Potent neutralizing monoclonal antibodies against Ebola virus isolated from vaccinated donors
Bibliographic record
Abstract
Ebola virus (EBOV) can cause severe hemorrhagic fever in humans, and no approved treatment is currently available. Although several antibodies have achieved good protection in animal models, the potential emerging isolates of ebolavirus and the unknown effects of experimental antibodies in humans underscore the need to develop additional antibodies to address the threat of Ebola. Here, we isolated a series of memory B cell-derived monoclonal antibodies from healthy Chinese adults vaccinated with Ad5-EBOV. These antibodies were encoded by diverse germline genes and had high levels of somatic hypermutation. Most antibodies were cross-reactive and could bind at least two ebolavirus glycoproteins (GPs). Seven neutralizing antibodies were identified using HIV-EBOV GP-Luc pseudovirus, and they effectively neutralized authentic EBOV. In particular, monoclonal antibody 2G1 exhibited potent cross-neutralization against HIV-EBOV/SUDV/BDBV GP-Luc bearing different ebolavirus GPs. We used truncated GPs, competition assays, and software prediction to analyze seven neutralizing antibodies, which bound four different epitopes on GP. Importantly, three of these antibodies provided complete protection in mice when administered one day post-infection. Our study expands the list of candidate antibodies and the options for successfully treating ebolavirus infection.
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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".