Spread of Endemic SARS-CoV-2 Lineages in Russia Before April 2021

doi:10.21203/rs.3.rs-827916/v1

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Spread of Endemic SARS-CoV-2 Lineages in Russia Before April 2021

https://doi.org/10.21203/rs.3.rs-827916/v1

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In 2021, the COVID-19 pandemic is characterized by global spread of several lineages with evidence for increased transmissibility. Russia is among the countries with the highest number of confirmed COVID-19 cases, making it a potential hotspot for emergence of novel variants. Here, we show that among the globally significant variants of concern, alpha (B.1.1.7), beta (B.1.351) or gamma (P.1), none have been sampled in Russia before January 2021. Instead, between summer 2020 and spring 2021, the epidemic in Russia has been characterized by the spread of two lineages that are rare elsewhere: B.1.1.317 and a sublineage of B.1.1 including B.1.1.397 (hereafter, B.1.1.397+). Their frequency has increased in different parts of Russia. Mutational composition and frequency dynamics suggest that B.1.1.317 and B.1.1.397+ may be more transmissible than the previously predominant B.1.1. On top of these lineages, in January 2021, B.1.1.7 emerged in Russia, reaching the frequency of 17.4% (95% C.I.: 12.0%-24.4%) in March 2021. Additionally, we identify three novel distinct lineages, AT.1, B.1.1.524 and B.1.1.525, that have started to spread, together reaching the frequency of 11.8% (95% C.I.: 7.5%-18.1%) in March 2021. These lineages carry combinations of several notable mutations, including the S:E484K mutation of concern, deletions at a recurrent deletion region of the spike glycoprotein (S:Δ140-142, S:Δ144 or S:Δ136-144), and nsp6:Δ106-108 (also known as ORF1a:Δ3675-3677). Community-based PCR testing indicates that these variants have continued to spread in April 2021, with the frequency of B.1.1.7 reaching 21.7% (95% C.I.: 12.3%-35.6%), and the joint frequency of B.1.1.524 and B.1.1.525, 15.2% (95% C.I.: 7.6%-28.2%). Although these variants have been displaced by the onset of delta variant in May-June 2021, the frequency increase of lineages B.1.1.317, B.1.1.397+, AT.1, B.1.1.524 and B.1.1.525 suggest that the combinations of mutations observed in them could have increased the rate of their spread.

Computational Biology

Bioinformatics

Infectious Diseases

COVID-19 pandemic

glycoprotein

combinations of mutations

Continuing evolution of SARS-CoV-2 in humans leads to emergence of new variants with novel epidemiological and/or antigenic properties. In spring 2020, the S:D614G change has spread globally due to its fitness advantage^1,2. Subsequently, a number of variants of concern, including alpha (B.1.1.7) first sampled in Great Britain in September, beta (B.1.351) first sampled in South Africa in October, gamma (P.1) first sampled in Brazil in December and delta (B.1.617.2) fist sampled in India in October, were shown to be associated with increased transmissibility^3–5. These variants are characterized by overlapping sets of changes in spike receptor-binding domain which affect ACE2 binding and antibody recognition, as well as other changes with demonstrated functional and antigenic effects. Emergence of SARS-CoV-2 variants with evidence for change in transmissibility, and possibly other properties, highlights the importance of continued surveillance of novel variants. In particular, locally arising variants that grow in frequency over time may suggest a transmission advantage, although such an increase may also occur by chance⁶.

Here, we show that before spring 2021, the outbreak in Russia was characterized by a spread of two lineages, B.1.1.317 and B.1.1.397+, which were highly prevalent in Russia but rarely appeared in non-Russian samples. We trace the accumulation of sequential mutations in the evolution of these lineages, and single out the spike mutations that were followed by bursts in frequency. If the frequency increase of B.1.1.317 and B.1.1.397 + has been indeed driven by changes in the intrinsic properties of the virus rather than by epidemiology, it is these mutations in spike that most likely have led to this increase, although importantly we lack direct transmission data to verify causality. We also describe three novel candidate variants of interest that are characterized by a rapid increase in frequency and combinations of important spike mutations, including the E484K mutation of concern.

High-frequency variants in Russia

We analyzed 4,487 SARS-CoV-2 sequences with known sampling dates obtained in Russia between February 25, 2020 - March 28, 2021. 2,842 of these samples are deposited to GISAID⁷, while the remaining 1,645 (all dating to February-November 2020) will be made available through another repository. All 968 analyzed samples from December 2020 - March 2021 are available on GISAID. The vast majority of samples over this period came from several genomic surveillance programs which were not targeted towards particular variants, although representation of Russia’s regions varied with time.

Throughout the pandemic, the SARS-CoV-2 diversity in Russia has been predominated by the B.1.1 Pango lineage which is frequent in Europe, as well as lineages descendant from it⁸ (Fig. 1). Three B.1.1-derived lineages with the highest prevalence in Russia in the beginning of 2021 were B.1.1.7, which was firstly introduced in Russia at the end of 2020, as well as two other lineages, B.1.1.317 and B.1.1.397, that appeared in Russia in April and July, 2020, respectively. B.1.1.317 was first sampled in Vietnam on March 27, 2020⁹; within Russia, it was first sampled on April 5, 2020 in Moscow, spreading across the country throughout 2020 (Fig. 2). B.1.1.397 was first sampled in the Krasnoyarsk Region of Russia on July 22, 2020. By summer 2020, both B.1.1.317 and B.1.1.397 were frequent throughout Russia (Fig. 2).

To find the non-reference amino acid variants that gained in frequency in Russia, we selected the positions at which the mean frequency of the non-reference variant in Russian samples exceeded 5% (for the spike) or 10% (for other proteins) in February-March 2021. We found 21 such positions in spike and 21 such positions in other proteins. Among these changes, two (RdRp:P323L and S:D614G) were fixed early in the global evolution of SARS-CoV-2; other two (N:R203K and N:G204R) are the lineage-defining mutations of B.1.1.

The frequency dynamics of the derived variants at the remaining 38 positions is shown in Fig. 3. These include the mutations characterizing the B.1.1.7 variant which has been increasing in frequency in Russia since January 2021 (Fig. 1), as well as some of the other globally spreading mutations of concern or interest, including the E484K mutation in spike. However, at many of these sites, the non-reference variants were rare outside Russia (Fig. 3). Most of these variants showed similar temporal dynamics in Moscow and St. Petersburg regions, as well as in the European and Asian parts of Russia (Fig. S1), indicating that their increase in frequency is not a result of sampling bias.

We aimed to identify the high-frequency variants carrying these mutations. Many of these sites were highly homoplasic, and overall we found the resulting phylogenies not to be robust. Instead, we defined the most frequent variants composed of these mutations, independent of the alleles at other sites (Fig. 4).

We considered the allele combinations that were most frequent in Russia in February-March 2021, and noticed that they belonged to several nested sets. The most frequent combination (99 out of the 461 samples) carried the N:A211V mutation which is characteristic of the B.1.1.317 Pango lineage; the second most frequent combination carried the S:D138Y and ORF8:V62L combination of mutations which are characteristic of the B.1.1.397 lineage; the third combination carried the set of characteristic mutations of B.1.1.7.

Still, there was no one-to-one correspondence between the frequent combinations of mutations and Pango lineages. For example, while the most frequent variant carrying S:M153T was that also including N:M234I, S:D138Y and ORF8:V62L (column 2 in Fig. 4), and classified as Pango lineage B.1.1.397, the variants carrying S:M153T alone, the S:M153T + N:M234I combination and the S:M153T + N:M234I + S:N679K combination were also frequent (columns 4, 9 and 5 in Fig. 4 respectively) but were classified by PANGOLIN as other lineages (B.1.1, B.1.1.141, B.1.1.28 and others). Similarly, while B.1.1.317 is defined by the N:A211V mutation, the frequency of the variant carrying this mutation alone was relatively low (column 12 in Fig. 4), while most samples carrying it also carried 8 additional high-frequency mutations, including four potentially important changes in spike (Q675R + D138Y + S477N + A845S; column 1 in Fig. 4). The frequencies of such “non-canonical” combinations of mutations increased throughout 2020–2021 (Fig. 5).

Finally, we observe three high-frequency combinations of mutations, including the S:E484K mutation of concern as well as other mutations of interest (notably S:Δ140–142, S:Δ136–144 and nsp6:Δ106–108, also referred to as ORF1a:Δ3675–3677; columns 7, 8 and 10 in Fig. 4). These combinations have recently got B.1.1.524, AT.1 and B.1.1.525 Pango designations (for columns 7, 8 and 10 in Fig. 4, respectively).

Frequency dynamics of the variants prevalent in Russia

To study potential effects of individual mutations composing a variant on the frequency dynamics of this variant, we fit the logistic growth model for the 10 most-frequent combinations of mutations and for N:A211V (which was the 12th most-frequent combination), and compared the dynamics of nested combinations with each other (Figs. 6–8).

The variant carrying just the N:A211V change (largely coincident with the B.1.1.317 Pango lineage) has increased in frequency since the start of the epidemic in Russia. However, since fall 2020, it was being displaced by the variant with 8 additional mutations, including four in spike: Q675R + D138Y + S477N + A845S. When the logistic growth model was fit to the N:A211V variant alone, it demonstrated modest growth (Fig. 6A); however, its combination with S:Q675R + D138Y + S477N + A845S demonstrated a much more rapid frequency increase, with the estimated daily growth rate of 1.93% (95% CI: 1.8%‒2.06%; Fig. 6B). While this led to a frequency increase of the N:A211V mutation independent of the background (Fig. 6C), this suggests that the frequency increase was more likely driven by the S:Q675R + D138Y + S477N + A845S combination than the N:A211V change defining the B.1.1.317 Pango lineage.

By contrast, the frequency of the S:M153T mutation grew independently of the presence of other mutations from our list (Fig. 7). While subsequent mutations may add to the estimated growth rates, these rates were comparable when the S:M153T mutation occurred alone or in combination with N:M234I, N:M234I + S:N679K, or N:M234I + S:D138Y, and all these combinations were still frequent many months after they had originated (Fig. 5, 7).

Finally, the five remaining variants which also reached high frequency in February-March carried unnested, although partially overlapping sets of mutations. These included B.1.1.7 (Fig. 8A); a variant carrying the S:P681H mutation of interest in the absence of other high-frequency mutations (Fig. 8B); as well as three novel variants carrying the following combinations of mutations: (i) nsp6:Δ106–108 + S:P9L + S:Δ140–142 (which recently got the B.1.1.524 Pango designation; Fig. 8C); (ii) S:P9L + S:Δ136–144 + S:E484K (which recently got the AT.1 Pango designation; Fig. 8D); and (iii) nsp6:Δ106–108 + S:Δ144 + S:E484K (which recently got the B.1.1.525 Pango designation; Fig. 8E). Variants B.1.1.524, AT.1 and B.1.1.525 were only observed in 13–14 samples each, but are of interest because this constitutes an appreciable fraction of samples obtained in February-March (3.0%, 2.8% and 2.6% respectively), and also because they are composed of known mutations of interest or concern. The daily growth rate estimated for these variants by the logistic growth model was in the range of 2.44–7.18% (Fig. 8C-E).

The continued spread of some of these variants between February-April 2021 was confirmed by community-based PCR testing. To obtain independent frequency estimates, we made use of a PCR system sensitive to the presence of nsp6:Δ106–108 and S:Δ69–70 deletions (see Methods) to detect the B.1.1.7, B.1.1.524 and B.1.1.525 variants. Specifically, S:Δ69–70⁺ nsp6:Δ106–108⁺ samples correspond to B.1.1.7, while S:Δ69–70⁻ nsp6:Δ106–108⁺ samples correspond to either the B.1.1.524 or the B.1.1.525 variant (Fig. 4). While the frequency estimates were highly uncertain (Table 1), they indicate that B.1.1.7, and one or both of variants B.1.1.524 and B.1.1.525, were wide-spread in April (Fig. 9, Table 1). A considerable fraction (59.6%) of PCR samples from February and March were included in our main analysis, as their sequences were in GISAID. However, the frequency increase was also observed in the 136 PCR samples for which no sequencing data was available (Fig. S2), providing independent validation of the NGS results. Similarly, it was observed when the PCR tests only for St. Petersburg were analyzed (Fig. S3, S4), indicating that the prevalence of these variants increased at least in this city as opposed to being an artefact of changing sampling between regions.

Table 1

Frequencies of (B.1.1.524 or B.1.1.525) and B.1.1.7 estimated from PCR data. The point estimate and the 95% confidence intervals (Wilson score intervals) are shown.
	2021-Feb	2021-Mar	2021-Apr
nsp6:Δ106–108 + S:Δ69-70- (B.1.1.524 or B.1.1.525)	10.5% (2.3%-31.4%)	6.9% (4.1%-11.2%)	15.2% (7.6%-28.2%)
nsp6:Δ106–108 + S:Δ69–70+ (B.1.1.7)	10.5% (2.9%-31.4%)	13.7% (9.7%-19.1%)	21.7% (12.3%-35.6%)

	2021-Feb	2021-Mar	2021-Apr
nsp6:Δ106–108 + S:Δ69-70- (B.1.1.524 or B.1.1.525)	10.5% (2.3%-31.4%)	6.9% (4.1%-11.2%)	15.2% (7.6%-28.2%)
nsp6:Δ106–108 + S:Δ69–70+ (B.1.1.7)	10.5% (2.9%-31.4%)	13.7% (9.7%-19.1%)	21.7% (12.3%-35.6%)

Mutational composition of the high-frequency variants

In this section, we discuss the mutations that constitute the variants that were spreading in Russia before April 2021.

B.1.1.317. This lineage is defined by the presence of the N:A211V mutation. Changes at nucleocapsid position 211 experienced both persistent (according to the FEL model of HyPhy¹⁰) and episodic (according to the MEME model¹¹) positive selection both in the global¹² and in the Russian dataset (p = 0.0396 for the MEME model and p = 0.0268 for the FEL model, the likelihood-ratio test), as well as a rapid increase in frequency of non-reference variants in the global dataset¹². While the global frequency of 211V has remained low (< 0.4%), in Russia, it has reached 26.9% in February-March 2021. According to immunoinformatic analysis, site N:211 is included in one of the four regions of the nucleocapsid protein with the highest affinity to multiple MHC-I alleles¹³. Nevertheless, the frequency of the variant carrying the N:A211V mutation alone has declined since October 2020 (Fig. 5), suggesting that it is unlikely to confer transmission advantage against the background of other variants that were frequent in early 2021 (Fig. 6).

A subclade within B.1.1.317 that was spreading rapidly during the studied period carried the (Q675R + D138Y + S477N + A845S) combination of changes in spike. Two of these mutations are of interest. S:D138Y, first described as one of the lineage-defining mutations of the P.1 lineage¹⁴, is a change in the N-terminal domain (NTD) of spike. Site 138 is adjacent to the NTD antigenic supersite, and together with other NTD mutations of P.1, S:D138Y was suggested to be the cause of disruption of binding with mAb159¹⁵ which is one of the most potent inhibitory antibodies¹⁶. S:S477N is positioned in the receptor-binding motif (RBM) of the S-protein near the antibody binding site (Fig. 10) and was reported to promote resistance to multiple antibodies and plasma from convalescent patients ^17,18. Additionally, S477N is thought to increase ACE2 binding¹⁹. It is one of the lineage-defining mutations of the B.1.160 (20A.EU2) variant of interest prevalent in Europe in Autumn 2020²⁰, and the only one among them to occur in the S-protein; it also defines one of the two subclades of the B.1.526 variant of interest that were spreading in the USA in the beginning of 2021^21,22. S:Q675R is located at the central part of S1 (Fig. 10), and S:A845S, in S2; the significance of these two mutations is unknown.

B.1.1.397+. S:M153T is a characteristic mutation of B.1.1.397, which also comprises several other mutations. However, the frequency of S:M153T in Russia also increased in the absence of these other mutations (Fig. 7). This increase has been ongoing since late spring 2020 (Fig. 5), and has been noticed in Russia^23,24. S:M153T, however, has remained rare outside Russia. S:153 is the first position of the 6-amino acid insertion specific to SARS-CoV-2 and some closely related bat betacoronaviruses that was absent in SARS-CoV²⁵. While the effect of S:M153T on antigenic properties is unknown, S:153 is a part of the N3 loop of the NTD. This loop is a part of the NTD antigenic supersite (Fig. 10; ²⁶), and nearby residues, including S:152, were recently shown to bind highly neutralizing 4A8 antibody from a convalescent patient²⁷. Besides Russia, S:M153T was prevalent in Kazakhstan²⁸ which has a long border with Russia, suggesting common ancestry of this change in these countries.

The most frequent subclade within B.1.1.397+, and the one with some evidence for an independent increase in frequency (Fig. 7), was defined by the presence of two additional mutations of interest: S:D138Y discussed above in the context of the B.1.1.317 lineage (but acquired in the B.1.1.397 lineage independently); and N:M234I. Position N:234 is a part of a disordered linker domain of the nucleocapsid protein²⁹. Outside B.1.1.397, the N:M234I change has also occurred independently in several lineages that attracted attention. It is among the lineage-defining mutations of the B.1.160 (20A.EU2) lineage as well as the B.1.526 lineage that increased in frequency in the USA at rates comparable to those of B.1.1.7²¹. It was also one of the changes defining a lineage (preliminarily identified as B.1.x) which also contained S:N501Y and S:P681H and seemed to spread rapidly in the USA³⁰. Independent emergence of N:M234I in several variants of interest may reflect its impact on at least one of multiple functions of the N protein³¹.

Other notable variants. The five other combinations of mutations observed at high frequencies in Russia in February-March 2021 were B.1.1.7, the best-known variant with increased transmissibility before the emergence of B.1.617.2 lineage; the variant carrying the S:P681H mutation alone; and three novel variants.

S:P681H is one of the nine spike changes that characterized B.1.1.7 lineage³; and S:P681R is one of lineage-defining mutations of B.1.617.2 lineage; however, changes in this site are absent from the two other lineages of concern, B.1.351 and P.1, indicating that it is not essential for increased transmissibility. The 681 position is adjacent to the furin cleavage site; this site is absent in non-human CoV, and is assumed to have contributed to pathogenicity in humans³². Changes at this position experienced both persistent and episodic positive selection¹². P681H appeared to increase in frequency globally³³, although it was hard to disentangle this increase from that of the other changes constituting B.1.1.7 lineage that was rapidly spreading. We find that the frequency of this mutation in Russia in the absence of other B.1.1.7 mutations did not increase (Fig. 8), indicating that it does not increase transmissibility by itself.

The three remaining high-frequency variants with evidence for rapid frequency increase in early 2021 carried combinations of the following high-frequency mutations: S:P9L, S:Δ140–142 (or S:Δ136–144), S:E484K, and nsp6:Δ106–108. The sets of mutations in these variants are in conflict (i.e., not nested within each other; Fig. 4), indicating that at least some of these mutations emerged in them independently. These mutations are of interest or concern. Specifically, S:E484K (present in AT.1 and B.1.1.525) is involved in several famous variants including the B.1.351⁴, P.1 ^5,34 and P.2^5,34 lineages, and has been shown by several groups to cause escape from neutralizing antibodies^35–37. nsp6:Δ106–108 (also referred to as ORF1a:Δ3675–3677, and present in B.1.1.524 and B.1.1.525) is a part of three variants of concern (B.1.1.7, B.1.351, P.1). S:Δ140–142 (present in B.1.1.524), S:Δ144 (present in B.1.1.525) and S:Δ136–144 (present in AT.1) are distinct deletions at a recurrent deletion region of the spike glycoprotein which confer resistance to neutralizing antibodies³⁸.

Russia has been relatively well isolated in the course of COVID-19 pandemic: both the first cases of COVID-19 and the arrival of variants of concern, notably the B.1.1.7, have happened here later than in many European countries ^8,39. Together with the large size of the outbreak in Russia, such isolation could have created conditions for emergence of novel important domestic variants.

A steady increase in frequency of lineages B.1.1.317 and B.1.1.397+, as well as the presence of multiple mutations with potential effect on antigenic properties, notably S:D138Y, merit classification of these two lineages as variants of interest^40,41. Nevertheless, the rate of spread of B.1.1.317 and B.1.1.397 + has been lower than that of VOCs (e.g., ~ 7% for B.1.1.7⁴²). In particular, while B.1.1.317 has been observed in Russia since April 2020, the subclade of B.1.1.317 carrying the three spike mutations, since July 2020, and B.1.1.397+, since April 2020, all these lineages have remained at frequencies below 30% in Russia, and the logistic growth rates estimated by our model are all below 2% (Fig. 6–8). Besides, these variants were missing spike changes L452R, E484K or N501Y which occur in other VOCs⁴¹. All these variants have been displaced with the advent of the delta variant in summer 2021.

The combinations of mutations seen in the three variants that emerged in 2021, AT.1, B.1.1.524 and B.1.1.525 (Fig. 8C-E), look more suspicious, because their estimated rate of frequency increase was higher and because they include mutations with known effects and occurring in other variants of interest or concern. While these variants have also become rare after the spread of the delta variant, the match between their mutational composition and that of other globally spreading variants of concern indicates that the mutations comprising them could have contributed to their rapid frequency increase.

Dataset preparation. 1,060,545 sequences of SARS-CoV-2 were downloaded from GISAID on April 15, 2021 (Supplementary File 2) and aligned with MAFFT⁴³ v7.45324 against the reference genome Wuhan-Hu-1/2019 (NCBI ID: MN908947.3) with --addfragments --keeplength options. 100 nucleotides from the beginning and from the end of the alignment were trimmed. After that, we excluded sequences (1) shorter than 29,000 bp, (2) with more than 3,000 (for Russian sequences) or 300 (for all other countries) positions of missing data (Ns), (3) excluded by Nextstrain⁴⁴, (4) in non-human animals, (5) with a genetic distance to the reference genome more than four standard deviations from the epi-week mean genetic distance to the reference, or (6) with incomplete collection dates. As our focus was on the spread of lineages in Russia, and since Russia is relatively poorly sampled, we chose a less stringent threshold at step (2) for Russian compared to non-Russian sequences in order to keep more Russian data in the dataset. To this dataset, we added the 1,645 Russian sequences described in our earlier study⁴⁵ and 344 samples produced by the CoRGI consortium which had not been yet available in GISAID on April 15 (but have been deposited to GISAID since then). The final dataset consisting of 830,249 sequences, including 4,487 Russian samples, was then annotated by the PANGOLIN package (v2.3.8). For the categories in Fig. 1, we selected Pango lineages represented by at least 100 Russian samples (there were six such lineages: B.1, B.1.1, B.1.1.397, B.1.1.28, B.1.1.317 and B.1.1.294), added the two earliest lineages A and B and the VOC lineage B.1.1.7, and aggregated A, B, B.1 and B.1.1 with lineages nested in them except those included in other categories into A.*, B.*, B.1.* and B.1.1.*, respectively.

Data analysis. To trace the frequency dynamics of mutations, for each non-reference nucleotide at each position in each month, we calculated the fraction of samples with this nucleotide among all samples where this position contained an ambiguous nucleotide. We did this separately for the Russian and non-Russian samples, and selected changes with frequency more than 5% in S protein and more than 10% in other proteins in February-March 2021 (a total of 461 sequences) in Russian samples for consideration. Calculations were performed with custom Perl and R scripts. Wilson score intervals were estimated using Hmisc R package⁴⁶, and results were visualized with the ggplot2 package⁴⁷ of R language⁴⁸. Upset plots were built with the UpSetR package⁴⁹ for R. Logistic growth models were fit to frequencies of each variant averaged across 14-days sliding windows with nls() function of R language⁴⁸. Only windows with total number of samples > 20 were taken into account. Confidence intervals for estimated parameters were obtained with confint2 function from nlstools R package⁵⁰, similar to ⁴². Results were visualized with R packages ggplot2⁴⁷, tidyverse⁵¹ and gridExtra⁵².

To estimate positive selection, we employed MEME and FEL models implemented in the HyPhy package^10,11. For this analysis, we added Russian sequences with incomplete collection dates to the main dataset, which resulted in 5006 Russian sequences. The tree for the selection analysis was built upon the whole-genome alignment of Russian sequences with the RAxML package v.8.0.26 (model GTRCAT)⁵³.

Mapping residues onto the S-protein structure. To visualize spike mutations, we utilized two different spike protein structures, corresponding to S-protein in complex bound with 4A8 (PDB ID: 7c2l) or with P5A-1B9 (PDB ID: 7czx) antibody. The NTD antibody binding epitope is defined as in²⁶. The two structures were structurally aligned and visualized with Open-Source PyMOL⁵⁴.

PCR data. Community-based PCR tests aimed at detection of S:Δ69–70 and nsp6:Δ106–108 deletions were performed for 739 samples. We further analyzed only those samples for which both tests were performed and produced unambiguous results. There were 269 such samples from 22 regions (including 170 from Saint Petersburg, 43 from Sverdlovsk Oblast, and 12 from Leningrad Oblast) obtained between February-April, 2021. For S:Δ69–70 detection⁵⁵, we used the Yale69/70del RT-PCR assay described elsewhere. For nsp6:Δ106–108 detection, we used a newly designed RT-PCR assay. 133 of these 269 samples were also sequenced (sequence data made available through GISAID); for 126 of them (94.7%), the results on the presence of S:Δ69–70 and nsp6:Δ106–108 were consistent between the NGS and PCR data, indicating that our PCR tests are highly specific.

Acknowledgements

We are grateful to all GISAID submitting and originating labs (Supplementary File 1) for rapid open release of SARS-CoV-2 sequencing data. We thank Sergei L Kosakovsky Pond for help with HyPhy analyses, and Evgeniya Alekseeva and members of the Bazykin lab for fruitful discussions. This work was supported by the RFBR grant 20-54-80014 to G.A.B.

Author contributions

Conceptualization: GVK KRS GAB

Investigation: GVK KRS SKG MM EN ABK DL CORGI

Data Curation: ABK DL CORGI

Formal analysis: GVK KRS MM GAB

Visualization: GVK KRS SKG

Supervision: GAB

Writing—original draft: GVK GAB

Writing—review & editing: GVK KRS EN GAB

Funding acquisition: GAB

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No competing interests reported.

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Spread of Endemic SARS-CoV-2 Lineages in Russia Before April 2021

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