A Proline‐Tyrosine Nuclear Localization Signal (<scp>PY‐NLS</scp>) Is Required for the Nuclear Import of Fission Yeast <scp>PAB2</scp>, but Not of Human <scp>PABPN1</scp>
Bibliographic record
Abstract
Nuclear poly(A)-binding proteins (PABPs) are evolutionarily conserved proteins that play key roles in eukaryotic gene expression. In the fission yeast Schizosaccharomyces pombe, the major nuclear PABP, Pab2, functions in the maturation of small nucleolar RNAs as well as in nuclear RNA decay. Despite knowledge about its nuclear functions, nothing is known about how Pab2 is imported into the nucleus. Here, we show that Pab2 contains a proline-tyrosine nuclear localization signal (PY-NLS) that is necessary and sufficient for its nuclear localization and function. Consistent with the role of karyopherin β2 (Kapβ2)-type receptors in the import of PY-NLS cargoes, we show that the fission yeast ortholog of human Kapβ2, Kap104, binds to recombinant Pab2 and is required for Pab2 nuclear localization. The absence of arginine methylation in a basic region N-terminal to the PY-core motif of Pab2 did not affect its nuclear localization. However, in the context of a sub-optimal PY-NLS, we found that Pab2 was more efficiently targeted to the nucleus in the absence of arginine methylation, suggesting that this modification can affect the import kinetics of a PY-NLS cargo. Although a sequence resembling a PY-NLS motif can be found in the human Pab2 ortholog, PABPN1, our results indicate that neither a functional PY-NLS nor Kapβ2 activity are required to promote entry of PABPN1 into the nucleus of human cells. Our findings describe the mechanism by which Pab2 is imported into the nucleus, providing the first example of a PY-NLS import system in fission yeast. In addition, this study suggests the existence of alternative or redundant nuclear import pathways for human PABPN1.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".