1887

Abstract

Analysing the flanking sequences surrounding genes of interest is often highly relevant to understanding the role of mobile genetic elements (MGEs) in horizontal gene transfer, particular for antimicrobial-resistance genes. Here, we present Flanker, a Python package that performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of MGEs without prior knowledge of their structure. These clusters, known as ‘flank patterns’ (FPs), are based on Mash distances, allowing for easy comparison of similarity across sequences. Additionally, Flanker can be flexibly parameterized to fine-tune outputs by characterizing upstream and downstream regions separately, and investigating variable lengths of flanking sequence. We apply Flanker to two recent datasets describing plasmid-associated carriage of important carbapenemase genes ( and ) and show that it successfully identifies distinct clusters of FPs, including both known and previously uncharacterized structural variants. For example, Flanker identified four Tn profiles that could not be sufficiently characterized using TETyper or MobileElementFinder, demonstrating the utility of Flanker for flanking-gene characterization. Similarly, using a large (=226) European isolate dataset, we confirm findings from a previous smaller study demonstrating association between Tn and upregulation and demonstrate 17 FPs (compared to the 5 previously identified). More generally, the demonstration in this study that FPs are associated with geographical regions and antibiotic-susceptibility phenotypes suggests that they may be useful as epidemiological markers. Flanker is freely available under an MIT license at https://github.com/wtmatlock/flanker.

Funding
This study was supported by the:
  • Medical Research Council (Award MR/T001151/1)
    • Principle Award Recipient: SamuelLipworth
  • Medical Research Foundation (Award MRF-145-0004-TPG-AVISO)
    • Principle Award Recipient: WilliamMatlock
  • 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.0.000634
2021-09-24
2021-10-24
Loading full text...

Full text loading...

/deliver/fulltext/mgen/7/9/mgen000634.html?itemId=/content/journal/mgen/10.1099/mgen.0.000634&mimeType=html&fmt=ahah

References

  1. Lipworth S, Vihta K-D, Chau K, Barker L, George S et al. Molecular epidemiology of Escherichia coli and Klebsiella species bloodstream infections in Oxfordshire (UK) 2008-2018. medRxiv 2021 [View Article]
    [Google Scholar]
  2. Vihta K-D, Stoesser N, Llewelyn MJ, Quan TP, Davies T et al. Trends over time in Escherichia coli bloodstream infections, urinary tract infections, and antibiotic susceptibilities in Oxfordshire, UK, 1998–2016: a study of electronic health records. Lancet Infect Dis 2018; 18:1138–1149 [View Article] [PubMed]
    [Google Scholar]
  3. Buetti N, Atkinson A, Marschall J, Kronenberg A. Swiss Centre for Antibiotic Resistance (ANRESIS) Incidence of bloodstream infections: a nationwide surveillance of acute care hospitals in Switzerland 2008–2014. BMJ Open 2017; 7:e013665 [View Article]
    [Google Scholar]
  4. Thanner S, Drissner D, Walsh F. Antimicrobial resistance in agriculture. mBio 2016; 7:e02227–15 [View Article] [PubMed]
    [Google Scholar]
  5. Wyres KL, Holt KE. Klebsiella pneumoniae as a key trafficker of drug resistance genes from environmental to clinically important bacteria. Curr Opin Microbiol 2018; 45:131–139 [View Article] [PubMed]
    [Google Scholar]
  6. Collis RM, Burgess SA, Biggs PJ, Midwinter AC, French NP et al. Extended-spectrum beta-lactamase-producing enterobacteriaceae in dairy farm environments: a New Zealand perspective. Foodborne Pathog Dis 2019; 16:5–22 [View Article] [PubMed]
    [Google Scholar]
  7. Velasova M, Smith RP, Lemma F, Horton RA, Duggett NA et al. Detection of extended-spectrum β-lactam, AmpC and carbapenem resistance in Enterobacteriaceae in beef cattle in Great Britain in 2015. J Appl Microbiol 2019; 126:1081–1095 [View Article] [PubMed]
    [Google Scholar]
  8. von Wintersdorff CJH, Penders J, van Niekerk JM, Mills ND, Majumder S et al. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front Microbiol 2016; 7:173 [View Article] [PubMed]
    [Google Scholar]
  9. Passarelli-Araujo H, Palmeiro JK, Moharana KC, Pedrosa-Silva F, Dalla-Costa LM et al. Genomic analysis unveils important aspects of population structure, virulence, and antimicrobial resistance in Klebsiella aerogenes. FEBS J 2019; 286:3797–3810 [View Article] [PubMed]
    [Google Scholar]
  10. Nakamura K, Murase K, Sato MP, Toyoda A, Itoh T et al. Differential dynamics and impacts of prophages and plasmids on the pangenome and virulence factor repertoires of Shiga toxin-producing Escherichia coli O145:H28. Microb Genom 2020; 6:000323 [View Article] [PubMed]
    [Google Scholar]
  11. Decano AG, Downing T. An Escherichia coli ST131 pangenome atlas reveals population structure and evolution across 4,071 isolates. Sci Rep 2019; 9:17394 [View Article] [PubMed]
    [Google Scholar]
  12. Inouye M, Dashnow H, Raven L-A, Schultz MB, Pope BJ et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med 2014; 6:90 [View Article] [PubMed]
    [Google Scholar]
  13. Seemann T. Mlst; 2019 https://github.com/tseemann/mlst accessed 12 Jul 2019
  14. Lam MMC, Wick RR, Wyres KL, Holt KE. Genomic surveillance framework and global population structure for Klebsiella pneumoniae. bioRxiv 2020 [View Article]
    [Google Scholar]
  15. Beghain J, Bridier-Nahmias A, Le Nagard H, Denamur E, Clermont O. ClermonTyping: an easy-to-use and accurate in silico method for Escherichia genus strain phylotyping. Microb Genom 2018; 4:000192 [View Article] [PubMed]
    [Google Scholar]
  16. Lees JA, Harris SR, Tonkin-Hill G, Gladstone RA, Lo SW et al. Fast and flexible bacterial genomic epidemiology with PopPUNK. Genome Res 2019; 29:304–316 [View Article] [PubMed]
    [Google Scholar]
  17. Robertson J, Nash JHE. MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. Microb Genom 2018; 4:000206 [View Article] [PubMed]
    [Google Scholar]
  18. Acman M, van Dorp L, Santini JM, Balloux F. Large-scale network analysis captures biological features of bacterial plasmids. Nat Commun 2020; 11:2452 [View Article] [PubMed]
    [Google Scholar]
  19. Sheppard AE, Stoesser N, German-Mesner I, Vegesana K, Walker AS et al. TETyper: a bioinformatic pipeline for classifying variation and genetic contexts of transposable elements from short-read whole-genome sequencing data. Microb Genom 2018; 4:000232 [View Article] [PubMed]
    [Google Scholar]
  20. Johansson MHK, Bortolaia V, Tansirichaiya S, Aarestrup FM, Roberts AP et al. Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. J Antimicrob Chemother 2021; 76:101–109 [View Article] [PubMed]
    [Google Scholar]
  21. Wang R, van Dorp L, Shaw LP, Bradley P, Wang Q et al. The global distribution and spread of the mobilized colistin resistance gene mcr-1. Nat Commun 2018; 9:1179 [View Article] [PubMed]
    [Google Scholar]
  22. Ludden C, Raven KE, Jamrozy D, Gouliouris T, Blane B et al. One health genomic surveillance of Escherichia coli demonstrates distinct lineages and mobile genetic elements in isolates from humans versus livestock. mBio 2019; 10:e02693-18 [View Article] [PubMed]
    [Google Scholar]
  23. David S, Cohen V, Reuter S, Sheppard AE, Giani T et al. Integrated chromosomal and plasmid sequence analyses reveal diverse modes of carbapenemase gene spread among Klebsiella pneumoniae. Proc Natl Acad Sci USA 2020; 117:25043–25054 [View Article] [PubMed]
    [Google Scholar]
  24. Acman M, Wang R, van Dorp L, Shaw LP, Wang Q et al. Role of the mobilome in the global dissemination of the carbapenem resistance gene blaNDM. bioRxiv 2021 [View Article]
    [Google Scholar]
  25. 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]
  26. Seemann T. Abricate; 2019 https://github.com/tseemann/abricate accessed 05 Jul 2019
  27. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 2009; 25:1422–1423 [View Article] [PubMed]
    [Google Scholar]
  28. Hendrickx APA, Landman F, de Haan A, Witteveen S, van Santen-Verheuvel MG. blaOXA-48-like genome architecture among carbapenemase-producing Escherichia coli and Klebsiella pneumoniae in the Netherlands. Microb Genom 2021; 7: [View Article] [PubMed]
    [Google Scholar]
  29. David S, Reuter S, Harris SR, Glasner C, Feltwell T. Epidemic of carbapenem-resistant Klebsiella pneumoniae in Europe is driven by nosocomial spread. Nat Microbiol 2019; 4:1919–1929 [View Article] [PubMed]
    [Google Scholar]
  30. Hagberg A, Swart P S, Chult D. Exploring network structure, dynamics, and function using NetworkX. Los Alamos National Lab.(LANL), Los Alamos, NM (United States; 2008 https://www.osti.gov/biblio/960616
  31. Wick R. Assembly-dereplicator. Github; 2021 https://github.com/rrwick/Assembly-Dereplicator accessed 02 Feb 2021
  32. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P et al. vegan: Community Ecology Package; 2019 https://CRAN.R-project.org/package=vegan
  33. EUCAST European committee on antimicrobial susceptibility testing; 2021 https://www.eucast.org/clinical_breakpoints/
  34. Wickham H. ggplot2: Elegant Graphics for Data Analysis; 2016 https://ggplot2.tidyverse.org
  35. Wilkins D. gggenes: Draw Gene Arrow Maps in “ggplot2.”; 2019 https://CRAN.R-project.org/package=gggenes
  36. Yu G, Smith DK, Zhu H, Guan Y, Lam TT. Ggtree: An r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 2017; 8:28–36 [View Article]
    [Google Scholar]
  37. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
    [Google Scholar]
  38. Katz L, Griswold T, Morrison S, Caravas J, Zhang S. Mashtree: a rapid comparison of whole genome sequence files. JOSS 2019; 4:1762 [View Article]
    [Google Scholar]
  39. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O. 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]
  40. Pitout JDD, Peirano G, Kock MM, Strydom K-. A, Matsumura Y. The global ascendency of OXA-48-type carbapenemases. Clin Microbiol Rev 2019; 33:e00102-19 [View Article] [PubMed]
    [Google Scholar]
  41. Beyrouthy R, Robin F, Delmas J, Gibold L, Dalmasso G et al. IS1R-mediated plasticity of IncL/M plasmids leads to the insertion of blaOXA-48 into the Escherichia coli chromosome. Antimicrob Agents Chemother 2014; 58:3785–3790 [View Article] [PubMed]
    [Google Scholar]
  42. Carrër A, Poirel L, Eraksoy H, Cagatay AA, Badur S et al. Spread of OXA-48-positive carbapenem-resistant Klebsiella pneumoniae isolates in Istanbul, Turkey. Antimicrob Agents Chemother 2008; 52:2950–2954 [View Article] [PubMed]
    [Google Scholar]
  43. Chen L, Mathema B, Chavda KD, DeLeo FR, Bonomo RA et al. Carbapenemase-producing Klebsiella pneumoniae: molecular and genetic decoding. Trends Microbiol 2014; 22:686–696 [View Article] [PubMed]
    [Google Scholar]
  44. Cuzon G, Naas T, Nordmann P. Functional characterization of Tn4401, a Tn3-based transposon involved in blaKPC gene mobilization. Antimicrob Agents Chemother 2011; 55:5370–5373 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000634
Loading
/content/journal/mgen/10.1099/mgen.0.000634
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

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