The presence of the TAR RNA structure alters the programmed -1 ribosomal frameshift efficiency of the human immunodeficiency virus type 1 (HIV-1) by modifying the rate of translation initiation
Bibliographic record
Abstract
HIV-1 uses a programmed -1 ribosomal frameshift to synthesize the precursor of its enzymes, Gag-Pol. The frameshift efficiency that is critical for the virus replication, is controlled by an interaction between the ribosome and a specific structure on the viral mRNA, the frameshift stimulatory signal. The rate of cap-dependent translation initiation is known to be altered by the TAR RNA structure, present at the 5' and 3' end of all HIV-1 mRNAs. Depending upon its concentration, TAR activates or inhibits the double-stranded RNA-dependent protein kinase (PKR). We investigated here whether changes in translation initiation caused by TAR affect HIV-1 frameshift efficiency. CD4+ T cells and 293T cells were transfected with a dual-luciferase construct where the firefly luciferase expression depends upon the HIV-1 frameshift. Translation initiation was altered by adding TAR in cis or trans of the reporter mRNA. We show that HIV-1 frameshift efficiency correlates negatively with changes in the rate of translation initiation caused by TAR and mediated by PKR. A model is presented where changes in the rate of initiation affect the probability of frameshifting by altering the distance between elongating ribosomes on the mRNA, which influences the frequency of encounter between these ribosomes and the frameshift stimulatory signal.
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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) | 0.000 | 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".