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Record W3157456279 · doi:10.1002/wrna.1660

Choosing the right exit: How functional plasticity of the nuclear pore drives selective and efficient <scp>mRNA</scp> export

2021· review· en· W3157456279 on OpenAlexafffund
Pierre Bensidoun, Daniel Zenklusen, Marlene Oeffinger

Bibliographic record

VenueWiley Interdisciplinary Reviews - RNA · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNuclear poreNuclear transportNuclear export signalRNACell biologyNucleoporinMessenger RNACytoplasmMediatorNucleusChemistryBiologyCell nucleusGeneBiochemistry

Abstract

fetched live from OpenAlex

The nuclear pore complex (NPC) serves as a central gate for mRNAs to transit from the nucleus to the cytoplasm. The ability for mRNAs to get exported is linked to various upstream nuclear processes including co-transcriptional RNP assembly and processing, and only export competent mRNPs are thought to get access to the NPC. While the nuclear pore is generally viewed as a monolithic structure that serves as a mediator of transport driven by transport receptors, more recent evidence suggests that the NPC might be more heterogenous than previously believed, both in its composition or in the selective treatment of cargo that seek access to the pore, providing functional plasticity to mRNA export. In this review, we consider the interconnected processes of nuclear mRNA metabolism that contribute and mediate export competence. Furthermore, we examine different aspects of NPC heterogeneity, including the role of the nuclear basket and its associated complexes in regulating selective and/or efficient binding to and transport through the pore. This article is categorized under: RNA Export and Localization > Nuclear Export/Import RNA Turnover and Surveillance > Turnover/Surveillance Mechanisms RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.030
GPT teacher head0.310
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2021
Admission routes2
Has abstractyes

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