Papers - Tomita, Masaru

Genomic heterogeneity in a natural archaeal population suggests a model of tRNA gene disruption

Sugahara J, Fujishima K, Nunoura T, Takaki Y, Takami H, Takai K, Tomita M, Kanai A.

PLoS ONE (PLoS ONE)  7 ( 3 ) 1-8 2012.03

Research paper (scientific journal), Joint Work

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Understanding the mechanistic basis of the disruption of tRNA genes, as manifested in the intron-containing and split tRNAs found in Archaea, will provide considerable insight into the evolution of the tRNA molecule. However, the evolutionary processes underlying these disruptions have not yet been identified. Previously, a composite genome of the deep-branching archaeon Caldiarchaeum subterraneum was reconstructed from a community genomic library prepared from a C. subterraneum-dominated microbial mat. Here, exploration of tRNA genes from the library reveals that there are at least three types of heterogeneity at the tRNA Thr(GGU) gene locus in the Caldiarchaeum population. All three involve intronic gain and splitting of the tRNA gene. Of two fosmid clones found that encode tRNA Thr(GGU), one (tRNA Thr-I) contains a single intron, whereas another (tRNA Thr-II) contains two introns. Notably, in the clone possessing tRNA Thr-II, a 5′ fragment of the tRNA Thr-I (tRNA Thr-F) gene was observed 1.8-kb upstream of tRNA Thr-II. The composite genome contains both tRNA Thr-II and tRNA Thr-F, although the loci are >500 kb apart. Given that the 1.8-kb sequence flanked by tRNA Thr-F and tRNA Thr-II is predicted to encode a DNA recombinase and occurs in six regions of the composite genome, it may be a transposable element. Furthermore, its dinucleotide composition is most similar to that of the pNOB8-type plasmid, which is known to integrate into archaeal tRNA genes. Based on these results, we propose that the gain of the tRNA intron and the scattering of the tRNA fragment occurred within a short time frame via the integration and recombination of a mobile genetic element. © 2012 Sugahara et al.

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Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Sugimoto, Masahiro; Kawakami, Masato; Robert, Martin; Soga, Tomoyoshi; Tomita, Masaru

Current Bioinformatics (Current Bioinformatics)  7 ( 1 ) 96-108 - 108 2012.03

Research paper (scientific journal), Joint Work,  ISSN  15748936

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Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader. © 2012 Bentham Science Publishers.

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Measures of Compositional Strand Bias Related to Replication Machinery and its Applications

Arakawa K., Tomita M.

Current Genomics (Current Genomics)  13 ( 1 ) 4 - 15 2012.03

ISSN  13892029

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The compositional asymmetry of complementary bases in nucleotide sequences implies the existence of a mutational or selectional bias in the two strands of the DNA duplex, which is commonly shaped by strand-specific mechanisms in transcription or replication. Such strand bias in genomes, frequently visualized by GC skew graphs, is used for the computational prediction of transcription start sites and replication origins, as well as for comparative evolutionary genomics studies. The use of measures of compositional strand bias in order to quantify the degree of strand asymmetry is crucial, as it is the basis for determining the applicability of compositional analysis and comparing the strength of the mutational bias in different biological machineries in various species. Here, we review the measures of strand bias that have been proposed to date, including the ΔGC skew, the B 1 index, the predictability score of linear discriminant analysis for gene orientation, the signal-to-noise ratio of the oligonucleotide bias, and the GC skew index. These measures have been predominantly designed for and applied to the analysis of replication-related mutational processes in prokaryotes, but we also give research examples in eukaryotes. ©2012 Bentham Science Publishers.

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Metatranscriptomic Analysis of Microbes in an Oceanfront Deep-Subsurface Hot Spring Reveals Novel Small RNAs and Type-Specific tRNA Degradation

Murakami S., Fujishima K., Tomita M., Kanai A.

Applied and Environmental Microbiology (Applied and Environmental Microbiology)  78 ( 4 ) 1015-1022 - 1022 2012.02

Research paper (scientific journal), Joint Work,  ISSN  00992240

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Studies of small noncoding RNAs (sRNAs) have been conducted predominantly using culturable organisms, and the acquisition of further information about sRNAs from global environments containing uncultured organisms now is very important. In this study, hot spring water (57°C, pH 8.1) was collected directly from the underground environment at depths of 250 to 1,000min Yunohama, Japan, and small RNA sequences obtained from the environment were analyzed. A phylogenetic analysis of both archaeal and bacterial 16S rRNA gene sequences was conducted, and the results suggested the presence of unique species in the environment, corresponding to the Archaeal Richmond Mine Acidophilic Nanoorganisms (ARMAN) group and three new Betaproteobacteria. A metatranscriptomic analysis identified 64,194 (20,057 nonredundant) cDNA sequences. Of these cDNAs, 90% were either tRNAs, tRNA fragments, rRNAs, or rRNA fragments, whereas 2,181 reads (10%) were classified as previously uncharacterized putative candidate sRNAs. Among these, 15 were particularly abundant, 14 of which showed no sequence similarity to any known noncoding RNA, and at least six of which form very stable RNA secondary structures. The analysis of a large number of tRNA fragments suggested that unique relationships exist between the anticodons of the tRNAs and the sites of tRNA degradation. Previous bacterial tRNA degradation studies have been limited to specific organisms, such as Escherichia coli and Streptomyces coelicolor, and the current results suggest that specific tRNA decay occurs more frequently than previously expected. © 2012, American Society for Microbiology.

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MMMDB: Mouse multiple tissue metabolome database

Sugimoto M., Ikeda S., Niigata K., Tomita M., Sato H., Soga T.

Nucleic Acids Research (Nucleic Acids Research)  40 ( D1 ) D809 - 14 2012.01

ISSN  03051048

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The Mouse Multiple Tissue Metabolome Database (MMMDB) provides comprehensive and quantitative metabolomic information for multiple tissues from single mice. Manually curated databases that integrate literature-based individual metabolite information have been available so far. However, data sets on the absolute concentration of a single metabolite integrated from multiple resources are often difficult to be used when different metabolomic studies are compared because the relative balance of the multiple metabolite concentrations in the metabolic pathways as a snapshot of a dynamic system is more important than the absolute concentration of a single metabolite. We developed MMMDB by performing non-targeted analyses of cerebra, cerebella, thymus, spleen, lung, liver, kidney, heart, pancreas, testis and plasma using capillary electrophoresis time-of-flight mass spectrometry and detected 428 non-redundant features from which 219 metabolites were successfully identified. Quantified concentrations of the individual metabolites and the corresponding processed raw data; for example, the electropherograms and mass spectra with their annotations, such as isotope and fragment information, are stored in the database. MMMDB is designed to normalize users' data, which can be submitted online and used to visualize overlaid electropherograms. Thus, MMMDB allows newly measured data to be compared with the other data in the database. MMMDB is available at: http:// mmmdb.iab.keio.ac.jp. © The Author(s) 2011.

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Time-structure of the yeast metabolism in vivo

Sasidharan K., Tomita M., Aon M., Lloyd D., Murray D.B.

Advances in Experimental Medicine and Biology  2012

ISSN  00652598

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The third plasmid pVV8 from Thermus thermophilus HB8: isolation, characterization, and sequence determination

Ohtani., N., Tomita, M. and Itaya, M.

Extremophiles (Extremophiles)  16 ( 2 ) 237-44 - 244 2012

Research paper (scientific journal), Joint Work,  ISSN  14310651

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The extremely thermophilic bacterium Thermus thermophilus is a model organism for structural biology and systems biology, and the so-called "Structural and Functional Whole-Cell Project for T. thermophilus HB8" is in progress. The released genomic sequence of the strain HB8 is composed of chromosome, pTT27 megaplasmid, and pTT8 plasmid. In this paper, however, a third plasmid was demonstrated and its sequence was determined. Although this plasmid pVV8 had been reported before, limited information and an unfortunate dropout in the substrain, whose genomic sequence was determined, would have prevented the plasmid from coming to public attention. The intrinsic circular plasmid, which was estimated to be six to ten copies in a cell, is 81151 bp and its G + C content is 68%. Among the identified 91 ORFs, a single gene has been experimentally analyzed before and is known as xylose isomerase. The phnCDEGHIJKLMX operon related to phosphonate metabolism, alkaline phosphatase, putative transcriptional regulators, several sets of toxin-antitoxin system, and transposase-like ORFs are also encoded on the pVV8 plasmid. Although association with cell aggregation was the one phenotypic characteristic of the plasmid that had been reported, it was never confirmed. Comparison of T. thermophilus HB8 strains suggests that the pVV8 is nonessential for growth. © 2011 Springer.

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Prediction of axillary lymph node metastasis in primary breast cancer patients using a decision tree-based model

Takada, M., Sugimoto, M., Naito, Y., Moon, H., G., Han, W., Noh, D., Y., Kondo, M., Kuroi, K., Sasano, H., Inamoto, T., Tomita, M., and Toi, M.

BMC Medical Informatics and Decision Making (BMC Medical Informatics and Decision Making)  12 ( 1 ) 54 2012

Research paper (scientific journal), Joint Work, Accepted

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Background: The aim of this study was to develop a new data-mining model to predict axillary lymph node (AxLN) metastasis in primary breast cancer. To achieve this, we used a decision tree-based prediction method-The alternating decision tree (ADTree). Methods: Clinical datasets for primary breast cancer patients who underwent sentinel lymph node biopsy or AxLN dissection without prior treatment were collected from three institutes (institute A, n = 148; institute B, n = 143; institute C, n = 174) and were used for variable selection, model training and external validation, respectively. The models were evaluated using area under the receiver operating characteristics (ROC) curve analysis to discriminate node-positive patients from node-negative patients. Results: The ADTree model selected 15 of 24 clinicopathological variables in the variable selection dataset. The resulting area under the ROC curve values were 0.770 [95% confidence interval (CI), 0.689-0.850] for the model training dataset and 0.772 (95% CI: 0.689-0.856) for the validation dataset, demonstrating high accuracy and generalization ability of the model. The bootstrap value of the validation dataset was 0.768 (95% CI: 0.763-0.774). Conclusions: Our prediction model showed high accuracy for predicting nodal metastasis in patients with breast cancer using commonly recorded clinical variables. Therefore, our model might help oncologists in the decision-making process for primary breast cancer patients before starting treatment. © 2012 Takada et al.; licensee BioMed Central Ltd.

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Rapid DNA, RNA and protein extraction protocols optimized for slow continuously growing yeast cultures

Sasidharan, K., Amariei, C., Tomita, M., and Murray, D, B.

Yeast (Yeast)  29 ( 8 ) 311-22 - 322 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  0749503X

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Conventional extraction protocols for yeast have been developed for relatively rapid-growing low cell density cultures of laboratory strains and often do not have the integrity for frequent sampling of cultures. Therefore, these protocols are usually inefficient for cultures under slow growth conditions or of non-laboratory strains. We have developed a combined mechanical and chemical disruption procedure using vigorous bead-beating that can consistently disrupt yeast cells (> 95%), irrespective of cell cycle and metabolic state. Using this disruption technique coupled with quenching, we have developed DNA, RNA and protein extraction protocols that are optimized for a large number of samples from slow-growing high-density industrial yeast cultures. Additionally, sample volume, the use of expensive reagents/enzymes, handling times and incubations were minimized. We have tested the reproducibility of our methods using triplicate/time-series extractions and compared these with commonly used protocols or commercially available kits. Moreover, we utilized a simple flow-cytometric approach to estimate the mitochondrial DNA copy number. Based on the results, our methods have shown higher reproducibility, yield and quality. © 2012 John Wiley & Sons, Ltd.

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Predictions of the pathological response to neoadjuvant chemotherapy in patients with primary breast cancer using a data mining technique

Takada, M., Sugimoto, M., Ohno, S., Kuroi, K., N. Sato , H., Bando, H., Masuda, N., Iwata, H., Kondo, M., Sasano, H., Chow, L., W., C., Inamoto , T., Naito, Y., Tomita, M. and Toi, M.

Breast Cancer Research and Treatment 134 ( 2 ) 661-70 - 670 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  01676806

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Nomogram, a standard technique that utilizes multiple characteristics to predict efficacy of treatment and likelihood of a specific status of an individual patient, has been used for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. The aim of this study was to develop a novel computational technique to predict the pathological complete response (pCR) to NAC in primary breast cancer patients. A mathematical model using alternating decision trees, an epigone of decision tree, was developed using 28 clinicopathological variables that were retrospectively collected from patients treated with NAC (n = 150), and validated using an independent dataset from a randomized controlled trial (n = 173). The model selected 15 variables to predict the pCR with yielding area under the receiver operating characteristics curve (AUC) values of 0.766 [95 % confidence interval (≥I)], 0.671-0.861, P value< 0.0001) in cross-validation using training dataset and 0.787 (95 % CI 0.716-0.858, P value < 0.0001) in the validation dataset. Among three subtypes of breast cancer, the luminal subgroup showed the best discrimination (AUC = 0.779, 95 % CI 0.641-0.917, P value = 0.0059). The developed model (AUC = 0.805, 95 % CI 0.716-0.894, P value\0.0001) outperformed multivariate logistic regression (AUC = 0.754, 95 % CI 0.651-0.858, P value = 0.00019) of validation datasets without missing values (n = 127). Several analyses, e.g. bootstrap analysis, revealed that the developed model was insensitive to missing values and also tolerant to distribution bias among the datasets. Our model based on clinicopathological variables showed high predictive ability for pCR. This model might improve the prediction of the response to NAC in primary breast cancer patients. © Springer Science+Business Media, LLC. 2012.

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Non-targeted metabolite profiling in activated macrophage secretion

Sugimoto, M., Sakagami, H., Yokote, Y., Onuma, H., Kaneko, M., Mori, M., Sakaguchi, Y., Soga, T. and Tomita, M.

Metabolomics 8 ( 4 ) 624–633 - 633 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  15733882

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Periodontal diseases are inflammatory infectious diseases that affect the periodontal tissue. Macrophages play a central role in inflammatory conditions, leading to the destruction of tissues. Identifying the signaling molecules secreted by macrophages would be valuable to the study of these diseases. Here, we present non-targeted analysis using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) for the profiling of extracellular metabolites released during macrophage activation. Lipopolysaccharide (LPS)-induced activation of a mouse macrophage-like cell line RAW264. 7 was used as a model system. Cells were treated without (control) or with LPS for 22 h and, after washing, were incubated for 1 h in phosphate-buffered saline. The accumulation of metabolites in the culture supernatant was monitored. LPS treatment significantly enhanced the accumulation of prostaglandins, tumor necrosis factor-α, nitric oxide and citrulline in the culture medium. RAW264. 7 cells produced 46 metabolites and 66% of these showed significant changes (P < 0.05) following cell activation. In particular, the production of leucine, hypoxanthine, choline, putrecine, N 8-acetylspermidine, succinate, itaconate, and 4-methyl-2-oxopentanoate was significantly increased by cell activation (P < 0.001). Significantly elevated production of lactate and glycine was also observed. Here, we present the first catalog of the up and down-regulation of the various metabolites secreted by macrophages following inflammatory activation. © 2011 Springer Science+Business Media, LLC.

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Mass spectrum sequential subtraction speeds up searching large peptide MS/MS spectra datasets against large nucleotide databases for proteogenomics

Helmy, M., Sugiyama, N., Tomita, M. and Ishihama, Y.

Genes to Cells (Genes to Cells)  17 ( 8 ) 633-44 - 644 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  13569597

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We have developed a novel bioinformatics method called mass spectrum sequential subtraction (MSSS) to search large peptide spectra datasets produced by liquid chromatography/mass spectrometry (LC-MS/MS) against protein and large-sized nucleotide sequence databases. The main principle in MSSS is to search the peptide spectra set against the protein database, followed by removal of the spectra corresponding to the identified peptides to create a smaller set of the remaining peptide spectra for searching against the nucleotide sequences database. Therefore, we reduce the number of spectra to be searched to limit the peptide search space. Comparing MSSS and conventional search approach using a dataset of 27 LC-MS/MS runs of rice culture cells indicated that MSSS reduced the search queries to 50% and the search time to 75% on average. In addition, MSSS had no effect on the identification false-positive rate (FPR) or the novel peptide sequences identification ability. We used MSSS to analyze another dataset of 34 LC-MS/MS runs, resulting in identifying additional 74 novel peptides. Proteogenomic analysis with these additional peptides yielded 47 new genomic features in 24 rice genes plus 24 intergenic peptides. These results show that the utility of MSSS in searching large databases with large MS/MS datasets for proteogenomics. © 2012 The Authors Journal compilation © 2012 by the Molecular Biology Society of Japan/Blackwell Publishing Ltd.

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Investigation of stochasticity in TRAIL signaling cancer model

Piras, V., Hayashi, K., Tomita, M. and Selvarajoo, K.

IEEE/ICME Com. Med. Eng.    609-614. - 614 2012

Research paper (scientific journal), Joint Work, Accepted

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In cancer, apoptosis or programmed cell death has been demonstrated through the tumor necrosis factor related apoptosis-inducing ligand (TRAIL) signal transduction. As a result, TRAIL-based therapies have been widely investigated to fight cancers. However, several malignant cancer types still remain resistant to TRAIL. Recently, we developed a dynamic computational model to investigate the resistance mechanisms in TRAIL-stimulated human fibrosarcoma (HT1080) cells. The macroscopic average-cell response model, based on the law of mass action and signaling flux conservation, successfully simulates the semi-quantitative temporal profiles of cell survival (I?B, JNK, p38) and apoptotic (caspase-8 and -3) molecules in wildtype and several mutants (FADD, RIP1 and TRAF2 knockdowns or KD). However, cancer populations are known to be highly heterogeneous, and various studies have demonstrated the importance of stochasticity and variability for phenotypic diversity between identical cells. Here, we extend our original model to investigate the effect of such fluctuations on TRAIL signaling response by adopting probabilities of signaling reactions through the Gillespie algorithm. Notably, when we stimulated the model 1000 times to indicate the variability of 1000 single cell responses in all 4 experimental conditions with different levels of stochasticity, we notice that TRAF2 KD produced the most variable signaling response. This variance subsequently affected the level of cellular apoptosis analysed through the cell-survival metric (CSM). Our work highlights the necessity to understand variable responses of cell signaling reactions to different levels of stochasticity. Thus, prior to the actual development of potential drug targets for killing cancer cells, the effect of stochastic variance could be investigated through dynamic models. © 2012 IEEE.

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A Yeast Metabolite Extraction Protocol Optimised for Time-Series Analyses

Sasidharan, K., Soga, T., Tomita, M. and Murray, D., B.

PLoS ONE (PLoS ONE)  7 ( 8 ) e44283 2012

Research paper (scientific journal), Joint Work, Accepted

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There is an increasing call for the absolute quantification of time-resolved metabolite data. However, a number of technical issues exist, such as metabolites being modified/degraded either chemically or enzymatically during the extraction process. Additionally, capillary electrophoresis mass spectrometry (CE-MS) is incompatible with high salt concentrations often used in extraction protocols. In microbial systems, metabolite yield is influenced by the extraction protocol used and the cell disruption rate. Here we present a method that rapidly quenches metabolism using dry-ice ethanol bath and methanol N-ethylmaleimide solution (thus stabilising thiols), disrupts cells efficiently using bead-beating and avoids artefacts created by live-cell pelleting. Rapid sample processing minimised metabolite leaching. Cell weight, number and size distribution was used to calculate metabolites to an attomol/cell level. We apply this method to samples obtained from the respiratory oscillation that occurs when yeast are grown continuously. © 2012 Sasidharan et al.

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Metabolic regulation analysis of wild-type and arcA mutant Escherichia coli under nitrate conditions using different levels of omics data

Toya, Y., Nakahigashi, K., Tomita, M., and Shimizu, K.

Molecular BioSystems (Molecular BioSystems)  8 ( 10 ) 2593-604. - 2604 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  1742206X

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It is of practical interest to investigate the effect of nitrates on bacterial metabolic regulation of both fermentation and energy generation, as compared to aerobic and anaerobic growth without nitrates. Although gene level regulation has previously been studied for nitrate assimilation, it is important to understand this metabolic regulation in terms of global regulators. In the present study, therefore, we measured gene expression using DNA microarrays, intracellular metabolite concentrations using CE-TOFMS, and metabolic fluxes using the ¹³C-labeling technique for wild-type E. coli and the ΔarcA (a global regulatory gene for anoxic response control, ArcA) mutant to compare the metabolic state under nitrate conditions to that under aerobic and anaerobic conditions without nitrates in continuous culture conditions at a dilution rate of 0.2 h^-1. In wild-type, although the measured metabolite concentrations changed very little among the three culture conditions, the TCA cycle and the pentose phosphate pathway fluxes were significantly different under each condition. These results suggested that the ATP production rate was 29% higher under nitrate conditions than that under anaerobic conditions, whereas the ATP production rate was 10% lower than that under aerobic conditions. The flux changes in the TCA cycle were caused by changes in control at the gene expression level. In ΔarcA mutant, the TCA cycle flux was significantly increased (4.4 times higher than that of the wild type) under nitrate conditions. Similarly, the intracellular ATP/ADP ratio increased approximately two-fold compared to that of the wild-type strain. © 2012 The Royal Society of Chemistry.

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Temporal profiling of lapatinib-suppressed phosphorylation signals inEGFR/HER2 pathways

Imami, K., Sugiyama, N., Imamura, H., Wakabayashi, M., Tomita, M., Taniguchi, M., Ueno, T., Toi, M. and Ishihama, Y.

Mol Cell Proteomics (Molecular and Cellular Proteomics)  11 ( 12 ) [Epub ahead of print] - 1757 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  15359476

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Lapatinib is a clinically potent kinase inhibitor for breast cancer patients because of its outstanding selectivity for epidermal growth factor receptor (EGFR) and EGFR2 (also known as HER2). However, there is only limited information about the in vivo effects of lapatinib on EGFR/HER2 and downstream signaling targets. Here, we profiled the lapatinib-induced time- and dose-dependent phosphorylation dynamics in SKBR3 breast cancer cells by means of quantitative phosphoproteomics. Among 4953 identified phosphopeptides from 1548 proteins, a small proportion (5-7%) was regulated at least twofold by 1-10 μM lapatinib. We obtained a comprehensive phosphorylation map of 21 sites on EGFR/HER2, including nine novel sites on HER2. Among them, serine/threonine phosphosites located in a small region of HER2 (amino acid residues 1049-1083) were up-regulated by the drug, whereas all other sites were down-regulated. We show that cAMP-dependent protein kinase is involved in phosphorylation of this particular region of HER2 and regulates HER2 tyrosine kinase activity. Computational analyses of quantitative phosphoproteome data indicated for the first time that protein-protein networks related to cytoskeletal organization and transcriptional/translational regulation, such as RNP complexes (i.e. hnRNP, snRNP, telomerase, ribosome), are linked to EGFR/HER2 signaling networks. To our knowledge, this is the first report to profile the temporal response of phosphorylation dynamics to a kinase inhibitor. The results provide new insights into EGFR/HER2 regulation through region-specific phosphorylation, as well as a global view of the cellular signaling networks associated with the anti-breast cancer action of lapatinib. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc.

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Sheathless capillary electrophoresis-mass spectrometry with a high- sensitivity porous sprayer for cationic metabolome analysis

Hirayama, A., Tomita, M. and Soga, T.

Analyst (Analyst)  137 ( 21 ) 5026-5033 - 5033 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  00032654

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Sheath-flow capillary electrophoresis-mass spectrometry (CE-MS) has emerged as a new tool for comprehensive analysis of charged metabolites. However, it needs to be more sensitive. Here, we report a sheathless capillary electrophoresis-electrospray ionization-mass spectrometry method for cationic metabolome analysis. This system used a high-sensitivity porous sprayer interface and 10% (v/v) acetic acid as the background electrolyte (BGE). Under optimized conditions, 53 cationic metabolites, including amino acids and their derivatives, amines, nucleic acids and small peptides, were successfully separated and selectively detected with a time-of-flight mass spectrometer. At a signal-to-noise ratio of three, the concentration detection limits for these compounds were between 0.004 and 0.8 μmol L-1 (amount detection limit, 0.01 to 2 fmol) with pressure injection at 20.7 kPa for 5 s (2.6 nL). Compared with conventional sheath-flow CE-MS, the detection limit of the present method was increased more than 5-fold for 21 (40%) of the compounds detected. When the method was applied to the analysis of cationic metabolites obtained from human urine, there was a 10-fold increase in the number of detected peaks compared with conventional methods. More than 180 successive runs could be conducted without any problems, and only the BGE needed to be changed. © 2012 The Royal Society of Chemistry.

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Pilot Study of Changes in Salivary Metabolic Profiles Induced by Template Therapy

Tanaka, S., Taga, H., Maehara, K., Kaneshima, A., Machino, M., Onuma, H., Kaneko, M., Sakagami, H., Sugimoto, M., Soga, T. and Tomita, M.

InVivo. (In Vivo)  26 ( 6 ) 1015-1020 - 1020 2012

Research paper (scientific journal), Joint Work, Accepted,  ISSN  0258851X

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Background: Occlusal raising method (so-called 'Template therapy') has been reported to alleviate various diseases and symptoms, but the underlying mechanism is not clear. We searched the low-molecular weight metabolite(s) in the saliva, the concentration of which is significantly changed by the template therapy. Materials and Methods: One female patient with headache underwent the template therapy for 12 days, and her total saliva was subjected to non-targeted analysis using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). Results: One hundred and thirteen substances were identified in the saliva. Glycine was the most abundant amino acid in the saliva, followed by alanine, serine and proline. After the start of the template therapy, her headache was alleviated, accompanied by a significant (p=0.042) increase of salivary concentration of glycine, as compared with total amino acids whereas that of other amino acids was not significantly changed. In the metabolomics profile, salivary concentration of large number of metabolites as compared with total metabolite concentration decreased, including N-acetylneuraminate (p=0.025) and p-hydroxyphenylacetate (p=0.039). Conclusion: This pilot study demonstrated, to our knowledge for the first time, that only glycine exhibited unique changes among total metabolites, suggesting its significant role in template therapy.

Is central dogma a global property of cellular information flow?

Piras, V., Tomita, M. and Selvarajoo, K

Front. Physiol. (Frontiers in Physiology)  3   439 2012

Research paper (scientific journal), Joint Work, Accepted

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The central dogma of molecular biology has come under scrutiny in recent years. Here, we reviewed high-throughput mRNA and protein expression data of Escherichia coli, Saccharomyces cerevisiae, and several mammalian cells. At both single cell and population scales, the statistical comparisons between the entire transcriptomes and proteomes show clear correlation structures. In contrast, the pair-wise correlations of single transcripts to proteins show nullity. These data suggest that the organizing structure guiding cellular processes is observed at omics-wide scale, and not at single molecule level. The central dogma, thus, globally emerges as an average integrated flow of cellular information. © 2012 Piras, Tomita and Selvarajoo.

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Metabolomics: A New Omics Technology for Traditional Herbal Medicine Analysis

Iino, K., Sugimoto, M., Soga, T. and Tomita, M.

Chinese Medicine: Acupuncture, Herbal Medicine and Therapies    45-55 2012

Research paper (scientific journal), Joint Work, Accepted