Regulation of protein kinase Cδ Nuclear Import and Apoptosis by Mechanistic Target of Rapamycin Complex-1
Bibliographic record
Abstract
Inactivation of the protein complex 'mechanistic target of rapamycin complex 1' (mTORC1) can increase the nuclear content of transcriptional regulators of metabolism and apoptosis. Previous studies established that nuclear import of signal transducer and activator of transcription-1 (STAT1) requires the mTORC1-associated adaptor karyopherin-α1 (KPNA1) when mTORC1 activity is reduced. However, the role of other mTORC1-interacting proteins in the complex, including 'protein kinase C delta' (PKCδ), have not been well characterized. In this study, we demonstrate that PKCδ, a STAT1 kinase, contains a functional 'target of rapamycin signaling' (TOS) motif that directs its interaction with mTORC1. Depletion of KPNA1 by RNAi prevented the nuclear import of PKCδ in cells exposed to the mTORC1 inhibitor rapamycin or amino acid restriction. Mutation of the TOS motif in PKCδ led to its loss of regulation by mTORC1 or karyopherin-α1, resulting in increased constitutive nuclear content. In cells expressing wild-type PKCδ, STAT1 activity and apoptosis were increased by rapamycin or interferon-β. Those expressing the PKCδ TOS mutant exhibited increased STAT1 activity and apoptosis; further enhancement by rapamycin or interferon-β, however, was lost. Therefore, the TOS motif in PKCδ is a novel structural mechanism by which mTORC1 prevents PKCδ and STAT1 nuclear import, 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".