Leukocyte-specific protein 1 regulates neutrophil recruitment in acute lung inflammation
Bibliographic record
Abstract
The mechanisms of excessive migration of activated neutrophils into inflamed lungs, credited with tissue damage, are not fully understood. We explored the hitherto unknown expression of leukocyte-specific protein 1 (LSP1) in human and mouse lungs and neutrophils and examined its role in neutrophil migration in acute lung inflammation. Autopsied septic human lungs showed increased LSP1 labeling in epithelium, endothelium, and leukocytes, including in their nuclei compared with normal lungs. We induced acute lung inflammation through intranasal administration of E. coli lipopolysaccharide (LPS) (80 μg) in LSP1-deficient (Lsp1(-/-)) and wild-type (WT) 129/SvJ mice. Immunocytochemistry and Western blots showed increased expression of LSP1 and phosphorylated LSP1 in lungs of LPS-treated WT mice. Histology showed more congestion, inflammation, and Gr-1(+) neutrophils in lung of WT mice than Lsp1(-/-) mice. LPS-treated WT mice had significantly more neutrophils in bronchoalveolar lavage (BAL) and myeloperoxidase levels in lungs compared with Lsp1(-/-) mice. However, there were no differences in lung tissue and BAL concentrations of keratinocyte-derived chemokine, monocyte chemoattractant protein-1, macrophage inflammatory protein-1α and -1β, vascular permeability, and phosphorylated p38 MAPK between LPS-treated WT and Lsp1(-/-) mice, whereas TNF-α concentration was higher in BAL fluid from LPS-treated WT. Immunoelectron microscopy showed increased LSP1 in the nuclei of LPS-treated neutrophils. We also found increased levels of phosphorylated LSP1 associated with plasma membrane, nucleus, and cytosol at various times after LPS treatment of murine bone marrow-derived neutrophils, suggesting its role in modulation of neutrophil cytoskeleton and the membrane. These data collectively show increased expression of LSP1 in inflamed mouse and human lungs and its role in neutrophil recruitment and lung inflammation.
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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 | 0.001 |
| 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".