1887

Abstract

and spp. are important human pathogens that cause a wide spectrum of clinical disease. In healthcare settings, sinks and other wastewater sites have been shown to be reservoirs of antimicrobial-resistant and spp., particularly in the context of outbreaks of resistant strains amongst patients. Without focusing exclusively on resistance markers or a clinical outbreak, we demonstrate that many hospital sink drains are abundantly and persistently colonized with diverse populations of , and , including both antimicrobial-resistant and susceptible strains. Using whole-genome sequencing of 439 isolates, we show that environmental bacterial populations are largely structured by ward and sink, with only a handful of lineages, such as ST635, being widely distributed, suggesting different prevailing ecologies, which may vary as a result of different inputs and selection pressures. Whole-genome sequencing of 46 contemporaneous patient isolates identified one (2 %; 95 % CI 0.05–11 %) urine infection-associated isolate with high similarity to a prior sink isolate, suggesting that sinks may contribute to up to 10 % of infections caused by these organisms in patients on the ward over the same timeframe. Using metagenomics from 20 sink-timepoints, we show that sinks also harbour many clinically relevant antimicrobial resistance genes including , and , and may act as niches for the exchange and amplification of these genes. Our study reinforces the potential role of sinks in contributing to Enterobacterales infection and antimicrobial resistance in hospital patients, something that could be amenable to intervention. This article contains data hosted by Microreact.

Funding
This study was supported by the:
  • , NIHR Oxford Biomedical Research Centre , (Award HPRU-2012-10041)
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000391
2020-06-18
2020-07-02
Loading full text...

Full text loading...

/deliver/fulltext/mgen/10.1099/mgen.0.000391/mgen000391.html?itemId=/content/journal/mgen/10.1099/mgen.0.000391&mimeType=html&fmt=ahah

References

  1. Public Health England English Surveillance Programme for Antimicrobial Utilisation and Resistance (ESPAUR) Report 2018–2019. Public Health England; 2019. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/843129/English_Surveillance_Programme_for_Antimicrobial_Utilisation_and_Resistance_2019.pdf. Accessed December 17, 2019.
  2. Tenaillon O, Skurnik D, Picard B, Denamur E. The population genetics of commensal Escherichia coli . Nat Rev Microbiol 2010; 8: 207 217 [CrossRef] [PubMed]
    [Google Scholar]
  3. Hendriksen RS, Munk P, Njage P, van Bunnik B, McNally L et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun 2019; 10: 1124 [CrossRef] [PubMed]
    [Google Scholar]
  4. Public Health England Preventing healthcare associated gram-negative bloodstream infections: An improvement resource. Public Health England; 2017. https://improvement.nhs.uk/documents/984/Gram-negative_IPCresource_pack.pdf .
  5. Kizny Gordon AE, Mathers AJ, Cheong EYL, Gottlieb T, Kotay S et al. The hospital water environment as a reservoir for carbapenem-resistant organisms causing hospital-acquired Infections-A systematic review of the literature. Clin Infect Dis 2017; 64: 1435 1444 [CrossRef] [PubMed]
    [Google Scholar]
  6. Mathers AJ, Crook D, Vaughan A, Barry KE, Vegesana K et al. Klebsiella quasipneumoniae provides a window into carbapenemase gene transfer, plasmid rearrangements, and patient interactions with the hospital environment. Antimicrob Agents Chemother 2019; 63: e02513-18, /aac/63/6/AAC.02513-18.atom. [CrossRef] [PubMed]
    [Google Scholar]
  7. Hopman J, Tostmann A, Wertheim H, Bos M, Kolwijck E et al. Reduced rate of intensive care unit acquired gram-negative bacilli after removal of sinks and introduction of 'water-free' patient care. Antimicrob Resist Infect Control 2017; 6: 59 [CrossRef] [PubMed]
    [Google Scholar]
  8. Mathers AJ, Vegesana K, German Mesner I, Barry KE, Pannone A et al. Intensive care unit wastewater interventions to prevent transmission of multispecies Klebsiella pneumoniae carbapenemase-producing organisms. Clin Infect Dis 2018; 67: 171 178 [CrossRef] [PubMed]
    [Google Scholar]
  9. Quainoo S, Coolen JPM, van Hijum SAFT, Huynen MA, Melchers WJG et al. Whole-Genome sequencing of bacterial pathogens: the future of nosocomial outbreak analysis. Clin Microbiol Rev 2017; 30: 1015 1063 [CrossRef] [PubMed]
    [Google Scholar]
  10. Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 2017; 35: 833 844 [CrossRef] [PubMed]
    [Google Scholar]
  11. Gweon HS, Shaw LP, Swann J et al. The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples. Microbiology 2019
    [Google Scholar]
  12. Sukhum KV, Diorio-Toth L, Dantas G. Genomic and metagenomic approaches for predictive surveillance of emerging pathogens and antibiotic resistance. Clin Pharmacol Ther 2019; 106: 512 524 [CrossRef] [PubMed]
    [Google Scholar]
  13. Johnson RC, Deming C, Conlan S, Zellmer CJ, Michelin AV et al. Investigation of a cluster of Sphingomonas koreensis infections. N Engl J Med 2018; 379: 2529 2539 [CrossRef] [PubMed]
    [Google Scholar]
  14. Chau K. WGS investigation of three OXA-48 carbapenemase bacteraemias and sink colonisation on a haematology unit. Poster presented at the Madrid, Spain: 2018
    [Google Scholar]
  15. Public Health England Standards for Microbiology Investigations (UK SMI) Public Health England; 2014
    [Google Scholar]
  16. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15: R46 [CrossRef] [PubMed]
    [Google Scholar]
  17. Seemann T. Snippy. https://github.com/tseemann/snippy. Accessed December 17, 2019.
  18. Seemann T. Shovill. https://github.com/tseemann/shovill. Accessed December 17, 2019.
  19. Eyre D. RunListCompare. https://github.com/davideyre/runListCompare. Accessed December 17, 2019.
  20. 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 [CrossRef] [PubMed]
    [Google Scholar]
  21. Didelot X, Wilson DJ. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol 2015; 11: e1004041 [CrossRef] [PubMed]
    [Google Scholar]
  22. Ondov BD, Starrett GJ, Sappington A, Kostic A, Koren S et al. Mash screen: high-throughput sequence containment estimation for genome discovery. Genome Biol 2019; 20: 232 [CrossRef] [PubMed]
    [Google Scholar]
  23. Lees JA, Harris SR, Tonkin-Hill G et al. Fast and flexible bacterial genomic epidemiology with PopPUNK. bioRxiv 2018
    [Google Scholar]
  24. Hunt M, Mather AE, Sánchez-Busó L, Page AJ, Parkhill J et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom 2017; 3: e000131 [CrossRef] [PubMed]
    [Google Scholar]
  25. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M et al. Card 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res October 2019; 10: gkz935 [CrossRef]
    [Google Scholar]
  26. Galili T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 2015; 31: 3718 3720 [CrossRef] [PubMed]
    [Google Scholar]
  27. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20: 257 [CrossRef] [PubMed]
    [Google Scholar]
  28. 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 [CrossRef] [PubMed]
    [Google Scholar]
  29. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 2018; 34: 3094 3100 [CrossRef] [PubMed]
    [Google Scholar]
  30. Constantinides B, Robertson D. Kindel: indel-aware consensus for nucleotide sequence alignments. JOSS 2017; 2: 282 [CrossRef]
    [Google Scholar]
  31. Breitwieser FP, Salzberg SL. Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification. Bioinformatics September 2019; 17: btz715 [CrossRef]
    [Google Scholar]
  32. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T et al. SciPy 1.0: fundamental algorithms for scientific computing in python. Nat Methods 2020; 17: 261 272 [CrossRef] [PubMed]
    [Google Scholar]
  33. Perez F, Granger BE. IPython: a system for interactive scientific computing. Comput Sci Eng 2007; 9: 21 29 [CrossRef]
    [Google Scholar]
  34. Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng 2007; 9: 90 95 [CrossRef]
    [Google Scholar]
  35. Argimón S, Abudahab K, Goater RJE, Fedosejev A, Bhai J et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2016; 2: e000093 [CrossRef] [PubMed]
    [Google Scholar]
  36. Carattoli A, Villa L, Feudi C, Curcio L, Orsini S et al. Novel plasmid-mediated colistin resistance mcr-4 gene in Salmonella and Escherichia coli, Italy 2013, Spain and Belgium, 2015 to 2016. Euro Surveill 2017; 22: 30589 [CrossRef] [PubMed]
    [Google Scholar]
  37. Bush SJ, Foster D, Eyre DW et al. Genomic diversity affects the accuracy of bacterial SNP calling pipelines. bioRxiv 2019
    [Google Scholar]
  38. Zhi S, Banting G, Stothard P, Ashbolt NJ, Checkley S et al. Evidence for the evolution, clonal expansion and global dissemination of water treatment-resistant naturalized strains of Escherichia coli in wastewater. Water Res 2019; 156: 208 222 [CrossRef] [PubMed]
    [Google Scholar]
  39. Holt KE, Wertheim H, Zadoks RN, Baker S, Whitehouse CA et al. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health. Proc Natl Acad Sci U S A 2015; 112: E3574 E3581 [CrossRef] [PubMed]
    [Google Scholar]
  40. Lowe C, Willey B, O'Shaughnessy A, Lee W, Lum M et al. Outbreak of extended-spectrum β-lactamase-producing Klebsiella oxytoca infections associated with contaminated handwashing sinks(1). Emerg Infect Dis 2012; 18: 1242 1247 [CrossRef] [PubMed]
    [Google Scholar]
  41. He S, Chandler M, Varani AM, Hickman AB, Dekker JP et al. Mechanisms of evolution in High-Consequence drug resistance plasmids. mBio 2016; 7: e01987-16 [CrossRef] [PubMed]
    [Google Scholar]
  42. Weingarten RA, Johnson RC, Conlan S, Ramsburg AM, Dekker JP et al. Genomic analysis of hospital plumbing reveals diverse reservoir of bacterial plasmids conferring carbapenem resistance. mBio 2018; 9: e02011 02017 [CrossRef] [PubMed]
    [Google Scholar]
  43. Kotay SM, Donlan RM, Ganim C, Barry K, Christensen BE et al. Droplet- rather than Aerosol-Mediated dispersion is the primary mechanism of bacterial transmission from contaminated hand-washing sink traps. Appl Environ Microbiol 2018; 85: e01997-18, /aem/85/2/AEM.01997-18.atom [CrossRef]
    [Google Scholar]
  44. Aranega-Bou P, George RP, Verlander NQ, Paton S, Bennett A et al. Carbapenem-resistant Enterobacteriaceae dispersal from sinks is linked to drain position and drainage rates in a laboratory model system. J Hosp Infect 2019; 102: 63 69 [CrossRef] [PubMed]
    [Google Scholar]
  45. Carling PC. Wastewater drains: epidemiology and interventions in 23 carbapenem-resistant organism outbreaks. Infect Control Hosp Epidemiol 2018; 39: 972 979 [CrossRef] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000391
Loading
/content/journal/mgen/10.1099/mgen.0.000391
Loading

Data & Media loading...

Supplements

Supplementary material 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