Development of neutralizing monoclonal antibodies (mAbs) against marburg virus
Bibliographic record
Abstract
Marburg virus (MARV) causes Marburg virus disease (MVD) in humans and non-human primates. Outbreaks of MVD are intermittent and have mostly happened in Central Africa. The mortality rates of these outbreaks are normally more than 50%. It is generally considered that MARV is not a major public health concern. The biggest outbreak of MVD happened in Angola in 2005, which caused 374 cases and 329 deaths. However, the outbreak of Ebola virus (EBOV) between 2013 and 2016 highlighted the need for more treatment and vaccine candidates against the unpredictable outbreaks of MVD in the future. Neutralizing antibodies are thought to be one of the best treatment candidates against filoviruses. Nevertheless, neutralizing antibodies against MARV have not yet been generated from vaccinated animals, which is different from anti-EBOV neutralizing antibodies. All the neutralizing anti-MARV mAbs are derived from human survivors. In this case, the differences between MARV and EBOV viral antigens could be the key. In this study, I focus on the differences of the mucin-like domain (MLD) on the glycoprotein (GP) between MARV and EBOV. The efficacy of Vesicular Stomatitis Virus (VSV)-based vaccines expressing MARV-GP or MARV mucin-deleted (ΔMuc) GP were evaluated in BALB/c mice. The results showed deleting the MLD on the vaccine will decrease vaccine efficacy. On the other hand, the VSV-MARV-ΔMuc GP vaccine leads to an earlier IgG response than the VSV-MARV-GP. A low level of neutralizing antibodies was observed in some of the MARV infected mice. All the survivors in two vaccine groups had a high level of anti-MARV GP IgG. Monoclonal antibodies (mAbs) against MARV were also generated. No neutralization was detected from the hybridoma supernatant and serum from immunized mice, despite high levels of IgG antibodies detected by ELISA. Deletion of the MLD did not enhance anti-MARV neutralizing antibody generation compared to the native form. Overall, the results from evaluating vaccine efficacy as well as mAbs development do not support the hypothesis that a vaccine expressing the ΔMuc GP of Marburg virus will yield more and better neutralizing mAbs than a vaccine expressing the full GP.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".