SARS-CoV-2 spike protein–induced cell fusion activates the cGAS-STING pathway and the interferon response
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the unprecedented coronavirus disease 2019 (COVID-19) pandemic. Critical cases of COVID-19 are characterized by the production of excessive amounts of cytokines and extensive lung damage, which is partially caused by the fusion of SARS-CoV-2–infected pneumocytes. Here, we found that cell fusion caused by the SARS-CoV-2 spike (S) protein induced a type I interferon (IFN) response. This function of the S protein required its cleavage by proteases at the S1/S2 and the S2′ sites. We further showed that cell fusion damaged nuclei and resulted in the formation of micronuclei that were sensed by the cytosolic DNA sensor cGAS and led to the activation of its downstream effector STING. Phosphorylation of the transcriptional regulator IRF3 and the expression of IFNB , which encodes a type I IFN, were abrogated in cGAS-deficient fused cells. Moreover, infection with VSV-SARS-CoV-2 also induced cell fusion, DNA damage, and cGAS-STING–dependent expression of IFNB . Together, these results uncover a pathway underlying the IFN response to SARS-CoV-2 infection. Our data suggest a mechanism by which fused pneumocytes in the lungs of patients with COVID-19 may enhance the production of IFNs and other cytokines, thus exacerbating disease severity.
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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 | 0.001 |
| 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".