IL-17 Promotes p38 MAPK-Dependent Endothelial Activation Enhancing Neutrophil Recruitment to Sites of Inflammation
Bibliographic record
Abstract
Neutrophilic inflammation plays an important role in lung tissue destruction occurring in many chronic pulmonary diseases. Neutrophils can be recruited to sites of inflammation via the action of the cytokine IL-17. In this study, we report that IL-17RA and IL-17RC mRNA expression is significantly increased in asthmatic bronchoscopic biopsies and that these receptors are not only expressed on epithelial and inflammatory cells but also on endothelial cells. IL-17 potently stimulates lung microvascular endothelial cells to produce chemoattractants (CXCL8 and derivatives of the 5-lipoxygenase pathway) that selectively drive neutrophil but not lymphocyte chemotaxis. Moreover, IL-17 promotes endothelial activation by inducing the expression of endothelial adhesion markers (E-selectin, VCAM-1, and ICAM-1) in a p38 MAPK-dependent manner. This increased expression of adhesion molecules stimulates the trans-endothelial migration of neutrophils, as well as the transmigration of HT-29 colon carcinoma cells, suggesting a further role in promoting lung metastasis. Finally, IL-17 increased neutrophil adhesion to the endothelium in vivo as determined by intravital microscopy of mice cremaster muscle. Overall, our results demonstrate that IL-17 is a potent activator of the endothelium in vivo leading to neutrophil infiltration. Therefore, preventing neutrophil recruitment by blocking the action of IL-17 on endothelial cells may prove to be highly beneficial in diseases in which neutrophilic inflammation plays a key role.
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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) | 0.004 | 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".