Tumor necrosis factor alpha protects heart cultures against hypoxic damage via activation of PKA and phospholamban to prevent calcium overload
Bibliographic record
Abstract
This study aims to elucidate the mechanisms by which tumor necrosis factor alpha (TNFα) provides protection from hypoxic damage to neonatal rat cardiomyocyte cultures. We show that when intracellular Ca(2+) ([Ca(2+)]i) levels are elevated by extracellular Ca(2+) ([Ca(2+)]o) or by hypoxia, then TNFα decreased [Ca(2+)]i in individual cardiomyocytes. However, TNFα did not reduce [Ca(2+)]i after its increase by thapsigargin, (a SERCA2a inhibitor), indicating that TNFα attenuates Ca(2+) overload through Ca(2+) uptake by SERCA2a. TNFα did not reduce [Ca(2+)]i, following its elevation when [Ca(2+)]o levels were elevated in TNFα receptor knock-out mice. H-89, a protein kinase A (PKA) inhibitor, attenuated the protective effect of TNFα when the cardiomyoctyes were subjected to hypoxia, as determined by lactate dehydrogenase (LDH) and creatine kinase (CK) released and from the cardiomyocytes. Moreover, when the levels of [Ca(2+)]i were increased by hypoxia, H-89, but not KN93, (a calmodulin kinase II inhibitor), prevented the reduction in [Ca(2+)]i by TNFα. TNFα increased the phosphorylation of PKA in normoxic and hypoxic cardiomyoctes, indicating that the cardioprotective effect of TNFα against hypoxic damage was via PKA activation. Hypoxia decreased phosphorylated phospholamban levels; however, TNFα attenuated this decrease following hypoxia. It is suggested that TNFα activates phospholamban phosphorylation in hypoxic heart cultures via PKA to stimulate SERCA2a activity to limit Ca(2+) overload.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".