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Phosphorylation of Eukaryotic Translation Initiation Factor 4E Is Critical for Growth

2002· article· en· W2171220723 on OpenAlexaff
Pascal E. D. Lachance, Mathieu Miron, Brian Raught, Nahum Sonenberg, Paul Lasko

Bibliographic record

VenueMolecular and Cellular Biology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyEukaryotic translationPhosphorylationTranslation (biology)Initiation factorCell biologyEukaryotic translation initiation factor 4 gammaeIF2Eukaryotic initiation factorEIF4EComputational biologyGeneticsGeneMessenger RNA

Abstract

fetched live from OpenAlex

Eukaryotic translation initiation factor 4E (eIF4E) binds to the cap structure at the 5' end of mRNAs and is a critical target for the control of protein synthesis. eIF4E is phosphorylated in many systems in response to extracellular stimuli, but biochemical evidence to date has been equivocal as to the biological significance of this modification. Here we use a genetic approach to this problem. We show that, in Drosophila melanogaster, homozygous eIF4E mutants arrest growth during larval development. In Drosophila eIF4EI, Ser251 corresponds to Ser209 of mammalian eIF4E, which is phosphorylated in response to extracellular signals. We find that, in vivo, eIF4EI Ser251 mutants cannot incorporate labeled phosphate. Furthermore, transgenic Drosophila organisms expressing eIF4E(Ser251Ala) in an eIF4E mutant background have reduced viability. Escapers develop more slowly than control siblings and are smaller. These genetic data provide evidence that eIF4E phosphorylation is biologically significant and is essential for normal growth and development.

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)0.0010.000
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)0.0010.001

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 head0.022
GPT teacher head0.246
Teacher spread0.225 · 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

Citations175
Published2002
Admission routes1
Has abstractyes

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