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Abstract

Genomic data contribute invaluable information to the epidemiological investigation of pathogens of public health importance. However, whole-genome sequencing (WGS) of bacteria typically relies on culture, which represents a major hurdle for generating such data for a wide range of species for which culture is challenging. In this study, we assessed the use of culture-free target-enrichment sequencing as a method for generating genomic data for two bacterial species: (1) which causes anthrax in both people and animals and whose culture requires high-level containment facilities; and (2) , a fastidious emerging human respiratory pathogen. We obtained high-quality genomic data for both species directly from clinical samples, with sufficient coverage (>15×) for confident variant calling over at least 80% of the baited genomes for over two thirds of the samples tested. Higher qPCR cycle threshold () values (indicative of lower pathogen concentrations in the samples), pooling libraries prior to capture, and lower captured library concentration were all statistically associated with lower capture efficiency. The value had the highest predictive value, explaining 52 % of the variation in capture efficiency. Samples with values ≤30 were over six times more likely to achieve the threshold coverage than those with a > 30. We conclude that target-enrichment sequencing provides a valuable alternative to standard WGS following bacterial culture and creates opportunities for an improved understanding of the epidemiology and evolution of many clinically important pathogens for which culture is challenging.

Funding
This study was supported by the:
  • Academy of Medical Sciences (GB) (Award SBF005\1023)
    • Principle Award Recipient: TayaL. Forde
  • Biotechnology and Biological Sciences Research Council (Award BB/R012075/1)
    • Principle Award Recipient: TayaL. Forde
  • H2020 European Research Council (Award 852957)
    • Principle Award Recipient: NotApplicable
  • Medical Research Council (Award MC_PC_16045)
    • Principle Award Recipient: JoE. B. Halliday
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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/content/journal/mgen/10.1099/mgen.0.000836
2022-05-27
2024-07-23
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References

  1. Brand A, Brand H, Schulte in den Bäumen T. The impact of genetics and genomics on public health. Eur J Hum Genet 2008; 16:5–13 [View Article] [PubMed]
    [Google Scholar]
  2. Kao RR, Haydon DT, Lycett SJ, Murcia PR. Supersize me: how whole-genome sequencing and big data are transforming epidemiology. Trends Microbiol 2014; 22:282–291 [View Article] [PubMed]
    [Google Scholar]
  3. Sajib MSI, Tanmoy AM, Hooda Y, Rahman H, Andrews JR et al. Tracking the emergence of azithromycin resistance in multiple genotypes of typhoidal Salmonella. mBio 2021; 12:e03481-20 [View Article] [PubMed]
    [Google Scholar]
  4. Brown AC, Bryant JM, Einer-Jensen K, Holdstock J, Houniet DT et al. Rapid whole-genome sequencing of Mycobacterium tuberculosis isolates directly from clinical samples. J Clin Microbiol 2015; 53:2230–2237 [View Article] [PubMed]
    [Google Scholar]
  5. Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM et al. Emerging technologies for molecular diagnosis of sepsis. Clin Microbiol Rev 2018; 31:e00089-17 [View Article] [PubMed]
    [Google Scholar]
  6. Mamanova L, Coffey AJ, Scott CE, Kozarewa I, Turner EH et al. Target-enrichment strategies for next-generation sequencing. Nat Methods 2010; 7:111–118 [View Article] [PubMed]
    [Google Scholar]
  7. Gaudin M, Desnues C. Hybrid capture-based next generation sequencing and its application to human infectious diseases. Front Microbiol 2018; 9:2924 [View Article] [PubMed]
    [Google Scholar]
  8. Gnirke A, Melnikov A, Maguire J, Rogov P, LeProust EM et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol 2009; 27:182–189 [View Article] [PubMed]
    [Google Scholar]
  9. Hodges E, Rooks M, Xuan Z, Bhattacharjee A, Benjamin Gordon D et al. Hybrid selection of discrete genomic intervals on custom-designed microarrays for massively parallel sequencing. Nat Protoc 2009; 4:960–974 [View Article] [PubMed]
    [Google Scholar]
  10. Carpi G, Walter KS, Bent SJ, Hoen AG, Diuk-Wasser M et al. Whole genome capture of vector-borne pathogens from mixed DNA samples: a case study of Borrelia burgdorferi. BMC Genomics 2015; 16:434 [View Article] [PubMed]
    [Google Scholar]
  11. Christiansen MT, Brown AC, Kundu S, Tutill HJ, Williams R et al. Whole-genome enrichment and sequencing of Chlamydia trachomatis directly from clinical samples. BMC Infect Dis 2014; 14:591 [View Article] [PubMed]
    [Google Scholar]
  12. Clark SA, Doyle R, Lucidarme J, Borrow R, Breuer J. Targeted DNA enrichment and whole genome sequencing of Neisseria meningitidis directly from clinical specimens. Int J Med Microbiol 2018; 308:256–262 [View Article] [PubMed]
    [Google Scholar]
  13. Carlson CJ, Kracalik IT, Ross N, Alexander KA, Hugh-Jones ME et al. The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nat Microbiol 2019; 4:1337–1343 [View Article] [PubMed]
    [Google Scholar]
  14. WHO Anthrax in Humans and Animals 2008
    [Google Scholar]
  15. D’Amelio E, Gentile B, Lista F, D’Amelio R. Historical evolution of human anthrax from occupational disease to potentially global threat as bioweapon. Environ Int 2015; 85:133–146 [View Article] [PubMed]
    [Google Scholar]
  16. Pitcher DG, Windsor D, Windsor H, Bradbury JM, Yavari C et al. Mycoplasma amphoriforme sp. nov., isolated from a patient with chronic bronchopneumonia. Int J Syst Evol Microbiol 2005; 55:2589–2594 [View Article] [PubMed]
    [Google Scholar]
  17. Pereyre S, Renaudin H, Touati A, Charron A, Peuchant O et al. Detection and susceptibility testing of Mycoplasma amphoriforme isolates from patients with respiratory tract infections. Clin Microbiol Infect 2010; 16:1007–1009 [View Article] [PubMed]
    [Google Scholar]
  18. Ling CL, Oravcova K, Beattie TF, Creer DD, Dilworth P et al. Tools for detection of Mycoplasma amphoriforme: a primary respiratory pathogen?. J Clin Microbiol 2014; 52:1177–1181 [View Article] [PubMed]
    [Google Scholar]
  19. Gillespie SH, Ling CL, Oravcova K, Pinheiro M, Wells L et al. Genomic Investigations unmask Mycoplasma amphoriforme, a new respiratory pathogen. Clin Infect Dis 2015; 60:381–388 [View Article] [PubMed]
    [Google Scholar]
  20. van Schaik ML, Patberg KW, Wallinga JG, Wolfhagen MJHM, Bruijnesteijn van Coppenraet LES. Mycoplasma amphoriforme vs M. pneumoniae: similarities and differences between patient characteristics in a regional hospital in the Netherlands. J Med Microbiol 2018; 67:1348–1350 [View Article] [PubMed]
    [Google Scholar]
  21. Rehman SU, Day J, Afshar B, Rowlands RS, Billam H et al. Molecular exploration for Mycoplasma amphoriforme, Mycoplasma fermentans and Ureaplasma spp. in patient samples previously investigated for Mycoplasma pneumoniae infection. Clin Microbiol Infect 2021; 27:1697 [View Article]
    [Google Scholar]
  22. Van Ert MN, Easterday WR, Huynh LY, Okinaka RT, Hugh-Jones ME et al. Global genetic population structure of Bacillus anthracis. PLoS ONE 2007; 2:e461 [View Article] [PubMed]
    [Google Scholar]
  23. Treangen TJ, Ondov BD, Koren S, Phillippy AM. The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biol 2014; 15:524 [View Article] [PubMed]
    [Google Scholar]
  24. Ravel J, Jiang L, Stanley ST, Wilson MR, Decker RS et al. The complete genome sequence of Bacillus anthracis Ames “Ancestor.”. J Bacteriol 2009; 191:445–446 [View Article] [PubMed]
    [Google Scholar]
  25. Carver TJ, Rutherford KM, Berriman M, Rajandream M-A, Barrell BG et al. ACT: the artemis comparison tool. Bioinformatics 2005; 21:3422–3423 [View Article] [PubMed]
    [Google Scholar]
  26. Smit A, Hubley R, Green P. RepeatMasker Open-4.0; 2013 http://www.repeatmasker.org
  27. Dennis TPW. Bactocap’ Github repo; 2022 https://github.com/tristanpwdennis/bactocap
  28. Aminu OR, Lembo T, Zadoks RN, Biek R, Lewis S et al. Practical and effective diagnosis of animal anthrax in endemic low-resource settings. PLOS Negl Trop Dis 2020; 14:e0008655 [View Article] [PubMed]
    [Google Scholar]
  29. Turner P, Turner C, Jankhot A, Helen N, Lee SJ et al. A longitudinal study of Streptococcus pneumoniae carriage in A cohort of infants and their mothers on the Thailand-Myanmar border. PLoS One 2012; 7:e38271 [View Article] [PubMed]
    [Google Scholar]
  30. Dennis TPW, Mable B, Brunelle B, Devault A, Carter R et al. Supplementary Tables A & B; 2022 http://dx.doi.org/10.5525/gla.researchdata.1249
  31. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018; 34:i884–i890 [View Article] [PubMed]
    [Google Scholar]
  32. Li H. Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM Oxford University Press;arXiv:1303.3997;
    [Google Scholar]
  33. Van der Auwera GA, O’Connor BD. Genomics in the Cloud O’Reilly Media, Inc; 2020
    [Google Scholar]
  34. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008; 456:53–59 [View Article] [PubMed]
    [Google Scholar]
  35. R Core Team R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2021 https://www.R-project.org/
  36. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw 2015; 67:1–48
    [Google Scholar]
  37. Harrison XA. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2014; 2:e616 [View Article] [PubMed]
    [Google Scholar]
  38. Burnham KP, Anderson DR. Model Selection and Multimodel Inference, 2nd ed. New York: Springer-Verlag; 2002
    [Google Scholar]
  39. Lüdecke D. sjPlot: Data Visualization for Statistics in Social Science. n.d https://CRAN.R-project.org/package=sjPlot
  40. Beale MA, Marks M, Sahi SK, Tantalo LC, Nori AV et al. Genomic epidemiology of syphilis reveals independent emergence of macrolide resistance across multiple circulating lineages. Nat Commun 2019; 10:3255 [View Article] [PubMed]
    [Google Scholar]
  41. Hadfield J, Harris SR, Seth-Smith HMB, Parmar S, Andersson P et al. Comprehensive global genome dynamics of Chlamydia trachomatis show ancient diversification followed by contemporary mixing and recent lineage expansion. Genome Res 2017; 27:1220–1229 [View Article] [PubMed]
    [Google Scholar]
  42. Enk J, Rouillard J-M, Poinar H. Quantitative PCR as a predictor of aligned ancient DNA read counts following targeted enrichment. Biotechniques 2013; 55:300–309 [View Article] [PubMed]
    [Google Scholar]
  43. Furtwängler A, Neukamm J, Böhme L, Reiter E, Vollstedt M et al. Comparison of target enrichment strategies for ancient pathogen DNA. Biotechniques 2020; 69:455–459 [View Article] [PubMed]
    [Google Scholar]
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