Deep sequencing of serially passaged Sudan virus in guinea pigs uncovers adaptive mutations
Bibliographic record
Abstract
. Sudan virus (SUDV) is a highly pathogenic orthoebolavirus that causes severe disease in humans. In the last 45 years, SUDV has been responsible for several outbreaks in eastern Africa, particularly in Sudan and Uganda, with an average case fatality rate of approximately 50 %. Despite having caused numerous outbreaks, including a recent outbreak in 2022, no licensed therapeutics or prophylactics currently exist for Sudan virus disease. Small animal models like mice, hamsters, and guinea pigs have paved the way for the initial evaluation of filovirus countermeasures; however, since filoviruses are apathogenic in immunocompetent rodents, they must first be adapted through serial passaging. As a result of this process, viruses acquire genomic changes that may contribute to increased virulence and lethality. Currently, only a single immunocompetent small animal model exists for SUDV, where the virus was serially passaged in guinea pigs until uniform lethality was observed. To better understand the serial passaging process, we used next-generation sequencing to identify and quantify the mutations that arose throughout the adaptation process in guinea pigs. We identified 7 nonsynonymous and 9 synonymous mutations that were present at frequencies near 100 % at the end of serial passaging. The glycoprotein and virion protein (VP) 40 harboured many of the substitutions, most of which were nonsynonymous, while VP35 and VP24 each maintained a single nonsynonymous mutation. These results are consistent with the previously determined genome sequence, obtained by Sanger sequencing, with two notable exceptions: we identified a novel mutation in VP40, but we were unable to confirm the mutation in the genome leader, due to poor sequence coverage. This analysis has allowed us to identify adaptive hotspots within the viral genome, which may hint at the molecular determinants contributing to pathogenicity.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".