Open AccessPredicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
Author(s) -
Khaledi Ariane,
Weimann Aaron,
Schniederjans Monika,
Asgari Ehsaneddin,
Kuo TzuHao,
Oliver Antonio,
Cabot Gabriel,
Kola Axel,
Gastmeier Petra,
Hogardt Michael,
Jonas Daniel,
Mofrad Mohammad RK,
Bremges Andreas,
McHardy Alice C,
Häussler Susanne
Publication year - 2020
Publication title -
embo molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.923
H-Index - 107
eISSN - 1757-4684
pISSN - 1757-4676
DOI - 10.15252/emmm.201910264
Subject(s) - pseudomonas aeruginosa , antibiotic resistance , antimicrobial , microbiology and biotechnology , computational biology , biology , computer science , bacteria , antibiotics , genetics
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.