisiae. In the absence of functional FACT or Spt6, transcription generates nucleosome loss, which is partially compensated for by the increased activity of non-transcription-coupled histone chaperones. The random incorporation of transcription-evicted modified histones scrambles epigenomic information. Our work highlights the importance of local recycling of modified histones by FACT and Spt6 during transcription in the maintenance of the epigenomic landscape.

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 score0.011
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.246
Teacher spread0.238 · 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

Citations115
Published2019
Admission routes2
Has abstractyes

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