Population Genomics of SARS-CoV-2 in the Constituent Entities of Siberian and Far Eastern Federal Districts

The aim of the study was to analyze the genetic structure of populations and the patterns of evolutionary variability of the novel coronavirus infection in the Siberian and Far Eastern Federal Districts. Materials and methods. 1033 SARS-CoV-2 genomes from samples from individuals diagnosed with COVID-19 from eight entities of the Siberia and Far East between December 2020 and November 2021 were assessed. Sequencing was performed on the MinION Oxford Nanopore platform using the ARTIC v.3 protocol. The degree of SARS-CoV-2 genetic isolation was estimated applying the Fst criterion. Phylogenetic analysis was carried out using maximum likelihood method and Bayesian phylogenetic inference. A nonparametric Bayesian Skyline Plot (BSP) model was used to reconstruct population dynamics. Results and discussion. The original SARS-CoV-2 variant (B.1) was identified in 100 % of the cases at the initial stages. The Alpha variant was detected in March-June, 2021; Beta – in single samples in March-May, 2021. Delta was first identified in April, 2021. The maximum degree of SARS-CoV-2 genetic isolation (Fst=0.18) was established for the most remote territories (Altai Territory ↔ Republic of Buryatia and Altai Territory ↔ Irkutsk Region). A relatively free circulation of the virus was detected between Irkutsk Region, Republic of Buryatia and Krasnoyarsk Territory. According to the results of population genetic tests, a sharp increase in the effective virus population size was the determining mechanism of SARS-CoV-2 genetic diversity formation. Reconstruction of population dynamics in BEAST (BSP model) has revealed the consistency of trends in the genetic diversity of the virus and the number of active cases. Two subclusters have been identified in the Delta cluster, consisting predominantly of samples isolated in the Irkutsk Region and Krasnoyarsk Territory. Change in the dominant variant of SARS-CoV-2 has been traced in dynamics. Molecular-epidemiological data point to the multiple pathways of spatial expansion of different SARS-CoV-2 genotypes into the constituent entities with generation of individual monophyletic clusters and further intra- and extraterritorial spread of the decedents.

1. Frost S.D.W., Magalis B.R., Kosakovsky Pond S.L. Neutral theory and rapidly evolving viral pathogens. Mol. Biol. Evol. 2018; 35(6):1348–54. DOI: 10.1093/molbev/msy088.

2. Ciotti M., Ciccozzi M., Pieri M., Bernardini S. The COVID‑19 pandemic: viral variants and vaccine efficacy. Crit. Rev. Clin. Lab. Sci. 2022; 59(1):66–75. DOI: 10.1080/10408363.2021.1979462.

3. Duchene S., Featherstone L., Haritopoulou-Sinanidou M., Rambaut A., Lemey P., Baele G. Temporal signal and the phylodynamic threshold of SARS-CoV‑2. Virus Evol. 2020; 6(2):veaa061. DOI: 10.1093/ve/veaa061.

4. Safari I., Elahi E. Evolution of the SARS-CoV‑2 genome and emergence of variants of concern. Arch. Virol. 2022; 167(2):293–305. DOI: 10.1007/s00705-021-05295-5.

5. Martin D.P., Weaver S., Tegally H., San J.E., Shank S.D., Wilkinson E., Lucaci A.G., Giandhari J., Naidoo S., Pillay Y., Singh L., Lessells R.J., NGS-SA, COVID‑19 Genomics UK (COG-UK), Gupta R.K., Wertheim J.O., Nekturenko A., Murrell B., Harkins G.W., Lemey P., MacLean O.A., Robertson D.L., de Oliveira T., Kosakovsky Pond S.L. The emergence and ongoing convergent evolution of the SARS-CoV‑2 N501Y lineages. Cell. 2021; 184(20):5189–5200.e5187. DOI: 10.1016/j.cell.2021.09.003.

6. Mascola J.R., Graham B.S., Fauci A.S. SARS-CoV‑2 viral variants-tackling a moving target. JAMA. 2021; 325(13):1261–2. DOI: 10.1001/jama.2021.2088.

7. 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. DOI: 10.1093/bib/bbx108.

8. Larsson A. AliView: a fast and lightweight alignment viewer and editor for large datasets. Bioinformatics. 2014; 30(22):3276–8. DOI: 10.1093/bioinformatics/btu531.

9. Rozas J., Ferrer-Mata A., Sánchez-DelBarrio J.C., Guirao-Rico S., Librado P., Ramos-Onsins S.E., Sánchez-Gracia A. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 2017; 34(12):3299–302. DOI: 10.1093/molbev/msx248.

10. 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. DOI: 10.1093/molbev/msu300.

11. Bouckaert R., Vaughan T.G., Barido-Sottani J., Duchene S., Fourment M., Gavryushkina A., Heled J., Jones G., Kuhnert D., De Maio N., Matschiner M., Mendes F.K., Muller N.F., Ogilvie H.A., du Plessis L., Popinga A., Rambaut A., Rasmussen D., Siveroni I., Suchard M.A., Wu C.H., Xie D., Zhang C., Stadler T., Drummond A.J. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 2019; 15(4):e1006650. DOI: 10.1371/journal.pcbi.1006650.

12. Kalyaanamoorthy S., Minh B.Q., Wong T.K.F., von Haeseler A., Jermiin L.S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods. 2017; 14(6):587–9. DOI: 10.1038/nmeth.4285.

13. Minh B.Q., Nguyen M.A.T., von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 2013; 30(5):1188–95. DOI: 10.1093/molbev/mst024.

14. Singh D., Yi S.V. On the origin and evolution of SARSCoV‑2. Exp. Mol. Med. 2021; 53(4):537–47. DOI: 10.1038/s12276-021-00604-z.

15. Wang S., Xu X., Wei C., Li S., Zhao J., Zheng Y., Liu X., Zeng X., Yuan W., Peng S. Molecular evolutionary characteristics of SARS-CoV‑2 emerging in the United States. J. Med. Virol. 2022; 94(1):310–7. DOI: 10.1002/jmv.27331.

16. Motayo B.O., Oluwasemowo O.O., Olusola B.A., Akinduti P.A., Arege O.T., Obafemi Y.D., Faneye A.O., Isibor P.O., Aworunse O.S., Oranusi S.U. Evolution and genetic diversity of SARS-CoV‑2 in Africa using whole genome sequences. Int. J. Infect. Dis. 2021; 103:282–7. DOI: 10.1016/j.ijid.2020.11.190.

17. Shen S., Zhang Z., He F. The phylogenetic relationship within SARS-CoV‑2s: An expanding basal clade. Mol. Phylogenet. Evol. 2021; 157:107017. DOI: 10.1016/j.ympev.2020.107017.

18. Pybus O.G., Rambaut A. Evolutionary analysis of the dynamics of viral infectious disease. Nat. Rev. Genet. 2009; 10(8):540–50. DOI: 10.1038/nrg2583.

19. Hall M.D., Woolhouse M.E., Rambaut A. The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: A simulation study. Virus Evol. 2016; 2(1):vew003. DOI: 10.1093/ve/vew003.