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Record W2163474660 · doi:10.1101/gad.1654808

<i>PU.1</i> expression is modulated by the balance of functional sense and antisense RNAs regulated by a shared <i>cis</i>-regulatory element

2008· article· en· W2163474660 on OpenAlexaff
Alexander K. Ebralidze, Florence C. Guibal, Ulrich Steidl, Pu Zhang, Sanghoon Lee, Boris Bartholdy, Meritxell Alberich-Jordà, Victoria Petkova, Frank Rosenbauer, Gang Huang, Tajhal Dayaram, Johanna Klupp, Karen B. O’Brien, Britta Will, Maarten Hoogenkamp, Katherine L. B. Borden, Constanze Bonifer, Daniel G. Tenen

Bibliographic record

VenueGenes & Development · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNational Cancer InstituteNational Institutes of HealthLymphoma Research Foundation
KeywordsBiologySense (electronics)Long non-coding RNAAntisense RNARegulatorTranslation (biology)RNASmall nucleolar RNACell biologyRegulation of gene expressionmicroRNAGeneGene expressionTranscription (linguistics)Function (biology)Transcription factorGeneticsComputational biologyMessenger RNA

Abstract

fetched live from OpenAlex

The transcription factor PU.1 is an important regulator of hematopoiesis; precise expression levels are critical for normal hematopoietic development and suppression of leukemia. We show here that noncoding antisense RNAs are important modulators of proper dosages of PU.1. Antisense and sense RNAs are regulated by shared evolutionarily conserved cis-regulatory elements, and we can show that antisense RNAs inhibit PU.1 expression by modulating mRNA translation. We propose that such antisense RNAs will likely be important in the regulation of many genes and may be the reason for the large number of overlapping complementary transcripts with so far unknown function.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations173
Published2008
Admission routes1
Has abstractyes

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