The nuclear localization of SET mediated by impα3/impβ attenuates its cytosolic toxicity in neurons
Bibliographic record
Abstract
SET is a multi-functional protein in proliferating cells. Some of the proposed functions of SET suggest an important nuclear role. However, the nuclear import pathway of SET is also unknown and the function of SET in neurons is unclear. Presently, using cortical neurons, we report that the nuclear import of SET is mediated by an impalpha/impbeta-dependent pathway. Nuclear localization signal, (168)KRSSQTQNKASRKR(181), in SET interacts with impalpha3, which recruits impbeta to form a ternary complex, resulting in efficient transportation of SET into nucleus. By in vitro nuclear import assay based on digitonin-permeabilized neurons, we further demonstrated that the nuclear import of SET relies on Ran GTPase. We provide evidence that this nuclear localization of SET is important in neuronal survival. Under basal conditions, SET is predominately nuclear. However, upon death induced by genotoxic stress, endogenous SET decreases in the nucleus and increases in the cytoplasm. Consistent with a toxic role of SET in the cytoplasm, targeted expression of SET to the cytoplasm exacerbates death compared to wild type SET expression which is protective following DNA damage. Taken together, our results indicate that SET is imported into the nucleus through its association with impalpha3/impbeta, and that localization of SET is important in regulation of neuronal death.
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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 | 0.000 |
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".