Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes

Abstract

Background: Antimicrobial resistance (AMR) is an intensifying threat that requires urgent mitigation to avoid a post-antibiotic era. Pseudomonas aeruginosa represents one of the greatest AMR concerns due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is gaining traction for in silico AMR profiling due to its unambiguity and transferability; however, accurate and comprehensive AMR prediction from P. aeruginosa genomes remains an unsolved problem.

Methods: We first curated the most comprehensive database yet of known P. aeruginosa AMR variants. Next, we performed comparative genomics and microbial genome-wide association study analysis across a Global isolate Dataset (n = 1877) with paired antimicrobial phenotype and genomic data to identify novel AMR variants. Finally, the performance of our P. aeruginosa AMR database, implemented in our AMR detection and prediction tool, ARDaP, was compared with three previously published in silico AMR gene detection or phenotype prediction tools-abritAMR, AMRFinderPlus, ResFinder-across both the Global Dataset and an analysis-naïve Validation Dataset (n = 102).

Results: Our AMR database comprises 3639 mobile AMR genes and 728 chromosomal variants, including 75 previously unreported chromosomal AMR variants, 10 variants associated with unusual antimicrobial susceptibility, and 281 chromosomal variants that we show are unlikely to confer AMR. Our pipeline achieved a genotype-phenotype balanced accuracy (bACC) of 85% and 81% across 10 clinically relevant antibiotics when tested against the Global and Validation Datasets, respectively, vs. just 56% and 54% with abritAMR, 58% and 54% with AMRFinderPlus, and 60% and 53% with ResFinder. ARDaP's superior performance was predominantly due to the inclusion of chromosomal AMR variants, which are generally not identified with most AMR identification tools.

Conclusions: Our ARDaP software and associated AMR variant database provides an accurate tool for predicting AMR phenotypes in P. aeruginosa, far surpassing the performance of current tools. Implementation of ARDaP for routine AMR prediction from P. aeruginosa genomes and metagenomes will improve AMR identification, addressing a critical facet in combatting this treatment-refractory pathogen. However, knowledge gaps remain in our understanding of the P. aeruginosa resistome, particularly the basis of colistin AMR.

Keywords: AMR database; AMR prediction; AMR software; Antibiotic; Bioinformatics; Genomics; High-throughput sequencing; Metagenomics; Whole-genome sequencing.

Fig. 1

Balanced accuracy of ARDaP, abritAMR, AMRFinderPlus, and ResFinder software for antimicrobial resistance (AMR) prediction in Pseudomonas aeruginosa. Software comparisons across ten clinically relevant antibiotics were undertaken against A the Global Dataset (n = 1877 isolates) and B the Validation Dataset (n = 102 isolates). For both datasets, and for all 10 antibiotics, ARDaP outperformed abritAMR, AMRFinderPlus, and ResFinder. To enable comparison with existing AMR prediction software, isolates with intermediate resistance were removed prior to analysis. Abbreviations: AMK, amikacin; CAZ, ceftazidime; CIP, ciprofloxacin; CST, colistin; FEP, cefepime; FQs, fluoroquinolones; IPM, imipenem; MEM, meropenem; PIP, piperacillin; TZP, piperacillin/tazobactam; TOB, tobramycin. “*” symbol indicates the following: no strains in the Validation Dataset exhibited CST AMR; as such, balanced accuracy could not be calculated for this antibiotic

Fig. 2

Precision and recall of ARDaP, abritAMR, AMRFinderPlus, and ResFinder software across the Global Dataset (n = 1877 strains). Precision and recall metrics for both antimicrobial-sensitive and antimicrobial-resistant (AMR) strains were highest using ARDaP (A; range 73–96%) vs. abritAMR (B; range 54–62%), AMRFinderPlus (C; range 54–62%) and ResFinder (D; range 42–68%). To enable software comparisons, isolates with intermediate resistance were removed prior to analysis. Abbreviations: AMK, amikacin; CAZ, ceftazidime; CIP, ciprofloxacin; CST, colistin; FEP, cefepime; FQs, fluoroquinolones; IPM, imipenem; MEM, meropenem; PIP, piperacillin; TZP, piperacillin/tazobactam; TOB, tobramycin. N.B. Precision (AMR) is also known as positive predictive value, and precision (sensitivity) is also known as negative predictive value