Data from Oncogenic Ras Promotes Reovirus Spread by Suppressing IFN-β Production through Negative Regulation of <i>RIG-I</i> Signaling
Bibliographic record
Abstract
Abstract Reovirus is the first naturally occurring human virus reported to exploit activated Ras signaling in the host cell for infection, and is currently undergoing clinical trials as a cancer therapeutic. Recent evidence suggests that Ras transformation promotes three reoviral replication steps during the first round of infection: uncoating of the incoming virion, generation of progeny viruses with enhanced infectivity, and virus release through enhanced apoptosis. Whether oncogenic Ras also enhances reovirus spread in subsequent rounds of infection through other mechanisms has not been examined. Here, we show that compared with nontransformed cells, Ras-transformed cells are severely compromised not only in their response to IFN-β, but also in the induction of IFN-β mRNA following reovirus infection. Defects in both IFN-β production and response allow for efficient virus spread in Ras-transformed cells. We show that the MEK/ERK pathway downstream of Ras is responsible for inhibiting IFN-β expression by blocking signaling from the retinoic acid–inducible gene I (RIG-I) which recognizes viral RNAs. Overexpression of wild-type RIG-I restores INF-β expression in reovirus-infected Ras-transformed cells. In vitro–synthesized viral mRNAs also invoke robust RIG-I–mediated IFN-β production in transfected nontransformed cells, but not in Ras-transformed cells. Collectively, our data suggest that oncogenic Ras promotes virus spread by suppressing viral RNA–induced IFN-β production through negative regulation of RIG-I signaling. Cancer Res; 70(12); 4912–21. ©2010 AACR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".