The sequestration of miR-642a-3p by a complex formed by HIV-1 Gag and human Dicer increases AFF4 expression and viral production
Bibliographic record
Abstract
ABSTRACT Micro (mi)RNAs are critical regulators of gene expression in human cells, the functions of which can be affected during viral replication. Here, we show that the human immunodeficiency virus type 1 (HIV-1) structural precursor Gag protein interacts with the miRNA processing enzyme Dicer. RNA immunoprecipitation and sequencing experiments show that Gag modifies the retention of a specific miRNA subset without affecting Dicer’s pre- miRNA processing activity. Among the retained miRNAs, miR-642a-3p shows an enhanced occupancy on Dicer in the presence of Gag and is predicted to target AFF4 mRNA, which encodes an essential scaffold protein for HIV-1 transcriptional elongation. miR-642a-3p gain- or loss-of-function negatively or positively regulates AFF4 protein expression at mRNA and protein levels with concomitant modulations of HIV-1 production, consistent with an antiviral activity. By sequestering miR-642a-3p with Dicer, Gag enhances AFF4 expression and HIV- 1 production without affecting miR-642a-3p levels. These results identify miR-642a-3p as a strong suppressor of HIV-1 replication and uncover a novel mechanism by which a viral structural protein directly disrupts an miRNA function for the benefit of its own replication. IMPORTANCE: Virus-host relationships occur at different levels and the human immunodeficiency virus type 1 (HIV-1) can modify the expression of microRNAs in different cells. Here, we identify a virus- host interaction between the HIV-1 structural protein Gag and the miRNA-processing enzyme Dicer. Gag does not affect the microRNA processing function of Dicer but affects the functionality of a subset of microRNAs that are enriched on the Dicer-Gag complex compared to on Dicer alone. We show that miR-642a-3p, the most enriched microRNA on the Dicer- Gag complex targets and degrades AFF4 mRNA coding for a protein from the super transcription elongation complex, essential for HIV-1 and cellular transcription. Interestingly, the silencing capacity by miR-642a-3p is hindered by Gag and heightened in its absence, consequently affecting HIV-1 transcription. These findings unveil a new paradigm that a microRNA function rather than its abundance can be affected by a viral protein through its enhanced retention on Dicer.
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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 | 0.001 |
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".