1887

Abstract

Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 10 genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016–2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000867
2022-09-28
2024-07-15
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/9/mgen000867.html?itemId=/content/journal/mgen/10.1099/mgen.0.000867&mimeType=html&fmt=ahah

References

  1. Van Poelvoorde L, Delcourt T, Vuylsteke M, De Keersmaecker SCJ, Thomas I et al. A general approach to identify low-frequency variants within influenza samples collected during routine surveillance. Figshare 2022
    [Google Scholar]
  2. Webster RG, Bean WJ, Gorman OT, Chambers TM, Kawaoka Y. Evolution and ecology of influenza a viruses. Microbiol Rev 1992; 56:152–179 [View Article]
    [Google Scholar]
  3. Mosnier A, Caini S, Daviaud I, Nauleau E, Bui TT et al. Clinical characteristics are similar across type A and B influenza virus nfections. PLoS ONE 2015; 10:1–13 [View Article]
    [Google Scholar]
  4. Kosik I, Yewdell JW. Influenza hemagglutinin and neuraminidase: yang proteins coevolving to thwart mmunity. Viruses 2019; 11:E346 [View Article]
    [Google Scholar]
  5. WHO Influenza (seasonal). Influenza n.d https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal) accessed 26 March 2018
    [Google Scholar]
  6. Sanjuán R, Nebot MR, Chirico N, Mansky LM, Belshaw R. Viral mutation rates. J Virol 2010; 84:9733–9748 [View Article] [PubMed]
    [Google Scholar]
  7. Nowak MA. What is a quasispecies?. Trends Ecol Evol 1992; 7:118–121 [View Article]
    [Google Scholar]
  8. Lauring AS, Andino R. Quasispecies theory and the behavior of RNA viruses. PLoS Pathog 2010; 6:e1001005 [View Article]
    [Google Scholar]
  9. Andino R, Domingo E. Viral quasispecies. Virology 2015; 479–480:46–51 [View Article]
    [Google Scholar]
  10. Webster RG, Laver WG, Air GM, Schild GC. Molecular mechanisms of variation in influenza viruses. Nature 1982; 296:115–121 [View Article]
    [Google Scholar]
  11. Hurt AC, Barr IG. Influenza viruses with reduced sensitivity to the neuraminidase inhibitor drugs in untreated young children. Commun Dis Intell Q Rep 2008; 32:57–62
    [Google Scholar]
  12. Forns X, Purcell RH, Bukh J. Quasispecies in viral persistence and pathogenesis of hepatitis C virus. Trends Microbiol 1999; 7:402–410 [View Article] [PubMed]
    [Google Scholar]
  13. Van Poelvoorde LAE, Saelens X, Thomas I, Roosens NH. Next-generation sequencing: an eye-opener for the surveillance of antiviral resistance in nfluenza. Trends Biotechnol 2020; 38:360–367 [View Article]
    [Google Scholar]
  14. Zhou B, Donnelly ME, Scholes DT, St George K, Hatta M et al. Single-reaction genomic amplification accelerates sequencing and vaccine production for classical and Swine origin human influenza A viruses. J Virol 2009; 83:10309–10313 [View Article] [PubMed]
    [Google Scholar]
  15. Boivin G. Detection and management of antiviral resistance for influenza viruses. Influenza Other Respir Viruses 2013; 7 Suppl 3:18–23 [View Article] [PubMed]
    [Google Scholar]
  16. Xu Y, Lewandowski K, Downs LO, Kavanagh J, Hender T et al. Nanopore metagenomic sequencing of influenza virus directly from respiratory samples: diagnosis, drug resistance and nosocomial transmission, United Kingdom, 2018/19 influenza season. Euro Surveill 2021; 26: [View Article]
    [Google Scholar]
  17. Koel BF, Burke DF, Bestebroer TM, van der Vliet S, Zondag GCM et al. Substitutions near the receptor binding site determine major antigenic change during influenza virus evolution. Science 2013; 342:976–979 [View Article]
    [Google Scholar]
  18. Simon B, Pichon M, Valette M, Burfin G, Richard M et al. Whole genome sequencing of A(H3N2) influenza viruses reveals variants associated with severity during the 2016. Viruses 2019; 11:E108 [View Article]
    [Google Scholar]
  19. Posada-Céspedes S, Seifert D, Topolsky I, Jablonski KP, Metzner KJ et al. V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data. Bioinformatics 2021; 37:1673–1680 [View Article] [PubMed]
    [Google Scholar]
  20. Tonkin-Hill G, Martincorena I, Amato R, Lawson ARJ, Gerstung M et al. Patterns of within-host genetic diversity in SARS-CoV-2. Elife 2021; 10:e66857 [View Article]
    [Google Scholar]
  21. Luksza M, Lässig M. A predictive fitness model for influenza. Nature 2014; 507:57–61 [View Article] [PubMed]
    [Google Scholar]
  22. Pompei S, Loreto V, Tria F. Phylogenetic properties of RNA viruses. PLoS ONE 2012; 7:e44849 [View Article]
    [Google Scholar]
  23. Varble A, Albrecht RA, Backes S, Crumiller M, Bouvier NM et al. Influenza a virus transmission bottlenecks are defined by infection route and recipient host. Cell Host Microbe 2014; 16:691–700 [View Article]
    [Google Scholar]
  24. Xue KS, Stevens-Ayers T, Campbell AP, Englund JA, Pergam SA et al. Parallel evolution of influenza across multiple spatiotemporal scales. Elife 2017; 6:e26875 [View Article]
    [Google Scholar]
  25. Rogers MB, Song T, Sebra R, Greenbaum BD, Hamelin M-E et al. Intrahost dynamics of antiviral resistance in influenza A virus reflect complex patterns of segment linkage, reassortment, and natural selection. mBio 2015; 6:e02464-14 [View Article]
    [Google Scholar]
  26. Ghedin E, Laplante J, DePasse J, Wentworth DE, Santos RP et al. Deep sequencing reveals mixed infection with 2009 pandemic influenza A (H1N1) virus strains and the emergence of oseltamivir resistance. J Infect Dis 2011; 203:168–174 [View Article] [PubMed]
    [Google Scholar]
  27. Trebbien R, Pedersen SS, Vorborg K, Franck KT, Fischer TK. Development of oseltamivir and zanamivir resistance in influenza A(H1N1)pdm09 virus, Denmark, 2014. Euro Surveill 2017; 22:1–8 [View Article]
    [Google Scholar]
  28. McCrone JT, Lauring AS. Measurements of intrahost viral diversity are extremely sensitive to systematic errors in variant alling. J Virol 2016; 90:6884–6895 [View Article]
    [Google Scholar]
  29. Xue KS, Bloom JD. Linking influenza virus evolution within and between human hosts. Virus Evol 2020; 6:veaa010 [View Article]
    [Google Scholar]
  30. Dinis JM, Florek KR, Fatola OO, Moncla LH, Mutschler JP et al. Deep sequencing reveals potential antigenic variants at low frequencies in influenza a virus-infected humans. J Virol 2016; 90:3355–3365 [View Article] [PubMed]
    [Google Scholar]
  31. Debbink K, McCrone JT, Petrie JG, Truscon R, Johnson E et al. Vaccination has minimal impact on the intrahost diversity of H3N2 influenza viruses. PLOS Pathog 2017; 13:e1006194 [View Article]
    [Google Scholar]
  32. Van den Hoecke S, Verhelst J, Vuylsteke M, Saelens X. Analysis of the genetic diversity of influenza a viruses using next-generation DNA sequencing. BMC Genomics 2015; 16:1–23 [View Article] [PubMed]
    [Google Scholar]
  33. Kundu S, Lockwood J, Depledge DP, Chaudhry Y, Aston A et al. Next-generation whole genome sequencing identifies the direction of norovirus transmission in linked patients. Clin Infect Dis 2013; 57:407–414 [View Article] [PubMed]
    [Google Scholar]
  34. Robasky K, Lewis NE, Church GM. The role of replicates for error mitigation in next-generation sequencing. Nat Rev Genet 2014; 15:56–62 [View Article] [PubMed]
    [Google Scholar]
  35. Van Poelvoorde LAE, Bogaerts B, Fu Q, De Keersmaecker SCJ, Thomas I et al. Whole-genome-based phylogenomic analysis of the Belgian 2016-2017 influenza A(H3N2) outbreak season allows improved surveillance. Microb Genom 2021; 7: [View Article]
    [Google Scholar]
  36. Van Poelvoorde LAE, Vanneste K, De Keersmaecker SCJ, Thomas I, Van Goethem N et al. n.d whole-genome viral sequence analysis reveals mutations associated with influenza patient data. Front Microbiol
    [Google Scholar]
  37. Thomas I, Barbezange C, Hombrouck A, Gucht SV, Weyckmans J et al. Virological surveillance of influenza in Belgium; season 2016-2017. Sciensano Influenza Report 20171–33
    [Google Scholar]
  38. Brittain-Long R, Nord S, Olofsson S, Westin J, Anderson L-M et al. Multiplex real-time PCR for detection of respiratory tract infections. J Clin Virol 2008; 41:53–56 [View Article] [PubMed]
    [Google Scholar]
  39. Hombrouck A, Sabbe M, Van Casteren V, Wuillaume F, Hue D et al. Viral aetiology of influenza-like illness in Belgium during the influenza A(H1N1)2009 pandemic. Eur J Clin Microbiol Infect Dis 2012; 31:999–1007 [View Article]
    [Google Scholar]
  40. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng 2007; 9:90–95 [View Article]
    [Google Scholar]
  41. Invitrogen Advanced analysis with the superscript iv oone-step rt-pcr system; 2018 https://assets.thermofisher.com/TFS-Assets/BID/Reference-Materials/advanced analysis-superscript-iv-one-step-rt-pcr-system-white-paper.pdf accessed 18 January 2022
  42. Leinonen R, Sugawara H, Shumway M. International Nucleotide Sequence Database Collaboration The sequence read archive. Nucleic Acids Res 2011; 39:D19–21 [View Article]
    [Google Scholar]
  43. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
    [Google Scholar]
  44. Brister JR, Ako-Adjei D, Bao Y, Blinkova O. NCBI viral genomes resource. Nucleic Acids Res 2015; 43:D571–7 [View Article]
    [Google Scholar]
  45. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010; 20:1297–1303 [View Article]
    [Google Scholar]
  46. Broad Institute Data pre-processing for variant discovery. GATK; n.d https://gatk.broadinstitute.org/hc/en-us/articles/%20360035535912-Data-pre-processing-for-variant-discovery accessed 14 January 2022
  47. Marx V. How to deduplicate PCR. Nat Methods 2017; 14:473–476 [View Article] [PubMed]
    [Google Scholar]
  48. Kassahn KS, Holmes O, Nones K, Patch A-M, Miller DK et al. Somatic point mutation calling in low cellularity tumors. PLOS ONE 2013; 8:e74380 [View Article]
    [Google Scholar]
  49. Tian S, Yan H, Kalmbach M, Slager SL. Impact of post-alignment processing in variant discovery from whole exome data. BMC Bioinformatics 2016; 17:403 [View Article] [PubMed]
    [Google Scholar]
  50. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article] [PubMed]
    [Google Scholar]
  51. Wilm A, Aw PPK, Bertrand D, Yeo GHT, Ong SH et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res 2012; 40:11189–11201 [View Article] [PubMed]
    [Google Scholar]
  52. van Rossum G. Python tutorial. CWI Report CS-R9526; 1995 pp 1–65
  53. Kenmoe S, Tchendjou P, Moyo Tetang S, Mossus T, Njankouo Ripa M et al. Evaluating the performance of a rapid antigen test for the detection of influenza virus in clinical specimens from children in Cameroon. Influenza Other Respir Viruses 2014; 8:131–134 [View Article]
    [Google Scholar]
  54. Caselton DL, Arunga G, Emukule G, Muthoka P, Mayieka L et al. Does the length of specimen storage affect influenza testing results by real-time reverse transcription-polymerase chain reaction? An analysis of influenza surveillance specimens, 2008 to 2010. Euro Surveill 2014; 19: [View Article]
    [Google Scholar]
  55. Duchamp MB, Casalegno JS, Gillet Y, Frobert E, Bernard E et al. Pandemic A(H1N1)2009 influenza virus detection by real time RT-PCR: is viral quantification useful?. Clin Microbiol Infect 2010; 16:317–321 [View Article]
    [Google Scholar]
  56. Gerstung M, Beisel C, Rechsteiner M, Wild P, Schraml P et al. Reliable detection of subclonal single-nucleotide variants in tumour cell populations. Nat Commun 2012; 3:811 [View Article]
    [Google Scholar]
  57. Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res 2016; 44:e108 [View Article]
    [Google Scholar]
  58. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing; 2012 http://arxiv.org/abs/1207.3907 accessed 18 January 2022
  59. Verbist BMP, Thys K, Reumers J, Wetzels Y, Van der Borght K et al. VirVarSeq: a low-frequency virus variant detection pipeline for Illumina sequencing using adaptive base-calling accuracy filtering. Bioinformatics 2015; 31:94–101 [View Article] [PubMed]
    [Google Scholar]
  60. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 2012; 22:568–576 [View Article]
    [Google Scholar]
  61. Mohammed KS, Kibinge N, Prins P, Agoti CN, Cotten M et al. Evaluating the performance of tools used to call minority variants from whole genome short-read data. Wellcome Open Res 2018; 3:21 [View Article]
    [Google Scholar]
  62. Deng Z-L, Dhingra A, Fritz A, Götting J, Münch PC et al. Evaluating assembly and variant calling software for strain-resolved analysis of large DNA viruses. Brief Bioinform 2021; 22:bbaa123 [View Article]
    [Google Scholar]
  63. Stead LF, Sutton KM, Taylor GR, Quirke P, Rabbitts P. Accurately identifying low-allelic fraction variants in single samples with next-generation sequencing: applications in tumor subclone resolution. Hum Mutat 2013; 34:1432–1438 [View Article] [PubMed]
    [Google Scholar]
  64. Gelbart M, Harari S, Ben-Ari Y, Kustin T, Wolf D et al. Drivers of within-host genetic diversity in acute infections of viruses. PLOS Pathog 2020; 16:e1009029 [View Article]
    [Google Scholar]
  65. Orton RJ, Wright CF, Morelli MJ, King DJ, Paton DJ et al. Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data. BMC Genomics 2015; 16:229 [View Article] [PubMed]
    [Google Scholar]
  66. King DJ, Freimanis G, Lasecka-Dykes L, Asfor A, Ribeca P et al. A systematic evaluation of high-throughput sequencing approaches to identify low-frequency single nucleotide variants in viral populations. Viruses 2020; 12:E1187 [View Article] [PubMed]
    [Google Scholar]
  67. Honce R, Schultz-Cherry S. They are what you eat: Shaping of viral populations through nutrition and consequences for virulence. PLOS Pathog 2020; 16:e1008711 [View Article]
    [Google Scholar]
  68. Lin S-R, Yang T-Y, Peng C-Y, Lin Y-Y, Dai C-Y et al. Whole genome deep sequencing analysis of viral quasispecies diversity and evolution in HBeAg seroconverters. JHEP Rep 2021; 3:100254 [View Article]
    [Google Scholar]
  69. Chen H, Cohen P, Chen S. How Big is a Big Odds Ratio? Interpreting the Magnitudes of Odds Ratios in Epidemiological Studies. Communications in Statistics - Simulation and Computation 2010; 39:860–864 [View Article]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000867
Loading
/content/journal/mgen/10.1099/mgen.0.000867
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error