Identification of a putative nuclear localization signal in maspin protein shed light into its nuclear import regulation
Bibliographic record
Abstract
Abstract Maspin (SERPINB5) is a potential tumor suppressor gene with pleiotropic biological activities, including regulation of cell proliferation, death, adhesion, migration and gene expression. Several studies suggest that subcellular localization plays an essential role on maspin tumor suppression activity. In this study we investigated the molecular mechanisms underlying maspin nucleocytoplasmic shuttling. An in vitro nuclear-import assay using digitonin-permeabilized HeLa cells demonstrated that maspin enters the nucleus by an energy-and carrier-independent mechanism. However, previous studies indicated that maspin subcellular localization is regulated in the cell. Using a nuclear localization signal (NLS) prediction software, we identified a putative NLS in the maspin amino acid sequence. To distinguish between passive and regulated nuclear translocation, maspinNLS or the full-length protein (MaspinFL) were fused to 5GFP, rendering the construct too large to enter the nucleus passively. Unexpectedly, 5GFP-maspinNLS, but not maspinFL-5GFP, entered the nucleus of HeLa cells. Dominant-negative Ran-GTPase mutants RanQ69L or RanT24N, suppressed 5GFP-maspinNLS nuclear localization. In summary, we provide evidence that maspin translocates to the nucleus passively. In addition, we identified a peptide in the maspin protein sequence, which is able to drive a 5GFP construct to the nucleus in an energy-dependent manner.
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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) | 0.002 | 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".