Genomic surveillance of SARS-CoV-2 in Ontario, Canada reveals biases in mutational patterns between successive epochs delimited by major public health events (Preprint)
Bibliographic record
Abstract
BACKGROUND The emergence of SARS-CoV-2 variants with mutations associated with increased transmissibility and virulence is an ongoing public health concern in Ontario, Canada. Characterizing how the mutational patterns of the SARS-CoV-2 genome have changed over time can shed light on the driving factors, including selection for increased fitness and host immune response, that may contribute to the emergence of novel variants. Moreover, the study of SARS-CoV-2 in the microcosm of Ontario, Canada can reveal how different province-specific public health policies over time may be associated with observed mutational patterns as a model system. OBJECTIVE This study is a comprehensive analysis of single base substitution types, counts, and genomic locations observed in SARS-CoV-2 genomic sequences sampled in Ontario, Canada. Comparisons of mutational patterns were conducted between sequences sampled during four different epochs delimited by major public health events to track the evolution of the SARS-CoV-2 mutational landscape over two years. METHODS In total, 24,244 SARS-CoV-2 genomic sequences and associated metadata sampled in Ontario, Canada from January 1, 2020 to December 31, 2021 were retrieved from the GISAID database. Sequences were assigned to four epochs, delimited by major public health events based on the sampling date. Single base substitutions from each SARS-CoV-2 sequence were identified relative to the MN996528.1 reference genome. Catalogues of single base substitution types and counts were generated to estimate the impact of selection in each open reading frame, and identify mutation clusters. The estimation of mutational fitness over time was calculated using the Augur pipeline. RESULTS In total, 24,244 SARS-CoV-2 genomic sequences and associated metadata sampled in Ontario, Canada from January 1, 2020 to December 31, 2021 were retrieved from the GISAID database. Sequences were assigned to four epochs, delimited by major public health events based on the sampling date. Single base substitutions from each SARS-CoV-2 sequence were identified relative to the MN996528.1 reference genome. Catalogues of single base substitution types and counts were generated to estimate the impact of selection in each open reading frame, and identify mutation clusters. The estimation of mutational fitness over time was calculated using the Augur pipeline. CONCLUSIONS Quantitative analysis of mutational patterns of the SARS-CoV-2 genome in the microcosm of Ontario, Canada within early consecutive epochs of a pandemic tracks the mutational dynamics in a context of public health events that instigate significant shifts in selection and mutagenesis. We observed positive selection of ORF1A and S, highlighting the challenges in the rational design of effective pan-variant therapeutics targeting these regions. The differences in ORF selection, mutation clusters, and mutation diversity may be driven in part by viral evolution in a human population with different immune responses. Continued genomic surveillance of emergent variants will be useful for the design of public health policies in response to the evolving COVID-19 pandemic.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".