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In April 2001, Keio University established the Institute for Advanced Biosciences, which specializes in Systems Biology, in Tsuruoka City (Yamagata Prefecture), Japan. The ultimate objective of the research at this institute is to construct a computer model of the cellular metabolism, and it has given high priority to metabolome research since its opening. As of 2005, the institute is equipped with the following world-class instrumentation for metabolome analysis: 19 CE systems, six LC systems, two GC/MS systems, nine quadrupole MS systems, four ion-trap MS systems, two Triple QMS/MS systems, six electrospray ionization time-of-flight mass spectrometers (ESI-TOF-MS), one Q-TOF-MS and one nuclear magnetic resonance (NMR) spectrometer.

The major projects being conducted at the Institute include: “ Modeling Project” funded by the New Energy and Industrial Technology Development Organization (NEDO) of the Ministry of Economy, Trade and Industry of Japan, with the ultimate aim of designing useful microorganisms; “Leading Project for Biosimulation” funded by the Ministry of Education, Culture, Sports, Science and Technology (Monbusho), which is performing metabolome analysis and simulation of red blood cells; and the Grants for Scientific Research and Scientific Research of Priority Areas (funded also by the Ministry of Education, Culture, Sports, Science and Technology), and 21st Century COE program at Keio University entitled “The Understanding and Control of Life’s Function via Systems Biology,” which are developing the basic technologies for metabolomics and cell simulation. In addition, the Faculty of Medicine at the University of Tokyo, Japan, established the Center for Metabolome in February 2003. This group is focusing primarily on lipid metabolomics using LC/MS technology.

Finally, Keio University and its partners invested in the establishment of a “bio-venture” company, Human Metabolome Technologies (HMT) Inc., in Tsuruoka City in July 2003. Utilizing the metabolome technology of the Institute for Advanced Biosciences of Keio University, HMT is now conducting joint research with major food companies, to understand bacterial metabolism of fermentation used in the food industry. Their future plan is to apply this technology to the fields of medical sciences through collaborations with drug companies.

Metabolic profiling by FT-ICR-MS and ESI-Q-TOF-MS is an effective method to capture a rough image of metabolism. It should be performed after the accuracy is confirmed by using the authentic sample after an elaborate calibration is done, when an individual metabolite is identified from the accurate mass for FT-ICR-MS. Moreover, when ESI-Q-TOF-MS is used, the CID spectrum should also be measured, and compared with that of the authentic sample.

The focused analysis for the metabolites obtained by rough separation with the differences in solubility compared to the extraction solvent is very effective, because further separation such as by LC or CE conditions suitable for the chemical properties of these metabolites is more easily selected. This is true for all metabolomics studies, especially in the detection of minor components. By using the differences in solubility, metabolites can be categorized into different groups of chemical and physiological nature. For each specified category of the metabolites, specified fields of metabolomics such as for peptidome, glycome, and lipidome can exist. It is very important that new strategies of analytical methods and databases for each of these categories of metabolites are created.

This chapter has described the analytical methods of metabolites by HPLC and LC/MS, based on the functional group. Amino acids and organic acids can be detected by using specific derivatization reagents for amino groups and carboxyl groups, respectively. Sugar phosphates are detected by selected reaction monitoring, using the characteristic cleavage between the phosphate and sugar moiety or by pulsed amperometry using a reducing sugar.

It is quite difficult to perform comprehensive and simultaneous analyses of a variety of intracellular metabolites by HPLC, as well as by other methods. We propose that the intracellular metabolites should first be classified by functional groups. A hundred amines, several dozen organic acids, and several dozen phosphate esters can be observed as peaks by HPLC. The combination of every profile results in a comprehensive analysis of the intracellular metabolites.

The HPLC method based on functional groups is also useful for the determination of intracellular metabolites, especially by LC/MS/MS. The methods are applicable for the specific determination of amino acids, TCA cycle intermediates, and glycolysis and pentose phosphate pathway intermediates, which are the most fundamental metabolites and intermediates in cells.

Integration of the method and instrument development are essential for a significant advancement of metabolomics, and we believe that the methods mentioned in this chapter provide useful tips toward this end.

Metabolic profiling by FT-ICR-MS and ESI-Q-TOF-MS is an effective method to capture a rough image of metabolism. It should be performed after the accuracy is confirmed by using the authentic sample after an elaborate calibration is done, when an individual metabolite is identified from the accurate mass for FT-ICR-MS. Moreover, when ESI-Q-TOF-MS is used, the CID spectrum should also be measured, and compared with that of the authentic sample.

Metabolic profiling by FT-ICR-MS and ESI-Q-TOF-MS is an effective method to capture a rough image of metabolism. It should be performed after the accuracy is confirmed by using the authentic sample after an elaborate calibration is done, when an individual metabolite is identified from the accurate mass for FT-ICR-MS. Moreover, when ESI-Q-TOF-MS is used, the CID spectrum should also be measured, and compared with that of the authentic sample.

There are bright prospects for the modeling of metabolic systems. However, in most cells except cells in which no genetic expression at all occurs, such as human erythrocytes, metabolism is controlled by genes. That is, the maximum activity of each enzyme is regulated by the gene expression, and changes dynamically according to the circumstance. Moreover, because one group of enzyme genes is not expressed under normal conditions, some cases in each pathway are expunged. In such cases and only under specific conditions, the enzyme group associated with that pathway is expressed and operated. It is therefore unlikely the metabolic pathway map created with the aid of data mining from genome sequencing using pathway databases and the GEM system could be applied in its present form. That is, it is not likely that detailed cell simulation could be obtained through metabolic modeling only; rather, the cell simulation would likely be a combination of a gene expression model and signal transmission model controlling it.

Although the principles differ from this hybrid method for metabolic systems, we are currently developing a new method to enable modeling of gene expression systems and signal transduction pathways.

Routine use of micro HPLC will need the development of several important constituents; the reproducible preparation of high-performance columns, small-volume pumps and gradient systems, and improvement of an injection system. Subjects to be studied are the development of high-performance monolithic silica columns for a variety of separation modes, multidimensional micro LC systems, and optimization of an interface between LC and MS instruments. Large peak capacities realized by highly efficient micro HPLC systems or multidimensional HPLC will greatly contribute to metabolomics studies when coupled with MS instruments.

The metabolite profiles of the cells are similarly independent on the carbon sources regardless of whether they suppress others or are suppressed by others. All the similar profiles were measured at the maximum growth rate, suggesting that has a predetermined metabolite profile optimized for the maximum growth rate. Differences in carbon sources induced local perturbations in the predetermined profile. One of such perturbations was the accumulation of the starting metabolites in the suppressed carbon sources. Combined analysis of the metabolite profile and DNA microarrays revealed that the first reaction in the catabolism was rate-limiting when was grown on suppressed carbon sources, although the enzyme genes of the reactions were upregulated. The present analysis suggests that the decrease or increase in the gene expression of an enzyme does not always result in the accumulation or decrease in its substrates or products, because of the multiplicity of metabolic pathway networks. Metabolome and transcriptome data that supplement each other provide much informatiion to study the global regulation of metabolism.

This chapter has described the analytical methods of metabolites by HPLC and LC/MS, based on the functional group. Amino acids and organic acids can be detected by using specific derivatization reagents for amino groups and carboxyl groups, respectively. Sugar phosphates are detected by selected reaction monitoring, using the characteristic cleavage between the phosphate and sugar moiety or by pulsed amperometry using a reducing sugar.

It is quite difficult to perform comprehensive and simultaneous analyses of a variety of intracellular metabolites by HPLC, as well as by other methods. We propose that the intracellular metabolites should first be classified by functional groups. A hundred amines, several dozen organic acids, and several dozen phosphate esters can be observed as peaks by HPLC. The combination of every profile results in a comprehensive analysis of the intracellular metabolites.

The HPLC method based on functional groups is also useful for the determination of intracellular metabolites, especially by LC/MS/MS. The methods are applicable for the specific determination of amino acids, TCA cycle intermediates, and glycolysis and pentose phosphate pathway intermediates, which are the most fundamental metabolites and intermediates in cells.

Integration of the method and instrument development are essential for a significant advancement of metabolomics, and we believe that the methods mentioned in this chapter provide useful tips toward this end.