Paxillin S273 Phosphorylation Regulates Adhesion Dynamics and Cell Migration through a Common Protein Complex with PAK1 and βPIX
Bibliographic record
Abstract
Cell migration is an important biological phenomenon involved in many homeostatic and aberrant physiological processes. Phosphorylation of the focal adhesion adaptor protein, paxillin, on serine 273 (S273) has been implicated as a key regulator of cell migration. Here, it is shown that phosphorylation on paxillin S273 leads to highly migratory cells with small dynamic adhesions. Adhesions at protrusive edges of the cell were more dynamic than adhesions at retracting edges. Temporal image correlation microscopy revealed that these dynamic adhesions undergo rapid binding of paxillin, PAK1 and βPIX. We identified membrane proximal adhesion subdomains in protrusive regions of the cell that show rapid protein binding that is dependent on paxillin S273 phosphorylation, PAK1 kinase activity and phosphatases. These dynamic adhesion subdomains corresponded to regions of the adhesion that also show co-binding of paxillin/PAK1 and paxillin/βPIX complexes. It is likely that parts of individual adhesions are more dynamic while others are less dynamic due to their association with the actin cytoskeleton. Variable adhesion and binding dynamics are regulated via differential paxillin S273 phosphorylation across the cell and within adhesions and are required for regulated cell migration. Dysregulation through phosphomutants, PAK1-KD or βPIX mutants resulted in large stable adhesions, long protein binding times and slow cell migration. Dysregulation through phosphomimics or PAK1-CA led to small dynamic adhesions and rapid cell migration reminiscent of highly migratory cancer cells. Thus, phosphorylation of paxillin S273 is a key regulator of cell migration through recruitment of βPIX and PAK1 to sites of adhesion.
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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".