Nonsense-mediated decay controls a negative feedback loop in innate immune sensing
Bibliographic record
Abstract
Nonsense-mediated decay (NMD) is an mRNA decay pathway which degrades potential harmful transcripts that contain premature termination codons. However, NMD's importance also extends to the control of isoform abundance under physiological conditions. During viral infection, NMD is inhibited through numerous mechanisms; however, NMD has been shown to have both antiviral as well as proviral activities, raising further questions into the role and control of NMD during viral infection. These observations have led us to investigate the potential involvement of NMD in dsRNA sensing as a mechanism that might explain these discrepancies. Using EIF4A2 exon 10B inclusion as an example of AS-NMD isoform accumulating during viral infection, we show that dsRNA sensing inhibits NMD. This effect is correlated with translational blockade and is driven primarily by RNaseL activation, and by PKR in the absence of RNaseL activation. Surprisingly, NMD inhibition limits the induction of IFN-β as well as interferon-stimulated genes, and this effect is upstream of IRF3 phosphorylation and translocation to the nucleus. NMD inhibition also decreases PKR and RNaseL activation as well as PIC-mediated cell death by decreasing the dsRNA content, suggesting NMD directly controls dsRNA sensing by controlling the dsRNA load. Therefore, inhibition of NMD upon dsRNA sensing provides a negative feedback loop that contributes to shaping the innate immune sensing pathways. SIGNIFICANCE: Nonsense-mediated decay (NMD) is a translation-dependent mRNA decay pathway that plays an important role in shaping the transcriptome. In this manuscript, we show that dsRNA sensing, as is typical during viral infection, inhibits NMD mainly through the translational inhibition caused by RNaseL activation. This NMD inhibition forms a negative-feedback loop that limits dsRNA sensing, thus preventing overactivation of dsRNA-mediated pathways. These findings contribute to a better understanding of the molecular mechanisms that limit antiviral responses as well as inflammation and inform the critical role that mRNA processes plays in innate immunity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".