Introduction: The rapid emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and their potential to endangering the global health has increased the demand for a fast-tracking method in comparison to the next-generation sequencing (NGS) as a gold standard assay, particularly in developing countries. This study was designed to evaluate the performance of a commercial multiplex real-time PCR technique (GA SARS-CoV-2 OneStep RT-PCR Kit, Iran) for identification of SARS-CoV-2 variants of concern (VOCs) compared to the Oxford Nanopore NGS assay. Methods: A total of 238 SARS-CoV-2-positive respiratory samples from different waves of COVID-19 in Iran were randomly selected in this study. To determine the SARS-CoV-2 VOC, the samples were analyzed via the commercial triple target assay, GA SARS-CoV-2 OneStep RT-PCR Kit, and NGS as well. Results: The results revealed good concordance between GA SARS-CoV-2 OneStep RT-PCR Kit and NGS for identification of SARS-CoV-2 VOCs. GA SARS-CoV-2 OneStep RT-PCR Kit identified Wuhan, Alpha, and Delta variants with 100% relative sensitivity and specificity. Regarding Omicron subvariants of BA.1, BA.2, and BA.4/5, the relative sensitivity of 100%, 100%, and 81.5% and the relative specificity of 95.3%, 93.5%, and 100% were observed. Conclusion: Overall, GA SARS-CoV-2 OneStep RT-PCR Kit can be used as a rapid and cost-effective alternative to NGS for identification of SARS-CoV-2 VOCs.

Key Points

  • Identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) is mainly done by methods such as next-generation sequencing (NGS).

  • The RT-qPCR-based assays are an appropriate and reliable alternative for NGS in tracking the SARS-CoV-2 VOCs.

Since the causative agent of coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first discovered in Wuhan, Hubei Province, China, on December 31, 2019, more than 600 million confirmed cases including 6 million deaths have been reported to the World Health Organization (WHO) [1, 2]. The 30 kb single-stranded positive-sense RNA genome of this novel pathogen, which is a member of the Betacoronavirus genus, encodes nearly 25 proteins [3]. Similar to other coronaviruses, the SARS-CoV-2 genome is continuously changing due to errors in the process of gene replication [4]. The phylogenetic classification of SARS-CoV-2 has proven to be very challenging due to the emergence of multiple lineages that differ in some nucleotides. To make this process easily accessible, two frequently used platforms have been introduced for epidemiological surveillance of SARS-CoV-2: the Phylogenetic Assignment of Named Global Outbreak (PANGO) lineage, used to classify the genetic diversity, and NextStrain, a collection of open-source tools for visualizing the genetics behind the spread of viral outbreaks and an online application for entire analysis of FASTA sequences [5]. Alongside these tools, the generated sequences are shared and available via the Global Initiative on Sharing All Influenza Data (GISAID) [6]. To prepare the public health for the potential consequences, the World Health Organization (WHO) and its expert partners classify the emerging variants into variants of interest and variants of concern (VOCs) [7]. It is critical to be able to identify new VOCs as soon as possible because of their rapid emergence and spread and potential to evade immune responses. Currently, whole genome sequencing is the gold standard for identifying SARS-CoV-2 variants, while real-time PCR (RT-PCR) is thought to be the most precise and specific method to detect SARS-CoV-2 infections [8, 9]. An alternative to whole genome sequencing are RT-PCR genotyping methods, which are faster and more affordable [10]. In this study, we evaluated a RT-PCR-based genotyping kit as a proxy assay for rapid SARS-CoV-2 VOC detection in comparison to next-generation sequencing (NGS).

Sample Collection

The COVID-19 National Reference Laboratory (CNRL), Pasteur Institute of Iran, received samples of Oro/nasopharyngeal swabs in Viral Transport Medium (VTM) between February 2021 and September 2022 from different regions of Iran. Out of the received specimens, those that passed were tested. Ethical approval for this study was obtained from the Ethics Committee of the Pasteur Institute of Iran (IR.PII.REC.1399.073).

From the received specimens which passed quality control (≃2000 samples), a total number of 238 SARS-CoV-2 positive samples were randomly chosen, of which 60 samples were selected from “February 2020 to June 2020,” during the first wave of the pandemic, i.e., the Wuhan strain outbreak, 58 samples from “March 2021 to August 2021,” representing a period of Alpha variant predominance, 61 samples were selected from the Delta variant outbreak, “April 2021 to September 2021,” and the other 62 samples were chosen from “February 2022 to August 2022,” representing the Omicron variant outbreak. The sequence of the workflow is diagnostic PCR, then PCR-genotyping assay, and finally, variant identification by NGS.

Viral Genomic RNA Extraction

Total RNA was extracted using Nucleic Acid Extraction Kit (Zybio, China) by the automated nucleic acid isolation system EXM3000 (Zybio, China) in Biological Safety Level 2 (BSL-2) environment. Briefly, 200 μL of samples and 15 μL of proteinase K were loaded onto the plate, followed by binding to magnetic beads. After washing the beads to remove any unbound samples, the RNA was finally eluted and stored at −20°C until further analyses.

SARS-CoV-2 RT-PCR

The extracted RNA was tested for SARS-CoV-2 infection using 2019-nCoV Nucleic Acid Diagnostic Kit (Sansure Biotech, Changsha, China) as previously described [11]. Briefly, the RNA was reverse transcribed at 50°C for 30 min, followed by primary denaturation at 95°C for 1 min, 45 cycles of 15 s at 95°C for secondary denaturation, and 30 s at 60°C for annealing/extension, performed by Qiagen Rotor-Gene Q (Qiagen, Germany).

Variant Identification Based on GA SARS-CoV-2 OneStep RT-PCR Kit (GeneovA, Iran)

The GA SARS-CoV-2 OneStep RT-PCR Kit (GeneovA, Iran) which is a commercial triple target assay intended for detection of specific mutations in S, N & ORF1a genes of SARS-CoV-2, was utilized according to the manufacturer’s instructions. The GA SARS-CoV-2 OneStep RT-PCR Kit’s principle of discrimination is established based on two deletions including 3,675–3,677 and 21,765–21770 in ORF1a, and Spike gene (69/70 Del), respectively. As noted in (Table 1) presence or absence of one, both, or none of these two deletions determines which variant should be interpreted. Briefly, following the addition of 10 µL of extracted RNA to 10 µL of reaction mix, the thermal cycling program, 53°C for 15 min, 95°C for 3 min, and 46 cycle of 5 s at 93°C for denaturation and 25 s at 60°C for annealing/extension was operated using Qiagen Rotor-Gene Q (Qiagen, Germany). The detection of S, N & ORF1a mutations was performed at FAM, HEX, TEXAS RED, and CY5 channels, respectively.

Table 1.

Interpretation of the GA SARS-CoV-2 OneStep RT-PCR Kit results based on the excited fluorescence

Variants/subvariantsFluorescent dye channels
FAMTEXAS REDCY5
Wuhan Detected Detected Detected 
Alpha Detected Not detected Not detected 
Delta Detected Detected Detected 
Omicron 21K Detected Detected Not detected 
Omicron 21L Detected Not detected Detected 
Omicron 22A/22B Detected Not detected Not detected 
Variants/subvariantsFluorescent dye channels
FAMTEXAS REDCY5
Wuhan Detected Detected Detected 
Alpha Detected Not detected Not detected 
Delta Detected Detected Detected 
Omicron 21K Detected Detected Not detected 
Omicron 21L Detected Not detected Detected 
Omicron 22A/22B Detected Not detected Not detected 

Presence or absence of one, both, or none of these two deletions, 3675–3677 in ORF1a and 21765–21770 in the Spike gene (69/70 Del), determines which variant should be interpreted.

Variant Identification Based on Oxford Nanopore Technologies (ONT) NGS

In order to evaluate the applicability of samples for NGS, they were filtered by cycle threshold (Ct) < 25 for N & ORF1ab genes by RT-qPCR assay. The library preparation for NGS was accomplished using Midnight RT-PCR Expansion (EXP-MRT001) and rapid barcoding SQK-RBK110.96 (ONT, UK). First, cDNA was synthesized using LunaScript RT SuperMix, then the whole genome was amplified using Q5 HS Master Mix (ONT, UK) and two sets of primers, Midnight Primer Pool A (MP A), Midnight Primer Pool B (MP B). The PCR products were then combined and their concentrations were quantified via Qubit 4 Fluorometer (ThermoFisher Scientific, USA) using the Qubit dsDNA HS Assay Kit (ThermoFisher Scientific, USA). The high-quality PCR products were barcoded with Rapid Barcode Plate RB96 (ONT, UK). Then the final pooled library was cleaned up using SPRI beads (ONT, UK) and ligated with Rapid Adapter F (RAP F). Finally, the prepared library was loaded on the Flow Cell (R9.4.1, ONT, UK) and ONT GridION machine. MinKNOW software (ONT, UK) was used for basecalling, FASTQ quality control (QC), variant calling, and FASTA Generation. Then QC of the passed FASTA files is aligned to the SARS-CoV-2 reference genome (NCBI RefSeq NC_045512.2) using Nextclade [12], which is an online application for aligning sequences to reference genome, identifying mutations, determining clades, and quality controlling [13].

Statistical Analysis

Quantitative variables were summarized using the mean (standard deviation), median, and interquartile range. Distribution of Omicron lineages was assessed, defined with number and relative frequencies. Association between Omicron lineage and SARS-CoV-2 infection outcome was assessed with Pearson χ2 test. To investigate the level of agreement between real-time RT-PCR GA SARS-CoV-2 OneStep RT-PCR Kit and NGS results, Kappa statistic was estimated. Kappa values above 0.8 were considered to be in near perfect agreement. Also, p < 0.05 showed agreements that were beyond chance alone. To evaluate the diagnostic accuracy of real-time RT-PCR GA SARS-CoV-2 OneStep RT-PCR Kit in detecting Alpha, Delta, BA.4/5, BA.2, and BA.1 variants, sensitivity, specificity, positive and negative likelihood ratio, and positive and negative predictive values were calculated considering NGS results as the gold standard. These indices included sensitivity, specificity, positive and negative likelihood ratio, and positive and negative predictive value. Data were analyzed using Stata SE (V.4).

Patients Clinical Features

A total of 238 SARS-CoV-2-infected cases from different timeframes of the COVID-19 pandemic in Iran were analyzed for SARS-CoV-2 VOCs using real-time RT-PCR GA SARS-CoV-2 OneStep RT-PCR Kit and NGS, as well. Participants’ mean age was 43.2 years (SD = 18.7). They were equally of both genders (male = 53.1%). COVID-19-associated hospitalization and death were collectively confirmed in 31.5% of participants. Based on the NGS results, the frequency of the Wuhan strain and three VOCs (Alpha, Delta, and Omicron) were relatively the same (Table 2).

Table 2.

Distribution of participants’ demographics and SARS-CoV-2 variants

Variablen (%)
Age 
 Mean (SD) 43.2 (18.7) 
 Min - max 0, 89 
Gender 
 Male 122 (51.3) 
 Female 116 (48.7) 
COVID-19 outcome 
 Outpatient 144 (68.6) 
 Hospitalization 56 (26.7) 
 Death 10 (4.8) 
Variablen (%)
Age 
 Mean (SD) 43.2 (18.7) 
 Min - max 0, 89 
Gender 
 Male 122 (51.3) 
 Female 116 (48.7) 
COVID-19 outcome 
 Outpatient 144 (68.6) 
 Hospitalization 56 (26.7) 
 Death 10 (4.8) 

Comparison of Real-Time RT-PCR GA SARS-CoV-2 OneStep RT-PCR Kit with NGS

Consequently, all screened samples including 60 Wuhan, 58 Alpha, 61 Delta, and 62 Omicron (BA.1, BA.2, BA.4/BA.5) were analyzed by NGS testing. As noted in Table 2 which includes information about the NGS results, 59 Wuhan (19A-B.4), one 20B, 58 Alpha (20I), 7 Delta (21A), 54 Delta (21J), 21 Omicron (21K), 19 Omicron (21L), 2 Omicron (22A), and 20 Omicron (22B) variants were identified.

There was full agreement between NGS and real-time RT-PCR GA SARS-CoV-2 OneStep RT-PCR Kit method in identifying SARS-CoV-2 VOCs (Kappa = 100%; p < 0.0001). The agreement between both methods at the Omicron subvariant level was very good (Kappa = 0.88%; p < 0.0001). Similarly, the discrimination power of the real-time RT-PCR GA SARS-CoV-2 OneStep RT-PCR Kit in identifying SARS-CoV-2 variants was perfect. The real-time RT-PCR GA SARS-CoV-2 OneStep RT-PCR Kit had excellent discrimination power at the level of Omicron subvariants, with 100% sensitivity in detecting BA.1 and BA.2 and 81.5% sensitivity in detecting BA.4/5 (Tables 3-4).

Table 3.

Agreement between next-generation sequencing (NGS) and GeneovA in differentiation of SARS-CoV-2 variants at variant and subvariant levels

GeneovANGS methodKappa (SE)*p value
WuhanAlphaDeltaOmicron
BA4/5BA2BA1
 Wuhan 60 0.97 <0.0001 
Alpha 58 
Delta 61 
Omicron BA.4/5 22 
BA.2 16 
BA.1 19 
GeneovANGS methodKappa (SE)*p value
WuhanAlphaDeltaOmicron
BA4/5BA2BA1
 Wuhan 60 0.97 <0.0001 
Alpha 58 
Delta 61 
Omicron BA.4/5 22 
BA.2 16 
BA.1 19 
Next-generation sequencing (NGS) typing resultsn (%)
Wuhan/19A-B.4 59 (24.5) 
 20B 1 (0.4) 
 20I/Alpha 58 (24.1) 
Delta 61 (25.4) 
 21A/Delta 7 (2.9) 
 21J/Delta 54 (22.4) 
Omicron 62 (25.8) 
 21K (BA.1) 9 (3.75) 
 21K (BA.1.1) 10 (4.2) 
 21K (BA.1.17) 2 (0.8) 
 21L (BA.2) 18 (7.5) 
 21L (BA.2.38) 1 (0.4) 
 22A (BA.4) 1 (0.4) 
 22A (BA.4.1) 1 (0.4) 
 22B (BA.5.2) 17 (7.1) 
 22B (BA.5.2.1) 2 (0.8) 
 22B (BA.5.6) 1 (0.4) 
Next-generation sequencing (NGS) typing resultsn (%)
Wuhan/19A-B.4 59 (24.5) 
 20B 1 (0.4) 
 20I/Alpha 58 (24.1) 
Delta 61 (25.4) 
 21A/Delta 7 (2.9) 
 21J/Delta 54 (22.4) 
Omicron 62 (25.8) 
 21K (BA.1) 9 (3.75) 
 21K (BA.1.1) 10 (4.2) 
 21K (BA.1.17) 2 (0.8) 
 21L (BA.2) 18 (7.5) 
 21L (BA.2.38) 1 (0.4) 
 22A (BA.4) 1 (0.4) 
 22A (BA.4.1) 1 (0.4) 
 22B (BA.5.2) 17 (7.1) 
 22B (BA.5.2.1) 2 (0.8) 
 22B (BA.5.6) 1 (0.4) 
GeneovA typing resultsn (%)
Wuhan 60 (24.9) 
Alpha 58 (24.1) 
Delta 61 (25.3) 
Omicron 62 (25.7) 
 BA.1 19 (7.9) 
 BA.2 16 (6.6) 
 BA.4/BA.5 27 (11.2) 
GeneovA typing resultsn (%)
Wuhan 60 (24.9) 
Alpha 58 (24.1) 
Delta 61 (25.3) 
Omicron 62 (25.7) 
 BA.1 19 (7.9) 
 BA.2 16 (6.6) 
 BA.4/BA.5 27 (11.2) 

SE, standard error. *Agreement (Kappa) between two methods in differentiating at variant level (i.e., Wuhan, Alpha, Delta, and Omicron) was 100% (SE: 0.03; p < 0.0001). Agreement (Kappa) between two methods in differentiating Omicron sub-lineage was 0.88 (SE: 0.08; p < 0.0001).

Table 4.

Sensitivity of GeneovA in detection of the SARS-CoV-2 at variant and subvariant levels

GeneovANext-generation sequencing (NGS)Sensitivity (95% CI)Specificity (95% CI)LR (+)LR (−)PPV (95% CI)NPV (95% CI)
positivenegative
Wuhan 
 Positive 60 100 (94.0, 100.0) 100 (98.0, 100.0) 100 (94.0, 100.0) 100 (98.0, 100.0) 
 Negative 181 
Alpha 
 Positive 58 100 (93.8, 100.0) 100 (98.0, 100.0) 100 (93.8, 100.0) 100 (98.0, 100.0) 
 Negative 183 
Delta 
 Positive 61 100 (94.1, 100.0) 100 (98.0, 100.0) 100 (94.1, 100.0) 100 (98.0, 100.0) 
 Negative 180 
Omicron 
 Positive 62 100 (94.2, 100.0) 100 (98.0, 100.0) 100 (94.2, 100.0) 100 (98.0, 100.0) 
 Negative 179 
Omicron subvariants 
 BA.4/5 
  Positive 22 81.5 (61.9, 93.7) 100 (90, 100) 0.2 (0.1, 0.4) 100 (84.6, 100) 87.5 (73.2, 95.8) 
  Negative 35 
 BA.2 
  Positive 16 100 (79.4, 100) 93.5 (82.1, 98.6) 15.3 (5.1, 45.8) 84.2 (60.4, 96.6) 100 (91.8, 100) 
  Negative 43 
 BA.1 
  Positive 19 100 (82.4, 100) 95.3 (84.2, 99.4) 21.5 (5. 6, 83.2) 90.5 (69.6, 98.8) 100 (91.4, 100) 
  Negative 41 
GeneovANext-generation sequencing (NGS)Sensitivity (95% CI)Specificity (95% CI)LR (+)LR (−)PPV (95% CI)NPV (95% CI)
positivenegative
Wuhan 
 Positive 60 100 (94.0, 100.0) 100 (98.0, 100.0) 100 (94.0, 100.0) 100 (98.0, 100.0) 
 Negative 181 
Alpha 
 Positive 58 100 (93.8, 100.0) 100 (98.0, 100.0) 100 (93.8, 100.0) 100 (98.0, 100.0) 
 Negative 183 
Delta 
 Positive 61 100 (94.1, 100.0) 100 (98.0, 100.0) 100 (94.1, 100.0) 100 (98.0, 100.0) 
 Negative 180 
Omicron 
 Positive 62 100 (94.2, 100.0) 100 (98.0, 100.0) 100 (94.2, 100.0) 100 (98.0, 100.0) 
 Negative 179 
Omicron subvariants 
 BA.4/5 
  Positive 22 81.5 (61.9, 93.7) 100 (90, 100) 0.2 (0.1, 0.4) 100 (84.6, 100) 87.5 (73.2, 95.8) 
  Negative 35 
 BA.2 
  Positive 16 100 (79.4, 100) 93.5 (82.1, 98.6) 15.3 (5.1, 45.8) 84.2 (60.4, 96.6) 100 (91.8, 100) 
  Negative 43 
 BA.1 
  Positive 19 100 (82.4, 100) 95.3 (84.2, 99.4) 21.5 (5. 6, 83.2) 90.5 (69.6, 98.8) 100 (91.4, 100) 
  Negative 41 

LR, likelihood ratio; PPV, positive predictive value; NPV, negative predictive value.

In order to control infection, hospitalization, and mortality rates, it is essential to keep an eye on the novel SARS-CoV-2 variants. NGS and Sanger sequencing are regarded as the gold standard for tracking SARS-CoV-2 mutations. Alternatively, RT-qPCR targeting specific mutations can be utilized to track newly discovered variants in routine clinical diagnosis. This study compared a commercially available kit (GA SARS-CoV-2 OneStep RT-PCR Kit) with NGS for identification of SARS-CoV-2 VOCs. GA SARS-CoV-2 OneStep RT-PCR Kit could identify Wuhan, Alpha, Delta variants with 100% sensitivity and specificity, and Omicron subvariants with 100%, 100%, and 81.5% sensitivity, and 95.3%, 93.5%, and 100% specificity, for BA.1, BA.2, and BA.4/5, respectively (Table 4).

Other studies noted that NGS and RT-qPCR-based variant detection can both detect known mutations, which can enhance the epidemiological surveillance of SARS-CoV-2 [1, 14]. However, both approaches are sensitive and reliable, and RT-qPCR can provide known mutation detection with specific designed primers and probes. NGS, in contrast, has the ability to detect full genome mutations in a single test reaction. As a result, the RT-qPCR-based results are fundamental and might require NGS for conformation. Additionally, RT-qPCR assays needed to be redesigned due to emerging new variants [9, 15]. In addition to these restrictions, RT-PCR genotyping can be used as a test for samples with low viral loads that NGS cannot detect [15]. According to the results, this study recommends using RT-qPCR-specific mutation tracking approach due to its low cost, time saving, and user-friendly analysis, almost as trustworthy as NGS results.

The authors expressed their gratitude to all personnel at the COVID-19 National Reference Laboratory.

Ethical approval for this study was obtained from the Ethics Committee of Pasteur Institute of Iran (IR.PII.REC.1399.073).

The authors declare that they have no conflict of interest related to this work.

This study was financially supported by the Pasteur Institute of Iran (Grant No. 1824).

Zahra Ahmadi and Ali Maleki equally wrote the manuscript. Zahra Fereydouni, Setareh Kashanian, and Laya Farhan Asadi carried out the experiments. Amir Hesam Nemati contributed to the sample preparation. Mahsa Tavakoli and Sana Eybpoosh analyzed the data. Mostafa Salehi-Vaziri designed and supervised the project. Zahra Ahmadi, Ali Maleki, and Mostafa Salehi-Vaziri contributed to the final version of the manuscript.

Additional Information

Zahra Ahmadi and Ali Maleki contributed equally to this work.

Data are not publicly available due to ethical reasons. Further enquiries can be directed to the corresponding author. A preprint version of this article is available as well [16].

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