Choosing the right exit: How functional plasticity of the nuclear pore drives selective and efficient <scp>mRNA</scp> export
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".