key: cord-0971661-bjoyswiw authors: Losada, Carmen; Rico-Luna, Carla; Otero-Sobrino, Álvaro; Molero-Salinas, Andrea; Buenestado-Serrano, Sergio; Candela, Ana; Pérez-Lago, Laura; Muñoz, Patricia; Catalán, Pilar; de Viedma, Darío García title: Shared Mutations in Emerging SARS-CoV-2 Circulating Variants May Lead to Reverse Transcription-PCR (RT-PCR)-Based Misidentification of B.1.351 and P.1 Variants of Concern date: 2021-10-13 journal: Microbiol Spectr DOI: 10.1128/spectrum.00816-21 sha: 985818bea6c7e753a3e20952df63f8952b104f41 doc_id: 971661 cord_uid: bjoyswiw Reverse transcription-PCRs (RT-PCRs) targeting SARS-CoV-2 variant of concern (VOC) mutations have been developed to simplify their tracking. We evaluated an assay targeting E484K/N501Y to identify B.1.351/P1. Whole-genome sequencing (WGS) confirmed only 72 (59.02%) of 122 consecutive RT-PCR P.1/B.1.351 candidates. Prescreening RT-PCRs must target a wider set of mutations, updated from WGS data from emerging variants. T he recent emergence and successful spread of SARS-CoV-2 variants of concern (VOCs) have triggered alarm (1) . Definitive identification of these variants requires whole-genome sequencing (WGS) to identify the constellation of variant-specific mutations (2) . In the search for a higher throughput, lower costs, and faster availability of data, alternative reverse transcription-PCR (RT-PCR)-based approaches have been proposed to prescreen these variants. For the first VOC triggering a global alarm, namely, B.1.1.7, an RT-PCR approach (Thermo Fisher TaqPath RT-PCR) took advantage of spike gene (S-gene) impaired detection, caused by the 69/70 deletion, which is a B.1.1.7 genetic marker (3) . This RT-PCR demonstrated its usefulness to simplify and accelerate the B.1.1.7 screening in microbiology diagnostic settings. Similarly, other RT-PCR-based assays have been developed by other companies to screen two new worrying VOCs, B.1.351 and P.1, targeting the E484K and N501Y mutations in the S-gene, which are considered markers for these VOCs. Our group has implemented a two-step procedure to screen VOCs. First, detection of SARS-CoV-2 is performed using routine RT-PCR (Thermo Fisher Taq-Path RT-PCR) on nasopharyngeal specimens. This step allows for identification of case candidates infected by the B.1.1.7 variant, based on the demonstration of S-gene target failure due to the spike 69/70 deletion. Second, cases where S-gene target failure was not identified are further screened using one of the new assays for early detection of B. Our findings indicate that incorrect B.1.351/P.1 assignment may occur when using a targeted RT-PCR prescreening approach, based on the identification of a limited number of marker single nuclear polymorphisms (SNPs). The emergent presence of the B.1.621 VOI, sharing the two E484K/N501Y mutations with P.1/B.1.351, is mainly responsible for the incorrect assignments in our study. The misassignments found in our study are not associated with specific technical limitations of the test applied; they are the result of assuming that a proper assignment of VOCs can be performed by targeting a low number of marker mutations. Thus, caution is recommended when applying targeted RT-PCR for VOC screening. In the current context of constant emergence of new variants, commercial solutions devoted to prescreen VOCs should rely on a wider set of mutations to ensure specificity. Mutations targeted by these approaches should be constantly evaluated considering updated WGS data on circulating variants. We used 11 ml of RNA as the template for reverse transcription using Invitrogen SuperScript IV reverse transcriptase (Thermo Fisher Scientific, MA, USA) and random hexamers (Thermo Fisher Scientific). Whole-genome amplification of the coronavirus was done with an Artic_nCov-2019_V3 panel of primers (Integrated DNA Technologies, Inc., Coralville, IA, USA) (artic.network/ncov-2019) and the Q5 Hot Start DNA polymerase (New England Biolabs, Ipswich, MA, USA). Libraries were prepared using the Nextera Flex DNA library preparation kit (Illumina, Inc., CA, USA) following the manufacturer's instructions. Libraries were quantified with the Quantus fluorometer (Promega, WI, USA) before being pooled at equimolar concentrations (4 nM). Next, they were sequenced in pools of up to 17 libraries on the MiSeq system (Illumina, Inc., CA, USA) and the MiSeq reagent microkit v2 (2 Â 151 bp) or in pools of up to 96 libraries with the MiSeq reagent (2 Â 201 bp). FastA files above the GISAID thresholds were deposited at GISAID (Table 1 ). An inhouse analysis pipeline was applied to analyze the sequencing reads. The pipeline can be accessed at https://github.com/pedroscampoy/covid_multianalysis. Briefly, the pipeline goes through the following steps: (i) removal of human reads with Kraken (6), (ii) preprocessing and quality assessment of FASTQ files using fastp v0.20.1 (7) (arguments: -cut tail, -cut-window-size, -cut-mean-quality, -max_len1,-max_len2) and fastQC v0.11.9 (8), (iii) mapping with the Burrows-Wheeler Aligner (BWA) v0.7.17 and variant calling using iVAR v1.2.3 (9) using the Wuhan-1 strain sequence (GenBank accession number NC_045512.2) as the reference, and (iv) recalibration of punctual low-coverage positions using joint variant calling. When necessary, informative noncovered positions were analyzed by standard Sanger sequencing with the corresponding flanking primers from the ARTIC v3 set. Novel SARS-CoV-2 variants: the pandemics within the pandemic The importance of genomic analysis in cracking the coronavirus pandemic S-gene target failure as a marker of variant B.1.1.7 among SARS-CoV-2 isolates in the greater Toronto area COVID-19 weekly epidemiological update, special edition: proposed working definitions of SARS-CoV-2 variants of interest and variants of concern Characterization of the emerging B.1.621 variant of interest of SARS-CoV-2 Kraken: ultrafast metagenomic sequence classification using exact alignments A quality control tool for high throughput sequence data FastQC: a quality control tool for high throughput sequence data An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar We are grateful to Dainora Jaloveckas for editing and proofreading assistance.