RESUMO
Cystic fibrosis (CF) represents one of the major genetic and chronic lung diseases affecting Caucasians of European descent. Patients with CF suffer from recurring infections that lead to further damage of the lungs. Pulmonary infection due to Pseudomonas aeruginosa is most prevalent, further increasing CF-related mortality. The present study describes the phenotypic and genotypic variations among 36 P. aeruginosa isolates obtained serially from a non-CF and five CF patients before, during and after lung transplantation (LTx). The classical and genomic investigation of these isolates revealed a common mucoid phenotype and only subtle differences in the genomes. Isolates originating from an individual patient shared ≥98.7% average nucleotide identity (ANI). However, when considering isolates from different patients, substantial variations in terms of sequence type (ST), virulence factors and antimicrobial resistance (AMR) genes were observed. Whole genome multi-locus sequence typing (MLST) confirmed the presence of unique STs per patient regardless of the time from LTx. It was supported by the monophyletic clustering found in the genome-wide phylogeny. The antibiogram shows that ≥91.6% of the isolates were susceptible to amikacin, colistin and tobramycin. For other antibiotics from the panel, isolates frequently showed resistance. Alternatively, a comparative analysis of the 36 P. aeruginosa isolates with 672 strains isolated from diverse ecologies demonstrated clustering of the CF isolates according to the LTx patients from whom they were isolated. We observed that despite LTx and associated measures, all patients remained persistently colonized with similar isolates. The present study shows how whole genome sequencing (WGS) along with phenotypic analysis can help us understand the evolution of P. aeruginosa over time especially its antibiotic resistance.
RESUMO
Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories.
