Translation of Pr55 <sup> <i>gag</i> </sup> Augments Packaging of Human Immunodeficiency Virus Type 1 RNA in a <i>Cis</i> -Acting Manner
Bibliographic record
Abstract
The full-length RNA of human immunodeficiency virus type 1 (HIV-1) serves both as a messenger (mRNA) to direct the translation of Pr55(gag) proteins and as genomic or viral particle RNA (vpRNA) to be packaged into virions. In this study, we have assessed a putative cis-acting effect of Pr55(gag) translation on HIV-1 RNA packaging. To pursue this subject, we have measured the relative competence of two distinct types of HIV-1 RNA for being packaged by virus particles under conditions in which only one of them is permissive for production of Pr55(gag). Not surprisingly, wild-type BH10 RNA was packaged at far higher efficiency than that associated with mutant viral RNA that was deleted of RNA packaging signals and incapable of Pr55(gag) production. However, when production of Pr55(gag) was eliminated from the wild-type BH10 viral RNA by insertion of stop codons either in matrix (MA) or in capsid (CA) sequences, regardless of retention of wild-type RNA packaging signals, these Pr55(gag)-deficient viral RNAs were packaged at low levels similar to those observed with viral RNA species that lack RNA packaging signals and are capable of Pr55(gag) generation. Moreover, loss of Pr55(gag) production did not affect stability of the relevant viral RNA; this observation rules out the possibility that lowered packaging efficiency associated with Pr55(gag)-deficient HIV-1 RNA is a result of reduced RNA stability. Taken together, our data demonstrate that cis translation of Pr55(gag) is needed for efficient packaging of HIV-1 RNA.
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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 | 0.001 | 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".