The NSP5, ORF6 and NSP13 of SARS‐CoV‐2 Cooperate to Modulate Inflammatory Cell Death Activation
Bibliographic record
Abstract
Programmed cell death is a pivotal mechanism of cell-autonomous immune defense against viral infections. Recent studies indicate that both blocking and promoting cell death negatively affect coronavirus replication, implying that coronaviruses may fine-tune cell death pathways to optimize their propagation. However, the mechanisms underlying this remain poorly understood. Here, it is verified that coronaviruses induce the formation of a Z-DNA-binding protein 1 (ZBP1)-initiated cell death complex involving ZBP1, Z-RNA, receptor-interacting serine/threonine-protein kinase 3 (RIPK3), and caspase-8, thereby triggering apoptosis, pyroptosis, and necroptosis in human bronchial epithelial cells. To impede the activation of apoptosis and pyroptosis, NSP5 and ORF6 of SARS-CoV-2 concurrently inhibit caspase-8 activity by targeting its large and small subunits, respectively. Additionally, NSP13, the viral helicase, interacts with RIPK3 to impair its binding to ZBP1, thus suppressing ZBP1-initiated necroptosis. This inhibitory effect on cell death is likely conserved across β-coronaviruses. Furthermore, co-infection of influenza A virus and SARS-CoV-2 is demonstrated to exacerbate disease severity, although the mechanisms remain unclear. These findings suggest that β-coronavirus-induced inhibition of cell death enhances influenza A virus replication and worsens inflammation during their co-infection, ultimately increasing mortality in mice. This research provides valuable insights into the regulation of coronavirus-induced cell death, offering potential therapeutic strategies for combating highly pathogenic coronavirus infections.
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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".