Dataset Information

The ability of virulence factor expression by Pseudomonas aeruginosa to predict clinical disease in hospitalized patients.

ABSTRACT:

Background

Pseudomonas aeruginosa is an opportunistic pathogen that frequently causes hospital acquired colonization and infection. Accurate identification of host and bacterial factors associated with infection could aid treatment decisions for patients with P. aeruginosa cultured from clinical sites.

Methods

We identified a prospective cohort of 248 hospitalized patients with positive P. aeruginosa cultures. Clinical data were analyzed to determine whether an individual met predefined criteria for infection versus colonization. P. aeruginosa isolates were tested for the expression of multiple phenotypes previously associated with virulence in animal models and humans. Logistic regression models were constructed to determine the degree of association between host and bacterial factors with P. aeruginosa infection of the bloodstream, lung, soft tissue and urinary tract.

Results

One host factor (i.e. diabetes mellitus), and one bacterial factor, a Type 3 secretion system positive phenotype, were significantly associated with P. aeruginosa infection in our cohort. Subgroup analysis of patients with P. aeruginosa isolated from the urinary tract revealed that the presence of a urinary tract catheter or stent was an additional factor for P. aeruginosa infection.

Conclusions

Among hospitalized patients with culture-documented P. aeruginosa, infection is more likely to be present in those with diabetes mellitus and those harboring a Type 3 secretion positive bacterial strain.

SUBMITTER: Ledizet M

PROVIDER: S-EPMC3495863 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

The ability of virulence factor expression by Pseudomonas aeruginosa to predict clinical disease in hospitalized patients.

Ledizet Michel M Murray Thomas S TS Puttagunta Sailaja S Slade Martin D MD Quagliarello Vincent J VJ Kazmierczak Barbara I BI

PloS one 20121112 11

<h4>Background</h4>Pseudomonas aeruginosa is an opportunistic pathogen that frequently causes hospital acquired colonization and infection. Accurate identification of host and bacterial factors associated with infection could aid treatment decisions for patients with P. aeruginosa cultured from clinical sites.<h4>Methods</h4>We identified a prospective cohort of 248 hospitalized patients with positive P. aeruginosa cultures. Clinical data were analyzed to determine whether an individual met pred ...[more]

PMID: 23152923

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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

The ability of virulence factor expression by Pseudomonas aeruginosa to predict clinical disease in hospitalized patients.

Background

Methods

Results

Conclusions

Publications

The ability of virulence factor expression by Pseudomonas aeruginosa to predict clinical disease in hospitalized patients.

Similar Datasets

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