Point mutation bias in SARS-CoV-2 variants results in increased ability to stimulate inflammatory responses
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection induces severe pneumonia and is the cause of a worldwide pandemic. Coronaviruses, including SARS-CoV-2, have RNA proofreading enzymes in their genomes, resulting in fewer gene mutations than other RNA viruses. Nevertheless, variants of SARS-CoV-2 exist and may induce different symptoms; however, the factors and the impacts of these mutations are not well understood. We found that there is a bias to the mutations occurring in SARS-CoV-2 variants, with disproportionate mutation to uracil (U). These point mutations to U are mainly derived from cytosine (C), which is consistent with the substrate specificity of host RNA editing enzymes, APOBECs. We also found the point mutations which are consistent with other RNA editing enzymes, ADARs. For the C-to-U mutations, the context of the upstream uracil and downstream guanine from mutated position was found to be most prevalent. Further, the degree of increase of U in SARS-CoV-2 variants correlates with enhanced production of cytokines, such as TNF-a and IL-6, in cell lines when compared with stimulation by the ssRNA sequence of the isolated virus in Wuhan. Therefore, RNA editing is a factor for mutation bias in SARS-CoV-2 variants, which affects host inflammatory cytokines production.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".