ArticleGenome medicine2024
Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes.
Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Digital Printing of Nanoenabled Enzymes on Textiles: An Integrated Antimicrobial and Antibiofilm Approach.ACS omega · 2026Article
- L-type pyocins inhibit the BAM complex to kill without cell entry.Nature communications · 2026Article
- In vitro exposure to non-antipseudomonal antibiotics (NAPA) induces Pseudomonas aeruginosa resistance to antipseudomonal antibiotics (APA).The Journal of antimicrobial chemotherapy · 2026Article
- Prevalence of Pseudomonas aeruginosa in Australian wild birds, native wildlife, livestock and domestic animals.Scientific reports · 2026Article
- Article
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- Evaluation of a machine learning system for genomic antimicrobial susceptibility determination on a clinically representative test set.Microbiology spectrum · 2026Article
- Evaluation of Phenotypic and Genotypic Susceptibility Testing Methods for Newer β-lactam/β-lactamase Inhibitor Combinations in Multidrug Resistant Pseudomonas aeruginosa.The Journal of infectious diseases · 2026Article
- Antimicrobial resistance databases: opportunities and challenges for public health.npj antimicrobials and resistance · 2026Review
- stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.Bioinformatics advances · 2026Article
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Detection of Genes Associated with Polymyxin and Antimicrobial Peptide Resistance in Isolates ofInternational journal of molecular sciences · 2025Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes.Genome medicine · 2024Article
- Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus onMicrobial genomics · 2023Article
- Article
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6 authors.
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Abstract
backgroundAntimicrobial 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.
methodsWe 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).
resultsOur 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.
conclusionsOur 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.
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