The application of metabolite profiling to Mycobacterium spp. Determination of metabolite changes associated with growth. / Drapal, Margit; Perez-Fons, Laura; Wheeler, Paul R.; Fraser, Paul D.

In: Journal of microbiological methods, Vol. 106, 11.2014, p. 23-32.

Research output: Contribution to journalArticlepeer-review



In order to decipher the complex biological networks underlying biochemical and physiological processes, cellular regulation at all levels must be studied. The metabolites determined by metabolomics represent the end-point of cellular regulation and thus vital components of any integrative network. In the case of pathogenic agents such as Mycobacterium tuberculosis metabolomics offers an ideal opportunity to gain a better understanding of how this species adapts to environmental conditions and antimicrobial treatments. In the present study a metabolite profiling protocol for Mycobacterium including optimised quenching, extraction and analysis has been devised. These methods have been applied to three different Mycobacterium spp. demonstrating potential translation across the genus. Steady-state levels of metabolites during growth have been determined for Mycobacterium smegmatis, Mycobacterium phlei and Mycobacterium bovis BCG (Bacillus Calmette-Guerin). The changes of designated biomarkers emphasised phenotypical differences (e.g. nitrogen metabolism) and similarities (e.g. cysteine biosynthesis) between the bacteria. Each time point showed distinguishable metabolic characteristics from early lag to late stationary phase/beginning of non-replicating phase. The combination of the metabolic results with published "omics" data indicated that transcription appeared to be the most predominant mode of cellular regulation utilised by these bacteria studied. (C) 2014 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)23-32
Number of pages10
JournalJournal of microbiological methods
Early online date7 Aug 2014
Publication statusPublished - Nov 2014
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 25255171