A Fc-enhanced NTD-binding non-neutralizing antibody delays virus spread and synergizes with a nAb to protect mice from lethal SARS-CoV-2 infection
Bibliographic record
Abstract
Emerging evidence indicates that both neutralizing and Fc-mediated effector functions of antibodies contribute to protection against SARS-CoV-2. It is unclear whether Fc-effector functions alone can protect against SARS-CoV-2. Here, we isolated CV3-13, a non-neutralizing antibody, from a convalescent individual with potent Fc-mediated effector functions. The cryoelectron microscopy structure of CV3-13 in complex with the SARS-CoV-2 spike reveals that the antibody binds from a distinct angle of approach to an N-terminal domain (NTD) epitope that only partially overlaps with the NTD supersite recognized by neutralizing antibodies. CV3-13 does not alter the replication dynamics of SARS-CoV-2 in K18-hACE2 mice, but its Fc-enhanced version significantly delays virus spread, neuroinvasion, and death in prophylactic settings. Interestingly, the combination of Fc-enhanced non-neutralizing CV3-13 with Fc-compromised neutralizing CV3-25 completely protects mice from lethal SARS-CoV-2 infection. Altogether, our data demonstrate that efficient Fc-mediated effector functions can potently contribute to the in vivo efficacy of anti-SARS-CoV-2 antibodies.
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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".