Lack of S100A8 impairs lung protective immunity against Streptococcus pneumoniae in mice
Bibliographic record
Abstract
We recently found that the alarmin S100A9 is a key regulator of lung antibacterial immunity against S. pneumoniae in mice and a potential biomarker to discriminate between bacterial and viral pneumonia in humans. However, the specific role of S100A8 in pneumococcal pneumonia is understudied. Here we show that human S100A8 protein levels were significantly increased in BAL fluids of patients with bacterial compared to viral pneumonia. Similarly, WT mice responded with coordinated S100A8 and S100A9 protein release after challenge with S. pneumoniae. Opposed to WT, S100A8 KO mice responded with significantly increased bacterial loads in BAL fluid, pleural lavage fluid (PLF) and lung tissue after challenge with S. pneumoniae. Accordingly, histopathological examination of lung tissue sections of S. pneumoniae-infected S100A8 KO mice revealed severe purulent bronchopneumonia with interstitial and alveolar edema and intravascular coagulation contributing to early mortality in the KO mice. Mechanistically, S. pneumoniae-infected S100A8 KO mice showed significantly increased levels of neutrophil elastase (NE) in BALF and lung tissue accompanied by substantial degradation of antibacterial opsonins SP-A and SP-D. Incubation of WT BALF with exogenous NE confirmed NE dependency of SP-D degradation in vitro, which was blocked in the presence of the NE-specific inhibitor sivelestat. Collectively, selective deletion of S100A8 severely disturbs lung protective immunity against S. pneumoniae in mice. At the same time, S100A8 protein levels in clinical samples may help to distinguish between patients with bacterial or 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".