NOD1 Promotes Antiviral Signaling by Binding Viral RNA and Regulating the Interaction of MDA5 and MAVS
Bibliographic record
Abstract
Abstract Nucleotide oligomerization domain–like receptors (NLRs) and RIG-I–like receptors (RLRs) detect diverse pathogen-associated molecular patterns to activate the innate immune response. The role of mammalian NLR NOD1 in sensing bacteria is well established. Although several studies suggest NOD1 also plays a role in sensing viruses, the mechanisms behind this are still largely unknown. In this study, we report on the synergism and antagonism between NOD1 and MDA5 isoforms in teleost. In zebrafish, the overexpression of NOD1 enhances the antiviral response and mRNA abundances of key antiviral genes involved in RLR-mediated signaling, whereas the loss of NOD1 has the opposite effect. Notably, spring viremia of carp virus–infected NOD1−/− zebrafish exhibit reduced survival compared with wild-type counterparts. Mechanistically, NOD1 targets MDA5 isoforms and TRAF3 to modulate the formation of MDA5–MAVS and TRAF3–MAVS complexes. The cumulative effects of NOD1 and MDA5a (MDA5 normal form) were observed for the binding with poly(I:C) and the formation of the MDA5a–MAVS complex, which led to increased transcription of type I IFNs and ISGs. However, the antagonism between NOD1 and MDA5b (MDA5 truncated form) was clearly observed during proteasomal degradation of NOD1 by MDA5b. In humans, the interactions between NOD1–MDA5 and NOD1–TRAF3 were confirmed. Furthermore, the roles that NOD1 plays in enhancing the binding of MDA5 to MAVS and poly(I:C) are also evolutionarily conserved across species. Taken together, our findings suggest that mutual regulation between NOD1 and MDA5 isoforms may play a crucial role in the innate immune response and that NOD1 acts as a positive regulator of MDA5/MAVS normal form–mediated immune signaling in vertebrates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".