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EMBO Mol Med. 2020 Feb 12:e10264. doi: 10.15252/emmm.201910264. [Epub ahead of print]

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

Author information

1
Department of Molecular Bacteriology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
2
Molecular Bacteriology Group, TWINCORE-Centre for Experimental and Clinical Infection Research, Hannover, Germany.
3
Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
4
German Center for Infection Research (DZIF), Braunschweig, Germany.
5
Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA.
6
Servicio de Microbiología y Unidad de Investigación Hospital Universitario Son Espases, Instituto de Investigación Sanitaria Illes Balears (IdISPa), Palma de Mallorca, Spain.
7
Institute of Hygiene and Environmental Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.
8
Institute of Medical Microbiology and Infection Control, University Hospital Frankfurt, Frankfurt/Main, Germany.
9
Faculty of Medicine, Institute for Infection Prevention and Hospital Epidemiology, Medical Center-University of Freiburg, Freiburg, Germany.
10
Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Lab, Berkeley, CA, USA.

Abstract

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

KEYWORDS:

antibiotic resistance; biomarkers; clinical isolates; machine learning; molecular diagnostics

PMID:
32048461
DOI:
10.15252/emmm.201910264
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