RIG-I Is Required for the Inhibition of Measles Virus by Retinoids
Bibliographic record
Abstract
Vitamin A can significantly decrease measles-associated morbidity and mortality. Vitamin A can inhibit the replication of measles virus (MeV) in vitro through an RARα- and type I interferon (IFN)-dependent mechanism. Retinoid-induced gene I (RIG-I) expression is induced by retinoids, activated by MeV RNA and is important for IFN signaling. We hypothesized that RIG-I is central to retinoid-mediated inhibition of MeV in vitro. We demonstrate that RIG-I expression is increased in cells treated with retinoids and infected with MeV. The central role of RIG-I in the retinoid-anti-MeV effect was demonstrated in the Huh-7/7.5 model; the latter cells having non-functional RIG-I. RAR-dependent retinoid signaling was required for the induction of RIG-I by retinoids and MeV. Retinoid signaling was also found to act in combination with IFN to induce high levels of RIG-I expression. RIG-I promoter activation required both retinoids and MeV, as indicated by markers of active chromatin. IRF-1 is known to be regulated by retinoids and MeV, but we found recruitment of IRF-1 to the RIG-I promoter by retinoids alone. Using luciferase expression constructs, we further demonstrated that the IRF-1 response element of RIG-I was required for RIG-I activation by retinoids or IFN. These results reveal that retinoid treatment and MeV infection induces significant RIG-I. RIG-I is required for the retinoid-MeV antiviral response. The induction is dependent on IFN, retinoids and IRF-1.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".