Shear-Dependent Capping of L-Selectin and P-Selectin Glycoprotein Ligand 1 by E-Selectin Signals Activation of High-Avidity β2-Integrin on Neutrophils
Bibliographic record
Abstract
Two adhesive events critical to efficient recruitment of neutrophils at vascular sites of inflammation are up-regulation of endothelial selectins that bind sialyl Lewis(x) ligands and activation of beta(2)-integrins that support neutrophil arrest by binding ICAM-1. We have previously reported that neutrophils rolling on E-selectin are sufficient for signaling cell arrest through beta(2)-integrin binding of ICAM-1 in a process dependent upon ligation of L-selectin and P-selectin glycoprotein ligand 1 (PSGL-1). Unresolved are the spatial and temporal events that occur as E-selectin binds to human neutrophils and dynamically signals the transition from neutrophil rolling to arrest. Here we show that binding of E-selectin to sialyl Lewis(x) on L-selectin and PSGL-1 drives their colocalization into membrane caps at the trailing edge of neutrophils rolling on HUVECs and on an L-cell monolayer coexpressing E-selectin and ICAM-1. Likewise, binding of recombinant E-selectin to PMNs in suspension also elicited coclustering of L-selectin and PSGL-1 that was signaled via mitogen-activated protein kinase. Binding of recombinant E-selectin signaled activation of beta(2)-integrin to high-avidity clusters and elicited efficient neutrophil capture of beta(2)-integrin ligands in shear flow. Inhibition of p38 and p42/44 mitogen-activated protein kinase blocked the cocapping of L-selectin and PSGL-1 and the subsequent clustering of high-affinity beta(2)-integrin. Taken together, the data suggest that E-selectin is unique among selectins in its capacity for clustering sialylated ligands and transducing signals leading to neutrophil arrest in shear flow.
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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) | 0.002 | 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".