1887

Abstract

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

Funding
This study was supported by the:
  • Academy of Finland (Award 309048)
    • Principle Award Recipient: VeliMäkinen
  • Norges Forskningsråd (Award 144501)
    • Principle Award Recipient: TeemuKallonen
  • European Research Council (Award 742158)
    • Principle Award Recipient: JukkaCorander
  • Joint Programming Initiative on Antimicrobial Resistance (Award MR/R00241X/1)
    • Principle Award Recipient: JukkaCorander
  • Academy of Finland (Award Finnish Center for Artificial Intelligence FCAI)
    • Principle Award Recipient: NotApplicable
  • Academy of Finland (Award 310261)
    • Principle Award Recipient: AnttiHonkela
  • 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.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000691
2021-11-15
2021-12-04
Loading full text...

Full text loading...

/deliver/fulltext/mgen/7/11/mgen000691.html?itemId=/content/journal/mgen/10.1099/mgen.0.000691&mimeType=html&fmt=ahah

References

  1. Mäklin T, Kallonen T, Alanko J, Samuelsen Ø, Hegstad K et al. Bacterial genomic epidemiology with mixed samples Figshare; 2021 [View Article]
    [Google Scholar]
  2. Deng X, den Bakker HC, Hendriksen RS. Genomic epidemiology: whole-genome-sequencing-powered surveillance and outbreak investigation of foodborne bacterial pathogens. Annu Rev Food Sci Technol 2016; 7:353–374 [View Article] [PubMed]
    [Google Scholar]
  3. Tang P, Croxen MA, Hasan MR, Hsiao WWL, Hoang LM. Infection control in the new age of genomic epidemiology. Am J Infect Control 2017; 45:170–179 [View Article] [PubMed]
    [Google Scholar]
  4. Van Goethem N, Descamps T, Devleesschauwer B, Roosens NHC, Boon NAM et al. Status and potential of bacterial genomics for public health practice: a scoping review. Implement Sci 2019; 14:79 [View Article] [PubMed]
    [Google Scholar]
  5. Grad YH, Lipsitch M. Epidemiologic data and pathogen genome sequences: a powerful synergy for public health. Genome Biol 2014; 15:538 [View Article] [PubMed]
    [Google Scholar]
  6. Kwong JC, Mccallum N, Sintchenko V, Howden BP. Whole genome sequencing in clinical and public health microbiology. Pathology 2015; 47:199–210 [View Article] [PubMed]
    [Google Scholar]
  7. Rossen JWA, Friedrich AW, Moran-Gilad J. Practical issues in implementing whole-genome-sequencing in routine diagnostic microbiology. Clin Microbiol Infect 2018; 24:355–360 [View Article] [PubMed]
    [Google Scholar]
  8. Scholz M, Ward DV, Pasolli E, Tolio T, Zolfo M et al. Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Nat Methods 2016; 13:435–438 [View Article] [PubMed]
    [Google Scholar]
  9. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 2017; 27:824–834 [View Article] [PubMed]
    [Google Scholar]
  10. Li D, Luo R, Liu C-M, Leung C-M, Ting H-F et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 2016; 102:3–11 [View Article] [PubMed]
    [Google Scholar]
  11. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de BRUIJN graph. Bioinformatics 2015; 31:1674–1676 [View Article] [PubMed]
    [Google Scholar]
  12. Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012; 28:1420–1428 [View Article] [PubMed]
    [Google Scholar]
  13. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 2018; 3:836–843 [View Article] [PubMed]
    [Google Scholar]
  14. Kang DD, Li F, Kirton E, Thomas A, Egan R et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019; 7:e7359 [View Article] [PubMed]
    [Google Scholar]
  15. Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016; 32:605–607 [View Article] [PubMed]
    [Google Scholar]
  16. Breitwieser FP, Lu J, Salzberg SL. A review of methods and databases for metagenomic classification and assembly. Brief Bioinform 2019; 20:1125–1136 [View Article] [PubMed]
    [Google Scholar]
  17. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S et al. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat Methods 2017; 14:1063–1071 [View Article] [PubMed]
    [Google Scholar]
  18. McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol 2017; 18:182 [View Article] [PubMed]
    [Google Scholar]
  19. Vollmers J, Wiegand S, Kaster A-K. Comparing and evaluating metagenome assembly tools from a microbiologist’s perspective - not only size matters! Rodriguez-Valera F, editor. PLOS ONE 2017; 12:e0169662 [View Article] [PubMed]
    [Google Scholar]
  20. Meyer F, Hofmann P, Belmann P, Garrido-Oter R, Fritz A et al. AMBER: Assessment of Metagenome BinnERs. Gigascience 2018; 7:giy069 [View Article]
    [Google Scholar]
  21. Greenblum S, Carr R, Borenstein E. Extensive strain-level copy-number variation across human gut microbiome species. Cell 2015; 160:583–594 [View Article] [PubMed]
    [Google Scholar]
  22. Ellegaard KM, Engel P. Beyond 16S rRNA community profiling: Intra-species diversity in the gut microbiota. Front Microbiol 2016; 7:1475 [View Article]
    [Google Scholar]
  23. Schlager TA, Hendley JO, Bell AL, Whittam TS. Clonal diversity of Escherichia coli colonizing stools and urinary tracts of young girls. Infect Immun 2002; 70:1225–1229 [View Article] [PubMed]
    [Google Scholar]
  24. Moreno E, Andreu A, Pérez T, Sabaté M, Johnson JR et al. Relationship between Escherichia coli strains causing urinary tract infection in women and the dominant faecal flora of the same hosts. Epidemiol Infect 2006; 134:1015–1023 [View Article] [PubMed]
    [Google Scholar]
  25. Lidin-Janson G, Kaijser B, Lincoln K, Olling S, Wedel H. The homogeneity of the faecal coliform flora of normal school-girls, characterized by serological and biochemical properties. Med Microbiol Immunol 1978; 164:247–253 [View Article] [PubMed]
    [Google Scholar]
  26. Mosavie M, Blandy O, Jauneikaite E, Caldas I, Ellington MJ et al. Sampling and diversity of Escherichia coli from the enteric microbiota in patients with Escherichia coli bacteraemia. BMC Res Notes 2019; 12:335 [View Article] [PubMed]
    [Google Scholar]
  27. Dixit OVA, O’Brien CL, Pavli P, Gordon DM. Within-host evolution versus immigration as a determinant of Escherichia coli diversity in the human gastrointestinal tract. Environ Microbiol 2018; 20:993–1001 [View Article] [PubMed]
    [Google Scholar]
  28. Zlitni S, Bishara A, Moss EL, Tkachenko E, Kang JB et al. Strain-resolved microbiome sequencing reveals mobile elements that drive bacterial competition on a clinical timescale. Genome Med 2020; 12:50 [View Article] [PubMed]
    [Google Scholar]
  29. Kirkup BC. Bacterial strain diversity within wounds. Adv Wound Care (New Rochelle) 2015; 4:12–23 [View Article]
    [Google Scholar]
  30. Paterson GK, Harrison EM, Murray GGR, Welch JJ, Warland JH et al. Capturing the cloud of diversity reveals complexity and heterogeneity of MRSA carriage, infection and transmission. Nat Commun 2015; 6:6560 [View Article] [PubMed]
    [Google Scholar]
  31. Stoesser N, Sheppard AE, Moore CE, Golubchik T, Parry CM et al. Extensive within-host diversity in fecally carried extended-spectrum-beta-lactamase-producing Escherichia coli isolates: implications for transmission analyses. J Clin Microbiol 2015; 53:2122–2131 [View Article] [PubMed]
    [Google Scholar]
  32. Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res 2017; 27:626–638 [View Article] [PubMed]
    [Google Scholar]
  33. Whelan FJ, Waddell B, Syed SA, Shekarriz S, Rabin HR et al. Culture-enriched metagenomic sequencing enables in-depth profiling of the cystic fibrosis lung microbiota. Nat Microbiol 2020; 5:379–390 [View Article] [PubMed]
    [Google Scholar]
  34. Ivy MI, Thoendel MJ, Jeraldo PR, Greenwood-Quaintance KE, Hanssen AD et al. Direct detection and identification of prosthetic joint infection pathogens in synovial fluid by metagenomic shotgun sequenDetection and Identification of Prosthetic Joint Infection Pathogens in Synovial Fluid by Metagenomic Shotgun Sequencing. J Clin Microbiol 2018; 56:00402-18
    [Google Scholar]
  35. Gu W, Miller S, Chiu CY. Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol Mech Dis 2019; 14:319–338 [View Article]
    [Google Scholar]
  36. Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 2017; 35:833–844 [View Article] [PubMed]
    [Google Scholar]
  37. Lagier J-C, Edouard S, Pagnier I, Mediannikov O, Drancourt M et al. Current and past strategies for bacterial culture in clinical microbiology. Clin Microbiol Rev 2015; 28:208–236 [View Article] [PubMed]
    [Google Scholar]
  38. Brodrick HJ, Raven KE, Kallonen T, Jamrozy D, Blane B et al. Longitudinal genomic surveillance of multidrug-resistant Escherichia coli carriage in a long-term care facility in the United Kingdom. Genome Med 2017; 9:70 [View Article] [PubMed]
    [Google Scholar]
  39. Raven KE, Reuter S, Gouliouris T, Reynolds R, Russell JE et al. Genome-based characterization of hospital-adapted Enterococcus faecalis lineages. Nat Microbiol 2016; 1:15033 [View Article] [PubMed]
    [Google Scholar]
  40. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016; 34:525–527 [View Article] [PubMed]
    [Google Scholar]
  41. Mäklin T, Kallonen T, David S, Boinett CJ, Pascoe B et al. High-resolution sweep metagenomics using fast probabilistic inference. Wellcome Open Res 2020; 5:14 [View Article]
    [Google Scholar]
  42. Seemann T. Shovill ; 2018 https://github.com/tseemann/shovill
  43. Kallonen T, Brodrick HJ, Harris SR, Corander J, Brown NM et al. Systematic longitudinal survey of invasive Escherichia coli in England demonstrates a stable population structure only transiently disturbed by the emergence of ST131. Genome Res 2017; 27:1437–1449 [View Article]
    [Google Scholar]
  44. Lees JA, Harris SR, Tonkin-Hill G, Gladstone RA, SW L et al. Fast and flexible bacterial genomic epidemiology with PopPUNK. Genome Res 2019; 29:304–316 [View Article] [PubMed]
    [Google Scholar]
  45. Ruiz-Garbajosa P, Bonten MJM, Robinson DA, Top J, Nallapareddy SR et al. Multilocus sequence typing scheme for Enterococcus faecalis reveals hospital-adapted genetic complexes in a background of high rates of recombination. J Clin Microbiol 2006; 44:2220–2228 [View Article] [PubMed]
    [Google Scholar]
  46. Jolley KA, Bray JE, Maiden MCJ. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Res 2018; 3:124 [View Article] [PubMed]
    [Google Scholar]
  47. Seemann T. mlst GitHub; 2015 https://github.com/tseemann/mlst
  48. Paulsen IT, Banerjei L, Myers GSA, Nelson KE, Seshadri R et al. Role of mobile DNA in the evolution of vancomycin-resistant Enterococcus faecalis . Science 2003; 299:2071–2074 [View Article] [PubMed]
    [Google Scholar]
  49. Holden MTG, Hsu L-Y, Kurt K, Weinert LA, Mather AE et al. A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic. Genome Res 2013; 23:653–664 [View Article] [PubMed]
    [Google Scholar]
  50. Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol 2019; 37:540–546 [View Article] [PubMed]
    [Google Scholar]
  51. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018; 34:i884–90 [View Article] [PubMed]
    [Google Scholar]
  52. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PloS one 2014; 9:e112963 [View Article] [PubMed]
    [Google Scholar]
  53. Gagie T, Manzini G, Sirén J. Wheeler graphs: A framework for BWT-based data structures. Theor Comput Sci 2017; 698:67–78 [View Article] [PubMed]
    [Google Scholar]
  54. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article] [PubMed]
    [Google Scholar]
  55. Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A et al. Assembling genomes and mini-metagenomes from highly chimeric reads. Deng M, Jiang R, Sun F, Zhang X. eds In Research in Computational Molecular Biology Berlin, Heidelberg: Springer; 2013 pp 158–170
    [Google Scholar]
  56. Seemann T. snippy: fast bacterial variant calling from NGS reads GitHub; 2014 https://github.com/tseemann/snippy
  57. Page AJ, Taylor B, Delaney AJ, Soares J, Seemann T et al. SNP-sites: Rapid efficient extraction of SNPs from multi-FASTA alignments. Microb Genomics 2016; 2:e000056
    [Google Scholar]
  58. Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 2019; 35:4453–4455 [View Article] [PubMed]
    [Google Scholar]
  59. Revell LJ. phytools: an R package for phylogenetic comparative biology (and other things. Methods Ecol Evol 2012; 3:217–223 [View Article]
    [Google Scholar]
  60. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2019; 35:526–528 [View Article] [PubMed]
    [Google Scholar]
  61. Bürkner P-C. Brms: An R package for bayesian multilevel models using stan. J Stat Softw 2017; 80:1–28
    [Google Scholar]
  62. Bürkner P-C. Advanced bayesian multilevel modeling with the R package brms. The R Journal 2018; 10:395–411 [View Article]
    [Google Scholar]
  63. Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B et al. Stan: a probabilistic programming language. J Stat Softw 2017; 76:1–32 [View Article]
    [Google Scholar]
  64. Harris SR. SKA: Split Kmer Analysis toolkit for bacterial genomic epidemiology. bioRxiv 2018; 453142:
    [Google Scholar]
  65. Goig GA, Blanco S, Garcia-Basteiro AL, Comas I. Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability. BMC Biol 2020; 18:24 [View Article] [PubMed]
    [Google Scholar]
  66. Beghini F, McIver LJ, Blanco-Mìguez A, Dubois L, Asnicar F et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 2021; 10:e65088 [View Article] [PubMed]
    [Google Scholar]
  67. Lieberman TD, Flett KB, Yelin I, Martin TR, McAdam AJ et al. Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures. Nat Genet 2014; 46:82–87 [View Article] [PubMed]
    [Google Scholar]
  68. Golubchik T, Batty EM, Miller RR, Farr H, Young BC et al. Within-host evolution of Staphylococcus aureus during asymptomatic carriage. PLoS One 2013; 8:e61319 [View Article]
    [Google Scholar]
  69. Worby CJ, Lipsitch M, Hanage WP. Within-host bacterial diversity hinders accurate reconstruction of transmission networks from genomic distance data. PLoS Comput Biol 2014; 10:e1003549 [View Article]
    [Google Scholar]
  70. Brodrick HJ, Raven KE, Harrison EM, Blane B, Reuter S et al. Whole-genome sequencing reveals transmission of vancomycin-resistant Enterococcus faecium in a healthcare network. Genome Med 2016; 8:4 [View Article] [PubMed]
    [Google Scholar]
  71. Maciel JF, Gressler LT, Silveira BP, Dotto E, Balzan C et al. Caution at choosing a particular colony-forming unit from faecal Escherichia coli: it may not represent the sample profile. Lett Appl Microbiol 2020; 70:130–136 [View Article] [PubMed]
    [Google Scholar]
  72. Forbes JD, Knox NC, Ronholm J, Pagotto F, Reimer A. Metagenomics: the next culture-independent game changer. Front Microbiol 2017; 8:1069 [View Article]
    [Google Scholar]
  73. McArdle AJ, Kaforou M. Sensitivity of shotgun metagenomics to host DNA: abundance estimates depend on bioinformatic tools and contamination is the main issue. Access Microbiol 2020; 2:000104 [View Article]
    [Google Scholar]
  74. 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]
  75. De Maio N, Worby CJ, Wilson DJ, Stoesser N. Bayesian reconstruction of transmission within outbreaks using genomic variants. PLOS Comput Biol 2018; 14:e1006117 [View Article]
    [Google Scholar]
  76. Worby CJ, Lipsitch M, Hanage WP. Shared genomic variants: identification of transmission routes using pathogen deep-sequence data. Am J Epidemiol 2017; 186:1209–1216 [View Article] [PubMed]
    [Google Scholar]
  77. Skums P, Zelikovsky A, Singh R, Gussler W, Dimitrova Z et al. QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data. Bioinformatics 2018; 34:163–170 [View Article] [PubMed]
    [Google Scholar]
  78. Stewart EJ. Growing unculturable bacteria. J Bacteriol 2012; 194:4151–4160 [View Article] [PubMed]
    [Google Scholar]
  79. Vartoukian SR, Palmer RM, Wade WG. Strategies for culture of ‘unculturable’ bacteria. FEMS Microbiol Lett 2010; 309:1–7 [View Article] [PubMed]
    [Google Scholar]
  80. Ito T, Sekizuka T, Kishi N, Yamashita A, Kuroda M. Conventional culture methods with commercially available media unveil the presence of novel culturable bacteria. Gut Microbes 2019; 10:77–91 [View Article] [PubMed]
    [Google Scholar]
  81. Forster SC, Kumar N, Anonye BO, Almeida A, Viciani E et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat Biotechnol 2019; 37:186–192 [View Article] [PubMed]
    [Google Scholar]
  82. Zou Y, Xue W, Luo G, Deng Z, Qin P et al. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat Biotechnol 2019; 37:179–185 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000691
Loading
/content/journal/mgen/10.1099/mgen.0.000691
Loading

Data & Media loading...

Supplements

Loading data from figshare Loading data from figshare

Most cited this month Most Cited RSS feed

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