Optimization of a sample preparation method for the metabolomic analysis of clinically relevant bacteria
2011 (English)In: Journal of Microbiological Methods, ISSN 0167-7012, E-ISSN 1872-8359, Vol. 87, no 1, 24-31 p.Article in journal (Refereed) Published
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 600nm. 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.
Place, publisher, year, edition, pages
Elsevier , 2011. Vol. 87, no 1, 24-31 p.
Bacteria metabolomics, Extraction method optimization, GC-MS/TOF profiling, Multivariate data analysis
IdentifiersURN: urn:nbn:se:umu:diva-46072DOI: 10.1016/j.mimet.2011.07.001PubMedID: 21763728OAI: oai:DiVA.org:umu-46072DiVA: diva2:436921