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

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. 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.

Authors:

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)

Publication Date:: Wed Feb 12 00:00:00 EST 2020

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

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1627933

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: EMBO Molecular Medicine

Additional Journal Information:: Journal Volume: 12; Journal Issue: 3; Journal ID: ISSN 1757-4676

Publisher:: EMBOpress

Country of Publication:: United States

Language:: English

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

Citation Formats


                    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., and Häussler, Susanne. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.  United States: N. p., 2020. 
Web.  doi:10.15252/emmm.201910264.

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                    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. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.  United States.  https://doi.org/10.15252/emmm.201910264

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                    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., and Häussler, Susanne. Wed .  
"Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics".  United States.  https://doi.org/10.15252/emmm.201910264.  https://www.osti.gov/servlets/purl/1627933.

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@article{osti_1627933,

  title        = {Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics},

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

  abstractNote = {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.},

  doi          = {10.15252/emmm.201910264},

  journal      = {EMBO Molecular Medicine},

  number       = 3,

  volume       = 12,

  place        = {United States},

  year         = {Wed Feb 12 00:00:00 EST 2020},

  month        = {Wed Feb 12 00:00:00 EST 2020}

}

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