Respiratory Syncytial Virus (RSV) optimizes the translational landscape during infection
Bibliographic record
Abstract
Abstract Viral infection often triggers eukaryotic initiator factor 2α (eIF2α) phosphorylation, leading to global 5’-cap-dependent translation inhibition. RSV encodes messenger RNAs (mRNAs) mimicking 5’-cap structures of host mRNAs and thus inhibition of cap-dependent translation initiation would likely also reduce viral translation. 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. We found that AU-rich host transcripts that are less efficiently translated under normal conditions become more efficient at recruiting ribosomes, similar to RSV transcripts. Viral transcripts are transcribed in cytoplasmic inclusion bodies, where the viral AU-rich binding protein M2-1 has been shown to bind viral transcripts and shuttle them into the cytoplasm. We further demonstrated that M2-1 is found on polysomes, and that M2-1 might deliver host AU-rich transcripts for translation. Importance 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. In this study, we found that respiratory syncytial virus (RSV) infection results in redistribution of 80S monosomes into the polysomes. High-throughput sequencing of translating transcripts revealed that low translation efficiency transcripts become more efficient at ribosome recruitment which are virus-resembling AU-rich host transcripts. Finally, we also uncover that AU-rich RNA binding protein RSV-M2-1 interacts with polysomes through contacts to mRNA. These findings revealed that RSV optimizes the translational landscape 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 | 0.000 |
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".