Focal adhesion kinase (FAK)‐related non‐kinase (FRNK) negatively regulates eosinophil recruitment (146.4)
Bibliographic record
Abstract
Focal adhesion kinase (FAK)‐related non‐kinase (FRNK) is an independently expressed protein containing the C‐terminal domain of FAK. A recent study showed that FRNK negatively regulates lung fibrosis. In this study we asked if FRNK could negatively regulate human eosinophil recruitment in response to IL‐4. Exogenous expression of FRNK blocked >90% of eosinophil firm adhesion and transmigration to IL‐4‐stimulated endothelial cells (HUVEC) under flow conditions. FRNK blocked eosinophil recruitment by preventing the transcription and translation of VCAM‐1 and eotaxin‐3 (CCL26), two proteins we previously showed were critical for eosinophil recruitment. GATA6 has been shown to regulate VCAM‐1 in response to TNF, but its role in IL‐4‐mediated VCAM‐1 expression is not known. We found the IL‐4 increased GATA6 expression and down‐regulating GATA6 with siRNA blocked VCAM‐1 expression and function. Expressing FRNK in HUVEC prevented GATA6 upregulation by IL‐4 suggesting that FRNK blocks VCAM‐1 expression through its actions on GATA6. FRNK can act directly by binding to target molecules or indirectly by displacing FAK at focal adhesions and blocking FAK’s kinase activity. To determine if FRNK was acting through FAK, we down‐regulated FAK with siRNA and found that knocking down FAK had no effect on GATA6, VCAM‐1 or CCL26 expression. These data suggest that FRNK acts independent of FAK to negatively regulate eosinophil recruitment. Grant Funding Source : Supported by the Canadian Institutes of Health Research
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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 | 0.001 |
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".