Multi-omics analysis on an agroecosystem reveals the significant role of organic nitrogen to increase agricultural crop yield.

Both inorganic fertilizer inputs and crop yields have increased globally, with the concurrent increase in the pollution of water bodies due to nitrogen leaching from soils. Designing agroecosystems that are environmentally friendly is urgently required. Since agroecosystems are highly complex and consist of entangled webs of interactions between plants, microbes, and soils, identifying critical components in crop production remain elusive. To understand the network structure in agroecosystems engineered by several farming methods, including environmentally friendly soil solarization, we utilized a multiomics approach on a field planted with Brassica rapa We found that the soil solarization increased plant shoot biomass irrespective of the type of fertilizer applied. Our multiomics and integrated informatics revealed complex interactions in the agroecosystem showing multiple network modules represented by plant traits heterogeneously associated with soil metabolites, minerals, and microbes. Unexpectedly, we identified soil organic nitrogen induced by soil solarization as one of the key components to increase crop yield. A germ-free plant in vitro assay and a pot experiment using arable soils confirmed that specific organic nitrogen, namely alanine and choline, directly increased plant biomass by acting as a nitrogen source and a biologically active compound. Thus, our study provides evidence at the agroecosystem level that organic nitrogen plays a key role in plant growth.

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.

Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables.

Deep neural networks (DNNs), which are kinds of the machine learning approaches, are powerful tools for analyzing big sets of data derived from biological and environmental systems. However, DNNs are not applicable to metabolomics studies because they have difficulty in identifying contribution factors, e.g., biomarkers, in constructed classification and regression models. In this paper, we describe an improved DNN-based analytical approach that incorporates an importance estimation for each variable using a mean decrease accuracy (MDA) calculation, which is based on a permutation algorithm; this approach is called DNN-MDA. The performance of the DNN-MDA approach was evaluated using a data set of metabolic profiles derived from yellowfin goby that lived in various rivers throughout Japan. Its performance was compared with that of conventional multivariate and machine learning methods, and the DNN-MDA approach was found to have the best classification accuracy (97.8%) among the examined methods. In addition to this, the DNN-MDA approach facilitated the identification of important variables such as trimethylamine N-oxide, inosinic acid, and glycine, which were characteristic metabolites that contributed to the discrimination of the geographical differences between fish caught in the Kanto region and those caught in other regions. As a result, the DNN-MDA approach is a useful and powerful tool for determining the geographical origins of specimens and identifying their biomarkers in metabolomics studies that are conducted in biological and environmental systems.

NALT M cells are important for immune induction for the common mucosal immune system.

Nasopharynx-associated lymphoid tissue (NALT) is one of the major constituents of the mucosa-associated lymphoid tissue (MALT), and has the ability to induce antigen-specific immune responses. However, the molecular mechanisms responsible for antigen uptake from the nasal cavity into the NALT remain largely unknown. Immunohistochemical analysis showed that CCL9 and CCL20 were co-localized with glycoprotein 2 (GP2) in the epithelium covering NALT, suggesting the existence of M cells in NALT. In analogy with the reduced number of Peyer's patch M cells in CCR6-deficient mice, the number of NALT M cells was drastically decreased in CCR6-deficient mice compared with the wild-type mice. Translocation of nasally administered Salmonella enterica serovar Typhimurium into NALT via NALT M cells was impaired in CCR6-deficient mice, whereas S. Typhimurium demonstrated consistent co-localization with NALT M cells in wild-type mice. When wild-type mice were nasally administered with an attenuated vaccine strain of S. Typhimurium, the mice were protected from a subsequent challenge with wild-type S. Typhimurium. Antigen-specific fecal and nasal IgA was detected after nasal immunization with the attenuated vaccine strain of S. Typhimurium only in wild-type mice but not in CCR6-deficient mice. Taken together, these observations demonstrate that NALT M cells are important as a first line of defense against infection by enabling activation of the common mucosal immune system (CMIS).

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.

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.

Human metabolic, mineral, and microbiota fluctuations across daily nutritional intake visualized by a data-driven approach.

Daily intake information is important for an understanding of the metabolic fluctuation of humans exposed to environmental stimuli. However, little investigation has been performed on the variations in dietary intake as an input and the relationship with human fecal, urinary, and salivary metabolic fluctuations as output information triggered by daily dietary intake. In the present study, we describe a data-driven approach for visualizing the daily intake information on a nutritional scale and for evaluating input-output responses under uncontrolled diets in a human study. For the input evaluation of nutritional intake, we collected information about daily dietary intake and converted this information to numeric data of nutritional elements. Furthermore, for the evaluation of output metabolic, mineral, and microbiota responses, we characterized the metabolic, mineral, and microbiota variations of noninvasive human samples of feces, urine, and saliva. The data-driven approach captured significant differences in the fluctuation of intestinal microbiota and some metabolites caused by a high-protein and a high-fat diet in daily life. This approach should contribute to the metabolic assessment of humans affected by environmental and nutritional factors under unlimited and uncontrolled diets.

Pretreatment and integrated analysis of spectral data reveal seaweed similarities based on chemical diversity.

Extracting useful information from high dimensionality and large data sets is a major challenge for data-driven approaches. The present study was aimed at developing novel integrated analytical strategies for comprehensively characterizing seaweed similarities based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass samples were analyzed using multiple techniques, including Fourier transform infrared (FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy, thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using non-negative matrix factorization (NMF) and NMF combined with multivariate curve resolution-alternating least-squares (MCR-ALS) methods in order to separate individual component information from the overlapping and/or broad spectral peaks. Integrated analysis of the preprocessed chemical data demonstrated distinct discrimination of differential seaweed species. Further network analysis revealed a close correlation between the heavy metal elements and characteristic components of brown algae, such as cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential basis for its metal-sorbing potential. These results suggest that this integrated analytical strategy is useful for extracting and identifying the chemical characteristics of diverse seaweeds based on large chemical data sets, particularly complicated overlapping spectral data.

Comparative analysis of chemical and microbial profiles in estuarine sediments sampled from Kanto and Tohoku regions in Japan.

Estuarine environments accumulate large quantities of organic matter from land masses adjoining the sea, and this is consumed as part of the detritus cycle. These environments are rich in biodiversity, and their ecosystem services greatly benefit humans. However, the estuarine environments have complicated aqueous ecosystems, thus the comprehensive evaluation of biotic interactions and stability is difficult using conventional hypothesis-driven approaches. In this study, we describe the advancement of an evaluation strategy for characterizing and visualizing the interactions and relationships among the microorganisms and chemicals in sediment ecosystems of estuarine environments by a combination of organic matter and elemental profiling as well as microbial profiling. We also report our findings from a comparative analysis of estuarine and coastal environmental samples collected from the Kanto and Tsunami-affected Tohoku regions in Japan. The microbial-gated correlation deployed from the coefficient of microbiota from the correlation matrix and network analysis was able to visualize and summarize the different relationships among the microbial communities, sediment organic matter, and element profiles based on geographical differences in Kanto and Tohoku regions. We demonstrated remarkable estuarine eutrophication in the Kanto region based on abundant sediment polypeptide signals and water nitrogen ions catabolized by microbiota. Therefore, we propose that this data-driven approach is a powerful method for analyzing, visualizing, and evaluating complex metabolic dynamics and networks in sediment microbial ecosystems and can be applied to other environmental ecosystems, such as deep sea sediments and agronomic and forest soils.

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

Biological information is intricately intertwined with several factors. Therefore, comprehensive analytical methods such as integrated data analysis, combining several data measurements, are required. In this study, we describe a method of data preprocessing that can perform comprehensively integrated analysis based on a variety of multimeasurement of organic and inorganic chemical data from Sargassum fusiforme and explore the concealed biological information by statistical analyses with integrated data. Chemical components including polar and semipolar metabolites, minerals, major elemental and isotopic ratio, and thermal decompositional data were measured as environmentally responsive biological data in the seasonal variation. The obtained spectral data of complex chemical components were preprocessed to isolate pure peaks by removing noise and separating overlapping signals using the multivariate curve resolution alternating least-squares method before integrated analyses. By the input of these preprocessed multimeasurement chemical data, principal component analysis and self-organizing maps of integrated data showed changes in the chemical compositions during the mature stage and identified trends in seasonal variation. Correlation network analysis revealed multiple relationships between organic and inorganic components. Moreover, in terms of the relationship between metal group and metabolites, the results of structural equation modeling suggest that the structure of alginic acid changes during the growth of S. fusiforme, which affects its metal binding ability. This integrated analytical approach using a variety of chemical data can be developed for practical applications to obtain new biochemical knowledge including genetic and environmental information.

A Data-Driven Approach to Sugarcane Breeding Programs with Agronomic Characteristics and Amino Acid Constituent Profiling.

Sugarcane (Saccharum spp. hybrids) and its processed products have supported local industries such as those in the Nansei Islands, Japan. To improve the sugarcane quality and productivity, breeders select better clones by evaluating agronomic characteristics, such as commercially recoverable sugar and cane yield. However, other constituents in sugarcane remain largely unutilized in sugarcane breeding programs. This study aims to establish a data-driven approach to analyze agronomic characteristics from breeding programs. This approach also determines a correlation between agronomic characteristics and free amino acid composition to make breeding programs more efficient. Sugarcane was sampled in clones in the later stage of breeding selection and cultivars from experimental fields on Tanegashima Island. Principal component analysis and hierarchical cluster analysis using agronomic characteristics revealed the diversity and variability of each sample, and the data-driven approach classified cultivars and clones into three groups based on yield type. A comparison of free amino acid constituents between these groups revealed significant differences in amino acids such as asparagine and glutamine. This approach dealing with a large volume of data on agronomic characteristics will be useful for assessing the characteristics of potential clones under selection and accelerating breeding programs.

Solid-, solution-, and gas-state NMR monitoring of ¹³C-cellulose degradation in an anaerobic microbial ecosystem.

Anaerobic digestion of biomacromolecules in various microbial ecosystems is influenced by the variations in types, qualities, and quantities of chemical components. Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for characterizing the degradation of solids to gases in anaerobic digestion processes. Here we describe a characterization strategy using NMR spectroscopy for targeting the input solid insoluble biomass, catabolized soluble metabolites, and produced gases. ¹³C-labeled cellulose produced by Gluconacetobacter xylinus was added as a substrate to stirred tank reactors and gradually degraded for 120 h. The time-course variations in structural heterogeneity of cellulose catabolism were determined using solid-state NMR, and soluble metabolites produced by cellulose degradation were monitored using solution-state NMR. In particular, cooperative changes between the solid NMR signal and ¹³C-¹³C/¹³C-¹²C isotopomers in the microbial degradation of ¹³C-cellulose were revealed by a correlation heat map. The triple phase NMR measurements demonstrated that cellulose was anaerobically degraded, fermented, and converted to methane gas from organic acids such as acetic acid and butyric acid.

Metabolic sequences of anaerobic fermentation on glucose-based feeding substrates based on correlation analyses of microbial and metabolite profiling.

Degradation processes in various biomasses are managed by complex metabolic dynamics created by diverse and extensive interactions and competition in microbial communities and their environments. It is important to develop visualization methods to provide a bird's-eye view when characterizing the entire sequential metabolic process in an environmental ecosystem. Here, we describe an approach for the visualization of the metabolic sequences in anaerobic fermentation ecosystems, characterizing the entire metabolic dynamics using a combination of microbial community profiles and metabolic profiles. By evaluating their time-dependent variation, we found that microbial community profiles and metabolite production processes were characteristically affected by the feeding of different glucose-based substrates (glucose, starch, cellulose), although the compositions of the major microbial community and the metabolites detected were likely to be similar in all experiments. This combinatorial approach to variation in microbial communities and metabolic profiles was used successfully to visualize metabolic sequences in anaerobic fermentation ecosystems, in addition to mining candidate microbiota for cellulose degradation. Thus, this approach provides a powerful tool for visualizing and evaluating metabolic sequences within the biomass degradation process in an environmental ecosystem. This is the first report to visualize the entire metabolic dynamic in an anaerobic fermentation ecosystem as metabolic sequences.

The epithelia-specific membrane trafficking factor AP-1B controls gut immune homeostasis in mice.

BACKGROUND & AIMS: Epithelial cells that cover the intestinal mucosal surface maintain immune homeostasis and tolerance in the gastrointestinal tract. However, little is known about the molecular mechanisms that regulate epithelial immune functions. Epithelial cells are distinct in that they are highly polarized; this polarity is, at least in part, established by the epithelium-specific polarized sorting factor adaptor protein (AP)-1B. We investigated the role of AP-1B-mediated protein sorting in the maintenance of gastrointestinal immune homeostasis. METHODS: The role of AP-1B in intestinal immunity was examined in AP-1B-deficient mice (Ap1m2(-/-)) by monitoring their phenotypes, intestinal morphology, and epithelial barrier functions. AP-1B-mediated protein sorting was examined in polarized epithelial cells from AP-1B knockdown and Ap1m2(-/-) mice. RESULTS: Ap1m2(-/-) mice developed spontaneous chronic colitis, characterized by accumulation of interleukin-17A-producing, T-helper 17 cells. Deficiency of AP-1B caused epithelial immune dysfunction, such as reduced expression of antimicrobial proteins and impaired secretion of immunoglobulin A. These defects promoted intestinal dysbiosis and increased bacterial translocation within the mucosa. Importantly, AP-1B deficiency led to mistargeting of a subset of basolateral cytokine receptors to the apical plasma membrane in a polarized epithelial cell line and in colonic epithelial cells from mice. AP1M2 expression was reduced significantly in colonic epithelium samples from patients with Crohn's disease. CONCLUSIONS: AP-1B is required for proper localization of a subset of cytokine receptors in polarized epithelial cells, which allows them to respond to cytokine signals from underlying lamina propria cells. The AP-1B-mediated protein sorting machinery is required for maintenance of immune homeostasis and prevention of excessive inflammation.

Sugarcane Metabolome Compositional Stability in Pretreatment Processes for NMR Measurements.

Sugarcane is essential for global sugar production and its compressed juice is a key raw material for industrial products. Sugarcane juice includes various metabolites with abundances and compositional balances influencing product qualities and functionalities. Therefore, understanding the characteristic features of the sugarcane metabolome is important. However, sugarcane compositional variability and stability, even in pretreatment processes for nuclear magnetic resonance (NMR)-based metabolomic studies, remains elusive. The objective of this study is to evaluate sugarcane juice metabolomic variability affected by centrifugation, filtration, and thermal pretreatments, as well as the time-course changes for determining optimal conditions for NMR-based metabolomic approach. The pretreatment processes left the metabolomic compositions unchanged, indicating that these pretreatments are compatible with one another and the studied metabolomes are comparable. The thermal processing provided stability to the metabolome for more than 32 h at room temperature. Based on the determined analytical conditions, we conducted an NMR-based metabolomic study to discriminate the differences in the harvest period and allowed for successfully identifying the characteristic metabolome. Our findings denote that NMR-based sugarcane metabolomics enable us to provide an opportunity to collect a massive amount of data upon collaboration between multiple researchers, resulting in the rapid construction of useful databases for both research purposes and industrial use.

Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework.

Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T2 relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties.

Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3-95.4%. Markov blanket-based feature selection method with an ecological-chemical-physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.

Nitrogen removal performance using anaerobic ammonium oxidation at low temperatures.

An anaerobic ammonium oxidation (anammox) process for ammonia-rich wastewater treatment has not been reported at temperatures below 15 degrees C. This study used a gel carrier with entrapped anammox bacteria to obtain a stable nitrogen removal performance at low temperatures. In a continuous feeding test, a high nitrogen conversion rate (6.2 kg N m(-3) day(-1)) was confirmed at 32 degrees C. Nitrogen removal activity decreased gradually with decreasing operation temperature; however, it still occurred at 6 degrees C. Nitrogen conversion rates at 22 and 6.3 degrees C were 2.8 and 0.36 kg N m(-3) day(-1), respectively. Moreover, the stability of anammox activity below 20 degrees C was confirmed for more than 130 days. In batch experiments, anammox gel carriers were characterized with respect to temperature. The optimum temperature for anammox bacteria was found to be 37 degrees C. Furthermore, it was clear that the temperature dependence changed at about 28 degrees C. The apparent activation energy in the temperature range from 22 to 28 degrees C was calculated as 93 kJ mol(-1), and that in the range from 28 to 37 degrees C was 33 kJ mol(-1). This value agrees with the result of a continuous feeding test (94 kJ mol(-1), between 6 and 22 degrees C). The nitrogen removal performance demonstrated at the low temperatures used in this study will open the door for the application of anammox processes to many types of industrial wastewater treatment.

Application of ensemble deep neural network to metabolomics studies.

Deep neural network (DNN) is a useful machine learning approach, although its applicability to metabolomics studies has rarely been explored. Here we describe the development of an ensemble DNN (EDNN) algorithm and its applicability to metabolomics studies. As a model case, the developed EDNN approach was applied to metabolomics data of various fish species collected from Japan coastal and estuarine environments for evaluation of a regression performance compared with conventional DNN, random forest, and support vector machine algorithms. This study also revealed that the metabolic profiles of fish muscles were correlated with fish size (growth) in a species-dependent manner. The performance of EDNN regression for fish size based on metabolic profiles was superior to that of DNN, random forest, and support vector machine algorithms. The EDNN approach, therefore, should be helpful for analyses of regression and concerns pertaining to classification in metabolomics studies.

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.