Focal adhesion kinase-related nonkinase (FRNK) negatively regulates IL-4-mediated inflammation
Bibliographic record
Abstract
Focal adhesion kinase (FAK)-related nonkinase (PTK2 isoform 6 in humans, hereafter referred to as FRNK) is a cytoskeletal regulatory protein that has recently been shown to dampen lung fibrosis, yet its role in inflammation is unknown. Here, we show for the first time that expression of FRNK negatively regulates IL-4-mediated inflammation in a human model of eosinophil recruitment. Mechanistically, FRNK blocks eosinophil accumulation, firm adhesion and transmigration by preventing transcription and protein expression of VCAM-1 and CCL26. IL-4 activates STAT6 to induce VCAM-1 and CCL26 transcription. We now show that IL-4 also increases GATA6 to induce VCAM-1 expression. FRNK blocks IL-4-induced GATA6 transcription but has little effect on GATA6 protein expression and no effect on STAT6 activation. FRNK can block FAK or Pyk2 signaling and we, thus, downregulated these proteins using siRNA to determine whether signaling from either protein is involved in the regulation of VCAM-1 and CCL26. Knockdown of FAK, Pyk2 or both had no effect on VCAM-1 or CCL26 expression, which suggests that FRNK acts independently of FAK and Pyk2 signaling. Finally, we found that IL-4 induces the late expression of endogenous FRNK. In summary, FRNK represents a novel mechanism to negatively regulate IL-4-mediated 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) | 0.000 | 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) | 0.002 | 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".