Live Imaging of SARS-CoV-2 Infection in Mice Reveals Neutralizing Antibodies Require Fc Function for Optimal Efficacy
Bibliographic record
Abstract
SUMMARY Neutralizing antibodies (NAbs) are effective in treating COVID-19 but the mechanism of immune protection is not fully understood. Here, we applied live bioluminescence imaging (BLI) to monitor the real-time effects of NAb treatment in prophylaxis and therapy of K18-hACE2 mice intranasally infected with SARS-CoV-2-nanoluciferase. We could visualize virus spread sequentially from the nasal cavity to the lungs and thereafter systemically to various organs including the brain, which culminated in death. Highly potent NAbs from a COVID-19 convalescent subject prevented, and also effectively resolved, established infection when administered within three days. In addition to direct Fab-mediated neutralization, Fc effector interactions of NAbs with monocytes, neutrophils and natural killer cells were required to effectively dampen inflammatory responses and limit immunopathology. Our study highlights that both Fab and Fc effector functions of NAbs are essential for optimal in vivo efficacy against SARS-CoV-2.
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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) | 0.002 | 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".