A nucleoporin, Nup60p, affects the nuclear and cytoplasmic localization of <i>ASH1</i> mRNA in <i>S. cerevisiae</i>
Bibliographic record
Abstract
The biogenesis of a localization-competent mRNP begins in the nucleus. It is thought that the coordinated action of nuclear and cytoplasmic components of the localization machinery is required for the efficient export and subsequent subcellular localization of these mRNAs in the cytoplasm. Using quantitative poly(A)(+) and transcript-specific fluorescent in situ hybridization, we analyzed different nonessential nucleoporins and nuclear pore-associated proteins for their potential role in mRNA export and localization. We found that Nup60p, a nuclear pore protein located on the nucleoplasmic side of the nuclear pore complex, was required for the mRNA localization pathway. In a Δnup60 background, localized mRNAs were preferentially retained within the nucleus compared to nonlocalized transcripts. However, the export block was only partial and some transcripts could still reach the cytoplasm. Importantly, downstream processes were also affected. Localization of ASH1 and IST2 mRNAs to the bud was impaired in the Δnup60 background, suggesting that the assembly of a localization competent mRNP ("locasome") was inhibited when NUP60 was deleted. These results demonstrate transcript specificity of a nuclear mRNA retention defect and identify a specific nucleoporin as a functional component of the localization pathway in budding yeast.
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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 | 0.000 | 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".