Cooperative involvement of zipper-interacting protein kinase (ZIPK) and the dual-specificity cell-division cycle 14A phosphatase (CDC14A) in vascular smooth muscle cell migration
Bibliographic record
Abstract
Zipper-interacting protein kinase (ZIPK) is a Ser/Thr protein kinase with regulatory involvement in vascular smooth muscle cell (VSMC) actin polymerization and focal adhesion assembly dynamics. ZIPK silencing can induce cytoskeletal remodeling with disassembly of actin stress fiber networks and coincident loss of focal adhesion kinase (FAK)-pY397 phosphorylation. The link between ZIPK inhibition and FAK phosphorylation is unknown, and critical interactor(s) and regulator(s) are not yet defined. In this study, we further analyzed the ZIPK-FAK relationship in VSMCs. The application of HS38, a selective ZIPK inhibitor, to coronary artery vascular smooth muscle cells (CASMCs) suppressed cell migration, myosin light chain phosphorylation (pT18&pS19) and FAK-pY397 phosphorylation as well. This was associated with the translocation of cytoplasmic FAK to the nucleus. ZIPK inhibition with HS38 was consistently found to suppress the activation of FAK and attenuate the phosphorylation of other focal adhesion protein components (i.e., pCas130, paxillin, ERK). In addition, our study showed a decrease in human cell-division cycle 14A phosphatase (CDC14A) levels with ZIPK-siRNA treatment and increased CDC14A with transient transfection of ZIPK. Proximity ligation assays (PLA) revealed CDC14A localized with ZIPK and FAK. Silencing CDC14A showed an increase of FAK-pY397 phosphorylation. Ultimately, the data presented herein strongly support a regulatory mechanism of FAK in CASMCs by a ZIPK-CDC14A partnership; ZIPK may act as a key signal integrator to control CDC14A and FAK during VSMC migration.
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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".