Innate immune evasion and general host shutoff strategies of human respiratory RNA viruses
Bibliographic record
Abstract
To replicate and spread efficiently, RNA viruses must inhibit the host immune signaling pathways and/or induce host shutoff and thereby evade host immunity. However, not all RNA viruses have the same consequences in terms of disease severity and ability to cause reinfections. What are the reasons for such differences? Are there any unique or common features of viral immune evasion? Answering these questions is imperative to elucidate the role of the balance between damaging pro-inflammatory and protective antiviral responses and to inform development of new therapeutic strategies. In this review, we provide a comparative overview of the knowledge regarding the innate immune evasion and general host shutoff strategies used by respiratory RNA viruses to enable their persistent spread in humans. We demonstrate that these viruses encode multiple and often redundant mechanisms of innate immune suppression, with potent general host shutoff being characteristic of viruses with wider host range and greater ability to cause reinfections. Accordingly, we argue that increased research effort in characterizing host shutoff mechanisms of different viruses and their contribution to virus fitness is needed to determine if targeting host shutoff represents a viable avenue for developing new antiviral therapies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".