Competition between HIV-1-encoded RRE RNA and miRNA-TRBP interactions alters RNA interference activity and gene expression
Bibliographic record
Abstract
We used a model based on miRNA Let7 activity on a reporter gene (RL or EGFP) linked to a complementary sequence (cLet7) to measure RNAi activity or its suppression. We used RNA-immunoprecipitation (IP) and gel mobility shift assays to compare TRBP binding to RRE or siRNAs. We studied RRE activity on RNAi in the context of the entire HIV-1, a lentiviral or an adenoviral vector. We observed that RRE, acts as an RNAi suppressor with no modification of the endogenous RISC (Daniels et al., submitted). In contrast, RRE RNA displaces siARNs from TRBP, which suggests a change in miRNA incorporation into the RISC. RNAi remains functional in HIV-1 infected cells, whereas a lentiviral vector expressing RRE has a suppressive activity. The suppression is alleviated when Rev or GagPol is expressed. Adenovirus is known to be suppressed by RNAi and RRE reverses this inhibition as seen by increased viral replication. RRE is a new RNAi suppressor, which acts by competition with siRNA and miRNA for binding to TRBP and therefore incorporation into the RISC. This could explain in part the alteration of certain gene expression and modifications of the cell metabolism in patients with long-term HIV-1 infection.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".