Variable routes to genomic and host adaptation among coronaviruses
Bibliographic record
Abstract
Abstract Natural selection operating on the genomes of viral pathogens in different host species strongly contributes to adaptation facilitating host colonization. Here, we analyse, quantify and compare viral adaptation in genomic sequence data derived from seven zoonotic events in the Coronaviridae family among primary, intermediate and human hosts. Rates of nonsynonymous (dN) and synonymous (dS) changes on specific amino acid positions were quantified for each open reading frame (ORF). Purifying selection accounted for 77% of all sites under selection. Diversifying selection was most frequently observed in viruses infecting the primary hosts of each virus and predominantly occurred in the orf1ab genomic region. Within all four intermediate hosts, diversifying selection on the spike gene was observed either solitarily or in combination with orf1ab and other genes. Consistent with previous evidence, pervasive diversifying selection on coronavirus spike genes corroborates the role this protein plays in host cellular entry, adaptation to new hosts and evasion of host cellular immune responses. Structural modelling of spike proteins identified a significantly higher proportion of sites for SARS‐CoV‐2 under positive selection in close proximity to sites of glycosylation relative to the other coronaviruses. Among human coronaviruses, there was a significant inverse correlation between the number of sites under positive selection and the estimated years since the virus was introduced into the human population. Abundant diversifying selection observed in SARS‐CoV‐2 suggests the virus remains in the adaptive phase of the host switch, typical of recent host switches. A mechanistic understanding of where, when and how genomic adaptation occurs in coronaviruses following a host shift is crucial for vaccine design, public health responses and predicting future pandemics.
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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 | 0.000 |
| 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) | 0.001 | 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".