Afucosylated IgG Promote Thrombosis in Mouse Injected with SARS-CoV-2 Spike Expressing Megakaryocytes
Bibliographic record
Abstract
Despite the prevalence of fucosylated IgG in plasma, specific IgGs with low core fucosylation sporadically emerge in response to virus infections and blood cell alloantigens. This low fucosylation of IgG is implicated in the pathogenesis of SARS-CoV-2 and dengue infections. In COVID-19, the presence of IgGs with low core fucosylation (afucosylated IgGs) targeting spike protein predicts disease progression to a severe form and actively mediates this progression. This study reveals that SARS-CoV-2 infection of megakaryocytes promotes the generation of pathogenic afucosylated anti-spike IgGs, leading to outcomes, such as pulmonary vascular thrombosis, acute lung injury, and mortality in FcγRIIa-transgenic mice. Platelets from mice injected with virus-infected human megakaryocytes express significant activation biomarkers, indicating a direct link between the immune response and platelet activation. Mice injected with virus-infected human megakaryocytes demonstrate an elevated rate of thrombus formation induced by FeCl3 (4%) and a reduction in bleeding time, emphasizing the intricate interplay of viral infection, immune response, and hemostatic complications. Treatment with inhibitors targeting FcγRIIa, serotonin, or complement anaphylatoxins of mice injected with spike-expressing MKs successfully prevents observed platelet activation, thrombus formation, and bleeding abnormalities, offering potential therapeutic strategies for managing severe outcomes associated with afucosylated IgGs in COVID-19 and related disorders.
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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".