Arginine methylation of SARS-Cov-2 nucleocapsid protein regulates RNA binding, its ability to suppress stress granule formation, and viral replication
Bibliographic record
Abstract
Viral proteins are known to be methylated by host protein arginine methyltransferases (PRMTs) necessary for the viral life cycle, but it remains unknown whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins are methylated. Herein, we show that PRMT1 methylates SARS-CoV-2 nucleocapsid (N) protein at residues R95 and R177 within RGG/RG motifs, preferred PRMT target sequences. We confirmed arginine methylation of N protein by immunoblotting viral proteins extracted from SARS-CoV-2 virions isolated from cell culture. Type I PRMT inhibitor (MS023) or substitution of R95 or R177 with lysine inhibited interaction of N protein with the 5'-UTR of SARS-CoV-2 genomic RNA, a property required for viral packaging. We also defined the N protein interactome in HEK293 cells, which identified PRMT1 and many of its RGG/RG substrates, including the known interacting protein G3BP1 as well as other components of stress granules (SGs), which are part of the host antiviral response. Methylation of R95 regulated the ability of N protein to suppress the formation of SGs, as R95K substitution or MS023 treatment blocked N-mediated suppression of SGs. Also, the coexpression of methylarginine reader Tudor domain-containing protein 3 quenched N protein-mediated suppression of SGs in a dose-dependent manner. Finally, pretreatment of VeroE6 cells with MS023 significantly reduced SARS-CoV-2 replication. Because type I PRMT inhibitors are already undergoing clinical trials for cancer treatment, inhibiting arginine methylation to target the later stages of the viral life cycle such as viral genome packaging and assembly of virions may represent an additional therapeutic application of these drugs.
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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".