Multiple viral protein genome-linked proteins compensate viral translation in a +ssRNA virus infection
Bibliographic record
Abstract
ABSTRACT Viral protein genome-linked (VPg) protein plays an essential role in protein-primed replication of plus stranded RNA viruses. VPg is covalently linked to the 5’ end of the viral RNA genome via a phosphodiester bond typically at a conserved amino acid. Whereas most viruses have a single VPg, some viruses encode multiple VPgs that are proposed to have redundant yet undefined roles in viral replication. Here, we use the dicistrovirus, cricket paralysis virus (CrPV), which encodes four non-identical copies of VPg, as a model to characterize the role of VPg copies in infection. Dicistroviruses encode two main open reading frames (ORFs) that are driven by distinct IRESs. We systematically generated single and combinatorial deletions and mutations of VPg1-4 within the CrPV infectious clone and monitored viral yield in Drosophila S2 cells. Deletion of one to three VPg copies progressively decreased viral yield and delayed viral replication, suggesting a threshold number of VPgs for productive infection. Mass spectrometry analysis of CrPV VPg-linked RNAs revealed viral RNA linkage to either a serine or threonine in VPg, from which mutations in all VPgs attenuated infection. Mutating serine 4 in a single VPg abolished viral infection, indicating a dominant-negative effect. Using viral minigenome reporters that monitor dicistrovirus 5’ untranslated (UTR) and intergenic internal ribosome entry site (IRES) translation revealed a relationship between VPg copy number and the ratio of distinct IRES translation. We uncover a novel viral strategy whereby VPg copies in dicistrovirus genomes compensate for the relative IRES translation efficiencies to promote infection. Importance Genetic duplication is exceedingly rare in small RNA viral genomes as there is selective pressure to prevent RNA genomes from expanding. However, some small RNA viruses encode multiple copies of a viral protein, most notably an unusual viral protein that is linked to the viral RNA genome. Here, we investigate a family of viruses that contains multiple viral protein genome-linked proteins and reveal a novel viral strategy whereby viral protein copy number counterbalances differences in viral protein synthesis mechanisms.
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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) | 0.001 | 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".