Global metabolic response of Enterococcus faecalis to oxygen.

Oxygen and oxidative stress have become relevant components in clarifying the mechanism that weakens bacterial cells in parallel to the mode of action of bactericidal antibiotics. Given the importance of oxidative stress in the overall defense mechanism of bacteria and their apparent role in the antimicrobial mode of action, it is important to understand how bacteria respond to this stress at a metabolic level. The aim of this study was to determine the impact of oxygen on the metabolism of the facultative anaerobe Enterococcus faecalis using continuous culture, metabolomics, and (13)C enrichment of metabolic intermediates. When E. faecalis was rapidly transitioned from anaerobic to aerobic growth, cellular metabolism was directed toward intracellular glutathione production and glycolysis was upregulated 2-fold, which increased the supply of critical metabolite precursors (e.g., glycine and glutamate) for sulfur metabolism and glutathione biosynthesis as well as reducing power for cellular respiration in the presence of hemin. The ultimate metabolic response of E. faecalis to an aerobic environment was the upregulation of fatty acid metabolism and benzoate degradation, which was linked to important changes in the bacterial membrane composition as evidenced by changes in membrane fatty acid composition and the reduction of membrane-associated demethylmenaquinone. These key metabolic pathways associated with the response of E. faecalis to oxygen may represent potential new targets to increase the susceptibility of this bacterium to bactericidal drugs.

Alkylation or Silylation for Analysis of Amino and Non-Amino Organic Acids by GC-MS?

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique in metabolomics. GC provides the highest resolution of any standard chromatographic separation method, and with modern instrumentation, retention times are very consistent between analyses. Electron impact ionization and fragmentation is generally reproducible between instruments and extensive libraries of spectra are available that enhance the identification of analytes. The major limitation is the restriction to volatile analytes, and hence the requirement to convert many metabolites to volatile derivatives through chemical derivatization. Here we compared the analytical performance of two derivatization techniques, silylation (TMS) and alkylation (MCF), used for the analysis of amino and non-amino organic acids as well as nucleotides in microbial-derived samples. The widely used TMS derivatization method showed poorer reproducibility and instability during chromatographic runs while the MCF derivatives presented better analytical performance. Therefore, alkylation (MCF) derivatization seems to be preferable for the analysis of polyfunctional amines, nucleotides and organic acids in microbial metabolomics studies.

Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry.

This protocol describes an analytical platform for the analysis of intra- and extracellular metabolites of microbial cells (yeast, filamentous fungi and bacteria) using gas chromatography-mass spectrometry (GC-MS). The protocol is subdivided into sampling, sample preparation, chemical derivatization of metabolites, GC-MS analysis and data processing and analysis. This protocol uses two robust quenching methods for microbial cultures, the first of which, cold glycerol-saline quenching, causes reduced leakage of intracellular metabolites, thus allowing a more reliable separation of intra- and extracellular metabolites with simultaneous stopping of cell metabolism. The second, fast filtration, is specifically designed for quenching filamentous micro-organisms. These sampling techniques are combined with an easy sample-preparation procedure and a fast chemical derivatization reaction using methyl chloroformate. This reaction takes place at room temperature, in aqueous medium, and is less prone to matrix effect compared with other derivatizations. This protocol takes an average of 10 d to complete and enables the simultaneous analysis of hundreds of metabolites from the central carbon metabolism (amino and nonamino organic acids, phosphorylated organic acids and fatty acid intermediates) using an in-house MS library and a data analysis pipeline consisting of two free software programs (Automated Mass Deconvolution and Identification System (AMDIS) and R).