• 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; et al. (EMBO, 2020-02-12)
      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.
    • Pseudomonas aeruginosa ceftolozane-tazobactam resistance development requires multiple mutations leading to overexpression and structural modification of AmpC.

      Cabot, Gabriel; Bruchmann, Sebastian; Mulet, Xavier; Zamorano, Laura; Moyà, Bartolomé; Juan, Carlos; Haussler, Susanne; Oliver, Antonio; Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany. (2014-06)
      We compared the dynamics and mechanisms of resistance development to ceftazidime, meropenem, ciprofloxacin, and ceftolozane-tazobactam in wild-type (PAO1) and mutator (PAOMS, ΔmutS) P. aeruginosa. The strains were incubated for 24 h with 0.5 to 64× MICs of each antibiotic in triplicate experiments. The tubes from the highest antibiotic concentration showing growth were reinoculated in fresh medium containing concentrations up to 64× MIC for 7 consecutive days. The susceptibility profiles and resistance mechanisms were assessed in two isolated colonies from each step, antibiotic, and strain. Ceftolozane-tazobactam-resistant mutants were further characterized by whole-genome analysis through RNA sequencing (RNA-seq). The development of high-level resistance was fastest for ceftazidime, followed by meropenem and ciprofloxacin. None of the mutants selected with these antibiotics showed cross-resistance to ceftolozane-tazobactam. On the other hand, ceftolozane-tazobactam resistance development was much slower, and high-level resistance was observed for the mutator strain only. PAO1 derivatives that were moderately resistant (MICs, 4 to 8 μg/ml) to ceftolozane-tazobactam showed only 2 to 4 mutations, which determined global pleiotropic effects associated with a severe fitness cost. High-level-resistant (MICs, 32 to 128 μg/ml) PAOMS derivatives showed 45 to 53 mutations. Major changes in the global gene expression profiles were detected in all mutants, but only PAOMS mutants showed ampC overexpression, which was caused by dacB or ampR mutations. Moreover, all PAOMS mutants contained 1 to 4 mutations in the conserved residues of AmpC (F147L, Q157R, G183D, E247K, or V356I). Complementation studies revealed that these mutations greatly increased ceftolozane-tazobactam and ceftazidime MICs but reduced those of piperacillin-tazobactam and imipenem, compared to those in wild-type ampC. Therefore, the development of high-level resistance to ceftolozane-tazobactam appears to occur efficiently only in a P. aeruginosa mutator background, in which multiple mutations lead to overexpression and structural modifications of AmpC.