Emergence of a mutation in the nucleocapsid gene of SARS-CoV-2 interferes with PCR detection in Canada
Bibliographic record
Abstract
The emergence of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) was met with rapid development of robust molecular-based detection assays. Many SARS-CoV-2 molecular tests target multiple genetic regions of the virus to maximize detection and protect against diagnostic escape. Despite the relatively moderate mutational rate of SARS-CoV-2, numerous mutations with known negative impact on diagnostic assays have been identified. In early 2021, we identified four samples positive for SARS-CoV-2 with a nucleocapsid (N) gene drop out on Cepheid Xpert® Xpress SARS-CoV-2 assay. Sequencing revealed a single common mutation in the N gene C29200T. Spatiotemporal analysis showed that the mutation was found in at least six different Canadian provinces from May 2020 until May 2021. Phylogenetic analysis showed that this mutation arose multiple times in Canadian samples and is present in six different variants of interest and of concern. The Cepheid testing platform is commonly used in Canada including in remote regions. As such, the existence of N gene mutation dropouts required further investigation. While commercial SARS-CoV-2 molecular detection assays have contributed immensely to the response effort, many vendors are reluctant to make primer/probe sequences publicly available. Proprietary primer/probe sequences create diagnostic 'blind spots' for global SARS-CoV-2 sequence monitoring and limits the ability to detect and track the presence and prevalence of diagnostic escape mutations. We hope that our industry partners will seriously consider making primer/probe sequences available, so that diagnostic escape mutants can be identified promptly and responded to appropriately to maintain diagnostic accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".