Effective Inhibition of HIV-1 Production by Short Hairpin RNAs and Small Interfering RNAs Targeting a Highly Conserved Site in HIV-1 Gag RNA Is Optimized by Evaluating Alternative Length Formats
Bibliographic record
Abstract
We have previously identified a target site in HIV-1 RNA that was particularly accessible to a ribozyme and a short hairpin RNA (shRNA). To design small interfering RNAs (siRNAs) targeting this site, we evaluated the effects of siRNAs with different lengths on HIV-1 production. The potency and efficacy of these siRNAs were dependent on the length of their intended sense strand with trends for symmetrical and asymmetrical formats that were similar. Although a typical canonical format with a 21-nucleotide (nt) sense strand was effective at inhibiting HIV-1 production, Dicer substrate siRNAs (dsiRNAs) with the longest lengths (27 to 29 nucleotides) were the most effective. Induction of double-stranded RNA immune responses and effects on cell viability were not detected in cells transfected with different siRNAs, suggesting that the differences observed were not related to indirect effects on HIV-1 production. For the corresponding shRNA designs, a different trend in potency and efficacy against HIV-1 production was observed, with the most effective shRNAs having stem lengths from 20 to 27 bp. Our results highlight the importance of evaluating different designs to identify the best siRNA and shRNA formats for any particular target site and provide a set of highly effective molecules for further development as drug and gene therapies for HIV-1 infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".