The SUMO-specific isopeptidase SENP2 associates dynamically with nuclear pore complexes through interactions with karyopherins and the Nup107-160 nucleoporin subcomplex
Bibliographic record
Abstract
The association of small, ubiquitin-related modifier-specific isopeptidases (also known as sentrin-specific proteases, or SENPs) with nuclear pore complexes (NPCs) is conserved in eukaryotic organisms ranging from yeast to mammals. However, the functional significance of this association remains poorly understood, particularly in mammalian cells. In this study, we have characterized the molecular basis for interactions between SENP2 and NPCs in human cells. Using fluorescence recovery after photobleaching, we demonstrate that SENP2, although concentrated at the nuclear basket, is dynamically associated with NPCs. This association is mediated by multiple targeting elements within the N-terminus of SENP2 that function cooperatively to mediate NPC localization. One of these elements consists of a high-affinity nuclear localization signal that mediates indirect tethering to FG-repeat-containing nucleoporins through karyopherins. A second element mediates interactions with the Nup107-160 nucleoporin subcomplex. A third element consists of a nuclear export signal. Collectively, our findings reveal that SENP2 is tethered to NPCs through a complex interplay of interactions with nuclear import and export receptors and nucleoporins. Disruption of these interactions enhances SENP2 substrate accessibility, suggesting an important regulatory node in the SUMO pathway.
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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".