Spatio-temporal characterization of the antiviral activity of the XRN1-DCP1/2 aggregation against cytoplasmic RNA viruses to prevent cell death
Bibliographic record
Abstract
Host nucleases are implicated in antiviral response through the processing of pathogen-derived nucleic acids. Among many host RNases, decapping enzymes DCP1 and 2, and 5'→3' exonuclease XRN1, which are components of the RNA decay machinery, have been extensively studied in prokaryotes, plants, and invertebrates but less so in mammalian systems. As a result, the implication of XRN1 and DCPs in viral replication, in particular, the spatio-temporal dynamics during RNA viral infections remains elusive. Here, we highlight that XRN1 and DCPs play a critical role in limiting several groups of RNA viral infections. This antiviral activity was not obvious in wild-type cells but clearly observed in type I interferon (IFN-I)-deficient cells. Mechanistically, infection with RNA viruses induced the enrichment of XRN1 and DCPs in viral replication complexes (vRCs), hence forming distinct cytoplasmic aggregates. These aggregates served as sites for direct interaction between XRN1, DCP1/2, and viral ribonucleoprotein that contains viral RNA (vRNA). Although these XRN1-DCP1/2-vRC-containing foci resemble antiviral stress granules (SGs) or P-body (PB), they did not colocalize with known SG markers and did not correlate with critical PB functions. Furthermore, the presence of 5' mono- and 5' triphosphate structures on vRNA was not required for the formation of XRN1-DCP1/2-vRC-containing foci. On the other hand, single-, double-stranded, and higher-ordered vRNA species play a role but are not deterministic for efficient formation of XRN1-DCP1/2 foci and consequent antiviral activity in a manner proportional to RNA length. These results highlight the mechanism behind the antiviral function of XRN1-DCP1/2 in RNA viral infections independent of IFN-I response, protein kinase R and PB function.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".