Molecular epidemiological analysis of SARS-CoV-2 genovariants in Moscow and Moscow region
- Authors: Ozhmegova E.N.1, Savochkina T.E.1, Prilipov A.G.1, Tikhomirov E...1, Larichev V.F.1, Sayfullin M.A.1,2, Grebennikova T.V.1
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Affiliations:
- 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
- Issue: Vol 67, No 6 (2022)
- Pages: 496-505
- Section: ORIGINAL RESEARCH
- URL: https://journals.rcsi.science/0507-4088/article/view/125756
- DOI: https://doi.org/10.36233/0507-4088-146
- ID: 125756
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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.
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##article.viewOnOriginalSite##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, MoscowTatyana 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, MoscowAlexey 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, MoscowE. .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, MoscowMukhammad 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, MoscowTatyana 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, MoscowReferences
- GISAID. Available at: https://gisaid.org/
- 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
- (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
- 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
- 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
- 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
- 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
- Outbreak.info. Available at: https://outbreak.info/
- 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
- WHO. Coronavirus disease (COVID-19) pandemic. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Auspice. Available at: https://auspice.us
- 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
- 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
- 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
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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