Inactivation of Mammalian Target of Rapamycin Increases STAT1 Nuclear Content and Transcriptional Activity in α4- and Protein Phosphatase 2A-dependent Fashion
Bibliographic record
Abstract
Target of rapamycin (TOR) is a highly conserved serine/threonine kinase that controls cell growth, primarily via regulation of protein synthesis. In Saccharomyces cerevisiae, TOR can also suppress the transcription of stress response genes by a mechanism involving Tap42, a serine/threonine phosphatase subunit, and the transcription factor Msn2. A physical association between mammalian TOR (mTOR) and the transcription factor signal transducer and activator of transcription-1 (STAT1) was recently identified in human cells, suggesting a similar role for mTOR in the transcription of interferon-gamma-stimulated genes. In the current study, we identified a macromolecular protein complex composed of mTOR, STAT1, the Tap42 homologue alpha4, and the protein phosphatase 2A catalytic subunit (PP2Ac). Inactivation of mTOR enhanced its association with STAT1 and increased STAT1 nuclear content in PP2Ac-dependent fashion. Depletion of alpha4, PP2A, or mTOR enhanced the induction of early (i.e. IRF-1) and late (i.e. caspase-1, hiNOS, and Fas) STAT1-dependent genes. The regulation of IRF-1 or caspase-1 by mTOR was independent of other known mTOR effectors p70 S6 kinase and Akt. These results describe a new role for mTOR and alpha4/PP2A in the control of STAT1 nuclear content, and the expression of interferon-gamma-sensitive genes involved in immunity and apoptosis.
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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) | 0.001 | 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".