Assessment of an anti-HIV-1 combination gene therapy strategy using the antisense RNA and multimeric hammerhead ribozymes
Bibliographic record
Abstract
A combination gene therapy strategy using an ASPsi-gag antisense RNA (targeted against the packaging signal and the gag-coding region) and a multimeric hammerhead ribozyme Rz1-9 (targeted against nine sites within the env-coding region) or Rz1-14 (targeted against 14 sites within the 5' leader and the pro-, pol-, vif- and env-coding regions) was assessed for inhibiting HIV-1 replication. A murine stem cell virus (MSCV)-based MGIN vector was used to express Rz1-9, Rz1-14, ASPsi-gag, Rz1-9ASPsi-gag, or Rz1-14ASPsi-gag RNA in a CD4+ T lymphoid cell line. Stable transductants were shown to express similar levels of interfering RNA. HIV-1 replication was inhibited in cells expressing Rz1-9 and Rz1-14. Little inhibition of HIV-1 replication was observed in cells expressing ASPsi-gag RNA. Thus, the multimeric hammerhead ribozymes inhibit HIV-1 replication better than the antisense RNA. Inhibition of HIV-1 replication in cells expressing Rz1-9ASPsi-gag or Rz1-14ASPsi-gag RNA was worse than that obtained with the multimeric ribozymes alone. This result suggests that co-expression of antisense RNA decreases the anti-HIV potential of ribozymes. The multimeric ribozymes and the antisense RNA were designed to target different sites within the HIV-1 RNA. They are not expected to interact with each other. Neither are they expected to compete with each other for binding to the HIV-1 RNA. Instead, the antisense RNA binding to its (1553 nt-long) target site may have resulted in a decreased ribozyme turn over. Furthermore, since the antisense RNA/HIV-1 RNA hybrids are degraded by the cells, the co-expressed antisense RNA may have led to ribozyme degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".