Neglected SARS-CoV-2 variants and potential concerns for molecular diagnostics: a framework for nucleic acid amplification test target site quality assurance
Notice bibliographique
Résumé
ABSTRACT During the COVID-19 pandemic, SARS-CoV-2 detection using nucleic acid amplification tests (NAATs) played a key role in clinical management and public health interventions. However, mutations could jeopardize NAAT-based detection if they occur in the NAAT target site, potentially resulting in false negative results. However, mutation monitoring is challenged as the exact location of commercial NAAT target sites is not divulged by manufacturers. This study sequenced commercial SARS-CoV-2 NAAT target sites to assess the impact of mutations occurring in these regions. The resulting sequences for the Xpert, Cobas, and ID NOW SARS-CoV-2 assays were queried against SARS-CoV-2 genome databases to identify mutations in circulating strains. Synthetic DNAs and clinical specimens harboring NAAT target site mutations were used to assess mutation impact. Of 17,600 NAAT target site mutation occurrences in a genome database, 269 compromised target detection. These represented 24 unique mutations that reduced NAAT target sensitivity and nine led to target detection failure. Only seven of these mutations were previously recognized. Overall, this reactive strategy along with passive surveillance identified 29 novel mutations that compromised detection with Xpert and Cobas targets. Knowledge of commercial NAAT target sites, paired with a strategy for mutation impact assessment and ongoing genetic surveillance, provided a robust framework for commercial NAAT target site quality assurance. The question remains of who should be responsible for NAAT target site quality assurance, but collaborative efforts between methods users, industry, and regulatory agencies would be ideal. IMPORTANCE Molecular tests like polymerase chain reaction were widely used during the COVID-19 pandemic but as the pandemic evolved, so did SARS-CoV-2. This virus acquired mutations, prompting concerns that mutations could compromise molecular test results and be falsely negative. While some manufacturers may have in-house programs for monitoring mutations that could impact their assay performance, it is important to promptly report mutations in circulating viral strains that could adversely impact a diagnostic test result. However, commercial test target sites are proprietary, making independent monitoring difficult. In this study, SARS-CoV-2 test target sites were sequenced to monitor and assess mutations impact, and 29 novel mutations impacting SARS-CoV-2 detection were identified. This framework for molecular test target site quality assurance could be adapted to any molecular test, ensuring accurate diagnostic test results and disease diagnoses.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».