The TAR RNA-binding Protein, TRBP, Stimulates the Expression of TAR-containing RNAs in Vitro and in VivoIndependently of Its Ability to Inhibit the dsRNA-dependent Kinase PKR
Bibliographic record
Abstract
TRBP (HIV-1 transactivating response (TAR) RNA-binding protein) and PKR, the interferon-induced dsRNA-regulated protein kinase, contain two dsRNA binding domains. They both bind to HIV-1 TAR RNAs through different sites. Binding to dsRNA activates PKR that phosphorylates the eukaryotic initiation factor eIF-2alpha leading to protein synthesis inhibition. TRBP and PKR can heterodimerize, which inhibits the kinase function of PKR and has a positive effect on HIV-1 expression. In this study, an in vitro reticulocyte assay revealed the poor expression of TAR containing CAT RNAs compared with CAT RNAs. Addition of TRBP restored translation efficiency of TAR-CAT RNA and decreased the phosphorylation status of eIF-2alpha, confirming its role as a PKR inhibitor. Unexpectedly, eIF-2alpha was phosphorylated in the presence of TAR-CAT as well as CAT RNA devoid of the TAR structure. TRBP inhibited eIF-2alpha phosphorylation in both cases, suggesting that it restores the translation of TAR-CAT RNA independently and in addition to its ability to inhibit PKR. TRBP activity on gene expression was then analyzed in a PKR-free environment using PKR-deficient murine embryo fibroblasts. In a transient reporter gene assay, TRBP stimulated the expression of a TAR-containing luciferase 3.8-fold whereas the reporter gene with mutated TAR structures or devoid of TAR was stimulated 1.5- to 2.4-fold. Overall, the activity of TRBP2 was higher when the 5'-end of the mRNA was structured and was mediated independently by each dsRBD in TRBP. Increasing concentrations of TRBP showed no significant modification of the luciferase RNA levels, suggesting that TRBP stimulates translation of TAR-containing RNAs. Therefore, TRBP is an important cellular factor for efficient translation of dsRNA containing transcripts, both by inhibiting PKR and in a PKR-independent pathway.
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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) | 0.000 | 0.001 |
| 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) | 0.002 | 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".