Peroxynitrite/PKR Axis Modulates the NLRP3 Inflammasome of Cardiac Fibroblasts
Bibliographic record
Abstract
Sepsis/endotoxemia activates the NLRP3 inflammasome of macrophages leading to the maturation and release of IL-1β, an important mediator of the inflammatory response. Reactive oxygen species have been implicated in NLRP3 inflammasome activation. Further, our preliminary studies indicated that LPS challenge of cardiac fibroblasts could phosphorylate protein kinase R (PKR) on threonine 451 and increase message for pro-IL-1 β. Thus, the major aim of the present was to address the role of PKR and the oxidant, peroxynitrite, in the two-tiered function of the NLRP3 inflammasome (priming and activation). Materials and Methods: Isolated murine fibroblasts were primed with LPS (1 µg/ml) for 6 hrs and subsequently activated by an ATP (3 mM) challenge for 30 min to induce optimum functioning of the inflammasome. Increased levels of NLRP3 and pro-IL-1β protein (Western) were used as readouts for inflammasome priming, while activation of caspase 1 (p20) (Western) and secretion of IL-1β (ELISA) were indicative of inflammasome activation. Results: Inhibition of PKR (PKR inhibitor or siRNA) prior to priming with LPS prevented the LPS-induced increase in NLRP3 and pro-IL-1β expression. Further, inhibition of PKR after priming, but before activation did not affect NLRP3 or pro-IL-1β protein levels, but markedly reduced the activation of caspase 1 and secretion of mature IL-1β. In a similar fashion, a peroxynitrite decomposition catalyst (Fe-TPPS) prevented both the priming and activation of the NLRP3 inflammasome. Finally, pretreatment of the fibroblasts with Fe-TPPS prevented the LPS-induced PKR phosphorylation (T451). Conclusion, our results indicate that peroxynitrite-/PKR pathway modulates priming and activation of NLRP3 inflammasome in LPS/ATP challenged cardiac fibroblasts.
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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) | 0.002 | 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".