Small Interfering RNAs against the TAR RNA Binding Protein, TRBP, a Dicer Cofactor, Inhibit Human Immunodeficiency Virus Type 1 Long Terminal Repeat Expression and Viral Production
Bibliographic record
Abstract
RNA interference (RNAi) is now widely used for gene silencing in mammalian cells. The mechanism uses the RNA-induced silencing complex, in which Dicer, Ago2, and the human immunodeficiency virus type 1 (HIV-1) TAR RNA binding protein (TRBP) are the main components. TRBP is a protein that increases HIV-1 expression and replication by inhibition of the interferon-induced protein kinase PKR and by increasing translation of viral mRNA. After HIV infection, TRBP could restrict the viral RNA through its activity in RNAi or could contribute more to the enhancement of viral replication. To determine which function will be predominant in the virological context, we analyzed whether the inhibition of its expression could enhance or decrease HIV replication. We have generated small interfering RNAs (siRNAs) against TRBP and found that they decrease HIV-1 long terminal repeat (LTR) basal expression 2-fold, and the LTR Tat transactivated level up to 10-fold. In the context of HIV replication, siRNAs against TRBP decrease the expression of viral genes and inhibit viral production up to fivefold. The moderate increase in PKR expression and activation indicates that it contributes partially to viral gene inhibition. The moderate decrease in micro-RNA (miRNA) biogenesis by TRBP siRNAs suggests that in the context of HIV replication, TRBP functions other than RNAi are predominant. In addition, siRNAs against Dicer decrease viral production twofold and impede miRNA biogenesis. These results suggest that, in the context of HIV replication, TRBP contributes mainly to the enhancement of virus production and that Dicer does not mediate HIV restriction by RNAi.
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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 | 0.001 |
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".