Unprecedented short-term <i>in vivo</i> protection in mice with a single immunization of a DNA GP vaccine against heterologous mouse-adapted Ebola virus challenge
Bibliographic record
Abstract
Abstract The recent Ebola outbreak in West Africa is a reminder that correlates of protection against filovirus infection are still not well understood. DNA vectors are a serology-independent platform that allow for vector re-administration with minimal side-effects. We designed a new micro-consensus DNA vaccine that expresses a Zaire Ebolavirus (EBOV) glycoprotein (GP) based on 2002–2008 EBOV outbreak strains. This novel glycoprotein is 3% distant from the GP expressed by the 1976 EBOV Mayinga outbreak strain. The optimized GP DNA vaccine was administered in mice by intramuscular injection followed by electroporation (IM-EP) and elicited strong total IgG antibody and T cell responses. To assess protection from challenge, we administered the GP DNA vaccine or pVax1 control (40ug) to BALB/c mice (n=10/group) by IM-EP at days 0, -7, -14, or -28 before lethal challenge (1000LD50) with a mouse-adapted EBOV Mayinga strain. Importantly we observed 100% protection against the lethal heterologous challenge when the DNA vaccine was administered 28 days before challenge illustrating the potency of this vaccine in this model. More importantly, we observed 100% protection and 90% protection when the DNA vaccine was administered at days -14 and day -7, respectively. Most surprising was the observation of 40% survival in the group that received vaccine at Day 0 (2–3 hours) before challenge. Rapid, short-term protection has been previously observed with a VSV-ZEBOVGP viral vector vaccine but never before with a DNA vaccine. The data suggests that protection in mice following administration of a heterologous DNA vaccine can be afforded by mechanisms independent solely of antibody titers. Ongoing studies are underway in NHPs to investigate these findings.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".