Neuraminidase inhibitors rewire neutrophil function <i>in vivo</i> in murine sepsis and <i>ex vivo</i> in COVID-19
Bibliographic record
Abstract
ABSTRACT Neutrophil overstimulation plays a crucial role in tissue damage during severe infections. Neuraminidase (NEU)-mediated cleavage of surface sialic acid has been demonstrated to regulate leukocyte responses. Here, we report that antiviral NEU inhibitors constrain host NEU activity, surface sialic acid release, ROS production, and NETs released by microbial-activated human neutrophils. In vivo , treatment with Oseltamivir results in infection control and host survival in peritonitis and pneumonia models of sepsis. Single-cell RNA sequencing re-analysis of publicly data sets of respiratory tract samples from critical COVID-19 patients revealed an overexpression of NEU1 in infiltrated neutrophils. Moreover, Oseltamivir or Zanamivir treatment of whole blood cells from severe COVID-19 patients reduces host NEU-mediated shedding of cell surface sialic acid and neutrophil overactivation. These findings suggest that neuraminidase inhibitors can serve as host-directed interventions to dampen neutrophil dysfunction in severe infections. At a Glance In a severe systemic inflammatory response, such as sepsis and COVID-19, neutrophils play a central role in organ damage. Thus, finding new ways to inhibit the exacerbated response of these cells is greatly needed. Here, we demonstrate that in vitro treatment of whole blood with the viral neuraminidase inhibitors Oseltamivir or Zanamivir, inhibits the activity of human neuraminidases as well as the exacerbated response of neutrophils. In experimental models of severe sepsis, oseltamivir decreased neutrophil activation and increased the survival rate of mice. Moreover, Oseltamivir or Zanamivir ex vivo treatment of whole blood cells from severe COVID-19 patients rewire neutrophil function.
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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 | 0.000 |
| 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".