Towards gene therapy against HIV-1: new therapeutic target in gag RNA accessible to ribozymes and RNA interference molecules
Bibliographic record
Abstract
We screened HIV-1 RNA to identify conserved target sites for new HDV-Rzs [ 2 ]. We designed new SOFA-HDV-Rzs against the Gag RNA and developed a rapid test to evaluate the inhibition of HIV-1 production. We designed small interfering (si) RNAs targeting the same region and tested their activity on HIV-1 replication. We identified 13 conserved regions in the gag RNA and constructed the corresponding Rzs. We transfected HEK293T cells with these Rzs and HIV-1 molecular clones. We identified one Rz that was particularly efficacious. We then constructed siRNAs and short hairpin (sh) RNAs targeting the same sequence. The shRNA was very active against HIV-1 clades B, C and A/G. Neither the Rz, nor the shRNA disturbs the cellular transcriptome, suggesting no toxicity. In lymphocytic cell lines, both the Rz and the shRNA inhibit long-term HIV-1 replication [ 2 ]. We identified new SOFA-HDV-Rz, siRNA and shRNA targeting HIV-1 Gag RNA. The shRNA is as active as the only shRNA that has advanced to clinical trials and targets more strains. Long-term inhibitory activity of these molecules shows that this site is particularly accessible to other antisense molecules. These molecules have a high potential to be used in combination gene therapy or as drugs with appropriate delivery methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".