Transactivation of programmed ribosomal frameshifting by a viral protein
Bibliographic record
Abstract
Programmed -1 ribosomal frameshifting (-1 PRF) is a widely used translational mechanism facilitating the expression of two polypeptides from a single mRNA. Commonly, the ribosome interacts with an mRNA secondary structure that promotes -1 frameshifting on a homopolymeric slippery sequence. Recently, we described an unusual -2 frameshifting (-2 PRF) signal directing efficient expression of a transframe protein [nonstructural protein 2TF (nsp2TF)] of porcine reproductive and respiratory syndrome virus (PRRSV) from an alternative reading frame overlapping the viral replicase gene. Unusually, this arterivirus PRF signal lacks an obvious stimulatory RNA secondary structure, but as confirmed here, can also direct the occurrence of -1 PRF, yielding a third, truncated nsp2 variant named "nsp2N." Remarkably, we now show that both -2 and -1 PRF are transactivated by a protein factor, specifically a PRRSV replicase subunit (nsp1β). Embedded in nsp1β's papain-like autoproteinase domain, we identified a highly conserved, putative RNA-binding motif that is critical for PRF transactivation. The minimal RNA sequence required for PRF was mapped within a 34-nt region that includes the slippery sequence and a downstream conserved CCCANCUCC motif. Interaction of nsp1β with the PRF signal was demonstrated in pull-down assays. These studies demonstrate for the first time, to our knowledge, that a protein can function as a transactivator of ribosomal frameshifting. The newly identified frameshifting determinants provide potential antiviral targets for arterivirus disease control and prevention. Moreover, protein-induced transactivation of frameshifting may be a widely used mechanism, potentially including previously undiscovered viral strategies to regulate viral gene expression and/or modulate host cell translation upon infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".