Dataset Information

Evaluation of a FRET-peptide substrate to predict virulence in Pseudomonas aeruginosa.

ABSTRACT: Pseudomonas aeruginosa produces a number of proteases that are associated with virulence and disease progression. A substrate able to detect P. aeruginosa-specific proteolytic activity could help to rapidly alert clinicians to the virulence potential of individual P. aeruginosa strains. For this purpose we designed a set of P. aeruginosa-specific fluorogenic substrates, comprising fluorescence resonance energy transfer (FRET)-labeled peptides, and evaluated their applicability to P. aeruginosa virulence in a range of clinical isolates. A FRET-peptide comprising three glycines (3xGly) was found to be specific for the detection of P. aeruginosa proteases. Further screening of 97 P. aeruginosa clinical isolates showed a wide variation in 3xGly cleavage activity. The absence of 3xGly degradation by a lasI knock out strain indicated that 3xGly cleavage by P. aeruginosa could be quorum sensing (QS)-related, a hypothesis strengthened by the observation of a strong correlation between 3xGly cleavage, LasA staphylolytic activity and pyocyanin production. Additionally, isolates able to cleave 3xGly were more susceptible to the QS inhibiting antibiotic azithromycin (AZM). In conclusion, we designed and evaluated a 3xGly substrate possibly useful as a simple tool to predict virulence and AZM susceptibility.

SUBMITTER: Kaman WE

PROVIDER: S-EPMC3841150 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Evaluation of a FRET-peptide substrate to predict virulence in Pseudomonas aeruginosa.

Kaman Wendy E WE Arkoubi-El Arkoubi Nora El NE Roffel Sanne S Endtz Hubert P HP van Belkum Alex A Bikker Floris J FJ Hays John P JP

PloS one 20131126 11

Pseudomonas aeruginosa produces a number of proteases that are associated with virulence and disease progression. A substrate able to detect P. aeruginosa-specific proteolytic activity could help to rapidly alert clinicians to the virulence potential of individual P. aeruginosa strains. For this purpose we designed a set of P. aeruginosa-specific fluorogenic substrates, comprising fluorescence resonance energy transfer (FRET)-labeled peptides, and evaluated their applicability to P. aeruginosa v ...[more]

PMID: 24303047

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Project description:Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm's performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes.IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

Dataset Information

Evaluation of a FRET-peptide substrate to predict virulence in Pseudomonas aeruginosa.

Publications

Evaluation of a FRET-peptide substrate to predict virulence in Pseudomonas aeruginosa.

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