Lack of S100A8 Impairs Lung-Protective Immunity Against <i>Streptococcus pneumoniae</i>
Bibliographic record
Abstract
BACKGROUND: We recently showed that S100A9 is indispensable for lung antibacterial immunity, making it crucial for the survival of pneumococcal pneumonia. However, the role of S100A8 in lung antibacterial immunity is ill-defined. METHODS: S100A8 levels in the bronchoalveolar lavage fluid (BALF) of patients with pneumonia were quantified by enzyme-linked immunosorbent assay. Wild type and S100A8 knockout mice were orotracheally infected with Streptococcus pneumoniae, and bacterial clearance, disease progression, lung histopathology, and leukocyte recruitment were analyzed at defined time points. RESULTS: S100A8 protein levels were particularly increased in the BALF of patients with bacterial pneumonia as compared with viral pneumonia. Similarly, wild type mice responded with S100A8 and S100A9 protein release upon pneumococcal challenge. However, S100A8 deficiency led to decreased S100A9 levels and significantly increased bacterial loads in the lungs of S pneumoniae-challenged mice. S pneumoniae-infected S100A8 knockout mice developed a severe neutrophil-dominated purulent bronchopneumonia with interstitial and alveolar edema and intravascular coagulation, leading to early mortality. Mechanistically, S100A8 deficiency resulted in neutrophil elastase (NE)-dependent degradation of surfactant proteins A and D in the lungs of mice. Incubation of wild type BALF with recombinant NE confirmed NE-dependent surfactant protein D degradation in vitro, which could be blocked by the NE-specific inhibitor sivelestat. Therapy with recombinant S100A8/A9 protein rescued S100A8 knockout mice from fatal pneumococcal pneumonia. CONCLUSIONS: Deletion of S100A8 disturbs lung-protective immunity against S pneumoniae in mice. At the same time, analysis of the S100A8/A9 protein complex in clinical samples may help to distinguish patients with bacterial vs viral pneumonia.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".