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Journal article
Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes
Genome medicine, Vol.16, pp.1-17
2024
PMID: 38849863
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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.
Details
- Title
- Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes
- Authors
- Danielle E. Madden - University of the Sunshine Coast, Queensland, Centre for BioinnovationTimothy Baird - University of the Sunshine Coast, Queensland, Centre for BioinnovationScott C. Bell - Prince Charles HospitalKate L. Mccarthy - The University of QueenslandErin P. Price - University of the Sunshine Coast, Queensland, Centre for BioinnovationDerek S. Sarovich (Corresponding Author) - University of the Sunshine Coast, Queensland, Centre for Bioinnovation
- Publication details
- Genome medicine, Vol.16, pp.1-17
- Publisher
- Springer Nature
- Date published
- 2024
- DOI
- 10.1186/s13073-024-01346-z
- ISSN
- 1756-994X
- PMID
- 38849863
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
- Grant note
- This work was supported by Advance Queensland [AQIRF0362018 to D.S.S., AQRF13016-17RD2 to E.P.P.], the Wishlist Sunshine Coast Health Foundation [2019-14 to T.B., E.P.P., D.S.S.].
- Organisation Unit
- School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 991046397802621
- Output Type
- Journal article
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