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Record W2098716539 · doi:10.1083/jcb.200504039

A role for the eIF4E-binding protein 4E-T in P-body formation and mRNA decay

2005· article· en· W2098716539 on OpenAlexaff
Maria Ferraiuolo, Sanjukta Basak, Josée Dostie, Elizabeth Murray, Daniel R. Schoenberg, Nahum Sonenberg

Bibliographic record

VenueThe Journal of Cell Biology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Cancer Institute
KeywordsEIF4EMessenger RNACell biologyChemistryBiologyTranslation (biology)GeneBiochemistry

Abstract

fetched live from OpenAlex

4E-transporter (4E-T) is one of several proteins that bind the mRNA 5'cap-binding protein, eukaryotic initiation factor 4E (eIF4E), through a conserved binding motif. We previously showed that 4E-T is a nucleocytoplasmic shuttling protein, which mediates the import of eIF4E into the nucleus. At steady state, 4E-T is predominantly cytoplasmic and is concentrated in bodies that conspicuously resemble the recently described processing bodies (P-bodies), which are believed to be sites of mRNA decay. In this paper, we demonstrate that 4E-T colocalizes with mRNA decapping factors in bona fide P-bodies. Moreover, 4E-T controls mRNA half-life, because its depletion from cells using short interfering RNA increases mRNA stability. The 4E-T binding partner, eIF4E, also is localized in P-bodies. 4E-T interaction with eIF4E represses translation, which is believed to be a prerequisite for targeting of mRNAs to P-bodies. Collectively, these data suggest that 4E-T interaction with eIF4E is a priming event in inducing messenger ribonucleoprotein rearrangement and transition from translation to decay.

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.270
Teacher spread0.260 · 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

Citations256
Published2005
Admission routes1
Has abstractyes

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