1887

Abstract

Plasmid prediction may be of great interest when studying bacteria of medical importance such as Enterobacteriaceae as well as Staphylococcus aureus or Enterococcus. Indeed, many resistance and virulence genes are located on such replicons with major impact in terms of pathogenicity and spreading capacities. Beyond strain outbreak, plasmid outbreaks have been reported in particular for some extended-spectrum beta-lactamase- or carbapenemase-producing Enterobacteriaceae. Several tools are now available to explore the ‘plasmidome’ from whole-genome sequences with various approaches, but none of them are able to combine high sensitivity and specificity. With this in mind, we developed PlaScope, a targeted approach to recover plasmidic sequences in genome assemblies at the species or genus level. Based on Centrifuge, a metagenomic classifier, and a custom database containing complete sequences of chromosomes and plasmids from various curated databases, PlaScope classifies contigs from an assembly according to their predicted location. Compared to other plasmid classifiers, PlasFlow and cBar, it achieves better recall (0.87), specificity (0.99), precision (0.96) and accuracy (0.98) on a dataset of 70 genomes of Escherichia coli containing plasmids. In a second part, we identified 20 of the 21 chromosomal integrations of the extended-spectrum beta-lactamase coding gene in a clinical dataset of E. coli strains. In addition, we predicted virulence gene and operon locations in agreement with the literature. We also built a database for Klebsiella and correctly assigned the location for the majority of resistance genes from a collection of 12 Klebsiella pneumoniae strains. Similar approaches could also be developed for other well-characterized bacteria.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000211
2018-09-28
2024-04-23
Loading full text...

Full text loading...

/deliver/fulltext/mgen/4/9/mgen000211.html?itemId=/content/journal/mgen/10.1099/mgen.0.000211&mimeType=html&fmt=ahah

References

  1. Arredondo-Alonso S, Willems RJ, van Schaik W, Schürch AC. On the (im)possibility of reconstructing plasmids from whole-genome short-read sequencing data. Microb Genom 2017; 3:e000128 [View Article][PubMed]
    [Google Scholar]
  2. Laczny CC, Galata V, Plum A, Posch AE, Keller A. Assessing the heterogeneity of in silico plasmid predictions based on whole-genome-sequenced clinical isolates. Brief Bioinform 2017; 5: [View Article][PubMed]
    [Google Scholar]
  3. Antipov D, Hartwick N, Shen M, Raiko M, Lapidus A et al. plasmidSPAdes: assembling plasmids from whole genome sequencing data. Bioinformatics 2016; 32:3380–3387 [View Article][PubMed]
    [Google Scholar]
  4. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 2014; 58:3895–3903 [View Article][PubMed]
    [Google Scholar]
  5. Krawczyk PS, Lipinski L, Dziembowski A. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res 2018; 46:e35 [View Article][PubMed]
    [Google Scholar]
  6. Rozov R, Brown Kav A, Bogumil D, Shterzer N, Halperin E et al. Recycler: an algorithm for detecting plasmids from de novo assembly graphs. Bioinformatics 2017; 33:475–482 [View Article][PubMed]
    [Google Scholar]
  7. Zhou F, Xu Y. cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data. Bioinformatics 2010; 26:2051–2052 [View Article][PubMed]
    [Google Scholar]
  8. Lanza VF, de Toro M, Garcillán-Barcia MP, Mora A, Blanco J et al. Plasmid flux in Escherichia coli ST131 sublineages, analyzed by plasmid constellation network (PLACNET), a new method for plasmid reconstruction from whole genome sequences. PLoS Genet 2014; 10:e1004766 [View Article][PubMed]
    [Google Scholar]
  9. Orlek A, Phan H, Sheppard AE, Doumith M, Ellington M et al. A curated dataset of complete Enterobacteriaceae plasmids compiled from the NCBI nucleotide database. Data Brief 2017; 12:423–426 [View Article][PubMed]
    [Google Scholar]
  10. Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 2016; 26:1721–1729 [View Article][PubMed]
    [Google Scholar]
  11. 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]
  12. Branger C, Ledda A, Billard-Pomares T, Doublet B, Fouteau S et al. Extended-spectrum β-lactamase-genes are spreading on a wide range of Escherichia coli plasmids existing prior the use of third generation cephalosporins. Microb Genom 2018; 4:000203
    [Google Scholar]
  13. van Zwetselaar M. 2017; ident-16s Rapid identification of bacterial species from FASTA contigs [Internet]. Github. Available from https://github.com/zwets/ident-16s [cited 14 January 2018]
  14. Mikheenko A, Valin G, Prjibelski A, Saveliev V, Gurevich A. Icarus: visualizer for de novo assembly evaluation. Bioinformatics 2016; 32:3321–3323 [View Article][PubMed]
    [Google Scholar]
  15. Falgenhauer L, Imirzalioglu C, Ghosh H, Gwozdzinski K, Schmiedel J et al. Circulation of clonal populations of fluoroquinolone-resistant CTX-M-15-producing Escherichia coli ST410 in humans and animals in Germany. Int J Antimicrob Agents 2016; 47:457–465 [View Article][PubMed]
    [Google Scholar]
  16. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 2012; 67:2640–2644 [View Article][PubMed]
    [Google Scholar]
  17. Talevich E, Invergo BM, Cock PJ, Chapman BA. Bio.Phylo: a unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython. BMC Bioinformatics 2012; 13:209 [View Article][PubMed]
    [Google Scholar]
  18. Galardini M, Koumoutsi A, Herrera-Dominguez L, Cordero Varela JA, Telzerow A et al. Phenotype inference in an Escherichia coli strain panel. eLife 2017; 6:e31035 [View Article][PubMed]
    [Google Scholar]
  19. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 2016; 17:132 [View Article][PubMed]
    [Google Scholar]
  20. Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res 2016; 44:W242–W245 [View Article][PubMed]
    [Google Scholar]
  21. Johnson TJ, Wannemuehler YM, Nolan LK. Evolution of the iss gene in Escherichia coli. Appl Environ Microbiol 2008; 74:2360–2369 [View Article][PubMed]
    [Google Scholar]
  22. Mainil JG, Gérardin J, Jacquemin E. Identification of the F17 fimbrial subunit- and adhesin-encoding (f17A and f17G) gene variants in necrotoxigenic Escherichia coli from cattle, pigs and humans. Vet Microbiol 2000; 73:327–335 [View Article][PubMed]
    [Google Scholar]
  23. Burkhardt R, Braun V. Nucleotide sequence of the fhuC and fhuD genes involved in iron (III) hydroxamate transport: domains in FhuC homologous to ATP-binding proteins. Mol Gen Genet 1987; 209:49–55 [View Article][PubMed]
    [Google Scholar]
  24. Kammler M, Schön C, Hantke K. Characterization of the ferrous iron uptake system of Escherichia coli. J Bacteriol 1993; 175:6212–6219 [View Article][PubMed]
    [Google Scholar]
  25. Liu J, Duncan K, Walsh CT. Nucleotide sequence of a cluster of Escherichia coli enterobactin biosynthesis genes: identification of entA and purification of its product 2,3-dihydro-2,3-dihydroxybenzoate dehydrogenase. J Bacteriol 1989; 171:791–798 [View Article][PubMed]
    [Google Scholar]
  26. Pressler U, Staudenmaier H, Zimmermann L, Braun V. Genetics of the iron dicitrate transport system of Escherichia coli. J Bacteriol 1988; 170:2716–2724 [View Article][PubMed]
    [Google Scholar]
  27. Schubert S, Picard B, Gouriou S, Heesemann J, Denamur E. Yersinia high-pathogenicity island contributes to virulence in Escherichia coli causing extraintestinal infections. Infect Immun 2002; 70:5335–5337 [View Article][PubMed]
    [Google Scholar]
  28. Debroy C, Sidhu MS, Sarker U, Jayarao BM, Stell AL et al. Complete sequence of pEC14_114, a highly conserved IncFIB/FIIA plasmid associated with uropathogenic Escherichia coli cystitis strains. Plasmid 2010; 63:53–60 [View Article][PubMed]
    [Google Scholar]
  29. Johnson TJ, Johnson SJ, Nolan LK. Complete DNA sequence of a ColBM plasmid from avian pathogenic Escherichia coli suggests that it evolved from closely related ColV virulence plasmids. J Bacteriol 2006; 188:5975–5983 [View Article][PubMed]
    [Google Scholar]
  30. Simner PJ, Antar AAR, Hao S, Gurtowski J, Tamma PD et al. Antibiotic pressure on the acquisition and loss of antibiotic resistance genes in Klebsiella pneumoniae. J Antimicrob Chemother 20181796–1803 [View Article][PubMed]
    [Google Scholar]
  31. Gao Q, Wang X, Xu H, Xu Y, Ling J et al. Roles of iron acquisition systems in virulence of extraintestinal pathogenic Escherichia coli: salmochelin and aerobactin contribute more to virulence than heme in a chicken infection model. BMC Microbiol 2012; 12:143 [View Article]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000211
Loading
/content/journal/mgen/10.1099/mgen.0.000211
Loading

Data & Media loading...

Supplements

Supplementary File 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