Adenoviral Gene Delivery of Elafin and Secretory Leukocyte Protease Inhibitor Attenuates NF-κB-Dependent Inflammatory Responses of Human Endothelial Cells and Macrophages to Atherogenic Stimuli
Bibliographic record
Abstract
Atherosclerosis is a chronic inflammatory disease affecting arterial vessels. Strategies to reduce the inflammatory responses of endothelial cells and macrophages may slow lesion development and prevent complications such as plaque rupture. The human protease human neutrophil elastase (HNE), oxidized low density lipoprotein, LPS, and TNF-alpha were chosen as model stimuli of arterial wall inflammation and led to production of the chemokine IL-8 in endothelial cells. To counteract the activity of HNE, we have examined the effects of adenoviral gene delivery of the anti-elastases elafin, previously demonstrated within human atheroma, and murine secretory leukocyte protease inhibitor (SLPI), a related molecule, on the inflammatory responses of human endothelial cells and macrophages to atherogenic stimuli. We developed a technique of precomplexing adenovirus with cationic lipid to augment adenoviral infection efficiency in endothelial cells and to facilitate infection in macrophages. Elafin overexpression protected endothelial cells from HNE-induced IL-8 production and cytotoxicity. Elafin and murine SLPI also reduced endothelial IL-8 release in response to oxidized low density lipoprotein, LPS, and TNF-alpha and macrophage TNF-alpha production in response to LPS. This effect was associated with reduced activation of the inflammatory transcription factor NF-kappaB, through up-regulation of IkappaBalpha, in both cell types. Our work suggests a novel and extended anti-inflammatory role for these HNE inhibitors working as effectors of innate immunity to protect tissues against maladaptive inflammatory responses. Our findings indicate that elafin and SLPI may be gene therapy targets for the treatment of atheroma.
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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 | 0.000 |
| 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".