title: Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates.
abstract: Sequence analyses of pathogen genomes facilitate the tracking of disease outbreaks and allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are generally used after an outbreak has happened. Here, we show that support vector machine analysis of bovine E. coli O157 isolate sequences can be applied to predict their zoonotic potential, identifying cattle strains more likely to be a serious threat to human health. Notably, only a minor subset (less than 10%) of bovine E. coli O157 isolates analyzed in our datasets were predicted to have the potential to cause human disease; this is despite the fact that the majority are within previously defined pathogenic lineages I or I/II and encode key virulence factors. The predictive capacity was retained when tested across datasets. The major differences between human and bovine E. coli O157 isolates were due to the relative abundances of hundreds of predicted prophage proteins. This finding has profound implications for public health management of disease because interventions in cattle, such a vaccination, can be targeted at herds carrying strains of high zoonotic potential. Machine-learning approaches should be applied broadly to further our understanding of pathogen biology.
authors.affiliation: Division of Immunity and Infection, The Roslin Institute and The Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
authors.forename: Nadejda
authors.initials: N
authors.lastname: Lupolova
authors.affiliation: Public Health England, National Infection Service, London NW9 5EQ, United Kingdom.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Dallman
authors.affiliation: Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom.
authors.forename: Louise
authors.identifier: 0000-0003-4420-8367
authors.initials: L
authors.lastname: Matthews
authors.affiliation: US Meat Animal Research Center, Agricultural Research Service, United States Department of Agriculture, Clay Center, NE 68933.
authors.forename: James L
authors.identifier: 0000-0001-5146-1171
authors.initials: JL
authors.lastname: Bono
authors.affiliation: Division of Immunity and Infection, The Roslin Institute and The Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom; dgally@ed.ac.uk.
authors.forename: David L
authors.initials: DL
authors.lastname: Gally
mesh_terms: D000818:Animals; D002417:Cattle; D002418:Cattle Diseases / Q000453:epidemiology / Q000382:microbiology*; D004196:Disease Outbreaks; D004927:Escherichia coli Infections / Q000453:epidemiology / Q000382:microbiology*; D019453:Escherichia coli O157 / Q000302:isolation & purification*; D006801:Humans; D010802:Phylogeny; D022641:Shiga Toxin 2 / Q000378:metabolism; D060388:Support Vector Machine; D006113:United Kingdom / Q000453:epidemiology; D015047:Zoonoses / Q000453:epidemiology / Q000382:microbiology*
chemical_list: D022641:Shiga Toxin 2
keywords: E. coli; Shiga toxin; cattle; machine learning; zoonosis