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

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...

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Bibliographic Details
Published in:PLoS computational biology 2018-12, Vol.14 (12), p.e1006258-e1006258
Main Authors: Moradigaravand, Danesh, Palm, Martin, Farewell, Anne, Mustonen, Ville, Warringer, Jonas, Parts, Leopold
Format: Article
Language:English
Subjects:
Anti-Bacterial Agents - pharmacology
Antibiotic resistance
Antibiotics
Antimicrobial agents
Artificial intelligence
Bioinformatics
Biology and Life Sciences
Ciprofloxacin
Computer and Information Sciences
Computer science
Decision trees
Deep learning
Deoxyribonucleic acid
Diagnostic software
Diagnostic systems
DNA
DNA, Bacterial - genetics
Drug resistance
Drug Resistance, Bacterial - genetics
Drug Resistance, Multiple, Bacterial - drug effects
E coli
Engineering and Technology
Epidemiology
Escherichia coli - genetics
Escherichia coli Infections
Forecasting - methods
Gene polymorphism
Gene sequencing
Genetic testing
Genome - genetics
Genome, Bacterial
Genomes
Genomics
Global health
Health risks
Humans
Learning algorithms
Machine learning
Medicine and Health Sciences
Microbial Sensitivity Tests
Microbiology
Microorganisms
Mikrobiologi
Model accuracy
Molecular biology
Mutation
Nucleotides
Phylogenetics
Physical Sciences
Polymorphism
Population structure
Predictions
Research and Analysis Methods
Sequence Analysis, DNA - methods
Supervision
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Summary: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 0.79 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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1006258