Neutrophil adhesion under flow conditions induces zyxin nuclear translocation in endothelial cells
Bibliographic record
Abstract
High shear stress causes zyxin translocation from focal adhesions (FA) to stress fibers. We previously showed that leukocyte adhesion to stimulated endothelial cells (EC) activated mechanosensitive signaling pathways in ECs at low shear stress, which in turn modulated leukocyte transmigration. In this study, we examined the remodeling of the FA protein zyxin following neutrophil (PMN) adhesion under shear stress and determined if downregulating this protein altered PMN transmigration. Zyxin localized to FAs and stress fibers under static conditions. Buffer perfusion across TNFα‐stimulated ECs did not change zyxin localization at lower shear stresses, but resulted in modest nuclear translocation of zyxin at a shear stress of 4 dyne/cm 2 . In contrast, PMN recruitment to TNFα‐stimulated ECs resulted in a loss of zyxin from FAs and translocation of zyxin to the nucleus at shear stresses as low as 1 dyne/cm 2 . Translocation specifically occured in ECs with adherent PMNs and did not occur in ECs distant from regions containing adherent PMNs. Perfusion of fixed PMNs, which roll but to do not adhere or transmigrate, did not induce changes in zyxin localization. Finally, zyxin downregulation in ECs attenuated PMN transmigration. These data suggest that PMN trafficking across stimulated ECs leads to focal adhesion remodeling and that EC zyxin nuclear translocation during this process may play a potential role in gene transcription. Funding: Canadian Institutes of Health Research, Alberta Heritage Foundation for Medical Research.
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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".