Dataset Information

A novel targeted/untargeted GC-Orbitrap metabolomics methodology applied to Candida albicans and Staphylococcus aureus biofilms

ABSTRACT: Introduction: Combined infections from Candida albicans and Staphylococcus aureus are a leading cause of death in the developed world. Evidence suggests that Candida enhances the virulence of Staphylococcus - hyphae penetrate through tissue barriers, while S. aureus tightly associates with the hyphae to obtain entry to the host organism. Indeed, in a biofilm state, C. albicans enhances the antimicrobial resistance characteristics of S. aureus. The association of these microorganisms is also associated with significantly increased morbidity and mortality. Due to this tight association we hypothesised that metabolic effects were also in evidence.
Objectives: To explore the interaction, we used a novel GC-Orbitrap – based mass spectrometer, the QExactive GC, which combines the high peak capacity and chromatographic resolution of gas chromatography with the sub-ppm mass accuracy of an Orbitrap system. This allows the capability to leverage the widely available electron ionisation libraries for untargeted applications, along with expanding accurate mass libraries and targeted matches based around authentic standards.
Methods: Optimised C. albicans and S. aureus mono- and co-cultured biofilms were analysed in addition to the fresh and spent bacterial growth media.
Results: The targeted analysis experiment was based around 36 sugars and sugar phosphates, 22 amino acids and five organic acids. We detected an additional 22 highly scoring compounds from untargeted analysis.
Conclusion: Many of the results were as expected – rapid consumption of glucose and fructose from the medium regardless of the cell type. We also detected trehalose from the untargeted data, only in medium that contained C. albicans, commensurate with it being a predominantly fungal sugar. Notable from the results is that the pentose phosphate pathway appears to be enhanced in the cells from co-cultured biofilms.

INSTRUMENT(S): ITQ 900 (Thermo Scientific), QExactive GC (Thermo Scientific)

SUBMITTER: Stefan Weidt

PROVIDER: MTBLS370 | MetaboLights | 2017-02-20

REPOSITORIES: MetaboLights

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A novel targeted/untargeted GC-Orbitrap metabolomics methodology applied to Candida albicans and Staphylococcus aureus biofilms.

Weidt Stefan S Haggarty Jennifer J Kean Ryan R Cojocariu Cristian I CI Silcock Paul J PJ Rajendran Ranjith R Ramage Gordon G Burgess Karl E V KE

Metabolomics : Official journal of the Metabolomic Society 20161105 12

<h4>Introduction</h4>Combined infections from Candida albicans and Staphylococcus aureus are a leading cause of death in the developed world. Evidence suggests that Candida enhances the virulence of Staphylococcus-hyphae penetrate through tissue barriers, while S. aureus tightly associates with the hyphae to obtain entry to the host organism. Indeed, in a biofilm state, C. albicans enhances the antimicrobial resistance characteristics of S. aureus ...[more]

PMID: 28003796

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Project description:Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to determine if the pathogen is bacterial, fungal or neither of the two. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional dataset comprising Cryptococcus neoformans infections. Furthermore, the noise-robustness of the classifier suggests high rates of correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances. Analysis of innate immune activation on the basis of gene expression of whole blood cells during ex vivo whole blood infection with bacterial (Staphylococcus aureus, Escherichia coli) and fungal pathogens (Candida albicans, Aspergillus fumigatus) in comparison to mock-treated blood.

Dataset Information

A novel targeted/untargeted GC-Orbitrap metabolomics methodology applied to Candida albicans and Staphylococcus aureus biofilms

Publications

A novel targeted/untargeted GC-Orbitrap metabolomics methodology applied to <i>Candida albicans</i> and <i>Staphylococcus aureus</i> biofilms.

Similar Datasets

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