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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 Laboratory (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. https://doi.org/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. https://doi.org/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 = {Thu Jan 11 00:00:00 EST 2018},
month = {Thu Jan 11 00:00:00 EST 2018}
}

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  • DOI: 10.3390/app9122486

A crash course in sequencing for a microbiologist
journal, January 2019

  • Kozińska, Aleksandra; Seweryn, Paulina; Sitkiewicz, Izabela
  • Journal of Applied Genetics, Vol. 60, Issue 1
  • DOI: 10.1007/s13353-019-00482-2

Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks
journal, January 2020


Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks
journal, January 2020


Surveillance to maintain the sensitivity of genotype-based antibiotic resistance diagnostics
journal, November 2019


Using Genomics to Track Global Antimicrobial Resistance
journal, September 2019

  • Hendriksen, Rene S.; Bortolaia, Valeria; Tate, Heather
  • Frontiers in Public Health, Vol. 7
  • DOI: 10.3389/fpubh.2019.00242

“It Takes a Village”: Mechanisms Underlying Antimicrobial Recalcitrance of Polymicrobial Biofilms
journal, September 2019

  • Orazi, Giulia; O’Toole, George A.
  • Journal of Bacteriology, Vol. 202, Issue 1
  • DOI: 10.1128/jb.00530-19

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella
journal, October 2018

  • Nguyen, Marcus; Long, S. Wesley; McDermott, Patrick F.
  • Journal of Clinical Microbiology, Vol. 57, Issue 2
  • DOI: 10.1128/jcm.01260-18

Genomic characterization and computational phenotyping of nitrogen-fixing bacteria isolated from Colombian sugarcane fields
journal, April 2021

  • Medina-Cordoba, Luz K.; Chande, Aroon T.; Rishishwar, Lavanya
  • Scientific Reports, Vol. 11, Issue 1
  • DOI: 10.1038/s41598-021-88380-8

Genomic analysis of variability in Delta-toxin levels between Staphylococcus aureus strains
journal, March 2020

  • Su, Michelle; Lyles, James T.; Petit III, Robert A.
  • PeerJ, Vol. 8
  • DOI: 10.7717/peerj.8717

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data
journal, December 2018


Genome-Based Prediction of Bacterial Antibiotic Resistance
journal, October 2018

  • Su, Michelle; Satola, Sarah W.; Read, Timothy D.
  • Journal of Clinical Microbiology, Vol. 57, Issue 3
  • DOI: 10.1128/jcm.01405-18

Surveillance to maintain the sensitivity of genotype-based antibiotic resistance diagnostics
journal, November 2019


Genome-Based Prediction of Bacterial Antibiotic Resistance
journal, October 2018

  • Su, Michelle; Satola, Sarah W.; Read, Timothy D.
  • Journal of Clinical Microbiology, Vol. 57, Issue 3
  • DOI: 10.1128/jcm.01405-18

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data
journal, September 2019


Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data
journal, December 2018


VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning
journal, January 2020


Current Affairs of Microbial Genome-Wide Association Studies: Approaches, Bottlenecks and Analytical Pitfalls
journal, January 2020

  • San, James Emmanuel; Baichoo, Shakuntala; Kanzi, Aquillah
  • Frontiers in Microbiology, Vol. 10
  • DOI: 10.3389/fmicb.2019.03119