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Masaru Tomita on Scale and Metabolomics Pipeline at Keio University

HEIDELBERG, Germany — A pioneer in the field of in silico modeling of biological systems, Masaru Tomita built the first computational model of a single-cell organism — Mycoplasma genitalium — in 1997. But according to Tomita, director of the E-Cell project at the Institute for Advanced Biosciences at Japan’s Keio University, “the major bottleneck [in cellular modeling] is not CPU power, but the lack of quantitative data, especially metabolic data.”

At the recent International Conference on Systems Biology, held here Oct. 9-13, Tomita discussed work underway at his lab to overcome this bottleneck by collecting metabolic data in a high-throughput manner. The primary workhorse for this task is a method first developed by Tomita and his colleagues two years ago called capillary electrophoresis mass spectrometry, or CE-MS, in which metabolites are first separated by capillary electrophoresis based on charge and size, and then identified using mass spectrometry. CE-MS can identify multiple metabolites in a sample at once, with molecular weights that range from 70 daltons to 1,000 daltons. The method performs better on charged molecules than liquid chromatography and requires less sample preparation time than gas chromatography, Tomita said.

Tomita said that his group of around 70 researchers at the Institute for Advanced Biosciences has so far been able to identify around 800 metabolites using CE-MS. The team has refined the method a bit, and has one CE-MS system devoted to positively charged molecules and another for negatively charged metabolites. For neutral metabolites, which won’t work with CE-MS, the IAB team uses liquid chromatography-mass spectrometry.

In the case of Escherichia coli, Tomita said IAB was able to identify 260 metabolites out of 586 peaks — 28 of the 260 are not available in the KEGG database. “Identifying the unknown peaks is very difficult,” he said, but he added that there is a bright side: “Once you do it, you don’t have to do it again.”

In order to facilitate the identification process, Tomita said that IAB is in the process of obtaining all commercially available standard samples for metabolites in order to run them through the system and determine what their spectral patterns are. Estimating that there are around 2,000 of these samples available, he said that this still goes only part of the way toward solving the problem — KEGG alone lists around 7,000 metabolites, he said, “and we can’t obtain all of them.”

To address this challenge, the IAB team relies on a combination of mass spec-based methods to characterize unknown peaks. Using CE/TOF-MS and LC/TOF-MS, they can determine the molecular weights of metabolites, and with CE/MS/MS and LC/MS/MS, they can establish the chemical structure. The group has also developed a computational method to predict migration times of molecules in capillary electrophoresis and CE-MS using artificial neural networks. Tomita said that by combining this wealth of experimental and predicted information, “most of the unknown peaks can eventually be disambiguated.”

Tomita said that his group’s goal is the “complete metabolome,” which he defined as “not just a list of metabolites, but the entire map of pathways in the cell.” He said that the IAB team combines a “top-down” approach based on functional genomics and the “bottom-up” approach of metabolite analysis to reconstruct metabolic pathways. Gene expression experiments, RT-PCR, Western blotting, and other methods can provide only “parts of the pathways,” he said, which can then be filled in after measuring the metabolites.

“We link these via bioinformatics to get a picture of the whole pathway,” he said. IAB uses its own GEMS (Genome-based E-Cell Modeling System) software, along with ARM (Atomic Reconstruction of Metabolites), a software package developed at the University of Tokyo, to reconstruct the metabolic pathways from genomic, proteomic, and metabolomic data, Tomita said.

One short-term goal for this massive experimental and computational undertaking is the creation of an in silico model of E. coli. Tomita said that IAB has recently completed a knock-out library of 3,917 out of 4,289 genes in the E. coli genome — an important tool for elucidating the function of the bacteria’s genome, for which only about half of the genes have been characterized so far. “We will use this resource to get systematic ‘omics’ data for all the possible combinations of genes,” he said. The IAB researchers are also knocking out entire segments of genes — a process called “genome deletion” — for 128 regions of the E. coli genome.

In addition, Tomita said, the IAB team is studying protein localization in E. coli by imaging GFP-fused proteins, and is also studying protein-protein interactions using his-tag purification and mass spectrometry. So far, he said, the researchers have identified 2,669 proteins in 11,531 interactions using this method.

While many of the goals of the E-Cell project are a long way off, Keio University sees near-term commercial promise in Tomita’s high-throughput approach to metabolics. The university spun off a company in September 2003 called Human Metabolome Technologies to commercialize the CE-MS approach developed by Tomita and his colleagues. So far, Tomita said, the company has signed agreements with two Japanese food companies — Mizkan and Ajinomoto — to study the metabolism of food products produced by microbial fermentation.

Ultimately, Human Metabolome Technologies plans to live up to its name and measure the thousands of metabolites in human biological samples, but Tomita pointed out that that high-throughput metabolomics can be useful for other industries besides healthcare. “Even a 10-percent improvement in fermentation will improve the profit for these companies greatly,” he said.

— Bernadette Toner ([email protected])

Metabolomics Instruments Deployed at the Institute for Advanced Biosciences and Human Metabolome Technologies:

  • Capillary Electrophoresis (15)
  • Liquid Chromatography (6)
  • Gas Chromatography/Mass Spectrometry (2)
  • Quadrupole Mass Spectrometry (9)
  • Ion-Trap Mass Spectrometry (4)
  • Electrospray Ionization Time-of-Flight Mass Spectrometry (1)
  • Quadrupole Time-of-Flight Mass Spectrometry (1)
  • Triple Quadrupole MS/MS (2)
  • Nucleic Magnetic Resonance (2)
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