Collaboration Between Host and Viral Factors Shape SARS-CoV-2 Evolution
Bibliographic record
Abstract
Abstract SARS-CoV-2 continues to evolve, resulting in several ‘variants of concern’ with novel properties. The factors driving SARS-CoV-2 fitness and evolution in the human respiratory tract remain poorly defined. Here, we provide evidence that both viral and host factors co-operate to shape SARS-CoV-2 genotypic and phenotypic change. Through viral whole-genome sequencing, we explored the evolution of two clinical isolates of SARS-CoV-2 during passage in unmodified Vero-derived cell lines and in parallel, in well-differentiated primary nasal epithelial cell (WD-PNEC) cultures. We identify a consistent, rich genetic diversity arising in vitro, variants of which could rapidly rise to near-fixation with 2 passages. Within isolates, SARS-CoV-2 evolution was dependent on host cells, with Vero-derived cells facilitating more profound genetic changes. However, most mutations were not shared between strains. Furthermore, comparison of both Vero-grown isolates on WD-PNECs disclosed marked growth attenuation mapping to the loss of the polybasic cleavage site (PBCS) in Spike while the strain with mutations in NSP12 (T293I), Spike (P812R) and a truncation of ORF7a remained viable in WD-PNECs. Our work highlights the significant genetic plasticity of SARS-CoV-2 while uncovering an influential role for collaboration between viral and host cell factors in shaping viral evolution and fitness in human respiratory epithelium.
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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 | 0.001 | 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".