A non-canonical role for Rgnef (p190RhoGEF) in promoting integrin-stimulated focal adhesion kinase activation
Bibliographic record
Abstract
Rgnef (also known as p190RhoGEF or ARHGEF28) is a Rho guanine-nucleotide-exchange factor (GEF) that binds focal adhesion kinase (FAK). FAK is recruited to adhesions and activated by integrin receptors binding to matrix proteins, such as fibronectin (FN). Canonical models place Rgnef downstream of integrin-FAK signaling in regulating Rho GTPase activity and cell movement. Herein, we establish a new, upstream role for Rgnef in enhancing FAK localization to early peripheral adhesions and promoting FAK activation upon FN binding. Rgnef-null mouse embryo fibroblasts (MEFs) exhibit defects in adhesion formation, levels of FAK phosphotyrosine (pY)-397 and FAK localization to peripheral adhesions upon re-plating on FN. Rgnef re-expression rescues these defects, but requires Rgnef-FAK binding. A mutation in the Rgnef pleckstrin homology (PH) domain inhibits adhesion formation, FAK localization, and FAK-Y397 and paxillin-Y118 phosphorylation without disrupting the Rgnef-FAK interaction. A GEF-inactive Rgnef mutant rescues FAK-Y397 phosphorylation and early adhesion localization, but not paxillin-Y118 phosphorylation. This suggests that, downstream of FN binding, paxillin-pY118 requires Rgnef GEF activity through a mechanism distinct from adhesion formation and FAK activation. These results support a scaffolding role for Rgnef in FAK localization and activation at early adhesions in a PH-domain-dependent but GEF-activity-independent manner.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".