Genomics and Susceptibility Profiles of Extensively Drug-Resistant Pseudomonas aeruginosa Isolates from Spain

Abstract

This study assessed the molecular epidemiology, resistance mechanisms, and susceptibility profiles of a collection of 150 extensively drug-resistant (XDR) clinical isolates obtained from a 2015 Spanish multicenter study, with a particular focus on resistome analysis in relation to ceftolozane-tazobactam susceptibility. Broth microdilution MICs revealed that nearly all (>95%) of the isolates were nonsusceptible to piperacillin-tazobactam, ceftazidime, cefepime, aztreonam, imipenem, meropenem, and ciprofloxacin. Most of them were also resistant to tobramycin (77%), whereas nonsusceptibility rates were lower for ceftolozane-tazobactam (31%), amikacin (7%), and colistin (2%). Pulsed-field gel electrophoresis-multilocus sequence typing (PFGE-MLST) analysis revealed that nearly all of the isolates belonged to previously described high-risk clones. Sequence type 175 (ST175) was detected in all 9 participating hospitals and accounted for 68% ( = 101) of the XDR isolates, distantly followed by ST244 ( = 16), ST253 ( = 12), ST235 ( = 8), and ST111 ( = 2), which were detected only in 1 to 2 hospitals. Through phenotypic and molecular methods, the presence of horizontally acquired carbapenemases was detected in 21% of the isolates, mostly VIM (17%) and GES enzymes (4%). At least two representative isolates from each clone and hospital ( = 44) were fully sequenced on an Illumina MiSeq. Classical mutational mechanisms, such as those leading to the overexpression of the β-lactamase AmpC or efflux pumps, OprD inactivation, and/or quinolone resistance-determining regions (QRDR) mutations, were confirmed in most isolates and correlated well with the resistance phenotypes in the absence of horizontally acquired determinants. Ceftolozane-tazobactam resistance was not detected in carbapenemase-negative isolates, in agreement with sequencing data showing the absence of mutations. The unique set of mutations responsible for the XDR phenotype of ST175 clone documented 7 years earlier were found to be conserved, denoting the long-term persistence of this specific XDR lineage in Spanish hospitals. Finally, other potentially relevant mutations were evidenced, including those in penicillin-binding protein 3 (PBP3), which is involved in β-lactam (including ceftolozane-tazobactam) resistance, and FusA1, which is linked to aminoglycoside resistance.

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

“…Particularly, two SNPs in the quinolone resistance-determining region (QRDR) of gyrA and parC had the strongest impact on the classification (Dataset EV3). This is an expected finding as quinolone antibiotics act by binding to their targets, gyrase, and topoisomerase IV (Bruchmann et al, 2013); and target-mediated resistance caused by specific mutations in the encoding genes is the most common and clinically significant form of resistance (del Barrio-Tofiño et al, 2017 Each panel depicts the results for a different anti-pseudomonal drug (CAZ: ceftazidime; CIP: ciprofloxacin; MEM: meropenem; TOB: tobramycin) for the best data type combination (GPA+EXPR/SNPs). Misclassified and correctly classified samples for the training dataset (80%) were inferred in a 10-fold cross-validation.…”

“…Particularly, two SNPs in the quinolone resistance‐determining region (QRDR) of gyrA and parC had the strongest impact on the classification (). This is an expected finding as quinolone antibiotics act by binding to their targets, gyrase, and topoisomerase IV (Bruchmann et al , ); and target‐mediated resistance caused by specific mutations in the encoding genes is the most common and clinically significant form of resistance (del Barrio‐Tofiño et al , ). Although the sensitivity to predict resistance and susceptibility from only gene expression data were also high toward ciprofloxacin, there was no added value of using information on gene expression in addition to SNP information.…”

Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

“…Particularly two SNPs in the quinolone resistance determining region (QRDR) of gyrA and parC had the strongest impact on the classification ( Supplementary Table S3). This is an expected finding as quinolone antibiotics act by binding to their targets, gyrase and topoisomerase IV (Bruchmann et al 2013) ; and target-mediated resistance caused by specific mutations in the encoding genes is the most common and clinically significant form of resistance (del Barrio-Tofiño et al 2017) . Although the sensitivity to predict resistance and susceptibility from only gene expression data was also high towards ciprofloxacin, there was no added value of using information on gene expression in addition to SNP information.…”

Defining the core essential genome ofPseudomonas aeruginosa

“…et al 4 sequences -Because insertions are assigned to a specific TA site in a specific gene based on the mapping of the genomic sequences flanking the ends of a transposon onto the entire genome, we removed from consideration TA sites whose flanking regions are not unique because of the possibility of mis-mapping reads. In PA14, 1122 such sites were found surrounding TA sites, 204 of which are from non-homologous genes.…”