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Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

Journal article
Authors Danesh Moradigaravand
Martin Palm
Anne Farewell
Ville Mustonen
Jonas Warringer
Leopold Parts
Published in PLoS computational biology
Volume 14
Issue 12
Pages e1006258
ISSN 1553-7358
Publication year 2018
Published at Department of Chemistry and Molecular Biology
Centre for antibiotic resistance research, CARe
Pages e1006258
Language en
Links dx.doi.org/10.1371/journal.pcbi.100...
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Microbiology

Abstract

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.90 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

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