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Abstract

Multi-drug-resistant infection is a significant public health risk. Rapidly detecting and antimicrobial-resistant (AMR) determinants by metagenomic sequencing of urine is possible, although high levels of host DNA and overgrowth of contaminating species hamper sequencing and limit genome coverage. We performed Nanopore sequencing of nucleic acid amplification test-positive urine samples and culture-positive urethral swabs with and without probe-based target enrichment, using a custom SureSelect panel, to investigate whether selective enrichment of DNA improves detection of both species and AMR determinants. Probes were designed to cover the entire genome, with tenfold enrichment of probes covering selected AMR determinants. Multiplexing was tested in a subset of samples. The proportion of sequence bases classified as increased in all samples after enrichment, from a median (IQR) of 0.05 % (0.01–0.1 %) to 76 % (42–82 %), giving a corresponding median improvement in fold genome coverage of 365 times (112–720). Over 20-fold coverage, required for robust AMR determinant detection, was achieved in 13/15(87 %) samples, compared to 2/15(13 %) without enrichment. The four samples multiplexed together also achieved >20-fold genome coverage. Coverage of AMR determinants was sufficient to predict resistance conferred by changes in chromosomal genes, where present, and genome coverage also enabled phylogenetic relationships to be reconstructed. Probe-based target enrichment can improve genome coverage when sequencing DNA extracts directly from urine or urethral swabs, allowing for detection of AMR determinants. Additionally, multiplexing prior to enrichment provided enough genome coverage for AMR detection and reduces the costs associated with this method.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2024-03-26
2024-04-18
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References

  1. Unemo M, Shafer WM. Antimicrobial resistance in Neisseria gonorrhoeae in the 21st century: past, evolution, and future. Clin Microbiol Rev 2014; 27:587–613 [View Article] [PubMed]
    [Google Scholar]
  2. Omeershffudin UNM, Kumar S. Emerging threat of antimicrobial resistance in Neisseria gonorrhoeae: pathogenesis, treatment challenges, and potential for vaccine development. Arch Microbiol 2023; 205:330 [View Article] [PubMed]
    [Google Scholar]
  3. Eyre DW, Town K, Street T, Barker L, Sanderson N et al. Detection in the United Kingdom of the Neisseria gonorrhoeae FC428 clone, with ceftriaxone resistance and intermediate resistance to azithromycin, october to december 2018. Euro Surveill 2019; 24:1900147 [View Article] [PubMed]
    [Google Scholar]
  4. Golparian D, Vestberg N, Södersten W, Jacobsson S, Ohnishi M et al. Multidrug-resistant Neisseria gonorrhoeae isolate SE690: mosaic penA-60.001 gene causing ceftriaxone resistance internationally has spread to the more antimicrobial-susceptible genomic lineage, Sweden, September 2022. Euro Surveill 2023; 28:2300125 [View Article] [PubMed]
    [Google Scholar]
  5. Sawatzky P, Demczuk W, Lefebvre B, Allen V, Diggle M et al. Increasing azithromycin resistance in Neisseria gonorrhoeae due to NG-MAST 12302 clonal spread in Canada, 2015 to 2018. Antimicrob Agents Chemother 2022; 66:e01688-21 [View Article] [PubMed]
    [Google Scholar]
  6. European Centre for Disease Prevention and Control. Gonorrhoea ECDC. Annual Epidemiological Report for 2019. Stockholm: ECDC; 2023 https://www.ecdc.europa.eu/en/publications-data/gonorrhoea-annual-epidemiological-report-2019
  7. Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet 2019; 20:341–355 [View Article] [PubMed]
    [Google Scholar]
  8. Street TL, Barker L, Sanderson ND, Kavanagh J, Hoosdally S et al. Optimizing DNA extraction methods for Nanopore sequencing of Neisseria gonorrhoeae directly from urine samples. J Clin Microbiol 2020; 58:e01822-19 [View Article] [PubMed]
    [Google Scholar]
  9. Sanderson ND, Swann J, Barker L, Kavanagh J, Hoosdally S et al. High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic Nanopore sequencing. Genome Res 2020; 30:1354–1363 [View Article] [PubMed]
    [Google Scholar]
  10. Cai W, Nunziata S, Rascoe J, Stulberg MJ. SureSelect targeted enrichment, a new cost effective method for the whole genome sequencing of Candidatus liberibacter asiaticus. Sci Rep 2019; 9:18962 [View Article] [PubMed]
    [Google Scholar]
  11. 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]
  12. Bowden KE, Joseph SJ, Cartee JC, Ziklo N, Danavall D et al. Whole-genome enrichment and sequencing of Chlamydia trachomatis directly from patient clinical vaginal and rectal swabs. mSphere 2021; 6:e01302-20 [View Article] [PubMed]
    [Google Scholar]
  13. Doyle RM, Burgess C, Williams R, Gorton R, Booth H et al. Direct whole-genome sequencing of sputum accurately identifies drug-resistant Mycobacterium tuberculosis faster than MGIT culture sequencing. J Clin Microbiol 2018; 56:e00666-18 [View Article] [PubMed]
    [Google Scholar]
  14. Sanderson ND, Street TL, Foster D, Swann J, Atkins BL et al. Real-time analysis of nanopore-based metagenomic sequencing from infected orthopaedic devices. BMC Genomics 2018; 19:714 [View Article] [PubMed]
    [Google Scholar]
  15. Seemann T. Snippy: Rapid haploid variant calling and core genome alignment; 2015 https://github.com/tseemann/snippy
  16. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 2015; 32:268–274 [View Article] [PubMed]
    [Google Scholar]
  17. Didelot X, Wilson DJ. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol 2015; 11:e1004041 [View Article]
    [Google Scholar]
  18. Miller S, Chiu C. The role of metagenomics and next-generation sequencing in infectious disease diagnosis. Clin Chem 2021; 68:115–124 [View Article] [PubMed]
    [Google Scholar]
  19. Street TL, Sanderson ND, Atkins BL, Brent AJ, Cole K et al. Molecular diagnosis of orthopedic-device-related infection directly from sonication fluid by metagenomic sequencing. J Clin Microbiol 2017; 55:2334–2347 [View Article] [PubMed]
    [Google Scholar]
  20. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 2014; 12:87 [View Article] [PubMed]
    [Google Scholar]
  21. Paniagua Voirol LR, Valsamakis G, Yu M, Johnston PR, Hilker M. How the “kitome” influences the characterization of bacterial communities in lepidopteran samples with low bacterial biomass. J Appl Microbiol 2021; 130:1780–1793 [View Article] [PubMed]
    [Google Scholar]
  22. Ni Y, Liu X, Simeneh ZM, Yang M, Li R. Benchmarking of Nanopore R10.4 and R9.4.1 flow cells in single-cell whole-genome amplification and whole-genome shotgun sequencing. Comput Struct Biotechnol J 2023; 21:2352–2364 [View Article] [PubMed]
    [Google Scholar]
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