Promiscuous Inflammasomes: The False Dichotomy of RNA/DNA Virus-Induced Inflammasome Activation and Pyroptosis
Bibliographic record
Abstract
It is well-known that viruses activate various inflammasomes, which can initiate the programmed cell death pathway known as pyroptosis, subsequently leading to cell lysis and release of inflammatory cytokines IL-1β and IL-18. This pathway can be triggered by various sensors, including, but not limited to, NLRP3, AIM2, IFI16, RIG-I, and NLRC4. Many viruses are known either to activate or inhibit inflammasomes as a part of the innate immune response or as a mechanism of pathogenesis. Early research in the field of virus-induced pyroptosis suggested a dichotomy, with RNA viruses activating the NLRP3 inflammasome and DNA viruses activating the AIM2 inflammasome. More recent research has shown that this dichotomy may not be as distinct as once thought. It seems many viruses activate multiple inflammasome sensors. Here, we detail which viruses fit the dichotomy as well as many that appear to defy this clearly false dichotomy. It seems likely that most, if not all, viruses activate multiple inflammasome sensors, and future research should focus on expanding our understanding of inflammasome activation in a variety of tissue types as well as virus activation of multiple inflammasomes, challenging biases that stemmed from early literature in this field. Here, we review primarily research performed on human viruses but also include details regarding animal viruses whenever possible.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".