Arginine Methylation of the Human Immunodeficiency Virus Type 1 Tat Protein by PRMT6 Negatively Affects Tat Interactions with both Cyclin T1 and the Tat Transactivation Region
Bibliographic record
Abstract
Arginine methylation has been shown to regulate signal transduction, protein subcellular localization, gene transcription, and protein-protein interactions that ultimately alter gene expression. Although the role of cellular protein arginine methyltransferases (PRMT) in viral gene expression is largely unknown, we recently showed that the Tat protein of human immunodeficiency virus type 1 (HIV-1) is a substrate for one such enzyme, termed PRMT6. However, the mechanism by which arginine methylation impairs the transactivation potential of Tat and the sites of arginine methylation within Tat remain obscure. We now show that Tat is a specific in vitro and in vivo substrate of PRMT6 which targets the Tat R52 and R53 residues for arginine methylation. Such Tat methylation led to decreased interaction with the Tat transactivation region (TAR) of viral RNA. Furthermore, arginine methylation of Tat negatively affected Tat-TAR-cyclin T1 ternary complex formation and diminished cyclin T1-dependent Tat transcriptional activation. Overexpression of wild-type PRMT6, but not a methylase-inactive PRMT6 mutant, reduced levels of Tat transactivation of HIV-1 long terminal repeat chloramphenicol acetyltransferase and luciferase reporter plasmids in a dose-dependent manner. In cell-based assays, knockdown of PRMT6 resulted in increased HIV-1 production and faster viral replication. Thus, PRMT6 can compromise Tat transcriptional activation and may represent a form of innate cellular immunity in regard to HIV-1 replication. Finding a way of inhibiting or stimulating PRMT6 activity might help to drive quiescently infected cells out of latency or combat HIV-1 replication, respectively.
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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".