Multiplex RT-qPCR strategy for SARS-CoV-2 variants detection in developing countries without ngs: The Bolivian experience

The rapid evolution of SARS-CoV-2 has led to the emergence of variants of concern (VOCs) characterized by increased transmissibility, pathogenicity, and resistance to neutralizing antibodies. Identifying these variants is essential for guiding public health efforts to control COVID-19. Although whole genome sequencing (WGS) is the gold standard for variant identification, its implementation is often limited in developing countries due to resource constraints. In Bolivia, genomic surveillance is a challenge due to its limited technological infrastructure and resources. An RT-qPCR-based strategy was designed to address these limitations and detect the mutations associated with VOCs and variants of interest (VOIs). The multiplex RT-qPCR commercial kits Allplex<sup>TM</sup> Master and Variants I (Seegene®) and the ValuPanel<sup>TM</sup> (Biosearch®) were used to target mutations such as HV69/70del, E484K, N501Y, P681H, and K417N/T. They are characteristic of the Alpha (B.1.1.7), Beta (B.1.531), Gamma (P.1), Omicron (B.1.1.529), Mu (B.1.621), and Zeta (P.2) variants. A total of 157 samples collected in Cochabamba from January to November 2021 were evaluated, identifying 44 Gamma, 2 Zeta, 20 Mu, and 10 Omicron were identified. The strategy's effectiveness was validated against WGS data generated with Oxford Nanopore<sup>TM</sup> technology, showing a concordance rate of 0.96. This highlights the value of the RT-qPCR strategy in guiding the selection of samples for WGS, enabling broader detection of new variants that cannot be identified by RT-qPCR alone.

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Publication Details

Journal
Epidemiology and Infection
Published
2025-01-01
DOI
https://doi.org/10.1017/s095026882510037x
Citations
1
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.36

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article

Multiplex RT-qPCR strategy for SARS-CoV-2 variants detection in developing countries without ngs: The Bolivian experience

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1 citations
Epidemiology and Infection
COVID-19 diagnosis using AI
1.36
article

Multiplex RT-qPCR strategy for SARS-CoV-2 variants detection in developing countries without ngs: The Bolivian experience

Yercin Mamani Ortiz, Javier Méndez, Nair A. Montaño, Lineth García, Catalina Pardo‐Roa, Irenice Coronado-Arrázola, Rafael Medina, Eugenia Fuentes-Luppichini, Nattaly Grecia Torrico Villarroel, Leonardo I. Almonacid, Rudy Parrado, Carolina X Cuba-Grandy, Betty Melgarejo
article en
1 citations

Abstract

The rapid evolution of SARS-CoV-2 has led to the emergence of variants of concern (VOCs) characterized by increased transmissibility, pathogenicity, and resistance to neutralizing antibodies. Identifying these variants is essential for guiding public health efforts to control COVID-19. Although whole genome sequencing (WGS) is the gold standard for variant identification, its implementation is often limited in developing countries due to resource constraints. In Bolivia, genomic surveillance is a challenge due to its limited technological infrastructure and resources. An RT-qPCR-based strategy was designed to address these limitations and detect the mutations associated with VOCs and variants of interest (VOIs). The multiplex RT-qPCR commercial kits Allplex<sup>TM</sup> Master and Variants I (Seegene®) and the ValuPanel<sup>TM</sup> (Biosearch®) were used to target mutations such as HV69/70del, E484K, N501Y, P681H, and K417N/T. They are characteristic of the Alpha (B.1.1.7), Beta (B.1.531), Gamma (P.1), Omicron (B.1.1.529), Mu (B.1.621), and Zeta (P.2) variants. A total of 157 samples collected in Cochabamba from January to November 2021 were evaluated, identifying 44 Gamma, 2 Zeta, 20 Mu, and 10 Omicron were identified. The strategy's effectiveness was validated against WGS data generated with Oxford Nanopore<sup>TM</sup> technology, showing a concordance rate of 0.96. This highlights the value of the RT-qPCR strategy in guiding the selection of samples for WGS, enabling broader detection of new variants that cannot be identified by RT-qPCR alone.

Epidemiology and InfectionVol. 153
Emory University (US), Pontificia Universidad Católica de Chile (CL), Emory and Henry College (US), Universidad Mayor de San Simón (BO), Instituto de Investigaciones en Ciencias de la Salud (AR), National Influenza Center (MN), Institut d'Investigacions Biomèdiques de Barcelona (ES), Universidad Privada Abierta Latinoamericana (BO)
University of Cambridge, National Institutes of Health, National Institute of Allergy and Infectious Diseases
Partnerships for the goals
Openalex Percentile: Top 17%
COVID-19 diagnosis using AI
1.36
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