Assessment of Arf6 Deletion in PLB-985 Differentiated in Neutrophil-Like Cells and in Mouse Neutrophils: Impact on Adhesion and Migration
Bibliographic record
Abstract
Chemoattractant sensing, adhesiveness, and migration are critical events underlying the recruitment of neutrophils (PMNs) to sites of inflammation or infection. Defects in leukocyte adhesion or migration result in immunodeficiency disorders characterized by recurrent infections. In this study, we evaluated the role of Arf6 on PMN adhesion in vitro and on migration to inflammatory sites using PMN-Arf6 conditional knockout (cKO) mice. In PMN-like PLB-985 silenced for Arf6 fMLP-mediated adhesion to the β2 integrin ligands, ICAM-1 and fibrinogen or the β1/β2 integrin ligand fibronectin was significantly reduced. Furthermore, overexpression of wild-type Arf6 promoted basal and fMLP-induced adhesion to immobilized integrin ligands, while overexpression of the dominant-negative Arf6 has the opposite effects. Using the Elane-Cre deleting mouse strains, we report that the level of Arf6 deletion in inflammatory PMNs isolated from the dorsal air pouches was stronger when compared to naïve cells isolated from the bone marrow. In PMN-Arf6 cKO mice, the recruitment of PMNs into the dorsal air pouch injected with LPS or the chemoattractant fMLP was significantly diminished. Impaired cell migration correlated with reduced cell surface expression of CD11a and CD11b in Arf6 cKO PMNs. Our results highlight that Arf6 regulates the activity and possibly the recycling of PMN integrins, and this compromises PMN migration to inflammatory sites.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".