Expression of an RNA glycosidase inhibits HIV-1 transactivation of transcription
Bibliographic record
Abstract
HIV-1 (human immunodeficiency virus) transcription is primarily controlled by the virally encoded Tat (transactivator of transcription) protein and its interaction with the viral TAR (transcription response element) RNA element. Specifically, binding of a Tat-containing complex to TAR recruits cellular factors that promote elongation of the host RNA polymerase engaging the viral DNA template. Disruption of this interaction halts viral RNA transcription. In the present study, we investigated the effect of pokeweed antiviral protein (PAP), an RNA glycosidase (EC#: 3.2.2.22) synthesized by the pokeweed plant (Phytolacca americana), on transcription of HIV-1 mRNA. We show that co-expression of PAP with a proviral clone in culture cells resulted in a Tat-dependent decrease in viral mRNA levels. PAP reduced HIV-1 transcriptional activity by inhibiting Tat protein synthesis. The effects of PAP expression on host factors AP-1 (activator protein 1), NF-κB (nuclear factor kappa-light-chain-enhancer of activated B-cells) and specificity protein 1, which modulate HIV-1 transcription by binding to the viral LTR (5′-long terminal repeat), were also investigated. Only AP-1 showed a modest JNK pathway-dependent increase in activity in the presence of PAP; however, this activation was not sufficient to significantly enhance transcription from a partial viral LTR containing AP-1 binding sites. Therefore, the primary effect of PAP on HIV-1 transcription is to reduce viral RNA synthesis by decreasing the abundance of Tat. These findings provide a mechanistic explanation for the observed decrease in viral RNAs in cells expressing PAP and contribute to our understanding of the antiviral effects of this plant protein.
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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".