75 Inhibition of GRK2 Suppressed TNFα-Induced Inflammatory Signaling and Pro-Fibrotic Factor in Cardiomyocytes
Bibliographic record
Abstract
TNFα is significantly elevated in burned patients and plays a role in the pathogenesis of cardiac dysfunction. In addition, increased G protein-coupled receptor kinase 2 (GRK2) modulates cardiac dysfunction. Therefore inhibition of GRK2 and TNFα signaling may be a viable option for burn induced cardiac dysfunction. However, the relationship between the two pathways has not been elucidated. In the present study, we explored the effect of GRK2 inhibitor (GRK2-I) on TNFα-induced pro-inflammatory and pro-fibrotic signaling in cardiomyocytes. H9c2 cardiomyocytes were exposed to TNFα and were treated with or without GRK2-I. Cellular proteins were analyzed with Western blot and immunofluorescence assay. TNFα treatments dramatically increased pro-inflammatory cytokine MCP1, which plays a major role in myocarditis and ischemia/reperfusion injury in cardiomyocytes. Pre-treatment with GRK2-I inhibited TNFα-induced MCP-1 secretion in dose-dependent fashion. Simultaneously, GRK2-I suppressed TNFα-induced NF-κB activation as evidenced by blocking NF-κB p65 nuclear translocation and inhibiting TNFα-stimulated IκBα phosphorylation and degradation. NF-κB chemical inhibitors abrogated TNFα-induced MCP-1 secretion in H9c2 cells. Our data demonstrated that GRK2-I significantly decreased endogenous and inflammatory stimulator-induced fibronectin expression in H9c2 cells. GRK2-I inhibited TNFα-induced inflammation through the NF-κB pathway and attenuated the expression of pro-fibrosis protein fibronectin. GRK2-I may represent a promising cardio-protective agent for burn patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".