論文

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.

We propose an approach designed to reconstitute a metabolic pathway composed of multistep biochemical reactions, rather than to dissect the individual reactions that make up the pathway. A synthetic in vitro glycolysis was reconstructed from ten purified Escherichia coli (E. coli) enzymes to obtain a better understanding of the regulation of sequential enzymatic reactions. The key to the success of this approach is the ability to perform direct and simultaneous determination of the diverse metabolic intermediates in the pathway by capillary electrophoresis-mass spectrometry. We observed that the pathway is regulated by a delicate balance between the changing metabolite concentrations and behaves like a natural biological oscillating network that has hitherto not been reported for E. coli glycolysis. The end-product, pyruvate, was periodically synthesized from glucose at an overall efficiency of 30%, corresponding to an average of 90% conversion efficiency for each of the ten steps involved. This approach is likely useful for the synthesis of natural products requiring complex sequential biocatalytic reactions.

We describe a method, based on pressure-assisted capillary electrophoresis coupled to electrospray ionization mass spectrometry (PACE/ESI-MS), that allows the simultaneous and quantitative analysis of multivalent anions, such as citrate isomers, nucleotides, nicotinamide-adenine dinucleotides, and flavin adenine dinucleotide, and coenzyme A (CoA) compounds. Key to the analysis was using a noncharged polymer, poly(dimethylsiloxane), coated to the inner surface of the capillary to prevent anionic species from adsorbing onto the capillary wall. It was also necessary to drive a constant liquid flow toward the MS by applying air pressure to the inlet capillary during electrophoresis to maintain a conductive liquid junction between the capillary and the electrospray needle. Although theoretical plates were inferior to those obtained by CE/ESI-MS using a cationic polymer-coated capillary, the PACE/ESI-MS method improved reproducibility and sensitivity of these anions. Eighteen anions were separated by PACE and selectively detected by a quadrupole mass spectrometer with a sheath-flow electrospray ionization interface. The relative standard deviations (n = 6) of the method were better than 0.6% for migration times and between 1.4% and 6.2% for peak areas. The detection limits for these species were between 0.4 and 3.7 μmol/L with pressure injection of 50 mbar for 30 s (30 nL), that is, mass detection limits calculated in the range from 12 to 110 fmol at a signal-to-noise ratio of 3. The utility of the method was demonstrated by analysis of citrate isomers, nucleotides, dinucleotides, and CoA compounds extracted from Bacillus subtilis cells.

We describe a method to identify and quantify amino acids using capillary electrophoresis-electrospray ionization-triple-quadrupole tandem mass spectrometry (CE-ESI-MS/MS). Amino acids, including physiological amino acids, were first separated by CE under acidic pH conditions and then detected by MS/MS. To efficiently introduce the whole sample into the capillary, no electrical potential was applied to the electrospray probe until running electrophoresis. The position of the electrosprayer with respect to the MS capillary entrance drastically affected sensitivity and generation of cluster ions. MS/MS with multiple reaction monitoring (MRM) detection was performed to obtain sufficient selectivity and sensitivity. Under optimized CE-MS/MS conditions, the minimum detectable levels for 32 free amino acids normally found in proteins and other physiological amino acids were between 0.1 and 14 micromol/L with pressure injection of 50 mbar for 3 s (3 nL) at a signal-to-noise ratio of 3. For most amino acids, this constitutes a severalfold increase in sensitivity compared to CE-MS. The relative standard deviations (% RSD) for all amino acids were better than 0.4% for migration times and between 1.4% and 8.6% for peak areas (n = 10). Since amino acids exhibited characteristic MS/MS spectra, this approach is useful for the simultaneous, selective, quantitative, and reproducible analysis of amino acids in physiological and biological samples that contain various kinds of matrices. The power of the method was demonstrated by analyzing amino acids in human urine.

The study of the metabolomics of primary metabolites using conventional chemical analyses requires a high‐throughput method. Chemical derivatizations are a prerequisite for gas‐chromatographic separation, and a large sample quantity is needed for liquid‐chromatographic separation and nuclear magnetic resonance detection systems. Recently, we have developed a capillary electrophoresis–mass spectrometry (CE–MS) technology that can simultaneously quantify a large number of primary metabolites, using only a small quantity of samples, and without any chemical derivatizations. Parallel use of a capillary electrophoresis–diode array detector (CE–DAD) system further enables almost all water‐soluble intracellular metabolites to be analyzed. We demonstrate, with rice leaves, a simple and rapid method of sample preparation for CE analysis; using this method, we have successfully measured the levels of 88 main metabolites involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, photorespiration, and amino acid biosynthesis.

We developed a computational technique to assist in the large-scale identification of charged metabolites. The electrophoretic mobility of metabolites in capillary electrophoresis-mass spectrometry (CE-MS) was predicted from their structure, using an ensemble of artificial neural networks (ANNs). Comparison between relative migration times of 241 various cations measured by CE-MS and predicted by a trained ANN ensemble produced a correlation coefficient of 0.931. When we used our technique to characterize all metabolites listed in the KEGG ligand database, the correct compounds among the top three candidates were predicted in 78.0% of cases. We suggest that this approach can be used for the prediction of the migration time of any cation and that it represents a powerful method for the identification of uncharacterized CE-MS peaks in metabolome analysis.

For many decades microorganisms have been used for industrial purposes; traditional fermentations such as brewing and production of food additives, aroma molecules, organic acids and pharmaceutical-like antibiotics or recombinant proteins are instances of the industrial microorganism utilization. Therefore, microorganism modeling and simulation have been required for engineering purposes, because of demands for design, optimization and quality control of large-scale fermentation plants. Modeling has recently become more highly developed, aided by the deciphering of microorganism genomes, the completion of metabolic databases, the development of analytical methodologies and improvements in the performance of computers. This paper reviews past and recent metabolic simulation of microorganisms, and also discusses the metabolome analytical techniques and the construction of large-scale microorganism models which are now being developed in our group.

A simple rule stating that the signal in conductivity detection in capillary zone electrophoresis is proportional to the difference between the analyte mobility and mobility of the background electrolyte (BGE) co‐ion is valid only for systems with fully ionized electrolytes. In zone electrophoresis systems with weak electrolytes both conductivity signal and electromigration dispersion of analyte peaks depend on the conductivity and pH effects. This allows optimization of the composition of BGEs to give a good conductivity signal of analytes while still keeping electromigration dispersion near zero, regardless of the injected amount of sample. The demands to achieve minimum electromigration dispersion and high sensitivity in conductivity detection can be accomplished at the same time. PeakMaster software is used for inspection of BGEs commonly used for separation of sugars (carbohydrates, saccharides) at highly alkaline pH. It is shown that the terms direct and indirect conductivity detection are misleading and should not be used.

Metabolomics is an emerging tool that can be used to gain insights into cellular and physiological responses. Here we present a metabolome differential display method based on capillary electrophoresis time-of-flight mass spectrometry to profile liver metabolites following acetaminophen-induced hepatotoxicity. We globally detected 1,859 peaks in mouse liver extracts and highlighted multiple changes in metabolite levels, including an activation of the ophthalmate biosynthesis pathway. We confirmed that ophthalmate was synthesized from 2-aminobutyrate through consecutive reactions with gamma-glutamylcysteine and glutathione synthetase. Changes in ophthalmate level in mouse serum and liver extracts were closely correlated and ophthalmate levels increased significantly in conjunction with glutathione consumption. Overall, our results provide a broad picture of hepatic metabolite changes following acetaminophen treatment. In addition, we specifically found that serum ophthalmate is a sensitive indicator of hepatic GSH depletion, and may be a new biomarker for oxidative stress. Our method can thus pinpoint specific metabolite changes and provide insights into the perturbation of metabolic pathways on a large scale and serve as a powerful new tool for discovering low molecular weight biomarkers.

Background and aims: Previous studies have shown that transgenic rice plants overexpressing YK1, which possesses dihydroflavonol-4-reductase (DFR) activity, showed biotic and abiotic stress tolerance. High throughput profiles of metabolites have also been shown in such transgenic plants by Fourier transform ion cyclotron mass spectrometry. In this study, capillary electrophoresis mass spectrometry analysis (CE/MS) was employed to identify precise metabolites such as organic acids, amino acids and sugars. Methods: Using CE/MS, we analysed several metabolites of glycolysis, the tricarboxylic acid (TCA) cycle and the pentose phosphate pathway. In addition, the concentrations of sugars and ion were quantified. Key results: In YK1 (DFR)-overexpressing plants, the concentrations of cis-aconitate, isocitrate and 2-oxoglutarate were higher in leaves, whereas those of fructose-1,6-bisphosphate and glyceraldehyde-3-phosphate were lower in roots. In seeds, the amounts of free amino acids and metals were altered, whereas sugars in seeds were kept constant. In YK1 calli, an approx. 3-fold increase in glutathione was observed, whereas the activities of glutathione peroxidase and glutathione reductase were concomitantly increased. Conclusions: The overexpression of YK1 (DFR) was associated with slight changes in the amounts of several metabolites analysed in whole plants, whilst glutathione derivatives were substantially increased in suspension-cultured cells.

The search for novel enzymes is an important but difficult task in functional genomics. Here, we present a systematic method based on in vitro assays in combination with metabolite profiling to discover novel enzymatic activities. A complex mixture of metabolites is incubated with purified candidate proteins and the reaction mixture is subsequently profiled by capillary electrophoresis electrospray ionization mass spectrometry (CE-MS). Specific changes in the metabolite composition can directly suggest the presence of an enzymatic activity while subsequent identification of the compounds whose level changed specifically can pinpoint the actual substrate(s) and product(s) of the reaction. We first evaluated the method using several Escherichia coli metabolic enzymes and then applied it to the functional screening of uncharacterized proteins. In this manner, YbhA and YbiV proteins were found to display both phosphotransferase and phosphatase activity toward different sugars/sugar phosphates. Our approach should be broadly applicable and useful for enzyme discovery in any system.

Background: Protein identification based on mass spectrometry (MS) has previously been performed using peptide mass fingerprinting (PMF) or tandem MS (MS/MS) database searching. However, these methods cannot identify proteins that are not already listed in existing databases. Moreover, the alternative approach of de novo sequencing requires costly equipment and the interpretation of complex MS/MS spectra. Thus, there is a need for novel high-throughput protein-identification methods that are independent of existing predefined protein databases. Results: Here, we present a hybrid method for genome-fingerprint scanning, known as HybGFS. This technique combines genome sequence-based peptide MS/MS ion searching with liquid-chromatography elution-time (LC-ET) prediction, to improve the reliability of identification. The hybrid method allows the simultaneous identification and mapping of proteins without a priori information about their coding sequences. The current study used standard LC-MS/MS data to query an in silico-generated six-reading-frame translation and the enzymatic digest of an entire genome. Used in conjunction with precursor/product ion-mass searching, the LC-ETs increased confidence in the peptide-identification process and reduced the number of false-positive matches. The power of this method was demonstrated using recombinant proteins from the Escherichia coli K12 strain. Conclusion: The novel hybrid method described in this study will be useful for the large-scale experimental confirmation of genome coding sequences, without the need for transcriptome-level expression analysis or costly MS database searching.

Analysis of cellular components at multiple levels of biological information can provide valuable functional insights. We performed multiple high-throughput measurements to study the response of Escherichia coli cells to genetic and environmental perturbations. Analysis of metabolic enzyme gene disruptants revealed unexpectedly small changes in messenger RNA and proteins for most disruptants. Overall, metabolite levels were also stable, reflecting the rerouting of fluxes in the metabolic network. In contrast, E. coli actively regulated enzyme levels to maintain a stable metabolic state in response to changes in growth rate. E. coli thus seems to use complementary strategies that result in a metabolic network robust against perturbations.

Chaperonins are absolutely required for the folding of a subset of proteins in the cell. An earlier proteome-wide analysis of Escherichia coli chaperonin GroEL/GroES (GroE) interactors predicted obligate chaperonin substrates, which were termed Class III substrates. However, the requirement of chaperonins for in vivo folding has not been fully examined. Here, we comprehensively assessed the chaperonin requirement using a conditional GroE expression strain, and concluded that only approximately 60% of Class III substrates are bona fide obligate GroE substrates in vivo. The in vivo obligate substrates, combined with the newly identified obligate substrates, were termed Class IV substrates. Class IV substrates are restricted to proteins with molecular weights that could be encapsulated in the chaperonin cavity, are enriched in alanine/glycine residues, and have a strong structural preference for aggregation-prone folds. Notably, approximately 70% of the Class IV substrates appear to be metabolic enzymes, supporting a hypothetical role of GroE in enzyme evolution.

We developed a computational method to predict the retention times of peptides in HPLC using artificial neural networks (ANN). We performed stepwise multiple linear regressions and selected for ANN input amino acids that significantly affected the LC retention time. Unlike conventional linear models, the trained ANN accurately predicted the retention time of peptides containing up to 50 amino acid residues. In 834 peptides, there was a strong correlation (R2 = 0.928) between measured and predicted retention times. We demonstrated the utility of our method by the prediction of the retention time of 121,273 peptides resulting from LysC-digestion of the Escherichia coli proteome. Our approach is useful for the proteome-wide characterization of peptides and the identification of unknown peptide peaks obtained in proteome analysis.

Background: With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods. Results: We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites. Conclusion: Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics.

Background: Density plot visualizations (also referred to as heat maps or color maps) are widely used in different fields including large-scale omics studies in biological sciences. However, the current color-codings limit the visualizations to single datasets or pairwise comparisons. Results: We propose a color-coding approach for the representation of three-way comparisons. The approach is based on the HSB (hue, saturation, brightness) color model. The three compared values are assigned specific hue values from the circular hue range (e.g. red, green, and blue). The hue value representing the three-way comparison is calculated according to the distribution of three compared values. If two of the values are identical and one is different, the resulting hue is set to the characteristic hue of the differing value. If all three compared values are different, the resulting hue is selected from a color gradient running between the hues of the two most distant values (as measured by the absolute value of their difference) according to the relative position of the third value between the two. The saturation of the color representing the three-way comparison reflects the amplitude (or extent) of the numerical difference between the two most distant values according to a scale of interest. The brightness is set to a maximum value by default but can be used to encode additional information about the three-way comparison. Conclusion: We propose a novel color-coding approach for intuitive visualization of three-way comparisons of omics data.

When erythrocytes are exposed to hypoxia, hemoglobin (Hb) stabilizes in the T-state by capturing 2,3-bisphosphoglycerate. This process could reduce the intracellular pool of glycolytic substrates, jeopardizing cellular energetics. Recent observations suggest that hypoxia-induced activation of glycolytic enzymes is correlated with their release from Band III (BIII) on the cell membrane. Based on these data, we developed a mathematical model of erythrocyte metabolism and compared hypoxia-induced differences in predicted activities of the enzymes, their products, and cellular energetics between models with and without the interaction of Hb with BIII. The models predicted that the allostery-dependent Hb interaction with BIII accelerates consumption of upstream glycolytic substrates such as glucose 6-phosphate and increases downstream products such as phosphoenolpyruvate. This prediction was consistent with metabolomic data from capillary electrophoresis mass spectrometry. The hypoxia-induced alterations in the metabolites resulted from acceleration of glycolysis, as judged by increased conversion of [13C]glucose to [13C]lactate. The allostery-dependent interaction of Hb with BIII appeared to contribute not only to maintenance of energy charge but also to further synthesis of 2,3-bisphosphoglycerate, which could help sustain stabilization of T-state Hb during hypoxia. Furthermore, such an activation of glycolysis was not observed when Hb was stabilized in R-state by treating the cells with CO. These results suggest that Hb allostery in erythrocytes serves as an O2-sensing trigger that drives glycolytic acceleration to stabilize intracellular energetics and promote the ability to release O2 from the cells.

Metabolomics is expected to boost data driven research. In biomarker discovery, powerful filtering methods to remove noise and outliers are essential for screening significant candidates from the huge volume of omic data. Here we propose a post-measurement peak filtering method (named P-BOSS) for CE electrospray ionization–time-of-flight MS (CE–TOFMS) data. Combining outlier detection method functions in parallel, we applied P-BOSS to the data using Escherichia coli knockout mutants of the tryptophan and purine biosynthesis pathways. As the result, P-BOSS showed remarkably superior performance, reducing 65% of all peaks, while leaving significant peaks.