Underestimation of SARS-CoV-2 in wastewater due to single or double mutations in the N1 qPCR probe binding region
Bibliographic record
Abstract
Wastewater surveillance using RT-qPCR has now been widely adopted to track circulating levels of SARS-CoV-2 virus in many sewersheds. The CDC qPCR assays targeting two regions (N1 and N2) within the N gene are commonly used, but a discrepancy between the two biomarkers has been noticed by independent studies using these methods since late 2021. The reason is presumed to be due to mutations in regions targeted by the N1 qPCR probe. In this study, we systematically investigated and unequivocally confirmed that the underlying reason for this discrepancy was mutations in the N1 probe target, and that a single mutation could cause a significant drop in signal. We first confirmed the proportion of related mutations in wastewater samples (Jan 2021-Dec 2022) using nested PCR and LC-MS. Based on relative proportions of N1 alleles, we separated the wastewater data into four time periods corresponding to different variant waves: Period I (Alpha and Delta waves with 0 mutation), Period II (BA.1/BA.2 waves with a single mutation found in all Omicron strains), Period III (BA.5.2* wave with two mutations), and Period IV (BQ.1* wave with two mutations). Significantly lower N1 copies relative to N2 copies in samples from Periods II-IV compared to those from Period I was observed in wastewater. To further pinpoint the extent to which each mutation impacted N1 quantification, we compared the qPCR response among different synthetic oligomers with corresponding mutations. This study highlighted the impact of even just one or two mutations on qPCR-based wastewater surveillance of SARS-CoV-2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".