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

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  • Danesh Moradigaravand
  • Martin Palm
  • Anne Farewell
  • Ville Mustonen
  • Jonas Warringer
  • Leopold Parts

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 0.79 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.Author summary: One of the major health threats of 21st century is emergence of antibiotic resistance. To manage its human health and economic impact, efforts are made to develop novel diagnostic tools that rapidly detect resistant strains in clinical settings. In our study, we employed a range of powerful machine learning tools to predict antibiotic resistance from whole genome sequencing data for E. coli. We used the presence or absence of genes, population structure and isolation year of isolates as predictors, and could attain average precision of 0.92 and recall of 0.83, without prior knowledge about the causal mechanisms. These results demonstrate the potential application of machine learning methods as a diagnostic tool in healthcare settings.

Suggested Citation

  • Danesh Moradigaravand & Martin Palm & Anne Farewell & Ville Mustonen & Jonas Warringer & Leopold Parts, 2018. "Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-17, December.
  • Handle: RePEc:plo:pcbi00:1006258
    DOI: 10.1371/journal.pcbi.1006258
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    References listed on IDEAS

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    1. Johan Hallin & Kaspar Märtens & Alexander I. Young & Martin Zackrisson & Francisco Salinas & Leopold Parts & Jonas Warringer & Gianni Liti, 2016. "Powerful decomposition of complex traits in a diploid model," Nature Communications, Nature, vol. 7(1), pages 1-9, December.
    2. Erki Aun & Age Brauer & Veljo Kisand & Tanel Tenson & Maido Remm, 2018. "A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-17, October.
    3. Kaspar Märtens & Johan Hallin & Jonas Warringer & Gianni Liti & Leopold Parts, 2016. "Predicting quantitative traits from genome and phenome with near perfect accuracy," Nature Communications, Nature, vol. 7(1), pages 1-8, September.
    4. John A. Lees & Minna Vehkala & Niko Välimäki & Simon R. Harris & Claire Chewapreecha & Nicholas J. Croucher & Pekka Marttinen & Mark R. Davies & Andrew C. Steer & Steven Y. C. Tong & Antti Honkela & J, 2016. "Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes," Nature Communications, Nature, vol. 7(1), pages 1-8, November.
    5. Nicole E Wheeler & Paul P Gardner & Lars Barquist, 2018. "Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica," PLOS Genetics, Public Library of Science, vol. 14(5), pages 1-20, May.
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    Cited by:

    1. Allison L Hicks & Nicole Wheeler & Leonor Sánchez-Busó & Jennifer L Rakeman & Simon R Harris & Yonatan H Grad, 2019. "Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-21, September.
    2. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Xuanji Li & Asker Brejnrod & Jonathan Thorsen & Trine Zachariasen & Urvish Trivedi & Jakob Russel & Gisle Alberg Vestergaard & Jakob Stokholm & Morten Arendt Rasmussen & Søren Johannes Sørensen, 2023. "Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Jason C. Hyun & Jonathan M. Monk & Richard Szubin & Ying Hefner & Bernhard O. Palsson, 2023. "Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    5. David C. Marciano & Chen Wang & Teng-Kuei Hsu & Thomas Bourquard & Benu Atri & Ralf B. Nehring & Nicholas S. Abel & Elizabeth A. Bowling & Taylor J. Chen & Pamela D. Lurie & Panagiotis Katsonis & Susa, 2022. "Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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