Integrated analysis of seaweed components during seasonal fluctuation by data mining across heterogeneous chemical measurements with network visualization

Computational estimation of sediment symbiotic bacterial structures of seagrasses overgrowing downstream of onshore aquaculture

Coastal seagrass meadows are essential in blue carbon and aquatic ecosystem services. However, this ecosystem has suffered severe eutrophication and destruction due to the expansion of aquaculture. Therefore, methods for the flourishing of seagrass are still being explored. Here, data from 49 public coastal surveys on the distribution of seagrass and seaweed around the onshore aquaculture facilities are revalidated, and an exceptional area where the seagrass Zostera marina thrives was found near the shore downstream of the onshore aquaculture facility. To evaluate the characteristics of the sediment for growing seagrass, physicochemical properties and bacterial ecological evaluations of the sediment were conducted. Evaluation of chemical properties in seagrass sediments confirmed a significant increase in total carbon and a decrease in zinc content. Association analysis and linear discriminant analysis refined bacterial candidates specified in seagrass overgrown- and nonovergrown-sediment. Energy landscape analysis indicated that the symbiotic bacterial groups of seagrass sediment were strongly affected by the distance close to the seagrass-growing aquaculture facility despite their bacterial population appearing to fluctuate seasonally. The bacterial population there showed an apparent decrease in the pathogen candidates belonging to the order Flavobacteriales. Moreover, structure equation modeling and a linear non-Gaussian acyclic model based on the machine learning data estimated an optimal sediment symbiotic bacterial group candidate for seagrass growth as follows: the Lachnospiraceae and Ruminococcaceae families as gut-inhabitant bacteria, Rhodobacteraceae as photosynthetic bacteria, and Desulfobulbaceae as cable bacteria modulating oxygen or nitrate reduction and oxidation of sulfide. These observations confer a novel perspective on the sediment symbiotic bacterial structures critical for blue carbon and low-pathogenic marine ecosystems in aquaculture.

An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach

The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.

Materials informatics approach using domain modelling for exploring structure-property relationships of polymers

In the development of polymer materials, it is an important issue to explore the complex relationships between domain structure and physical properties. In the domain structure analysis of polymer materials, ¹H-static solid-state NMR (ssNMR) spectra can provide information on mobile, rigid, and intermediate domains. But estimation of domain structure from its analysis is difficult due to the wide overlap of spectra from multiple domains. Therefore, we have developed a materials informatics approach that combines the domain modeling (http://dmar.riken.jp/matrigica/) and the integrated analysis of meta-information (the elements, functional groups, additives, and physical properties) in polymer materials. Firstly, the ¹H-static ssNMR data of 120 polymer materials were subjected to a short-time Fourier transform to obtain frequency, intensity, and T₂ relaxation time for domains with different mobility. The average T₂ relaxation time of each domain is 0.96 ms for Mobile, 0.55 ms for Intermediate (Mobile), 0.32 ms for Intermediate (Rigid), and 0.11 ms for Rigid. Secondly, the estimated domain proportions were integrated with meta-information such as elements, functional group and thermophysical properties and was analyzed using a self-organization map and market basket analysis. This proposed method can contribute to explore structure–property relationships of polymer materials with multiple domains.

Noninvasive fecal metabolic profiling for the evaluation of characteristics of thermostable lactic acid bacteria, Weizmannia coagulans SANK70258, for broiler chickens

Weizmannia coagulans SANK70258 is a spore-forming thermostable lactic acid bacterium and an effective probiotic for the growth of livestock animals, but its growth-promoting mechanism remains unclear. Here, the composition of fecal metabolites in broilers continuously administered with W. coagulans SANK70258 was assessed under a regular program with antibiotics, which was transiently given for 6 days after birth. Oral administration of W. coagulans to broiler chicks tended to increase the average daily gain of body weights thereafter. The composition of fecal metabolites in the early chick stage (Day 10 after birth) was dramatically altered by the continuous exposure. The levels of short-chain fatty acids (SCFAs) propionate and butyrate markedly increased, while those of acetate, one of the SCFAs, and lactate were reduced. Simultaneously, arabitol, fructose, mannitol, and erythritol, which are carbohydrates as substrates for gut microbes to produce SCFAs, also increased along with altered correlation. Correlation network analyses classified the modularity clusters (|r| > 0.7) among carbohydrates, SCFAs, lactate, amino acids, and the other metabolites under the two conditions. The characteristic diversities by the exposure were visualized beyond the perspective associated with differences in metabolite concentrations. Further, enrichment pathway analyses showed that metabolic composition related to biosynthesis and/or metabolism for SCFAs, amino acids, and energy were activated. Thus, these observations suggest that W. coagulans SANK70258 dramatically modulates the gut metabolism of the broiler chicks, and the metabolomics profiles during the early chick stages may be associated with growth promotion.

Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs

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.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

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.

Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional 1H-NMR Spectral Database

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.

Noise Reduction in Solid-State NMR Spectra Using Principal Component Analysis

A noise reduction method was developed for solid-state nuclear magnetic resonance spectroscopy using multivariate analysis. Principal component analysis was first applied for cross-polarization/magic angle spinning and ¹³C spin-lattice relaxation measurements of solid-state nuclear magnetic resonance array spectra. The contact time of cross-polarization/magic angle spinning and the delay time in spin-lattice relaxation measurements were continuously changed to obtain a series of spectra, which were used for noise reduction using principal component analysis. The noise reduction method successfully produced spectra with improved signal-to-noise ratios. This noise reduction method shortens the measurement time and allows for detection of components with minute signals.

InterSpin: Integrated Supportive Webtools for Low- and High-Field NMR Analyses Toward Molecular Complexity

InterSpin (http://dmar.riken.jp/interspin/) comprises integrated, supportive, and freely accessible preprocessing webtools and a database to advance signal assignment in low- and high-field NMR analyses of molecular complexities ranging from small molecules to macromolecules for food, material, and environmental applications. To support handling of the broad spectra obtained from solid-state NMR or low-field benchtop NMR, we have developed and evaluated two preprocessing tools: sensitivity improvement with spectral integration, which enhances the signal-to-noise ratio by spectral integration, and peaks separation, which separates overlapping peaks by several algorithms, such as non-negative sparse coding. In addition, the InterSpin Laboratory Information Management System (SpinLIMS) database stores numerous standard spectra ranging from small molecules to macromolecules in solid and solution states (dissolved in polar/nonpolar solvents), and can be searched under various conditions using the following molecular assignment tools. SpinMacro supports easy assignment of macromolecules in natural mixtures via solid-state ¹³C peaks and dimethyl sulfoxide-dissolved ¹H-¹³C correlation peaks. InterAnalysis improves the accuracy of molecular assignment by integrated analysis of ¹H-¹³C correlation peaks and ¹H-J correlation peaks of small molecules dissolved in D₂O or deuterated methanol, which supports easy narrowing down of metabolite candidates. Finally, by enabling database interoperability, SpinLIMS's client software will ultimately support scientific discovery by facilitating sharing and reusing of NMR data.

Profiling physicochemical and planktonic features from discretely/continuously sampled surface water

There is an increasing need for assessing aquatic ecosystems that are globally endangered. Since aquatic ecosystems are complex, integrated consideration of multiple factors utilizing omics technologies can help us better understand aquatic ecosystems. An integrated strategy linking three analytical (machine learning, factor mapping, and forecast-error-variance decomposition) approaches for extracting the features of surface water from datasets comprising ions, metabolites, and microorganisms is proposed herein. The three developed approaches can be employed for diverse datasets of sample sizes and experimentally analyzed factors. The three approaches are applied to explore the features of bay water surrounding Odaiba, Tokyo, Japan, as a case study. Firstly, the machine learning approach separated 681 surface water samples within Japan into three clusters, categorizing Odaiba water into seawater with relatively low inorganic ions, including Mg, Ba, and B. Secondly, the factor mapping approach illustrated Odaiba water samples from the summer as rich in multiple amino acids and some other metabolites and poor in inorganic ions relative to other seasons based on their seasonal dynamics. Finally, forecast-error-variance decomposition using vector autoregressive models indicated that a type of microalgae (Raphidophyceae) grows in close correlation with alanine, succinic acid, and valine on filters and with isobutyric acid and 4-hydroxybenzoic acid in filtrate, Ba, and average wind speed. Our integrated strategy can be used to examine many biological, chemical, and environmental physical factors to analyze surface water.

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (Plectropomus leopardus)

Aquaculture is currently a major source of fish and has the potential to become a major source of protein in the future. These demands require efficient aquaculture. The intestinal microbiota plays an integral role that benefits the host, providing nutrition and modulating the immune system. Although our understanding of microbiota in fish gut has increased, comprehensive studies examining fish microbiota and host metabolism remain limited. Here, we investigated the microbiota and host metabolism in the coral leopard grouper, which is traded in Asian markets as a superior fish and has begun to be produced via aquaculture. We initially examined the structural changes of the gut microbiota using next-generation sequencing and found that the composition of microbiota changed between fasting and feeding conditions. The dominant phyla were Proteobacteria in fasting and Firmicutes in feeding; interchanging the dominant bacteria required 12 hours. Moreover, microbiota diversity was higher under feeding conditions than under fasting conditions. Multivariate analysis revealed that Proteobacteria are the key bacteria in fasting and Firmicutes and Fusobacteria are the key bacteria in feeding. Subsequently, we estimated microbiota functional capacity. Microbiota functional structure was relatively stable throughout the experiment; however, individual function activity changed according to feeding conditions. Taken together, these findings indicate that the gut microbiota could be a key factor to understanding fish feeding conditions and play a role in interactions with host metabolism. In addition, the composition of microbiota in ambient seawater directly affects the fish; therefore, it is important to monitor the microbiota in rearing tanks and seawater circulating systems.

Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem

Data-driven approaches were applied to investigate the temporal and spatial changes of 1,022 individuals of wild yellowfin goby and its potential interaction with the estuarine environment in Japan. Nuclear magnetic resonance (NMR)-based metabolomics revealed that growth stage is a primary factor affecting muscle metabolism. Then, the metabolic, elemental and microbial profiles of the pooled samples generated according to either the same habitat or sampling season as well as the river water and sediment samples from their habitats were measured using NMR spectra, inductively coupled plasma optical emission spectrometry and next-generation 16 S rRNA gene sequencing. Hidden interactions in the integrated datasets such as the potential role of intestinal bacteria in the control of spawning migration, essential amino acids and fatty acids synthesis in wild yellowfin goby were further extracted using correlation clustering and market basket analysis-generated networks. Importantly, our systematic analysis of both the seasonal and latitudinal variations in metabolome, ionome and microbiome of wild yellowfin goby pointed out that the environmental factors such as the temperature play important roles in regulating the body homeostasis of wild fish.

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet

Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general “linear” multivariate analysis alone limits interpretations due to “non-linear” variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input–output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Trans-omics approaches used to characterise fish nutritional biorhythms in leopard coral grouper (Plectropomus leopardus)

Aquaculture is now a major supplier of fish, and has the potential to be a major source of protein in the future. Leopard coral groupers are traded in Asian markets as superior fish, and production via aquaculture has commenced. As feeding efficiency is of great concern in aquaculture, we sought to examine the metabolism of leopard coral groupers using trans-omics approaches. Metabolic mechanisms were comprehensively analysed using transcriptomic and metabolomic techniques. This study focused on the dynamics of muscular metabolites and gene expression. The omics data were discussed in light of circadian rhythms and fasting/feeding. The obtained data suggest that branched-chain amino acids played a role in energy generation in the fish muscle tissues during fasting. Moreover, glycolysis, TCA cycles, and purine metabolic substances exhibited circadian patterns, and gene expression also varied. This study is the first step to understanding the metabolic mechanisms of the leopard coral grouper.

NMR window of molecular complexity showing homeostasis in superorganisms

NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.

FoodPro: A Web-Based Tool for Evaluating Covariance and Correlation NMR Spectra Associated with Food Processes

Foods from agriculture and fishery products are processed using various technologies. Molecular mixture analysis during food processing has the potential to help us understand the molecular mechanisms involved, thus enabling better cooking of the analyzed foods. To date, there has been no web-based tool focusing on accumulating Nuclear Magnetic Resonance (NMR) spectra from various types of food processing. Therefore, we have developed a novel web-based tool, FoodPro, that includes a food NMR spectrum database and computes covariance and correlation spectra to tasting and hardness. As a result, FoodPro has accumulated 236 aqueous (extracted in D₂O) and 131 hydrophobic (extracted in CDCl₃) experimental bench-top 60-MHz NMR spectra, 1753 tastings scored by volunteers, and 139 hardness measurements recorded by a penetrometer, all placed into a core database. The database content was roughly classified into fish and vegetable groups from the viewpoint of different spectrum patterns. FoodPro can query a user food NMR spectrum, search similar NMR spectra with a specified similarity threshold, and then compute estimated tasting and hardness, covariance, and correlation spectra to tasting and hardness. Querying fish spectra exemplified specific covariance spectra to tasting and hardness, giving positive covariance for tasting at 1.31 ppm for lactate and 3.47 ppm for glucose and a positive covariance for hardness at 3.26 ppm for trimethylamine N-oxide.

Modification of plant cell wall structure accompanied by enhancement of saccharification efficiency using a chemical, lasalocid sodium

The cell wall is one major determinant of plant cell morphology, and is an attractive bioresource. Here, we report a novel strategy to modify plant cell wall property by small molecules. Lasalocid sodium (LS) was isolated by chemical screening to identify molecules that affect the cell morphology of tobacco BY-2 cells. LS treatment led to an increase in cell wall thickness, whilst the quantity and sugar composition of the cell wall remained unchanged in BY-2 cells. The chemical also disordered the cellular arrangement of hypocotyls of Arabidopsis plants, resulting in a decrease in hypocotyl length. LS treatment enhanced enzymatic saccharification efficiency in both BY-2 cells and Arabidopsis plants. Microarray analysis on Arabidopsis showed that exposure to LS upregulated type III peroxidase genes, of which some are involved in lignin biogenesis, and jasmonic acid response genes, and phloroglucinol staining supported the activation of lignification by the LS treatment. As jasmonic acid-mediated lignification is a typical reaction to cell wall damage, it is possible that LS induces cell wall loosening, which can trigger cell wall damage response. Thus, LS is a unique chemical for modification of cell wall and morphology through changes in cell wall architecture.

Improvement of physical, chemical, and biological properties of aridisol from Botswana by the incorporation of torrefied biomass

Effective use of agricultural residual biomass may be beneficial for both local and global ecosystems. Recently, biochar has received attention as a soil enhancer, and its effects on plant growth and soil microbiota have been investigated. However, there is little information on how the physical, chemical, and biological properties of soil amended with biochar are affected. In this study, we evaluated the effects of the incorporation of torrefied plant biomass on physical and structural properties, elemental profiles, initial plant growth, and metabolic and microbial dynamics in aridisol from Botswana. Hemicellulose in the biomass was degraded while cellulose and lignin were not, owing to the relatively low-temperature treatment in the torrefaction preparation. Water retentivity and mineral availability for plants were improved in soils with torrefied biomass. Furthermore, fertilization with 3% and 5% of torrefied biomass enhanced initial plant growth and elemental uptake. Although the metabolic and microbial dynamics of the control soil were dominantly associated with a C1 metabolism, those of the 3% and 5% torrefied biomass soils were dominantly associated with an organic acid metabolism. Torrefied biomass was shown to be an effective soil amendment by enhancing water retentivity, structural stability, and plant growth and controlling soil metabolites and microbiota.

Application of Market Basket Analysis for the Visualization of Transaction Data Based on Human Lifestyle and Spectroscopic Measurements

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.

Visualization of Microfloral Metabolism for Marine Waste Recycling

Marine biomass including fishery products are precious protein resources for human foods and are an alternative to livestock animals in order to reduce the virtual water problem. However, a large amount of marine waste can be generated from fishery products and it is not currently recycled. We evaluated the metabolism of digested marine waste using integrated analytical methods, under anaerobic conditions and the fertilization of abandoned agricultural soils. Dynamics of fish waste digestion revealed that samples of meat and bony parts had similar dynamics under anaerobic conditions in spite of large chemical variations in input marine wastes. Abandoned agricultural soils fertilized with fish waste accumulated some amino acids derived from fish waste, and accumulation of l-arginine and l-glutamine were higher in plant seedlings. Therefore, we have proposed an analytical method to visualize metabolic dynamics for recycling of fishery waste processes.

SENSI: signal enhancement by spectral integration for the analysis of metabolic mixtures

The method provided here can overcome the low S/N problem in ¹³C NMR by the integration of plural spectra to increase the resolution based on non-bucketing analysis without measurements.

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.

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.

Metabolic dynamics analysis by massive data integration: application to tsunami-affected field soils in Japan

A new metabolic dynamics analysis approach has been developed in which massive data sets from time-series of (1)H and (13)C NMR spectra are integrated in combination with microbial variability to characterize the biomass degradation process using field soil microbial communities. On the basis of correlation analyses that revealed relationships between various metabolites and bacteria, we efficiently monitored the metabolic dynamics of saccharides, amino acids, and organic acids, by assessing time-course changes in the microbial and metabolic profiles during biomass degradation. Specific bacteria were found to support specific steps of metabolic pathways in the degradation process of biomass to short chain fatty acids. We evaluated samples from agricultural and abandoned fields contaminated by the tsunami that followed the Great East earthquake in Japan. Metabolic dynamics and activities in the biomass degradation process differed considerably between soil from agricultural and abandoned fields. In particular, production levels of short chain fatty acids, such as acetate and propionate, which were considered to be produced by soil bacteria such as Sedimentibacter sp. and Coprococcus sp., were higher in the soil from agricultural fields than from abandoned fields. Our approach could characterize soil activity based on the metabolic dynamics of microbial communities in the biomass degradation process and should therefore be useful in future investigations of the environmental effects of natural disasters on soils.

Profiling planktonic biomass using element-specific, multicomponent nuclear magnetic resonance spectroscopy

Planktonic metabolism plays crucial roles in Earth's elemental cycles. Chemical speciation as well as elemental stoichiometry is important for advancing our understanding of planktonic roles in biogeochemical cycles. In this study, a multicomponent solid-state nuclear magnetic resonance (NMR) approach is proposed for chemical speciation of cellular components, using several advanced NMR techniques. Measurements by ssNMR were performed on (13)C and (15)N-labeled Euglena gracilis, a flagellated protist. 3D dipolar-assisted rotational resonance, double-cross-polarization (1)H-(13)C correlation spectroscopy, and (1)H-(13)C solid-state heteronuclear single quantum correlation spectroscopy successively allowed characterization of cellular components. These techniques were then applied to E. gracilis cultured in high and low ammonium media to demonstrate the power of this method for profiling and comparing cellular components. Cellular NMR spectra indicated that ammonium induced both paramylon degradation and amination. Arginine was stored as a nitrogen reserve and ammonium replaced by arginine catabolism via the arginine dihydrolase pathway. (15)N and (31)P cellular ssNMR indicated arginine and polyphosphate accumulation in E. gracilis, respectively. This chemical speciation technique will contribute to environmental research by providing detailed information on environmental chemical properties.

Comparative metabolomic and ionomic approach for abundant fishes in estuarine environments of Japan

Environmental metabolomics or ionomics is widely used to characterize the effects of environmental stressors on the health of aquatic organisms. However, most studies have focused on liver and muscle tissues of fish, and little is known about how the other organs are affected by environmental perturbations and effects such as metal pollutants or eutrophication. We examined the metabolic and mineral profiles of three kinds of abundant fishes in estuarine ecosystem, yellowfin goby, urohaze-goby, and juvenile Japanese seabass sampled from Tsurumi River estuary, Japan. Multivariate analyses, including nuclear magnetic resonance-based metabolomics and inductively coupled plasma optical emission spectrometry-based ionomics approaches, revealed that the profiles were clustered according to differences among body tissues rather than differences in body size, sex, and species. The metabolic and mineral profiles of the muscle and fin tissues, respectively, suggest that these tissues are most appropriate for evaluating environmental perturbations. Such analyses will be highly useful in evaluating the environmental variation and diversity in aquatic ecosystems.

Noninvasive analysis of metabolic changes following nutrient input into diverse fish species, as investigated by metabolic and microbial profiling approaches

An NMR-based metabolomic approach in aquatic ecosystems is valuable for studying the environmental effects of pharmaceuticals and other chemicals on fish. This technique has also contributed to new information in numerous research areas, such as basic physiology and development, disease, and water pollution. We evaluated the microbial diversity in various fish species collected from Japan’s coastal waters using next-generation sequencing, followed by evaluation of the effects of feed type on co-metabolic modulations in fish-microbial symbiotic ecosystems in laboratory-scale experiments. Intestinal bacteria of fish in their natural environment were characterized (using 16S rRNA genes) for trophic level using pyrosequencing and noninvasive sampling procedures developed to study the metabolism of intestinal symbiotic ecosystems in fish reared in their environment. Metabolites in feces were compared, and intestinal contents and feed were annotated based on HSQC and TOCSY using SpinAssign and network analysis. Feces were characterized by species and varied greatly depending on the feeding types. In addition, feces samples demonstrated a response to changes in the time series of feeding. The potential of this approach as a non-invasive inspection technique in aquaculture is suggested.

Chemical profiling of Jatropha tissues under different torrefaction conditions: application to biomass waste recovery

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.