Design of SARS-CoV-2 Variant-Specific PCR Assays Considering Regional and Temporal Characteristics

Abstract

Monitoring the prevalence of SARS-CoV-2 variants is necessary to make informed public health decisions during the COVID-19 pandemic. PCR assays have received global attention, facilitating a rapid understanding of variant dynamics because they are more accessible and scalable than genome sequencing. However, as PCR assays target only a few mutations, their accuracy could be reduced when these mutations are not exclusive to the target variants. Here we introduce PRIMES, an algorithm that evaluates the sensitivity and specificity of SARS-CoV-2 variant-specific PCR assays across different geographical regions by incorporating sequences deposited in the GISAID database. Using PRIMES, we determined that the accuracy of several PCR assays decreased when applied beyond the geographic scope of the study in which the assays were developed. Subsequently, we used this tool to design Alpha and Delta variant-specific PCR assays for samples from Illinois, USA. In silico analysis using PRIMES determined the sensitivity/specificity to be 0.99/0.99 for the Alpha variant-specific PCR assay and 0.98/1.00 for the Delta variant-specific PCR assay in Illinois, respectively. We applied these two variant-specific PCR assays to six local sewage samples and determined the dominant SARS-CoV-2 variant of either the wild type, the Alpha variant, or the Delta variant. Using next-generation sequencing (NGS) of the spike (S) gene amplicons of the Delta variant-dominant samples, we found six mutations exclusive to the Delta variant (S:T19R, S:Δ156/157, S:L452R, S:T478K, S:P681R, and S:D950N). The consistency between the variant-specific PCR assays and the NGS results supports the applicability of PRIMES. IMPORTANCE Monitoring the introduction and prevalence of variants of concern (VOCs) and variants of interest (VOIs) in a community can help the local authorities make informed public health decisions. PCR assays can be designed to keep track of SARS-CoV-2 variants by measuring unique mutation markers that are exclusive to the target variants. However, the mutation markers may not be exclusive to the target variants because of regional and temporal differences in variant dynamics. We introduce PRIMES, an algorithm that enables the design of reliable PCR assays for variant detection. Because PCR is more accessible, scalable, and robust for sewage samples than sequencing technology, our findings will contribute to improving global SARS-CoV-2 variant surveillance.

Keywords: PCR assays; PRIMES; SARS-CoV-2 variants; in silico analysis; wastewater-based epidemiology.

FIG 1

(a) Schematic describing the workflow for designing PCR assays while considering regional and temporal variations in GISAID samples. After selecting a target variant, we identified variant-specific mutations, which we ranked in terms of sensitivity and specificity by using the introduced tool PRIMES. Finally, we designed primers for mutations with high sensitivity and specificity for the geographical region of interest. (b) Illustration showing the effect of regional lineages on the accuracy of variant-specific PCR assays.

FIG 2

In silico analysis of PCR assay targeting the S:Δ69/70 mutation (15) to detect the Alpha variant for GISAID samples from Israel (n = 13,932) (a), Slovenia (n = 25,528) (b), Central African Republic (n = 49) (c), and Congo (n = 183) (d). Dotted lines in the graphs on the left indicate the number of sequences used for the in silico analyses.

FIG 3

In silico analysis of PCR assay targeting the S:T478K mutation (16) to detect the Delta variant in the United States (n = 1,187,412) (a) and Mexico (n = 28,956) (b). Dotted lines in the graphs on the left indicate the number of sequences used for the in silico analyses.

FIG 4

(a and b) Variant dynamics determined by Pangolin using GISAID samples from the state of Illinois in the United States (n = 20,165) (a) and from the United States (n = 1,187,412) (b). (c and d) Focusing on the spike protein mutations in the Delta variant, we showed the sensitivity and specificity of assigning the Delta variant based on the presence of each mutation in GISAID samples from Illinois (c) and the United States (d). (e and f) Estimated assignment of GISAID samples from Illinois (e) and the United States (f) to variants using the primer designed to target mutation S:P681R.

FIG 5

Determination of sensitivities (i.e., LOQs and LODs) of RT-qPCR assays for total SARS-CoV-2, Alpha variant, and Delta variant. (a) Dashed lines indicate the coefficient of variation at 0.35. (b) The trend lines for positive rate (solid lines) were calculated using equation 6. The LODs were the RNA concentration at which the positive rate was 0.95 (dashed lines).

FIG 6

Cross-reactivity of PCR assays for Alpha and Delta variants. RT-qPCR assays for the Alpha (a) and Delta (b) variants were applied to the corresponding variant and the WT to determine the specificity (i.e., cross-reactivity with the WT).

FIG 7

Dominant variants of the mixtures of synthetic RNA controls determined by RT-qPCR assays. Total SARS-CoV-2 concentrations were determined by the N gene concentrations at 10⁴ gc/μL (a) and 10¹ gc/μL (b). Prevalences on the y axis indicate the ratio of the concentration of each variant to the total virus concentration. The x axis shows the mixing ratios of different synthetic RNA controls (W, A, and D represent WT, Alpha variant, and Delta variant, respectively). The label at the top of each graph (others, Alpha, or Delta) indicates the dominant variant determined by the RT-qPCR assays. A one-sample t test or a two-sample t test was conducted to compare the prevalences between the Alpha variant and 0.5 or the prevalences between the Alpha and Delta variants (nonsignificant [ns], P > 0.05; *, 0.001 < P < 0.05; **, P < 0.001), respectively.