Respiratory syncytial virus (RSV) enhances translation of virus-resembling AU-rich host transcripts
Bibliographic record
Abstract
BACKGROUND: Viruses strongly rely on the host's translational machinery to produce viral proteins required for replication. However, it is unknown how viruses that do not globally inhibit cap-dependent translation compete with abundant host transcripts for ribosomes. Viral infection often triggers eukaryotic initiator factor 2α (eIF2α) phosphorylation, leading to global 5'-cap-dependent translation inhibition. Respiratory syncytial virus (RSV) encodes mRNAs mimicking 5'-cap structures of host mRNAs and thus inhibition of cap-dependent translation initiation would likely also reduce viral translation. METHODS: RSV-infected HEp-2 and A549 cells were analyzed to determine translation levels using western blotting, indirect immunofluorescent staining and polysome profiling. Transcriptome-wide translation efficiencies of virus-infected cells were compared against mock-infected cells using high-throughput sequencing of poly(A)-tail enriched total mRNA and transcripts associated with heavy polysomes. RESULTS: We confirmed that RSV limits widespread translation initiation inhibition and unexpectedly found that the fraction of ribosomes within polysomes increases during infection, indicating higher ribosome loading on mRNAs during infection. High-throughput sequencing revealed that virus-resembling, AU-rich host transcripts become more efficient at ribosome recruitment. Using a previously published dataset, we observe similar trends in another negative-sense single-stranded RNA virus, vesicular stomatitis virus (VSV). CONCLUSIONS: These findings revealed that RSV changes the translational landscape by enhancing translation of virus-resembling AU-rich host transcripts rather than inhibiting host translation.
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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 | 0.001 |
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".