Evidence for selection on SARS-CoV-2 RNA translation revealed by the evolutionary dynamics of mutations in UTRs and CDSs
Bibliographic record
Abstract
RNA translation is the rate-limiting step when cells synthesize proteins. Elevating translation efficiency (TE) is intuitively beneficial. Particularly, when viruses invade host cells, how to compete with endogenous RNAs for efficient translation is a major issue to be resolved. We collected millions of worldwide SARS-CoV-2 sequences during the past year and traced the dynamics of allele frequency of every mutation. We defined adaptive and deleterious mutations according to the rise and fall of their frequencies along time. For 5ʹUTR and synonymous mutations in SARS-CoV-2, the selection on TE is evident near start codons. Adaptive mutations generally decrease GC content while deleterious mutations increase GC content. This trend fades away with increasing distance to start codons. Mutations decreasing GC content near start codons would unravel the complex RNA structure and facilitate translation initiation, which are beneficial to SARS-CoV-2, and vice versa. During this evolutionary arms race between human and virus, SARS-CoV-2 tries to improve its cis elements to compete with host RNAs for rapid translation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".