Deficiency in platelet 12-lipoxygenase exacerbates inflammation and disease severity during SARS-CoV-2 infection
Bibliographic record
Abstract
Platelets, known for maintaining blood balance, also participate in antimicrobial defense. Upon severeacute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, platelets become hyperactivated, releasing molecules such as cytokines, granule contents, and bioactive lipids. The key effector biolipids produced by platelets include 12-hydroxyeicosatetraenoic acid (12-HETE) and 12-hydroxyeicosatrienoic acid (12-HETrE), produced by 12-lipoxygenase (12-LOX), and prostaglandins and thromboxane, produced by cyclooxygenase-1. While prostaglandin E2 and thromboxane B2 were previously associated with lung inflammation in severe COVID-19, the role of platelet 12-LOX in SARS-CoV-2 infection remains unclear. Using mice deficient for platelets' 12-LOX, we report that SARS-CoV-2 infection resulted in higher lung inflammation characterized by histopathological tissue analysis, increased leukocyte infiltrates, and cytokine production relative to wild-type mice. In addition, distinct platelet and lung transcriptomic changes, including alterations in NOD-like receptor (NLR) family pyrin domain-containing 1 (NLRP1) inflammasome-related gene expression, were observed. Mass spectrometry lipidomic analysis in 12-LOX-deficient-infected mice revealed significant changes in bioactive lipid content, including reduced levels of 12-HETrE that inversely correlated with disease severity. Finally, platelet 12-LOX deficiency was associated with increased morbidity and lower survival rates relative to wild type (WT) mice. Overall, this study highlights the complex interplay between 12-LOX-related lipid metabolism and inflammatory responses during SARS-CoV-2 infection. The findings provide valuable insights into potential therapeutic targets aimed at mitigating severe outcomes, emphasizing the pivotal role of platelet enzymes in the host response to viral 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".