In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches
Tóm tắt
This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. We report a single mutation found in the ORF1ab gene (nucleotide position 18,440) that appears to be significantly enriched within the Cypriot population. The amino acid change is denoted as S6059F, which maps to the SARS-CoV-2 NSP14 protein. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. Investigating Cyprus-specific mutations for SARS-CoV-2 can lead to a better understanding of viral pathogenicity. Researching these mutations can generate potential links between viral-specific mutations and the unique genomics of the Cypriot population. This can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide.
Tài liệu tham khảo
Andino R, Domingo E. Viral quasispecies. Virology. 2015;479-480:46.
Zrelovs N, Ustinova M, Silamikelis I, Birzniece L, Megnis K, Rovite V, et al. First report on the Latvian SARS-CoV-2 isolate genetic diversity. Front Med. 2021;8:241. https://doi.org/10.3389/fmed.2021.626000.
Geoghegan JL, Douglas J, Ren X, Storey M, Hadfield J, Silander OK, et al. The power and limitations of genomics to track COVID-19 outbreaks: a case study from New Zealand. Emerging Infectious Diseases. 2020.
Elizondo V, Harkins GW, Mabvakure B, Smidt S, Zappile P, Marier C, et al. SARS-CoV-2 genomic characterization and clinical manifestation of the COVID-19 outbreak in Uruguay. Emerg Microbes Infect. 2021;10(1):51–65. https://doi.org/10.1080/22221751.2020.1863747.
Kozlovskaya L, Piniaeva A, Ignatyev G, Selivanov A, Shishova A, Kovpak A, et al. Isolation and phylogenetic analysis of SARS-CoV-2 variants collected in Russia during the COVID-19 outbreak. Int J Infect Dis. 2020;99:40–6. https://doi.org/10.1016/j.ijid.2020.07.024.
Taboada B, Vazquez-Perez JA, Muñoz-Medina JE, Ramos-Cervantes P, Escalera-Zamudio M, Boukadida C, et al. Genomic analysis of early SARS-CoV-2 variants introduced in Mexico. J Virol. 2020;94(18). https://doi.org/10.1128/JVI.01056-20.
Zhang W, Govindavari JP, Davis BD, Chen SS, Kim JT, Song J, et al. Analysis of genomic characteristics and transmission routes of patients with confirmed SARS-CoV-2 in Southern California during the early stage of the US COVID-19 pandemic. JAMA Netw Open. 2020;3(10):e2024191. https://doi.org/10.1001/jamanetworkopen.2020.24191.
Gómez-Carballa A, Bello X, Pardo-Seco J, Del Molino MLP, Martinón-Torres F, Salas A. Phylogeography of SARS-CoV-2 pandemic in Spain: a story of multiple introductions, micro-geographic stratification, founder effects, and super-spreaders. Zool Res. 2020;41(6):605–20. https://doi.org/10.24272/j.issn.2095-8137.2020.217.
Sekizuka T, Itokawa K, Hashino M, Kawano-Sugaya T, Tanaka R, Yatsu K, et al. A genome epidemiological study of SARS-CoV-2 introduction into Japan. mSphere. 2020;5:e00786-20.
Richter J, Fanis P, Tryfonos C, Koptides D, Krashias G, Bashiardes S, et al. Molecular epidemiology of SARS-CoV-2 in Cyprus. PLoS One. 2021;16:e0248792. https://doi.org/10.1371/journal.pone.0248792.
Oulas A, Zanti M, Tomazou M, Zachariou M, Minadakis G, Bourdakou MM, et al. Generalized linear models provide a measure of virulence for specific mutations in SARS-cov-2 strains. PLoS One. 2021;16(1):e0238665. https://doi.org/10.1371/journal.pone.0238665.
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25. https://doi.org/10.1186/gb-2009-10-3-r25.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9. https://doi.org/10.1093/bioinformatics/btp352.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303. https://doi.org/10.1101/gr.107524.110.
Rambaut A, Holmes EC, O’Toole Á, Hill V, McCrone JT, Ruis C, et al. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol. 2020;5(11):1403–7. https://doi.org/10.1038/s41564-020-0770-5.
RC T. R: a language and environment for statistical computing. Vienna: Austria R Found Stat Comput; 2013.
Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, et al. NextStrain: real-time tracking of pathogen evolution. Bioinformatics. 2018;34:4121–3.
Katoh K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30(14):3059–66. https://doi.org/10.1093/nar/gkf436.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3. https://doi.org/10.1093/bioinformatics/btu033.
Yang J, Zhang Y. Protein structure and function prediction using I-TASSER. Curr Protoc Bioinformatics. 2015;52(1):5.8.1–5.8.15. https://doi.org/10.1002/0471250953.bi0508s52.
Ittisoponpisan S, Islam SA, Khanna T, Alhuzimi E, David A, Sternberg MJE. Can predicted protein 3D structures provide reliable insights into whether missense variants are disease associated? J Mol Biol. 2019;431:2197–212.
Rodrigues CH, Pires DE, Ascher DB, RenéRen I, Rachou R, Oswaldo CF. DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res. 2018;46(W1):W350–5. https://doi.org/10.1093/nar/gky300.
Jubb HC, Higueruelo AP, Ochoa-Montaño B, Pitt WR, Ascher DB, Blundell TL. Arpeggio: a web server for calculating and visualising interatomic interactions in protein structures. J Mol Biol. 2017;429(3):365–71. https://doi.org/10.1016/j.jmb.2016.12.004.
Vajda S, Yueh C, Beglov D, Bohnuud T, Mottarella SE, Xia B, et al. New additions to the ClusPro server motivated by CAPRI. Proteins Struct Funct Bioinforma. 2017;85:435–44.
Yan Y, Tao H, He J, Huang SY. The HDOCK server for integrated protein–protein docking. Nat Protoc. 2020;15:1829–52.
Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics. 2016;32:3676–8.
He J, Tao H, Huang SY. Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models. Bioinformatics. 2019;35:4994–5002.
Arya R, Kumari S, Pandey B, Mistry H, Bihani SC, Das A, et al. Structural insights into SARS-CoV-2 proteins. J Mol Biol. 2021;433(2):166725. https://doi.org/10.1016/j.jmb.2020.11.024.
Rambaut A, Loman N, Pybus O, Barclay W, Barrett J, Carabelli A, et al. Preliminary genomic characterisation of an emergent SARS-CoV-2 lineage in the UK defined by a novel set of spike mutations. VirologicalOrg. 2020.
Davies NG, Abbott S, Barnard RC, Jarvis CI, Kucharski AJ, Munday J, et al. Estimated transmissibility and severity of novel SARS-CoV-2 Variant of Concern 202012/01 in England. Science. 2020.
Volz E, Mishra S, Chand M, Barrett JC, Johnson R, Hopkins S, et al. Transmission of SARS-CoV-2 lineage B.1.1.7 in England: insights from linking epidemiological and genetic data. medRxiv. 2021;2020.12.30.20249034.
Ogando NS, Zevenhoven-Dobbe JC, van der Meer Y, Bredenbeek PJ, Posthuma CC, Snijder EJ. The enzymatic activity of the nsp14 exoribonuclease is critical for replication of MERS-CoV and SARS-CoV-2. J Virol. 2020;94(23). https://doi.org/10.1128/JVI.01246-20.
Eckerle LD, Lu X, Sperry SM, Choi L, Denison MR. High fidelity of murine hepatitis virus replication is decreased in nsp14 exoribonuclease mutants. J Virol. 2007;81(22):12135–44. https://doi.org/10.1128/JVI.01296-07.
Eckerle LD, Becker MM, Halpin RA, Li K, Venter E, Lu X, et al. Infidelity of SARS-CoV Nsp14-exonuclease mutant virus replication is revealed by complete genome sequencing. PLoS Pathog. 2010;6:1–15.
Graham RL, Becker MM, Eckerle LD, Bolles M, Denison MR, Baric RS. A live, impaired-fidelity coronavirus vaccine protects in an aged, immunocompromised mouse model of lethal disease. Nat Med. 2012;18:1820–6.
Jiang C, Komazin-Meredith G, Tian W, Coen DM, Hwang CBC. Mutations that increase DNA binding by the processivity factor of herpes simplex virus affect virus production and DNA replication fidelity. J Virol. 2009.
Smith EC, Denison MR. Coronaviruses as DNA wannabes: a new model for the regulation of RNA virus replication fidelity. PLoS Pathog. 2013;9(12):e1003760. https://doi.org/10.1371/journal.ppat.1003760.
Hou Y, Zhao J, Martin W, Kallianpur A, Chung MK, Jehi L, et al. New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysis. BMC Med. 2020;18(1):216. https://doi.org/10.1186/s12916-020-01673-z.
Secolin R, de Araujo TK, Gonsales MC, Rocha CS, Naslavsky M, De Marco L, et al. Genetic variability in COVID-19-related genes in the Brazilian population. Hum Genome Var. 2021;8(1):15. https://doi.org/10.1038/s41439-021-00146-w.
Hsu JCC, Laurent-Rolle M, Pawlak JB, Wilen CB, Cresswell P. Translational shutdown and evasion of the innate immune response by SARS-CoV-2 NSP14 protein. Proc Natl Acad Sci U S A. 2021;118(24):e2101161118. https://doi.org/10.1073/pnas.2101161118.
Zhang Q, Liu Z, Moncada-Velez M, Chen J, Ogishi M, Bigio B, et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science (80- ). 2020.
Shannon A, Le NTT, Selisko B, Eydoux C, Alvarez K, Guillemot JC, et al. Remdesivir and SARS-CoV-2: structural requirements at both nsp12 RdRp and nsp14 exonuclease active-sites. Antivir Res. 2020;178:104793. https://doi.org/10.1016/j.antiviral.2020.104793.