Keeping up with the pathogens: Improved antimicrobial resistance detection and prediction fromPseudomonasaeruginosa genomes

Background Antimicrobial resistance (AMR) is an intensifying threat that requires urgent mitigation to avoid a post-antibiotic era. The ESKAPE pathogen, Pseudomonas aeruginosa , represents one of the greatest AMR concerns due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is quickly 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 ARDaP software, 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 733 AMR-conferring chromosomal variants, including 75 chromosomal variants not previously reported, and 284 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. Conclusions Our ARDaP software and associated AMR variant database provides the most accurate tool yet for predicting AMR phenotypes in P. aeruginosa , far surpassing the performance of current tools. Implementation of our ARDaP-compatible database 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.