VSVΔG/EBOV GP-Induced Innate Protection Enhances Natural Killer Cell Activity to Increase Survival in a Lethal Mouse Adapted Ebola Virus Infection
Bibliographic record
Abstract
Members of the species Zaire ebolavirus cause severe hemorrhagic fever with up to a 90% mortality rate in humans. The VSVΔG/EBOV GP vaccine has provided 100% protection in the mouse, guinea pig, and nonhuman primate (NHP) models, and has also been utilized as a post-exposure therapeutic to protect mice, guinea pigs, and NHPs from a lethal challenge of Ebola virus (EBOV). EBOV infection causes rapid mortality in human and animal models, with death occurring as early as 6 days after infection, suggesting a vital role for the innate immune system to control the infection before cells of the adaptive immune system can assume control. Natural killer (NK) cells are the predominant cell of the innate immune response, which has been shown to expand with VSVΔG/EBOV GP treatment. In the current study, an in vivo mouse model of the VSVΔG/EBOV GP post-exposure treatment was used for a mouse adapted (MA)-EBOV infection, to determine the putative VSVΔG/EBOV GP-induced protective mechanism of NK cells. NK depletion studies demonstrated that mice with NK cells survive longer in a MA-EBOV infection, which is further enhanced with VSVΔG/EBOV GP treatment. NK cell mediated cytotoxicity and IFN-γ secretion was significantly higher with VSVΔG/EBOV GP treatment. Cell mediated cytotoxicity assays and perforin knockout mice experiments suggest that there are perforin-dependent and -independent mechanisms involved. Together, these data suggest that NK cells play an important role in VSVΔG/EBOV GP-induced protection of EBOV by increasing NK cytotoxicity, and IFN-γ secretion.
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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".