Nuclear-localized focal adhesion kinase regulates inflammatory VCAM-1 expression
Bibliographic record
Abstract
Vascular cell adhesion molecule-1 (VCAM-1) plays important roles in development and inflammation. Tumor necrosis factor-α (TNF-α) and focal adhesion kinase (FAK) are key regulators of inflammatory and integrin-matrix signaling, respectively. Integrin costimulatory signals modulate inflammatory gene expression, but the important control points between these pathways remain unresolved. We report that pharmacological FAK inhibition prevented TNF-α-induced VCAM-1 expression within heart vessel-associated endothelial cells in vivo, and genetic or pharmacological FAK inhibition blocked VCAM-1 expression during development. FAK signaling facilitated TNF-α-induced, mitogen-activated protein kinase activation, and, surprisingly, FAK inhibition resulted in the loss of the GATA4 transcription factor required for TNF-α-induced VCAM-1 production. FAK inhibition also triggered FAK nuclear localization. In the nucleus, the FAK-FERM (band 4.1, ezrin, radixin, moesin homology) domain bound directly to GATA4 and enhanced its CHIP (C terminus of Hsp70-interacting protein) E3 ligase-dependent polyubiquitination and degradation. These studies reveal new developmental and anti-inflammatory roles for kinase-inhibited FAK in limiting VCAM-1 production via nuclear localization and promotion of GATA4 turnover.
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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".