Endothelium-derived Toll-like receptor-4 is the key molecule in LPS-induced neutrophil sequestration into lungs
Bibliographic record
Abstract
The rapid and selective accumulation of neutrophils into the lungs is thought to underlie the pulmonary failure that leads to sepsis-related death. In this study we investigated whether neutrophil TLR4 is important in LPS-induced pulmonary neutrophil recruitment by creating chimeric mice (transferring bone marrow between TLR4(+/+) and TLR4(-/-) mice). In TLR4(+/+) mice receiving TLR4(-/-) bone marrow, 6 weeks after transplant TLR4 was absent in all circulating leukocytes as well as in resident macrophages (these mice were termed LeukocyteTLR4(-/-)), and these cells were completely nonresponsive to LPS. In TLR4(-/-) mice receiving TLR4(+/+) bone marrow, endothelial cells but not leukocytes were deficient in TLR4 (EndotheliumTLR4(-/-)). Surprisingly, systemic LPS (0.5 mg/kg) induced a dramatic increase in neutrophil sequestration into the lungs of LeukocyteTLR4(-/-) mice over the first 4 hours. Concomitantly, numbers of circulating leukocytes decreased by 90%. By contrast, EndotheliumTLR4(-/-) mice showed very little increase in neutrophil sequestration in the lungs, suggesting that endothelium rather than leukocyte TLR4 was important. Intravital microscopy of peripheral microcirculation in the cremaster muscle revealed about 30-fold more leukocyte-endothelial cell interactions in LPS-treated EndotheliumTLR4(-/-) mice than in LPS-treated LeukocyteTLR4(-/-) mice. This is consistent with less sequestration of leukocytes into the lungs of EndotheliumTLR4(-/-) mice. In conclusion, our data challenge the view that LPS directly activates neutrophils to trap in lungs and suggest a far more important role than previously appreciated for the endothelial cells.
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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 | 0.001 |
| 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".