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

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.

Rapid fluoroquinolone resistance detection in Pseudomonas aeruginosa using mismatch amplification mutation assay-based real-time PCR.

Background. Antimicrobial resistance (AMR) is an ever-increasing global health concern. One crucial facet in tackling the AMR epidemic is earlier and more accurate AMR diagnosis, particularly in the dangerous and highly multi-drug-resistant ESKAPE pathogen, Pseudomonas aeruginosa.Objectives. We aimed to develop two SYBR Green-based mismatch amplification mutation assays (SYBR-MAMAs) targeting GyrA T83I (gyrA248) and GyrA D87N, D87Y and D87H (gyrA259). Together, these variants cause the majority of fluoroquinolone (FQ) AMR in P. aeruginosa.Methods. Following assay validation, the gyrA248 and gyrA259 SYBR-MAMAs were tested on 84 Australian clinical P. aeruginosa isolates, 46 of which demonstrated intermediate/full ciprofloxacin resistance according to antimicrobial susceptibility testing.Results. Our two SYBR-MAMAs correctly predicted an AMR phenotype in the majority (83%) of isolates with intermediate/full FQ resistance. All FQ-sensitive strains were predicted to have a sensitive phenotype. Whole-genome sequencing confirmed 100 % concordance with SYBR-MAMA genotypes.Conclusions. Our GyrA SYBR-MAMAs provide a rapid and cost-effective method for same-day identification of FQ AMR in P. aeruginosa. An additional SYBR-MAMA targeting the GyrB S466Y/S466F variants would increase FQ AMR prediction to 91 %. Clinical implementation of our assays will permit more timely treatment alterations in cases where decreased FQ susceptibility is identified, leading to improved patient outcomes and antimicrobial stewardship.

Express Yourself: Quantitative Real-Time PCR Assays for Rapid Chromosomal Antimicrobial Resistance Detection in Pseudomonas aeruginosa.

The rise of antimicrobial-resistant (AMR) bacteria is a global health emergency. One critical facet of tackling this epidemic is more rapid AMR diagnosis in serious multidrug-resistant pathogens like Pseudomonas aeruginosa. Here, we designed and then validated two multiplex quantitative real-time PCR (qPCR) assays to simultaneously detect differential expression of the resistance-nodulation-division efflux pumps MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MexXY-OprM, the AmpC ß-lactamase, and the porin OprD, which are commonly associated with chromosomally encoded AMR. Next, qPCRs were tested on 15 sputa from 11 participants with P. aeruginosa respiratory infections to determine AMR profiles in vivo. We confirmed multiplex qPCR testing feasibility directly on sputa, representing a key advancement in in vivo AMR diagnosis. Notably, comparison of sputa with their derived isolates grown in Luria-Bertani broth (±2.5% NaCl) or a 5-antibiotic cocktail showed marked expression differences, illustrating the difficulty in replicating in vivo expression profiles in vitro. Cystic fibrosis sputa showed significantly reduced mexE and mexY expression compared with chronic obstructive pulmonary disease sputa, despite harboring fluoroquinolone- and aminoglycoside-resistant strains, indicating that these loci do not contribute to AMR in vivo. oprD was also significantly downregulated in cystic fibrosis sputa, even in the absence of contemporaneous carbapenem use, suggesting a common adaptive trait in chronic infections that may affect carbapenem efficacy. Sputum ampC expression was highest in participants receiving carbapenems (6.7 to 15×), some of whom were simultaneously receiving cephalosporins, the latter of which would be rendered ineffective by the upregulated ampC. Our qPCR assays provide valuable insights into the P. aeruginosa resistome, and their use on clinical specimens will permit timely treatment alterations that will improve patient outcomes and antimicrobial stewardship measures.

Taking the next-gen step: Comprehensive antimicrobial resistance detection from Burkholderia pseudomallei.

BACKGROUND: Antimicrobial resistance (AMR) poses a major threat to human health. Whole-genome sequencing holds great potential for AMR identification; however, there remain major gaps in accurately and comprehensively detecting AMR across the spectrum of AMR-conferring determinants and pathogens. METHODS: Using 16 wild-type Burkholderia pseudomallei and 25 with acquired AMR, we first assessed the performance of existing AMR software (ARIBA, CARD, ResFinder, and AMRFinderPlus) for detecting clinically relevant AMR in this pathogen. B. pseudomallei was chosen due to limited treatment options, high fatality rate, and AMR caused exclusively by chromosomal mutation (i.e. single-nucleotide polymorphisms [SNPs], insertions-deletions [indels], copy-number variations [CNVs], inversions, and functional gene loss). Due to poor performance with existing tools, we developed ARDaP (Antimicrobial Resistance Detection and Prediction) to identify the spectrum of AMR-conferring determinants in B. pseudomallei. FINDINGS: CARD, ResFinder, and AMRFinderPlus failed to identify any clinically-relevant AMR in B. pseudomallei; ARIBA identified AMR encoded by SNPs and indels that were manually added to its database. However, none of these tools identified CNV, inversion, or gene loss determinants, and ARIBA could not differentiate AMR determinants from natural genetic variation. In contrast, ARDaP accurately detected all SNP, indel, CNV, inversion, and gene loss AMR determinants described in B. pseudomallei (n≈50). Additionally, ARDaP accurately predicted three previously undescribed determinants. In mixed strain data, ARDaP identified AMR to as low as ~5% allelic frequency. INTERPRETATION: Existing AMR software packages are inadequate for chromosomal AMR detection due to an inability to detect resistance conferred by CNVs, inversions, and functional gene loss. ARDaP overcomes these major shortcomings. Further, ARDaP enables AMR prediction from mixed sequence data down to 5% allelic frequency, and can differentiate natural genetic variation from AMR determinants. ARDaP databases can be constructed for any microbial species of interest for comprehensive AMR detection. FUNDING: National Health and Medical Research Council (BJC, EPP, DSS); Australian Government (DEM, ES); Advance Queensland (EPP, DSS).