Induction of Broad Cytotoxic T Cells by Protective DNA Vaccination Against Marburg and Ebola
Bibliographic record
Abstract
Marburg and Ebola hemorrhagic fevers have been described as the most virulent viral diseases known to man due to associative lethality rates of up to 90%. Death can occur within days to weeks of exposure and there is currently no licensed vaccine or therapeutic. Recent evidence suggests an important role for antiviral T cells in conferring protection, but little detailed analysis of this response as driven by a protective vaccine has been reported. We developed a synthetic polyvalent-filovirus DNA vaccine against Marburg marburgvirus (MARV), Zaire ebolavirus (ZEBOV), and Sudan ebolavirus (SUDV). Preclinical efficacy studies were performed in guinea pigs and mice using rodent-adapted viruses, whereas murine T-cell responses were extensively analyzed using a novel modified assay described herein. Vaccination was highly potent, elicited robust neutralizing antibodies, and completely protected against MARV and ZEBOV challenge. Comprehensive T-cell analysis revealed cytotoxic T lymphocytes (CTLs) of great magnitude, epitopic breadth, and Th1-type marker expression. This model provides an important preclinical tool for studying protective immune correlates that could be applied to existing platforms. Data herein support further evaluation of this enhanced gene-based approach in nonhuman primate studies for in depth analyses of T-cell epitopes in understanding protective efficacy.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".