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Abstract

Salmonella enterica and Escherichia coli are bacterial species that colonize different animal hosts with sub-types that can cause life-threatening infections in humans. Source attribution of zoonoses is an important goal for infection control as is identification of isolates in reservoir hosts that represent a threat to human health. In this study, host specificity and zoonotic potential were predicted using machine learning in which Support Vector Machine (SVM) classifiers were built based on predicted proteins from whole genome sequences. Analysis of over 1000 S. enterica genomes allowed the correct prediction (67 –90 % accuracy) of the source host for S. Typhimurium isolates and the same classifier could then differentiate the source host for alternative serovars such as S. Dublin. A key finding from both phylogeny and SVM methods was that the majority of isolates were assigned to host-specific sub-clusters and had high host-specific SVM scores. Moreover, only a minor subset of isolates had high probability scores for multiple hosts, indicating generalists with genetic content that may facilitate transition between hosts. The same approach correctly identified human versus bovine E. coli isolates (83 % accuracy) and the potential of the classifier to predict a zoonotic threat was demonstrated using E. coli O157. This research indicates marked host restriction for both S. enterica and E. coli, with only limited isolate subsets exhibiting host promiscuity by gene content. Machine learning can be successfully applied to interrogate source attribution of bacterial isolates and has the capacity to predict zoonotic potential.

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2017-10-03
2019-10-17
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References

  1. Kaper JB, Nataro JP, Mobley HL. Pathogenic Escherichia coli. Nat Rev Microbiol 2004;2:123–140 [CrossRef][PubMed]
    [Google Scholar]
  2. Chaudhuri RR, Henderson IR. The evolution of the Escherichia coli phylogeny. Infect Genet Evol 2012;12:214–226 [CrossRef][PubMed]
    [Google Scholar]
  3. Langridge GC, Fookes M, Connor TR, Feltwell T, Feasey N et al. Patterns of genome evolution that have accompanied host adaptation in Salmonella. Proc Natl Acad Sci USA 2015;112:863–868 [CrossRef][PubMed]
    [Google Scholar]
  4. Bäumler A, Fang FC. Host specificity of bacterial pathogens. Cold Spring Harb Perspect Med 2013;3:a010041 [CrossRef][PubMed]
    [Google Scholar]
  5. Okoro CK, Barquist L, Connor TR, Harris SR, Clare S et al. Signatures of adaptation in human invasive Salmonella Typhimurium ST313 populations from sub-Saharan Africa. PLoS Negl Trop Dis 2015;9:e0003611 [CrossRef][PubMed]
    [Google Scholar]
  6. Clermont O, Christenson JK, Denamur E, Gordon DM. The clermont Escherichia coli phylo-typing method revisited: improvement of specificity and detection of new phylo-groups. Environ Microbiol Rep 2013;5:58–65 [CrossRef][PubMed]
    [Google Scholar]
  7. Clermont O, Olier M, Hoede C, Diancourt L, Brisse S et al. Animal and human pathogenic Escherichia coli strains share common genetic backgrounds. Infect Genet Evol 2011;11:654–662 [CrossRef][PubMed]
    [Google Scholar]
  8. von Mentzer A, Connor TR, Wieler LH, Semmler T, Iguchi A et al. Identification of enterotoxigenic Escherichia coli (ETEC) clades with long-term global distribution. Nat Genet 2014;46:1321–1326 [CrossRef][PubMed]
    [Google Scholar]
  9. Mainda G, Lupolova N, Sikakwa L, Bessell PR, Muma JB et al. Phylogenomic approaches to determine the zoonotic potential of Shiga toxin-producing Escherichia coli (STEC) isolated from Zambian dairy cattle. Sci Rep 2016;6:26589 [CrossRef][PubMed]
    [Google Scholar]
  10. White AP, Sibley KA, Sibley CD, Wasmuth JD, Schaefer R et al. Intergenic sequence comparison of Escherichia coli isolates reveals lifestyle adaptations but not host specificity. Appl Environ Microbiol 2011;77:7620–7632 [CrossRef][PubMed]
    [Google Scholar]
  11. Bauchart P, Germon P, Brée A, Oswald E, Hacker J et al. Pathogenomic comparison of human extraintestinal and avian pathogenic Escherichia coli-search for factors involved in host specificity or zoonotic potential. Microb Pathog 2010;49:105–115 [CrossRef][PubMed]
    [Google Scholar]
  12. The HC, Thanh DP, Holt KE, Thomson NR, Baker S. The genomic signatures of Shigella evolution, adaptation and geographical spread. Nat Rev Microbiol 2016;14:235–250 [CrossRef][PubMed]
    [Google Scholar]
  13. Lupolova N, Dallman TJ, Matthews L, Bono JL, Gally DL. Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates. Proc Natl Acad Sci USA 2016;113:11312–11317 [CrossRef][PubMed]
    [Google Scholar]
  14. 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 [CrossRef][PubMed]
    [Google Scholar]
  15. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–2069 [CrossRef][PubMed]
    [Google Scholar]
  16. Seemann T. 2017; MLST. GitHub – tseemann/mlst: scan contig files against PubMLST typing schemes. https://github.com/tseemann/mlst [accessed 2017]
  17. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015;31:3691–3693 [CrossRef][PubMed]
    [Google Scholar]
  18. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014;30:1312–1313 [CrossRef][PubMed]
    [Google Scholar]
  19. R package e1071, version 1.6-7 2015; Functions of the Department of Statistics, Probability Theory Group, TU Wien. https://cran.r-project.org/web/packages/e1071/
  20. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273–297 [CrossRef]
    [Google Scholar]
  21. Enterobase [online] Enterobase. http://enterobase.warwick.ac.uk/ [Accessed 2016–2017]
  22. Parsons BN, Humphrey S, Salisbury AM, Mikoleit J, Hinton JC et al. Invasive non-typhoidal Salmonella typhimurium ST313 are not host-restricted and have an invasive phenotype in experimentally infected chickens. PLoS Negl Trop Dis 2013;7:e2487 [CrossRef][PubMed]
    [Google Scholar]
  23. Foodborne Outbreak Tracking and Reporting [Internet] 2016; Centers for disease control and prevention. http://wwwn.cdc.gov/foodborneoutbreaks/
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