1. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics
- Author
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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
- Subjects
Microbiology ,Biological Sciences ,Genetics ,Vaccine Related ,Infectious Diseases ,Antimicrobial Resistance ,Emerging Infectious Diseases ,Prevention ,Biotechnology ,Human Genome ,Clinical Research ,Biodefense ,Detection ,screening and diagnosis ,Development of treatments and therapeutic interventions ,5.1 Pharmaceuticals ,4.1 Discovery and preclinical testing of markers and technologies ,Infection ,Good Health and Well Being ,Anti-Bacterial Agents ,Drug Resistance ,Bacterial ,Genome ,Bacterial ,Machine Learning ,Microbial Sensitivity Tests ,Pathology ,Molecular ,Pseudomonas aeruginosa ,Transcriptome ,antibiotic resistance ,biomarkers ,clinical isolates ,machine learning ,molecular diagnostics ,Medical and Health Sciences ,Biochemistry and cell biology - 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.
- Published
- 2020