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 04:00:00 UTC 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 Lab. (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, Journal Name: Scientific Reports Journal Issue: 1 Vol. 8; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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