Abl Kinases Regulate HGF/Met Signaling Required for Epithelial Cell Scattering, Tubulogenesis and Motility
Bibliographic record
Abstract
Tight regulation of receptor tyrosine kinases (RTKs) is crucial for normal development and homeostasis. Dysregulation of RTKs signaling is associated with diverse pathological conditions including cancer. The Met RTK is the receptor for hepatocyte growth factor (HGF) and is dysregulated in numerous human tumors. Here we show that Abl family of non-receptor tyrosine kinases, comprised of Abl (ABL1) and Arg (ABL2), are activated downstream of the Met receptor, and that inhibition of Abl kinases dramatically suppresses HGF-induced cell scattering and tubulogenesis. We uncover a critical role for Abl kinases in the regulation of HGF/Met-dependent RhoA activation and RhoA-mediated actomyosin contractility and actin cytoskeleton remodeling in epithelial cells. Moreover, treatment of breast cancer cells with Abl inhibitors markedly decreases Met-driven cell migration and invasion. Notably, expression of a transforming mutant of the Met receptor in the mouse mammary epithelium results in hyper-activation of both Abl and Arg kinases. Together these data demonstrate that Abl kinases link Met activation to Rho signaling and Abl kinases are required for Met-dependent cell scattering, tubulogenesis, migration, and invasion. Thus, inhibition of Abl kinases might be exploited for the treatment of cancers driven by hyperactivation of HGF/Met signaling.
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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".