Identification of functional microRNAs released through asymmetrical processing of HIV-1 TAR element
Bibliographic record
Abstract
The interaction between human immunodeficiency virus type 1 (HIV-1) and RNA silencing pathways is complex and multifaceted. Essential for efficient viral transcription and supporting Tat-mediated transactivation of viral gene expression, the trans-activation responsive (TAR) element is a structured RNA located at the 5' end of all transcripts derived from HIV-1. Here, we report that this element is a source of microRNAs (miRNAs) in cultured HIV-1-infected cell lines and in HIV-1-infected human CD4+ T lymphocytes. Using primer extension and ribonuclease (RNase) protection assays, we delineated both strands of the TAR miRNA duplex deriving from a model HIV-1 transcript, namely miR-TAR-5p and miR-TAR-3p. In vitro RNase assays indicate that the lack of a free 3' extremity at the base of TAR may contribute to its low processing reactivity in vivo. Both miR-TAR-5p and miR-TAR-3p down-regulated TAR miRNA sensor activity in a process that required an integral miRNA-guided RNA silencing machinery. miR-TAR-3p exerted superior gene downregulatory effects, probably due to its preferential release from HIV-1 TAR RNA by the RNase III Dicer. Our study suggests that the TAR element of HIV-1 transcripts releases functionally competent miRNAs upon asymmetrical processing by Dicer, thereby providing novel insights into viral miRNA biogenesis.
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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 | 0.000 |
| 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".