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00925njm 22002777a 4500 dc Khaledi, Ariane author Weimann, Aaron author Schniederjans, Monika author Asgari, Ehsaneddin author Kuo, Tzu-Hao author Oliver, Antonio author Cabot, Gabriel author Kola, Axel author Gastmeier, Petra author Hogardt, Michael author Jonas, Daniel author Mofrad, Mohammad RK author Bremges, Andreas author McHardy, Alice C author Haeussler, Susanne author 2020-03-06 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. Khaledi A, Weimann A, Schniederjans M, Asgari E, Kuo TH, Oliver A, et al. Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics. EMBO Mol Med. 2020 Mar 06;12(3):e10264. Epub 2020 Feb 12. 10.15252/emmm.201910264 1757-4684 1757-4676 Embo Molecular Medicine http://hdl.handle.net/20.500.13003/11753 32048461 L2004245372 2-s2.0-85079431987 https://hdl.handle.net/20.500.12105/23050 512724500001 Antibiotic resistance Biomarkers Clinical isolates Machine learning Molecular diagnostics Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics