Yuuri Tsuboi

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.

Top-down Phenomics of Arabidopsis thaliana METABOLIC PROFILING BY ONE- AND TWO-DIMENSIONAL NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY AND TRANSCRIPTOME ANALYSIS OF ALBINO MUTANTS

Elucidating the function of each gene in a genome is important for understanding the whole organism. We previously constructed 4000 disruptant mutants of Arabidopsis by insertion of Ds transposons. Here, we describe a top-down phenomics approach based on metabolic profiling that uses one-dimensional 1H and two-dimensional 1H,13C NMR analyses and transcriptome analysis of albino mutant lines of Arabidopsis. One-dimensional 1H NMR metabolic fingerprinting revealed global metabolic changes in the albino mutants, notably a decrease in aromatic metabolites and changes in aliphatic metabolites. NMR measurements of plants fed with 13C6-glucose showed that the albino lines had dramatically different 13C-labeling patterns and increased levels of several amino acids, especially Asn and Gln. Microarray analysis of one of the albino lines revealed a unique expression profile and showed that changes in the expression of genes encoding metabolic enzymes did not correspond with changes in the levels of metabolites. Collectively, these results suggest that albino mutants lose the normal carbon/nitrogen balance, presumably mainly through lack of photosynthesis. Our study offers an idea of how much the metabolite network is affected by chloroplast function in plants and shows the effectiveness of NMR-based metabolic analysis for metabolite profiling. On the basis of these findings, we propose that future investigations of plant systems biology combine transcriptomic, metabolomic, and phenomic analyses of gene disruptant lines.

Exploring the conformational space of amorphous cellulose using NMR chemical shifts

(13)C-labeled amorphous cellulose and (13)C NMR chemical shifts by 2D (13)C-(13)C correlation spectroscopy were obtained in the regenerated solid-state from ionic liquids. On the basis of the assigned chemical shifts, combined with information from molecular dynamics and quantum chemistry computer simulations a twisted structure for amorphous cellulose is proposed exposing more hydrophilic surface than that of extended crystalline cellulose.

SpinCouple: Development of a Web Tool for Analyzing Metabolite Mixtures via Two-Dimensional J-Resolved NMR Database

A new Web-based tool, SpinCouple, which is based on the accumulation of a two-dimensional (2D) (1)H-(1)H J-resolved NMR database from 598 metabolite standards, has been developed. The spectra include both J-coupling and (1)H chemical shift information; those are applicable to a wide array of spectral annotation, especially for metabolic mixture samples that are difficult to label through the attachment of (13)C isotopes. In addition, the user-friendly application includes an absolute-quantitative analysis tool. Good agreement was obtained between known concentrations of 20-metabolite mixtures versus the calibration curve-based quantification results obtained from 2D-Jres spectra. We have examined the web tool availability using nine series of biological extracts, obtained from animal gut and waste treatment microbiota, fish, and plant tissues. This web-based tool is publicly available via http://emar.riken.jp/spincpl.

Metabolomic profiling of 13C-labelled cellulose digestion in a lower termite: insights into gut symbiont function.

Termites consume an estimated 3–7 billion tonnes of lignocellulose annually, a role in nature which is unique for a single order of invertebrates. Their food is digested with the help of microbial symbionts, a relationship that has been recognized for 200 years and actively researched for at least a century. Although DNA- and RNA-based approaches have greatly refined the details of the process and the identities of the participants, the allocation of roles in space and time remains unclear. To resolve this issue, a pioneer study is reported using metabolomics to chart the in situ catabolism of 13C-cellulose fed to the dampwood species Hodotermopsis sjostedti. The results confirm that the secretion of endogenous cellulases by the host may be significant to the digestive process and indicate that a major contribution by hindgut bacteria is phosphorolysis of cellodextrins or cellobiose. This study provides evidence that essential amino acid acquisition by termites occurs following the lysis of microbial tissue obtained via proctodaeal trophallaxis.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as “reliable” or “unreliable” based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance (1H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named “cluster-aided MCR-ALS,” will facilitate the attainment of more reliable results in the metabolomics datasets.

Methylated Cytokinins from the Phytopathogen Rhodococcus fascians Mimic Plant Hormone Activity

Methylated cytokinins contribute to pathogenesis as hormone-mimics. Cytokinins (CKs), a class of phytohormones that regulate plant growth and development, are also synthesized by some phytopathogens to disrupt the hormonal balance and to facilitate niche establishment in their hosts. Rhodococcus fascians harbors the fasciation (fas) locus, an operon encoding several genes homologous to CK biosynthesis and metabolism. This pathogen causes unique leafy gall symptoms reminiscent of CK overproduction; however, bacterial CKs have not been clearly correlated with the severe symptoms, and no virulence-associated unique CKs or analogs have been identified. Here, we report the identification of monomethylated N6-(∆2-isopentenyl)adenine and dimethylated N6-(∆2-isopentenyl)adenine (collectively, methylated cytokinins [MeCKs]) from R. fascians. MeCKs were recognized by a CK receptor and up-regulated type-A ARABIDOPSIS THALIANA RESPONSE REGULATOR genes. Treatment with MeCKs inhibited root growth, a hallmark of CK action, whereas the receptor mutant was insensitive. MeCKs were retained longer in planta than canonical CKs and were poor substrates for a CK oxidase/dehydrogenase, suggesting enhanced biological stability. MeCKs were synthesized by S-adenosyl methionine-dependent methyltransferases (MT1 and MT2) that are present upstream of the fas genes. The best substrate for methylation was isopentenyl diphosphate. MT1 and MT2 catalyzed distinct methylation reactions; only the MT2 product was used by FAS4 to synthesize monomethylated N6-(∆2-isopentenyl)adenine. The MT1 product was dimethylated by MT2 and used as a substrate by FAS4 to produce dimethylated N6-(∆2-isopentenyl)adenine. Chemically synthesized MeCKs were comparable in activity. Our results strongly suggest that MeCKs function as CK mimics and play a role in this plant-pathogen interaction.

Biogeochemical Typing of Paddy Field by a Data-Driven Approach Revealing Sub-Systems within a Complex Environment - A Pipeline to Filtrate, Organize and Frame Massive Dataset from Multi-Omics Analyses

We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.

Multidimensional High-Resolution Magic Angle Spinning and Solution-State NMR Characterization of 13 C-labeled Plant Metabolites and Lignocellulose

Lignocellulose, which includes mainly cellulose, hemicellulose, and lignin, is a potential resource for the production of chemicals and for other applications. For effective production of materials derived from biomass, it is important to characterize the metabolites and polymeric components of the biomass. Nuclear magnetic resonance (NMR) spectroscopy has been used to identify biomass components; however, the NMR spectra of metabolites and lignocellulose components are ambiguously assigned in many cases due to overlapping chemical shift peaks. Using our 13C-labeling technique in higher plants such as poplar samples, we demonstrated that overlapping peaks could be resolved by three-dimensional NMR experiments to more accurately assign chemical shifts compared with two-dimensional NMR measurements. Metabolites of the 13C-poplar were measured by high-resolution magic angle spinning NMR spectroscopy, which allows sample analysis without solvent extraction, while lignocellulose components of the 13C-poplar dissolved in dimethylsulfoxide/pyridine solvent were analyzed by solution-state NMR techniques. Using these methods, we were able to unambiguously assign chemical shifts of small and macromolecular components in 13C-poplar samples. Furthermore, using samples of less than 5 mg, we could differentiate between two kinds of genes that were overexpressed in poplar samples, which produced clearly modified plant cell wall components.

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each other at the biochemical level. Nuclear magnetic resonance (NMR) spectroscopy is one of several technologies, including gas chromatography-mass spectrometry (GC-MS), with considerable promise for such studies. Advantages of NMR are that it is suitable for untargeted analyses, provides structural information and spectra can be queried in quantitative and statistical manners against recently available databases of individual metabolite spectra. In addition, NMR spectral data can be combined with data from other omics levels (e.g. transcriptomics, genomics) to provide a more comprehensive understanding of the physiological responses of taxa to each other and the environment. However, NMR is less sensitive than other metabolomic techniques, making it difficult to apply to natural microbial systems where sample populations can be low-density and metabolite concentrations low compared to metabolites from well-defined and readily extractable sources such as whole tissues, biofluids or cell-cultures. Consequently, the few direct environmental metabolomic studies of microbes performed to date have been limited to culture-based or easily defined high-density ecosystems such as host-symbiont systems, constructed co-cultures or manipulations of the gut environment where stable isotope labeling can be additionally used to enhance NMR signals. Methods that facilitate the concentration and collection of environmental metabolites at concentrations suitable for NMR are lacking. Since recent attention has been given to the environmental metabolomics of organisms within the aquatic environment, where much of the energy and material flow is mediated by the planktonic community, we have developed a method for the concentration and extraction of whole-community metabolites from planktonic microbial systems by filtration. Commercially available hydrophilic poly-1,1-difluoroethene (PVDF) filters are specially treated to completely remove extractables, which can otherwise appear as contaminants in subsequent analyses. These treated filters are then used to filter environmental or experimental samples of interest. Filters containing the wet sample material are lyophilized and aqueous-soluble metabolites are extracted directly for conventional NMR spectroscopy using a standardized potassium phosphate extraction buffer. Data derived from these methods can be analyzed statistically to identify meaningful patterns, or integrated with other omics levels for comprehensive understanding of community and ecosystem function.