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Virulence genome analysis of Pseudomonas aeruginosa VRFPA10 recovered from patient with scleritis.

ABSTRACT: Infectious keratitis is a major cause of blindness, next to cataract and majority of cases are mainly caused by gram negative bacterium Pseudomonas aeruginosa (P. aeruginosa). In this study, we investigated a P. aeruginosa VRFPA10 genome which exhibited susceptibility to commonly used drugs in vitro but the patient had poor prognosis due to its hyper virulent nature. Genomic analysis of VRFPA10 deciphered multiple virulence factors and P.aeruginosa Genomic Islands (PAGIs) VRFPA10 genome which correlated with hyper virulence nature of the organism. The genome sequence has been deposited in DDBJ/EMBL/GenBank under the accession numbers LFMZ01000001-LFMZ01000044.

SUBMITTER: Murugan N

PROVIDER: S-EPMC5302140 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Virulence genome analysis of Pseudomonas aeruginosa VRFPA10 recovered from patient with scleritis.

Murugan Nandagopal N Malathi Jambulingam J Umashankar Vetrivel V Madhavan Hajib Narahari Rao HN

Genomics data 20170206

Infectious keratitis is a major cause of blindness, next to cataract and majority of cases are mainly caused by gram negative bacterium Pseudomonas aeruginosa (P. aeruginosa). In this study, we investigated a P. aeruginosa VRFPA10 genome which exhibited susceptibility to commonly used drugs in vitro but the patient had poor prognosis due to its hyper virulent nature. Genomic analysis of VRFPA10 deciphered multiple virulence factors and P.aeruginosa</i ...[more]

PMID: 28217443

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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.

Project description:Pseudomonas aeruginosa is one of the first causes of acute nosocomial and chronic infections in patients with underlying respiratory pathologies such as cystic fibrosis (CF). It has been proposed that P. aeruginosa accumulates mutations driving to peptidoglycan modifications throughout the development of the CF-associated infection, as a strategy to lower the immune detection hence ameliorating the chronic persistence. As well, some studies dealing with peptidoglycan modifications driving to a better survival within the host have been published in other gram-negatives. According to these facts, the gram-negative peptidoglycan could be considered as a pathogen-associated molecular pattern with very important implications regarding the host's detection-response, worthy to dissect in detail. For this reason, in this work we characterized for the first time the peptidoglycans of three large collections [early CF, late CF and acute infection (bloodstream) P. aeruginosa strains] from qualitative (HPLC), quantitative and inflammatory capacity-related perspectives. The final goal was to identify composition trends potentially supporting the cited strategy of evasion/resistance to the immune system and providing information regarding the differential intrinsic adaptation depending on the type of infection. Although we found several punctual strain-specific particularities, our results indicated a high degree of inter-collection uniformity in the peptidoglycan-related features and the absence of trends amongst the strains studied here. These results suggest that the peptidoglycan of P. aeruginosa is a notably conserved structure in natural isolates regardless of transitory changes that some external conditions could force. Finally, the inverse correlation between the relative amount of stem pentapeptides within the murein sacculus and the resistance to immune lytic attacks against the peptidoglycan was also suggested by our results. Altogether, this work is a major step ahead to understand the biology of peptidoglycan from P. aeruginosa natural strains, hopefully useful in future for therapeutic alternatives design.

Dataset Information

Virulence genome analysis of Pseudomonas aeruginosa VRFPA10 recovered from patient with scleritis.

Publications

Virulence genome analysis of <i>Pseudomonas aeruginosa</i> VRFPA10 recovered from patient with scleritis.

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