Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses

Monitoring harmful microalgal species and their appearance in Tokyo Bay, Japan, using metabarcoding

During the recent decade, high-throughput sequencing (HTS) techniques, in particular, DNA metabarcoding, have facilitated increased detection of biodiversity, including harmful algal bloom (HAB) species. In this study, the presence of HAB species and their appearance patterns were investigated by employing molecular and light microscopy-based monitoring in Tokyo Bay, Japan. The potential co-appearance patterns between the HAB species, as well as with other eukaryotes and prokaryotes were investigated using correlation and association rule-based time-series analysis. In total, 40 unique HAB species were detected, including 12 toxin-producing HAB species previously not reported from the area. More than half of the HAB species were present throughout the sampling season (summer to autumn) and no structuring or succession patterns associated with the environmental conditions could be detected. Statistically significant (p < 0.05, rS ranging from −0.88 to 0.90) associations were found amongst the HAB species and other eukaryotic and prokaryotic species, including genera containing growth-limiting bacteria. However, significant correlations between species differed amongst the years, indicating that variability in environmental conditions between the years may have a stronger influence on the microalgal community structure and interspecies interactions than the variability during the sampling season. The association rule-based time-series analysis allowed the detection of a previously reported negative relationship between Synechococcus sp. and Skeletonema sp. in nature. Overall, the results support the applicability of metabarcoding and HTS-based microalgae monitoring, as it facilitates more precise species identification compared to light microscopy, as well as provides input for investigating potential interactions amongst different species/groups through simultaneous detection of multiple species/genera.

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.

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.

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.

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.

Evaluation of 16S rRNA Gene Primer Pairs for Monitoring Microbial Community Structures Showed High Reproducibility within and Low Comparability between Datasets Generated with Multiple Archaeal and Bacterial Primer Pairs

The application of next-generation sequencing technology in microbial community analysis increased our knowledge and understanding of the complexity and diversity of a variety of ecosystems. In contrast to Bacteria, the archaeal domain was often not particularly addressed in the analysis of microbial communities. Consequently, established primers specifically amplifying the archaeal 16S ribosomal gene region are scarce compared to the variety of primers targeting bacterial sequences. In this study, we aimed to validate archaeal primers suitable for high throughput next generation sequencing. Three archaeal 16S primer pairs as well as two bacterial and one general microbial 16S primer pairs were comprehensively tested by in-silico evaluation and performing an experimental analysis of a complex microbial community of a biogas reactor. The results obtained clearly demonstrate that comparability of community profiles established using different primer pairs is difficult. 16S rRNA gene data derived from a shotgun metagenome of the same reactor sample added an additional perspective on the community structure. Furthermore, in-silico evaluation of primers, especially those for amplification of archaeal 16S rRNA gene regions, does not necessarily reflect the results obtained in experimental approaches. In the latter, archaeal primer pair ArchV34 showed the highest similarity to the archaeal community structure compared to observed by the metagenomic approach and thus appears to be the appropriate for analyzing archaeal communities in biogas reactors. However, a disadvantage of this primer pair was its low specificity for the archaeal domain in the experimental application leading to high amounts of bacterial sequences within the dataset. Overall our results indicate a rather limited comparability between community structures investigated and determined using different primer pairs as well as between metagenome and 16S rRNA gene amplicon based community structure analysis. This finding, previously shown for Bacteria, was as well observed for the archaeal domain.

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.

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.