P-selectin Cross-links PSGL-1 and Enhances Neutrophil Adhesion to Fibrinogen and ICAM-1 in a Src Kinase-Dependent, but GPCR-Independent Mechanism
Bibliographic record
Abstract
Endothelial and platelet P-selectin (CD62P) and leukocyte integrin alpha(M)beta(2) (CD11bCD18, Mac-1) are cell adhesion molecules essential for host defense and innate immunity. Upon inflammatory challenges, P-selectin binds to PSGL-1 (P-selectin glycoprotein ligand-1, CD162) to mediate neutrophil rolling, during which integrins become activated by extracellular stimuli for their firm adhesion in a G-protein coupled receptor (GPCR)-dependent mechanism. Here we show that cross-linking of PSGL-1 by dimeric or multimeric forms of platelet P-selectin, P-selectin receptor-globulin, anti-PSGL-1 mAb and its F(ab')2 induced adhesion of human neutrophils to fibrinogen (Fg) and intercellular cell adhesion molecule-1 (ICAM-1, CD54) and triggered a moderate clustering of alpha(M)beta(2), but monomeric forms of soluble P-selectin and anti-PSGL-1 Fab did not. Interestingly, P-selectin did not induce a detectable interleukine-8 (IL-8) secretion (<0.1 ng/ml) in 30 minutes, whereas a high concentration of IL-8 (>50 ng/ml) was required to increase neutrophil adhesion to Fg. P-selectin-induced neutrophil adhesion was significantly inhibited by PP2 (a Src kinase inhibitor), but not by pertussis toxin (PTX; a GPCR inhibitor). Activated platelets also increased neutrophil binding to fibrinogen and triggered tyrosine phosphorylation of cellular proteins. Our results indicate that P-selectin-induced integrin activation (Src kinase-dependent) is distinct from that elicited by cytokines, chemokines, chemoattractants (GPCR-dependent), suggesting that these two signal transduction pathways may cooperate for maximal activation of leukocyte integrins.
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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".