Activation of Cdc42, Rac, PAK, and Rho-Kinase in Response to Hepatocyte Growth Factor Differentially Regulates Epithelial Cell Colony Spreading and Dissociation
Bibliographic record
Abstract
Hepatocyte growth factor (HGF), the ligand for the Met receptor tyrosine kinase, is a potent modulator of epithelial-mesenchymal transition and dispersal of epithelial cells, processes that play crucial roles in tumor development, invasion, and metastasis. Little is known about the Met-dependent proximal signals that regulate these events. We show that HGF stimulation of epithelial cells leads to activation of the Rho GTPases, Cdc42 and Rac, concomitant with the formation of filopodia and lamellipodia. Notably, HGF-dependent activation of Rac but not Cdc42 is dependent on phosphatidylinositol 3-kinase. Moreover, HGF-induced lamellipodia formation and cell spreading require phosphatidylinositol 3-kinase and are inhibited by dominant negative Cdc42 or Rac. HGF induces activation of the Cdc42/Rac-regulated p21-activated kinase (PAK) and c-Jun N-terminal kinase, and translocation of Rac, PAK, and Rho-dependent Rho-kinase to membrane ruffles. Use of dominant negative and activated mutants reveals an essential role for PAK but not Rho-kinase in HGF-induced epithelial cell spreading, whereas Rho-kinase activity is required for the formation of focal adhesions and stress fibers in response to HGF. We conclude that PAK and Rho-kinase play opposing roles in epithelial-mesenchymal transition induced by HGF, and provide new insight regarding the role of Cdc42 in these events.
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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".