The μ2 and λ1 Proteins of Mammalian Reovirus Modulate Early Events Leading to Induction of the Interferon Signaling Network
Bibliographic record
Abstract
It has been previously shown that amino acid polymorphisms in reovirus proteins μ2 and λ1 are associated with differing levels of interferon induction. In the present study, viruses carrying these polymorphisms in either or both proteins, were further studied. The two viral determinants exert a synergistic effect on the control of β-interferon induction at the protein and mRNA level, with a concomitant increase in RIG-I. In contrast, levels of phospho-Stat1 and interferon-stimulated genes are increased in singly substituted viruses but with no further increase when both substitutions were present. This suggests that the viral determinants are acting during initial events of viral recognition. Accordingly, difference between viruses was reduced when infection was performed with partially uncoated virions (ISVPs) and transfection of RNA recovered from early-infected cells recapitulates the differences between viruses harboring the different polymorphisms. Altogether, the data are consistent with a redundant or complementary role of μ2 and λ1, affecting either early disassembly or the nature of the viral RNA in the incoming viral particle. Proteins involved in viral RNA synthesis are thus involved in this likely critical aspect of the ability of different reovirus variants to infect various cell types, and to discriminate between parental and transformed/cancer cells.
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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".