miRNA-1236 Inhibits HIV-1 Infection of Monocytes by Repressing Translation of Cellular Factor VprBP
Bibliographic record
Abstract
Primary monocytes are refractory to HIV-1 infection and become permissive upon differentiation into monocyte-derived dendritic cells (MDDCs) or macrophages. Multiple mechanisms have been proposed to interpret HIV-1 restriction in monocytes. Human cellular miRNAs can modulate HIV-1 infection by targeting either conserved regions of the HIV-1 genome or host gene transcripts. We have recently reported that the translation of host protein pur-alpha is repressed by abundant cellular miRNAs to inhibit HIV-1 infection in monocytes. Here, we report that the transcript of another cellular factor, VprBP [Vpr (HIV-1)-binding protein], was repressed by cellular miRNA-1236, which contributes to HIV-1 restriction in monocytes. Transfection of miR-1236 inhibitors enhanced translation of VprBP in monocytes and significantly promoted viral infection; exogenous input of synthesized miR-1236 mimics into MDDCs suppressed translation of VprBP, and, accordingly, significantly impaired viral infection. Our data emphasize the role of miRNA in modulating differentiation-dependent susceptibility of the host cell to HIV-1 infection. Understanding the modulation of HIV-1 infection by cellular miRNAs may provide key small RNAs or the identification of new important protein targets regulated by miRNAs for the development of antiviral strategies.
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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 | 0.001 |
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".