A Novel Mechanism of E2F1 Regulation Via Nucleocytoplasmic Shuttling: Determinants of Nuclear Import and Export
Bibliographic record
Abstract
E2F1 is a transcription factor central for cell survival, proliferation, and repair following genomic insult. Depending on the cell type and conditions, E2F1 can induce apoptosis in transformed cells, behaving as a tumour suppressor, or impart growth advantages favouring tumour formation. The pleiotropic functions of E2F1 are a likely consequence of its ability to transcriptionally control a wide variety of target genes, and require tight regulation of its activity at multiple levels. Although sequestration of proteins to particular cellular compartments is a well-established regulatory mechanism, virtually nothing is known about its contribution to modulation of E2F1 target gene expression. We have examined the subcellular trafficking of E2F1 and, contrary to the widely held notion that this factor is constitutively nuclear, we now demonstrate that it is subjected to continuous nucleocytoplasmic shuttling. We have also defined two nuclear localization domains and a nuclear export region, which mediates CRM1-dependent transit out of the nucleus. The predominant subcellular location of E2F1 is likely determined by the balance between the activity of nuclear import and export domains, and can be modulated by differentiation stimuli in epidermal cells. Thus, we have identified a hitherto unrecognized mechanism to control E2F1 function through modulation of its subcellular localization.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".