The Versatile Mutational Resistome of Pseudomonas aeruginosa

López-Causapé, Carla; Cabot, Gabriel; Barrio-Tofiño, Ester del; Oliver, Antonio

doi:10.3389/fmicb.2018.00685

Cited by 173 publications

(132 citation statements)

References 74 publications

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“…For example, we complemented the prediction of genomic resistance to ciprofloxacin with a custom database of known mutations. 4 Similarly, no mutations leading to upregulation of the chromosomal cephalosporinase AmpC (blaPAO) were found in our data, but an exhaustive search would require additional analyses. Secondly, the regulatory pathways of some mechanisms are not fully understood, such as those that regulate AmpC.…”

Section: Discussionmentioning

confidence: 55%

“…27 Known AMR determinants were identified from raw sequence reads using ARIBA 21 and a curated database of known resistance genes and mutations 28 . Results were complemented with predictions using the Comprehensive Antibiotic Resistance Database (CARD 29 ), and a custom database of mutations in the quinolone resistancedetermining region of the gyrA/B and parC/E genes described for P. aeruginosa 4 . Genomic predictions of resistance were derived from the presence of known antimicrobial resistance genes and mutations identified in the genome sequences.…”

Section: Bioinformatics Analysismentioning

confidence: 99%

“…3 P. aeruginosa infections are resilient to treatment due to an extensive repertoire of intrinsic and acquired resistance mechanisms. 4 A strong association between the use of carbapenems and P. aeruginosa resistance has been documented. 1 Nonetheless, a recent study evaluating carbapenem restriction practices at a hospital in Manila found that 37% of the carbapenems prescriptions were non-compliant, thus pointing to ongoing challenges in antimicrobial stewardship.…”

Section: Introductionmentioning

confidence: 99%

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Genomic Surveillance of Pseudomonas aeruginosa in the Philippines from 2013-2014

Chilam

Argimón²,

Limas

et al. 2020

Preprint

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Pseudomonas aeruginosa is an opportunistic pathogen often causing nosocomial infections that are resilient to treatment due to an extensive repertoire of intrinsic and acquired resistance mechanisms. In recent years, increasing resistance rates to antibiotics such as carbapenems and extended-spectrum cephalosporins have been reported, as well as multi-drug resistant and possible extremely drug-resistant rates of approximately 21% and 15%, respectively. However, the molecular epidemiology and AMR mechanisms of this pathogen remains largely uncharacterized.We sequenced the whole genomes of 176 P. aeruginosa isolates collected in 2013-2014 by the Antimicrobial Resistance Surveillance Program. The multi-locus sequence type, presence of antimicrobial resistance (AMR) determinants, and relatedness between the isolates were derived from the sequence data. The concordance between phenotypic and genotypic resistance was also determined.Carbapenem resistance was associated namely with loss-of function of the OprD porin, and acquisition of the metallo-β-lactamase VIM. The concordance between phenotypic and genotypic resistance was 93.27% overall for 6 antibiotics in 3 classes, but varied widely between aminoglycosides. The population of P. aeruginosa in the Philippines was diverse, with clonal expansions of XDR genomes belonging to multilocus sequence types ST235, ST244, ST309, and ST773. We found evidence of persistence or reintroduction of the predominant clone ST235 in one hospital, as well as transfer between hospitals. Most of the ST235 genomes formed a distinct Philippine lineage when contextualized with international genomes, thus raising the possibility that this is a lineage unique to the Philippines. This was further supported by long-read sequencing of one representative XDR isolate, which revealed the presence of an integron carrying multiple resistance genes, including blaVIM-2, with differences in gene composition and synteny to other P. aeruginosa class 1 integrons described before.We produced the first comprehensive genomic survey of P. aeruginosa in the Philippines, which bridges the gap in genomic data from the Western Pacific region and will constitute the genetic background to contextualize ongoing prospective surveillance. Our results also highlight the importance of infection control interventions aimed to curtail the spread of international epidemic clone ST235 within the country.

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Section: Discussionmentioning

confidence: 55%

Section: Bioinformatics Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genomic Surveillance of Pseudomonas aeruginosa in the Philippines from 2013-2014

Chilam

Argimón²,

Limas

et al. 2020

Preprint

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“…Experimental evolution of antibiotic-resistant mutants from sensitive parent strains, followed by whole genome sequencing to identify resistance-conferring mutations, is an approach that has been applied to a number of species (16)(17)(18)(19)(20)(21), including P. aeruginosa (22)(23)(24)(25)(26)(27)(28). Studies have confirmed the involvement of genes previously proposed to be associated with resistance in P. aeruginosa.…”

Section: Introductionmentioning

confidence: 87%

A large-scale comparison shows that genetic changes causing antibiotic resistance in experimentally evolvedPseudomonas aeruginosapredict those in naturally evolved bacteria

Wardell¹,

Rehman²,

Martin³

et al. 2019

Preprint

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Pseudomonas aeruginosa is an opportunistic pathogen that causes a wide range of acute and chronic infections. An increasing number of isolates have acquired mutations that make them antibiotic resistant, making treatment more difficult. To identify resistance-associated mutations we experimentally evolved the antibiotic sensitive strain P. aeruginosa PAO1 to become resistant to three widely used anti-pseudomonal antibiotics, ciprofloxacin, meropenem and tobramycin. Mutants were able to tolerate up to 2048-fold higher concentrations of antibiotic than strain PAO1. Genome sequences were determined for thirteen mutants for each antibiotic. Each mutant had between 2 and 8 mutations. There were at least 8 genes mutated in more than one mutant per antibiotic, demonstrating the complexity of the genetic basis of resistance. Additionally, large deletions of up to 479kb arose in multiple meropenem resistant mutants. For all three antibiotics mutations arose in genes known to be associated with resistance, but also in genes not previously associated with resistance. To determine the clinical relevance of mutations uncovered in experimentallyevolved mutants we analysed the corresponding genes in 457 isolates of P. aeruginosa from patients with cystic fibrosis or bronchiectasis as well as 172 isolates from the general environment. Many of the genes identified through experimental evolution had changes predicted to be function-altering in clinical isolates but not in isolates from the general environment, showing that mutated genes in experimentally evolved bacteria can predict those that undergo mutation during infection. These findings expand understanding of the genetic basis of antibiotic resistance in P. aeruginosa as well as demonstrating the validity of experimental evolution in identifying clinically-relevant resistance-associated mutations.' ImportanceThe rise in antibiotic resistant bacteria represents an impending global health crisis. As such, understanding the genetic mechanisms underpinning this resistance can be a crucial piece of the puzzle to combatting it. The importance of this research is that by experimentally evolving P. aeruginosa to three clinically relevant antibiotics, we have generated a catalogue of genes that can contribute to resistance in vitro. We show that many (but not all) of these genes are clinically relevant, by identifying variants in clinical isolates of P. aeruginosa. This research furthers our understanding of the genetics leading to resistance in P. aeruginosa and provides tangible evidence that these genes can play a role clinically, potentially leading to new druggable targets or inform therapies.

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“…This delay also contributes to the spread of drug-resistance (López-Causapé et al 2018;Oliver et al 2015) .…”

Section: Introductionmentioning

confidence: 99%

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

Khaledi

Weimann

Schniederjans

et al. 2019

Preprint

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The growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated predictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers.The implementation of a molecular susceptibility test system in routine clinical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

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The Versatile Mutational Resistome of Pseudomonas aeruginosa

Cited by 173 publications

References 74 publications

Genomic Surveillance of Pseudomonas aeruginosa in the Philippines from 2013-2014

Genomic Surveillance of Pseudomonas aeruginosa in the Philippines from 2013-2014

A large-scale comparison shows that genetic changes causing antibiotic resistance in experimentally evolvedPseudomonas aeruginosapredict those in naturally evolved bacteria

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

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