Bidirectional Regulation of Nuclear Factor-κB and Mammalian Target of Rapamycin Signaling Functionally Links Bnip3 Gene Repression and Cell Survival of Ventricular Myocytes
Bibliographic record
Abstract
BACKGROUND: Tumor necrosis factor-α and other proinflammatory cytokines activate the canonical nuclear factor (NF)-κB pathway through the kinase IKKβ. Previously, we established that IKKβ is also critical for Akt-mediated NF-κB activation in ventricular myocytes. Akt activates the kinase mammalian target of rapamycin (mTOR), which mediates important processes such as cardiac hypertrophy. However, whether mTOR regulates cardiac myocyte cell survival is unknown. METHODS AND RESULTS: Herein, we demonstrate bidirectional regulation between NF-κB signaling and mTOR, the balance which determines ventricular myocyte survival. Overexpression of IKKβ resulted in mTOR activation and conversely overexpression of mTOR lead to NF-κB activation. Loss of function approaches demonstrated that endogenous levels of IKKβ and mTOR also signal through this pathway. NF-κB activation by mTOR was mediated by phosphorylation of the NF-κB p65 subunit increasing p65 nuclear translocation and activation of gene transcription. This circuit was also important for NF-κB activation by the canonical tumor necrosis factor-α pathway. Our previous work has shown that NF-κB signaling suppresses transcription of the death gene Bnip3 resulting in ventricular myocyte survival. Inhibition of mTOR with rapamycin decreased NF-κB activation resulting in increased Bnip3 expression and cell death. Conversely, mTOR overexpression suppressed Bnip3 levels and cell death of ventricular myocytes in response to hypoxia. CONCLUSIONS: To our knowledge, these data provide the first evidence for a bidirectional link between NF-κB signaling and mTOR that is critical in the regulation of Bnip3 expression and cardiac myocyte death. Hence, modulation of this axis may be cardioprotective during ischemia.
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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 | 0.001 |
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".