Enrichment of G‐to‐U Substitution in SARS‐CoV‐2 Functional Regions and Its Compensation via Concurrent Mutations
Bibliographic record
Abstract
We surveyed single nucleotide variant (SNV) patterns from 5 903 647 complete SARS-CoV-2 genomes. Among 10 012 SNVs, APOBEC-mediated C-to-U (C > U) deamination was the most prevalent, followed by G > U and other RNA editing-related substitutions including (A > G, U > C, G > A). However, C > U mutations were less frequent in functional regions, for example, S protein, intrinsic disordered regions, and nonsynonymous mutations, where G > U were over-represented. Notably, G-loss substitutions rarely appeared together. Instead, G-gain mutations tended to more frequently co-occur with others, with a marked preference in the S protein, suggesting a compensatory mechanism for G loss in G > U mutations. The temporal patterns revealed C > U frequency declined until late 2021 then resurged in early 2022. Conversely, G > U steadily decreased, with a pronounced drop in January 2022, coinciding with reduced COVID-19 severity. Vaccinated individuals exhibited a slightly but significantly higher C > U frequency and a notably lower G > U frequency compared to the unvaccinated group. Additionally, cancer patients had higher G > U frequency than general patients during the same period. Interestingly, none of the C > U SNVs were uniquely identified in 2724 environmental samples. These findings suggest novel functional roles of G > U in COVID-19 symptoms, potentially linked to oxidative stress and reactive oxygen species, while C > U remains the dominant substitution, likely driven by host immune-mediated RNA editing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".