Recognition of the Measles Virus Nucleocapsid as a Mechanism of IRF-3 Activation
Bibliographic record
Abstract
The mechanisms of cellular recognition for virus infection remain poorly understood despite the wealth of information regarding the signaling events and transcriptional responses that ensue. Host cells respond to viral infection through the activation of multiple signaling cascades, including the activation of NF-kappaB, c-Jun/ATF-2 (AP-1), and the interferon regulatory factors (IRFs). Although viral products such as double-stranded RNA (dsRNA) and the processes of viral binding and fusion have been implicated in the activation of NF-kappaB and AP-1, the mechanism(s) of IRF-1, IRF-3, and IRF-7 activation has yet to be fully elucidated. Using recombinant measles virus (MeV) constructs, we now demonstrate that phosphorylation-dependent IRF-3 activation represents a novel cellular detection system that recognizes the MeV nucleocapsid structure. At low multiplicities of infection, IRF-3 activation is dependent on viral transcription, since UV cross-linking and a deficient MeV containing a truncated polymerase L gene failed to induce IRF-3 phosphorylation. Expression of the MeV nucleocapsid (N) protein, without the requirement for any additional viral proteins or the generation of dsRNA, was sufficient for IRF-3 activation. In addition, the nucleocapsid protein was found to associate with both IRF-3 and the IRF-3 virus-activated kinase, suggesting that it may aid in the colocalization of the kinase and the substrate. Altogether, this study suggests that IRF-3 recognizes nucleocapsid structures during the course of an MeV infection and triggers the induction of interferon production.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".