Protein kinase R modulates NLRP3 inflammasome in cardiac fibroblasts in sepsis
Bibliographic record
Abstract
Background We have reported that sepsis leads to an activation of NLRP3 inflammasome in cardiac fibroblasts (CF) resulting in IL‐1β maturation and extracellular release. Increase in IL‐1β by the CF induces myocardial dysfunction. The aim of the present study is to identify a modulation pathway that activates NLRP3 inflammasome in the CF. Materials and Methods CF were isolated and cultured from adult mouse hearts. The CF were primed with LPS (1 μg/ml) for 6 hrs followed by 30 min of ATP (3 mM) treatment to activate NLRP3 inflammasome. PKR activation was assessed by PKR phosphorylation (Western). NLRP3 inflammasome activation was assessed by detecting CF caspase‐1 p20 (Western) and IL‐1β release (ELISA). Results LPS priming of CF resulted in PKR activation as indicated by increase in PKR phosphorylation at Thr451, and increase in NLRP3 and pro‐IL‐1β protein expression. Inhibition of PKR (PKR inhibitor and siRNA) prevented the LPS‐induced NLRP3 and pro‐IL‐1β expression in the CF. Inhibition of PKR in CF primed with LPS before ATP showed no effect on NLRP3 and pro‐IL‐1β expression, but prevented the NLRP3 inflammasome activation as indicated by abolishing the caspase‐1 activation and IL‐1β releasing. In addition, pretreatment of CF with peroxynitrite (ONOO − ) decomposition catalyst, FeTPPs, before LPS, prevented the LPS induced PKR phosphorylation, NLRP3 and pro‐IL‐1β expression in CF. Furthermore, pretreatment of CF with FeTPPs before ATP prevented the NLRP3 inflammsome activation in CF. Conclusion our results indicate that ONOO − /PKR pathway modulates priming and activation of NLRP3 inflammasome in CF in sepsis. Support or Funding Information IRF of Lawson Health Research Institute (IRF 2014–25); The Natural Science Foundation of Jiangsu Province, China (BK2015‐1332). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 |
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".