Influenza Virus mRNA Translation Revisited: Is the eIF4E Cap-Binding Factor Required for Viral mRNA Translation?
Bibliographic record
Abstract
Influenza virus mRNAs bear a short capped oligonucleotide sequence at their 5' ends derived from the host cell pre-mRNAs by a "cap-snatching" mechanism, followed immediately by a common viral sequence. At their 3' ends, they contain a poly(A) tail. Although cellular and viral mRNAs are structurally similar, influenza virus promotes the selective translation of its mRNAs despite the inhibition of host cell protein synthesis. The viral polymerase performs the cap snatching and binds selectively to the 5' common viral sequence. As viral mRNAs are recognized by their own cap-binding complex, we tested whether viral mRNA translation occurs without the contribution of the eIF4E protein, the cellular factor required for cap-dependent translation. Here, we show that influenza virus infection proceeds normally in different situations of functional impairment of the eIF4E factor. In addition, influenza virus polymerase binds to translation preinitiation complexes, and furthermore, under conditions of decreased eIF4GI association to cap structures, an increase in eIF4GI binding to these structures was found upon influenza virus infection. This is the first report providing evidence that influenza virus mRNA translation proceeds independently of a fully active translation initiation factor (eIF4E). The data reported are in agreement with a role of viral polymerase as a substitute for the eIF4E factor for viral mRNA translation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".