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Record W2942544581 · doi:10.1016/j.celrep.2019.04.008

Nuclear eIF4E Stimulates 3′-End Cleavage of Target RNAs

2019· article· uk· W2942544581 on OpenAlexafffund
Margaret R. Davis, Mildred Delaleau, Katherine L. B. Borden

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageuk
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsEIF4ECleavage and polyadenylation specificity factorCleavage stimulation factorPolyadenylationCleavage (geology)EIF4A1Nuclear export signalCleavage factorTranslation (biology)Messenger RNARNACell biologyEukaryotic translationBiologyEukaryotic translation initiation factor 4 gammaEukaryotic initiation factorCell nucleusGeneticsGeneCytoplasm

Abstract

fetched live from OpenAlex

The eukaryotic translation initiation factor eIF4E is nuclear and cytoplasmic where it plays roles in export and translation of specific transcripts, respectively. When we were studying its mRNA export activity, we unexpectedly discovered that eIF4E drives the protein expression of elements of the 3'-end core cleavage complex involved in cleavage and polyadenylation (CPA), including CPSF3, the enzyme responsible for cleavage, as well as its co-factors CPSF1, CPSF2, CPSF4, Symplekin, WDR33, and FIP1L1. Using multiple strategies, we demonstrate that eIF4E stimulates 3'-end cleavage of selected RNAs. eIF4E physically interacts with CPSF3, CPSF1, and uncleaved target RNA, suggesting it acts directly and indirectly on the pathway. Through these effects, eIF4E can generate better substrates for its mRNA export and translation activities. Thus, we identified an unanticipated function for eIF4E in 3'-end processing of specific target RNAs, and this function could potentially affect the expression of a broad range of oncoproteins.

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 categoriesMeta-epidemiology (narrow)
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 score0.066
Threshold uncertainty score1.000

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)0.0010.000

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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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.

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

Citations48
Published2019
Admission routes2
Has abstractyes

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