Identification of mutations in SARS-CoV-2 PCR primer regions

doi:10.21203/rs.3.rs-1838361/v1

Due to the constantly increasing number of mutations in the SARS-CoV-2 genome, concerns have emerged over the possibility of decreased diagnostic accuracy of reverse transcription-polymerase chain reaction (RT-PCR), the gold standard diagnostic test for SARS-CoV-2. We propose an analysis pipeline to discover genomic variations overlapping the target regions of commonly used PCR primer sets. We provide the list of these mutations in a publicly available format based on a dataset of more than 600,000 SARS-CoV-2 samples. Our approach distinguishes among mutations possibly having a damaging impact on PCR efficiency and ones anticipated to be neutral in this sense. Samples are categorized as „prone to misclassification” vs. „likely to be correctly detected” by a given PCR primer set based on the estimated effect of mutations present. Samples susceptible to misclassification are always present at a daily rate of 2% or lower, while the daily ratio of samples having a slight chance of misclassification with a particular primer set can reach 90%. As different variant strains may temporarily gain dominance in the worldwide SARS-CoV-2 viral population, the efficiency of a particular PCR primer set may change over time, therefore constant monitoring of variations in primer target regions is highly recommended.

The COVID-19 pandemic has been going on for over two years, and PCR-based diagnostics is still the major tool for the identification of SARS-CoV-2 infected people by successful amplification of the virus from nasopharyngeal or oropharyngeal swabs. The average mutation rate of the SARS-CoV-2 genome is estimated to be 1.05 × 10^–3 to 1.26 × 10^–3 nucleotide substitutions/site/year^1,2, which is in the same order of magnitude as that of SARS-CoV³. In contrast, the human genome-wide mutation rate is approximately 0.5 × 10⁻⁹ per base pair per year⁴. Given the highly mutation-prone property of viruses, genetic variations in the viral genome in the primer/probe-binding regions can lead to false-negative results during polymerase chain reaction (PCR) detection⁵. Diagnostic primer/probe alignments have been performed by laboratories with a limited number of viral sequences in the early stages of the pandemic and some mismatches have been reported^6,7, which may lead to false-negative results⁸. Since then, numerous publications have reported instances of false-negative diagnoses of COVID-19^9–12. Due to the great clinical relevance of these mutations, there is a requirement to monitor primer/probe variations using sequences from virus isolates worldwide.

Throughout the analyses and discussions of this manuscript, we intend to adhere to a rigorous terminology to avoid confusion. We define a „primer system” as the collection of the forward and reverse primers (and whenever applicable, the probe) designed for the amplification and detection of a single genomic region during PCR. We refer to the parts of the genome where the forward and reverse primers (along with the probe, when relevant) bind as „target regions” (TRs). Thus, a given primer system has two or three TRs in the virus genome, depending on the exact scheme of the laboratory procedure. In order to reliably detect the presence of SARS-CoV-2 in a sample, it is advantageous to amplify multiple parts of its genome to avoid possible false-negative cases. Thus, many developers employ multiple primer systems in their PCR tests as a fail-safe. We term the assortment of primer systems designed by the same developer and used concurrently in a single test a „primer set”.

The impact of a variant on the efficacy of PCR tests can be influenced by various factors. The most known components determining the consequence of a variant are its specific genomic location within the TR^13,14, and the total number of mutations overlapping the TR^15,16. An additional aspect to be considered is the type of variant (point mutation or insertion/deletion), and if the former, whether it is a transversion or a transition^17,18.

The potential complication presented by targeting highly polymorphic regions of the virus genome has been previously addressed by Davi et al.¹⁹ with the suggested solution of designing a primer set in silico optimized to target well-conserved sections instead. However, the study did not investigate either the actual number of variations affecting the TRs of previously developed primer sets or their ability to truly hamper the PCR process.

Table 1. SARS-CoV-2 PCR primer sets analyzed in this study. Primer set names are based on the first author’s last name of the reference.

Assay name	Source/country	Target gene(s) (Total number of TRs)	Technology
Chan-set²⁰	University of Hong Kong (HKU) / Queen Elizabeth Hospital (QMH), China	RdRp, N, S (3)	Taqman
Chu-set²¹	Li Ka Shing Faculty of Medicine, The University of Hong Kong (HKU), China	ORF1ab, N (2)	Taqman
Corman-set⁶	Charité Hospital, Germany	RdRp, N, E (3)	Taqman
Davi-set¹⁹	Federal University of Rio Grande do Norte (UFRN), Brazil	ORF1ab, S (9)	Taqman
DMSC-set²²	Ministry of Public Health (MOPH), Thailand	N (1)	Taqman
Huang-set²³	Wuhan Jinyintan Hospital (Jin Hos), China	E (1)	Taqman
IP-set²²	Institut Pasteur (IP), France	RdRp (2)	Taqman
Lu-set⁸	Centers for Disease Control and Prevention, USA (CDC-US)	N (3)	Taqman
Mollaei-set²⁴	Kerman University of Medical Sciences (KMU) / Pasteur Institute of Iran (IPI), Iran	ORF1ab, RdRp, N, E, S (5)	Traditional
Niu-set²⁵	Chinese Center for Disease Control and Prevention, China (CDC-China)	ORF1ab, RdRp, N, E (4)	Taqman
Sarkar-set²⁶	Jashore University of Science and Technology (JUST), Bangladesh	RdRp, N, E, S (4)	SYBR Green
Shirato-set²⁷	National Institute of Infectious Diseases (NIID), Japan	N (1)	Taqman
Tombuloglu-set²⁸	Institute for Research and Medical Consultations (IRMC), Saudi Arabia	RdRp, E (2)	Taqman
Won-set²⁹	Institute for Basic Science (IBS) / Seoul National University (SNU), South Korea	RdRp, N, E, S (9)	SYBR Green
Yip-set³⁰	Queen Mary Hospital (QMH) / The Chinese University of Hong Kong (HKSAR), Hong Kong	ORF1ab (1)	SYBR Green
Young-set³¹	National Centre for Infectious Diseases (NCID), Singapore	ORF1ab, N, S (3)	Taqman

Here we aim to create a workflow to detect genomic variations compared to the original Wuhan reference sequence (NC_045512.2) that overlap the TRs of commonly used PCR primer sets (Table 1; full sequences, location and additional information of primers and probes are available on GitHub at the repository github.com/csabaiBio/coveo_pcr_primers2021). To this end, we use the CoVEO database that assembles data of more than 650,000 good-quality SARS-CoV-2 samples sequenced from the start of 2021 to 31st December 2021, originally uploaded to the COVID-19 Data Portal (https://www.covid19dataportal.org³²).

The CoVEO database stores, in a coherent and searchable manner, the genomic variations of sequenced SARS-CoV-2 samples, which were produced by a freely accessible standardized variant calling workflow (see Methods). In order to verify our results on another dataset, the GISAID database (Global Initiative on Sharing All Influenza Data, https://www.gisaid.org³³) was utilized to collect genomic variations of the SARS-CoV-2 genomes that could be processed with the same post-processing workflow that was used on the CoVEO database.

One of our main goals is to provide a comprehensive, raw list of mutations overlapping PCR primer TRs in the investigated samples, which can be further filtered based on individual scientific needs when investigating the possible effects of mutations on PCR performance or designing new PCR primer sets.

In this work, based on literature, we differentiate between mutations likely to affect the efficiency of PCR and ones predicted to be harmless in this sense. Our further goal is to perform an analysis that can forewarn the possibility of specific primer sets becoming obsolete due to emerging mutations in the virus genome.

Ratio of samples affected by mutations in different primer system TRs

A raw list of mutations overlapping PCR primer TRs in the investigated samples is uploaded to a GitHub repository at github.com/csabaiBio/coveo_pcr_primers2021. For details on mutation filtering criteria, see Methods.

A total of 665,325 good-quality SARS-CoV-2 genomic samples were analyzed from the CoVEO database (see Methods) collected in 2021. Most of these samples were Alpha or Delta variants, while the proportion of other VOCs (Variants of Concern) was negligible. Samples that could not be unambiguously categorized to WHO-designated lineages or were classified to a lineage other than Alpha, Beta, Gamma, Delta or Omicron were assigned the umbrella term “other variants” (Table 2). The low number of Omicron samples in the dataset is due to the fact that sample collection was limited to the 1st of January to the 31st of December of the year 2021.

Table 2. Total number of SARS-CoV-2 samples analyzed, and ratio of samples affected by a genomic variation in at least one investigated TR

	All samples	Alpha	Beta	Gamma	Delta	Omicron	Other variants
Number of analyzed samples	665,325	152,891	2,664	4,219	367,727	2,459	135,365
Ratio of samples with variants in any of the investigated TRs (%)	96.24	99.33	98.46	98.91	99.26	99.35	84.39

We found reliable genetic variations in 1,826 of all 2,188 genomic positions overlapping the 141 primer or probe binding sites (TRs) in the investigated SARS-CoV-2 samples. In many cases, different primer sets target the same sections of the genome. For example, primer systems designed for the E gene of the genome necessarily share some of their TRs due to the short length of the gene (Figure 1a-b). The E gene also has a low estimated mutation rate (Figure 1c), in line with basic intuition that primer systems are best designed to target relatively conserved regions of the genome.

Most of the mutations affecting the TRs were point mutations (with a slightly higher frequency of transitions (1,677) than transversions (1,510)), while the numbers of distinct deletions (79) and insertions (23) were significantly lower.

The ratio of samples with any variants in the TR of a given primer system (any of its forward primer/probe/reverse primer regions) was calculated (Figure 2, bottom panel). We found that even for the primer system targeting the seemingly most conserved genomic regions (Mollaei-ORF1ab), 1,062 samples contained at least a single mutation in the TRs. On the other hand, the ratio of samples affected by at least one variant is below 10% for 43 of the 53 investigated primer systems. In the TRs of the remaining 10 primer systems a considerable fraction of the samples had at least one variant: almost 80% of the samples contained a mutation in the TRs of primer system Niu-N; about 55% of samples had a variant in the TRs of primer systems Niu-RdRp, Won-RdRp-1, Corman-RdRp, Tombuloglu-RdRp, and Sarkar-S, furthermore around 10-20% of samples were mutated in the TRs of primer system Davi-S-2, Davi-S-1, Young-S and Davi-ORF1a-4.

Different variant strains show highly diverse mutational patterns in various primer systems (Figure 2, top six panels). While many samples tend to have a mutated TR in the Niu-N primer system independent of their lineage, the TRs of many primer systems are almost exclusively mutated in samples of a specific variant (e.g. Davi-S-1 and Davi-S-2 systems are mainly affected in Alpha samples, the TRs of the Young-S system are usually mutated in Alpha and Beta samples, etc.).

This result suggests that the performance of a given primer set largely depends on the specific genomic characteristics of the presently circulating most dominant lineages. Thus, PCR efficacy should be dynamically reevaluated throughout the course of the pandemic.

Possible effects of mutations on PCR amplification

We calculated the ratio of samples with a single, two, and three or more genomic variations in the TRs of a given primer system. Our results show that samples have on average 1.16 mutations in the TRs of the investigated primer systems. As shown in Figure 3, most of the affected samples have only a single variant position (Figure 3a green bars) over the TRs. Nevertheless, there are a few samples for every primer system with two or more variations present in the TRs (Figure 3a yellow and red bars), but their number is generally below 1,000, accounting for less than 0.5% of all samples. Remarkably, more than 150,000 samples (about 23% of all samples) contained at least three mutations in the TRs of the Niu-N primer system, with one of the samples presenting seven variant positions.

As a next step, we examined the type of the detected variants and their location in the TRs of different primer systems and categorized them as either “high risk” or “moderate risk” mutations (see Methods for details). 0.012% to 55.25% of the samples contain high risk mutations for a particular primer set. The distribution of samples with variants belonging to different risk-categories is presented in Figure 3b. Most of the samples that had any mutations in the TRs of any given primer system contained only variants with no drastic effect on PCR efficiency based on their location. For example, the highly mutation-prone TRs of the Niu-N primer system usually contain variants at moderately risky positions which are unlikely to disrupt the PCR process. In contrast, the TRs of two primer systems (Niu-RdRp and Corman-RdRp) are mutated in high risk positions in many samples, comprising around 55% of the total samples analyzed.

Based on our results, the most common high and moderate risk mutations that were identifiable in the majority of samples are listed in Table 3.

Table 3. Summary of the most frequent mutations in the TRs of investigated PCR primer systems.

Primer	Mutation	Ratio of mutated samples in the CoVEO database	Ratio of mutated samples by WHO designation (*)
Corman-RdRp-F^H, Niu-RdRp-F^H, Tombuloglu-RdRp-F^M, Won-RdRp-1-F^M	SNP: G15451A	54.92%	Delta (90.32%), Other variant (24.53%), Beta (<1%), Gamma (<1%), Alpha (<1%)
Sarkar-S-F^M	SNP: C21618G	53.15%	Delta (89.14%), Other variant (19.07%)
Niu-N-F^M	SNP: G28881T	44.50%	Delta (77.19%), Other variant (9.02%), Alpha (<1%)
Davi-S-1-P^M, Davi-S-2-P^M	SNP: C23271A	23.09%	Alpha (95.97%), Other variant (5.11%), Delta (<1%)
Niu-N-R^M	SNP: C28977T	18.87%	Alpha (80.27%), Other variant (2.09%), Delta (<1%)
Young-S-F^H	Deletion: ATACATG21764A	17.09%	Alpha (71.53%), Other variant (3.16%), Beta (<1%), Delta (<1%)
Niu-N-F^H	„AAC”-triplet: G28881A, G28882A, G28883C	23.35%	Gamma (87.34%), Alpha (85.44%), Omicron (85.16%), Other variant (13.98%), Beta (<1%), Delta (<1%)

Primer names are based on the nomenclature: [first author last name]-[target gene name]-[id, when multiple primer systems target the same gene]-[type of oligo: forward (F), reverse (R) or probe (P)]. „M” marks the primers where the variant was defined as a moderate-risk mutation; „H” marks the primers if the variant was defined as a high risk mutation. Mutation names are based on the nomenclature: [reference base][genomic position of the start of the variant][alternate non-reference base]. Asterisk: ratio of samples which contain the mutation in a given WHO designation. Lineages with no mutated samples are not listed. Abbreviations: SNP - Single-Nucleotide Polymorphism.

Potential false-negative results due to misclassification

Since diagnostic COVID-19 tests generally aim to amplify several gene regions simultaneously, thus employing primer sets of multiple primer systems, we investigated whether there are samples with damaged TRs (see Methods for definition) in multiple primer systems of specific primer sets. We differentiated between samples having a “slight change of misclassification” and samples “susceptible to misclassification” with a primer set based on the number and ratio of damaged TRs in the primer systems of the given set. Samples with no damaged TRs in the set and sufficient sequencing depth for all of them were regarded as having “no reasonable chance of misclassification” (see Methods for details).

A relatively large number of samples had a slight chance of misclassification with the Niu-, Corman- or Young-sets, with respectively only 10.03%, 29.48% and 37.12% of them having evidence of absolutely no damaged TRs (Figure 4).

Nevertheless, there is only a negligible number of samples (with a maximum ratio of 0.51% for the Chu-set) susceptible to misclassification with any of the investigated primer sets, and in most cases, only very few TRs of a primer set are damaged simultaneously in each sample. Based on these observations, for most primer sets, a dominant part (49.47-96.83%) of the investigated samples could be reliably detected as positive ones if partially inconclusive results are not rejected automatically by the test protocol (i.e., if a primer set consists of three primer systems, and among them, one is damaged, the result of the PCR is not automatically considered as negative).

An important additional insight is that the ratio of ambiguous samples (not shown in Figure 4) with no satisfactory coverage across all TRs for a definite categorization vary greatly for different primer sets. This is partly explained by the fact that the number of primer systems employed by a given set is also highly variable and statistically there is a smaller chance to obtain a sample with high enough coverage in all TR positions for 9 primer systems (e.g. for the Won-set 50.53% of all samples were ambiguous) than it is for a single one (e.g. for the DMSC-set the same ratio was 2.99%). On the other hand, some primer sets are notable exceptions to this trend. For example the Davi-set, also containing 9 primer systems, had inconclusive results for only 27.35% of the samples. On the contrary, for the Young-set with only 3 primer systems 43.6% of the samples were ambiguous.

It is also worth noting that primer sets with an overall low proportion of samples susceptible to misclassification can have an increased chance of failure in cohorts of samples belonging to a specific variant. For example, the IP-set showed an appeasing 0.49% for the proportion of samples susceptible for misclassification across all sample groups, but particularly for Omicron samples this ratio increased to 6.75%.

These results suggest that to truly minimize the number of samples susceptible to misclassification, it can be beneficial to simultaneously use three or more primer systems within a single PCR test. This way, even with a damaged TR, more than 50% of the employed primer systems would yield a positive test result. Notably, primer sets with at least 5 primer systems (Won-set, Davi-set, Mollaei-set) were extremely unlikely to misclassify samples due to mutations present in the TRs (see the light red columns on Figure 4, bottom panel).

Additionally, given that primer sets perform differently across variant groups, it is important to continously surveil the ratio of samples prone to misclassification to determine whether the given primer set is suitable for the detection of SARS-CoV-2 samples of the presently spreading lineage.

Ratio of samples having a slight chance of or being susceptible to misclassification over time

It is also a matter of concern to monitor the relative occurrence of variants on the TRs of different primer systems over time to predict if a primer set is at danger of becoming obsolete as new strains of the virus emerge. The ratio of samples having a slight chance of (Figure 5a) and being susceptible to (Figure 5b) misclassification was calculated over time using a 30-day rolling average method.

Most of the primer sets analyzed in this work (with the exceptions of the Davi-, Sarkar- and Tombuloglu-sets) were designed in 2020 at the beginning of the pandemic, with only a few SARS-CoV-2 genomes available, hence the mutational patterns of the more recent Alpha and Delta lineages were inaccessible at the time.

With the appearance of the Alpha variant in early 2021, the number of samples with at least one damaged TR of the Niu- and the Young-sets increased. Around June, with the emergence of the Delta variant, the mutations that damaged the TRs of the Young-set disappeared from the dominant portion of the samples, as Delta variants lack this mutation. At the same time, new mutations appeared in the TRs of the Corman-set, causing the ratio of samples having a slight chance of misclassification with this primer set to increase. This trend seems to be reversing since the widespread arrival of Omicron samples, which also induced the decrease in the ratio of samples having a slight chance of misclassification with the Niu-set. However, the TRs of the Sarkar-set seem to be gaining damaging mutations in Omicron samples, thus samples having a slight chance of misclassification with this primer set are getting more frequent since November of 2021.

The ratio of samples susceptible to misclassification remained below 4% for the entire timeline for all investigated primer sets. It is interesting to observe, however, that the Tombuloglu-set, made public in March, 2021 would have been significantly less efficient on samples leading up to the publication of the primer set than on samples sequenced after March, 2021. This suggests that this primer set was optimized to detect strains emerging right around the time of its development, rendering it a state-of-the-art detection method of the time.

In case of the Chu-set, it appears that the then dominating Alpha variant may have acquired increasingly more frequent mutations that damaged the TRs of this primer set resulting in an elevated ratio of samples susceptible to misclassification, but the arrival of the new VoC (Delta) decreased their ratio by spreading a different mutational pattern. On the other hand, the emergence of the Omicron variant seems to be negatively affecting the performance of the relatively vulnerable IP-set employing only two primer systems.

It is important to note that either the spread of a new variant or simply the emergence of a damaging mutation within the dominant strain might drastically increase the number of samples prone to misclassification for any given primer set. Thus, it is essential to continuously monitor genomic variations overlapping the TRs of primer sets used in routine diagnostics.

Comparison with the GISAID database

We compared our results with genomic variants found in SARS-CoV-2 samples from the GISAID (www.gisaid.org³³) database collected in the same time period as our original sample set, where a total of 6,287,362 samples (Number of samples classified by WHO-lineages: Alpha: 901,778, Beta: 311,561, Gamma: 407,404, Delta: 3,949,899, Omicron: 418,792) were analyzed. We found genetic variants in all 2,188 genomic positions mapped to 141 primer or probe TRs in the investigated samples. In this analysis, we only focused on point mutations, as the number of insertions and deletions was difficult to determine with high confidence. We found that the ratio of GISAID samples containing either mutations of any kind, moderate risk mutations or high risk mutations in the TRs was similar to that of in the CoVEO database for all analyzed primer systems. The most frequent mutations overlapping the TRs in the CoVEO database are also present in the GISAID database with a similar frequency of affected samples (G15451A: 63.52%, C21618G: 63.94%, G28881T: 64.32%, C23271A: 17.51%, C28977T: 17.49%, „AAC”-triplet: 27.51%) (for comparison see Table 3). When analyzing GISAID samples over time, we found that samples susceptible to misclassification were present at a daily rate of 2% or lower. On the other hand, the daily ratio of samples having a slight chance of misclassification with a certain primer set could reach almost 100%, similarly to our results on the CoVEO database.

The consistent results acquired across multiple databases suggest that the mutations observed in CoVEO samples overlapping the TRs are not due to sequencing artifacts or the by-products of the bioinformatical analysis pipeline, but are in fact true genomic variants occurring frequently and possibly affecting PCR test accuracy. Even though the obtained results are in great agreement across data providers, it should be underlined that samples of the CoVEO database were processed with a single, standardized, publicly available workflow, while GISAID consensus sequences are generated individually by data uploaders. Moreover, the CoVEO database contains detailed information about genomic variants (sequencing depth, alternate allele frequency, alternate alleles by read orientation, etc.), which can be utilized to specifically filter variants based on different scientific research requirements.

This study comprehensively evaluated the genetic variability of 53 previously published SARS-CoV-2 diagnostic primer systems of 16 primer sets in PCR primer/probe-binding regions, including those recommended by the WHO. We found that the TRs of many of the investigated primers were prone to mutations in the analyzed samples, but further investigations were needed to determine if these variations had the potential to reduce PCR sensitivity in a clinical setting.

Zimmermann et al.³⁴ highlighted the fact that experimental data does not necessarily follow the theoretical predictions, particularly with regard to the magnitude of the Ct shift with mismatches close to the 3’ end. Moreover, the specific nucleotide composition of these mismatches also seemed to play a role in determining PCR efficacy³⁵. In some protocols³⁶, the results of the PCR test are automatically deemed inconclusive (thus not positive) if even a single primer system of the primer set fails to suitably amplify its targeted genomic region, which may also influence the correct evaluation of the samples. Furthermore, a common practice to reduce both testing time and cost is to pool samples prior to the PCR procedure, which inherently considerably limits sensitivity³⁷, thus could result in an increased susceptibility to errors caused by mutations in the TRs.

Given that both the number of variations in the TRs of the employed primers and their relative position to TR end sites can influence the efficacy of PCR reaction, we considered both of these factors in the investigated samples and assigned variants to be either high risk or moderate risk based on their relative position in a given TR. According to Bru et al.¹³, a single mutation can result in an underestimation of the gene copy number by up to 1000-fold. The number of mutations within a TR shows a negative correlation with the PCR amplification efficiency^16,38. Mismatches at the 3′ end are known for their deleterious effect on PCR amplification, and even a single 3′ end mismatch can lead to a failed PCR reaction³⁹. On the other hand, single mismatches, especially more than 5 bp away from the 3′ end, have only a moderate effect on PCR amplification and are unlikely to significantly affect the assay performance^5,18,35.

Our results showed that most of the samples containing any variation in the TRs of a primer/probe generally had a single mutation, which is in most cases unlikely to drastically influence the effectiveness of the PCR process. However, we found that the most frequent SNP overlapping any of the TRs (G15451A, see Table 3) could be identified in more than half of the samples, mainly belonging to the Delta variant. This SNP was defined as high risk in two forward primers. Vogels et al.⁴⁰ reported that this mutation was present in 100% of the samples they have tested. Regardless, there are samples with multiple variants in the TRs of some primers. The most common multiple variation (affecting the 5' end of the Niu-N forward primer binding site) was the „AAC” triplet (Table 3), which was already described in several publications^40-46, but the studies reported varying frequencies (13-37%) of the 'AAC' mutant in the GISAID samples they investigated. We also found that the His69_Val70del deletion of the Spike protein, overlapping the Young-S forward primer TR, was present in a relatively high proportion of the samples in the time range when the Alpha variant gained dominance worldwide. It has been previously demonstrated⁴⁷ that this causes S-gene target failure on the TaqPath COVID‑19 PCR test (ThermoFisher). Two sublineages of the Omicron strain (BA.1, BA.3) also contain this deletion, which might cause a renewed reduction in PCR efficiency for the Young-set and the TaqPath kit⁴⁷.

The CoVEO database used in this study provides the advantage of fast and straightforward mutation retrieval compared to databases containing only the consensus sequences of the samples. Even though the number of genomic variations occurring in the TRs of the investigated primer sets is generally low and the ratio of affected samples remains under 1%, a readily deployable pipeline for monitoring mutation frequency in the TRs is of utmost importance.

To improve COVID-19 diagnostic test efficiency and sensitivity, it is common practice to employ multiple primer systems in order to target multiple regions of the virus genome within a single PCR assay. We detected a relatively large number of samples that had at least one primer system within a primer set that had a damaged TR in the sequenced genome, defining these samples as having a slight chance of misclassification with the given assay. On the other hand, the number of samples that had damaged TRs for more than half of the primer systems in the set (samples “susceptible to misclassification”) was negligible for all investigated primer sets. This underlines the importance of using more than one target in diagnostic PCR tests already pointed out by previous studies^28,34.

To monitor whether samples with high risk mutations in the TRs of the different primer sets are becoming more frequent in time, we plotted the fraction of samples having a slight chance of misclassification and being susceptible to misclassification. We found that the frequency of samples prone to misclassification was changing during the analyzed time period in strong correlation with the emergence of the different VoCs. This result highlights the need for constantly overseeing emerging mutations, especially in the case of the appearance of a new SARS-CoV-2 lineage. This way the primer sets used in clinical and commercial settings can be regularly reevaluated and updated if necessary.

Recent efforts in similar aspects have been made by aligning a limited number of viral sequences with primers/probes to look for mismatches^40-46,48. Nayar et al.⁴⁸ found that there is a growing number of mismatches, with an increase of 2% per month, and emerging mutations are highly specific to various geographic locations. Their previous statement is in agreement with estimations on the general mutation rate of the virus, and in addition to this observation, our results also suggest that the mutational landscape of a new VoC does not automatically contain the same variations as the previous VoCs, i.e. a new VoC does not necessarily emerge from a previous, widespread variant. Peñarrubia et al.⁴⁶ found that about one-third of the genomes they tested included single mutations affecting the annealing of any PCR assay. Variations in the quarter of their investigated samples were considered high risk, whereas additional (less than ten percent) genomes presented low frequency single mutations that were predicted to yield no impact on sensitivity.

In conclusion, given the previously published data and the bioinformatic analysis performed in this study, currently, the known variability in the SARS-CoV-2 population has minimal or no impact on the sensitivity of existing molecular systems for virus detection. Furthermore, the majority of the observed variants were not high risk ones (near the 3′ end of the TR/multiple mutations/indels) that could potentially disrupt the PCR process. Only three exceptions should be highlighted: one trinucleotide mutation (G28881A, G28882A, G28883C), one deletion (ATACATG21764A), and one SNP in a primer TR near the 3′ end (G15451A), which occurred with high frequency in the samples. These results suggest that the detection of Alpha and Delta variants can be confidently performed with any of the investigated 16 primer sets.

Our approach providing these results is unique in both the sense that we only included good-quality samples and high-confidence variants determined from raw sequencing data in our analysis instead of investigating consensus sequences; and in its comprehensive way of differentiating between harmless and possibly damaging mutations. Our work is aimed to draw attention to the need of constant surveillance of mutations affecting already existing and yet-to-be-developed primer sets. Nevertheless, due to the scarce access to primer and probe sequences used in commercial SARS-CoV-2 PCR tests, our results are inherently limited to the publicly available, but in practical settings rarely used primer sets.

However, it should be mentioned that viral genomes harboring mutations that are truly capable of escaping PCR amplification during clinical testing are unlikely to be submitted to sequencing later. Thus, it is possible that the reliable identification of these extremely high risk mutations would be impossible by analyzing sequencing data.

Through international effort, the Versatile Emerging infectious disease Observatory (VEO, www.veo-europe.eu) consortium analyses and interprets genomic data from SARS-CoV-2 sequencing samples as one of its subprojects. Throughout its standardized pipeline, variants of the sequenced samples submitted to the European COVID-19 Data Portal (www.covid19dataportal.org³²) are identified and stored in VCF files, the results of which are then loaded into a PostgreSQL database, named CoVEO. This data is unique in the sense that besides the commonly available consensus sequences (for example in the GISAID database, www.gisaid.org³³), the raw sequencing data of the samples is also accessible. This allows for direct filtering of genomic positions based on sequencing depth and alternate allele frequency.

The standardized pipelines for variant calling are publicly available on GitHub^49,50.

In our analyses only those of the total 987,138 samples of the CoVEO database were included that were collected between 1st January 2021 and 31st December 2021 and had an estimated N-content of no more than 10%. (Estimated N-content was defined as the ratio of genomic positions in a sample with a sequencing depth of less than 10.) This filtering step resulted in 665,325 good-quality samples.

In order to restrict our analyses to highly reliable variants, genomic positions where the sequencing depth did not reach 100 and/or the alternate allele frequency was below 0.9 were discarded.

Mutation rate at each genomic position (Figure 1c) was calculated by dividing the number of samples with a high-confidence (see above) mutation at the given position by the total number of samples with a coverage of 100 or more in the same position.

Sequences and data for 53 (traditional and RT-Q) PCR SARS-CoV-2 detection primer systems, belonging to 16 different primer sets were collected from the literature^{6,8,19–21,23–31} or obtained from WHO²². In this study, primer system names are based on the nomenclature: [first author last name]-[target gene name]-[id, when multiple primer systems target the same gene]. The sequences of primers and probes were aligned to the Wuhan reference genome of SARS-CoV-2 (NC_045512.2) to determine their TRs within the genome using BLAST⁵¹. Only those mutations were considered that overlapped the TRs of the above primer systems.

Previous explorations of PCR efficacy^5,18,35,39 suggest that variations at the 3’ end of the TR of either the forward or reverse primer are more prone to hinder the PCR reaction than mutations in other parts of the TRs. In contrast, variants in the middle of the probe TR are more likely to reduce detection efficiency than near-end mutations^52,53. Therefore, we designated all insertions and deletions, along with mismatches that occur in the first 5 positions of the 3' end of the forward and backward primer TRs or the middle of the probe TR (5 base pairs inward from the two ends) as „high risk” mutations and assigned „moderate risk” to the rest of the variants.

It has also been experimentally demonstrated that an increased number of mutations (of any kind) in the TR of the forward/reverse primers or the probe can reduce duplex stability, thus impairing amplification and detection of the targeted genome regions. For primers with an approximate base length of 30, two to four internal (non-3’ terminal) mismatches had no significant effect on RT-PCR, however, 6 to 8 mismatches reduced the PCR product yield by approximately 22-100-fold respectively¹⁵. Samples with viral genomes that harbor either a high risk mutation in the TRs of a specific primer system and/or possess an increased number of variations (of any kind) in a single TR of the same primer system are at risk of escaping PCR amplification. To that end, primer sets usually consist of multiple primer systems to decrease the probability of a false-negative result. Theoretically, a sample containing the genome of SARS-CoV-2 will only be categorized as negative if all the primer systems of the applied test fail to amplify and/or detect their targeted genome regions. Recently Laine et al.⁵⁴ showed that samples having high risk mutations (a short 3 bp deletion and three subsequent mismatches) in the TR of the N gene, resulted in no signal for this primer system, however, the other primer system (targeting ORF1ab) of the primer set showed prominent signal, suggesting the presence of the SARS-CoV-2 genome in the sample. Thus, we consider samples „susceptible to misclassification” by a given primer set if more than 50% of the primer systems of the set have TRs that are damaged by mutations. A TR is defined to be damaged if at least a single high risk mutation or a minimum of 3 mutations of any kind are present in it. Samples with at least one damaged TR in the primer systems of the given primer set are regarded as having a „slight chance of misclassification” if no more than 50% of the primer systems of the given set have damaged TRs. Samples that had a coverage of 100 or more in all the genomic positions overlapping any of the TRs of a given primer set and none of these TRs were proven to be damaged were regarded as having “no reasonable chance of misclassification”. We emphasize that this is an extremely stringent categorization and is aimed at monitoring samples with even the slimmest probability of escaping PCR amplification. It should also be noted that the above three categories include only those samples for which a credible proof of either a minimum of a single damaged TR or of absolutely no damaged TRs exists. Thus, samples with ambiguous results (i.e. with no proof of a damaged TR but with insufficient coverage in any of them) are not considered.

Data Availability

The datasets generated and/or analysed during the current study are available in the coveo_pcr_primers2021 repository, https://github.com/csabaiBio/coveo_pcr_primers2021.

Acknowledgements

The authors thank Ádám Dán for fruitful discussions on the topic and valuable suggestions for research objectives.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No. 874735 (VEO) and No.101046203 (BY-COVID), and also from the National Research, Development and Innovation Fund of Hungary under Project no. FIEK_16-1-2016-0005.

Author information

Authors and Affiliations

1. Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary

Anikó Mentes

Krisztián Papp

Dávid Visontai

József Stéger

István Csabai

Anna Medgyes-Horváth

Orsolya Anna Pipek

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom

Guy Cochrane

Nadim Rahman

Carla Cummins

David Yu Yuan

Sandeep Selvakumar

Milena Mansurova

Colman O'Cathail

Alexey Sokolov

Ross Thorne

3. Department of Viroscience, Erasmus University Medical Center, Rotterdam, Netherlands

Marion Koopmans

David Nieuwenhuijse

Bas Oude-Munnink

Nathalie Worp

Clara Amid

Consortia

VEO Technical Working Group

Guy Cochrane

Nadim Rahman

Carla Cummins

David Yu Yuan

Sandeep Selvakumar

Milena Mansurova

Colman O'Cathail

Alexey Sokolov

Ross Thorne

Krisztián Papp

Dávid Visontai

József Stéger

István Csabai

Marion Koopmans

David Nieuwenhuijse

Bas Oude-Munnink

Nathalie Worp

Clara Amid

Author contributions statement

A.M. contributed to the analysis and interpretation of the data and to the writing of the manuscript. K.P., J.S. and D.V. contributed to the acquisition of the data. I. C. contributed to the conception of the study and the writing of the manuscript. The VEO Technical Working Group provided assistance with the CoVEO database. A.M-H and O.A.P. contributed to the coordination of the study and to the writing of the manuscript. All authors read and approved the final version of the manuscript

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No competing interests reported.

SupplementaryTable1.xlsx

Identification of mutations in SARS-CoV-2 PCR primer regions

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Abstract

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Introduction

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Discussion

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