Inhibition of HIV-1 expression and replication by SOFA-HDV ribozymes against Tat and Rev mRNA sequences
Bibliographic record
Abstract
RNA-based compounds are promising methods to inactivate viruses. New specific hepatitis delta virus (HDV)-derived ribozymes are natural molecules that can be engineered to specifically target a viral RNA. We have designed specific on-off adapted (SOFA) HDV-ribozymes targeting the regions of the HIV-1 RNA in the Tat and Rev sequences. We show that these SOFA-HDV ribozymes cleave their Tat RNA target in vitro. They inhibit the Tat-mediated transactivation of HIV-1 long terminal repeat by up to 62 and 86% in luciferase and beta-galactosidase assays, respectively. Inactivation of transfected HIV pNL4-3 molecular clone reached a fourfold inhibition by reverse transcriptase assay of the supernatant and an almost undetectable Gag protein synthesis. In vivo RNA cleavage reached 66 and 86% for two of the tested ribozymes showing that the decrease in HIV production is due to the direct decline in spliced and unspliced viral RNA. These SOFA-HDV-ribozymes were able to target four HIV-1 strains, showing an extended potential to act on multiple HIV variants. When transfected before HIV-1 infection, they prevented incoming virus to be expressed. Our results show that SOFA-HDV-ribozymes show a great potential to target HIV and to be used as therapeutic agents in gene therapy.
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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 | 0.000 |
| 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) | 0.001 | 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".