Nucleic Acid-Sensing Pathways During SARS-CoV-2 Infection: Expectations versus Reality
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected millions of people and crippled economies worldwide. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for this pandemic has triggered avid research on its pathobiology to better understand the pathophysiology of COVID-19. In the absence of approved antiviral therapeutic strategies or vaccine platforms capable of effectively targeting this global threat, the hunt for effective therapeutics has led to many candidates being actively evaluated for their efficacy in controlling or preventing COVID-19. In this review, we gathered current evidence on the innate nucleic acid-sensing pathways expected to be elicited by SARS-CoV-2 and the immune evasion mechanisms they have developed to promote viral replication and infection. Within the nucleic acid-sensing pathways, SARS-CoV-2 infection and evasion mechanisms trigger the activation of NOD-signaling and NLRP3 pathways leading to the production of inflammatory cytokines, IL-1β and IL-6, while muting or blocking cGAS-STING and interferon type I and III pathways, resulting in decreased production of antiviral interferons and delayed innate response. Therefore, blocking the inflammatory arm and boosting the interferon production arm of nucleic acid-sensing pathways could facilitate early control of viral replication and dissemination, prevent disease progression, and cytokine storm development. We also discuss the rationale behind therapeutic modalities targeting these sensing pathways and their implications in the treatment of COVID-19.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".