Abstract

Metabolomics, or metabolite profiling, is an approach that is increasingly used to study the metabolism of diverse organisms, elucidate biological processes and/or find characteristic biomarkers of physiological states. Here, we describe the optimization of a method for global metabolomic analysis of bacterial cultures, with the following steps. Cells are grown to log-phase, starting from an overnight culture and bacterial concentrations are monitored by measuring the optical density of the cultures at 600 nm. At an appropriate density they are harvested by centrifugation, washed three times with NaCl solution and metabolites are extracted using methanol and a bead-mill. Dried extracts are methoxymated and derivatized with methyltrimethylsilyltrifluoroacetamide (MSTFA) then analyzed using gas chromatography coupled to time-of-flight mass spectrometry (GC-MS/TOF). Finally, patterns in the acquired data are examined by multivariate data modeling. This method enabled us to obtain reproducible metabolite profiles of Yersinia pseudotuberculosis, with about 25% compound identification, based on comparison with entries in available GC-MS libraries. To assess the potential utility of the method for comparative analysis of other bacterial species we analyzed cultures of Pseudomonas aeruginosa, Salmonella typhimurium, Escherichia coli and methicillin-sensitive Staphylococcus aureus (MSSA). Multivariate analysis of the acquired data showed that it was possible to differentiate the species according to their metabolic profiles. Our results show that the presented procedure can be used for metabolomic analysis of a wide range of bacterial species of clinical interest. (C) 2011 Elsevier B.V. All rights reserved.

Keywords

Bacteria metabolomics; Extraction method optimization; GC-MS/TOF profiling; Multivariate data analysis

Published in

Journal of Microbiological Methods
2011, Volume: 87, number: 1, pages: 24-31
Publisher: ELSEVIER SCIENCE BV

SLU Authors

• Moritz, Thomas

  • Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Biochemistry and Molecular Biology


Publication identifier

DOI: https://doi.org/10.1016/j.mimet.2011.07.001

Permanent link to this page (URI)

https://res.slu.se/id/publ/46143