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Record W2123355529 · doi:10.1083/jcb.200805146

UBF levels determine the number of active ribosomal RNA genes in mammals

2008· article· en· W2123355529 on OpenAlexaff
Elaine Sanij, Gretchen Poortinga, Kerith Sharkey, Sandy Hung, Timothy P. Holloway, Jaclyn Quin, Elysia Robb, Lee H. Wong, Walter G. Thomas, Victor Y. Stefanovsky, Tom Moss, Lawrence Rothblum, Katherine M. Hannan, Grant A. McArthur, Richard B. Pearson, Ross D. Hannan

Bibliographic record

VenueThe Journal of Cell Biology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsUniversité LavalHôtel-Dieu de Québec
FundersCancer Council VictoriaNational Health and Medical Research CouncilNational Heart, Lung, and Blood InstituteMedical Research CouncilNational Institutes of Health
KeywordsBiologyRNA polymerase IChromatinRNA polymerase IIIRibosomal RNAMolecular biology5S ribosomal RNARNAGeneCell biologyGeneticsRNA polymerase18S ribosomal RNA

Abstract

fetched live from OpenAlex

In mammals, the mechanisms regulating the number of active copies of the approximately 200 ribosomal RNA (rRNA) genes transcribed by RNA polymerase I are unclear. We demonstrate that depletion of the transcription factor upstream binding factor (UBF) leads to the stable and reversible methylation-independent silencing of rRNA genes by promoting histone H1-induced assembly of transcriptionally inactive chromatin. Chromatin remodeling is abrogated by the mutation of an extracellular signal-regulated kinase site within the high mobility group box 1 domain of UBF1, which is required for its ability to bend and loop DNA in vitro. Surprisingly, rRNA gene silencing does not reduce net rRNA synthesis as transcription from remaining active genes is increased. We also show that the active rRNA gene pool is not static but decreases during differentiation, correlating with diminished UBF expression. Thus, UBF1 levels regulate active rRNA gene chromatin during growth and differentiation.

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 score0.019
Threshold uncertainty score0.224

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Citations212
Published2008
Admission routes1
Has abstractyes

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