Bronchial inflammation induced PKC<sub>ζ</sub> over-expression: involvement in mechanical properties of airway smooth muscle
Bibliographic record
Abstract
Protein kinase C variants (PKCs) have been involved in the control of airway smooth muscle (ASM) tone, and abnormalities in PKC-dependent signaling have been associated with respiratory diseases such as asthma. In this study, the role of atypical PKCζ in airway hyperresponsiveness was investigated, using an in-vitro model of TNFα-treated human bronchi and an in vivo guinea pig model of chronic asthma. Our results demonstrated that PKCζ-specific inhibition produced a significant increase in isoproterenol sensitivity in TNFα-treated bronchi and ovalbumin (OVA)-sensitized guinea pig bronchi. The role of epoxy-eicosanoids, known to exert anti-inflammatory effects in lung, on PKCζ expression and activity in these models was evaluated. An enhanced PKCζ protein expression was delineated in TNFα-treated bronchi when compared with control (untreated) and epoxy-eicosanoid-treated bronchi. Measurements of Ca(2+) sensitivity, performed in TNFα-treated bronchi, demonstrated that treatment with myristoylated (Myr) PKCζ peptide inhibitor resulted in significant reductions of pCa-induced tension. Epoxy-eicosanoid treatments had similar effects on Ca(2+) sensitivity in TNFα-treated bronchi. In control and epoxy-eicosanoid-treated bronchi, the phosphorylated forms of p38MAPK and CPI-17 were significantly decreased compared with the TNFα-treated bronchi. An enhanced expression of PKCζ was ascertained in our in-vivo model of allergic asthma. Hence an increased Ca(2+) sensitivity could be explained by the phosphorylation of p38-MAPK, which in turn leads to phosphorylation and activation of the CPI-17 regulatory protein. This process was reversed upon treatment with the Myr-PKCζ-peptide inhibitor. The present data provide relevant evidence regarding the role of PKCζ in human and rodent models of airways inflammation.
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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) | 0.002 | 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".