Regulation of Karyopherin α1 and Nuclear Import by Mammalian Target of Rapamycin
Bibliographic record
Abstract
Under conditions of reduced mitogen or nutritional substrate levels, the serine/threonine kinase target of rapamycin can augment the nuclear content of distinct transcription factors and promote the induction of stress response genes. In its latent (i.e., unphosphorylated) form, the transcription factor STAT1 regulates a subset of genes involved in immune modulation and apoptosis. Based on previous work indicating a functional relationship between mammalian target of rapamycin (mTOR) and the nuclear content of latent STAT1, we investigated the mechanism by which mTOR controls STAT1 nuclear import. By fluorescence confocal microscopy, inactivation of mTOR with rapamycin promoted the nuclear translocation of unphosphorylated STAT1, but not that of a STAT1 mutant incapable of binding its nuclear import adaptor karyopherin-α1 (KPNA1). By immunoprecipitation, KPNA1 was physically associated with mTOR and STAT1 in a complex that translocated to the nucleus in response to rapamycin. Although mTOR is not a kinase for KPNA1, the mTOR-associated phosphatase protein phosphatase 2A catalytic interacted directly with KPNA1 and regulated nuclear import of the mTOR-KPNA1 complex. KPNA1, or its interaction with STAT1, was required for the nuclear import of latent STAT1, transcriptional induction of the STAT1 gene, and caspase-3 activation under conditions of reduced mTOR activity (i.e. rapamycin, glucose starvation, serum withdrawal). Therefore, at low mitogen or nutrient levels, mTOR and protein phosphatase 2A catalytically control the constitutive nuclear import of latent STAT1 by KPNA1, which are key modulators of STAT1 expression and apoptosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".