TLR9 activation impairs phagocytosis-induced neutrophil apoptosis and prolongs <i>E. coli</i>-induced acute lung injury
Bibliographic record
Abstract
Abstract Neutrophil dysfunction, resulting in delayed apoptosis and inefficient bacterial clearance, is a characteristic feature of severe pathologies, including sepsis and cystic fibrosis. Human neutrophils detect and respond to bacterial DNA (CpG DNA) through TLR9. We investigated the impact of CpG DNA on phagocytosis, phagocytosis-induced neutrophil apoptosis and clearance of E coli. Culture of human neutrophils with CpG DNA (0.1–3.2 μg/ml) resulted in decreased phagocytosis of opsonized E. coli or yeast. CpG DNA upregulated C3R (CD11b) expression, downregulated C5aR (CD88) expression and induced release of neutrophil elastase. C5aR cleavage was prevented by a specific neutrophil elastase inhibitor and the broad-spectrum serine protease inhibitor PMSF. Consistently, CpG DNA reduced phagocytosis-induced NADPH oxidase-mediated activation of caspase8 and caspase-3. These actions of CpG DNA were blocked by the telomere-derived TLR9 inhibitory oligonucleotide 5′-TTT AGG GTT AGG GTT AGG G-3′. In mice, CpG DNA impaired pulmonary clearance of E coli, suppressed neutrophil apoptosis and delayed resolution of lung injury evoked by intratracheal instillation of live E. coli. Genetic deletion of TLR9 rendered mice unresponsive to CpG DNA. These results identify a novel mechanism, neutrophil elastase-mediated inactivation of C5aR-mediated phagocytosis, by which CpG DNA could contribute to neutrophil dysfunction and prolongation of tissue injury. Our findings also suggest a therapeutic potential for TLR9 antagonists or neutrophil elastase inhibitors for enhancing clearance of bacterial infections in an environment where bacterial DNA is abundantly present.
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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 | 0.001 | 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".