Predicting Pseudomonas aeruginosa susceptibility phenotypes from whole genome sequence resistome analysis

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Abstract

Objectives: The aim was to develop and validate a Pseudomonas aeruginosa genotypic resistance score, based on analysis of the whole genome sequence resistome, to predict antimicrobial susceptibility phenotypes. Methods: A scoring system based on the analysis of mutation-driven resistance in 40 chromosomal genes and horizontally acquired resistance (Resfinder) was developed for ceftazidime, ceftolozane/tazobactam, meropenem, ciprofloxacin and tobramycin. Resistance genes/mutations were scored from 0 (no effect) to 1 (EUCAST clinical resistance). One hundred wild-type strains obtained from 51 different hospitals during a 2017 multicentre study were fully sequenced and analysed in order to define a catalogue of natural polymorphisms in the 40 chromosomal resistance genes. The capacity of genotypic score to predict the susceptibility phenotype was tested in 204 isolates randomly selected from the 51 hospitals (four from each hospital). Results: The analysis of the 100 wild-type isolates yielded a catalogue of 455 natural polymorphisms in the 40 genes involved in mutational resistance. However, resistance mutations and high-risk clones (such as ST235) were also documented among a few wild-type isolates. Overall, the capacity of the genotypic score (<0.5) for predicting phenotypic susceptibility (S + I in the case of meropenem) was very high (95–100%). In contrast, the capacity of the genotypic score to predict resistance (≥1) was far more variable depending on the agent. Prediction of meropenem clinical resistance was particularly low (18/39, 46.1%), whereas it classified clinical ceftolozane/tazobactam resistance in 100% (7/7) of cases. Discussion: Although a margin for improvement was evidenced in this proof of concept study, an overall good correlation between the genotypic resistance score and the susceptibility profile was documented. Further refining of the scoring system, automatization and testing of large international cohorts should follow.

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Cortes-Lara, S., Barrio-Tofiño, E. del, López-Causapé, C., Oliver, A., Martínez-Martínez, L., Bou, G., … Oteo, J. (2021). Predicting Pseudomonas aeruginosa susceptibility phenotypes from whole genome sequence resistome analysis. Clinical Microbiology and Infection, 27(11), 1631–1637. https://doi.org/10.1016/j.cmi.2021.05.011

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