Dominant Role of the 5′ TAR Bulge in Dimerization of HIV-1 Genomic RNA, but No Evidence of TAR–TAR Kissing during in Vivo Virus Assembly
Bibliographic record
Abstract
The 5' untranslated region of HIV-1 genomic RNA (gRNA) contains two stem-loop structures that appear to be equally important for gRNA dimerization: the 57-nucleotide 5' TAR, at the very 5' end, and the 35-nucleotide SL1 (nucleotides 243-277). SL1 is well-known for containing the dimerization initiation site (DIS) in its apical loop. The DIS is a six-nucleotide palindrome. Here, we investigated the mechanism of TAR-directed gRNA dimerization. We found that the trinucleotide bulge (UCU24) of the 5' TAR has dominant impacts on both formation of HIV-1 RNA dimers and maturation of the formed dimers. The ΔUCU trinucleotide deletion strongly inhibited the first process and blocked the other, thus impairing gRNA dimerization as severely as deletion of the entire 5' TAR, and more severely than deletion of the DIS, inactivation of the viral protease, or most severe mutations in the nucleocapsid protein. The apical loop of TAR contains a 10-nucleotide palindrome that has been postulated to stimulate gRNA dimerization by a TAR-TAR kissing mechanism analogous to the one used by SL1 to stimulate dimerization. Using mutations that strongly destabilize formation of the TAR palindrome duplex, as well as compensatory mutations that restore duplex formation to a wild-type-like level, we found no evidence of TAR-TAR kissing, even though mutations nullifying the kissing potential of the TAR palindrome could impair dimerization by a mechanism other than hindering of SL1. However, nullifying the kissing potential of TAR had much less severe effects than ΔUCU. By not uncovering a dimerization mechanism intrinsic to TAR, our data suggest that TAR mutations exert their effect 3' of TAR, yet not on SL1, because TAR and SL1 mutations have synergistic effects on gRNA dimerization.
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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".