A Cap-to-Tail Guide to mRNA Translation Strategies in Virus-Infected Cells
Bibliographic record
Abstract
Although viruses require cellular functions to replicate, their absolute dependence upon the host translation machinery to produce polypeptides indispensable for their reproduction is most conspicuous. Despite their incredible diversity, the mRNAs produced by all viruses must engage cellular ribosomes. This has proven to be anything but a passive process and has revealed a remarkable array of tactics for rapidly subverting control over and dominating cellular regulatory pathways that influence translation initiation, elongation, and termination. Besides enforcing viral mRNA translation, these processes profoundly impact host cell-intrinsic immune defenses at the ready to deny foreign mRNA access to ribosomes and block protein synthesis. Finally, genome size constraints have driven the evolution of resourceful strategies for maximizing viral coding capacity. Here, we review the amazing strategies that work to regulate translation in virus-infected cells, highlighting both virus-specific tactics and the tremendous insight they provide into fundamental translational control mechanisms in health and disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".