Inhibiting focal adhesion kinase (FAK) blocks IL-4 induced VCAM-1 expression and eosinophil recruitment in vitro and in vivo
Bibliographic record
Abstract
Leukocyte recruitment plays a critical role during both normal inflammation and chronic inflammatory diseases, and ongoing studies endeavor to better understand the complexities of this process. Focal adhesion kinase (FAK) is well known for its role in cancer, yet it also has been shown to regulate aspects of neutrophil and B16 melanoma cell recruitment by rapidly influencing endothelial cell focal adhesion dynamics and junctional opening. Recently, we found that FAK related non-kinase (FRNK), a protein that is often used as a FAK dominant negative, blocked eosinophil transmigration by preventing the transcription of vascular cell adhesion molecule-1 (VCAM-1) and eotaxin-3 (CCL26). Surprisingly, the blocking occurred even in the absence of endogenous FAK. To better understand the role of FAK in leukocyte recruitment, we used a FAK-specific inhibitor (PF-573228) and determined the effect on IL-4 induced eosinophil recruitment in vitro and in vivo. PF-573228 prevented the expression of VCAM-1 and CCL26 expression in IL-4-stimulated human endothelial cells in vitro. As a result, eosinophil adhesion and transmigration were blocked. PF-572338 also prevented IL-4-induced VCAM-1 expression in vivo. Using brightfield intravital microscopy, we found that PF-573228 decreased leukocyte rolling flux, adhesion, and emigration. We specifically examined eosinophil recruitment in vivo by using an eosinophil-GFP reporter mouse and found PF-573228 attenuated eosinophil emigration. This study reveals that a FAK inhibitor influences inflammation through its action on eosinophil recruitment.
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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 | 0.000 |
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".