RNA Polymerase I-Driven Minigenome System for Ebola Viruses
Bibliographic record
Abstract
In general, Ebola viruses are well known for their ability to cause severe hemorrhagic fever in both human and nonhuman primates. However, despite substantial sequence homology to other members of the family Filoviridae, Reston ebolavirus displays reduced pathogenicity for nonhuman primates and has never been demonstrated to cause clinical disease in humans, despite its ability to cause infection. In order to develop a tool to explore potential roles for transcription and replication in the reduced pathogenicity of Reston ebolavirus, we developed an RNA polymerase I (Pol I)-driven minigenome system. Here we demonstrate successful Reston ebolavirus minigenome rescue, including encapsidation, transcription, and replication, as well as the packaging of minigenome transcripts into progeny particles. The Pol I-driven Reston ebolavirus minigenome system provides a higher signal intensity with less background (higher signal-to-noise ratio) than a comparable T7-driven Reston ebolavirus minigenome system which was developed simultaneously. Successful Reston ebolavirus minigenome rescue was also achieved by the use of helper plasmids derived from the closely related Zaire ebolavirus or the more distantly related Lake Victoria marburgvirus. The use of heterologous helper plasmids in the Reston ebolavirus minigenome system yielded levels of reporter expression which far exceeded the level produced by the homologous helper plasmids. This comparison between minigenomes and helper plasmids from different filovirus species and genera indicates that inherent differences in the transcription and/or replication capacities of the ribonucleoprotein complexes of pathogenic and apathogenic filoviruses may exist, as these observations were confirmed in a Lake Victoria marburgvirus minigenome system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".