Inflammasome activation in neutrophils of patients with severe COVID-19
Bibliographic record
Abstract
Infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) engages the inflammasome in monocytes and macrophages and leads to the cytokine storm in COVID-19. Neutrophils, the most abundant leukocytes, release neutrophil extracellular traps (NETs), which have been implicated in the pathogenesis of COVID-19. Our recent study shows that activation of the NLRP3 inflammasome is important for NET release in sterile inflammation. However, the role of neutrophil inflammasome formation in human disease is unknown. We hypothesized that SARS-CoV-2 infection may induce inflammasome activation in neutrophils. We also aimed to assess the localization of inflammasome formation (ie, apoptosis-associated speck-like protein containing a CARD [ASC] speck assembly) and timing relative to NETosis in stimulated neutrophils by real-time video microscopy. Neutrophils isolated from severe COVID-19 patients demonstrated that ∼2% of neutrophils in both the peripheral blood and tracheal aspirates presented ASC speck. ASC speck was observed in neutrophils with an intact poly-lobulated nucleus, suggesting early formation during neutrophil activation. Additionally, 40% of nuclei were positive for citrullinated histone H3, and there was a significant correlation between speck formation and nuclear histone citrullination. Time-lapse microscopy in lipopolysaccharide -stimulated neutrophils from fluorescent ASC reporter mice showed that ASC speck formed transiently and at the microtubule organizing center long before NET release. Our study shows that ASC speck is present in neutrophils from COVID-19 patients with respiratory failure and that it forms early in NETosis. Our findings suggest that inhibition of neutrophil inflammasomes may be beneficial in COVID-19.
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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 | 0.001 |
| 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 | 0.000 |
| 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".