Toll‐like receptor ligands induce polymorphonuclear leukocyte migration: key roles for leukotriene B <sub>4</sub> and platelet‐activating factor
Bibliographic record
Abstract
Activation of toll-like receptors (TLRs) and polymorphonuclear leukocyte (PMN) accumulation at infection sites are critical events of host defense. The involvement of leukotriene (LT) B(4) and platelet-activating factor (PAF) in TLR ligand-induced activation of inflammatory cell functions is essentially unknown. Using an in vitro model of human PMN migration through human endothelial cell monolayers, we demonstrate that prototypic ligands of TLR1/2, 2/6, 3, 4, 5, and 7/8 promote PMN migration, an effect markedly inhibited by 3 LTB(4) receptor antagonists (70-80% inhibition at 100 nM compared to vehicle-treated cells), 3 PAF receptor antagonists (20-50% inhibition at 10 nM), 3 LT biosynthesis inhibitors (75-85% inhibition at 100 nM), and 1 cytosolic phospholipase A(2)alpha (cPLA(2)alpha) inhibitor (90% inhibition at 1 microM). Accordingly, selected TLR ligands caused Ser-505-phosphorylation of cPLA(2)alpha and measurable LTB(4) and PAF biosynthesis in the transmigration assay. As negative controls, interleukin-8- and formyl-methionyl-leucyl-phenylalanine-elicited migration in vitro was not inhibited either by an LTB(4) receptor antagonist or by the cPLA(2)alpha inhibitor. Finally, LTB(4) and PAF receptor antagonists inhibited (up to approximately 65% at optimal doses) TLR ligand-induced PMN infiltration in the mouse air-pouch model. These studies unravel the critical involvement of de novo LTB(4) and PAF biosynthesis in PMN migration elicited by TLR ligands.
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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 | 0.000 | 0.000 |
| 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".