Published papers

AIM: To clarify the characteristics of metabolite profiles in virus-related hepatocellular carcinoma (HCC) patients using serum metabolome analysis. METHODS: The serum levels of low-molecular-weight metabolites in 68 patients with HCC were quantified using capillary electrophoresis chromatography and mass spectrometry. Thirty and 38 of the patients suffered from hepatitis B virus-related HCC (HCC-B) and hepatitis C virus-related HCC (HCC-C), respectively. RESULTS: The main metabolites characteristic of HCC were those associated with glutathione metabolism, notably 13 γ-glutamyl peptides, which are by-products of glutathione induction. Two major profiles, i.e. , concentration patterns, of metabolites were identified in HCC patients, and these were classified into two groups: an HCC-B group and an HCC-C group including some of the HCC-B cases. The receiver operating characteristic curve for the multiple logistic regression model discriminating HCC-B from HCC-C incorporating the concentrations of glutamic acid, methionine and γ-glutamyl-glycine-glycine showed a highly significant area under the curve value of 0.94 (95%CI: 0.89-1.0, P < 0.0001). CONCLUSION: The serum levels of γ-glutamyl peptides, as well as their concentration patterns, contribute to the development of potential biomarkers for virus-related HCC. The difference in metabolite profiles between HCC-B and HCC-C may reflect the respective metabolic reactions that underlie the different pathogeneses of these two types of HCC.

© 2018 The Authors Plasmids are genetic parasites of microorganisms. The genomes of naturally occurring plasmids are expected to be polished via natural selection to achieve long-term persistence in the microbial cell population. However, plasmid genomes are extremely diverse, and the rules governing plasmid genomes are not fully understood. Therefore, computationally designing plasmid genomes optimized for model and nonmodel organisms remains challenging. Here, we summarize current knowledge of the plasmid genome organization and the factors that can affect plasmid persistence, with the aim of constructing synthetic plasmids for use in gram-negative bacteria. Then, we introduce publicly available resources, plasmid data, and bioinformatics tools that are useful for computational plasmid design.

Cancer cells are highly variable and largely resistant to therapeutic intervention. Recently, the use of the tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induced treatment is gaining momentum due to TRAIL's ability to specifically target cancers with limited effect on normal cells. Nevertheless, several malignant cancer types still remain non-sensitive to TRAIL. Previously, we developed a dynamic computational model, based on perturbation-response differential equations approach, and predicted protein kinase C (PKC) as the most effective target, with over 95% capacity to kill human fibrosarcoma (HT1080) in TRAIL stimulation (1). Here, to validate the model prediction, which has significant implications for cancer treatment, we conducted experiments on two TRAIL-resistant cancer cell lines (HT1080 and HT29). Using PKC inhibitor bisindolylmaleimide I, we demonstrated that cell viability is significantly impaired with over 95% death of both cancer types, in consistency with our previous model. Next, we measured caspase-3, Poly (ADP-ribose) polymerase (PARP), p38, and JNK activations in HT1080, and confirmed cell death occurs through apoptosis with significant increment in caspase-3 and PARP activations. Finally, to identify a crucial PKC isoform, from 10 known members, we analyzed each isoform mRNA expressions in HT1080 cells and shortlisted the highest 4 for further siRNA knock-down (KD) experiments. From these KDs, PKCd produced the most cancer cell death in conjunction with TRAIL. Overall, our approach combining model predictions with experimental validation holds promise for systems biology based cancer therapy.

Inulin, a natural renewable polysaccharide resource produced by various plants in nature, has been reported to possess a significant number of diverse pharmaceutical and food applications. Recently, there has been rapid progress in high-throughput technologies and platforms to assay global mRNA, proteins, metabolites and gut microbiota. In this review, we will describe the current status of utilizing omics technologies of elucidating the impact of inulin and inulin-containing prebiotics at the transcriptome, proteome, metabolome and gut microbiome levels. Although many studies in this review have addressed the impact of inulin comprehensively, these omics technologies only enable us to understand physiological information at each different stage of mRNA, protein, metabolite and gut microbe. We believe that a synergistic approach is vital in order to fully illustrate the intricate beauty behind the relatively modest influence of food factors like inulin on host health.

A plethora of data is accumulating from high throughput methods on metabolites, coenzymes, proteins, and nucleic acids and their interactions as well as the signalling and regulatory functions and pathways of the cellular network. The frozen moment viewed in a single discrete time sample requires frequent repetition and updating before any appreciation of the dynamics of component interaction becomes possible. Even then in a sample derived from a cell population, time-averaging of processes and events that occur in out-of-phase individuals blur the detailed complexity of single cell organization. Continuously-grown cultures of yeast can become spontaneously self-synchronized, thereby enabling resolution of far more detailed temporal structure. Continuous on-line monitoring by rapidly responding sensors (O 2 electrode and membrane-inlet mass spectrometry for O2, CO2 and H2S; direct fluorimetry for NAD(P)H and flavins) gives dynamic information from time-scales of minutes to hours. Supplemented with capillary electophoresis and gas chromatography mass spectrometry and transcriptomics the predominantly oscillatory behaviour of network components becomes evident, with a 40 min cycle between a phase of increased respiration (oxidative phase) and decreased respiration (reductive phase). Highly pervasive, this ultradian clock provides a coordinating function that links mitochondrial energetics and redox balance to transcriptional regulation, mitochondrial structure and organelle remodelling, DNA duplication and cell division events. Ultimately, this leads to a global partitioning of anabolism and catabolism and the enzymes involved, mediated by a relatively simple ATP feedback loop on chromatin architecture. © 2013 IEEE.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) is usually used for staging or evaluation of treatment response rather than for cancer screening. However,18F-FDG PET/CT has also been used in Japan for cancer screening in people with no cancer symptoms, and accumulating evidence supports this application of18F-FDG PET/CT. Previously, we have observed a correlation between the saliva and tumor metabolomic profiles in patients with oral cancer. Hence, if salivary metabolites demonstrate a significant correlation with PET parameters such as the maximum standardized uptake value (SUVmax), they may have the potential to be used as a screening tool before PET/CT to identify patients with high SUVmax. Hence, in this study, we aimed to explore the relationship between salivary metabolites and SUVmax of18F-FDG PET/CT using previously collected data.18F-FDG PET/CT was performed for staging 26 patients with oral cancer. The collected data were integrated and analyzed along with quantified salivary hydrophilic metabolites obtained from the same patients with oral cancer and controls (n = 44). In total, 11 metabolites showed significant correlations with SUVmax in the delayed phases. A multiple logistic regression model of the two metabolites showed the ability to discriminate between patients with oral cancer and controls, with an area under the receiver operating characteristic curve of 0.738 (p = 0.001). This study uniquely confirmed a relationship between salivary metabolites and SUVmax of PET/CT in patients with oral cancer; salivary metabolites were significantly correlated with SUVmax. These salivary metabolites can be used as a screening tool before PET/CT to identify patients with high SUVmax, i.e., to detect the presence of oral cancer.

To determine the mechanism underlying the flow of genetic information, it is important to understand the relationship between a tRNA and its binding enzyme, a member of the aminoacyl-tRNA synthetase (aaRS) family. We have developed a novel method to project the interacting regions of tRNA-aaRS complexes, obtained from their three-dimensional structures, onto two-dimensional space. The interacting surface between each tRNA and its aaRS was successfully identified by determining these interactions with an atomic distance threshold of 3.3 Å. We analyzed their interactions, using 60 mainly bacterial and eukaryotic tRNA-aaRS complexes, and showed that the tRNA sequence regions that interacted most strongly with each aaRS are the anticodon loop and the CCA terminal region, followed by the D-stem. A sequence conservation analysis of the canonical tRNAs was conducted in 83 bacterial, 182 archaeal, and 150 eukaryotic species. Our results show that the three tRNA regions that interact with the aaRS and two additional loop regions (D-loop and TψC-loop) known to be important for formation of the tRNA L-shaped structure are broadly conserved. We also found sequence conservations near the tRNA discriminator in the Bacteria and Archaea, and an enormous number of noncanonical tRNAs in the Eukaryotes. This is the first global view of tRNA evolution based on its structure and an unprecedented number of sequence data.

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.

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.

We successfully identified the proteome expressed in Escherichia coli (E. coli) cells on a microarray scale using one-dimensional capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a 350 cm long, 100 μm i. d., monolithic silica-C18 capillary column. E. coli tryptic digest (4 μg) was injected onto the column, and a 41 h gradient was applied with a flow rate of 500 nL/min at less than 20 MPa. In total, 22 196 nonredundant tryptic peptides from 2602 proteins, including 830 membrane proteins, were identified from the E. coli cells (triplicate analysis), in which an equivalent number of genes was detected by transcriptome analysis. Approximately a 5-fold larger peak response on average was obtained in this system, compared with that obtained by conventional capillary LC-MS/MS analysis with a 15 cm long, 3 μm diameter C18 silica particle-packed column. The higher response suggests that the influence of ionization suppression was drastically reduced by the high-efficiency separation on the long monolithic silica column coupled with the shallow gradient. Because this high-resolution system does not require any additional separation prior to LC-MS/MS, this "one-shot" proteomics approach can simplify the workflow of shotgun proteomics and minimize the sample amount, as well as reduce the total analysis time, despite the use of prolonged shallow gradient elution. © 2010 American Chemical Society.

Purpose: We investigated the significance of CD24 and CD44 expression for predicting responses to chemotherapy and prognosis in primary breast cancer patients. Patients and Methods: Diagnosis of breast cancer was confirmed by core needle biopsy, and immunohistochemical studies were performed. Preoperatively, patients received anthracyclinecontaining chemotherapy. Expression of CD44 and CD24 was assessed immunohistochemically and the relationship with chemotherapy response and with prognosis was analyzed. Results: Between 2001 and 2004, 139 women were enrolled in this study. In the correlation analysis, CD24 expression was negatively associated with pathological response to chemotherapy (p=0.0003). A machine learning technique with an alternating decision tree (ADTree) showed that four logical rules are involved in predicting the response depending on the combination of CD24, HER2, tumor stage, CD44, progesterone receptor, and patient age. In the survival analysis, patients having CD44 (++) showed a significantly favorable prognosis as compared with others (p=0.0002). A multivariate analysis showed that CD44 expression had an independent prognostic value (p<0.001). Conclusion: We found a significant correlation between CD44 expression and prognosis and between CD24 expression and response to chemotherapy. CD24 and CD44 expressions would be useful predictive markers, although further studies are needed.

The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profi les of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path. © 2009 Yanashima, Kitagawa, Matsubara, Weatheritt, Oka, Kikuchi, Tomita and Ishizaki.

© 2018 The Authors Osteoporosis is a skeletal disorder characterized by compromised bone strength and increased risk of fracture. Low bone mass and/or pre-existing bone fragility fractures serve as diagnostic criteria in deciding when to start medication for osteoporosis. Although osteoporosis is a metabolic disorder, metabolic markers to predict reduced bone mass are unknown. Here, we show serum metabolomics profiles of women grouped as pre-menopausal with normal bone mineral density (BMD) (normal estrogen and normal BMD; NN), post-menopausal with normal BMD (low estrogen and normal BMD; LN) or post-menopausal with low BMD (low estrogen and low BMD; LL) using comprehensive metabolomics analysis. To do so, we enrolled healthy volunteer and osteoporosis patient female subjects, surveyed them with a questionnaire, measured their BMD, and then undertook a comprehensive metabolomics analysis of sera of the three groups named above. We identified 24 metabolites whose levels differed significantly between NN/LN and NN/LL groups, as well as 18 or 10 metabolites whose levels differed significantly between NN/LN and LN/LL, or LN/LL and NN/LN groups, respectively. Our data shows metabolomics changes represent useful markers to predict estrogen deficiency and/or bone loss.

Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.

Estrogen receptor (ER) positive breast cancer represents 75% of all breast cancers in women. Although patients with ER+ cancers receive endocrine therapies, more than 30% develop resistance and succumb to the disease, highlighting the need to understand endocrine resistance. Here we show an unexpected role for the cell polarity protein SCRIB as a tumor-promoter and a regulator of endocrine resistance in ER-positive breast cancer cells. SCRIB expression is induced by estrogen signaling in a MYC-dependent manner. SCRIB interacts with SLC3A2, a heteromeric component of leucine amino acid transporter SLC7A5. SLC3A2 binds to the N-terminus of SCRIB to facilitate the formation of SCRIB/SLC3A2/LLGL2/SLC7A5 quaternary complex required for membrane localization of the amino acid transporter complex. Both SCRIB and SLC3A2 are required for cell proliferation and tamoxifen resistance in ER+ cells identifying a new role for the SCRIB/SLC3A2 complex in ER+ breast cancer.

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. © The Royal Society of Chemistry.

We describe a sheath flow capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) method in the negative mode using a platinum electrospray ionization (ESI) spray needle, which allows the comprehensive analysis of anionic metabolites. The material of the spray needle had significant effect on the measurement of anions. A stainless steel spray needle was oxidized and corroded at the anodic electrode due to electrolysis. The precipitation of iron oxides (rust) plugged the capillary outlet, resulting in shortened capillary lifetime. Many anionic metabolites also formed complexes with the iron oxides or migrating nickel ion, which was also generated by electrolysis and moved toward the cathode (the capillary inlet). The metal-anion complex formation significantly reduced detection sensitivity of the anionic compounds. The use of a platinum ESI needle prevented both oxidation of the metals and needle corrosion. Sensitivity using the platinum needle increased from several- to 63-fold, with the largest improvements for anions exhibiting high metal chelating properties such as carboxylic acids, nucleotides, and coenzyme A compounds. The detection limits for most anions were between 0.03 and 0.87 μmol/L (0.8 and 24 fmol) at a signal-to-noise ratio of 3. This method is quantitative, sensitive, and robust, and its utility was demonstrated by the analysis of the metabolites in the central metabolic pathways extracted from mouse liver. © 2009 American Chemical Society.

The search for novel enzymes and enzymatic activities is important to map out all metabolic activities and reveal cellular metabolic processes in a more exhaustive manner. Here we present biochemical and physiological evidence for the function of the uncharacterized protein YihU in Escherichia coli using metabolite profiling by capillary electrophoresis time-of-flight mass spectrometry. To detect enzymatic activity and simultaneously identify possible substrates and products of the putative enzyme, we profiled a complex mixture of metabolites in the presence or absence of YihU. In this manner, succinic semialdehyde was identified as a substrate for YihU. The purified YihU Protein catalyzed in vitro the NADH-dependent reduction of succinic semialdehyde to γ-hydroxybutyrate. Moreover, a yihU deletion mutant displayed reduced tolerance to the cytotoxic effects of exogenous addition of succinic semialdehyde. Profiling of intracellular metabolites following treatment of E. coli with succinic semialdehyde supports the existence of a YihU-catalyzed reduction of succinic semialdehyde to γ-hydroxybutyrate in addition to its known oxidation to succinate and through the tricarboxylic acid cycle. These findings suggest that YihU is a novel γ-hydroxybutyrate dehydrogenase involved in the metabolism of succinic semialdehyde, and other potentially toxic intermediates that may accumulate under stress conditions in E. coli. © 2009 by The American Society for Biochemistry and Molecular Biology, Inc.

© 2007 - 2018 Frontiers Media S.A. All Rights Reserved. Antifolates are a class of drugs effective for treating malignant pleural mesothelioma (MPM). The majority of antifolates inhibit enzymes involved in purine and pyrimidine synthesis such as dihydrofolate reductase (DHFR), thymidylate synthase (TYMS), and glycinamide ribonucleotide formyltransferase (GART). In order to select the most suitable patients for effective therapy with drugs targeting specific metabolic pathways, there is a need for better predictive metabolic biomarkers. Antifolates can alter global metabolic pathways in MPM cells, yet the metabolic profile of treated cells has not yet been clearly elucidated. Here we found that MPM cell lines could be categorized into two groups according to their sensitivity or resistance to pemetrexed treatment. We show that pemetrexed susceptibility could be reversed and DNA synthesis rescued in drug-treated cells by the exogenous addition of the nucleotide precursors hypoxanthine and thymidine (HT). We observed that the expression of pemetrexed-targeted enzymes in resistant MPM cells was quantitatively lower than that seen in pemetrexed-sensitive cells. Metabolomic analysis revealed that glycine and choline, which are involved in one-carbon metabolism, were altered after drug treatment in pemetrexed-sensitive but not resistant MPM cells. The addition of HT upregulated the concentration of inosine monophosphate (IMP) in pemetrexed-sensitive MPM cells, indicating that the nucleic acid biosynthesis pathway is important for predicting the efficacy of pemetrexed in MPM cells. Our data provide evidence that may link therapeutic response to the regulation of metabolism, and points to potential biomarkers for informing clinical decisions regarding the most effective therapies for patients with MPM.