Nuclear mRNA metabolism drives selective basket assembly on a subset of nuclear pores in budding yeast
Bibliographic record
Abstract
ABSTRACT To determine which transcripts should reach the cytoplasm for translation, eukaryotic cells have established mechanisms to regulate selective mRNA export through the nuclear pore complex (NPC). The nuclear basket, a substructure of the NPC protruding into the nucleoplasm, is thought to function as a stable platform where mRNA-protein complexes (mRNPs) are rearranged and undergo quality control (QC) prior to export, ensuring that only mature mRNAs reach the cytoplasm. Here, we use proteomic, genetic, live-cell, and single-molecule resolution microscopy approaches in budding yeast to demonstrate that baskets assemble only on a subset of NPCs and that basket formation is dependent on RNA polymerase II (Pol II) transcription and subsequent mRNP processing. Specifically, we observe that the cleavage and polyadenylation machinery, the poly(A)-binding protein Pab1, and pre-mRNA-leakage factor Pml39 are required for basket assembly. We further show that while all nuclear pores can bind Mlp1, baskets assemble only on a subset of nucleoplasmic NPCs, and these basket-containing pores associate a distinct protein and RNA interactome. Taken together, our data points towards nuclear pore heterogeneity and an RNA-dependent mechanism for functionalization of nuclear pores in budding yeast through nuclear basket assembly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".