F1000ResearchF1000Research2046-1402F1000 Research LimitedLondon, UK10.12688/f1000research.109673.2Research ArticleArticlesDenaturing and dNTPs reagents improve SARS-CoV-2 detection
via single and multiplex RT-qPCR
[version 2; peer review: 2 approved with reservations]
Cadena-CaballeroCristian E.ConceptualizationData CurationFormal AnalysisInvestigationMethodologyResourcesValidationVisualizationWriting – Original Draft PreparationWriting – Review & Editinghttps://orcid.org/0000-0002-1502-49671Vera-CalaLina M.Project AdministrationResourcesSupervisionValidationVisualizationWriting – Review & Editinghttps://orcid.org/0000-0003-3174-81532Barrios-HernandezCarlosProject AdministrationResourcesSoftwareSupervisionValidationVisualizationWriting – Review & Editinghttps://orcid.org/0000-0002-3227-865113Rueda-PlataDiegoData CurationInvestigationMethodologySoftwareValidationhttps://orcid.org/0000-0003-2818-33231Forero-BuitragoLizeth J.Data CurationInvestigationhttps://orcid.org/0000-0003-1645-99861Torres-JimenezCarolina S.Data CurationInvestigationMethodologyhttps://orcid.org/0000-0002-3088-52551Lizarazo-GutierrezErikaData Curation1Agudelo-RodriguezMayraData CurationInvestigationhttps://orcid.org/0000-0002-9908-099X1Martinez-PerezFranciscoConceptualizationFormal AnalysisFunding AcquisitionInvestigationMethodologyProject AdministrationResourcesSupervisionValidationWriting – Original Draft PreparationWriting – Review & Editinghttps://orcid.org/0000-0003-1668-7671a13
Grupo de Investigación Computo Avanzado y a Gran Escala - CAGE, Universidad Industrial de Santander, Bucaramanga, Santander, 680006, Colombia
Grupo de Investigación en Demografía, Salud Pública y Sistemas de Salud - GUINDESS, Universidad Industrial de Santander, Bucaramanga, Santander, 680006, Colombia
Centro de Supercomputación y Cálculo Científico de la Universidad Industrial de Santander -SC3UIS, Universidad Industrial de Santander, Bucaramanga, Santander, 680006, Colombiafjmartin@uis.edu.co
The COVID-19 pandemic, caused by the SARS-CoV-2, can be effectively managed with diagnostic tools such as RT-qPCR. However, it can produce false-negative results due to viral mutations and RNA secondary structures from the target gene sequence.
Methods
With High Performance Computing, the complete SARS-CoV-2 genome was obtained from the GenBank/GISAID to generate consensus sequences to design primers/probes for RT-qPCR.
ORF8 gene was selected, meanwhile,
E and
N and
RNAse P were according to CDC protocol. Nasopharyngeal swab samples were collected from patients diagnosed with SARS-CoV-2. Total RNA was purified according MagMAX kit, it was used in single, and multiplex RT-qPCR. To avoid templated secondary structures, compensate nucleotide proportions and improve Ct values, a solution composed of tetraethylammonium chloride and dimethyl sulfoxide and other with corresponding to dNTPs proportions in accordance SARS-CoV-2 genome were included. Sensitivity and specificity according to Ct values were determined with the Caret package in R software.
Results
126,576 SARS-CoV-2 genomes from January to December 2020 comprised a database. From this, a region near of 5′
ORF8 gene showed three stem-loops was used for primers/FAM-probe. 49 samples were obtained, from them, 22 were positive to gene selected regions. Interestingly, samples from October to November 2020 were positive for all regions, however, in January 2021 different results were observed in
ORF8. An improvement in Ct with the adjuvant solutions was determined in all samples with others SARS-CoV-2 primers/probes, for both single and multiplex RT-qPCR. The inclusion of the denaturing solution in single RT-qPCR increased its sensitivity with respect to the commercial method, while in multiplex the opposite was generated.
Conclusions
Including adjuvant solutions to prevent the formation of RNA secondary structures and the adjustment of the nucleotide ratios of SARS-CoV-2 improved single and multiplex RT-qPCR for viral identification, demonstrating its potential application in health public.
SARS-CoV-2Diagnosis RT-qPCRAdjuvant formulationPrimer and probe designHigh performance computingMinistry of Science, Technology, and Innovation of Colombia (MinCiencias)contractNo.369-2020code1102101576900Vice-Rectory of Research and Extension of Industrial University of SantanderprojectNo.76900This research was supported by the Ministry of Science, Technology, and Innovation of Colombia (MinCiencias) [contract No. 369-2020, code 1102101576900] and by the Vice-Rectory of Research and Extension of Industrial University of Santander [project No. 76900].The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Amendments from Version 1
The Abstract was updated and rewritten to provide clarity in the methods and results section. The description on the use of the databases for the design of the primers/probe for the
ORF8 gene was expanded. In Table 1, the concentrations of the primers, probes and oligonucleotides substrate in total nmole were included. The methodology, result and discussion were included based on the calculations of sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) values for single and multiplex RT-qPCR reactions in SARS-CoV-2 are show in Table 4. The above was implemented with a script using the Caret package in the R software. Figure 2, which presented a syntax error in the primer names, was modified. In addition, the manuscript was corrected according to the comments suggested by the evaluator. Finally, the pertinent references were included in each section.
Introduction
On January 23, 2020, the SARS-CoV-2 virus (
Coronaviridae:
Betacoronavirus:
Severe acute respiratory syndrome-related coronavirus) was declared a public health emergency by the World Health Organization (WHO) International Health Regulations (IHR) Emergency Committee. At the time, the global public health authorities established that the spread of SARS-CoV-2 could be prevented if every nation adopted solid strategies for rapid and accurate disease detection (
WHO, 2020).
Diagnostic methods based on gene-specific primers and probes for the detection of viruses
via gene amplification include quantitative real-time polymerase chain reaction (RT-qPCR) or reverse transcription loop-mediated isothermal amplification (RT-LAMP), both of which can be conducted using oropharyngeal and nasopharyngeal swabs samples from patients (
Kevadiya
et al., 2021). Among these, the former method is considered the most sensitive and accurate for the detection of SARS-CoV-2 and other viruses (
Martín
et al., 2021). This procedure could be effectively implemented due to the characterization of the viral genome, which encompasses approximately 10 genes (
Zhu
et al., 2020). Therefore, WHO authorized the Berlin protocol which relies on the following genes:
ORF1ab, which encodes proteins that enable viral replication (
Nur
et al., 2015);
Spike (
S), which interacts with the receptor of the host’s angiotensin-converting enzyme 2 (ACE2) (
Wan
et al., 2020); and
E, which encodes the structural envelop protein (
Tahan
et al., 2021). Other protocols such as the 2019-nCoV TaqMan RT-qPCR Kit authorized by the United States Centers for Disease Control and Prevention (CDC) utilize two regions of the
N gene (N1-N2), which encode for the nucleocapsid phosphoprotein (
Navarathna
et al., 2021). Moreover, both protocols use the
RNase P gene as a control to assess the efficiency of the RT-qPCR protocol (
WHO, 2009).
Nevertheless, RT-qPCR-based diagnosis is conducted on a per-gene basis and therefore its widespread implementation would be both impractical and costly. Therefore, multiplex RT-qPCR (
e.g., the CDC kit or combined quantification of the
ORF1ab and
S genes) could be implemented as a promising approach to meet the current demand for accurate and cost-efficient diagnosis (
Kudo
et al., 2020). Sample pooling is another approach that could increase population-wide SARS-CoV-2 diagnosis rates and has therefore been approved and implemented in several countries (
Grobe
et al., 2021). However, sample numbers and sampling structure may limit the implementation of this strategy (
Grobe
et al., 2021).
Although RT-qPCR-based SARS-CoV-2 diagnostic tools are generally considered the gold standard for disease detection, several studies have determined that this approach is prone to return false-negative results due to gene mutations (which is often the case for the
E and
N genes) (
Hasan
et al., 2021;
Tahan
et al., 2021) or primer dimer formation (
Jaeger
et al., 2021). Another factor that affects RT-qPCR efficiency is the secondary structure of the RNA to be characterized/quantified (
Hammerling
et al., 2020).
Although previous studies have confirmed the influence of SARS-CoV-2 RNA secondary structure on evolutionary dynamics and transcript regulation, among other factors (
Andrews
et al., 2021;
Huston
et al., 2021;
Rangan
et al., 2020;
Wacker
et al., 2020), previous studies have not established whether this phenomenon directly or indirectly affects RT-qPCR efficiency using patient-derived samples. Moreover, the effects of adjuvants such as DMSO or ammonium salts on RT-qPCR efficiency and viral detection are also largely unknown (
Kovarova & Draber, 2000). Therefore, using high-performance computing (HPC), we consolidated a global repository of SARS-CoV-2 genomes published until December 2020, which we then used to select a region of the
ORF8 gene for both single and multiplex RT-qPCR-based viral detection using the
E gene (Berlin protocol) and the
N gene (CDC protocol) as targets. Furthermore, our findings demonstrated that adjusting the TMCA-DMSO and dNTP proportions of the denaturing solutions based on the nucleotide composition of the virus eliminates the secondary RNA structures that adversely affect the reaction. The detection and diagnosis implications of our findings will be discussed below.
MethodsEthics statement
This study was approved by the Research Ethics Committees of the Industrial University of Santander, the Chicamocha Clinic, and the Chicamocha Clinical Laboratory (L3C) (Bucaramanga, Santander, Colombia). The study participants were patients either hospitalized-diagnosed with SARS-CoV-2 or persons that presented to the emergency room or volunteers. All participants provided written informed consent and voluntarily participated in our study. Further, all participants were kept anonymous and were informed that the present study was conducted strictly for research purposes and that its outcomes were not intended to serve as treatment or diagnosis.
SARS-CoV-2 sample collection and storage
Nasopharyngeal swab samples were acquired by L3C personnel. Two samples were obtained per study participant, one for clinical diagnosis, as authorized by the Ministry of Health and Social Protection of Colombia, and the other for our study. The samples were placed in Universal Transport Medium (UTM) and stored at −80°C until required for downstream analyses (
Rogers
et al., 2020). The samples were collected over the course of one week and then appropriately packaged according to the sanitary legislation of Colombia (
Ministry of Health and Social Protection, 2020) prior to their transportation and processing. All samples were shipped to the Central Research Laboratory of the Industrial University of Santander Health Faculty (LCI-FS-UIS) and maintained at 4°C while the informed consents were processed and digitalized.
Afterwards, the samples were aliquoted in a Class II, Type A2 Biosafety Cabinet (Thermo Fisher Scientific). A total of three aliquots were obtained from each sample, including a 200-μL aliquot for total RNA extraction, which was used immediately, and two 650-μL aliquots used as backup samples, which were stored at −80°C.
SARS-CoV-2 genome database consolidation
Due to the appearance of mutations in several genes of the SARS-CoV-2 reference genome that could generate false negatives with the primers and probes authorized for identification by RT-qPCR. A region coding for the accessory protein ORF8 was selected, hypothesizing that it did not have a high mutation frequency. Therefore, monthly consensus sequences were generated to determine its identity pattern with respect to new genomes. Publicly available SARS-CoV-2 genome sequences were obtained from the GenBank and GISAID databases (RRID:SCR_002760; RRID:SCR_018251) (
Sayers
et al., 2021;
Shu & McCauley, 2017). To gather data from the GenBank database, a Python script (RRID:SCR_008394) was executed in the GUANE-1 High Performance and Scientific Computing Center of the Industrial University of Santander (SC3UIS) through the Entrez Programming Utilities interface using the Biopython package (RRID:SCR_013249; RRID:SCR_007173) (
Cock
et al., 2009;
Geer
et al., 2010), whereas all GISAID data were manually downloaded. Genomes with uncharacterized regions/nucleotides were discarded using another Python script (RRID:SCR_008394), which was used to conduct monthly FASTA sequence alignments using the MAFFT software (RRID:SCR_011811) (
Katoh & Standley, 2013) coupled with previously described DNA loss model parameters (
Martínez-Pérez
et al., 2002, 2007). The consensus sequences were generated using BioEdit version 7.2 (RRID:SCR_007361) with a 100% threshold frequency (
Hall, 1999).
Design and synthesis of
<italic toggle="yes">ORF8</italic>-specific primers, probes, and substrate oligos
Using the SARS-CoV-2 (NC_045512) reference genome and consensus sequences from January to April 2020 (
Zhu
et al., 2020), an approximately 150-base pair (bp) region of the
ORF8 gene was selected based on the secondary structure of its RNA transcript, which in turn was predicted using the algorithms proposed by Zuker (
Zuker & Jacobson, 1998) via the Mflod software (RRID:SCR_001360) (
Zuker, 2003). Thus, the last set containing the codons of the central region of the
ORF8 gene, which encodes the secretion protein ORF8, was chosen because it allows proper viral adhesion to the host cell (
Chan
et al., 2020). The obtained sequence was used as a template to create primers, a TaqMan FAM-BBQ probe, and substrate oligos for RT-qPCR. The specificity of the aforementioned molecules was confirmed via GenBank BLAST analyses (RRID:SCR_004870) (
Ye
et al., 2006). Genes
E and
N from the Berlin and CDC protocols were used as controls (
Biotek, 2020;
Corman
et al., 2020).
All molecules were synthesized by Bioneer (Korea). The primers were purified
via separation on a reverse-phase cartridge, whereas the probe and substrate oligos were purified
via high-performance liquid chromatography (HPLC) and polyacrylamide gel electrophoresis (PAGE), respectively. The
ORF8 RT-PCR conditions were implemented as described by the Berlin protocol (
Corman
et al., 2020) using the aforementioned molecules coupled with the 2019-nCoV TaqMan RT-PCR Kit (Norgen Biotek Corp) developed by the CDC (
Biotek, 2020).
Preparation of denaturing solutions and adjustment of dNTP concentrations
A:T and G:C ratios were calculated based on the monthly SARS-CoV-2 consensus sequences to obtain an average for each nucleotide. These averages were then used to determine the minimum concentrations of TEA (ABCAM-USA), DMSO (Scharlab-Spain), and dNTPs (100 mM each, Promega-USA) in molecular-grade ultrapure water (Promega-USA), in addition to the MgSO
4 concentration recommended by the Berlin protocol.
RNA extraction
Total RNA extraction was conducted using the MagMAX™ Viral/Pathogen II (MVP II) Nucleic Acid Isolation Kit (2000 RXNs) (Applied Biosystems-USA) using a KingFisher Duo Prime (5400110) DNA/RNA extraction system according to the manufacturer’s instructions (Thermo Fisher Scientific-USA) (
Fang
et al., 2007).
SARS-CoV-2 single and multiplex RT-qPCR
RT-qPCR was conducted using the
ORF8-specific primers and probe designed herein, in addition to the
E (Berlin protocol) and
N genes (N1-N2; CDC protocol). The
RNase P gene was used as an external control, as proposed by both of the aforementioned protocols. The reactions were conducted using the SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase (Ref. 11732088; Invitrogen-USA) and the 2019-nCoV TaqMan RT-PCR Kit (Ref. TM67100; Norgen-Biotek-Canada). Each reaction for each diagnostic system was conducted in either 15- or 25-μL reaction volumes consisting of 2 μL of patient-derived purified RNA, 2× One-Step RT-PCR Master Mix, 2× nuclease-free buffer, and the respective primers/probe at the concentrations recommended by the CDC. Moreover, a denaturing stock solution was added to obtain a final concentration of 0.7% TEA, 0.2% DMSO, and 0.8 mMol MgSO
4. The dNTP reagent had a final concentration of 12 mMol dATP-dTTP, 10 mMol dCTP-dGTP, and 0.8 mMol MgSO
4. The reaction conditions were the following: 55°C for 15 minutes, 95°C for 3 minutes followed by 45 cycles of 95°C for 15 s and 58°C for 30 s for the SuperScript™ III One-Step RT-PCR kit; and 95°C for 3 s followed by 55°C for 20 s for the 2019-nCoV TaqMan RT-PCR kit. Fluorescence signals were quantified using a QuantStudio 1 Real-Time PCR System (No. A40427) in a 96-well 0.2 μL block (Thermo Fisher Scientific-USA).
The above-described procedure was conducted using two different Multiplex One-Step RT-qPCR protocols. In the first instance, the primers and probes for the
E,
ORF8, and
N (N1) genes were mixed, whereas the other reaction was performed by mixing the N1 and N2 sets of the
N gene. The
RNase P gene was independently assessed in both cases. All reactions were performed as described above.
Calculation of sensitivity, specificity and predictive values of SARS-CoV-2
To calculate the sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of each of the reactions with the single and multiplex primers used, a script was implemented in R using the Caret package to calculate the values generated by each RT-qPCR with their respective primers/probes (
https://github.com/GenomicUIS/Sensitivity-specificity-PPV-and-NPV-for-SARS-CoV-2.git) (
Kuhn, 2008;
R Core Team 2020). Cycle threshold (Ct) was defined for each of the primers in the single reactions of each of the processed samples, where, the value of 18-35 Ct was considered true positive, Ct 35 > false positive, Ct 18 < true negative. Subsequently, the Cts values obtained were pooled and evaluated per sample. Therefore, if the result generated by a set of primers/probes was at the above-mentioned threshold, it was considered a true positive identification. Whereas, results outside the threshold were considered false negative and indeterminate. Cts were identical for multiplex RT-qPCR reactions, but the results were evaluated as a whole.
ResultsSARS-CoV-2 GenBank and GISAID databases
A total of 19,317 genomes were retrieved from the GenBank database from January to October 2020 based on our search criteria, whereas the GISAID database rendered 107,259 full genomes from January to December 2020. In both cases, the number of available sequences increased substantially each month. However, the frequency of base substitutions in the monthly consensus sequences for the
E,
ORF8, and
N genes was higher in the GISAID database compared to the GenBank database (
Figure 1).
Primers and probes to RT-qPCR to SARS-CoV-2.
The nucleotides correspond to: forward primer in green box and lower primer blue box. The Probes is in yellow box. The conceptual translation is indicated in the top of each alignment. The number indicate the nucleotide combination. The nucleotides values combination by position corresponds to international nomenclature. The nucleotide position according to SARS-CoV-2 reference genome is indicated in the lower consensus alignment.
Primer, probe, and substrate oligo design for SARS-CoV-2 detection based on RNA secondary structure
The region of the
E gene employed herein exhibited a 113 bp length, whereas the amplicons of the N1-N2 system of the
N gene were 72 and 67 bp in length, respectively, all of which exhibited loop-bubble structures. Interestingly, 154 bp of the first half of the
ORF8 gene presented a loop-bubble structure, which was similar to that of the other two genes. Nevertheless, the third loop of the N1 system and the second loop of the
ORF8 gene were formed via four and seven canonical pairings, respectively. These structures can be considered scorpion-type primers or probes, which are often used in RT-qPCR; however, the loop-bubble structures used for these purposes are typically formed
via 2–6 canonical pairings (
Figure 2).
Table 1 summarizes the primer sequences used in this study.
Secondary structures of the primers and probes used for genes
<italic toggle="yes">E</italic>,
<italic toggle="yes">ORF8</italic> and
<italic toggle="yes">N</italic> (N1-N2).
Stem and stem-bubble structures are observed in the 5' and 3' direction. The numbers represent the amount of nucleic acids used for each set and the parentheses individually delimit each primer and probe within each segment.
RT-qPCR primer, probes, and substrate oligos designed in this study.
A total of 49 samples were collected from October 25, 2020, to January 21, 2021, of which 22 tested positive and 27 were negative. The October samples were used to confirm that the
E,
ORF8, and
N genes could be used as effective indicators to detect SARS-CoV-2
via RT-qPCR (
Table 2,
Figure 3). However, given that our secondary structure and nucleotide ratio analyses indicated that the A:T ratio was always between 63% and 70% regardless of the consensus sequence, denaturing and dNTP solutions were also incorporated in these reactions (see the Methods for more details). These conditions generally improved the Ct values of the reactions compared to those of the commercial procedures; however, some exceptions were observed (
Table 2,
Figure 3).
RT-qPCR single gene SARS-CoV-2.
Sample
Collection/Process Date
Reagent
E
ORF8
N1
N2
RNase P
Sample
Collection/Process Date
Reagent
E
ORF8
N1
N2
RNase P
02
25/10/2020 18/11/2020
Comm
41.884
44.458
27.527
27.471
28.500
03
27/10/2020
19/11/2020
Comm
23.300
24.131
27.904
28.130
28.316
Desn
39.498
59.736
28.837
28.598
28.763
Desn
24.518
24.539
27.489
28.017
28.900
dNTPs
35.338
16.999
28.278
31.674
27.675
dNTPs
25.556
25.851
27.616
29.531
28.090
07
03/12/2020
04/12/2020
Comm
---
20.662
No determine
24.383
28.827
10
04/12/2020
17/12/2020
Comm
14.417
---
No determine
No determine
27.623
Desn
36.554
23.382
24.195
30.331
Desn
21.025
27.188
11
05/12/2020
11/12/2020
Comm
20.806
19.893
No determine
---
32.268
12
06/12/2020
17/12/2020
Comm
29.291
26.454
No determine
---
27.776
Desn
35.808
33.855
---
27.518
Desn
25.257
---
---
28.202
14
14/12/2020
19/12/2020
Comm
7.970
10.416 4.873
No determine
No determine
30.167
15
14/12/2020
20/12/2020
Comm
44.182
---
No determine
No determine
27.994
Desn
21.082
31.051
Desn
---
---
25.933
16-21
14-18/12/2020
16-18/12/2020
Comm
Similar results to sample 15
+++
22
18/12/2020
27/12/2020
Comm
20.559
---
No determine
32.003
26.813
Desn
+++
Desn
---
26.017
---
31.614
23
18/12/2020
27/12/2020
Comm
24.533
---
No determine
33.795
29.757
24
21/12/2020
27/12/2020
Comm
17.016
---
No determine
22.325
26.014
Desn
---
25.500
---
34.371
Desn
---
17.192
31.312
31.392
E,
ORF8, N1-N2 and
RNase P = Primers and probes to WHO, this work,
N gen from CDC and Universal control, respectively.
Values correspond to Ct result by RT-qPCR.
Dot line = Result not was generated.
Comm = Commercial reagent.
Desn = Denaturalization reagent.
dNTPs = dNTPs reagent.
+++ = Positive result.
RT-qPCR curves for the detection of SARS-CoV-2 from patient-derived samples.
RNA obtained from patients and primers and probes corresponding to the
E gene (WHO),
ORF8 (this study), and the
N gene (N1-N2; CDC). The green, yellow, and red arrows indicate the curves generated by denaturalization (Den), the dNTP reagent, and commercial kits, respectively. The
RNase P gene was used as a control.
Given that all samples were freshly acquired and never stored, we concluded that the gradual loss of amplicon systems and adjuvant solutions began in December. This was confirmed first for the
E gene system, then for the N2 system, and finally for
ORF8. This trend was determined using ten samples collected from December 14 to 21, 2020. The results of our experiments could be generally classified into three outcomes depending on the viral detection system: (1) the system worked following the manufacturer’s instructions with the addition of the denaturing solution (2); the system rendered positive results with the denaturing solution but negative without it; and (3) the opposite of the second outcome (
Table 2).
<italic toggle="yes">In silico</italic> estimation of
<italic toggle="yes">E</italic>,
<italic toggle="yes">ORF8</italic>, and
<italic toggle="yes">N</italic> RT-PCR primer and probe combinations
Our analysis of the global SARS-CoV-2 mutation patterns based on the GenBank and GISAID databases indicated that most mutations began in March 2020, and subsequently increased significantly, relative to the SARS-CoV-2 reference genome (GenBank access: NC_045512.2). This trend was particularly noticeable for the quencher and forward primer sequences of the
ORF8 and
N genes. Moreover, the potential primer and probe combinations for each gene increased substantially starting in November according to the GISAID database and were significantly higher in December. For example, the
ORF8 system and the N1 set exhibited 3.4×10
11 and 1.7×10
12 combinations, respectively, whereas the N2 set and
E had only 1.4×10
5 and 32 combinations (
Figure 1).
Multiplex RT-qPCR
The multiplex RT-qPCR controls of systems
E,
ORF8, and
N (N1) with the positive samples 02 and 03 rendered the expected results, which were thereafter confirmed using samples 05 and 06 obtained in October 2020. Nevertheless, using the same reaction conditions, the denaturing solution decreased the Ct values for the positive samples obtained in October but enhanced those of the samples obtained in November. In contrast, the dNTP solution had the opposite effect (
Table 3,
Figure 4).
RT-qPCR Multiplex to SARS-CoV-2.
Date
RT-PCR Multiplex
Reagent
Sample
02
03
05
06
Collection
25/10/2020
27/10/2020
12/11/2020
09/11/2020
Process
22/11/2020
E,
ORF8 and N1
Comm
27.377
18.290
33.299
35.341
Desn
27.109
18.050
35.149
35.919
dNTPs
28.057
18.845
32.792
34.256
RNase P
Comm
27.908
26.449
25.876
27.309
10
11
12
13
38
39
Collection
04/12/2020
05/12/2020
06/12/2020
7/12/2020
15/01/2021
14/01/2021
Process
18/01/2021
N1 and N2
Comm
19.961
30.558
28.461
36.556
24.581
---
Desn
19.219
27.389
27.947
---
23.230
23.961
RNase P
Comm
20.855
25.610
26.525
23.669
25.171
26.306
Desn
26.043
25.589
26.402
23.028
26.943
26.304
32
33
34
Collection
26/12/2020
29/12/2020
29/12/2020
Process
07/01/2021
E,
ORF8 and N1
Comm
---
---
---
Desn
31.194
36.784
35.523
RNase P
Comm
29.973
29.165
29.802
Desn
---
39.593
30.761
41
42
43
44
45
46
47
48
49
Collection
19/01/2021
21/01/2021
26/01/2021
24/01/2021
29/01/2021
29/01/2021
23/01/2021
23/01/2021
21/01/2021
Process
22/01/2021
30/01/2021
E,
ORF8 and N1
Comm
20.529
28.554
30.913
23.318
32.362
30.324
33.064
29.253
30.411
Desn
26.471
24.536
30.160
21.325
30.444
31.857
28.459
29.515
28.695
RNase P
Comm
29.098
24.786
23.353
23.130
28.733
25.742
24.343
24.664
26.368
Desn
---
23.472
23.414
24.260
26.898
25.297
24.488
25.290
25.976
Nomenclature in
Table 2.
Multiplex RT-qPCR (Mp) curves for the detection of SARS-CoV-2 using
<italic toggle="yes">E</italic>-,
<italic toggle="yes">ORF8</italic>-, and
<italic toggle="yes">N</italic> (N1)-specific.
The RNA samples from 6 different patients are indicated with the respective numbers. For each curve, the first number in the nomenclature indicated the top result and the second the lower result. The
RNase P gene was used as a control. The arrows and nomenclature are the same as in
Figure 3.
The influence of the denaturing and dNTP solutions was further confirmed using nine samples obtained from December 4 to 7, 2020, and from January 14 to 15, 2021, using the N1–N2 primers and probe as described by the CDC. A total of six samples exhibited the originally documented pattern and the three remaining samples tested negative, both in terms of curve trends and Ct values (
Table 3,
Figure 5). Therefore, the initial multiplex reaction effectively detected the SARS-CoV-2 virus in the nine samples obtained from January 19 to 29, 2021 (
Table 3).
RT-qPCR Multiplex (Mp) curves for the detection of SARS-CoV-2 using from
<italic toggle="yes">N</italic> gene (N1-N2).
Primers and probes, as indicated by the CDC coupled with the denaturalization reagent. The RNA samples from 9 patients are indicated with the respective numbers. The multiplex curves corresponding to N1-N2 are indicated at the top, whereas the results corresponding to the
RNase P primers and probe used as controls are indicated below. The abbreviators and nomenclature are the same as in
Figure 1.
Sensitivity, specificity and predictive values of SARS-CoV-2
The performance of single and multiplex RT-qPCRs with the solutions used showed that single denaturing was more sensitive than the commercial solution, but the latter is 33% more specific than the former. As for the multiplex reactions, the commercial solution showed non-significant difference in sensitivity with respect to the denaturing solution. Similarly, the specificity of the single reactions was better for the commercial solution with respect to the denaturing solution, but in the multiplex reactions this difference was not evident in the detection of true negatives as both solutions yielded results of 0. dNTPs were not evaluated due to the number of positive results (
Table 4).
Sensitivity, specificity, PPV and NPV values for single and multiplex RT-qPCR reactions of SARS-CoV-2.
Calculation of sensitivity and specificity for single RT-qPCR
Comm
Desn
Event
No Event
Event
No Event
Event
6
2
Event
2
8
No Event
8
1
No Event
0
0
Kappa:
-0.1333
Kappa:
0
Mcnemar's Test P-Value:
0.1138
Mcnemar's Test P-Value:
0.01333
Sensitivity:
0.4286
Sensitivity:
1.0
Specificity:
0.3333
Specificity:
0.0
Pos Pred Value (PPV) :
0.7500
Pos Pred Value (PPV) :
0.2
Neg Pred Value (NPV) :
0.1111
Neg Pred Value (NPV) :
NaN
Prevalence:
0.8235
Prevalence:
0.2
Calculation of sensitivity and specificity for multiplex RT-qPCR
Comm
Desn
Event
No Event
Event
No Event
Event
16
4
Event
15
2
No Event
2
0
No Event
5
0
Kappa:
-0.1379
Kappa:
-0.1493
Mcnemar's Test P-Value:
0.6831
Mcnemar's Test P-Value:
0.4497
Sensitivity:
0.8889
Sensitivity:
0.7500
Specificity:
0.0000
Specificity:
0.0000
Pos Pred Value (PPV) :
0.8000
Pos Pred Value (PPV) :
0.8824
Neg Pred Value (NPV) :
0.0000
Neg Pred Value (NPV) :
0.0000
Prevalence:
0.8182
Prevalence:
0.9091
Discussion
Since the first report of the SARS-CoV-2 virus and the beginning of the pandemic, the public health authorities indicated that the spread of this disease could be prevented by implementing rapid, cost-effective, and accurate diagnostic tools that would enable the analysis of large sample volumes across the globe. Many RT-qPCR-based diagnostic kits have since been developed based on SARS-CoV-2 marker genes (
Ruhan
et al., 2020;
Nalla
et al., 2020). However, similar to the Berlin protocol (
Corman
et al., 2020), several of these kits require two or three consecutive reactions that must be conducted after the first results, thus prolonging the diagnosis process. Importantly, excessively long test procedures are not only an inconvenience but also pose a serious risk to patient health and promote disease dissemination.
To the best of our knowledge, our study is the first to demonstrate the use of three genes both independently or in multiplex reactions to detect the SARS-CoV-2 virus. This was achieved by combining the primers and probes for the
E gene of the Berlin protocol, the
N gene (N1-N2 set) of the CDC protocol, and the
ORF8 gene with reagents that promote the elimination of secondary structures and compensate for the variations in the nucleotide proportions of the SARS-CoV-2 genome. More importantly, these modifications enhance the SARS-CoV-2 detection efficiency of RT-qPCR, thus rendering results in as little as 12 hours.
These achievements were largely facilitated by our HPC analyses, which allowed for full SARS-CoV-2 genome alignments and database curation in a matter of minutes or hours using publicly available sequences from the GISAID and GenBank databases dating back to the first genomic characterization of the virus up until December 31, 2020 (
Zhu
et al., 2020). This allowed for the design and
in silico validation of RT-qPCR primers and probes for viral detection. Nevertheless, even though our database consolidated genomic sequences from across the globe, it does not reflect the true distribution and frequency of viral variants by region, as the implementation of next-generation sequencing (NGS) technologies varies widely in different countries and regions depending on infrastructure and economic factors, thus creating biases in viral variant descriptions and genomic surveillance.
Given the aforementioned considerations, the consensus sequence-based primer and probe design conducted herein were also based on genome quality and quantity, as well as the computational capacity of our HPC cluster. This enabled the analysis of gene sequences in minutes or hours, thus allowing for the evolutionary and genomic characterization of the virus.
Another benefit of our global SARS-CoV-2 genome database is that it allowed for the prediction of secondary RNA and cDNA structures associated with the amplified region, thus providing insights into the final primer and probe nucleotide distributions and concentrations. This approach also enabled the specific formulation of denaturing solutions (
Kovarova & Draber, 2000) and nucleic acid synthesis to enhance RT-qPCR Ct values for different regions of the SARS-CoV-2 genome, as demonstrated in this study. All of these factors facilitate the validation of RT-qPCR-based assays and substantially decrease the likelihood of false-negative results.
The reagents and methods described herein allow for an immediate, facile, and cost-effective detection of the SARS-CoV-2 virus; which was demonstrated by comparing the Cts of the positive results used to determine sensitivity and specificity with those published for other countries (
Arakawa
et al., 2024;
Aranha
et al., 2021;
Chen
et al., 2022). Nevertheless, the proposed method still relied on the use of two or three SARS-CoV-2 genes in addition to the
RNase P universal control in the same RT-qPCR plate to ensure viral detection accuracy. Additionally, the high mutation rates of this virus will inevitably increase detection costs and decrease test accuracy over time, as demonstrated with the December 2020 samples. Therefore, the SARS-CoV-2 genome databases must be continually updated to preserve the accuracy and applicability of our proposed procedure.
Based on our findings, we concluded that multiplex RT-qPCR is an optimal solution for the aforementioned limitations, even though the Cts used to establish sensitivity and specificity did not show a difference in some samples between commercial methods compared to the one proposed here. However, different fluorescent dyes were required to differentiate the different probes, thus increasing operating costs associated not only with the dyes themselves but also the acquisition of multiple-channel thermocyclers to detect different fluorescent signals. Despite these disadvantages, combining the detection of the
E,
ORF8, and
N genes increases the likelihood of identifying a fluorescent signal, making SARS-CoV-2 detection more robust and reliable. In this sense, the timely and accurate detection of the virus would allow caregivers to promptly implement pertinent measures and prevent disease progression and transmission. Moreover, the RT-qPCR procedures proposed herein are highly modular, allowing health professionals to select only the gene-specific primers and probes that they deem necessary for different diagnostic needs.
Another advantage of multiplex RT-qPCR assays is that they enable the analysis of multiple samples from a single patient (
Grobe
et al., 2021). Specifically, preliminary studies have demonstrated that robust diagnoses can be achieved using four samples obtained at different dates. However, this requires additional monitoring (e.g., clinical records) to optimize pool design. Taken together, our findings indicate that integrating HPC, probe and primer design, denaturing and dNTP solutions, and single and multiplex RT-qPCR protocols will contribute to the timely detection of the SARS-CoV-2 virus, thus minimizing its spread.
Data availabilityUnderlying data
This genomic sequence is available in GenBank:
GenBank: Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome. Accession number NC_045512;
https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.
Extended data
GitHub: Steps to download and align genomic sequences from GenBank in the GUANE-1 supercomputer from High Performance and Scientific Computing Center of the Industrial University of Santander (SC3UIS). This software is also used for manually downloaded sequences such as GISAID. For both databases or others, the script eliminates the undetermined sequences indicated with “n” within the genome or study sequence.
https://github.com/druedaplata/bio
Zenodo: SARS-CoV-2 genomic data obtained for this study are available in: Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR.
https://doi.org/10.5281/zenodo.6337537 (
Cadena-Caballero
et al., 2022).
This project contains the following extended data:
SARS-CoV-2 genomic sequences from the GenBank and GISAID databases and their alignments.
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Acknowledgements
The authors would like to express their gratitude to the volunteers that participated in this study, as well as to the Central Research Laboratory of the Industrial University of Santander Health Faculty (LCI-FS-UIS), the Chicamocha Clinical Laboratory, the High Performance and Scientific Computing Center of the Industrial University of Santander (SC3UIS), the Vice-Rectory of Research and Extension of the Industrial University of Santander, and Ministerio de Ciencia, Tecnología e Innovación, MINCIENCIAS; invitation No. 1015, Mincienciaton. Contract 369-2020. We would also like to thank Dr. Francisco Mora at SciWrite Solutions for providing English editing.
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962212610.1017/S1355838298980116PMC136964910.5256/f1000research.163834.r469433Reviewer response for version 2Quintero BarbosaJuan Sebastian1Refereehttps://orcid.org/0000-0001-8020-5265
University of Virginia, Charlottesville, Virginia, USA
Competing interests: No competing interests were disclosed.
This manuscript presents an interesting exploratory study on the optimization of SARS-CoV-2 RT-qPCR detection through in silico design of an ORF8-targeted system and the evaluation of modified reaction conditions in both single and multiplex assays. The topic is relevant, and the integration of computational design with experimental testing in clinical samples is a clear strength of the work. Overall, I believe the study has value as a methodological contribution, but several aspects would benefit from clarification and a more cautious interpretation. For this reason, I responded “Partly” to all six evaluation questions.
For the question of whether the work is clearly and accurately presented and cites the current literature, I selected Partly because the manuscript is generally understandable and addresses a relevant body of literature, but some statements are stronger than the data support and some sections would benefit from clearer wording and greater precision.
For the question of whether the study design is appropriate and the work technically sound, I selected Partly because the overall concept is appropriate and the study includes both in silico and experimental components, but the validation is relatively limited and does not fully support the strength of some of the claims made by the authors.
For the question of whether sufficient details of methods and analysis are provided to allow replication, I selected Partly because the manuscript includes useful methodological information, but some aspects still require clarification, particularly how sample-level classifications were assigned and how the different assay formats were compared.
For the question of whether the statistical analysis and its interpretation are appropriate, I selected Partly because the authors provide performance metrics and comparative analyses, but the interpretation should be more cautious. In particular, the basis for sensitivity, specificity, and related measures should be explained more clearly, and their limitations should be acknowledged more explicitly.
For the question of whether all source data underlying the results are available to ensure full reproducibility, I selected Partly because the manuscript makes an effort toward transparency, but I was not fully convinced that all underlying information needed for complete reproducibility is presented in a sufficiently clear and accessible manner.
For the question of whether the conclusions are adequately supported by the results, I selected Partly because the study provides promising findings, but the conclusions regarding improved diagnostic performance, especially in multiplex RT-qPCR, seem somewhat broader than what the current dataset can firmly support.
In summary, I consider this a useful exploratory study with methodological interest, but I recommend clearer presentation of the methods, more cautious interpretation of the performance analyses, and moderation of the conclusions.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
Reviewer Expertise:
Virology, immunology, vaccine research, molecular biology, and experimental assay evaluation. My assessment is primarily focused on study design, methodological clarity, data interpretation, and whether the conclusions are adequately supported by the results.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Martinez-PerezFrancisco School of Biology, Industrial University of Santander, Bucaramanga, Santander, Colombia
Competing interests: No competing interests were disclosed.
3132026
Dear Reviewer 2,
We sincerely thank you for your careful and thoughtful evaluation of our manuscript. We appreciate your recognition that the study has methodological value and that the integration of
in silico design with experimental testing in patient-derived samples represents a strength of the work. We have carefully revised the manuscript in response to your comments and observations.
Below we summarize the principal revisions made to the manuscript in relation to your concerns.
1. Clarity of presentation and wording
Comment: You indicated that the manuscript addresses a relevant topic, but that some statements were stronger than the data support and that several sections would benefit from clearer wording and greater precision.
Response: We revised the Abstract, Introduction, Results, Discussion, and Conclusions to improve clarity, precision, and overall consistency throughout the manuscript. We also moderated statements that could be interpreted as overstating the findings. In particular, the study is now explicitly framed as an exploratory and methodological contribution, and the wording of the conclusions has been revised to avoid implying clinical validation or broad diagnostic superiority.
2. Study design and technical soundness
Comment: You considered the overall concept appropriate, but noted that the validation was limited and did not fully support some of the stronger claims made in the manuscript.
Response: We agree with this observation and revised the manuscript accordingly. The study is now more clearly described as exploratory in nature. In addition, we added a dedicated Limitations section to explicitly acknowledge the restricted sample size, the context of sample acquisition during the COVID-19 pandemic, and the absence of independent external validation. These changes were introduced to better align the interpretation of the results with the actual scope of the study.
3. Methodological clarity and reproducibility
Comment: You requested clarification regarding sample-level classification and the comparison of different assay formats, and also noted that the information required for reproducibility was not sufficiently clear and accessible.
Response: We substantially revised the Methods section to clarify these points. In particular, we added and reformulated the subsection now entitled “Exploratory Ct-based performance analysis of SARS-CoV-2 RT-qPCR assays.” In this section, we explain that the performance estimates were derived from predefined Ct-based operational criteria applied uniformly across assays, and that these criteria were used solely for internal exploratory comparison. We also clarified how Ct values were pooled and evaluated at the sample level for both single and multiplex assays.
We additionally revised the Data Availability section to more clearly describe the genomic datasets, sequence alignments, and scripts used in the analyses, and to improve transparency regarding the public repositories associated with these resources. These revisions were intended to facilitate reproducibility within the scope of the study.
4. Statistical analysis and interpretation
Comment: You noted that the interpretation of sensitivity, specificity, and related metrics should be more cautious, and that their basis and limitations should be explained more explicitly.
Response: We carefully revised both the Methods and Results sections in response to this concern. The Ct-based performance analysis is now explicitly presented as exploratory and internal, rather than as a measure of absolute diagnostic accuracy. In the Results section, these values are now described as apparent comparative metrics under a predefined Ct-based classification framework. We also added explicit language stating that, because no independent external clinical validation was available, these metrics should not be interpreted as evidence of clinical diagnostic performance.
5. Conclusions
Comment: You considered that the conclusions, particularly those related to improved diagnostic performance in multiplex RT-qPCR, were broader than what the dataset could firmly support.
Response: We revised the Discussion and Conclusions to moderate these statements. The manuscript no longer presents the findings as demonstrating clinical diagnostic superiority. Instead, the conclusions now emphasize that the evaluated denaturing and dNTP-based conditions were associated with changes in RT-qPCR behaviour under the experimental conditions tested, and that additional studies with larger datasets and independent validation would be required to assess broader applicability.
6. Additional clarification regarding study scope and clinical information
Response: To further clarify the context of the study, we now explicitly state in the manuscript that this was not a clinical validation study and that access to participant clinical characterization, including symptom severity and extent of infection, was restricted by the informed consent framework and applicable legal requirements. This clarification was added to the Limitations section to explain why these data were not available for further analysis.
We are grateful for your constructive comments, which helped us improve the manuscript substantially. We believe that the revised version is clearer, more methodologically transparent, more cautious in its interpretation, and better aligned with the exploratory scope of the work.
Sincerely,
Francisco Martinez-Perez
On behalf of all authors
Industrial University of Santander
10.5256/f1000research.121206.r135398Reviewer response for version 1BeggsAndrew D.1Refereehttps://orcid.org/0000-0003-0784-2967
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
Competing interests: No competing interests were disclosed.
The authors have set out to design a new primer set to target the ORF8 gene region of the SARS-CoV-2 genome, as well as understand the effect of denaturing agents on the efficiency of detection by reducing secondary structures and increasing RT-QPCR. It is certainly an interesting approach. However I have a few comments and suggestions to further improve the manuscript
The abstract is confusing. They state that they design ORF8 primers using E and N as targets but what they actually mean is that they use these as references. Also they state that the denaturing agents reduce secondary structure and increase efficiency but don't really get the results of this across in the abstract
They talk in detail about the characterisation in the GISAID/GenBank databases, but don't explain well that the reason they are doing this is to help design the ORF8 gene probes. This should be clarified.
The primer/probe concentrations are not given but should be to increase the reproducibility of the experiments
A 2xn table of the primer sets with a set threshold in positive/negative and quoted sensitivity, specificity etc would greatly aid interpretation of the data
The authors state in the discussion that "our study is the first to demonstrate the use of three genes both independently or in multiplex reactions to detect the SARS-CoV-2 virus" - the Thermo Taqpath assay uses ORF, N, and S genes for detection and so is multiplex.
In the discussion of their analysis of global SARS-CoV-2 mutations they discuss "thousands of millions" of mutations. This makes it seem like there have been this number of mutations rather than reports of mutations, as there will be genomes submitted that have overlapping mutations between genomes. Some clarification on language would be useful.
In addition a discussion of why they chose to target the ORF8 gene would be useful. I assume it is because of the relatively low mutational rate here, but it would be nice to see this explained.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Is the study design appropriate and is the work technically sound?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
Reviewer Expertise:
SARS-CoV-2 genetics.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Martinez-PerezFrancisco School of Biology, Industrial University of Santander, Bucaramanga, Santander, Colombia
Competing interests: No competing interests were disclosed.
1432024
Reviewer 1.
Andrew D. Beggs
Institute of Cancer and Genomic Sciences,
University of Birmingham, Birmingham, UK.
The authors have set out to design a new primer set to target the
ORF8 gene region of the SARS-CoV-2 genome, as well as understand the effect of denaturing agents on the efficiency of detection by reducing secondary structures and increasing RT-qPCR. It is certainly an interesting approach. However, I have a few comments and suggestions to further improve the manuscript.
Response: We greatly appreciate the comments, suggestions and corrections made by reviewer 1, which greatly enriched the work.
1. The abstract is confusing. They state that they design
ORF8 primers using
E and
N as targets but what they actually mean is that they use these as references. Also, they state that the denaturing agents reduce secondary structure and increase efficiency but don't really get the results of this across in the abstract.
Response: The correction is accepted. The abstract of the manuscript is modified and expanded, providing clarity in the methods and results sections.
Abstract
Background: The COVID-19 pandemic, caused by the SARS-CoV-2, can be effectively managed with diagnostic tools such as RT-qPCR. However, it can produce false-negative results due to viral mutations and RNA secondary structures from the target gene sequence.
Methods: With High Performance Computing, the complete SARS-CoV-2 genome was obtained from the GenBank/GISAID to generate consensus sequences to design primers/probes for RT-qPCR.
ORF8 gene was selected, meanwhile,
E and
N and
RNAse P were according to CDC protocol. Nasopharyngeal swab samples were collected from patients diagnosed with SARS-CoV-2. Total RNA was purified according MagMAX kit, it was used in single, and multiplex RT-qPCR. To avoid templated secondary structures, compensate nucleotide proportions and improve Ct values, a solution composed of tetraethylammonium chloride and dimethyl sulfoxide and other with corresponding to dNTPs proportions in accordance SARS-CoV-2 genome were included. Sensitivity and specificity according to Ct values were determined with the Caret package in R software.
Results: 126,576 SARS-CoV-2 genomes from January to December 2020 comprised a database. From this, a region near of 5'
ORF8 gene showed three stem-loops was used for primers/FAM-probe. 49 samples were obtained, from them, 22 were positive to gene selected regions. Interestingly, samples from October to November 2020 were positive for all regions, however, in January 2021 different results were observed in
ORF8. An improvement in Ct with the adjuvant solutions was determined in all samples with others SARS-CoV-2 primers/probes, for both single and multiplex RT-qPCR. The inclusion of the denaturing solution in single RT-qPCR increased its sensitivity with respect to the commercial method, while in multiplex the opposite was generated.
Conclusions: Including adjuvant solutions to prevent the formation of RNA secondary structures and the adjustment of the nucleotide ratios of SARS-CoV-2 improved single and multiplex RT-qPCR for viral identification, demonstrating its potential application in health public.
2. They talk in detail about the characterization in the GISAID/GenBank databases, but don't explain well that the reason they are doing this is to help design the ORF8 gene probes. This should be clarified.
Response: Clarity is provided in the methods to use of databases for
ORF8 design.
Due to the appearance of mutations in several genes of the SARS-CoV-2 reference genome that could generate false negatives with the primers and probes authorized for identification by RT-qPCR. A region coding for the accessory protein ORF8 was selected, hypothesizing that it did not have a high mutation frequency. Therefore, monthly consensus sequences were generated to determine its identity pattern with respect to new genomes.
3. The primer/probe concentrations are not given but should be to increase the reproducibility of the experiments.
Response: The concentration in nmole of the primers/probes is reported below. It could also be included in Table 1.
4. A 2xn table of the primer sets with a set threshold in positive/negative and quoted sensitivity, specificity etc. would greatly aid interpretation of the data.
Response: The calculation of the sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of the viral identification tests of the participants was included in the manuscript.
Methodology
Calculation of sensitivity, specificity and predictive values of SARS-CoV-2.
To calculate the sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of each of the reactions with the single and multiplex primers used, a script was implemented in R using the Caret package to calculate the values generated by each RT-qPCR with their respective primers/probes (https://github.com/GenomicUIS/Sensitivity-specificity-PPV-and-NPV-for-SARS-CoV-2.git) (Kuhn, 2008; R Core Team 2020). Cycle threshold (Ct) was defined for each of the primers in the single reactions of each of the processed samples, where, the value of 18-35 Ct was considered true positive, Ct 35 > false positive, Ct 18 < true negative. Subsequently, the Cts values obtained were pooled and evaluated per sample. Therefore, if the result generated by a set of primers/probes was at the above-mentioned threshold, it was considered a true positive identification. Whereas, results outside the threshold were considered false negative and indeterminate. Cts were identical for multiplex RT-qPCR reactions, but the results were evaluated as a whole.
R Core Team. R: A language and environment for statistical computing.
R Foundation for Statistical Computing. 2020; Vienna, Austria. URL
https://www.R-project.org/.
Kuhn M: Building Predictive Models in R Using the caret Package.
J Stat Softw. 2008; 28(5): 1–26. Doi: 10.18637/jss.v028.i05
Results
Sensitivity, specificity and predictive values of SARS-CoV-2
The performance of single and multiplex RT-qPCRs with the solutions used showed that single denaturing was more sensitive than the commercial solution, but the latter is 33% more specific than the former. As for the multiplex reactions, the commercial solution showed non-significant difference in sensitivity with respect to the denaturing solution. Similarly, the specificity of the single reactions was better for the commercial solution with respect to the denaturing solution, but in the multiplex reactions this difference was not evident in the detection of true negatives as both solutions yielded results of 0. dNTPs were not evaluated due to the number of positive results (Table 4).
Discussion
The reagents and methods described herein allow for an immediate, facile, and cost-effective detection of the SARS-CoV-2 virus; which was demonstrated by comparing the Cts of the positive results used to determine sensitivity and specificity with those published for other countries (Arakawa
et al., 2024; Aranha
et al., 2021; Chen
et al., 2022).
Arakawa Y, Nishida Y, Sakanashi D,
et al.: Clinical evaluation of a modified SARS-CoV-2 rapid molecular assay, ID NOW
TM COVID-19 2.0.
J. Infect. Chemother. 2024; S1341-321X(24): 00073-4. Doi: 10.1016/j.jiac.2024.02.032.
Aranha C, Patel V, Bhor V,
et al.: Cycle threshold values in RT-PCR to determine dynamics of SARS-CoV-2 viral load: An approach to reduce the isolation period for COVID-19 patients.
J. Med. Virol. 2021; 93(12): 6794-6797. Doi: 10.1002/jmv.27206.
Chen YY, Shen X, Wang YJ,
et al.: Evaluation of the cycle threshold values of RT-PCR for SARS-CoV-2 in COVID-19 patients in predicting epidemic dynamics and monitoring surface contamination.
J. Infect. Public. Health. 2022; 15(12): 1494-1496. Doi: 10.10 16/j.jiph.2022.11.012.
Based on our findings, we concluded that multiplex RT-qPCR is an optimal solution for the aforementioned limitations, even though the Cts used to establish sensitivity and specificity did not show a difference in some samples between commercial methods compared to the one proposed here.
5. The authors state in the discussion that "our study is the first to demonstrate the use of three genes both independently or in multiplex reactions to detect the SARS-CoV-2 virus" -the Thermo Taqpath assay uses ORF, N, and S genes for detection and so is multiplex.
Response: To our knowledge, at the time the experiments for this study were conducted (19 September 2020 to 30 January 2021), there was no kit that performed viral identification with three genes.
According to the official website of Thermo Fisher Scientific, the Taqpath kit is based on sequences obtained from GenBank and GISAID as of 7 May 2021 (https://www.thermofisher.com/co/en/home/clinical/clinical-genomics/pathogen-detection-solutions/covid-19-sars-cov-2/multiplex.html), and subsequently obtained a letter of approval for use by the Food and Drug Administration (FDA) on 18 May 2022 (https://www.fda.gov/media/136113/download). Our public database was generated by HPC between January and December 2020 (https://zenodo.org/records/6337537).
6. In the discussion of their analysis of global SARS-CoV-2 mutations they discuss "thousands of millions" of mutations. This makes it seem like there have been this number of mutations rather than reports of mutations, as there will be genomes submitted that have overlapping mutations between genomes. Some clarification on language would be useful.
Response: The correction is accepted, and clarity is given in the manuscript.
Our analysis of the global SARS-CoV-2 mutation patterns based on the GenBank and GISAID databases indicated that most mutations began in March 2020, and subsequently increased significantly, relative to the SARS-CoV-2 reference genome (GenBank access: NC_045512.2).
7. In addition, a discussion of why they chose to target the
ORF8 gene would be useful. I assume it is because of the relatively low mutational rate here, but it would be nice to see this explained.
Response: Correction accepted.
Using the SARS-CoV-2 (NC_045512) reference genome and consensus sequences from January to April 2020 (Zhu et al., 2020), an approximately 150-base pair (bp) region of the ORF8 gene was selected based on the secondary structure of its RNA transcript, which in turn was predicted using the algorithms proposed by Zuker (Zuker & Jacobson, 1998) via the Mflod software (RRID:SCR_001360) (Zuker, 2003). Thus, the last set containing the codons of the central region of the
ORF8 gene, which encodes the secretion protein ORF8, was chosen because it allows proper viral adhesion to the host cell (Chan et al., 2020).
Chan JF, Kok KH, Zhu Z,
et al.: Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan.
Emerg. Microbes. Infect. 2020; 9(1): 221-236. DOI: 10.1080/22221751.2020.1719902.