Neuraminidase is a host‐directed approach to regulate neutrophil responses in sepsis and COVID‐19
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neutrophil overstimulation plays a crucial role in tissue damage during severe infections. Because pathogen-derived neuraminidase (NEU) stimulates neutrophils, we investigated whether host NEU can be targeted to regulate the neutrophil dysregulation observed in severe infections. EXPERIMENTAL APPROACH: The effects of NEU inhibitors on lipopolysaccharide (LPS)-stimulated neutrophils from healthy donors or COVID-19 patients were determined by evaluating the shedding of surface sialic acids, cell activation, and reactive oxygen species (ROS) production. Re-analysis of single-cell RNA sequencing of respiratory tract samples from COVID-19 patients also was carried out. The effects of oseltamivir on sepsis and betacoronavirus-induced acute lung injury were evaluated in murine models. KEY RESULTS: Oseltamivir and zanamivir constrained host NEU activity, surface sialic acid release, cell activation, and ROS production by LPS-activated human neutrophils. Mechanistically, LPS increased the interaction of NEU1 with matrix metalloproteinase 9 (MMP-9). Inhibition of MMP-9 prevented LPS-induced NEU activity and neutrophil response. In vivo, treatment with oseltamivir fine-tuned neutrophil migration and improved infection control as well as host survival in peritonitis and pneumonia sepsis. NEU1 also is highly expressed in neutrophils from COVID-19 patients, and treatment of whole-blood samples from these patients with either oseltamivir or zanamivir reduced neutrophil overactivation. Oseltamivir treatment of intranasally infected mice with the mouse hepatitis coronavirus 3 (MHV-3) decreased lung neutrophil infiltration, viral load, and tissue damage. CONCLUSION AND IMPLICATIONS: These findings suggest that interplay of NEU1-MMP-9 induces neutrophil overactivation. In vivo, NEU may serve as a host-directed target to dampen neutrophil dysfunction during severe infections.
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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".