Transforming growth factor β-induced epithelial to mesenchymal transition requires the Ste20-like kinase SLK independently of its catalytic activity
Bibliographic record
Abstract
// Jillian Conway 1, 2 , Khalid N. Al-Zahrani 1, 2 , Benjamin R. Pryce 1, 2 , John Abou-Hamad 1,2 and Luc A. Sabourin 1, 2 1 University of Ottawa, Department of Cellular and Molecular Medicine, Ottawa, Ontario, K1H8M5, Canada 2 Ottawa Hospital Research Institute, Cancer Therapeutics, Ottawa, Ontario, K1H8L6, Canada Correspondence to: Luc A. Sabourin, email: lsabourin@ohri.ca Keywords: SLK; Stk2; EMT; TGFβ; invasion Received: June 06, 2017 Accepted: August 26, 2017 Published: October 19, 2017 ABSTRACT Invasion can be stimulated in vitro using the soluble ligand transforming growth factor-β (TGFβ) to induce a process called epithelial-to-mesenchymal transition (EMT) characterized by cell-cell junction breakdown and an invasive phenotype. We have previously demonstrated a role for Ste20-like kinase SLK cell migration and invasion. Here we show that SLK depletion in NMuMG mammary epithelial cells significantly impairs their TGFβ-induced migration and invasion. Immunofluorescence studies show that a fraction of SLK localizes to E-cadherin-positive adherens junction and that SLK impairs the breakdown of cell-cell contacts. We find that SLK-depleted cultures express significantly lower levels of vimentin protein as well as Snai1 and E-cadherin mRNA levels following TGF-β treatment. Surprisingly, our data show that SLK depletion does not affect the activation and nuclear translocation of Smad3. Furthermore, we show that expression of a dominant negative kinase does not impair tight junction breakdown and rescues Snai1 mRNA expression levels. Together these data suggest that SLK plays a novel role in TGFβ-induced EMT, independent of Smads, in a kinase 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".