Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica (2018)
© 2018 Wheeler et al. http://creativecommons.org/licenses/by/4.0/ Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality, associated with a narrowed host range. Our random forest classifier learned to perfectly discriminate long-established gastrointestinal and invasive serovars of Salmonella. Additionally, it was able to discriminate recently emerged Salmonella Enteritidis and Typhimurium lineages associated with invasive disease in immunocompromised populations in sub-Saharan Africa, and within-host adaptation to invasive infection. We dissect the architecture of the model to identify the genes that were most informative of phenotype, revealing a common theme of degradation of metabolic pathways in extraintestinal lineages. This approach accurately identifies patterns of gene degradation and diversifying selection specific to invasive serovars that have been captured by more labour-intensive investigations, but can be readily scaled to larger analyses.
KeywordsAnimals; Humans; Salmonella enterica; Salmonella Infections; Salmonella Infections, Animal; Bacterial Proteins; Adaptation, Physiological; Phylogeny; Virulence; Mutation; Host Specificity; Machine Learning
ANZSRC Fields of Research31 - Biological sciences::3107 - Microbiology::310701 - Bacteriology
31 - Biological sciences::3105 - Genetics::310509 - Genomics
31 - Biological sciences::3104 - Evolutionary biology::310403 - Biological adaptation
31 - Biological sciences::3101 - Biochemistry and cell biology::310109 - Proteomics and intermolecular interactions (excl. medical proteomics)
08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing
RightsCopyright: © 2018 Wheeler et al. 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 author and source are credited.
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