Development of the Ebola virus vaccine using a genetically modified dual serotype recombinant vesicular stomatitis virus platform technology
Bibliographic record
Abstract
T Ebola virus outbreak in West Africa continues to rage, with over 20,000 virus-infected individuals in 2014, over 50% which have resulted in death. The best and most effective way of controlling this devastating epidemic is to develop an efficacious vaccine. We have developed an Ebola virus vaccine using a genetically modified dual serotype recombinant vesicular stomatitis virus (VSV) platform technology. The VSV, one of the Rhabdo viruses, offers an ideal system for the creation of prime-boost vaccine vectors. In order to induce maximum immune responses, the priming recombinant viral vector should be antigenically distinct from the boosting vaccine vector to maximize the boost effect. Here we report robust humoral immune responses when two antigenically distinct genetically modified VSV vectors carrying the Ebola virus genes are used for prime-boost immunization. To examine the humoral immune responses against the Ebola virus proteins expressed from the genetically modified M gene variants of rVSV vectors, we generated rVSVs with the Zaire strain of Ebola virus GP, VP40 and NP genes. rVSVs carrying the Ebola virus GP, VP40, NP genes express high levels of proteins, and the GP and VP40 co-expression forms virus-like particles (VLP) that are secreted from the infected cells. From the various vaccination regimens tested in animals, priming with rVSVInd (GML)-EboGP+EboVP40, followed by an rVSVNJ (GMM)-EboGP+EboVP40 boosting, induced strong humoral immune responses against the Ebola virus GP and VP40 proteins. Increasing vaccine doses induced stronger humoral immune responses against the GP and VP40 proteins. Our results demonstrate that rVSVInd (GML) priming followed by rVSVNJ (GMM) boosting is the best system for inducing optimum adaptive immune responses. We are in the process of determining the efficacy of this Ebola virus vaccine in a BSL4 laboratory.
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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".