CaMK‐II promotes focal adhesion turnover and cell motility by inducing tyrosine dephosphorylation of FAK and paxillin
Bibliographic record
Abstract
Transient elevations in Ca2+ have previously been shown to promote focal adhesion disassembly and cell motility through an unknown mechanism. In this study, evidence is provided to show that CaMK-II, a Ca2+/calmodulin dependent protein kinase, influences fibroblast adhesion and motility. TIRF microscopy reveals a dynamic population of CaMK-II at the cell surface in migrating cells. Inhibition of CaMK-II with two mechanistically distinct, membrane permeant inhibitors (KN-93 and myr-AIP) freezes lamellipodial dynamics, accelerates spreading on fibronectin, enlarges paxillin-containing focal adhesions and blocks cell motility. In contrast, constitutively active CaMK-II is not found at the cell surface, reduces cell attachment, eliminates paxillin from focal adhesions and decreases the phospho-tyrosine levels of both FAK and paxillin; all of these events can be reversed with myr-AIP. Thus, both CaMK-II inhibition and constitutive activation block cell motility through over-stabilization or destabilization of focal adhesions, respectively. Coupled with the existence of transient Ca2+ elevations and a dynamic CaMK-II population, these findings provide the first direct evidence that CaMK-II enables cell motility by transiently and locally stimulating tyrosine dephosphorylation of focal adhesion proteins to promote focal adhesion turnover.
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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".