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. 2022 Oct 3;17(10):e0275150. doi: 10.1371/journal.pone.0275150

Validation of reduced S-gene target performance and failure for rapid surveillance of SARS-CoV-2 variants

Cyndi Clark 1,#, Joshua Schrecker 1,#, Matthew Hardison 1, Michael S Taitel 2,*
Editor: Padmapriya P Banada3
PMCID: PMC9529109  PMID: 36190984

Abstract

SARS-CoV-2, the virus that causes COVID-19, has many variants capable of rapid transmission causing serious illness. Timely surveillance of new variants is essential for an effective public health response. Ensuring availability and access to diagnostic and molecular testing is key to this type of surveillance. This study utilized reverse transcription polymerase chain reaction (RT-PCR) and whole genome sequencing results from COVID-19-positive patient samples obtained through a collaboration between Aegis Sciences Corporation and Walgreens Pharmacy that has conducted more than 8.5 million COVID-19 tests at ~5,200 locations across the United States and Puerto Rico. Viral evolution of SARS-CoV-2 can lead to mutations in the S-gene that cause reduced or failed S-gene amplification in diagnostic PCR tests. These anomalies, labeled reduced S-gene target performance (rSGTP) and S-gene target failure (SGTF), are characteristic of variants carrying the del69-70 mutation, such as Alpha and Omicron (B.1.1.529, BA.1, and BA.1.1) lineages. This observation has been validated by whole genome sequencing and can provide presumptive lineage data following completion of diagnostic PCR testing in 24–48 hours from collection. Active surveillance of trends in PCR and sequencing results is key to the identification of changes in viral transmission and emerging variants. This study shows that rSGTP and SGTF can be utilized for near real-time tracking and surveillance of SARS-CoV-2 variants, and is superior to the use of SGTF alone due to the significant proportion of Alpha and Omicron (B.1.1.529, BA.1, and BA.1.1) lineages known to carry the del69-70 mutation and observed to have S-gene amplification. Adopting new tools and techniques to both diagnose acute infections and expedite identification of emerging variants is critical to supporting public health.

Introduction

Throughout the duration of the COVID-19 pandemic, mutations in the SARS-CoV-2 genome have led to the emergence of numerous variants. As of March 2022, over 1,900 unique lineages of SARS-CoV-2 have been identified via whole genome sequencing [1], a methodology that has proven to be of great importance to public health surveillance during the pandemic [2]. Most new variants retain the same properties as their parent lineage, whereas others are classified as variants being monitored (VBM), variants of interest (VOI), variants of concern (VOC), or variants of high consequence (VOHC) based on an increased risk to global public health [3]. Since December 2020, there have been six VOCs: Alpha, Beta, Gamma, Delta, Epsilon, and Omicron based on data showing a reduced response to diagnostics, treatment, or vaccines, evidence of increased transmissibility, and/or demonstration of increased disease severity [4]. Subsequently, Alpha, Beta, Gamma, and Epsilon were reclassified as VBM amid the Delta surge in September of 2021. Omicron emerged in late November and has garnered much attention due to its significant increase in transmissibility and its expedient replacement of Delta as the predominant variant [5].

Surveillance data of circulating variants are typically available 2 weeks after the date of specimen collection due to the length of time required to complete whole genome sequencing. Efforts have been made to optimize sequencing methods and validate other methods to track the spread of viral lineages more efficiently. Some variants, such as Alpha and Omicron (B.1.1.529, BA.1, BA.1.1), have demonstrated unique result patterns during real-time reverse transcriptase polymerase chain reaction (RT-PCR) testing, allowing for rapid assessment of their presumptive presence in a patient specimen. When using the ThermoFisher TaqPath™ COVID-19 Combo Kit RT-PCR assay to detect N, ORF1ab, and S-genes, Alpha’s and Omicron’s mutated S-gene inhibits target amplification. This anomaly is labeled S-gene target failure (SGTF) [6] and is caused by deletions at positions 69 and 70 (del69-70) in the S protein [7]. Alpha, the dominant variant of concern in early 2021, shares the characteristic S-gene mutation (del69-70) with Omicron lineages B.1.1.529, BA.1, and BA.1.1. Surveillance of positive PCR results exhibiting SGTF allows for the identification of variants, such as Omicron, simultaneously with a positive PCR result within 24–48 hours; whereas strain identification to confirm lineage via SARS-CoV-2 whole genome sequencing can take up to 2 weeks after initial diagnostic testing.

Although the use of SGTF for rapid surveillance of viral spread has been reported in peer-reviewed literature, the rates of transmission are likely underreported based on analysis of SGTF alone [8]. This manuscript describes the validation of reduced S-gene target performance (rSGTP) analysis in addition to SGTF for early surveillance of variants with characteristic S-gene mutations, like del69-70. The use of additional measures for rapid surveillance of variants with rSGTP and SGTF allow for a real-time assessment of transmission and assist with identification of emerging variants during periods of transition.

Herein we show the use of a novel algorithm for rSGTP and SGTF based on the Ct values of the RT-PCR results as a proxy to rapidly assess the spread of certain SARS-CoV-2 lineages and validation of the algorithm with sequencing results in a randomized subset of specimens. With the emergence of BA.2, an Omicron lineage that does not share the del69-70 mutation or exhibit rSGTP or SGTF, we were also able to track the trajectory of B.1.1.529/BA.1/BA.1.1 versus BA.2 in near real-time using the algorithm and confirmed our analyses with sequencing data, demonstrating the utility of this tool during viral evolution and how this methodology may inform future surveillance measures.

Methods

This study describes SARS-CoV-2 RT-PCR and whole genome sequencing results obtained via a collaboration between Aegis Sciences Corporation and Walgreens Pharmacy that has administered ~8.5 million COVID-19 tests at ~5,200 locations across the United States and Puerto Rico at the time of development of this manuscript [9]. RT-PCR testing utilizes primers and probes that bind to regions along the viral genome and release fluorescent compounds through repeated cycles of gene target amplification. Samples tested via the ThermoFisher TaqPath™ COVID-19 Combo Kit are positive if the fluorescent signal is detected above threshold in two out of three gene targets, the positive control amplifies all three gene targets, and the negative control shows no amplification of the SARS-CoV-2 gene targets [10]. An internal control is included in each sample to account for extraction and PCR efficiency. SARS-CoV-2 lineages with characteristic S-gene mutations have altered amplification of the TaqPath™ S-gene target. This study analyzed rSGTP and SGTF for genome sequencing results meeting the following criteria:

  • Reduced S-gene Target Performance (rSGTP):
    • ○ Similar amplification of N and ORF1ab-genes, but reduced amplification of S-gene.
    • ○ S-gene Ct value ≥ 4 + Average (N-gene Ct value: ORF1ab-gene Ct value)
  • S-Gene Target Failure (SGTF):
    • ○ Similar aAmplification of N and ORF1ab genes, but no amplification of the S-gene.
    • ○ S-gene Ct value > 37 or null

These data were generated in a subset of COVID-19 positive specimens with an average N-gene and ORF1ab-gene Ct value ≤ 30 due to Ct fluctuations and poor sequencing results in samples with high Ct values and lower viral titers. Presumed Alpha and Omicron B.1.1.529/BA.1/BA.1.1 prevalence is calculated by taking the sum of rSGTP and SGTF RT-PCR positive results divided by the total number of RT-PCR positives for each collection date or a range of collection dates.

  • Presumed Alpha or Omicron B.1.1.529/BA.1/BA.1.1% = [(rSGTP + SGTF) / TOTAL POSITIVES] * 100.

Presumed Omicron BA.2 prevalence is calculated by subtracting the sum of rSGTP and SGTF RT-PCR positive results from the total number of RT-PCR positives divided by the total number of RT-PCR positives for each collection date or a range of collection dates.

  • Presumed Omicron BA.2% = [[TOTAL POSITIVES—(rSGTP + SGTF)] / TOTAL POSITIVES] * 100.

Whole genome sequencing (WGS) was performed using the Illumina® COVIDSeq Test and the NovaSeq 6000 sequencer. The Illumina® DRAGEN COVID Lineage App uses the FASTQ files to align reads to the SARS-CoV-2 reference genome (NC_045512.2) and reports coverage of targeted regions. Indeterminate (N) bases are assigned in regions covered with less than 20 unique reads or where the allele frequencies of A, T, G, or C are equal or too low to confidently make a call. Sequences with greater than 10% N bases were not included in the analysis. Variant calling, consensus sequence generation, and lineage/clade analysis is completed via Pangolin and NextClade pipelines.

Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) was determined for Presumed Alpha, Presumed Omicron B.1.1.529/BA.1/BA.1.1, and Presumed Omicron BA.2 calculations. The protocol for this study was institutional review board approved by the Pearl IRB™.

Results

A randomized subset of COVID-19 positive samples collected between 1/1/2021–3/23/2022 with an average N-gene and ORF1ab-gene Ct value ≤ 30 (N = 374,469) were sequenced. Lineages identified in these samples were: Alpha– 8.40% (n = 31,442), Delta– 60.15% (n = 225,226), Omicron B.1.1.529/BA.1/BA.1.1–21.32% (n = 79,826), Omicron BA.2 – 1.00% (n = 3,737) and Other– 9.14% (n = 34,238) (Table 1 and Fig 1). S-gene target failure (SGTF) was dominant in Alpha (79.35%) and Omicron B.1.1.529/BA.1/BA.1.1 (92.18%) but was not exhibited in all cases (Table 1 and Fig 1). Analysis of the non-SGTF samples that were classified as Alpha and Omicron B.1.1.529/BA.1/BA.1.1 showed lower amplification of the S-gene target when compared to the N-gene and ORF1ab-gene Ct values within the same sample.

Table 1. Rates of rSGTP and SGTF in WGS confirmed lineages.

Lineage Category Total Samples Analyzed by Lineage % of Total Samples Analyzed for Study Total Samples with SGTF by Lineage % of Samples with SGTF by Lineage Total Samples with rSGTP by Lineage % of Samples with rSGTP by Lineage % of Samples with SGTF or rSGTP by Lineage
Alpha 31,442 8.40% 24,949 79.35% 5,367 17.07% 96.42%
Delta 225,226 60.15% 283 0.13% 151 0.07% 0.19%
Omicron B.1.1.529/ BA.1/ BA.1.1 79,829 23.32% 73,583 92.18% 5,960 7.47% 99.65%
Omicron BA.2 3,737 1.00% 11 0.29% 2 0.05% 0.35%
Other 34,238 9.14% 1,135 3.32% 123 0.36% 3.67%
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Fig 1. Frequency of S-gene target status in WGS confirmed lineages.

Fig 1

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Reduced S-gene target performance (rSGTP) was validated by comparing the frequency that the S-gene amplified and the distribution of the Ct value differences between the S-gene and the average of N and ORF1ab-genes for each sample where the lineage was confirmed as Alpha, Delta, Omicron, or Other (Table 2). The average Ct value of N and ORF1ab-genes was chosen as the comparator because the mean and median Ct Value differences between N and ORF1ab-genes was less than 0.4 across all lineages analyzed (Table 2). The Ct value differences between the S-gene and the average of N and ORF1ab-genes amongst samples confirmed as Delta (Mean: 0.36, Median: 0.28), Omicron BA.2 (Mean: 0.49, Median: 0.42), or Other (Mean: 0.23, Median: 0.19) were negligible and the S-gene amplified in greater than 95% of cases. Conversely, S-gene amplification was observed in only 17.07% and 7.47% of Alpha and Omicron B.1.1.529/BA.1/BA.1.1 samples, respectively. Moreover, the Ct value of the S-gene was 3–4 cycles greater than the average Ct value of N and ORF1ab-genes in more than 90% of Alpha and Omicron B.1.1.529/BA.1/BA.1.1 cases in which S-gene amplification occurred (Fig 2). The S-gene amplified on average 5 cycles higher in Alpha and 6 cycles higher in Omicron B.1.1.529/BA.1/BA.1.1 samples demonstrating weak amplification, or reduced performance, of the S-gene target in samples where S-gene amplification occurred. (Fig 2). Using the algorithms for rSGTP: S-gene Ct value ≥ 4 + Average Ct value (N: ORF1ab) and SGTF: S-gene Ct value > 37, or null rSGTP + SGTF was observed in 96.42% of Alpha and 99.65% Omicron B.1.1.529/BA.1/BA.1.1 samples (Table 2).

Table 2. Ct value differences in WGS confirmed lineages with S-gene amplification.

Lineage Category Total Samples with S-gene Present Mean Ct Value Difference (S-gene Ct value–AVG Ct value (N: ORF1ab-genes)) Median Ct Value Difference (S-gene Ct value–AVG Ct value (N: ORF1ab-genes)) Mean Ct Value Difference (N gene Ct value–ORF1ab gene Ct value) Median Ct Value Difference (N gene Ct value–ORF1ab gene Ct value)
Alpha 6,493 5.08 5.31 -0.23 -0.17
Delta 224,943 0.36 0.28 0.25 0.30
Omicron B.1.1.529/BA.1/BA.1.1 6,243 6.01 6.07 0.16 0.24
Omicron BA.2 3,726 0.49 0.42 0.05 0.16
Other 33,103 0.23 0.19 0.38 0.39
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Fig 2. Distribution of Ct value differences between the S-gene and the average of N and ORF1ab-genes in Alpha and Omicron B.1.1.529/BA.1/BA.1.1 variants.

Fig 2

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Sensitivity and specificity of using rSGTP + SGTF or SGTF alone was calculated for Alpha, Omicron B.1.1.529/BA.1/BA.1.1, and Omicron BA.2 lineages that were confirmed by sequencing within the timeframe of first and last identification based on date of specimen collection (Table 3). Sensitivity improved by utilizing rSGTP + SGTF as a proxy for the identification of Alpha (96.4% vs. 79.3%) and Omicron B.1.1.529/BA.1/BA.1.1 (99.6% vs. 92.2%) in comparison to SGTF alone. Sensitivity did not change for the inverse of rSGTP + SGTF compared to the inverse of only SGTF in Omicron BA.2 (99.7%). Specificity remained consistent for both Alpha (99.5% vs. 99.6%) and Omicron B.1.1.529/BA.1/BA.1.1 (97.6% vs 97.8%) but increased for Omicron BA.2 (99.1 vs. 94.1%) when comparing rSGTP + SGTF to SGTF alone. The increase in sensitivity and negative predictive value (NPV) when rSGTP + SGTF is used as a proxy for surveillance of Alpha and Omicron B.1.1.529/BA.1/BA.1.1 lineages create a more accurate presumptive dataset with fewer false negatives. Furthermore, the utility of the inverse rSGTP + SGTF calculation is demonstrated by higher specificity and positive predictive value, or less false positives, in the presumed Omicron BA.2 dataset.

Table 3. Sensitivity, specificity, PPV, and NPV of proxy calculations by lineage.

Presumed Alpha
Alpha (Samples Collected: 1/1/2021-12/03/2021; n = 273,318) Sensitivity Specificity PPV NPV
SGTF 79.3% 99.6% 96.5% 97.4%
rSGTP + SGTF 96.4% 99.5% 96.4% 99.5%
Presumed Omicron B.1.1.529/BA.1/BA.1.1
Omicron - B.1.1.529/BA.1/BA.1.1 (Samples Collected: 11/24/2021-3/23/2022; n = 105,646) Sensitivity Specificity PPV NPV
SGTF 92.2% 97.8% 99.2% 80.2%
rSGTP + SGTF 99.6% 97.6% 99.2% 98.9%
Presumed Omicron BA.2
Omicron—BA.2 (Samples Collected: 1/3/2022-3/23/2022; n =: 59,920) Sensitivity Specificity PPV NPV
Inverse SGTF 99.7% 94.1% 53.1% 100.0%
Inverse rSGTP + SGTF 99.7% 99.1% 88.0% 100.0%
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The prevalence of rSGTP + SGTF samples was graphed over time and compared to the prevalence of WGS-confirmed Alpha, Delta, Omicron B.1.1.529/BA.1/BA.1.1, and Omicron BA.2 (Fig 3). These data demonstrate the strong correlation between rSGTP + SGTF positives and the emergence and spread of both Alpha and Omicron B.1.1.529/BA.1/BA.1.1 VOC within the population and, for the first time, supports the use of reduced S-gene amplification (rSGTP) in samples as a proxy for early detection and surveillance of SARS-CoV-2 variants.

Fig 3. Surveillance of SARS-CoV-2 variants via rSGTP + SGTF tracking versus WGS-lineage confirmation.

Fig 3

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Date range truncated at March 2021 due to low sample volume for sequenced samples prior to this date.

Discussion

SARS-CoV-2 genomes accumulate ~2 mutations per month, and over time, viral evolution gives rise to new lineages that are partitioned into phylogenetic trees and classified as variant strains [11]. Mutations in variants of concern (VOC), such as Omicron and previously Alpha, pose a challenge for some PCR primers to anneal to the S-gene. The phenomenon defined as reduced S-gene target performance (rSGTP) and failure (SGTF) is characteristic of both Alpha and Omicron B.1.1.529/BA.1/BA.1.1 variants. Our analyses revealed that although SGTF was present in the majority of cases confirmed as Alpha and Omicron B.1.1.529/BA.1/BA.1., there were many samples that had detectable S-gene amplification (Table 1). Identification of SGTF and rSGTP, instead of SGTF alone, can be used as a proxy to more accurately track emergence and spread of variants with the del69-70 mutation in real-time, enabling rapid dissemination of COVID-19 surveillance patterns. Additionally, the absence of rSGTP and SGTF has allowed for early assessment of transmission patterns of BA.2. Using the algorithm proposed for rSGTP and SGTF as a proxy for surveillance of Omicron and other variants of concern with these characteristic PCR result patterns, significantly accelerates the ability to identify gross changes in viral spread while waiting for viral genome sequencing to be completed.

Several previous publications have reported similar mechanisms for utilizing SGTF to increase efficiency of surveillance of certain variants of concern. To date, the publications have primarily focused on early detection of the Alpha and Omicron B.1.1.529/BA.1/BA.1.1. Investigators utilized similar methods for identification of SGTF as what is presented in this paper. This included identification of PCR results with amplification of the N and ORF1ab-genes in the absence of amplification for the S-gene. Similarly, other groups targeted PCR results with cycle threshold values ≤ 30 for the N and ORF1ab-genes to reduce the incidence of identifying a sample as exhibiting SGTF in cases where viral load was low, and amplification was near the limit of the detection of the assay. These publications typically focused on smaller sample sizes but did confirm SGTF as a proxy for identification of the Alpha and B.1.1.529/BA.1/BA.1.1 variants through subsequent genome sequencing [8, 12–16]. Similar to our results, the vast majority of samples with SGTF, which were expected to have been caused by commonly circulating variants at the time of the study, were confirmed as Alpha or Omicron B.1.1.529/BA.1/BA.1.1 via sequencing. Additionally, we determined sensitivity (92.2%) and specificity (97.8%) was similar to previously published calculations utilizing SGTF to detect Omicron (B.1.1.529, BA.1, BA.1.1 lineages) [17]. Our analyses also revealed a significant proportion of Alpha and Omicron B.1.1.529/BA.1/BA.1.1 samples with S-gene amplification (Table 1). For this reason, we believe that utilization of SGTF alone for early surveillance of variants with del69-70 would underestimate the actual changes in transmission of these variants.

Migueres et al. reported a similar phenomenon that we identified in both Alpha and Omicron B.1.1.529/BA.1/BA.1.1 cases, which we refer to as rSGTP, though the analysis was performed only during a time at which Alpha was in circulation [18]. These investigators labeled samples with reduced S-gene amplification in comparison to that of N and ORF1ab-genes as “S-gene target late detection,” or SGTL. More specifically, these samples were identified when S-gene Ct values were ≥ 5 cycles higher than N or ORF1ab-genes and were reported as totaling less than 1% of all Alpha cases. Our analysis defined samples as having rSGTP when the S-gene cycle threshold value was at least 4 cycles higher than the average Ct values of N and ORF1ab, and nearly 17% of Alpha and 8% of Omicron B.1.1.529/BA.1/BA.1.1 cases exhibited rSGTP. Table 1 indicates that our calculation for the percent of samples with SGTF or rSGTP was unable to capture 100% of the confirmed Alpha (96.42%) or Omicron B.1.1.529/BA.1/BA.1.1 (99.65%) samples. The samples missed were those with Ct values for the S-gene less than 4 cycles higher than the average of N and ORF1ab-genes. Fig 2 demonstrates the distribution of the differences between the S-gene Ct value and the average of N and ORF1ab-genes Ct value in Alpha and Omicron B.1.1.529/BA.1/BA.1.1 lineages. According to poisson distribution, the calculation of rSGTP presented here captures ~90% or more of the Alpha and Omicron B.1.1.529/BA.1/BA.1.1 lineages exhibiting S-gene amplification and supports the utility and validity of using the definitions of SGTF and rSGTP outlined in this manuscript. Finally, we are aware that a research team at the CDC contemporaneously performed another analysis and came to similar conclusions regarding a revised SGTF definition (H. Scobie, Z. Smith, personal communication, April 13, 2022).

The primary limitation of this study is that all PCR testing for SARS-CoV-2 was performed utilizing the ThermoFisher TaqPath™ COVID-19 Combo Kit RT-PCR assay to detect N, ORF1ab, and S-genes. Although there would not be an expectation that other commercially available assays that include testing for the S-gene would have significantly different capabilities in detecting amplification of this target, it is possible that results from testing performed by another assay may contribute to differences in the rates at which rSGTP and SGTF were identified. We did not perform testing via other methodologies to validate if rates of rSGTP and SGTF were similar across other assays that included testing for the S-gene.

An additional limitation of this study was its exclusion of test results from analysis with an average N-gene and ORF1ab-gene Ct value ≥ 30. These samples were excluded as those with moderate to low viral titer could have appeared to exhibit SGTF and rSGTP but may have had little to no detectable S-gene amplification due to proximity to the limit of detection of the assay. Additionally, samples with moderate to low viral titer can create limitations when performing whole genome sequencing as they often contribute to a significant increase in non-callable bases and result in less specific lineage calls [19]. The authors felt that inclusion of these low to moderate viral titer samples, which likely would have resulted in less specific lineage calls, would have negatively impacted the ability to compare findings between predominant lineages over time.

As we endure an ever-changing pandemic, the development of novel tools to both diagnose acute infections and to expedite identification of emerging variants will be critical during the transition into an endemic phase. Collaborative efforts between laboratories and healthcare providers to identify unique trends within large, shared datasets can provide support for public health decision making. Those engaged in protecting public health during the COVID-19 pandemic are urged to adopt new methods to identify and track this virus as it continues to mutate.

Supporting information

S1 File. Final COVID dataset full.

This Zip file contains the complete dataset used for all analyses, tables, and figures. Dates were adjusted by a random constant to assure deidentification. A data definition document is also included.

(ZIP)

Click here for additional data file. (8.5MB, zip)

Acknowledgments

The authors thank Adam Wallace for his expert support preparing the data for this study and Heather Scobie and Zachary Smith for their review and feedback on this manuscript.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

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Decision Letter 0

Padmapriya P Banada

8 Aug 2022

PONE-D-22-14356Validation of Reduced S-gene Target Performance and Failure for Rapid Surveillance of SARS-CoV-2 VariantsPLOS ONE

Dear Dr. Taitel,

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The study presents an important methodology and an algorithm to accurately survey for SARS-CoV-2 varaints of concern. However, reviewers and I have few questions and concerns which needs to be addressed. 

The general risk of this assay is in its inability to conclusively differentiate strains with the same deletion, as shown for alpha and omicron BA.1. however, it can differentiate variants with and without deletion mutant very effectively.

It is still not very clear to me how the S gene target performance reduction is measured. I understand you came up with a formula  but what is the comparator to dictate the performance is reduced? what if the primers/probe for S-gene are degraded or the sample has low viral load? how do you account for this? 

The following statement in discussion is very important in this study. I would recommend the authors to include this message concisely in the abstract as well "Our analyses also revealed a significant proportion of Alpha and Omicron B.1.1.529/BA.1/BA.1.1 samples with S-gene amplification (Table 1).For this reason, we believe that utilization of SGTF alone for early surveillance of variants with del69-70 would underestimate the actual changes in transmission of these variants".

please also highlight in the discussion, the limitation of the study and an explanation as in a scenario where any new emerging variants which does not carry the deletion at69-70 codon. in addition, please comment on the samples which fail due to the low viral load, which might be below the cut off of the assay, but might work fine for N and ORF1ab. 

you have briefly indicated that this was done with a specific RT PCR kit and in specific instrument. do you anticipate change in results due to change of qPCR instruments?

Please also add a comment on the descrepant samples, that were missed by the assay. 

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Padmapriya P Banada, PhD

Academic Editor

PLOS ONE

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Additional Editor Comments:

The study presents an important methodology and an algorithm to accurately survey for SARS-CoV-2 varaints of concern. However, reviewers and I have few questions and concerns which needs tobe addressed.

The general risk of this assay is in the ability to not conclusively be able to differentiate strains with the same deletion, as shown for alpha and omicron BA.1. however, it can differentiate variants with and without deletion mutant very effectively.

It is still not very clear to me how the S gene target performance reduction is measured. I understand you came up with a formula, but what is the comparator to dictate the performance is reduced? what if the primers/probe for S-gene are degraded or the sample has low viral load? how do you account for this?

The following statement in discussion is very important in this study. I would recommend the authors to include this message concisely in the abstract as well "Our analyses also revealed a significant proportion of Alpha and Omicron B.1.1.529/BA.1/BA.1.1 samples with S-gene amplification (Table 1).For this reason, we believe that utilization of SGTF alone for early surveillance of variants with del69-70 would underestimate the actual changes in transmission of these variants".

please also highlight in the discussion, the limitation of the study and an explanation as in a scenario where any new emerging variants which does not carry the deletion at69-70 codon. in addition, please comment on the samples which fail due to the low viral load, which might be below the cut off of the assay, but might work fine for N and ORF1ab.

you have briefly indicated that this was done with a specific RT PCR kit and in specific instrument. do you anticipate change in results due to change of qPCR instruments?

Please also add a comment on the descrepant samples. that were missed by the assay.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: General comments:

Clark et al. presents an interesting study using Ct differences between different targets to identify reduced S-gene target performance instead of solely S-gene target failure to increase sensitivity in the preliminary identification of SARS-CoV-2 variants for rapid surveillance purposes. This proof-of-concept approach to increase screening sensitivity could be applied if new VOCs with different target failure or reduction patterns emerge.

Line numbers would be useful for reviewing. Further clarification about the exact methods in the process would helpful.

Introduction

1. Page 2: Suggest aligning classification of VBM, VOI, VOC, VOHC with WHO classifications instead of CDC to make it internationally applicable.

2. Page 2: Suggest taking out Epsilon in the list of VOCs, as has not been listed as a VOC by the WHO and did not have worldwide impact.

Methods

3. Page 4: rSGTP criteria second point about S-gene Ct value >4 + Average (N-gene Ct value: ORF1ab-gene Ct value) is unclear. Could be rephrased clearer with wording instead of using “+” Perhaps S-gene Ct value 4 greater than the Average etc.

4. Page 4: Was there a Ct value threshold for the amplification of N and ORF1ab genes in the two criteria? Would include the threshold that is used for transparency.

5. Page 5-6: Methods section about sensitivity, specificity, PPV and NPV - would detail what the gold standard comparison was to identify true positives and negatives versus false. I assume it was to the sequences that underwent NGS. Were all samples first identified via the algorithmic method with RT-PCR screening and then 1:1 subsequently underwent gold standard NGS? Or was it a sample that went for NGS that was not connected 1:1?

Results

6. Page 6: First line of the second results paragraph may need to be rephrased for clarity.

7. Page 7: Would again rephrase the rSGTP criteria for clarity. Perhaps using the word Ct value 4 greater than the average etc.

8. Page 7: Unclear what is meant by inverse rSGTP + SGTF in the second paragraph. Recommend clarifying.

9. Page 7 & Figure 3: Visually quite striking in terms of correlation between presumed lineage vs confirmed lineage. Was any statistical testing done beyond visual with the statement of “strong correlation”?

10. Page 7 & Figure 3: Were the confirmed variants the same sample that was screened or was it all that was sequenced (ie a larger sample)?

Discussion

11. Page 8: First paragraph, last sentence – may want to reframe it less as a comparison between this algorithm vs WGS and rather this algorithm with rSGTP compared to just SGTF alone adds to the fidelity and confidence of using this screening approach in more timely surveillance. As the authors have stated, the SGTF approach is already being implemented as a timely surveillance method.

12. Page 10: Can see another limitation being samples with low viral load/high cycle Cts. Would be interesting to see as a percentage of overall samples. How many fit that criteria and was excluded from this validation study and therefore affect the sensitivity and correlation if looking at all samples?

13. Could the authors comment about this approach in the context of the CDC framework for evaluating surveillance systems? This will strengthen the paper in connecting it into the larger surveillance discussion instead of focused more on the laboratory side of things.

Figures

13. Table 1: For the column % of total samples analyzed for study – is there a reason it totals to 102%? I see the % for Omicron is different from the manuscript versus the table. Typo? Recommend putting a footnote of why that is if it’s not a typo.

14. Figure 1: If keeping this figure, consider putting in percentages if possible for the different stacked bars.

15. Table 3: Would recommend having a 2x2 table with the actual numbers of true positives, true negatives, false positives, and false negatives in addition to percentages for more clarity.

16. Figure 3: Suggest labeling the y-axis. May be a point to clarify is this all samples that underwent any type of SGTP screening or the subset samples that was part of this study as per previous points.

Reviewer #2: This was a good validation study. Please review paper for grammatical errors.

Page 4 & 5: Remove bullets. The criteria for rSGTP and SGTF can be written as sentences. The formulas can be submitted in a supplementary table.

Page 6:

• Table 1 states that 79,829 Omicron B.1.1.529/BA.1/BA.1.1 samples were analyzed, but paragraph one states 79,826 Omicron B.1.1.529/BA.1/BA.1.1 samples were sequenced. What happened to the remaining 3 Omicron samples?

• The last sentence in paragraph one might work better in the rSGTP paragraph.

Page 7:

• Sentence 1: Remove the criteria for rSGTP and SGTF. It’s stated in your method section.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Oct 3;17(10):e0275150. doi: 10.1371/journal.pone.0275150.r002

Author response to Decision Letter 0

24 Aug 2022

Response to Reviewers,

Thank you for the thorough review of our manuscript and the opportunity to submit a revised version. We have addressed each of the comments below and updated the manuscript where necessary.

• The general risk of this assay is in its inability to conclusively differentiate strains with the same deletion, as shown for Alpha and Omicron BA.1. however, it can differentiate variants with and without deletion mutants very effectively.

o Response: This is a limitation of the approach that we have addressed in the manuscript discussion.

• It is still not very clear to me how the S-gene target performance reduction is measured. I understand you came up with a formula but what is the comparator to dictate the performance is reduced?

o Response: The comparator to dictate the S-gene target performance is reduced is the performance of the N and ORF1ab gene targets within the same sample. The performance of all targets is measured by the amplification Ct value. There was a negligible difference between the Ct values of the N and ORF1ab-genes across all samples analyzed. The Mean and Median Ct Value difference between N and ORF1ab-genes for the lineages in this study has now been added to Table 2.

� The Mean and Median Ct Value difference between N and ORF1ab-genes is less than 0.4 across all lineages. Once this was determined, we used the average Ct value of these two targets to compare against the S-gene Ct value.

� The Mean and Median Ct Value difference between the S-gene and the average of N and ORF1ab-genes is less than 0.5 in Delta, Omicron BA.2, and Other lineages, indicating similar S-gene target amplification, or performance, in these lineages.

� The Mean and Median Ct Value difference between the S-gene and the average of N and ORF1ab-genes is 5.08 and 5.31 for Alpha and 6.01 and 6.07 for Omicron B.1.1.529/BA.1/BA.1.1 lineages, respectively. The higher S-gene Ct Values in this sample set demonstrates weak amplification, or reduced performance, of the assay target in those samples where S-gene amplification occurred.

o Response: Additionally, Table 1 and Figure 1 compares the frequency of rSGTP across all confirmed lineages in this study and further validates rSGTP in lineages with del69-70.

� The proportion of samples with rSGTP was 0.07%. 0.05%, and 0.36% in Delta, Omicron BA.2, and Other lineages, respectively.

� The proportion of samples with rSGTP was 17.07% and 7.47% in Alpha and Omicron B.1.1.529/BA.1/BA.1.1 lineages, respectively

• What if the primers/probe for S-gene are degraded or the sample has low viral load? How do you account for this?

o Response: Degradation of the primers and probes would be observed in the positive controls. There are four positive controls on each RT-PCR run and all three genes, S, N, and ORF1ab, must amplify for the run and samples to pass. This has been added to the methods.

o Response: To address samples with low viral loads, we specifically analyzed samples with an average N-gene and ORF1ab-gene Ct value < 30 to reduce the bias potentially introduced by these samples. Additionally, samples with lower viral load typically do not meet our quality criteria, or that which was set forth by the CDC, to make a definitive lineage call via our next-generation sequencing platform. These samples either result in incomplete sequencing, or result in a call for a less specific parent lineage. This is likely why the ”Other” category has an occurrence of SGTF and rSGTP that is higher than that found with Delta and BA.2. This is addressed in the methods and has been added as a limitation to the manuscript.

• The following statement in the discussion is very important in this study. I would recommend the authors to include this message concisely in the abstract as well "Our analyses also revealed a significant proportion of Alpha and Omicron B.1.1.529/BA.1/BA.1.1 samples with S-gene amplification (Table 1). For this reason, we believe that utilization of SGTF alone for early surveillance of variants with del69-70 would underestimate the actual changes in transmission of these variants".

o Response: This has been concisely added to the abstract and manuscript.

• Please also highlight in the discussion, the limitation of the study and an explanation as in a scenario where any new emerging variants which does not carry the deletion at 69-70 codon. In addition, please comment on the samples which fail due to the low viral load, which might be below the cut off of the assay, but might work fine for N and ORF1ab.

o Response: Statement of clarification and limitation added to the manuscript discussion.

• You have briefly indicated that this was done with a specific RT-PCR kit and in specific instrument. Do you anticipate change in results due to change of qPCR instruments?

o Response: No meaningful changes would be expected due to a change of qPCR instrumentation. There could potentially be slight changes in the Ct values associated with each sample, but this would be minimally impactful and within the normal expected variance range of repeated analysis using this molecular technique. Additionally, other labs using the same reagent kit with different qPCR instruments have also observed SGTF and rSGTP in these lineages.

• Please also add a comment on the discrepant samples, that were missed by the assay.

o Response: Table 1 indicates that our calculation for % of Samples with SGTF or rSGTP was unable to capture 100% of the confirmed Alpha (96.42%) or Omicron B.1.1.529/BA.1/BA.1.1 (99.65%) samples. The samples that were missed by our calculation were those where the Ct value of the S-gene was less than 4 cycles higher than the average of N and ORF1ab-gene targets. Figure 2 demonstrates the distribution of the differences between the S-gene Ct value and the average of N and ORF1ab-genes Ct value in Alpha and Omicron B.1.1.529/BA.1/BA.1.1 lineages. According to Poisson distribution, our calculation of rSGTP would capture ~90% or more of the Alpha and Omicron B.1.1.529/BA.1/BA.1.1 lineages with reduced S-gene target performance. This has been clarified in the results and discussion sections.

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Decision Letter 1

Padmapriya P Banada

12 Sep 2022

Validation of Reduced S-gene Target Performance and Failure for Rapid Surveillance of SARS-CoV-2 Variants

PONE-D-22-14356R1

Dear Dr. Taitel,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Padmapriya P Banada, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Padmapriya P Banada

21 Sep 2022

PONE-D-22-14356R1

Validation of Reduced S-gene Target Performance and Failure for Rapid Surveillance of SARS-CoV-2 Variants

Dear Dr. Taitel:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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on behalf of

Dr. Padmapriya P Banada

Academic Editor

PLOS ONE

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