In Vitro Evaluation of Antisense RNA Efficacy against Filovirus Infection, by Use of Reverse Genetics
Bibliographic record
Abstract
BACKGROUND: Recent reports indicate the possibility of using small interfering RNAs (siRNAs) to treat filovirus infections; however, they also show that the effectiveness of this approach is highly dependent on target site selection. Therefore, we explored the application of minigenomes as screening tools to identify functional siRNA targets under biosafety level 2 conditions. METHODS: siRNA candidates were screened using the minigenome system to identify those with potential antiviral activity, compared with controls with poor predicted function on the basis of design guidelines, or those that were noncomplementary to Zaire ebolavirus (ZEBOV). These findings were then validated in cell culture by use of a previously developed ZEBOV expressing green fluorescent protein (ZEBOV-GFP), which allowed siRNA function to be easily assessed via flow cytometry or focus formation. RESULTS: The most promising siRNA based on minigenome screening, targeting the nucleoprotein (NP) mRNA (ZNP1), also reduced protein expression and decreased viral titers after infection with ZEBOV-GFP to an extent similar to that reported for an siRNA recently shown to be therapeutic in guinea pigs. CONCLUSIONS: Minigenome screening appears to be an effective and convenient method of evaluating the therapeutic potential of siRNA targets, and findings suggest that its use would increase success rates in later stages of siRNA testing.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".