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

Khaledi, Ariane; Weimann, Aaron; Schniederjans, Monika; Asgari, Ehsaneddin; Kuo, Tzu‐Hao; Oliver, Antonio; Cabot, Gabriel; Kola, Axel; Gastmeier, Petra; Hogardt, Michael; Jonas, Daniel; Mofrad, Mohammad RK; Bremges, Andreas; McHardy, Alice C.; Häussler, Susanne

doi:10.15252/emmm.201910264

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

Journal Article · Wed Feb 12 00:00:00 EST 2020 · EMBO Molecular Medicine

DOI:https://doi.org/10.15252/emmm.201910264· OSTI ID:1627933

Khaledi, Ariane ^[1];

^[2]; Schniederjans, Monika ^[1]; Asgari, Ehsaneddin ^[3]; Kuo, Tzu‐Hao ^[4]; Oliver, Antonio ^[5]; Cabot, Gabriel ^[5]; Kola, Axel ^[6]; Gastmeier, Petra ^[6]; Hogardt, Michael ^[7]; Jonas, Daniel ^[8]; Mofrad, Mohammad RK ^[9];

^[10];

^[1]

Helmholtz Centre for Infection Research, Braunschweig (Germany). Dept. of Molecular Bacteriology; TWINCORE‐Centre for Experimental and Clinical Infection Research, Hannover (Germany). Molecular Bacteriology Group
TWINCORE‐Centre for Experimental and Clinical Infection Research, Hannover (Germany). Molecular Bacteriology Group; Helmholtz Centre for Infection Research Braunschweig, (Germany). Computational Biology of Infection Research; German Center for Infection Research (DZIF), Braunschweig (Germany)
Helmholtz Centre for Infection Research Braunschweig, (Germany). Computational Biology of Infection Research; Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering and Mechanical Engineering. Molecular Cell Biomechanics Lab.
Helmholtz Centre for Infection Research Braunschweig, (Germany). Computational Biology of Infection Research
Unidad de Investigación Hospital Universitario Son Espases, Palma de Mallorca (Spain). Instituto de Investigación Sanitaria Illes Balears (IdISPa). Servicio de Microbiología
Universitätsmedizin Berlin (Germany). Inst. of Hygiene and Environmental Medicine Charité
Univ. Hospital Frankfurt (Germany). Inst. of Medical Microbiology and Infection Control
Univ. of Freiburg (Germany). Inst. for Infection Prevention and Hospital Epidemiology Medical Center. Faculty of Medicine
Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering and Mechanical Engineering. Molecular Cell Biomechanics Lab.; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Biophysics and Integrated Bioimaging Division
Helmholtz Centre for Infection Research Braunschweig, (Germany). Computational Biology of Infection Research; German Center for Infection Research (DZIF), Braunschweig (Germany)

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. Finally, 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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1627933

Journal Information:: EMBO Molecular Medicine, Vol. 12, Issue 3; ISSN 1757-4676

Publisher:: EMBOpressCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
Research & Experimental Medicine
antibiotic resistance
biomarkers
clinical isolates
machine learning
molecular diagnostics

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

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