A new approach for the comprehensive and quantitative analysis of charged metabolites by capillary electrophoresis mass spectrometry (CE-MS) is proposed. Metabolites are first separated by CE based on charge and size and then selectively detected using MS by monitoring over a large range of m/z values. This method enabled the determination of 352 metabolic standards and its utility was demonstrated in the analysis of 1692 metabolites from Bacillus subtilis extracts, revealing significant changes in metabolites during B. subtilis sporulation.

A method for simultaneous determination of anionic metabolites based on capillary electrophoresis (CE) coupled to electrospray ionization mass spectrometry is described. To prevent current drop by the system, electroosmotic flow (EOF) reversal by using a cationic polymer-coated capillary was indispensable. A mixture containing 32 standards including carboxylic acids, phosphorylated carboxylic acids, phosphorylated saccharides, nucleotides, and nicotinamide and flavin adenine coenzymes of glycolysis and the tricarboxylic acid cycle pathways were separated by CE and selectively detected by a quadrupole mass spectrometer with a sheath-flow electrospray ionization interface. Key to the analysis was EOF reversal using a cationic polymer-coated capillary and an electrolyte system consisting of 50 mM ammonium acetate, pH 9.0. The relative standard deviations of the method were better than 0.4% for migration times and between 0.9% and 5.4% for peak areas. The concentration detection limits for these metabolites were between 0.3 and 6.7 micromol/L with pressure injection of 50 mbar for 30 s (30 nL); i.e., mass detection limits ranged from 9 to 200 fmol, at a signal-to-noise ratio of 3. This method was applied to the comprehensive analysis of metabolic intermediates extracted from Bacillus subtilis, and 27 anionic metabolites could be directly detected and quantified.

Metabolic changes in response to histidine starvation were observed in histidine-auxotrophic Escherichia coli using a capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS)-based metabolomics technique. Prior to the analysis, we prepared an E. coli metabolome list of 727 metabolites reported in the literature. An improved method for metabolite extraction was developed, which resulted in higher extraction efficiency in phosphate-rich metabolites, e.g., ATP and GTP. Based on the results, 375 charged, hydrophilic intermediates in primary metabolisms were analysed simultaneously, providing quantitative data of 198 metabolites. We confirmed that the intracellular levels of intermediates in histidine biosynthesis are rapidly accumulated in response to a drop in histidine level under histidine-starved conditions. Simultaneously, disciplined responses were observed in the glycolysis, tricarboxylic acid cycle, and amino acid and nucleotide biosynthesis pathways as regulated by amino acid starvation.

Metabolomics is an emerging technology that reveals homeostatic imbalances in biological systems. Global determination of metabolite concentrations in body fluid and tissues provides novel anatomical aspects of pathological conditions that cannot be obtained from target-specific measurements. Here, we characterised metabolic imbalance in Watanabe heritable hyperlipidaemic rabbits as a model of hypercholesterolaemia. Using a mass spectrometry-based system, we measured a total of 335 metabolites in plasma and tissues (liver, aorta, cardiac muscle, and brain) from WHHL and healthy control rabbits. From the comparison between two metabolomic profiles, pathophysiological features including glutathione and phosphatidylcholine metabolism indicated the occurrence of oxidative stress in several tissues. Especially for the liver, imbalanced purine catabolism shed light on the transcriptional activation of xanthine oxidase, which is thought to act in absorbing or possibly triggering oxidative stress. We also applied this system to assess the therapeutic effects of simvastatin administration. After the treatment, a portion of the metabolomic features in pathological conditions showed alterations suggesting restoration of metabolism to the healthy condition. These changes were considered to be due to the pleiotropic action of statin, including antioxidant effects, rather than its main inhibitory action on cholesterol biosynthesis.

Metabolic microenvironment of tumor cells is influenced by oncogenic signaling and tissue-specific metabolic demands, blood supply, and enzyme expression. To elucidate tumor-specific metabolism, we compared the metabolomics of normal and tumor tissues surgically resected pairwise from nine lung and seven prostate cancer patients, using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). Phosphorylation levels of enzymes involved in central carbon metabolism were also quantified. Metabolomic profiles of lung and prostate tissues comprised 114 and 86 metabolites, respectively, and the profiles not only well distinguished tumor from normal tissues, but also squamous cell carcinoma from the other tumor types in lung cancer and poorly differentiated tumors from moderately differentiated tumors in prostate cancer. Concentrations of most amino acids, especially branched-chain amino acids, were significantly higher in tumor tissues, independent of organ type, but of essential amino acids were particularly higher in poorly differentiated than moderately differentiated prostate cancers. Organ-dependent differences were prominent at the levels of glycolytic and tricarboxylic acid cycle intermediates and associated energy status. Significantly high lactate concentrations and elevated activating phosphorylation levels of phosphofructokinase and pyruvate kinase in lung tumors confirmed hyperactive glycolysis. We highlighted the potential of CE-TOFMS-based metabolomics combined with phosphorylated enzyme analysis for understanding tissue-specific tumor microenvironments, which may lead to the development of more effective and specific anticancer therapeutics.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-012-0452-2) contains supplementary material, which is available to authorized users.

To elucidate the biological functions of small (p)ppGpp synthetases YjbM and YwaC of Bacillus subtilis, we constructed RIK1059 and RIK1066 strains carrying isopropyl‐β‐D‐thiogalactopyranoside (IPTG) inducible yjbM and ywaC genes, respectively, in the ΔrelA ΔyjbM ΔywaC triple mutant background. While the uninduced and IPTG‐induced RIK1059 cells grew similarly in LB medium, the growth of RIK1066 cells was arrested following the addition of IPTG during the early exponential growth phase. Induction of YwaC expression by IPTG also severely decreased the intracellular GTP level and drastically altered the transcriptional profile in RIK1066 cells. Sucrose density gradient centrifugation analysis of the ribosomal fractions prepared from the IPTG‐induced RIK1066 cells revealed three peaks corresponding to 30S, 50S, and 70S ribosome particles, and also an extra peak. Electron microscope studies revealed that the extra peak fraction contained dimers of 70S ribosomes, which were similar to the Escherichia coli 100S ribosomes. Proteomic analysis revealed that the 70S dimer contained an extra protein, YvyD, in addition to those found in the 70S ribosome. Accordingly, strain resulting from the disruption of the yvyD gene in the RIK1066 cells was unable to form 70S dimers following IPTG induction, indicating that YvyD is required for the formation of these dimers in B. subtilis.

The practical realization of DNA data storage is a major scientific goal. Here we introduce a simple, flexible, and robust data storage and retrieval method based on sequence alignment of the genomic DNA of living organisms. Duplicated data encoded by different oligonucleotide sequences was inserted redundantly into multiple loci of the Bacillus subtilis genome. Multiple alignment of the bit data sequences decoded by B. subtilis genome sequences enabled the retrieval of stable and compact data without the need for template DNA, parity checks, or error-correcting algorithms. Combined with the computational simulation of data retrieval from mutated message DNA, a practical use of this alignment-based method is discussed.

BackgroundPrincipal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g., the top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences are made for these metabolites. However, this approach may lead to biased biological inferences because these metabolites are not objectively selected with statistical criteria.ResultsWe propose a statistical procedure that selects metabolites with statistical hypothesis testing of the factor loading in PCA and makes biological inferences about these significant metabolites with a metabolite set enrichment analysis (MSEA). This procedure depends on the fact that the eigenvector in PCA for autoscaled data is proportional to the correlation coefficient between the PC score and each metabolite level. We applied this approach to two sets of metabolomic data from mouse liver samples: 136 of 282 metabolites in the first case study and 66 of 275 metabolites in the second case study were statistically significant. This result suggests that to set the number of metabolites before the analysis is inappropriate because the number of significant metabolites differs in each study when factor loading is used in PCA. Moreover, when an MSEA of these significant metabolites was performed, significant metabolic pathways were detected, which were acceptable in terms of previous biological knowledge.ConclusionsIt is essential to select metabolites statistically to make unbiased biological inferences from metabolomic data when using factor loading in PCA. We propose a statistical procedure to select metabolites with statistical hypothesis testing of the factor loading in PCA, and to draw biological inferences about these significant metabolites with MSEA. We have developed an R package “mseapca” to facilitate this approach. The “mseapca” package is publicly available at the CRAN website.