MétaCan
Menu
Back to cohort
Record W1993287040 · doi:10.1083/jcb.200607020

eIF4E is a central node of an RNA regulon that governs cellular proliferation

2006· article· en· W1993287040 on OpenAlexaff
Biljana Čuljković, Ivan Topisirović, Lucy Skrabanek, Melisa Ruiz‐Gutierrez, Katherine L. B. Borden

Bibliographic record

VenueThe Journal of Cell Biology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNational Cancer InstituteNational Institutes of Health
KeywordsEIF4EBiologyMessenger RNARNANuclear export signalEukaryotic translationCell biologyTranslation (biology)Protein biosynthesisMolecular biologyGeneticsGene

Abstract

fetched live from OpenAlex

This study demonstrates that the eukaryotic translation initiation factor eIF4E is a critical node in an RNA regulon that impacts nearly every stage of cell cycle progression. Specifically, eIF4E coordinately promotes the messenger RNA (mRNA) export of several genes involved in the cell cycle. A common feature of these mRNAs is a structurally conserved, approximately 50-nucleotide element in the 3' untranslated region denoted as an eIF4E sensitivity element. This element is sufficient for localization of capped mRNAs to eIF4E nuclear bodies, formation of eIF4E-specific ribonucleoproteins in the nucleus, and eIF4E-dependent mRNA export. The roles of eIF4E in translation and mRNA export are distinct, as they rely on different mRNA elements. Furthermore, eIF4E-dependent mRNA export is independent of ongoing RNA or protein synthesis. Unlike the NXF1-mediated export of bulk mRNAs, eIF4E-dependent mRNA export is CRM1 dependent. Finally, the growth-suppressive promyelocytic leukemia protein (PML) inhibits this RNA regulon. These data provide novel perspectives into the proliferative and oncogenic properties of eIF4E.

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 score0.007
Threshold uncertainty score0.366

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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

Citations289
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Cell BiologySame topicPI3K/AKT/mTOR signaling in cancerFrench-language works237,207