Analysis Of Microbial Phenotype And Metabolites Based On Chromatography-Mass Spectrometry

Due to small size, simple structure, easy to raise, micro-organism easily relates to metabolism process and the cell function or the phenotype. The characterization of metabolomics at biochemistry level is closest to cell function response, and many changes of metabolite are in the downstream of gene and protein changes. Thus it is most effective that using metabolomics which is one branch of systems biology to fully understand the physiological functions of micro-organisms. As a new omics technique, metabolomics has greatly enriched systems biology research and become one of the most promising methods to the research of life sciences. The present analytical techniques used in metabolomics analysis include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and capillary electrophoresis-mass spectrometry technologies (CE-MS). Because of its high sensitivity and compounds standard spectral library (NIST), GC-MS was utilized more widely.E.colil.1566and five gene knockout mutants were selected to establish a GC-MS metabolomic analysis. The calibration curves for all the standards were satisfactory with regression coefficients better than0.99. The spiked recoveries of most standards were better than80%and RSD was within6%. Then we evaluated a slide-based metabolic quenching method by GC-MS analysis. It showed great improvement on metabolite extract efficiency compared to traditional strategies. Subsequently, this method was integrated with GC-MS for metabolic fingerprint analysis. The studied strains were separately cultured in sole carbon source media of glucose, xylose or fructose. The partial least square-discriminate analysis (PLS-DA) of intracellular metabolic fingerprints showed clear separation between the wild and the genetic modified strains; SAM analysis can find the compounds that contribute to disparity; the determinative effects, from environmental factors, on metabolic phenotypes could be retrieved by hierarchical cluster analysis (HCA); further analysis, at the level of metabolic network, indicated that the common active metabolic pathways in response to genetic modification were inconspicuous when the mutants were cultured in glucose medium. While mutants cultured in xylose and fructose medium showed common active response pathways and subnetworks involved in glycine, succinate metabolism and glyceryl, succinate metabolism respectively.Two Yeast strains were used to metabolism disparity analysis by metabolic fingerprint analysis method we have established before. Multivariate data analysis revealed two yeast strains can not only be a good distinction, but also to identify differences in the contribution of the compounds produced the largest of which is phosphoric acid, followed by lysine and so on.