Yasuyo Sekiyama

Statistical Indices for Simultaneous Large-Scale Metabolite Detections for a Single NMR Spectrum

NMR-based metabolomics has become a practical and analytical methodology for discovering novel genes, biomarkers, metabolic phenotypes, and dynamic cell behaviors in organisms. Recent developments in NMR-based metabolomics, however, have not concentrated on improvements of comprehensiveness in terms of simultaneous large-scale metabolite detections. To resolve this, we have devised and implemented a statistical index, the SpinAssign p-value, in NMR-based metabolomics for large-scale metabolite annotation and publicized this information. It enables simultaneous annotation of more than 200 candidate metabolites from the single (13)C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell extract.

Profiling Polar and Semipolar Plant Metabolites throughout Extraction Processes Using a Combined Solution-State and High-Resolution Magic Angle Spinning NMR Approach

In metabolomic analyses, care should be exercised as to which metabolites are extracted from the sample and which remain in the residue; the remaining metabolites are typically discarded following the extraction process. In this study, nuclear magnetic resonance (NMR)-based metabolomics was used to visualize plant metabolite profiles throughout a series of repeated extraction processes. Metabolites remaining in the extraction residues of (13)C-labeled Arabidopsis thaliana were recovered by repeated extraction using methanol-d(4) and deuterium oxide. The soluble extracts and residual pellets from each step of the extraction process were analyzed by both solution-state and high-resolution magic angle spinning NMR. Metabolic profiling based on chemical shifts in two-dimensional (1)H-(13)C heteronuclear single-quantum coherence spectra allowed the elucidation of both structural and chemical properties. In addition to the metabolite profile, there appears to be a relationship between metabolite structure and behavior throughout the repeated extraction process. These approaches suggest that metabolites are not always extracted in a single step and that the distribution of metabolites in an extraction scenario cannot be predicted solely on the basis of solubility or polarity. The composition of all metabolites in cells influences the solubility of each metabolite; thus, particular attention should be paid because changes in only a portion of the metabolites could influence the entire metabolite profile in a solvent extract.

Evaluation of a semipolar solvent system as a step toward heteronuclear multidimensional NMR-based metabolomics for 13C-labeled bacteria, plants, and animals.

Nuclear magnetic resonance (NMR) has become a key technology in metabolomics, with the use of stable isotope labeling and advanced heteronuclear multidimensional NMR techniques. In this paper, we focus on the evaluation of extraction solvents to improve NMR-based methodologies for metabolomics. Line broadening is a serious barrier to detecting signals and the annotation of metabolites using multidimensional NMR. We evaluated a series of NMR solvents for easy and versatile single-step extraction using the (13)C-labeled photosynthetic bacterium Rhodobacter sphaeroides, which shows pronounced broadening of NMR signals. The performance of each extraction solvent was judged using 2D (1)H-(13)C heteronuclear single quantum coherence (HSQC) spectra, considering three metrics: (1) distribution of the line width at half height, (2) number of observed signals, and (3) the total observed signal intensity. Considering the total rank values for the three metrics, we chose methanol-d(4) (MeOD) as a semipolar extraction solvent that can sufficiently sharpen the line width and affords better-quality NMR spectra. We also evaluated the series of extraction solvents by means of inductively coupled plasma optical emission spectroscopy (ICP-OES) based ionomics approach. It was also indicated that MeOD is useful for excluding paramagnetic ions as well as macromolecules in an easy single-step extraction. MeOD extraction also appeared to be effective for other bacterial and animal samples. An additional advantage of this semipolar solvent is that it supplements the aqueous (polar) buffer system reported by many groups. The flexible, appropriate application of polar and semipolar extraction should contribute to the large-scale analysis of metabolites.

A NMR-based, non-targeted multistep metabolic profiling revealed L-rhamnitol as a metabolite that characterised apples from different geographic origins.

This study utilises (1)H NMR-based metabolic profiling to characterise apples of five cultivars grown either in Japan (Fuji, Orin, and Jonagold) or New Zealand (Fuji, Jazz, and Envy). Principal component analysis (PCA) showed a clear separation between the Fuji-Orin-Jonagold class and the Jazz-Envy class, primarily corresponding to the differences in sugar signals, such as sucrose, glucose, and fructose. Multistep PCA removed the influence of dominant sugars and highlighted minor metabolites such as aspartic acid, 2-methylmalate, and an unidentified compound. These minor metabolites separated the apples into two classes according to different geographical areas. Subsequent partial least squares discriminant analysis (PLS-DA) indicated the importance of the unidentified metabolite. This metabolite was isolated using charcoal chromatography, and was identified as L-rhamnitol by 2D NMR and LC/MS analyses. The remarkable contribution of L-rhamnitol to geographic discrimination suggests that apples may be characterised according to various factors, including storage duration, cultivation method, and climate.

Metagenomic Analysis of the Bacterial Community Associated with the Taproot of Sugar Beet

We analyzed a metagenome of the bacterial community associated with the taproot of sugar beet (Beta vulgaris L.) in order to investigate the genes involved in plant growth-promoting traits (PGPTs), namely 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, indole acetic acid (IAA), N2 fixation, phosphate solubilization, pyrroloquinoline quinone, siderophores, and plant disease suppression as well as methanol, sucrose, and betaine utilization. The most frequently detected gene among the PGPT categories encoded β-1,3-glucanase (18 per 105 reads), which plays a role in the suppression of plant diseases. Genes involved in phosphate solubilization (e.g., for quinoprotein glucose dehydrogenase), methanol utilization (e.g., for methanol dehydrogenase), siderophore production (e.g. isochorismate pyruvate lyase), and ACC deaminase were also abundant. These results suggested that such PGPTs are crucially involved in supporting the growth of sugar beet. In contrast, genes for IAA production (iaaM and ipdC) were less abundant (~1 per 105 reads). N2 fixation genes (nifHDK) were not detected; bacterial N2 -fixing activity was not observed in the 15N2 -feeding experiment. An analysis of nitrogen metabolism suggested that the sugar beet microbiome mainly utilized ammonium and nitroalkane as nitrogen sources. Thus, N2 fixation and IAA production did not appear to contribute to sugar beet growth. Taxonomic assignment of this metagenome revealed the high abundance of Mesorhizobium, Bradyrhizobium, and Streptomyces, suggesting that these genera have ecologically important roles in the taproot of sugar beet. Bradyrhizobium-assigned reads in particular were found in almost all categories of dominant PGPTs with high abundance. The present study revealed the characteristic functional genes in the taproot-associated microbiome of sugar beet, and suggest the opportunity to select sugar beet growth-promoting bacteria.

Characterization of Leaf Blade- and Leaf Sheath-Associated Bacterial Communities and Assessment of Their Responses to Environmental Changes in CO2, Temperature, and Nitrogen Levels under Field Conditions

Rice shoot-associated bacterial communities at the panicle initiation stage were characterized and their responses to elevated surface water-soil temperature (ET), low nitrogen (LN), and free-air CO2 enrichment (FACE) were assessed by clone library analyses of the 16S rRNA gene. Principal coordinate analyses combining all sequence data for leaf blade- and leaf sheath-associated bacteria revealed that each bacterial community had a distinct structure, as supported by PC1 (61.5%), that was mainly attributed to the high abundance of Planctomycetes in leaf sheaths. Our results also indicated that the community structures of leaf blade-associated bacteria were more sensitive than those of leaf sheath-associated bacteria to the environmental factors examined. Among these environmental factors, LN strongly affected the community structures of leaf blade-associated bacteria by increasing the relative abundance of Bacilli. The most significant effect of FACE was also observed on leaf blade-associated bacteria under the LN condition, which was explained by decreases and increases in Agrobacterium and Pantoea, respectively. The community structures of leaf blade-associated bacteria under the combination of FACE and ET were more similar to those of the control than to those under ET or FACE. Thus, the combined effects of environmental factors need to be considered in order to realistically assess the effects of environmental changes on microbial community structures.

NMR-Based Metabolic Profiling of Field-Grown Leaves from Sugar Beet Plants Harbouring Different Levels of Resistance to Cercospora Leaf Spot Disease

Cercospora leaf spot (CLS) is one of the most serious leaf diseases for sugar beet (Beta vulgaris L.) worldwide. The breeding of sugar beet cultivars with both high CLS resistance and high yield is a major challenge for breeders. In this study, we report the nuclear magnetic resonance (NMR)-based metabolic profiling of field-grown leaves for a subset of sugar beet genotypes harbouring different levels of CLS resistance. Leaves were collected from 12 sugar beet genotypes at four time points: seedling, early growth, root enlargement, and disease development stages. 1H-NMR spectra of foliar metabolites soluble in a deuterium-oxide (D2O)-based buffer were acquired and subjected to multivariate analyses. A principal component analysis (PCA) of the NMR data from the sugar beet leaves shows clear differences among the growth stages. At the later time points, the sugar and glycine betaine contents were increased, whereas the choline content was decreased. The relationship between the foliar metabolite profiles and resistance level to CLS was examined by combining partial least squares projection to latent structure (PLS) or orthogonal PLS (OPLS) analysis and univariate analyses. It was difficult to build a robust model for predicting precisely the disease severity indices (DSIs) of each genotype; however, GABA and Gln differentiated susceptible genotypes (genotypes with weak resistance) from resistant genotypes (genotypes with resistance greater than a moderate level) before inoculation tests. The results suggested that breeders might exclude susceptible genotypes from breeding programs based on foliar metabolites profiled without inoculation tests, which require an enormous amount of time and effort.

A survey of metabolic changes in potato leaves by NMR-based metabolic profiling in relation to resistance to late blight disease under field conditions.

Non‐targeted nuclear magnetic resonance (NMR)‐based metabolic profiling was applied to potato leaves to survey metabolic changes associated with late blight resistance under field conditions. Potato plants were grown in an experimental field, and the compound leaves with no visible symptoms were collected from 20 cultivars/lines at two sampling time points: (i) the time of initial presentation of symptoms in susceptible cultivars and (ii) 12 days before this initiation. 1H NMR spectra of the foliar metabolites soluble in deuterium oxide‐ or methanol‐d4‐based buffers were measured and used for multivariate analysis. Principal component analysis for six cultivars at symptom initiation showed a class separation corresponding to their levels of late blight resistance. This separation was primarily explained by higher levels of malic acid, methanol, and rutin and a lower level of sucrose in the resistant cultivars than in the susceptible ones. Partial least squares regression revealed that the levels of these metabolites were strongly associated with the disease severity measured in this study under field conditions. These associations were observed only for the leaves harvested at the symptom initiation stage, but not for those collected 12 days beforehand. Subsequently, a simple, alternative enzymatic assay for l‐malic acid was used to estimate late blight resistance, as a model for applying the potential metabolic marker obtained. This study demonstrated the potential of metabolomics for field‐grown plants in combination with targeted methods for quantifying marker levels, moving towards marker‐assisted screening of new cultivars with durable late blight resistance. Copyright

Dietary intake of heat-killed Lactococcus lactis H61 delays age-related hearing loss in C57BL/6J mice

Age-related hearing loss (AHL) is a common disorder associated with aging. In this study, we investigated the effect of the intake of heat-killed Lactococcus lactis subsp. cremoris H61 (strain H61) on AHL in C57BL/6J mice. Measurement of the auditory brainstem response (ABR) demonstrated that female mice at 9 months of age fed a diet containing 0.05% strain H61 for 6 months maintained a significantly lower ABR threshold than control mice. The age-related loss of neurons and hair cells in the cochlea was suppressed by the intake of strain H61. Faecal analysis of bacterial flora revealed that the intake of strain H61 increased the prevalence of Lactobacillales, which is positively correlated with hearing ability in mice. Furthermore, plasma fatty acid levels were negatively correlated with hearing ability. Overall, the results supported that the intake of heat-killed strain H61 for 6 months altered the intestinal flora, affected plasma metabolite levels, including fatty acid levels, and retarded AHL in mice.

Nuclear magnetic resonance- and gas chromatography/mass spectrometry-based metabolomic characterization of water-soluble and volatile compound profiles in cabbage vinegar

Non-targeted metabolomic analyses employing nuclear magnetic resonance- and gas chromatography/mass spectrometry-based techniques were applied for an in-depth characterization of cabbage vinegar, an original agricultural product made from cabbage harvested in Tsumagoi, Japan. Water-soluble and volatile metabolite profiles of cabbage vinegar were compared with those of various vinegars: rice vinegar, grain vinegar, apple vinegar, and black vinegar (Japanese kurozu made of brown rice). Principal component analysis (PCA) of the water-soluble metabolites indicated that cabbage vinegars belonged to an isolated class by the contributions of fructose, pyroglutamic acid, choline, and methiin (S-methylcysteine sulfoxide). Regarding the volatile compounds, the PCA data represented that rice, black, and apple vinegars were characterized by most of the dominant volatiles, such as acetate esters, alcohols, ketones, and acids. Cabbage and grain vinegars were included in the same class although these two vinegars have different flavors. Orthogonal partial least squares-discrimination analysis exhibited the differences in volatile compound profile between cabbage and grain vinegars, revealing that cabbage vinegars were characterized by the presence of sulfides (dimethyl sulfide, dimethyl disulfide, and dimethyl trisulfide), nitriles (allyl cyanide and 4-methylthio-butanenitrile), 3-hexene-1-ol, and crotonic acid. The time-course changes in these highlighted compounds during the acetic acid fermentation of cabbage vinegar suggested that pyroglutamic and crotonic acids were produced through fermentation, whereas choline, methiin, sulfides, nitriles, and 3-hexene-1-ol were derived from cabbage, suggesting the key role of these compounds in the unique taste and flavor of cabbage vinegar.