A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.

ABSTRACT: Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation

SUBMITTER: Pincus NB 

PROVIDER: S-EPMC7448275 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature