Replication of Human Immunodeficiency Viruses Engineered with Heterologous Tat-Transactivation Response Element Interactions
Bibliographic record
Abstract
Human immunodeficiency viruses (HIVs) and the related bovine lentiviruses bovine immunodeficiency virus (BIV) and Jembrana disease virus (JDV) utilize the viral Tat protein to activate viral transcription. The arginine-rich RNA-binding domains of the Tat proteins bind to their cognate transactivation response element (TAR) RNA hairpins located at the 5' ends of the viral mRNAs, resulting in enhanced processivity of RNA polymerase II. It has previously been shown that HIV type 1 (HIV-1) Tat requires the cellular cyclin T1 protein for high-affinity RNA binding whereas BIV Tat and JDV Tat bind with high affinity on their own and adopt distinct beta-hairpin conformations when complexed to RNA. Here we have engineered the BIV and JDV Tat-TAR interactions into HIV-1 and show that the heterologous interactions support viral replication, correlating well with their RNA-binding affinities. Viruses engineered with a variant TAR able to bind all three Tat proteins replicate efficiently with any of the proteins. In one virus containing a noncognate Tat-TAR pair that neither interacts nor efficiently replicates (HIV-1 TAR and BIV Tat), viral revertants were isolated in which TAR had become mutated to generate a functional BIV Tat binding site. Our results support the view that incremental changes to TAR structure can provide routes for evolving new Tat-TAR complexes while maintaining active viral replication.
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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 | 0.001 |
| 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".