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Title: Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Abstract

Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ± 1 two-fold dilution factor, is 92%. Individual accuracies are >= 90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

Authors:
 [1];  [2]; ORCiD logo [3];  [3];  [3];  [2];  [2];  [2];  [2];  [2];  [2]
  1. Northern Illinois Univ., DeKalb, IL (United States); Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Houston Methodist Research Institute and Houston Methodist Hospital, Houston, TX (United States); Weill Cornell Medical College, New York, NY (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Institutes of Health (NIH); USDOE
OSTI Identifier:
1421958
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES

Citation Formats

Nguyen, Marcus, Brettin, Thomas, Long, S. Wesley, Musser, James M., Olsen, Randall J., Olson, Robert, Shukla, Maulik, Stevens, Rick L., Xia, Fangfang, Yoo, Hyunseung, and Davis, James J. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. United States: N. p., 2018. Web. doi:10.1038/s41598-017-18972-w.
Nguyen, Marcus, Brettin, Thomas, Long, S. Wesley, Musser, James M., Olsen, Randall J., Olson, Robert, Shukla, Maulik, Stevens, Rick L., Xia, Fangfang, Yoo, Hyunseung, & Davis, James J. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. United States. doi:10.1038/s41598-017-18972-w.
Nguyen, Marcus, Brettin, Thomas, Long, S. Wesley, Musser, James M., Olsen, Randall J., Olson, Robert, Shukla, Maulik, Stevens, Rick L., Xia, Fangfang, Yoo, Hyunseung, and Davis, James J. Thu . "Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae". United States. doi:10.1038/s41598-017-18972-w. https://www.osti.gov/servlets/purl/1421958.
@article{osti_1421958,
title = {Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae},
author = {Nguyen, Marcus and Brettin, Thomas and Long, S. Wesley and Musser, James M. and Olsen, Randall J. and Olson, Robert and Shukla, Maulik and Stevens, Rick L. and Xia, Fangfang and Yoo, Hyunseung and Davis, James J.},
abstractNote = {Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ± 1 two-fold dilution factor, is 92%. Individual accuracies are >= 90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.},
doi = {10.1038/s41598-017-18972-w},
journal = {Scientific Reports},
number = 1,
volume = 8,
place = {United States},
year = {2018},
month = {1}
}

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