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. 2024 Jan 12;19(1):e0294271.
doi: 10.1371/journal.pone.0294271. eCollection 2024.

Monitoring SARS-CoV-2 genetic variability: A post-market surveillance workflow for combined bioinformatic and laboratory evaluation of commercial RT-PCR assay performance

Affiliations

Monitoring SARS-CoV-2 genetic variability: A post-market surveillance workflow for combined bioinformatic and laboratory evaluation of commercial RT-PCR assay performance

Barbara Kosińska-Selbi et al. PLoS One. .

Abstract

Objective: The speed at which Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is mutating has made it necessary to frequently assess how these genomic changes impact the performance of diagnostic real-time polymerase chain reaction (RT-PCR) assays. Herein, we describe a generic three-step workflow to assess the effect of genomic mutations on inclusivity and sensitivity of RT-PCR assays.

Methods: Sequences collected from the Global Initiative on Sharing All Influenza Data (GISAID) were mapped to a SARS-CoV-2 reference genome to evaluate the position and prevalence of mismatches in the oligonucleotide-binding sites of the QIAstat-Dx, an RT-PCR panel designed to detect SARS-CoV-2. The frequency of mutations and their impact on melting temperature were assessed, and sequences flagged by risk-based criteria were examined in vitro.

Results: Out of 8,900,393 SARS-CoV-2 genome sequences analyzed, only 173 (0.0019%) genomes contained potentially critical mutations for the QIAstat-Dx; follow-up in-vitro testing confirmed no impact on the assays' performance.

Conclusions: The current study demonstrates that SARS-CoV-2 genetic variants do not affect the performance of the QIAstat-Dx device. It is recommended that manufacturers incorporate this workflow into obligatory post-marketing surveillance activities, as this approach could potentially enhance genetic monitoring of their product.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: All authors were QIAGEN employees at the time of writing this manuscript.

Figures

Fig 1
Fig 1. Scheme of operations for detecting mutations from regions extracted from the mapping.

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