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

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.

doi:10.1038/s41598-017-18972-w

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

Journal Article · Thu Jan 11 00:00:00 EST 2018 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-017-18972-w· OSTI ID:1421958

Nguyen, Marcus ^[1]; Brettin, Thomas ^[2];

^[3]; Musser, James M. ^[3]; Olsen, Randall J. ^[3]; Olson, Robert ^[2]; Shukla, Maulik ^[2]; Stevens, Rick L. ^[2]; Xia, Fangfang ^[2]; Yoo, Hyunseung ^[2]; Davis, James J. ^[2]

Northern Illinois Univ., DeKalb, IL (United States); Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Houston Methodist Research Institute and Houston Methodist Hospital, Houston, TX (United States); Weill Cornell Medical College, New York, NY (United States)

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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1421958

Journal Information:: Scientific Reports, Vol. 8, Issue 1; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 86 works

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Whole genome sequencing reveals high-resolution epidemiological links between clinical and environmental Klebsiella pneumoniae Runcharoen, Chakkaphan; Moradigaravand, Danesh; Blane, Beth Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.7258	text	January 2017

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Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks Aytan-Aktug, D.; Clausen, P. T. L. C.; Bortolaia, V. mSystems, Vol. 5, Issue 1 https://doi.org/10.1128/msystems.00774-19	journal	January 2020
Surveillance to maintain the sensitivity of genotype-based antibiotic resistance diagnostics Hicks, Allison L.; Kissler, Stephen M.; Lipsitch, Marc PLOS Biology, Vol. 17, Issue 11 https://doi.org/10.1371/journal.pbio.3000547	journal	November 2019
Using Genomics to Track Global Antimicrobial Resistance Hendriksen, Rene S.; Bortolaia, Valeria; Tate, Heather Frontiers in Public Health, Vol. 7 https://doi.org/10.3389/fpubh.2019.00242	journal	September 2019
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Genomic analysis of variability in Delta-toxin levels between Staphylococcus aureus strains Su, Michelle; Lyles, James T.; Petit III, Robert A. PeerJ, Vol. 8 https://doi.org/10.7717/peerj.8717	journal	March 2020
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Genome-Based Prediction of Bacterial Antibiotic Resistance Su, Michelle; Satola, Sarah W.; Read, Timothy D. Journal of Clinical Microbiology, Vol. 57, Issue 3 https://doi.org/10.1128/jcm.01405-18	journal	October 2018
Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data Hicks, Allison L.; Wheeler, Nicole; Sánchez-Busó, Leonor PLOS Computational Biology, Vol. 15, Issue 9 https://doi.org/10.1371/journal.pcbi.1007349	journal	September 2019
VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning Kim, Jiwoong; Greenberg, David E.; Pifer, Reed PLOS Computational Biology, Vol. 16, Issue 1 https://doi.org/10.1371/journal.pcbi.1007511	journal	January 2020
Current Affairs of Microbial Genome-Wide Association Studies: Approaches, Bottlenecks and Analytical Pitfalls San, James Emmanuel; Baichoo, Shakuntala; Kanzi, Aquillah Frontiers in Microbiology, Vol. 10 https://doi.org/10.3389/fmicb.2019.03119	journal	January 2020

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