Crosstalk between RNA viruses and DNA sensors: Role of the cGAS‐STING signalling pathway
Bibliographic record
Abstract
Despite only comprising half of all known viral species, RNA viruses are disproportionately responsible for many of the worst epidemics in human history, including outbreaks of influenza, poliomyelitis, Ebola, and most recently, the coronavirus disease-2019 (COVID-19) pandemic. The propensity for RNA viruses to replicate in cytosolic compartments has led to an evolutionary arms race and the emergence of cytosolic sensors to recognise and initiate the host innate immune response. Although significant progress has been made in identifying and characterising cytosolic RNA sensors as anti-viral innate immune factors, the potential role for cytosolic DNA sensors in RNA viral infection is only recently being appreciated. Among these, the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway has attracted increasing attention. The cGAS-STING signalling pathway has emerged as a key innate immune signalling axis that is implicated in diverse human diseases from infectious diseases to neurodegeneration and cancer. Here we review the existing literature on RNA viruses and their reciprocal interactions with the cGAS-STING pathway and share insights into RNA virus diversity by touching on the similarities and differences of RNA viral strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".