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Aquaculture contributes to the sustainable development of food security, marine resource conservation, and economy. Shifting aquaculture feed from fish meal and oil to terrestrial plant derivatives may result in cost savings. However, many carnivorous fish cannot be sustained on plant-derived materials, necessitating the need for the identification of important factors for farmed fish growth and the identification of whether components derived from terrestrial plants can be used in feed. Herein, we focused on the carnivorous fish leopard coral grouper (P. leopardus) to identify the essential growth factors and clarify their intake timing from feeds. Furthermore, we evaluated the functionality of starch, which are easily produced by terrestrial plants. Results reveal that carbohydrates, which are not considered essential for carnivorous fish, can be introduced as a major part of an artificial diet. The development of artificial feed using starch offers the possibility of increasing the growth of carnivorous fish in aquaculture. [ABSTRACT FROM AUTHOR]

Surface modification using hydrophilic polymer coatings is a sustainable approach for preventing membrane clogging due to foulant adhesion to water treatment membranes and reducing membrane-replacement frequency. Typically, both molecular descriptors and time-domain nuclear magnetic resonance (TD-NMR) data, which reveal physicochemical properties and polymer-chain dynamics, respectively, are required to predict the properties and understand the mechanisms of hydrophilic polymer coatings. However, studies on the selection of essential components from high-dimensional data and their application to the prediction of surface properties are scarce. Therefore, we developed a method for selecting features from combined high-dimensional molecular descriptors and TD-NMR data. The molecular descriptors of the monomers present in polyethylene terephthalate films were calculated using RDKit, an open-source chemoinformatics toolkit, and TD-NMR spectroscopy was performed over a wide time range using five-pulse sequences to investigate the mobility of the polymer chains. The model that analyzed the data using the random forest algorithm, after reducing the features using gradient boosting machine-based recursive feature elimination, achieved the highest prediction accuracy. The proposed method enables the extraction of important elements from both descriptors of surface properties and can contribute to the development of new sustainable materials and material-specific informatics methodologies encompassing multiple information modalities. [ABSTRACT FROM AUTHOR]

Aquaculture is receiving attention as one of the solutions to the global food problem. Therefore, it is essential to clarify the impact of fish and their environment on the stable supply and uniformity of the quality of fish provided as meat. Nuclear magnetic resonance can comprehensively acquire metabolite information in foods nondestructively and is suitable for measuring physical properties for quality control. Moreover, recent advances in machine learning methods and artificial neural network (ANN) analysis have contributed to the analysis of comprehensive information. In this study, we sampled a wide variety of fish from the natural sea and analyzed them using a scheme incorporating ANN. As a result, it was found that anserine, an antioxidant, was found to be reduced in fish muscles, and this destabilized the homeostasis of other metabolites at low water temperature. We also concluded that the fish muscle metabolic state was stabilized in warm water. Furthermore, a relationship between water temperature and the intestinal microbiota of fish was established. In this study, we evaluated the relationship between the metabolic profile changes in fish muscle and external environmental factors and predicted connection strength and order using machine learning and ANN. We conclude that our proposed scheme for estimating the degree and direction of the influence of environmental factors on organisms by using ANN will work. [ABSTRACT FROM AUTHOR]

The secondary cell wall (SCW) in the xylem is one of the largest sink organs of carbon in woody plants, and is considered a promising sustainable bioresource for biofuels and biomaterials. To enhance SCW formation in poplar (Populus sp.) xylem, we developed a self-reinforced system of SCW-related transcription factors from Arabidopsis thaliana , involving VASCULAR-RELATED NAC-DOMAIN7 (VND7), SECONDARY WALL-ASSOCIATED NAC-DOMAIN PROTEIN 1/NAC SECONDARY WALL THICKENING-PROMOTING FACTOR3 (SND1/NST3), and MYB46. In this system, these transcription factors were fused with the transactivation domain VP16 and expressed under the control of the Populus trichocarpa CesA18 (PtCesA18) gene promoter, creating the chimeric genes PtCesA18pro::AtVND7:VP16 , PtCesA18pro::AtSND1:VP16 , and PtCesA18pro::AtMYB46:VP16. The PtCesA18 promoter is active in tissues generating SCWs, and can be regulated by AtVND7, AtSND1, and AtMYB46; thus, the expression levels of PtCesA18pro::AtVND7:VP16 , PtCesA18pro::AtSND1:VP16 , and PtCesA18pro::AtMYB46:VP16 are expected to be boosted in SCW-generating tissues. In the transgenic hybrid aspens (Populus tremula × tremuloides T89) expressing PtCesA18pro::AtSND1:VP16 or PtCesA18pro::AtMYB46:VP16 grown in sterile half-strength Murashige and Skoog growth medium, SCW thickening was significantly enhanced in the secondary xylem cells, while the PtCesA18pro::AtVND7:VP16 plants showed stunted xylem formation, possibly because of the enhanced programmed cell death (PCD) in the xylem regions. After acclimation, the transgenic plants were transferred from the sterile growth medium to pots of soil in the greenhouse, where only the PtCesA18pro::AtMYB46:VP16 aspens survived. A nuclear magnetic resonance footprinting cell wall analysis and enzymatic saccharification analysis demonstrated that PtCesA18pro::AtMYB46:VP16 influences cell wall properties such as the ratio of syringyl (S) and guaiacyl (G) units of lignin, the abundance of the lignin β-aryl ether and resinol bonds, and hemicellulose acetylation levels. Together, these data indicate that we have created a self-reinforced system using SCW-related transcription factors to enhance SCW accumulation. [ABSTRACT FROM AUTHOR]

Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of "measurement descriptors" with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement via selection of adopted descriptors based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as α-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair. [ABSTRACT FROM AUTHOR]

Bio-based polymers have been considered as an alternative to oil-based materials for their "carbon-neutral" environmentally degrative features. However, degradation is a complex system in which environmental factors and preparation conditions are involved, and the relationship between degradation and these factors/conditions has not yet been clarified. Moreover, an efficient system that addresses multiple degradation factors has not been developed for practical use. Thus, we constructed a decomposition degree predictive model to explore degradation factors based on analytical data and experimental conditions. The predictive model was constructed by machine learning using a dataset. The objective variable was the molecular weight, and the explanatory variables were the moisture content in a compost environment, degradation period, degree of crystallinity pre-experiment, and features of solid-state nuclear magnetic resonance spectra. The good accuracy of this predictive model was confirmed by statistical variables. The moisture content in the compost environment was a critical factor for considering initial degradation; specific scores revealed the contribution of degradation factors. Furthermore, the optimum decomposition degree, various analytical values, and experimental conditions were predictable when this predictive model was combined with Bayesian optimization. Information obtained from virtual experiments is expected to promote the material design and development of bio-based plastics. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at . [ABSTRACT FROM AUTHOR]

Noninvasive evaluation of the spatial distribution of chemical composition and diffusion behavior of materials is becoming possible by advanced nuclear magnetic resonance (NMR) pulse sequence editing. However, there is room for improvement in the spectral resolution and analytical method for application to heterogeneous samples. Here, we develop applications for comprehensively evaluating compounds and their dynamics in intact bodies and heterogeneous systems from NMR data, including spatial z-position, chemical shift, and diffusion or relaxation. This experiment is collectively named spatial molecular-dynamically ordered spectroscopy (SMOOSY). Pseudo-three-dimensional (3D) SMOOSY spectra of an intact shrimp and two heterogeneous systems are recorded to evaluate this methodology. Information about dynamics is mapped onto two-dimensional (2D) chemical shift imaging spectra using a pseudo-spectral imaging method with a processing tool named SMOOSY processor. Pseudo-2D SMOOSY spectral images can non-invasively assess the different dynamics of the compounds at each spatial z-position of the shrimp's body and two heterogeneous systems. Modern NMR methods such as chemical shift imaging and mobility ordered spectroscopy offer direct insight into the spatial distribution of chemical species within heterogeneous systems. Here, spatial molecular-dynamically ordered spectroscopy (SMOOSY) is developed and used to characterise the spatial distribution and diffusion behavior of small molecules within a heterogeneous biological sample. [ABSTRACT FROM AUTHOR]

NMR offers tremendous advantages in the analyses of molecular complexity, such as crude bio-fluids, bio-extracts, and intact cells and tissues. Here we introduce recent applications of NMR approaches, as well as next generation sequencing (NGS), for the evaluation of human and environmental health (i.e., maintenance of a homeostatic state) based on metabolic and microbial profiling and data science. We describe useful databases and web tools that are used to support these studies by facilitating the characterization of metabolites from complex NMR spectra. Because the NMR spectra of metabolic mixtures can produce numerical matrix data (e.g., chemical shift versus intensity) with high reproducibility and inter-institution convertibility, advanced data science approaches, such as multivariate analysis and machine learning, are desirable; therefore, we also introduce informatics techniques derived from heterogeneously measured data, such as environmental microbiota, for the extraction of submerged information using data science approaches. We summarize recent studies of microbiomes that are based on these techniques and show that, particularly in human studies, NMR-based metabolic characterization of non-invasive samples, such as feces, can provide a large quantity of beneficial information regarding human health and disease. [ABSTRACT FROM AUTHOR]

In this study, we investigated the applicability of NMR-based metabolomics to discriminate strain-dependent fermentation characteristics of lactic acid bacteria (LAB), which are important microorganisms for fermented food production. To evaluate the discrimination capability, six type strains of Lactobacillus species and six additional L. brevis strains were used focusing on i) the difference between homo- and hetero-lactic fermentative species and ii) strain-dependent characteristics within L. brevis. Based on the differences in the metabolite profiles of fermented vegetable juices, non-targeted principal component analysis (PCA) clearly separated the samples into those inoculated with homo- and hetero-lactic fermentative species. The separation was primarily explained by the different levels of dominant metabolites (lactic acid, acetic acid, ethanol, and mannitol). Orthogonal partial least squares discrimination analysis, based on a regions-of-interest (ROIs) approach, revealed the contribution of low-abundance metabolites: acetoin, phenyllactic acid, p-hydroxyphenyllactic acid, glycerophosphocholine, and succinic acid for homolactic fermentation; and ornithine, tyramine, and γ-aminobutyric acid (GABA) for heterolactic fermentation. Furthermore, ROIs-based PCA of seven L. brevis strains separated their strain-dependent fermentation characteristics primarily based on their ability to utilize sucrose and citric acid, and convert glutamic acid and tyrosine into GABA and tyramine, respectively. In conclusion, NMR metabolomics successfully discriminated the fermentation characteristics of the tested strains and provided further information on metabolites responsible for these characteristics, which may impact the taste, aroma, and functional properties of fermented foods. [ABSTRACT FROM AUTHOR]

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 © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Improved signal identification for biological small molecules (BSMs) in a mixture was demonstrated by using multidimensional NMR on samples from 13C-enriched Rhododendron japonicum (59.5 atom%) cultivated in air containing 13C-labeled carbon dioxide for 14 weeks. The resonance assignment of 386 carbon atoms and 380 hydrogen atoms in the mixture was achieved. 42 BSMs, including eight that were unlisted in the spectral databases, were identified. Comparisons between the experimental values and the 13C chemical shift values calculated by density functional theory supported the identifications of unlisted BSMs. Tracing the 13C/12C ratio by multidimensional NMR spectra revealed faster and slower turnover ratios of BSMs involved in central metabolism and those categorized as secondary metabolites, respectively. The identification of BSMs and subsequent flow analysis provided insight into the metabolic systems of the plant. [ABSTRACT FROM AUTHOR]

The method provided here can overcome the low S/N problem in 13C NMR, by the integration of plural spectra to take advantage of high-resolution potential based on non-bucketing analysis without additional measurements. In addition, a new metabolite annotation approach using advanced STOCSY and quantum chemistry calculations was introduced in this study. [ABSTRACT FROM AUTHOR]

Gradual depletion of the world petroleum reserves and the impact of environmental pollution highlight the importance of developing alternative energy resources such as plant biomass. To address these issues, intensive research has focused on the plant Jatropha curcas, which serves as a rich source of biodiesel because of its high seed oil content. However, producing biodiesel from Jatropha generates large amounts of biomass waste that are difficult to use. Therefore, the objective of our research was to analyze the effects of different conditions of torrefaction on Jatropha biomass. Six different types of Jatropha tissues (seed coat, kernel, stem, xylem, bark, and leaf) were torrefied at four different temperature conditions (200°C, 250°C, 300°C, and 350°C), and changes in the metabolite composition of the torrefied products were determined by Fourier transform-infrared spectroscopy and nuclear magnetic resonance analyses. Cellulose was gradually converted to oligosaccharides in the temperature range of 200°C–300°C and completely degraded at 350°C. Hemicellulose residues showed different degradation patterns depending on the tissue, whereas glucuronoxylan efficiently decomposed between 300°C and 350°C. Heat-induced depolymerization of starch to maltodextrin started between 200°C and 250°C, and oligomer sugar structure degradation occurred at higher temperatures. Lignin degraded at each temperature, e.g., syringyl (S) degraded at lower temperatures than guaiacyl (G). Finally, the toxic compound phorbol ester degraded gradually starting at 235°C and efficiently just below 300°C. These results suggest that torrefaction is a feasible treatment for further processing of residual biomass to biorefinery stock or fertilizer. [ABSTRACT FROM AUTHOR]

Because they are strong and stable, lignocellulosic supramolecular structures in plant cell walls are resistant to decomposition. However, they can be degraded and recycled by soil microbiota. Little is known about the biomass degradation profiles of complex microbiota based on differences in cellulosic supramolecular structures without compositional variations. Here, we characterized and evaluated the cellulosic supramolecular structures and composition of rice straw biomass processed under different milling conditions. We used a range of techniques including solid- and solution-state nuclear magnetic resonance (NMR) and Fourier transform infrared spectroscopy followed by thermodynamic and microbial degradability characterization using thermogravimetric analysis, solution-state NMR, and denaturing gradient gel electrophoresis. These measured data were further analyzed using an “ECOMICS” web-based toolkit. From the results, we found that physical pretreatment of rice straw alters the lignocellulosic supramolecular structure by cleaving significant molecular lignocellulose bonds. The transformation from crystalline to amorphous cellulose shifted the thermal degradation profiles to lower temperatures. In addition, pretreated rice straw samples developed different microbiota profiles with different metabolic dynamics during the biomass degradation process. This is the first report to comprehensively characterize the structure, composition, and thermal degradation and microbiota profiles using the ECOMICS toolkit. By revealing differences between lignocellulosic supramolecular structures of biomass processed under different milling conditions, our analysis revealed how the characteristic compositions of microbiota profiles develop in addition to their metabolic profiles and dynamics during biomass degradation. [ABSTRACT FROM AUTHOR]

We have developed a plant growth system to analyze water dynamics in the roots of a small model plant, Arabidopsis thaliana, by nuclear magnetic resonance (NMR) microscopic imaging. Using the two-dimensional slice technique, we obtained a series of images with high signal-to-noise ratio indicating the water distribution in the root. To demonstrate light regulation of water transport in the root and involvement of aquaporin gene expression, we visualized the distribution of water in Arabidopsis roots under various light conditions and compared the data with the expression profiles of two aquaporin genes. 1H-NMR imaging revealed that water content in Arabidopsis roots is lower in the light than in the dark. This diurnal variation in water content was clearly observed in the basal zone of the root. In addition, an autonomous rhythm of water dynamics was observed under continuous light (LL) and darkness (DD). However, the circadian oscillation in water dynamics was obscured in the early-flowering 3 (elf3) mutant under LL. The expression of both the aquaporin genes, AtPIP1;2 and AtPIP2;1, oscillated with the circadian rhythm under LL conditions in wild-type seedlings, but not in the elf3 mutant. These results demonstrate the advantages of our technique for monitoring water dynamics in roots of living Arabidopsis seedlings, and suggest that the circadian clock modulates water dynamics and aquaporin expression. [ABSTRACT FROM AUTHOR]

Background: Metabolic phenotyping has become an important 'bird's-eye-view' technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of 'topdown' chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems. Methodology/Principal Findings: The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 ¹H and 13C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected .450 HSQC peaks in each 13C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient 13C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts. Conclusions/Significance: Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of 13C atoms of given metabolites on development-dependent changes in the 56 identified 13C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism. [ABSTRACT FROM AUTHOR]

Abstract: Novel technologies for measuring biological systems and methods for visualizing data have led to a revolution in the life sciences. Nuclear magnetic resonance (NMR) techniques can provide information on metabolite structure and metabolic dynamics at the atomic level. We have been developing a new method for measuring the dynamic metabolic network of crude extracts that combines [13C6]glucose stable isotope labeling of Arabidopsis thaliana and multi-dimensional heteronuclear NMR analysis, whereas most conventional metabolic flux analyses examine proteinogenic amino acids that are specifically labeled with partially labeled substrates such as [2-13C1]glucose or 10% [13C6]glucose. To show the validity of our method, we investigated how to obtain information about biochemical reactions, C–C bond formation, and the cleavage of the main metabolites, such as free amino acids, in crude extracts based on the analysis of the 13C–13C coupling pattern in 2D-NMR spectra. For example, the combination of different extraction solvents allows one to distinguish complicated 13C–13C fine couplings at the C2 position of amino acids. As another approach, f1–f3 projection of the HCACO spectrum also helps in the analysis of 13C–13C connectivities. Using these new methods, we present an example that involves monitoring the incorporation profile of [13C6]glucose into A. thaliana and its metabolic dynamics, which change in a time-dependent manner with atmospheric 12CO2 assimilation. [Copyright &y& Elsevier]

Nuclear magnetic resonance (NMR) will become a key technology in plant metabolomics with the use of stable isotope labeling and advanced hetero-nuclear NMR methodologies. To demonstrate the power of this approach, we performed multi-dimensional hetero-nuclear NMR analysis of metabolic movement of carbon and nitrogen nuclei in Arabidopsis thaliana. First, distinct ethanol-stress response was investigated using 13C-labeled wild type and an ethanol-hypersensitive mutant plants. Furthermore, we followed nitrogen fluxes in 15N-labeled seeds during the initiation of germination in vivo. The future role of stable isotope-labeling combined with advanced hetero-nuclear NMR in plant metabolomics is discussed. [ABSTRACT FROM AUTHOR]

Parkin, a product of the causative gene of autosomal-recessive juvenile parkinsonism (AR-JP), is a RING-type E3 ubiquitin ligase and has an amino-terminal ubiquitin-like (Ubl) domain. Although a single mutation that causes an Arg to Pro substitution at position 42 of the Ubl domain (the Arg 42 mutation) has been identified in AR-JP patients, the function of this domain is not clear. In this study, we determined the three-dimensional structure of the Ubl domain of parkin by NMR, in particular by extensive use of backbone 15N-1H residual dipolar-coupling data. Inspection of chemical-shift-perturbation data showed that the parkin Ubl domain binds the Rpn10 subunit of 26S proteasomes via the region of parkin that includes position 42. Our findings suggest that the Arg 42 mutation induces a conformational change in the Rpn10-binding site of Ubl, resulting in impaired proteasomal binding of parkin, which could be the cause of AR-JP. [ABSTRACT FROM AUTHOR]

Magnetic susceptibility (χ) and 51V NMR have been measured in (V1−xTix)2O3 near the phase boundary of the metal–insulator transition. It is established that the transition from antiferromagnetic insulating (AFI) to antiferromagnetic metallic phases near xc≈0.05 is not quantum critical, but is discontinuous with a jump of the transition temperature. In the AFI phase at 4.2 K, we observed the satellite in the zero-field 51V NMR spectrum around 181 MHz in addition to the ‘host’ resonance around 203 MHz. The satellite is also observable in the paramagnetic metallic phase of the x=0.055 sample. We associated the satellite with the V sites near Ti, which are in the V3+-like oxidation state, but has different temperature dependence of the NMR shift from that of the host V site. The host d-spin susceptibility for x=0.055 decreases below ∼60 K, but remains finite in the low-temperature limit. [Copyright &y& Elsevier]

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as Euglena gracilis. During cellulose degradation, 13C cross-polarization (CP)–magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO2. Using these methods, the 1H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design. [ABSTRACT FROM AUTHOR]

Conventional proton nuclear magnetic resonance (1H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional 1H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures. In this study, we introduced an integrated analytical strategy based on the combination of additional measurement using a diffusion filter, covariation peak separation, and matrix decomposition in a small-scale training dataset. This strategy is aimed to extract signal profiles of soluble macromolecular components from conventional 1H-NMR spectral data in a large-scale dataset without the requirement of re-measurement. We applied this method to the conventional 1H-NMR spectra of water-soluble fish muscle extracts and investigated the distribution characteristics of fish diversity and muscle soluble macromolecular components, such as lipids and collagens. We identified a cluster of fish species with low content of lipids and high content of collagens in muscle, which showed great potential for the development of functional foods. Because this mechanical data processing method requires additional measurement of only a small-scale training dataset without special sample pretreatment, it should be immediately applicable to extract macromolecular signals from accumulated conventional 1H-NMR databases of other complex gelatinous mixtures in foods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 [sup 13]C-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. [ABSTRACT FROM AUTHOR]

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 13C-labeled photosynthetic bacterium Rhodobacter sphaeroides, which shows pronounced broadening of NMR signals. The performance of each extraction solvent was judged using 2D 1H-13C 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-d4 (MeOD) as a semipolar extraction solvent that can sufficiently sharpen the line width and affords better-quality NMR spectra. We also evaluated the senes 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. [ABSTRACT FROM AUTHOR]

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 metabolize profiles throughout a series of repeated extraction processes. Metabolites remaining in the extraction residues of 13C-labeled Ambidopsis thuliana were recovered by repeated extraction using methanol-d4 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 1H-13C 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 metabolize 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. [ABSTRACT FROM AUTHOR]

Lignin is an aromatic polymer that makes a network by intertwining between cellulose fibers in plant. As the lignin network retards access to carbohydrates, it is regarded as a nuisance during biomass processing. When wood is processed into paper pulp or bioethanol, lignin is produced as a by-product and utilized as fuel or a soil amendment. Recently, there has been much interest in the aromatic structure of lignin in relation to the utilization of lignocellulose and the search for petroleum substitutes. Sulfur-free pulping methods, such as soda-anthraquinone cooking, provide more opportunity for using lignin than the alternative kraft process. Our aim was to expand the availability of soda lignin from Japanese cedar, the most planted tree in Japan, by fungal degradation. We performed degradation assays to identify suitable fungi for the efficient breakdown of soda lignin from cedar. Fourteen fungi from both white-rot and leaf-litter fungi were identified using the RBBR and Sundman and Näse assays. By nuclear magnetic resonance analysis we obtained water- and/or methanol-soluble degradation products from four fungi, and the patterns indicate specific degradation mechanisms for each fungi. These results suggest that the screened fungi have more than one mechanism for degrading soda lignin from Japanese cedar. [ABSTRACT FROM AUTHOR]

[Display omitted] • The T 2 relaxation curve provides the water affinity and physical properties of polymers. • Semisupervised NMF separates signals of bound and free water from the T 2 relaxation curve of water-soaked polymers. • Adipic acid and terephthalic acid are related to water affinity and molecular mobility of polymers. • GTMR predicts the polymer composition with desired properties. • Bayesian optimization provides expected values for polymers with desired properties. As global environmental sustainability becomes increasingly emphasized, the development of eco-friendly materials, including solutions to the issue of marine plastics, is thriving. However, the material parameter space is vast, making efficient search a challenge. Time-domain nuclear magnetic resonance offers material property information through the complex T 2 relaxation curves resulting from multiple mobilities. In this research, we used the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence to evaluate the binding state of water (water affinity) in polymers synthesized with various monomer compositions, which were immersed in seawater. We also assessed the T 2 relaxation property of the polymers using the magic sandwich echo, double quantum filter, and magic-and-polarization echo filter techniques. We separated the T 2 relaxation curves of CPMG into free and bound water for polymers by employing semisupervized nonnegative matrix factorization. By employing the features of separated bound water and polymer properties, a polymer composition optimization method offered crucial factors to monomers through random forests, predicted the components of the polymer using generative topography mapping regression, and determined expected values using Bayesian optimization for polymer composition candidates with the desired high water affinity and high rigidity. [ABSTRACT FROM AUTHOR]

Rice straw was mechanically milled using a process consuming 1.9 MJ/kg-biomass, and 10 g/L of unmilled or milled rice straw was used as the carbon source for methane fermentation in a digester containing carbon fiber textile as the supporting material. Milling increased methane production from 226 to 419 mL/L/day at an organic loading rate of 2180 mg-dichromate chemical oxygen demand/L/day, corresponding to 260 mL CH4 /g VS . Storage of the fermentation effluent at room temperature decreased the weight of the milled rice straw residue from 3.81 to 1.00 g/L. The supernatant of the effluent was subjected to nanofiltration. The black concentrates deposited on the nanofiltration membranes contained 53.0–57.9% lignin. Solution nuclear magnetic resonance showed that lignin aromatic components such as p -hydroxyphenyl (H), guaiacyl (G), and syringyl (S) were retained primarily, and major lignin interunit structures such as the β–O–4-H/G unit were absent. This combinational process will aid the complete utilization of rice straw. [ABSTRACT FROM AUTHOR]

With the innovation of high-throughput metabolic profiling methods such as nuclear magnetic resonance (NMR), data mining techniques that can reveal valuable information from substantial data sets are constantly desired in this field. In particular, for the analytical assessment of various human lifestyles, advanced computational methods are ultimately needed. In this study, we applied market basket analysis, which is generally applied in social sciences such as marketing, and used transaction data derived from dietary intake information and urinary chemical data generated using NMR and inductively coupled plasma optical emission spectrometry measurements. The analysis revealed several relationships, such as fish diets with high trimethylamine N-oxide excretion and N-methylnicotinamide excreted at higher levels in the morning and produced from a protein that was consumed one day prior. Therefore, market basket analysis can be applied to metabolic profiling to effectively understand the relationships between metabolites and lifestyle. [ABSTRACT FROM AUTHOR]

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 13C-HSQC (heteronuclear single quantum coherence) NMR spectrum of a single sample of cell extract. [ABSTRACT FROM AUTHOR]