Complete genome sequence of Bacillus subtilis subsp. natto NARUSE using PacBio sequencing.

We report the complete genome sequence of Bacillus subtilis subsp. natto NARUSE, which has been traditionally employed for fermenting soybeans in Japan. The genome was sequenced using the PacBio system, yielding a sequence, yielding a sequence length of 4,148,793 nucleotides for the circular chromosome and 62,770 nucleotides for the plasmid.

OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data.

Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course or rank order, but hypothesis testing to select significant metabolite candidates was not possible. Here, we modified the smoothed PCA as an orthogonal smoothed PCA (OS-PCA) so that statistical hypothesis testing in OS-PC loadings could be performed with the same PC projections provided by the smoothed PCA. Statistical hypothesis testing is especially useful in metabolomics because biological interpretations are made based on statistically significant metabolites. We applied the OS-PCA method to two real metabolome datasets, one for metabolic turnover analysis and the other for evaluating the taste of Japanese green tea. The OS-PCA successfully extracted similar PC scores as the smoothed PCA; these scores reflected the expected phenotypes. The significant metabolites that were selected using statistical hypothesis testing of OS-PC loading facilitated biological interpretations that were consistent with the results of our previous study. Our results suggest that OS-PCA combined with statistical hypothesis testing of OS-PC loading is a useful method for the analysis of metabolome data.

Highly Accurate Detection and Identification Methodology of Xenobiotic Metabolites Using Stable Isotope Labeling, Data Mining Techniques, and Time-Dependent Profiling Based on LC/HRMS/MS.

A generally applicable method to discover xenobiotic metabolites is important to safely and effectively develop xenobiotics. We propose an advanced method to detect and identify comprehensive xenobiotic metabolites using stable isotope labeling, liquid chromatography coupled with benchtop quadrupole Orbitrap high-resolution tandem mass spectrometry (LC/HRMS/MS), data mining techniques (alignment, peak picking, and paired-peaks filtering), in silico metabolism prediction, and time-dependent profiling. The LC/HRMS analysis was carried out using Arabidopsis T87 cultured cells treated with unlabeled or with 13C- or 2H-labeled 2,4-dichlorophenoxyacetic acid (2,4-D). Paired-peak filtering enabled the accurate detection of 83 candidates for 2,4-D metabolites without any false positive peaks derived from solvents or the biological matrix. We confirmed 10 previously reported 2,4-D metabolites and identified 16 novel 2,4-D metabolites. Our method provides accurate detection and identification of comprehensive xenobiotic metabolites and represents a potentially useful tool for elucidating xenobiotic metabolism.

Characterization of mutants expressing thermostable D1 and D2 polypeptides of photosystem II in the cyanobacterium Synechococcus elongatus PCC 7942.

Photosystem II complex embedded in thylakoid membrane performs oxygenic photosynthesis where the reaction center D1/D2 heterodimer accommodates all components of the electron transport chain. To express thermostable D1/D2 heterodimer in a cyanobacterium Synechococcus elongatus PCC 7942, we constructed a series of mutant strains whose psbA1 and psbD1 genes encoding, respectively, the most highly expressed D1 and D2 polypeptides were replaced with those of a thermophilic strain, Thermosynechococcus vulcanus. Because the C-terminal 16 amino acid sequences of D1 polypeptides should be processed prior to maturation but diverge from each other, we also constructed the psbA1ΔC-replaced strain expressing a thermostable D1 polypeptide devoid of the C-terminal extension. The psbA1/psbD1-replaced strain showed decreased growth rate and oxygen evolution rate, suggesting inefficient photosystem II. Immunoblot analyses for thermostable D1, D2 polypeptides revealed that the heterologous D1 protein was absent in thylakoid membrane from any mutant strains with psbA1, psbA1ΔC, and psbA1/psbD1-replacements, whereas the heterologous D2 protein was present in thylakoid membrane as well as purified photosystem II complex from the psbA1/psbD1-replaced strain. In the latter strain, the compensatory expression of psbA3 and psbD2 genes was elevated. These data suggest that heterologous D2 polypeptide could be combined with the host D1 polypeptide to form chimeric D1/D2 heterodimer, whereas heterologous D1 polypeptide even without the C-terminal extension was unable to make complex with the host D2 polypeptide. Since the heterologous D1 could not be detected even in the whole cells of psbA1/psbD1-replaced strain, the rapid degradation of unprocessed or unassembled heterologous D1 was implicated.

Molecular Components of Arabidopsis Intact Vacuoles Clarified with Metabolomic and Proteomic Analyses.

We analyzed the metabolites and proteins contained in pure intact vacuoles isolated from Arabidopsis suspension-cultured cells using capillary electrophoresis-mass spectrometry (CE-MS), Fourier transform-ion cyclotron resonance (FT-ICR)-MS and liquid chromatography (LC)-MS. We identified 21 amino acids and five organic acids as major primary metabolites in the vacuoles with CE-MS. Further, we identified small amounts of 27 substances including well-known vacuolar molecules, but also some unexpected substances (e.g. organic phosphate compounds). Non-target analysis of the vacuolar sample with FT-ICR-MS suggested that there are 1,106 m/z peaks that could predict the 5,090 molecular formulae, and we have annotated 34 compounds in these peaks using the KNapSAck database. By conducting proteomic analysis of vacuolar sap, we found 186 proteins in the same vacuole samples. Since the vacuole is known as a major degradative compartment, many of these were hydrolases, but we also found various oxidoreductases and transferases. The relationships between the proteins and metabolites in the vacuole are discussed.

Glutamate production from ammonia via glutamate dehydrogenase 2 activity supports cancer cell proliferation under glutamine depletion.

Cancer cells rapidly consume glutamine as a carbon and nitrogen source to support proliferation, but the cell number continues to increase exponentially after glutamine is nearly depleted from the medium. In contrast, cell proliferation rates are strongly depressed when cells are cultured in glutamine-free medium. How cancer cells survive in response to nutrient limitation and cellular stress remains poorly understood. In addition, rapid glutamine catabolism yields ammonia, which is a potentially toxic metabolite that is secreted into the extracellular space. Here, we show that ammonia can be utilized for glutamate production, leading to cell proliferation under glutamine-depleted conditions. This proliferation requires glutamate dehydrogenase 2, which synthesizes glutamate from ammonia and α-ketoglutarate and is expressed in MCF7 and T47D cells. Our findings provide insight into how cancer cells survive under glutamine deprivation conditions and thus contribute to elucidating the mechanisms of tumor growth.

Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis.

INTRODUCTION: Previously constructed Escherichia coli strains that produce 1-propanol use the native threonine pathway, or a heterologous citramalate pathway. However, based on the energy and cofactor requirements of each pathway, a combination of the two pathways produces synergistic effects that increase the theoretical maximum yield with a simultaneous unexplained increase in productivity. OBJECTIVE: Identification of key factors that contribute to synergistic effect leading to 1-propanol yield and productivity improvement in E. coli with native threonine pathway and heterologous citramalate pathway. METHOD: A combination of snapshot metabolomic profiling and dynamic metabolic turnover analysis were used to identify system-wide perturbations that contribute to the productivity improvement. RESULT AND CONCLUSION: In the presence of both pathways, increased glucose consumption and elevated levels of glycolytic intermediates are attributed to an elevated phosphoenolpyruvate (PEP)/pyruvate ratio that is known to increase the function of the native phosphotransferase. Turnover analysis of nitrogen containing byproducts reveals that ammonia assimilation, required for the threonine pathway, is streamlined when provided with an NAD(P)H surplus in the presence of the citramalate pathway. Our study illustrates the application of metabolomics in identification of factors that alter cellular physiology for improvement of 1-propanol bioproduction.

Increased Dynamics of Tricarboxylic Acid Cycle and Glutamate Synthesis in Obese Adipose Tissue: IN VIVO METABOLIC TURNOVER ANALYSIS.

Obesity is closely associated with various metabolic disorders. However, little is known about abnormalities in the metabolic change of obese adipose tissue. Here we use static metabolic analysis and in vivo metabolic turnover analysis to assess metabolic dynamics in obese mice. The static metabolic analyses showed that glutamate and constitutive metabolites of the TCA cycle were increased in the white adipose tissue (WAT) of ob/ob and diet-induced obesity mice but not in the liver or skeletal muscle of these obese mice. Moreover, in vivo metabolic turnover analyses demonstrated that these glucose-derived metabolites were dynamically and specifically produced in obese WAT compared with lean WAT. Glutamate rise in obese WAT was associated with down-regulation of glutamate aspartate transporter (GLAST), a major glutamate transporter for adipocytes, and low uptake of glutamate into adipose tissue. In adipocytes, glutamate treatment reduced adiponectin secretion and insulin-mediated glucose uptake and phosphorylation of Akt. These data suggest that a high intra-adipocyte glutamate level potentially relates to adipocyte dysfunction in obesity. This study provides novel insights into metabolic dysfunction in obesity through comprehensive application of in vivo metabolic turnover analysis in two obese animal models.

Two isoforms of TALDO1 generated by alternative translational initiation show differential nucleocytoplasmic distribution to regulate the global metabolic network.

Transaldolase 1 (TALDO1) is a rate-limiting enzyme involved in the pentose phosphate pathway, which is traditionally thought to occur in the cytoplasm. In this study, we found that the gene TALDO1 has two translational initiation sites, generating two isoforms that differ by the presence of the first 10 N-terminal amino acids. Notably, the long and short isoforms were differentially localised to the cell nucleus and cytoplasm, respectively. Pull-down and in vitro transport assays showed that the long isoform, unlike the short one, binds to importin α and is actively transported into the nucleus in an importin α/ß-dependent manner, demonstrating that the 10 N-terminal amino acids are essential for its nuclear localisation. Additionally, we found that these two isoforms can form homo- and/or hetero-dimers with different localisation dynamics. A metabolite analysis revealed that the subcellular localisation of TALDO1 is not crucial for its activity in the pentose phosphate pathway. However, the expression of these two isoforms differentially affected the levels of various metabolites, including components of the tricarboxylic acid cycle, nucleotides, and sugars. These results demonstrate that the nucleocytoplasmic distribution of TALDO1, modulated via alternative translational initiation and dimer formation, plays an important role in a wide range of metabolic networks.

Random sample consensus combined with partial least squares regression (RANSAC-PLS) for microbial metabolomics data mining and phenotype improvement.

In recent years, the advent of high-throughput omics technology has made possible a new class of strain engineering approaches, based on identification of possible gene targets for phenotype improvement from omic-level comparison of different strains or growth conditions. Metabolomics, with its focus on the omic level closest to the phenotype, lends itself naturally to this semi-rational methodology. When a quantitative phenotype such as growth rate under stress is considered, regression modeling using multivariate techniques such as partial least squares (PLS) is often used to identify metabolites correlated with the target phenotype. However, linear modeling techniques such as PLS require a consistent metabolite-phenotype trend across the samples, which may not be the case when outliers or multiple conflicting trends are present in the data. To address this, we proposed a data-mining strategy that utilizes random sample consensus (RANSAC) to select subsets of samples with consistent trends for construction of better regression models. By applying a combination of RANSAC and PLS (RANSAC-PLS) to a dataset from a previous study (gas chromatography/mass spectrometry metabolomics data and 1-butanol tolerance of 19 yeast mutant strains), new metabolites were indicated to be correlated with tolerance within certain subsets of the samples. The relevance of these metabolites to 1-butanol tolerance were then validated from single-deletion strains of corresponding metabolic genes. The results showed that RANSAC-PLS is a promising strategy to identify unique metabolites that provide additional hints for phenotype improvement, which could not be detected by traditional PLS modeling using the entire dataset.

Metabolomic approach for improving ethanol stress tolerance in Saccharomyces cerevisiae.

The budding yeast Saccharomyces cerevisiae is widely used for brewing and ethanol production. The ethanol sensitivity of yeast cells is still a serious problem during ethanol fermentation, and a variety of genetic approaches (e.g., random mutant screening under selective pressure of ethanol) have been developed to improve ethanol tolerance. In this study, we developed a strategy for improving ethanol tolerance of yeast cells based on metabolomics as a high-resolution quantitative phenotypic analysis. We performed gas chromatography-mass spectrometry analysis to identify and quantify 36 compounds on 14 mutant strains including knockout strains for transcription factor and metabolic enzyme genes. A strong relation between metabolome of these mutants and their ethanol tolerance was observed. Data mining of the metabolomic analysis showed that several compounds (such as trehalose, valine, inositol and proline) contributed highly to ethanol tolerance. Our approach successfully detected well-known ethanol stress related metabolites such as trehalose and proline thus, to further prove our strategy, we focused on valine and inositol as the most promising target metabolites in our study. Our results show that simultaneous deletion of LEU4 and LEU9 (leading to accumulation of valine) or INM1 and INM2 (leading to reduction of inositol) significantly enhanced ethanol tolerance. This study shows the potential of the metabolomic approach to identify target genes for strain improvement of S. cerevisiae with higher ethanol tolerance.

New Insight into the Role of the Calvin Cycle: Reutilization of CO2 Emitted through Sugar Degradation.

Ralstonia eutropha is a facultative chemolithoautotrophic bacterium that uses the Calvin-Benson-Bassham (CBB) cycle for CO2 fixation. This study showed that R. eutropha strain H16G incorporated (13)CO2, emitted by the oxidative decarboxylation of [1-(13)C1]-glucose, into key metabolites of the CBB cycle and finally into poly(3-hydroxybutyrate) [P(3HB)] with up to 5.6% (13)C abundance. The carbon yield of P(3HB) produced from glucose by the strain H16G was 1.2 times higher than that by the CBB cycle-inactivated mutants, in agreement with the possible fixation of CO2 estimated from the balance of energy and reducing equivalents through sugar degradation integrated with the CBB cycle. The results proved that the 'gratuitously' functional CBB cycle in R. eutropha under aerobic heterotrophic conditions participated in the reutilization of CO2 emitted during sugar degradation, leading to an advantage expressed as increased carbon yield of the storage compound. This is a new insight into the role of the CBB cycle, and may be applicable for more efficient utilization of biomass resources.

Bulk RNA degradation by nitrogen starvation-induced autophagy in yeast.

Autophagy is a catabolic process conserved among eukaryotes. Under nutrient starvation, a portion of the cytoplasm is non-selectively sequestered into autophagosomes. Consequently, ribosomes are delivered to the vacuole/lysosome for destruction, but the precise mechanism of autophagic RNA degradation and its physiological implications for cellular metabolism remain unknown. We characterized autophagy-dependent RNA catabolism using a combination of metabolome and molecular biological analyses in yeast. RNA delivered to the vacuole was processed by Rny1, a T2-type ribonuclease, generating 3'-NMPs that were immediately converted to nucleosides by the vacuolar non-specific phosphatase Pho8. In the cytoplasm, these nucleosides were broken down by the nucleosidases Pnp1 and Urh1. Most of the resultant bases were not re-assimilated, but excreted from the cell. Bulk non-selective autophagy causes drastic perturbation of metabolism, which must be minimized to maintain intracellular homeostasis.

Glutamate acts as a key signal linking glucose metabolism to incretin/cAMP action to amplify insulin secretion.

Incretins, hormones released by the gut after meal ingestion, are essential for maintaining systemic glucose homeostasis by stimulating insulin secretion. The effect of incretins on insulin secretion occurs only at elevated glucose concentrations and is mediated by cAMP signaling, but the mechanism linking glucose metabolism and cAMP action in insulin secretion is unknown. We show here, using a metabolomics-based approach, that cytosolic glutamate derived from the malate-aspartate shuttle upon glucose stimulation underlies the stimulatory effect of incretins and that glutamate uptake into insulin granules mediated by cAMP/PKA signaling amplifies insulin release. Glutamate production is diminished in an incretin-unresponsive, insulin-secreting ß cell line and pancreatic islets of animal models of human diabetes and obesity. Conversely, a membrane-permeable glutamate precursor restores amplification of insulin secretion in these models. Thus, cytosolic glutamate represents the elusive link between glucose metabolism and cAMP action in incretin-induced insulin secretion.

Changes in transcription and metabolism during the early stage of replicative cellular senescence in budding yeast.

Age-related damage accumulates and a variety of biological activities and functions deteriorate in senescent cells. However, little is known about when cellular aging behaviors begin and what cellular aging processes change. Previous research demonstrated age-related mRNA changes in budding yeast by the 18th to 20th generation, which is the average replicative lifespan of yeast (i.e. about half of the population is dead by this time point). Here, we performed transcriptional and metabolic profiling for yeast at early stages of senescence (4th, 7th, and 11th generation), that is, for populations in which most cells are still alive. Transcriptional profiles showed up- and down-regulation for ∼20% of the genes profiled after the first four generations, few further changes by the 7th generation, and an additional 12% of the genes were up- and down-regulated after 11 generations. Pathway analysis revealed that these 11th generation cells had accumulated transcripts coding for enzymes involved in sugar metabolism, the TCA cycle, and amino acid degradation and showed decreased levels of mRNAs coding for enzymes involved in amino acid biosynthetic pathways. These observations were consistent with the metabolomic profiles of aging cells: an accumulation of pyruvic acid and TCA cycle intermediates and depletion of most amino acids, especially branched-chain amino acids. Stationary phase-induced genes were highly expressed after 11 generations even though the growth medium contained adequate levels of nutrients, indicating deterioration of the nutrient sensing and/or signaling pathways by the 11th generation. These changes are presumably early indications of replicative senescence.

Novel strategy for non-targeted isotope-assisted metabolomics by means of metabolic turnover and multivariate analysis.

Isotope-labeling is a useful technique for understanding cellular metabolism. Recent advances in metabolomics have extended the capability of isotope-assisted studies to reveal global metabolism. For instance, isotope-assisted metabolomics technology has enabled the mapping of a global metabolic network, estimation of flux at branch points of metabolic pathways, and assignment of elemental formulas to unknown metabolites. Furthermore, some data processing tools have been developed to apply these techniques to a non-targeted approach, which plays an important role in revealing unknown or unexpected metabolism. However, data collection and integration strategies for non-targeted isotope-assisted metabolomics have not been established. Therefore, a systematic approach is proposed to elucidate metabolic dynamics without targeting pathways by means of time-resolved isotope tracking, i.e., "metabolic turnover analysis", as well as multivariate analysis. We applied this approach to study the metabolic dynamics in amino acid perturbation of Saccharomyces cerevisiae. In metabolic turnover analysis, 69 peaks including 35 unidentified peaks were investigated. Multivariate analysis of metabolic turnover successfully detected a pathway known to be inhibited by amino acid perturbation. In addition, our strategy enabled identification of unknown peaks putatively related to the perturbation.

Molar-based targeted metabolic profiling of cyanobacterial strains with potential for biological production.

Recently, cyanobacteria have become one of the most attractive hosts for biochemical production due to its high proliferative ability and ease of genetic manipulation. Several researches aimed at biological production using modified cyanobacteria have been reported previously. However, to improve the yield of bioproducts, a thorough understanding of the intercellular metabolism of cyanobacteria is necessary. Metabolic profiling techniques have proven to be powerful tools for monitoring cellular metabolism of various organisms and can be applied to elucidate the details of cyanobacterial metabolism. In this study, we constructed a metabolic profiling method for cyanobacteria using 13C-labeled cell extracts as internal standards. Using this method, absolute concentrations of 84 metabolites were successfully determined in three cyanobacterial strains which are commonly used as background strains for metabolic engineering. By comparing the differences in basic metabolic potentials of the three cyanobacterial strains, we found a well-correlated relationship between intracellular energy state and growth in cyanobacteria. By integrating our results with the previously reported biological production pathways in cyanobacteria, we found putative limiting step of carbon flux. The information obtained from this study will not only help gain insights in cyanobacterial physiology but also serve as a foundation for future metabolic engineering studies using cyanobacteria.

Metabolic distance estimation based on principle component analysis of metabolic turnover.

Visualization of metabolic dynamism is important for various types of metabolic studies including studies on optimization of bio-production processes and studies of metabolism-related diseases. Many methodologies have been developed for metabolic studies. Among these, metabolic turnover analysis (MTA) is often used to analyze metabolic dynamics. MTA involves observation of changes in the isotopomer ratio of metabolites over time following introduction of isotope-labeled substrates. MTA has several advantages compared with (13)C-metabolic flux analysis, including the diversity of applicable samples, the variety of isotope tracers, and the wide range of target pathways. However, MTA produces highly complex data from which mining useful information becomes difficult. For easy understanding of MTA data, a new approach was developed using principal component analysis (PCA). The resulting PCA score plot visualizes the metabolic distance, which is defined as distance between metabolites on the real metabolic map. And the score plot gives us some hints of interesting metabolism for further study. We used this method to analyze the central metabolism of Saccharomyces cerevisiae under moderated aerobic conditions, and time course data for 77 isotopomers of 14 metabolites were obtained. The PCA score plot for this dataset represented a metabolic map and indicated interesting phenomena such as activity of fumarate reductase under aerated condition. These findings show the importance of a multivariate analysis to MTA. In addition, because the approach is not biased, this method has potential application for analysis of less-studied pathways and organisms.

Current metabolomics: practical applications.

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.