Abstract 19126: Novel Linkage Between Metabolic Sensor Mechanistic Target of Rapamycin (mTOR) and Canonical NF-kB Signaling Pathway Preserves Mitochondrial Integrity in Ventricular Myocytes During Hypoxia
Bibliographic record
Abstract
Background: TNFα and other pro-inflammatory cytokines activate the canonical NF-kB pathway through the IkBa kinase (IKK) signaling complex. IKKβ kinase is also critical for Akt-mediated NF-κB activation in ventricular myocytes. Akt activates the kinase (mechanistic target of rapamycin (mTOR), which mediates important processes such as metabolism protein synthesis and autophagy. However, mTOR’s role in regulating cardiac myocyte cell survival is unknown. Methods and Results: Herein, we demonstrate bi-directional regulation between NF-kB signaling and mTOR, the balance of which determines ventricular myoctye 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-kB activation by mTOR was medated by phosphorylation of the NF-kB p65 subunit at S276 increasing p65 nuclear translocation and activation of gene transcription. This circuit was also important for NF-κB activation by the canonical TNFα pathway. Inhibition of mTOR with rapamycin or during hypoxia decreased NF-kB activation resulting in increased mitochondrial PTP opening, maladaptive autophagy and necrotic cell death. Conversely, mTOR over-expression suppressed mitochondrial injury, autophagy and cell death of ventricular myocytes during hypoxia as well as nutrient deprivation. Conclusions: To our knowledge, these data provide the first evidence for a bi-directional link between NF-kB signaling and mTOR that is critical in the regulation of cardiac myocyte death. Hence, modulation of this axis may be cardioprotective in attenuating metabolic stress induced during hypoxia.
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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) | 0.004 | 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".