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Replication of the Ordered, Nonredundant Library of Pseudomonas aeruginosa strain PA14 Transposon Insertion Mutants
Published on: May 4, 2018
Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa
Danielle E Madden1,2, Timothy Baird1,2,3, Scott C Bell4,5
1Centre for Bioinnovation, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
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.
Insights
Antimicrobial resistance (AMR) in Pseudomonas aeruginosa is a growing concern. A new tool, ARDaP, accurately predicts AMR phenotypes by incorporating chromosomal variants, outperforming existing methods.
Area of Science:
- Genomics
- Microbiology
- Computational Biology
Background:
- Antimicrobial resistance (AMR) poses a significant global health threat, potentially leading to a post-antibiotic era.
- Pseudomonas aeruginosa is a key pathogen of concern due to high rates of multi- and pan-drug resistance.
- Accurate in silico AMR profiling from genomic data is crucial but challenging for P. aeruginosa.
Purpose of the Study:
- To develop a comprehensive database of P. aeruginosa AMR variants.
- To identify novel AMR variants through comparative genomics and GWAS.
- To create and evaluate an improved tool for predicting AMR phenotypes.
Main Methods:
- Curated a comprehensive database of P. aeruginosa AMR variants.
- Performed comparative genomics and GWAS on a global isolate dataset (n=1877) with phenotype data.
- Developed and tested the ARDaP tool against existing AMR prediction software using global and validation datasets.
Main Results:
- The AMR database includes 3639 mobile genes and 728 chromosomal variants, with 75 novel variants identified.
- ARDaP achieved a balanced accuracy of 85% (Global) and 81% (Validation) for AMR prediction.
- ARDaP significantly outperformed abritAMR, AMRFinderPlus, and ResFinder, primarily due to the inclusion of chromosomal variants.
Conclusions:
- ARDaP provides a highly accurate tool for predicting P. aeruginosa AMR phenotypes, surpassing current methods.
- Routine use of ARDaP can enhance AMR identification for this difficult-to-treat pathogen.
- Further research is needed to fully understand the P. aeruginosa resistome, especially colistin resistance.
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