Generation and Characterization of a Mouse-Adapted Makona Variant of Ebola Virus
Bibliographic record
Abstract
Ebola virus (EBOV) is a zoonotic pathogen that poses a significant threat to public health, causing sporadic yet devastating outbreaks that have the potential to spread worldwide, as demonstrated during the 2013-2016 West African outbreak. Mouse models of infection are important tools for the development of therapeutics and vaccines. Exposure of immunocompetent mice to clinical isolates of EBOV is nonlethal; consequently, EBOV requires prior adaptation in mice to cause lethal disease. Until now, the only immunocompetent EBOV mouse model was based on the Mayinga variant, which was isolated in 1976. Here, we generated a novel mouse-adapted (MA)-EBOV based on the 2014 Makona isolate by inserting EBOV/Mayinga-MA mutations into the EBOV/Makona genome, followed by serial passaging of the rescued virus in suckling mice. The resulting EBOV/Makona-MA causes lethal disease in adult immunocompetent mice within 6 to 9 days and has a lethal dose (LD50) of 0.004 plaque forming units (PFU). Two additional mutations emerged after mouse-adaptation in the viral nucleoprotein (NP) and membrane-associated protein VP24. Using reverse genetics, we found the VP24 mutation to be critical for EBOV/Makona-MA virulence. EBOV/Makona-MA infected mice that presented with viremia, high viral burden in organs, increased release of pro-inflammatory cytokines/chemokines, and lymphopenia. Our mouse model will help advance pre-clinical development of countermeasures against contemporary EBOV variants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".