Comparison of analytical performance and economic value of two biosurveillance methods for tracking SARS-COV-2 variants of concern

ABSTRACT The development of biosurveillance programs with strong analytical performance and economically accessible protocols is essential for monitoring viral pathogens. Throughout the COVID-19 pandemic, whole-genome sequencing (WGS) has been the prevailing technology for SARS-CoV-2 variant of concern (VOC) detection. While WGS offers benefits, it is a lengthy process, financially and technically straining for scalable viral tracking. The aim of this study was to compare the analytical performance and economic feasibility of WGS and PCR mutation panels for distinguishing six known VOCs: Alpha (B.1.1.7 and Q.4), Gamma (P.1), Delta (B.1.617.2 and AY.4.2), and Omicron. (B.1.1.529.1). In all, 78 SARS-CoV-2-positive samples were collected from April to December 2021 at Northeastern University (Cabot Testing Site, Boston, MA, USA) for genotyping PCR and WGS analysis. MagMax Viral/Pathogen II Nucleic Acid Isolation and TaqPath COVID-19 Combo Kits were used for RNA extraction and SARS-CoV-2 confirmation. VOC discrimination was assessed using two TaqMan SARS-CoV-2 single nucleotide polymorphism (SNP) assay layouts, and Ion Torrent WGS. In November 2021, the mutation panel demonstrated marked versatility by detecting the emerging Omicron variant reported by South Africa. SNP panel analysis yielded the following 78 VOC identifications: Alpha B.1.1.7 (N = 20), Alpha Q.4 (N = 3), Gamma P.1 (N = 1), Delta B.1.617.2 (N = 30), Delta AY.4.2 (N = 3), and Omicron B.1.1.529.1 (N = 20) with one undetermined (N = 1) sample. Genotyping mutation panels designated lineages in 77 of 78 samples, 46/78 were confirmed by WGS, while 32 samples failed WGS lineage assignment. RT-PCR genotyping panels offer pronounced throughput and sensitivity and provide an economically advantageous technique for SARS-CoV-2 biosurveillance. IMPORTANCE The results presented in our manuscript demonstrate how the value of simplistic and reliable molecular assays coupled with the core scientific principle of standardization can be overlooked by the charm of more sophisticated assays and instrumentation. This effect can often be amplified during tumultuous public health events, such as the COVID-19 pandemic. By adapting standardized PCR mutation panels to detect prominently circulating SARS-CoV-2 variants, we were able to better assess the potential health impacts of rising positivity rates and transmission clusters within the Northeastern University population. While several literature publications utilizing genotyping PCR and NGS have a similar scope to ours, many investigations lack sufficiently standardized genotyping PCR and NGS bioinformatics inclusionary/exclusionary criteria for SARS-CoV-2 variant identification. Finally, the economic benefits of standardized PCR mutation panels would allow for global implementation of biosurveillance, rather than reserving biosurveillance to more economically developed nations.

KEYWORDS SARS-CoV-2, surveillance, genotyping, PCR, NGS, sequencing, variants of concern S ARS-CoV-2 variants of concern (VOC) and variants being monitored (VBM) have led to significant public health and epidemiological challenges throughout the COVID-19 pandemic as VOC/VBM have surfaced and competed for strain dominance since mid-to late 2020 (1).As COVID-19 transitions to an endemic state and newer VOCs arise, classification is centered around assessing how genetic mutations in viral genomes impact factors such as disease severity, immune evasion, in vitro diagnostic (IVD) test accuracy, and transmissibility and virulence (1)(2)(3).Alpha (B.1.1.7),Beta (B.1.351),and Gamma (P.1) lineages were most pronounced in early 2021, prior to Delta's (B.1.617.2) emergence in July 2021 and rapid succession over the previously dominant Alpha variant (1)(2)(3)(4).Currently, 99.9% of all SARS-CoV-2 cases in the United States originated from an Omicron (B.1.1.529)parent lineage, with subvariants continuously emerging in succession as dominant circulating strains (1).Tracking novel and recurring single nucleotide polymorphisms (SNP) expressed in SARS-CoV-2 VOCs could help inform scientists on which mutations have the greatest likelihood of influencing protein structure, viral assembly, virulence, and disease severity (4).In addition, leveraging genomic data gathered from genotyping and sequencing studies can cater to more proactive public health efforts with respect to IVD test design, next-generation vaccine development, and pharmacological and non-pharmacological treatment protocols (2)(3)(4)(5).While whole-genome sequencing (WGS) provides invaluable insights in these regards, it is a costly and time-consuming method for high-throughput biosurveillance (6,7).In addition, generating consistent, high-quality WGS data requires samples with high viral loads and significant nucleic acid purification (7)(8)(9).Genotyping PCR panels, however, have simpler workflows and allow greater success in determining VOC status with low viral loads (6,8,9).Moreover, SNP genotyping panels can be easily tailored to current variant activity in a timely manner (8,10).SNP genotyping performed in tandem with WGS may be the optimal choice for genomic surveillance programs as SNP genotyping can be used to target samples of interest for WGS.Samples of interest that would be expedited for WGS include those with inconclusive lineage determinations as well as samples with novel SNP combinations not found in actively circulating VOCs.
In this study, RT-PCR-based genotyping was used to design standardized muta tion panels to assess method performance and economic feasibility for determining SARS-CoV-2 VOC status in comparison to standard WGS variant surveillance.The sample cohort comprised 78 confirmed SARS-CoV-2-positive samples collected at the Life Sciences Testing Center, Northeastern University between 04 April and 27 December 2021, representing a timeframe where five predominant VOCs (Alpha, Beta, Gamma, Delta, and Omicron) were circulating (1).

RNA extraction and RT-qPCR-positive SARS-CoV-2 confirmation
In all, 78 anterior nasal swab specimens were collected and de-identified during the study period at the Life Sciences Testing Center, Northeastern University between 04 April and 27 December 2021.Viral RNA extraction and purification were performed using the MagMax Viral/Pathogen II Nucleic Acid Isolation Kit on Agilent Bravo Automated liquid handlers (Agilent Technologies, Inc.), with each sample well and a negative control well consisting of 5 µL of Proteinase K, 200 µL of patient sample suspended in viral transport medium (VTM; Redoxica, Little Rock, AR) or 200 µL of nuclease-free water (negative control), 275 µL of Binding Bead Mix (consisting of 265 µL Binding Solution and 10 µL MVP II magnetic Binding Beads), and 5 µL of a macrophage 2 (MS2) nega tive extraction control.Following extraction, RT-PCR was conducted using the TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific) on Applied Biosystems 7500 Fast Dx RT-PCR thermocyclers (Fig. 1A).The TaqPath COVID-19 Combo Kit consists of a master mix PCR buffer and multiplex RT-PCR assay containing oligonucleotide primer-probe pairs with complementary sequences to three SARS-CoV-2 gene targets; VIC dye-labeled nucleocapsid gene (Ngene); FAM dye-labeled open reading frame-1ab gene (ORF1ab-gene); and ABY dyelabeled spike glycoprotein gene (S-gene), and a JUN dye-labeled MS2 negative extraction control (Fig. 1A).RT-PCR set up for SARS-CoV-2 confirmation was performed by combining TaqPath 1-Step Multiplex Master Mix (No ROX) (4X) (6.875 µL × (n + 2); COVID-19 Real-Time PCR Assay Multiplex (1.375 µL × (n + 2); and nuclease-free water (8.25 µL × (n + 2) into a 50 mL Falcon tube, thoroughly vortexing the tube contents and aliquoting 15 µL of the prepared reaction mix into each sample well, the negative extraction control well, and the positive control well of a 0.1 mL 96-well optical plate.Following the addition of the reaction mix, 10 µL of eluted sample or negative extraction control was added to their respective wells using an Agilent Bravo Automated liquid handler to bring the final PCR volume to 25 µL.The COVID-19-positive control was prepared using TaqPath COVID-19 Control and diluting the stock concentration (1 × 10 4 copies/µL) down to a working solution of 25 copies/µL with the TaqPath COVID-19 Control Dilution Buffer.The TaqPath COVID-19 Control (1 × 10 4 copies/µL) was first diluted down to 200 copies/µL by aliquoting 2 µL of the stock-positive control into a microcentrifuge tube containing 98 µL of TaqPath COVID-19 Control Dilution Buffer.The positive control was then diluted to a working concentration of 25 copies/µL by aliquot ing 12.5 µL from the first dilution into a second microcentrifuge tube containing 87.5 µL of TaqPath COVID-19 Control Dilution Buffer (see SI). Finally, 4 µL of positive control and 6 µL of nuclease-free water were aliquoted into the positive control well of the optical plate containing 15 µL of reaction mix from the liquid handler RT-PCR setup step, bringing the final positive control PCR to 10 copies/25 µL.The optical plate was sealed with optical adhesive film, thoroughly vortexed, centrifuged at 1,000 × g for 1 minute and subsequently loaded onto an Applied Biosystems 7500 Fast Dx RT-PCR thermocycler for analysis.

Targeted RT-PCR-based genotyping
Genotyping PCR was performed on the same 78 SARS-CoV-2-positive samples using the TaqMan SARS-CoV-2 Mutation Panel (Thermo Fisher Scientific) comprised of an 8-assay or 12-assay layout (Fig. 1A).Criteria for VOC confirmation required amplification of at least two unique and defining SNPs expressed specifically by a single SARS-CoV-2 variant (Tables 1 and 2).Genotyping PCR panels were prepared by adding the following components row-wise (8-assay panel) or column-wise (12-assay panel) into a 1.0 mL Agilent 96-well plate in an assay-specific manner: TaqPath 1-Step RT-qPCR Master Mix,  1A).The RT-PCR-based genotyping panel consisted of TaqMan probes targeting both the SNPs present in circulating VOCs (Table 3) and the SARS-CoV-2 reference strain.Variant alleles were detected using a FAM dye-labeled probe while reference alleles were detected using a VIC dye-labeled probe.Allelic mutation status was determined using Applied Biosystems Design and Analysis Software Version 2.5.1.The detection of at least two unique and defining SNPs was required for VOC confirmation according to the discrimination criteria depicted in Tables 1 and 2.

Whole-Genome Sequencing
Fresh RNA was extracted and isolated from the same 78 samples prior to whole-genome library preparation using the Ion Ampliseq Library Kit Plus (Thermo Fisher Scientific) according to the manufacturer's instructions for use (see SI).The Ion 540 chip used in this investigation has a recommended limit of 80 samples per chip with the intent of ~1 million reads per sample (see SI).As such, 78 samples were chosen to improve the uniformity of base calling, sequencing reads, and genome depth coverage across the entire sample set.Extracted RNA was reverse transcribed using SuperScript IV VILO Master Mix added to each sample to generate initial cDNA libraries.Targeted amplification of the cDNA libraries was then performed using the Ion Ampliseq SARS-CoV-2 Insight Research Assay, which consists of two 5× primer pools targeting 237 amplicons specific to SARS-CoV-2 as well as five human controls (Hg19 human reference genome).Separate target amplification reactions were set up by dividing the cDNA libraries into two wells containing either primer pool 1 or primer pool 2. PCR amplification cycles were set according to respective sample Ct values to improve overall library yield, especially in samples with low viral loads.Following target amplification reactions, primer pool 1 and 2 libraries were combined and partially digested to remove any unreacted primers.The libraries were then barcoded using IonCode Barcode Adapters and purified using AMPure XP beads (Agencourt) and 70% ethanol.The purified libraries were subsequently rehydrated in Low Tris-EDTA buffer and quantified using the Qubit dsDNA HS Assay Kit and Qubit 4 Fluorometer (Invitrogen).Libraries were then diluted to final 70 pM concentrations and pooled into a barcoded Ion Chef PCR tube.Template preparation was performed using the Ion 540 kit on the Ion Chef Instrument.Sequencing was performed on an Ion 540 chip and run on the Ion Torrent GeneStudio S5 Plus platform (Fig. 1B).Data analysis was performed using Ion Torrent Suite software, Assembler SPAdes (Version 3.15.4),Pango lineage (Pangolin) Variant Call Format (VCF) files, and SARS_CoV_2_linea geID plugins (Fig. 1B).

Economic analysis of surveillance methods
An economic analysis of the two biosurveillance methods was conducted by evaluating manufacturer price points within three main categories: (1) principal cost of instrumen tation, (2) cost of consumables and reagents, and (3) cost of staffing rates multiplied by the hours of required hands-on-time.Ion Torrent WGS instrumentation included the following: QuantStudio

RESULTS
In total, 78 SARS-CoV-2 samples collected between 04 April and 27 December 2021 were identified as positive using the TaqPath COVID-19 Combo Kit assay and further analyzed using mutation panels comprised of targeted TaqMan RT-PCR-based genotyping assays.The same 78 samples then underwent whole-genome sequencing for further analysis using the Ion Torrent GeneStudio S5 Plus system.The RT-PCR approach included either an 8-assay (N = 25 samples tested) or a 12-assay (N = 53 samples tested) SNP assay mutation panel (Tables 1 and 2) for SARS-CoV-2 VOC and VBM discrimination.Using the interpretation criteria described in Tables 1 and 2 The Alpha VOC was most pronounced in April 2021, while the Delta variant was detected over the entire screening period with a peak in cases in August (Fig. 2).In April 2021, one Gamma VOC was detected, in addition to one sample that did not fit any VOC profile and could not be distinguished using WGS (Fig. 2).Finally, the Omicron variant became the most frequently detected VOC in December 2021 (Fig. 2).(N = 1) and undetermined (N = 1) samples were omitted from the analysis to maintain validity as ANOVA constraints required a minimum of at least three data points per VOC.Spike glycoprotein gene (S-gene) Ct values were also omitted from the ANOVA due to Alpha and Omicron variants exhibiting total S-gene Target Failure (SGTF) on the TaqPath COVID-19 Combo Kit.
An economic analysis was conducted comparing both eight and twelve-assay mutation panel layouts to Ion Torrent WGS for high-throughput variant surveillance.The factors used to calculate a cost-benefit analysis included the following: reagents and consumables (Fig. 4A), principal cost of instrumentation, staffing hands-on time, and computing power required for sequencing analysis and data storage (Fig. 4B).The average cost per sample for the 2twelve-assay genotyping panel was calculated to be >7 times cheaper than WGS, while the eight-assay genotyping panel was calculated to be >12 times cheaper per sample when compared to Ion Torrent WGS (Fig. 4A).Moreover, average turnaround times for each method from extraction to VOC result for the 78-sample cohort significantly favored the mutation panel approach (≤8 hours RT-PCR) to sequencing (≥96 hours WGS).

DISCUSSION
Surveillance of SARS-CoV-2 variants is essential for the timely implementation of public health measures aimed at limiting viral spread and the inevitable impacts of an endemic FIG 3 Box and Whisker plot distribution for visualization of ANOVA data table from Table 4.  (6,(8)(9)(10)12).While WGS has proven to be an invaluable tool for the identification of new variants, the cost, turnaround times, and viral load requirements for sequencing and data analysis remain challenging for wide implementation (6,7,9,10,(13)(14)(15).This study demonstrates that genotyping mutation panels offer a fast, reliable, and cost-effective method for monitoring known and unknown SARS-CoV-2 variants in addition to complementary WGS surveillance.
Results showed that mutation panels accurately assign lineages to VOCs as confirmed with WGS.Although genotyping assays can only assign known SARS-CoV-2 lineages, a key feature of genotyping SNP panels is the ability to rapidly adapt SNP panels as new VOC mutations arise (6)(7)(8)(9)(10)(11)(12)(13)(14)(15).The Delta (AY.4.2) subvariant and Omicron (B.1.1.529)variant provided excellent examples of how routine surveillance using well-designed mutation panels can help inform institutions on divergent mutational trends within a testing population.Delta (AY.4.2) first emerged in the US (United States) from a Washington State sample collected on 03 May 2021 (11).However, limited data on Delta (AY.4.2) whole genomes were available at the time, prolonging the response time for comprehensive investigations to delineate the mutational impact of the subvariant (11).In addition to early Delta (AY.4.2), the substituted 12-assay mutation panel (Table 2) was able to detect the first suspected Omicron infection on 30 November 2021 following a sample presenting with SGTF on the TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific).An aliquot of the sample was sent to the Massachusetts Department of Public Health where it was subsequently confirmed as Omicron via WGS (personal communication).The data presented here show a strong association between the emergence of Alpha, Delta, and Omicron variants, and marked spikes in SARS-CoV-2-positive infections within the Northeastern University population.This finding alone highlights how rapid tracking of variants with immune escape or higher transmissibility is essential for quickly responding to disease clusters as COVID-19 transitions into an endemic state.
This study showed that when comparing surveillance technical costs, using the 8-assay or 12-assay mutation panel afforded an average VOC determination of 98.72% at >7 to >12 times cheaper than WGS testing per sample.Notably, this targeted RT-PCRbased genotyping approach enables high-throughput variant surveillance and can be easily implemented in a routine testing laboratory including in developing regions with limited access to WGS.While allele-specific and genotyping PCR lab-developed tests (LDTs) have displayed promising results in tracking SARS-CoV-2 variants (6, 9), designing mutation panels using SNP assays from established manufacturers can afford clinical laboratories with greater benefits in scalability, standardization, and regulatory compliance.Furthermore, by confining the cohort of tested samples to a single observed swab collection site, any potential variances that could be introduced from unobserved swab collection, alternative collection methods (i.e., nasopharyngeal, oropharyngeal, or salivary collection), or impacts on sample stability from multiple courier methods allowed samples to be evaluated devoid of the aforementioned factors, respectively.With minimal automation, this genotyping approach generated VOC data for 77 samples of the 78-sample cohort with a turnaround of ≤4 hours, whereas WGS required 5-7 days to confirm VOC data in a fraction (46/78; 58.97%) of the 78 samples tested.Thus, expanding the use of a standardized RT-PCR-based mutation panel approach would enhance WGS efforts for detection, emergence, and surveillance of VOC.WGS could be prioritized to target samples that have not yet been assigned a VOC parent lineage, in addition to samples that display inconclusive SARS-CoV-2 IVD test results and ambiguous mutation panel genotypes.
By evaluating the performance of PCR-based mutation panels for VOC/VBM discrimination, this investigation outlines method development and establishes minimum SNP criteria for standardizing and maximizing mutation panel performance for the identification of SARS-CoV-2 variants and subvariants.One inherent deficiency of using standalone WGS for variant tracking is the lack of standardization in bioinformatic pipelines between sequencing platforms, variant calling software, and quality control metrics (7)(8)(9)15).Although recent efforts have been made to improve the consistency and availability of WGS VCF pipelines, such as the NIH ACTIV TRACE initiative, an Ion Torrent NIH ACTIV TRACE pipeline has yet to be developed, furthering the limitations of WGS as an independent biosurveillance tool (16).In addition, expanding the use of standardized genotyping panels could rapidly inform bioinformaticians of mutation status trends, helping expedite bioinformatic pipeline development and modernization.
Diligent, evidence-based consideration during mutation panel design and SNP inclusionary/exclusionary criteria should be the utmost priority for ensuring the accuracy and continuity of VOC discrimination.The use of mutation panels can further be valuable to measure the impact of VOC on parameters such as vaccine effectiveness in prevent ing SARS-CoV-2 infections (17)(18)(19)(20)(21).In addition, panel targets should be monitored and updated consistently to provide the most recent VOC data regarding frequently expressed, novel mutations that hold the potential to enhance viral fitness or alter the antigenic phenotype of SARS-CoV-2 (17,18).Mutations within the spike gene are of particular importance as the viral spike glycoprotein is responsible for binding the human ACE-2 receptor to mediate viral entry into cells (17).
Taken together, these results show that VOC genotyping using mutation panels is a cost-effective, versatile approach to monitor variant prevalence in real time and should be utilized concomitantly with WGS to enhance proactive epidemiology and public health measures.

FIG 1 (
FIG 1 (A) Panel 1 depicts the standard COVID-19 Combo Kit workflow for determining positive SARS-CoV-2 status extracted from an anterior nasal swab.In addition, Panel 1 displays a typical S-gene Target Failure (SGTF) PCR amplification plot.SGTF is highly indicative of a VOC (namely, Alpha, and Omicron) possessing the hallmark Histidine and Valine amino acid deletions at the S-gene 69/70 nucleotide positions (d.H69/V70).The d.H69/V70 six base pair deletion inhibits complementary binding of the TaqPath S-gene primer and probe sequences and DNA polymerase activation, thus failing S-gene PCR amplification, while the N and ORF-1ab targets remain unaffected and sufficient for SARS-CoV-2 detection.Panel 2 illustrates the SNP assay and sample setups for 8-assay and 12-assay mutation panel designs for SARS-CoV-2 variant surveillance.In the 8-assay panel, samples were run column-wise (color coded, e.g., S1 = sample-1; dark blue wells; S12 = sample-12; transparent wells), and SNP assays were run row-wise (arrows, e.g., A222V = top arrow left to right; Q27X = bottom arrow left to right).In the 12-assay panel, samples were run row-wise (color coded, e.g., S1 = sample-1; dark blue wells; S8 = sample-8; transparent wells), and SNP assays were run column-wise (arrows, e.g., A222V = left most arrow, from top to bottom; Q27X = right most arrow from top to bottom).(B) Schematic representation of the workflow for Ion Ampliseq library preparation and Ion Torrent GeneStudio S5 Plus SARS-CoV-2 semiconductor sequencing.

FIG 2
FIG 2 Frequency of identified/undetermined SARS-CoV-2 lineages in SARS-CoV-2-positive specimens collected from April to December 2021 using targeted RT-PCR genotyping assay mutation panels.

FIG 4 (
FIG 4 (A) Cost per sample comparison for consumables and reagents needed for the 78-sample cohort.Top: 8-assay mutation panel, middle: 12-assay Mutant Panel, bottom: Ion Torrent whole genome sequencing.(B) Analysis of initial expenses for instrumentation, computing capacity, and staffing/hands-on time necessary for launching a biosurveillance program using Ion Torrent whole-genome sequencing (blue), or TaqMan Mutation Panel using an 8-assay or 12-assay layout (orange).
a SNPs of the 12-assay mutation panel layout.b 12-assay mutation panel SNP criteria for SARS-CoV-2 VOC/VBM discrimination.c Ref: reference allele.d Mut: mutant allele.e f TABLE 3 From left to right: TaqMan SARS-CoV-2 mutation panel SNP assay, VOCs/VBMs resulting in mutant SNP expression, and assay shorthand description of TaqMan SNP assays SNP

TABLE 4
One-Way Analysis of Variance (ANOVA) of mean TaqPath COVID-19 Combo Kit Ct values for N-gene and ORF1ab-gene targets across five SARS-CoV-2 variants/subvariants of concern (N = 76)