Monitoring the SARS-CoV-2 pandemic: screening algorithm with single nucleotide polymorphism detection for the rapid identification of established and emerging variants
To evaluate a testing algorithm for the rapid identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that includes the use of PCR-based targeted single nucleotide polymorphism (SNP) detection assays preceded by a multiplex PCR sensitive to S-Gene Target Failure (SGTF...
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| Published in | Clinical microbiology and infection Vol. 28; no. 1; pp. 124 - 129 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
01.01.2022
European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1198-743X 1469-0691 1469-0691 |
| DOI | 10.1016/j.cmi.2021.09.007 |
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| Abstract | To evaluate a testing algorithm for the rapid identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that includes the use of PCR-based targeted single nucleotide polymorphism (SNP) detection assays preceded by a multiplex PCR sensitive to S-Gene Target Failure (SGTF).
PCR SNP assays targeting SARS-CoV-2 S-gene mutations ΔH69–V70, L452R, E484K, N501Y, H655Y and P681R using melting curve analysis were performed on 567 samples in which SARS-CoV-2 viral RNA was detected by a multiplex PCR. Viral whole-genome sequencing (WGS) was performed to confirm the presence of SNPs and to identify the Pangolin lineage. Additionally, 1133 SARS-CoV-2 positive samples with SGTF were further assessed by WGS to determine the presence of ΔH69–V70.
The N501Y-specific assay (n = 567) had an overall percentage agreement (OPA) of 98.5%. The ΔH69-V70-specific (n = 178) and E484K-specific (n = 401) assays had OPA of 96.6% and 99.7%, respectively. Assessment of H655Y (n = 139) yielded a 100.0% concordance when applied in the proposed algorithm. The L452R-specific (n = 67) and P681R-specific (n = 62) assays had an OPA of 98.2% and 98.1%, respectively. The proposed algorithm identified six variants of concern/interest (VOC/VOI)—Alpha (n = 149), Beta (n = 65), Gamma (n = 86), Delta (n = 49), Eta (n = 6), Kappa (n = 6)—and 205 non-VOC/VOI strains—including the variants under monitoring B.1.214.2 (n = 43) and B.1.1.318 (n = 18) and Epsilon (n = 1). An excellent concordance was observed for the identification of all SARS-CoV-2 lineages evaluated.
We present a flexible testing algorithm for the rapid detection of current and emerging SARS-CoV-2 VOC/VOIs, which can be easily adapted based on the local endemicity of specific variants. |
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| AbstractList | To evaluate a testing algorithm for the rapid identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that includes the use of PCR-based targeted single nucleotide polymorphism (SNP) detection assays preceded by a multiplex PCR sensitive to S-Gene Target Failure (SGTF).OBJECTIVESTo evaluate a testing algorithm for the rapid identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that includes the use of PCR-based targeted single nucleotide polymorphism (SNP) detection assays preceded by a multiplex PCR sensitive to S-Gene Target Failure (SGTF).PCR SNP assays targeting SARS-CoV-2 S-gene mutations ΔH69-V70, L452R, E484K, N501Y, H655Y and P681R using melting curve analysis were performed on 567 samples in which SARS-CoV-2 viral RNA was detected by a multiplex PCR. Viral whole-genome sequencing (WGS) was performed to confirm the presence of SNPs and to identify the Pangolin lineage. Additionally, 1133 SARS-CoV-2 positive samples with SGTF were further assessed by WGS to determine the presence of ΔH69-V70.METHODSPCR SNP assays targeting SARS-CoV-2 S-gene mutations ΔH69-V70, L452R, E484K, N501Y, H655Y and P681R using melting curve analysis were performed on 567 samples in which SARS-CoV-2 viral RNA was detected by a multiplex PCR. Viral whole-genome sequencing (WGS) was performed to confirm the presence of SNPs and to identify the Pangolin lineage. Additionally, 1133 SARS-CoV-2 positive samples with SGTF were further assessed by WGS to determine the presence of ΔH69-V70.The N501Y-specific assay (n = 567) had an overall percentage agreement (OPA) of 98.5%. The ΔH69-V70-specific (n = 178) and E484K-specific (n = 401) assays had OPA of 96.6% and 99.7%, respectively. Assessment of H655Y (n = 139) yielded a 100.0% concordance when applied in the proposed algorithm. The L452R-specific (n = 67) and P681R-specific (n = 62) assays had an OPA of 98.2% and 98.1%, respectively. The proposed algorithm identified six variants of concern/interest (VOC/VOI)-Alpha (n = 149), Beta (n = 65), Gamma (n = 86), Delta (n = 49), Eta (n = 6), Kappa (n = 6)-and 205 non-VOC/VOI strains-including the variants under monitoring B.1.214.2 (n = 43) and B.1.1.318 (n = 18) and Epsilon (n = 1). An excellent concordance was observed for the identification of all SARS-CoV-2 lineages evaluated.RESULTSThe N501Y-specific assay (n = 567) had an overall percentage agreement (OPA) of 98.5%. The ΔH69-V70-specific (n = 178) and E484K-specific (n = 401) assays had OPA of 96.6% and 99.7%, respectively. Assessment of H655Y (n = 139) yielded a 100.0% concordance when applied in the proposed algorithm. The L452R-specific (n = 67) and P681R-specific (n = 62) assays had an OPA of 98.2% and 98.1%, respectively. The proposed algorithm identified six variants of concern/interest (VOC/VOI)-Alpha (n = 149), Beta (n = 65), Gamma (n = 86), Delta (n = 49), Eta (n = 6), Kappa (n = 6)-and 205 non-VOC/VOI strains-including the variants under monitoring B.1.214.2 (n = 43) and B.1.1.318 (n = 18) and Epsilon (n = 1). An excellent concordance was observed for the identification of all SARS-CoV-2 lineages evaluated.We present a flexible testing algorithm for the rapid detection of current and emerging SARS-CoV-2 VOC/VOIs, which can be easily adapted based on the local endemicity of specific variants.CONCLUSIONSWe present a flexible testing algorithm for the rapid detection of current and emerging SARS-CoV-2 VOC/VOIs, which can be easily adapted based on the local endemicity of specific variants. To evaluate a testing algorithm for the rapid identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that includes the use of PCR-based targeted single nucleotide polymorphism (SNP) detection assays preceded by a multiplex PCR sensitive to S-Gene Target Failure (SGTF). PCR SNP assays targeting SARS-CoV-2 S-gene mutations ΔH69-V70, L452R, E484K, N501Y, H655Y and P681R using melting curve analysis were performed on 567 samples in which SARS-CoV-2 viral RNA was detected by a multiplex PCR. Viral whole-genome sequencing (WGS) was performed to confirm the presence of SNPs and to identify the Pangolin lineage. Additionally, 1133 SARS-CoV-2 positive samples with SGTF were further assessed by WGS to determine the presence of ΔH69-V70. The N501Y-specific assay (n = 567) had an overall percentage agreement (OPA) of 98.5%. The ΔH69-V70-specific (n = 178) and E484K-specific (n = 401) assays had OPA of 96.6% and 99.7%, respectively. Assessment of H655Y (n = 139) yielded a 100.0% concordance when applied in the proposed algorithm. The L452R-specific (n = 67) and P681R-specific (n = 62) assays had an OPA of 98.2% and 98.1%, respectively. The proposed algorithm identified six variants of concern/interest (VOC/VOI)-Alpha (n = 149), Beta (n = 65), Gamma (n = 86), Delta (n = 49), Eta (n = 6), Kappa (n = 6)-and 205 non-VOC/VOI strains-including the variants under monitoring B.1.214.2 (n = 43) and B.1.1.318 (n = 18) and Epsilon (n = 1). An excellent concordance was observed for the identification of all SARS-CoV-2 lineages evaluated. We present a flexible testing algorithm for the rapid detection of current and emerging SARS-CoV-2 VOC/VOIs, which can be easily adapted based on the local endemicity of specific variants. |
| Author | Mertens, Joachim Xavier, Basil Britto Goossens, Herman Lammens, Christine Vandamme, Sarah Matheeussen, Veerle Loens, Katherine Le Mercier, Marie Coppens, Jasmine Jansens, Hilde |
| Author_xml | – sequence: 1 givenname: Joachim surname: Mertens fullname: Mertens, Joachim organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium – sequence: 2 givenname: Jasmine surname: Coppens fullname: Coppens, Jasmine organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium – sequence: 3 givenname: Katherine surname: Loens fullname: Loens, Katherine organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium – sequence: 4 givenname: Marie surname: Le Mercier fullname: Le Mercier, Marie organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium – sequence: 5 givenname: Basil Britto surname: Xavier fullname: Xavier, Basil Britto organization: Laboratory of Medical Biochemistry, University of Antwerp, Wilrijk, Antwerp, Belgium – sequence: 6 givenname: Christine surname: Lammens fullname: Lammens, Christine organization: Laboratory of Medical Biochemistry, University of Antwerp, Wilrijk, Antwerp, Belgium – sequence: 7 givenname: Sarah surname: Vandamme fullname: Vandamme, Sarah organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium – sequence: 8 givenname: Hilde surname: Jansens fullname: Jansens, Hilde organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium – sequence: 9 givenname: Herman surname: Goossens fullname: Goossens, Herman organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium – sequence: 10 givenname: Veerle surname: Matheeussen fullname: Matheeussen, Veerle email: veerle.matheeussen@uza.be organization: Department of Microbiology and National Reference Centre for Respiratory Pathogens, University Hospital Antwerp, Edegem, Antwerp, Belgium |
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| Copyright | 2021 European Society of Clinical Microbiology and Infectious Diseases Copyright © 2021 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved. 2021 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved. 2021 European Society of Clinical Microbiology and Infectious Diseases |
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| Keywords | Polymerase chain reaction Severe acute respiratory syndrome coronavirus 2 Severe acute respiratory syndrome coronavirus 2 variants Testing algorithm Coronavirus disease 2019 Nucleic acid testing Whole-genome sequencing Melting curve analysis |
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