Molecular epidemiological analysis of SARS-CoV-2 genovariants in Moscow and Moscow region

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Abstract

Introduction. SARS-CoV-2, a severe acute respiratory illness virus that emerged in China in late 2019, continues to spread rapidly around the world, accumulating mutations and thus causing serious concern. Five virus variants of concern are currently known: Alpha (lineage B.1.1.7), Beta (lineage B.1.351), Gamma (lineage P.1), Delta (lineage B.1.617.2), and Omicron (lineage B.1.1.529). In this study, we conducted a molecular epidemiological analysis of the most prevalent genovariants in Moscow and the region.

The aim of the study is to estimate the distribution of various variants of SARS-CoV-2 in Moscow city and the Moscow Region.

Materials and methods. 227 SARS-CoV-2 sequences were used for analysis. Isolation of the SARS-CoV-2 virus was performed on Vero E6 cell culture. Sequencing was performed by the Sanger method. Bioinformatic analysis was carried out using software packages: MAFFT, IQ-TREE v1.6.12, jModelTest 2.1.7, Nextstrain, Auspice v2.34.

Results. As a result of phylogenetic analysis, we have identified the main variants of the virus circulating in Russia that have been of concern throughout the existence of the pandemic, namely: variant B.1.1.7, which accounted for 30% (9/30), AY.122, which accounted for 16.7% (5/30), BA.1.1 with 20% (6/30) and B.1.1 with 33.3% (10/30). When examining Moscow samples for the presence of mutations in SARS-CoV-2 structural proteins of different genovariants, a significant percentage of the most common substitutions was recorded: S protein – D614G (86.7%), P681H/R (63.3%), E protein – T9I (20.0%); M protein – I82T (30.0%), D3G (20.0%), Q19E (20.0%) and finally N protein – R203K/M (90.0%), G204R/P (73.3 %).

Conclusion. The study of the frequency and impact of mutations, as well as the analysis of the predominant variants of the virus are important for the development and improvement of vaccines for the prevention of COVID-19. Therefore, ongoing molecular epidemiological studies are needed, as these data provide important information about changes in the genome of circulating SARS-CoV-2 variants.

About the authors

Ekaterina N. Ozhmegova

National Research Center for Epidemiology and Microbiology named after honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation

Email: ozhmegova.eka@gmail.com
ORCID iD: 0000-0002-3110-0843

Researcher, Laboratory of leukemia viruses

Russian Federation, 123098, Moscow

Tatyana E. Savochkina

National Research Center for Epidemiology and Microbiology named after honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation

Email: tasavochkina@yandex.ru
ORCID iD: 0000-0003-4366-8476

Junior Researcher, Laboratory of Molecular Diagnostics

Russian Federation, 123098, Moscow

Alexey G. Prilipov

National Research Center for Epidemiology and Microbiology named after honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation

Email: a_prilipov@mail.ru
ORCID iD: 0000-0001-8755-1419

Doctor of Biological Sciences, Head Laboratory of Molecular Genetics Center

Russian Federation, 123098, Moscow

E. .E. Tikhomirov

National Research Center for Epidemiology and Microbiology named after honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation

Email: ozhmegova.eka@gmail.com
Russian Federation, 123098, Moscow

Victor F. Larichev

National Research Center for Epidemiology and Microbiology named after honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation

Email: vlaritchev@mail.ru
ORCID iD: 0000-0001-8262-5650

doctor of med. sci, Leading Researcher of laboratory of biology and indication of arbovirus infections

Russian Federation, 123098, Moscow

Mukhammad A. Sayfullin

National Research Center for Epidemiology and Microbiology named after honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation; Pirogov Russian National Research Medical University

Email: dr_saifullin@mail.ru
ORCID iD: 0000-0003-1058-3193

PhD, Associate Professor, Department of Infectious Diseases in Children, Faculty of Pediatrics, Sen. оf laboratory of biology and indication of arbovirus infections

Russian Federation, 123098, Moscow; 119997, Moscow

Tatyana V. Grebennikova

National Research Center for Epidemiology and Microbiology named after honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation

Author for correspondence.
Email: t_grebennikova@mail.ru
ORCID iD: 0000-0002-6141-9361

Doctor of Biological Sciences, Professor, Corresponding Member RAS, Head Laboratory of Molecular Diagnostics, Head of department

Russian Federation, 123098, Moscow

References

  1. GISAID. Available at: https://gisaid.org/
  2. Kistler K.E., Huddleston J., Bedford T. Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2. Cell Host Microbe. 2022; 30(4): 545–55е4. https://doi.org/10.1016/j.chom.2022.03.018
  3. (COVID-19 Genomics UK (COG-UK). An integrated national scale SARS-CoV-2 genomic surveillance network. Lancet Microbe. 2020; 1(3): e99–e100. https://doi.org/10.1016/S2666-5247(20)30054-9
  4. Endo A., Abbott S., Kucharski A.J., Funk S.; Group CftMMoIDC-W. Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Res. 2020; 5: 67. https://doi.org/10.12688/wellcomeopenres.15842.3
  5. Lewis D. Superspreading drives the COVID pandemic – and could help to tame it. Nature. 2021; 590(7847): 544–6. https://doi.org/10.1038/d41586-021-00460-x
  6. Sun K., Wang W., Gao L., Wang Y., Luo K., Ren L., et al. Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. Science. 2021; 371(6526): eabe2424. https://doi.org/10.1126/science.abe2424
  7. Akimkin V.G., Popova A.Yu., Ploskireva A.A., Ugleva S.V., Semenenko T.A., Pshenichnaya N.Yu., et al. COVID-19: the evolution of the pandemic in Russia. Report I: manifestations of the COVID-19 epidemic process. Zhurnal mikrobiologii, èpidemiologii i immunobiologii. 2022; 99(3): 269–86. https://doi.org/10.36233/0372-9311-276
  8. Outbreak.info. Available at: https://outbreak.info/
  9. Akimkin V.G., Popova A.Yu., Khafizov K.F., Dubodelov D.V., Ugleva S.V., Semenenko T.A., et al. COVID-19: evolution of the pandemic in Russia. Report II: dynamics of the circulation of SARS-CoV-2 genetic variants. Zhurnal mikrobiologii, èpidemiologii i immunobiologii. 2022;99(4):381–396. DOI: https://doi.org/10.36233/0372-9311-2959311-295
  10. WHO. Coronavirus disease (COVID-19) pandemic. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019
  11. Planas D., Veyer D., Baidaliuk A., Staropoli I., Guivel-Benhassine F., Rajah M.M., et al. Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization. Nature. 2021; 596(7871): 276–80. https://doi.org/10.1038/s41586-021-03777-9
  12. Chomczynski P., Sacchi N. The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on. Nat. Protoc. 2006; 1(2): 581–5. https://doi.org/10.1038/nprot.2006.83
  13. Katoh K., Rozewicki J., Yamada K.D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2019; 20(4): 1160–6. https://doi.org/10.1093/bib/bbx108
  14. Nguyen L.T., Schmidt H.A., von Haeseler A., Minh B.Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 2015; 32(1): 268–74. https://doi.org/10.1093/molbev/msu300
  15. Darriba D., Taboada G.L., Doallo R., Posada D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods. 2012; 9(8): 772. https://doi.org/10.1038/nmeth.2109
  16. Hadfield J., Megill C., Bell S.M., Huddleston J., Potter B., Callender C., et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018; 34(23): 4121–3. https://doi.org/10.1093/bioinformatics/bty407
  17. Sagulenko P., Puller V., Neher R.A. TreeTime: Maximum-likelihood phylodynamic analysis. Virus Evol. 2018; 4(1): vex042. https://doi.org/10.1093/ve/vex042
  18. Auspice. Available at: https://auspice.us
  19. Mahmanzar M., Houseini S.T., Rahimian K., Namini A.M., Gholamzad A., Tokhanbigli S., et al. The first geographic identification by country of sustainable mutations of SARS-COV2 sequence samples: worldwide natural selection trends. bioRxiv. 2022. Preprint. https://doi.org/10.1101/2022.07.18.500565
  20. Shen L., Bard J.D., Triche T.J., Judkins A.R., Biegel J.A., Gai X. Emerging variants of concern in SARS-CoV-2 membrane protein: a highly conserved target with potential pathological and therapeutic implications. Emerg. Microbes Infect. 2021; 10(1): 885–93. https://doi.org/10.1080/22221751.2021.1922097
  21. Komissarov A.B., Safina K.R., Garushyants S.K., Fadeev A.V., Sergeeva M.V., Ivanova A.A., et al. Genomic epidemiology of the early stages of the SARS-CoV-2 outbreak in Russia. Nat. Commun. 2021; 12(1): 649. https://doi.org/10.1038/s41467-020-20880-z
  22. Klink G.V., Safina K.R., Garushyants S.K., Moldovan M., Nabieva E., Komissarov A.B., et al. Spread of endemic SARS-CoV-2 lineages in Russia before April 2021. PLoS One. 2022; 17(7): e0270717. https://doi.org/10.1371/journal.pone.0270717
  23. Borisova N.I., Kotov I.A., Kolesnikov A.A., Kaptelova V.V., Speranskaya A.S., Kondrasheva L.Yu., et al. Monitoring the spread of the SARS-CoV-2 (Coronaviridae: Coronavirinae: Betacoronavirus; Sarbecovirus) variants in the Moscow region using targeted high-throughput sequencing. Voprosy virusologii. 2021; 66(4): 269–78. https://doi.org/10.36233/0507-4088-72 (in Russian)
  24. Kannan S., Shaik Syed Ali P., Sheeza A. Omicron (B.1.1.529) – variant of concern – molecular profile and epidemiology: a mini review. Eur. Rev. Med. Pharmacol. Sci. 2021; 25(24): 8019–22. https://doi.org/10.26355/eurrev_202112_27653
  25. Karim S.S.A., Karim Q.A. Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet. 2021; 398(10317): 2126–8. https://doi.org/10.1016/S0140-6736(21)02758-6
  26. Unni S., Aouti S., Thiyagarajan S., Padmanabhan B. Identification of a repurposed drug as an inhibitor of Spike protein of human coronavirus SARS-CoV-2 by computational methods. J. Biosci. 2020; 45(1): 130. https://doi.org/10.1007/s12038-020-00102-w
  27. Daniloski Z., Jordan T.X., Ilmain J.K., Guo X., Bhabha G., tenOever B.R., et al. The Spike D614G mutation increases SARS-CoV-2 infection of multiple human cell types. Elife. 2021; 10: e65365. https://doi.org/10.7554/eLife.65365
  28. Zuckerman N.S., Fleishon S., Bucris E., Bar-Ilan D., Linial M., Bar-Or I., et al. A unique SARS-CoV-2 spike protein P681H variant detected in Israel. Vaccines (Basel). 2021; 9(6): 616. https://doi.org/10.3390/vaccines9060616
  29. Baden L.R., El Sahly H.M., Essink B., Kotloff K., Frey S., Novak R., et al. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N. Engl. J. Med. 2021; 384(5): 403–16. https://doi.org/10.1056/NEJMoa2035389
  30. Polack F.P., Thomas S.J., Kitchin N., Absalon J., Gurtman A., Lockhart S., et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N. Engl. J. Med. 2020; 383(27): 2603–15. https://doi.org/10.1056/NEJMoa2034577
  31. Sadoff J., Gray G., Vandebosch A., Cárdenas V., Shukarev G., Grinsztejn B., et al. Safety and Efficacy of Single-Dose Ad26.COV2.S Vaccine against Covid-19. N. Engl. J. Med. 2021; 384(23): 2187–201. https://doi.org/10.1056/NEJMoa2101544
  32. Dong E., Du H., Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 2020; 20(5): 533–4. https://doi.org/10.1016/S1473-3099(20)30120-1
  33. Wang R., Chen J., Gao K., Wei G.W. Vaccine-escape and fast-growing mutations in the United Kingdom, the United States, Singapore, Spain, India, and other COVID-19-devastated countries. Genomics. 2021; 113(4): 2158–70. https://doi.org/10.1016/j.ygeno.2021.05.006
  34. Wu H., Xing N., Meng K., Fu B., Xue W., Dong P., et al. Nucleocapsid mutations R203K/G204R increase the infectivity, fitness, and virulence of SARS-CoV-2. Cell Host Microbe. 2021; 29(12): 1788–801.e6. https://doi.org/10.1016/j.chom.2021.11.005

Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. Maximum likelihood dendrogram for 30 Moscow sequences obtained from Infectious Clinical Hospital No. 1 together with 196 available sequences from the GISAID database (Nextstrain reference variants were used). The tree is rooted using a reference sample from Wuhan (hCoV-19/Wuhan/WIV04/2019 (WIV04)). Variants of concern and interest (VOC and VOI) are marked with color and captions.

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3. Fig. 2. Maximum likelihood dendrograms of Moscow samples. Illuminated dots indicate Moscow samples from IKH№1. The numbers at the tree nodes indicate highly supported branches (bootstrap support > 0.9). The names of the specimens are given according to the names from the GISAID database: a – line B.1.1 together with reference strains from Japan, USA and Russia, b – line B.1.1.7 (Alpha) with reference strains from Germany, USA and Sweden, c – line BA.1.1 (B.1.1.529; Omicron) together with reference strains from Luxembourg, USA and Italy, d – line AY.122 (B.1.617 .2; Delta) with reference strains from England, Scotland, USA, Switzerland, Germany and Singapore.

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4. Fig. 3. Frequency of mutations in SARS-CoV-2 structural proteins (S, E, M, N) in Moscow samples from Infectious Clinical Hospital No. 1.

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Copyright (c) 2023 Ozhmegova E.N., Savochkina T.E., Prilipov A.G., Tikhomirov E..., Larichev V.F., Sayfullin M.A., Grebennikova T.V.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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