Background: Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability.

An analytical method was set up suitable for the analysis of microbial metabolomes, consisting of an oximation and silylation derivatization reaction and subsequent analysis by gas chromatography coupled to mass spectrometry. Microbial matrixes contain many compounds that potentially interfere with either the derivatization procedure or analysis, such as high concentrations of salts, complex media or buffer components, or extremely high substrate and product concentrations. The developed method was extensively validated using different microorganisms, i.e., Bacillus subtilis, Propionibacterium freudenreichii, and Escherichia coli. Many metabolite classes could be analyzed with the method: alcohols, aldehydes, amino acids, amines, fatty acids, (phospho-) organic acids, sugars, sugar acids, (acyl-) sugar amines, sugar phosphate, purines, pyrimidines, and aromatic compounds. The derivatization reaction proved to be efficient (>50% transferred to derivatized form) and repeatable (relative standard deviations <10%). Linearity for most metabolites was satisfactory with regression coefficients better than 0.996. Quantification limits were 40-500 pg on-column or 0.1-0.7 mmol/g of microbial cells (dry weight). Generally, intrabatch precision (repeatability) and interbatch precision (reproducibility) for the analysis of metabolites in cell extracts was better than 10 and 15%, respectively. Notwithstanding the nontargeted character of the method and complex microbial matrix, analytical performance for most metabolites fit the requirements for target analysis in bioanalysis. The suitability of the method was demonstrated by analysis of E. coli samples harvested at different growth phases.

A general method is presented for combining mass spectrometry-based metabolomics data. Such data are becoming more and more abundant, and proper tools for fusing these types of data sets are needed. Fusion of metabolomics data leads to a comprehensive view on the metabolome of an organism or biological system. The ideas presented draw upon established techniques in data analysis. Hence, they are also widely applicable to other types of X-omics data provided there is a proper pretreatment of the data. These issues are discussed using a real-life metabolomics data set from a microbial fermentation process.

Actinobacillus sp. 130Z fermented glucose to the major products succinate, acetate, and formate. Ethanol was formed as a minor fermentation product. Under CO2-limiting conditions, less succinate and more ethanol were formed. The fermentation product ratio remained constant at pH values from 6.0 to 7.4. More succinate was produced when hydrogen was present in the gas phase. Actinobacillus sp. 130Z grew at the expense of fumarate and l-malate reduction, with hydrogen as an electron donor. Other substrates such as more-reduced carbohydrates (e.g., d-sorbitol) resulted in higher succinate and/or ethanol production. Actinobacillus sp. 130Z contained the key enzymes involved in the Embden-Meyerhof-Parnas and the pentose-phosphate pathways and contained high levels of phosphoenolpyruvate (PEP) carboxykinase, malate dehydrogenase, fumarase, fumarate reductase, pyruvate kinase, pyruvate formate-lyase, phosphotransacetylase, acetate kinase, malic enzyme, and oxaloacetate decarboxylase. The levels of PEP carboxykinase, malate dehydrogenase, and fumarase were significantly higher in Actinobacillus sp. 130Z than in Escherichia coli K-12 and accounted for the differences in succinate production. Key enzymes in end product formation in Actinobacillus sp. 130Z were regulated by the energy substrates.

Styrene oxide and 2-phenylethanol metabolism in the styrene-degrading Xanthobacter sp. strain 124X was shown to proceed via phenylacetaldehyde and phenylacetic acid. In cell extracts 2-phenylethanol was oxidized by a phenazine methosulfate-dependent enzyme, probably a pyrroloquinoline quinone enzyme. Xanthobacter sp. strain 124X also contains a novel enzymatic activity designated as styrene oxide isomerase. Styrene oxide isomerase catalyzes the isomerization of styrene oxide to phenylacetaldehyde. The enzyme was partially purified and shown to have a very high substrate specificity. Of the epoxides tested, styrene oxide was the only substrate transformed. The initial step in styrene metabolism in Xanthobacter sp. strain 124X is oxygen dependent and probably involves oxidation of the aromatic nucleus.