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Volume 7, Issue 6

Large-scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management Open Access

Abstract

The COVID-19 pandemic has spread rapidly throughout the world. In the UK, the initial peak was in April 2020; in the county of Norfolk (UK) and surrounding areas, which has a stable, low-density population, over 3200 cases were reported between March and August 2020. As part of the activities of the national COVID-19 Genomics Consortium (COG-UK) we undertook whole genome sequencing of the SARS-CoV-2 genomes present in positive clinical samples from the Norfolk region. These samples were collected by four major hospitals, multiple minor hospitals, care facilities and community organizations within Norfolk and surrounding areas. We combined clinical metadata with the sequencing data from regional SARS-CoV-2 genomes to understand the origins, genetic variation, transmission and expansion (spread) of the virus within the region and provide context nationally. Data were fed back into the national effort for pandemic management, whilst simultaneously being used to assist local outbreak analyses. Overall, 1565 positive samples (172 per 100 000 population) from 1376 cases were evaluated; for 140 cases between two and six samples were available providing longitudinal data. This represented 42.6 % of all positive samples identified by hospital testing in the region and encompassed those with clinical need, and health and care workers and their families. In total, 1035 cases had genome sequences of sufficient quality to provide phylogenetic lineages. These genomes belonged to 26 distinct global lineages, indicating that there were multiple separate introductions into the region. Furthermore, 100 genetically distinct UK lineages were detected demonstrating local evolution, at a rate of ~2 SNPs per month, and multiple co-occurring lineages as the pandemic progressed. Our analysis: identified a discrete sublineage associated with six care facilities; found no evidence of reinfection in longitudinal samples; ruled out a nosocomial outbreak; identified 16 lineages in key workers which were not in patients, indicating infection control measures were effective; and found the D614G spike protein mutation which is linked to increased transmissibility dominates the samples and rapidly confirmed relatedness of cases in an outbreak at a food processing facility. The large-scale genome sequencing of SARS-CoV-2-positive samples has provided valuable additional data for public health epidemiology in the Norfolk region, and will continue to help identify and untangle hidden transmission chains as the pandemic evolves.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): ARTIC , genome , genomic epidemiology , NGS , SARS-CoV-2 and sequencing
Funding
This study was supported by the:
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10356)
    • Principle Award Recipient: AndrewJ. Page
  • Biotechnology and Biological Sciences Research Council (Award BB/R012504/1)
    • Principle Award Recipient: NotApplicable
  • ISCIII (Award CD018/0123)
    • Principle Award Recipient: AnaP Tedim
  • Medical Research Council (Award COVID-19 Genomics UK (COG-UK) Consortium)
    • Principle Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10353)
    • Principle Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BB/R012490/1)
    • Principle Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BB/CCG1860/1)
    • Principle Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10352)
    • Principle Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10351)
    • Principle Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10349)
    • Principle Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10348)
    • Principle Award Recipient: NotApplicable
© 2021 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. The Microbiology Society waived the open access fees for this article.
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2021-06-29
2024-04-25
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References

  1. Huang C, Wang Y, Li X, Ren L, Zhao J et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 2020; 395:497–506 [View Article]
     [Google Scholar]
  2. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020; 20:533–534 [View Article] [PubMed]
     [Google Scholar]
  3. Jin JM, Bai P, He W, Wu F, Liu XF et al. Gender differences in patients With COVID-19: focus on severity and mortality. Front Public Health 2020; 8:
     [Google Scholar]
  4. Bryant J, Chewapreecha C, Bentley SD. Developing insights into the mechanisms of evolution of bacterial pathogens from whole-genome sequences. Future Microbiol 2012; 7:1283–1296 [View Article] [PubMed]
     [Google Scholar]
  5. Lu R, Zhao X, Li J, Niu P, Yang B et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet 2020; 395:565–574 [View Article]
     [Google Scholar]
  6. Alm E, Broberg EK, Connor T, Hodcroft EB, Komissarov AB et al. Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020. Euro Surveill 2020; 25:2001410 [View Article] [PubMed]
     [Google Scholar]
  7. Filipe ADS, Shepherd J, Williams T, Hughes J, Aranday-Cortes E et al. Genomic epidemiology of SARS-CoV-2 spread in Scotland highlights the role of European travel in COVID-19 emergence. medRxiv 2020; 9:
     [Google Scholar]
  8. Oude Munnink BB, Nieuwenhuijse DF, Stein M, Á O, Haverkate M et al. Rapid SARS-CoV-2 whole-genome sequencing and analysis for informed public health decision-making in the Netherlands. Nat Med 2020; 26:1405–1410
     [Google Scholar]
  9. Shu Y, McCauley J. GISAID: Global initiative on sharing all influenza data – from vision to reality. Euro Surveill 2017; 22:30494 [View Article] [PubMed]
     [Google Scholar]
  10. Smith GJD, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M et al. Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature 2009; 459:1122–1125 [View Article] [PubMed]
     [Google Scholar]
  11. Page AJ, Alikhan N-. F, Carleton HA, Seemann T, Keane JA et al. Comparison of classical multi-locus sequence typing software for next-generation sequencing data. Microb Genomics 2017; 3:
     [Google Scholar]
  12. Dallman T, Ashton P, Schafer U, Jironkin A, Painset A et al. SnapperDB: a database solution for routine sequencing analysis of bacterial isolates. Bioinformatics 2018; 34:3028–3029 [View Article] [PubMed]
     [Google Scholar]
  13. Rambaut A, Holmes EC, O’Toole Á, Hill V, McCrone JT et al. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol 2020; 5:1403–1407 [View Article]
     [Google Scholar]
  14. COG-UK An integrated national scale SARS-CoV-2 genomic surveillance network. Lancet Microbe 2020
     [Google Scholar]
  15. Volz E, Hill V, McCrone JT, Price A, Jorgensen D et al. Evaluating the effects of SARS-CoV-2 Spike mutation D614G on transmissibility and pathogenicity. Cell 2020
     [Google Scholar]
  16. Quick J. nCoV-2019 sequencing protocol v2. https://www.protocols.io/view/ncov-2019-sequencing-protocol-v2-bdp7i5rn
  17. Baker DJ, Aydin A, Le-Viet T, Kay GL, Rudder S et al. CoronaHiT: high-throughput sequencing of SARS-CoV-2 genomes. Genome Med 2021; 13:21 [View Article] [PubMed]
     [Google Scholar]
  18. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv13033997 Q-Bio 2013
     [Google Scholar]
  19. Grubaugh ND, Gangavarapu K, Quick J, Matteson NL, De Jesus JG et al. An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar. Genome Biol 2019; 20:8 [View Article] [PubMed]
     [Google Scholar]
  20. Turakhia Y, Maio ND, Thornlow B, Gozashti L, Lanfear R et al. Stability of SARS-CoV-2 phylogenies. PLOS Genet 2020; 16:e1009175 [View Article]
     [Google Scholar]
  21. De Maio N, Walker C, Borges R, Weilguny L, Slodkowicz G et al. Issues with SARS-CoV-2 sequencing data. Virological 2020
     [Google Scholar]
  22. Elbe S, Buckland‐Merrett G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob Chall 2017; 1:33–46
     [Google Scholar]
  23. Griffiths EJ, Timme RE, Page AJ, Alikhan N-. F, Fornika D et al. The PHA4GE SARS-CoV-2 contextual data specification for open genomic epidemiology. https://www.preprints.org/manuscript/202008.0220/v1
  24. Cochrane G, Karsch-Mizrachi I, Takagi T. Sequence Database Collaboration IN. The international nucleotide sequence database collaboration. Nucleic Acids Res 2016; 44:D48–50 [View Article] [PubMed]
     [Google Scholar]
  25. Connor TR, Loman NJ, Thompson S, Smith A, Southgate J et al. CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community. Microb Genomics 2016; 2:e000086 [View Article]
     [Google Scholar]
  26. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic Era. Mol Biol Evol 2020
     [Google Scholar]
  27. Hasegawa M, Kishino H, Yano T. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol 1985; 22:160–174 [View Article] [PubMed]
     [Google Scholar]
  28. Yang Z. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods. J Mol Evol 1994; 39:306–314 [View Article] [PubMed]
     [Google Scholar]
  29. Yu G. Using ggtRee to visualize data on tree-like structures. Curr Protoc Bioinforma 2020; 69:e96
     [Google Scholar]
  30. Page AJ, Taylor B, Delaney AJ, Soares J, Seemann T. Snp-sites: Rapid efficient extraction of Snps from multi-FASTA alignments. Microb Genomics 2016; 2:e000056
     [Google Scholar]
  31. Hadfield J, Croucher NJ, Goater RJ, Abudahab K, Aanensen DM et al. Phandango: an interactive viewer for bacterial population genomics. Bioinformatics 2018; 34:292–293 [View Article] [PubMed]
     [Google Scholar]
  32. Rogers AA, Baumann RE, Borillo GA, Kagan RM, Batterman HJ et al. Evaluation of transport media and specimen transport conditions for the detection of sars-cov-2 by use of real-time reverse transcription-pcr. J Clin Microbiol 2020; 58: [View Article] [PubMed]
     [Google Scholar]
  33. Farr B, Rajan D, Betteridge E, Shirley L, Quail M et al. COVID-19 ARTIC v3 Illumina library construction and sequencing protocol. https://www.protocols.io/view/covid-19-artic-v3-illumina-library-construction-an-bgq3jvyn
  34. Morel B, Barbera P, Czech L, Bettisworth B, Hübner L et al. Phylogenetic Analysis of SARS-CoV-2 Data Is Difficult. Mol Biol Evol 2020
     [Google Scholar]
  35. Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J et al. Tracking changes in sars-cov-2 spike: Evidence that d614g increases infectivity of the COVID-19 virus. Cell 2020; 182:812–827 [View Article] [PubMed]
     [Google Scholar]
  36. IUPAC-IUB Comm. on Biochem. Nomenclature (CBN) Abbreviations and symbols for nucleic acids, polynucleotides, and their constituents. Biochemistry 1970; 9:4022–4027
     [Google Scholar]
  37. NHS Equality and diversity NHS trusts and CCGs March 2019 [Internet]; 2019 https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics/nhs-workforce-statistics---march-2019-provisional-statistics
  38. Zou L, Ruan F, Huang M, Liang L, Huang H et al. SARS-COV-2 viral load in upper respiratory specimens of infected patients. N Engl J Med 2020; 382:1177–1179 [View Article] [PubMed]
     [Google Scholar]
  39. van Dorp L, Acman M, Richard D, Shaw LP, Ford CE et al. Emergence of genomic diversity and recurrent mutations in SARS-CoV-2. Infect Genet Evol 2020; 83:104351 [View Article] [PubMed]
     [Google Scholar]
  40. Argimón S, Abudahab K, Goater RJE, Fedosejev A, Bhai J et al. Microreact: Visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2016; 2:e000093 [View Article]
     [Google Scholar]
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Large-scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management
M Gen 7, 000589 (2021); https://doi.org/10.1099/mgen.0.000589
/content/journal/mgen/10.1099/mgen.0.000589
/content/journal/mgen/10.1099/mgen.0.000589
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