The 5′ UTR of HIV-1 full-length mRNA and the Tat viral protein modulate the programmed −1 ribosomal frameshift that generates HIV-1 enzymes
Bibliographic record
Abstract
Translation of the full-length messenger RNA (mRNA) of the human immunodeficiency virus type 1 (HIV-1) generates the precursor of the viral enzymes via a programmed -1 ribosomal frameshift. Here, using dual-luciferase reporters, we investigated whether the highly structured 5' untranslated region (UTR) of this mRNA, which interferes with translation initiation, can modulate HIV-1 frameshift efficiency. We showed that, when the 5' UTR of HIV-1 mRNA occupies the 5' end of the reporter mRNA, HIV-1 frameshift efficiency is increased about fourfold in Jurkat T-cells, compared with a control dual-luciferase reporter with a short unstructured 5' UTR. This increase was related to an interference with cap-dependent translation initiation by the TAR-Poly(A) region at the 5' end of the messenger. HIV-1 mRNA 5' UTR also contains an internal ribosome entry site (IRES), but we showed that, when the cap-dependent initiation mode is available, the IRES is not used or is weakly used. However, when the ribosomes have to use the IRES to translate the dual-luciferase reporter, the frameshift efficiency is comparable to that of the control dual-luciferase reporter. The decrease in cap-dependent initiation and the accompanying increase in frameshift efficiency caused by the 5' UTR of HIV-1 mRNA is antagonized, in a dose-dependent way, by the Tat viral protein. Tat also stimulates the IRES-dependent initiation and decreases the corresponding frameshift efficiency. A model is presented that accounts for the variations in frameshift efficiency depending on the 5' UTR and the presence of Tat, and it is proposed that a range of frameshift efficiencies is compatible with the virus replication.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".