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

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‐...

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Bibliographic Details
Published in:	EMBO molecular medicine 2020-03, Vol.12 (3), p.e10264-n/a
Main Authors:	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
Format:	Article
Language:	eng
Subjects:	Antibiotic resistance Antibiotics Antimicrobial agents Antimicrobial resistance Bacteria BASIC BIOLOGICAL SCIENCES biomarkers Ciprofloxacin clinical isolates Cloning Drug resistance Gene expression Genomes Genotype & phenotype Infections Learning algorithms Machine learning molecular diagnostics Mutation Nucleotide sequence Pathogens Phylogenetics Prediction models Pseudomonas aeruginosa Research & Experimental Medicine
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Summary:	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. Synopsis The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles. Genome and transcriptome data of 414 clinical isolates was combined for biomarker identification using information on gene expression, gene presence or absence, and single nucleotide variations. For some antibiotics, transcriptome information greatly improves resistance prediction. Depending on the antibiotic, 37–93 biomarkers are sufficient to obtain high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. Biomarkers include known resistance conferring genes (e.g. gyrA, oprD, ampC, efflux pumps) as well as unexpected and potential novel candidates. The spread of antibiotic resistance complicates infection treatment, requiring an optimization of current diagnostics. In this study, a machine learning approach identified a set of biomarkers suitable for the development of a molecular test system to determine antibiotic resistance profiles.
ISSN:	1757-4676 1757-4684