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

Variability of the Ionome of Wild Boar (Sus scrofa) and Red Deer (Cervus elaphus) in a Dutch National Park, with Implications for Biomonitoring

The ionome-an important expression of the physiological state of organisms-is poorly known for mammals. The focus on particular tissues-such as liver, kidney, and bones-in biomonitoring of environmental pollution and potential deficiencies is based on widely held assumptions rather than solid knowledge of full mammalian ionomes. We examined the full ionome of Red deer (Cervus elaphus) and Wild boar (Sus scrofa), two commonly used mammals for biomonitoring, in a Dutch protected nature reserve (Veluwezoom). We used four individuals per species. We dissected 13 tissues and organs from each individuals (eight in total) of each species and measured 22 elemental concentrations in each. We assessed, for each element, how concentrations varied across tissues within and between individuals. Based on existing literature, we put our findings in the context of their function in the mammalian body. We found that the ionome was highly variable between as well as within the two species. For most elements, tissues containing the highest and lowest concentration differed between individuals. No single tissue accurately represented the accumulation of toxic elements or potential deficiencies in the bodies. Our assessment of the element's biological roles revealed a serious lack of reference values. Our findings imply that analyses of commonly used tissues in biomonitoring do not necessarily capture bioaccumulation of toxins or potential deficiencies. We recommend establishing a centralized database of mammalian ionomes to derive reference values in future. To our knowledge, our study is one of the most complete assessments of mammalian ionomes to date.

Environmental metabolomics uncovers oxidative stress, amino acid dysregulation, and energy impairment in Daphnia magna with exposure to industrial effluents

Anthropogenic activities are regarded as point sources of pollution entering freshwater bodies worldwide. With over 350,000 chemicals used in manufacturing, wastewater treatment and industrial effluents are comprised of complex mixtures of organic and inorganic pollutants of known and unknown origins. Consequently, their combined toxicity and mode of action are not well understood in aquatic organisms such as Daphnia magna. In this study, effluent samples from wastewater treatment and industrial sectors were used to examine molecular-level perturbations to the polar metabolic profile of D. magna. To determine if the industrial sector and/or the effluent chemistries played a role in the observed biochemical responses, Daphnia were acutely (48 h) exposed to undiluted (100%) and diluted (10, 25, and 50%) effluent samples. Endogenous metabolites were extracted from single daphnids and analyzed using targeted mass spectrometry-based metabolomics. The metabolic profile of Daphnia exposed to effluent samples resulted in significant separation compared to the unexposed controls. Linear regression analysis determined that no single pollutant detected in the effluents was significantly correlated with the responses of metabolites. Significant perturbations were uncovered across many classes of metabolites (amino acids, nucleosides, nucleotides, polyamines, and their derivatives) which serve as intermediates in keystone biochemical processes. The combined metabolic responses are consistent with oxidative stress, disruptions to energy metabolism, and protein dysregulation which were identified through biochemical pathway analysis. These results provide insight into the molecular processes driving stress responses in D. magna. Overall, we determined that the metabolic profile of Daphnia could not be predicted by the chemical composition of environmentally relevant mixtures. The findings of this study demonstrate the advantage of metabolomics in conjunction with chemical analyses to assess the interactions of industrial effluents. This work further demonstrates the ability of environmental metabolomics to characterize molecular-level perturbations in aquatic organisms exposed to complex chemical mixtures directly.

Tracing and regulating redox homeostasis of model benthic ecosystems for sustainable aquaculture in coastal environments

Aquaculture in coastal environments has an increasingly important role in the world’s food supply; however, the accumulation of organic compounds on seafloors due to overfeeding adversely affects benthic ecosystems. To assess the ecological resilience of aquafarms to nutrient influx, we investigated the redox homeostasis of benthic ecosystems using a marine oligochaete as a model benthic organism in aquaculture fields. Real-time monitoring of the redox potential of a model benthic ecosystem constructed in an electrochemical reactor allowed evaluation of the homeostatic response of the system to nutrient addition. Although the detrimental effects of overfeeding were confirmed by irreversible potential changes in the sediment, redox homeostasis was reinforced through a cooperative relationship between oligochaetes and sediment microorganisms. Specifically, the oligochaetes exhibited reversible changes in metabolism and body position in response to dynamic changes in the sediment potential between −300 and 500 mV, thereby promoting the decomposition of organic compounds. The potential-dependent changes in metabolism and body position were reproduced by artificially manipulating the sediment potential in electrochemical reactors. Given the importance of benthic animals in sustaining coastal ecosystems, the electrochemical monitoring and physiologic regulation of marine oligochaetes could offer an intriguing approach toward sustainable aquaculture.

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.

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.

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

In 1840, Justus von Liebig proposed the theory of mineral plant nutrition, through the invention of the Haber–Bosch process, leading to the industrialization of chemical fertilizer (inorganic nitrogen) to feed the human population. Because the excessive use of chemical fertilizer has led to numerous environmental problems, understanding the agroecosystem that contains plants, microbes, and soils is necessary for sustainable agriculture. We revealed the network structure of an agroecosystem established with different management practices and identified that organic nitrogen is a key component contributing to crop yield under the condition of soil solarization, even in the presence of inorganic nitrogen. Our finding provides a potential solution to make crop production more sustainable by utilizing organic nitrogen induced by soil solarization.

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.

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization

Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time-frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T₂* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

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.

Metabolite profiling of Epinephelus fuscoguttatus infected with vibriosis reveals Omega 9 as potential metabolite biomarker

In this study, we report the starvation effect and vibriosis infection on a tropical fish, the tiger grouper (Epinephelus fuscoguttatus). The tiger groupers were infected with Vibrio vulnificus for 21 days. Gas chromatography-mass spectrometry combined with multivariate analysis was used to assess the variation in metabolite profiles of E. fuscoguttatus. Metabolite productions in infected fishes were significantly influenced by fatty acid production. The Omega 9 (ω-9) was abundant under the challenged conditions compared to Omega 3 (ω-3) and Omega 6 (ω-6). A total of six fatty acids from the ω-9 group were detected in high concentration in the infected fishes compared to the control groupers. These metabolites are Oleic acid, Palmitoleic acid, 6,9-Octadecenoic acid, 8,11-Eicosadienoic acid, cis-Erucic acid and 5,8,11-Eicosatrienoic acid. The production of ω-9 differed significantly (p ≤ 0.001) in the challenged samples. The detected ω-9 compounds were quantified based on three different extraction techniques with Supelco 37-component FAME mix (Supelco, USA). The highest concentration of ω-9 groups compared to the other fatty acids detected is 1320.79 mg/4 g and the lowest is 939 mg/4 g in challenged-starved; meanwhile, in challenged-fed, the highest concentration detected is 1220.87 mg/4 g and the lowest is 917.25 mg/4 g. These changes demonstrate that ω-9 can be used as a biomarker of infection in fish.

Ionomic profile and arsenic speciation in Semisulcospira cancellata, a freshwater shellfish from a mine-impacted river in China

Freshwater aquatic environment close to cities and industrial areas is more sensitive than marine environment. The freshwater shellfish Semisulcospira cancellate was introduced as a bioindicator to monitor the heavy metal contamination in the river through ionomic profiles and arsenic speciation. The shellfish samples were collected near four cities along the Xiang River in China. The concentrations of elements including Mg, Al, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, and Pb were determined using ICP-MS. Multivariate statistical analyses such as Pearson's correlation analysis and principle component analysis (PCA) were employed to identify the possible sources of the elements in the shellfish samples. Three principle components were extracted from the ionomic matrix and were associated with natural existence, biological pathways, and mining and smelting activities, respectively. The ionomic profiles of the shellfish samples were evaluated through hierarchical cluster analysis (HCA) which was exhibited in the form of heatmap. The shellfish samples were categorized according to the sampling sites with different contamination levels. Six As species including arsenite [As(III)], arsenate [As(V)], monomethylarsonic acid (MMA), dimethylarsinic acid (DMA), arsenobetaine (AsB), and arsenocholine (AsC) were separated and quantified using HPLC-ICP-MS. The concentrations of As(III) and As(V) were linearly increased with total As concentration increasing. However, the proportion of AsB was decreased with total As while the AsB concentration was irrelevant to total As.

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.

Risk assessment of pesticides in estuaries: a review addressing the persistence of an old problem in complex environments

Estuaries, coastal lagoons and other transition ecosystems tend to become the ultimate reservoirs of pollutants transported by continental runoff, among which pesticides constitute the class of most concern. High amounts of dissolved and particulated organic matter greatly contribute to the accumulation of pesticides that eventually become trapped in sediments or find their way along food chains. Perhaps not so surprisingly, it is common to find elevated levels of pesticides in estuarine sediments decades after their embargo. Still, it remains challenging to address ecotoxicity in circumstances that invariably imply mixtures of contaminants and multiple factors affecting bioavailability. Despite advances in methods for detecting pesticides in waters, sediments and organisms, chemical data alone are insufficient to predict risk. Many researchers have been opting for ex situ bioassays that mimic the concentrations of pesticides in estuarine waters and sediments using a range of ecologically relevant model organisms, with emphasis on fish, molluscs and crustaceans. These experimental procedures unravelled novel risk factors and important insights on toxicological mechanisms, albeit with some prejudice of ecological relevance. On the other hand, in situ bioassays, translocation experiments and passive biomonitoring strive to spot causality through an intricate mesh of confounding factors and cocktails of pollutants. Seemingly, the most informative works are integrative approaches that combine different assessment strategies, multiple endpoints and advanced computational and geographical models to determine risk. State-of-art System Biology approaches combining high-content screening approaches involving "omics" and bioinformatics, can assist discovering and predicting novel Adverse Outcome Pathways that better reflect the cumulative risk of persisting and emerging pesticides among the wide range of stressors that affect estuaries.

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.

Evaluation of 2,4-dichlorophenol exposure of Japanese medaka, Oryzias latipes, using a metabolomics approach

In this study, the metabolic effects of waterborne exposure of medaka (Oryzias latipes) to nominal concentrations of 20 (L group) and 2000 μg/L (H group) 2,4-dichlorophenol (DCP) were examined using a gas chromatography/mass spectroscopy (GC/MS) metabolomics approach. A principal component analysis (PCA) separated the L, H, and control groups along PC1 to explain the toxic effects of DCP at 24 h of exposure. Furthermore, the L and H groups were separated along PC1 at 96 h on the PCA score plots. These results suggest that the effects of DCP depended on exposure concentration and time. Changes in tricarboxylic cycle metabolites suggested that fish exposed to 2,4-DCP require more energy to metabolize and eliminate DCP, particularly at 96 h of exposure. A time-dependent response in the fish exposed to DCP was observed in the GC/MS data, suggesting that the higher DCP concentration had greater effects at 24 h than those observed in response to the lower concentration. In addition, several essential amino acids (arginine, histidine, lysine, isoleucine, leucine, methionine, phenylalanine, threonine, tryptophan, and valine) decreased after DCP exposure in the H group, and starvation condition and high concentration exposure of DCP could consume excess energy from amino acids.

Current knowledge of metabolomic approach in infectious fish disease studies

The approaches of transcriptomic and proteomic have been widely used to study host-pathogen interactions in fish diseases, and this is comparable to the recently emerging application of metabolomic in elucidating disease-resistant mechanisms in fish that gives new insight into potential therapeutic strategies to improve fish health. Metabolomic is defined as the large-scale study of all metabolites within an organism and represents the frontline in the 'omics' approaches, providing direct information on the metabolic responses and perturbations in metabolic pathways. In this review, the current research in infectious fish diseases using metabolomic approach will be summarized. The metabolomic approach in economically important fish infected with viruses, bacteria and nematodes will also be discussed. The potential of the metabolomic approach for management of these infectious diseases as well as the challenges and the limitations of metabolomic in fish disease studies will be explored. Current review highlights the impacts of metabolomic studies in infectious fish diseases, which proposed the potential of new therapeutic strategies to enhance disease resistance in fish.

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.

ICP-MS/MS-Based Ionomics: A Validated Methodology to Investigate the Biological Variability of the Human Ionome

We here describe the development, validation and application of a quantitative methodology for the simultaneous determination of 29 elements in human serum using state-of-the-art inductively coupled plasma triple quadrupole mass spectrometry (ICP-MS/MS). This new methodology offers high-throughput elemental profiling using simple dilution of minimal quantity of serum samples. We report the outcomes of the validation procedure including limits of detection/quantification, linearity of calibration curves, precision, recovery and measurement uncertainty. ICP-MS/MS-based ionomics was used to analyze human serum of 120 older adults. Following a metabolomic data mining approach, the generated ionome profiles were subjected to principal component analysis revealing gender and age-specific differences. The ionome of female individuals was marked by higher levels of calcium, phosphorus, copper and copper to zinc ratio, while iron concentration was lower with respect to male subjects. Age was associated with lower concentrations of zinc. These findings were complemented with additional readouts to interpret micronutrient status including ceruloplasmin, ferritin and inorganic phosphate. Our data supports a gender-specific compartmentalization of the ionome that may reflect different bone remodelling in female individuals. Our ICP-MS/MS methodology enriches the panel of validated "Omics" approaches to study molecular relationships between the exposome and the ionome in relation with nutrition and health.

Application of Passive Sampling to Characterise the Fish Exometabolome

The endogenous metabolites excreted by organisms into their surrounding environment, termed the exometabolome, are important for many processes including chemical communication. In fish biology, such metabolites are also known to be informative markers of physiological status. While metabolomics is increasingly used to investigate the endogenous biochemistry of organisms, no non-targeted studies of the metabolic complexity of fish exometabolomes have been reported to date. In environmental chemistry, Chemcatcher^® (Portsmouth, UK) passive samplers have been developed to sample for micro-pollutants in water. Given the importance of the fish exometabolome, we sought to evaluate the capability of Chemcatcher^® samplers to capture a broad spectrum of endogenous metabolites excreted by fish and to measure these using non-targeted direct infusion mass spectrometry metabolomics. The capabilities of C18 and styrene divinylbenzene reversed-phase sulfonated (SDB-RPS) Empore™ disks for capturing non-polar and polar metabolites, respectively, were compared. Furthermore, we investigated real, complex metabolite mixtures excreted from two model fish species, rainbow trout (Oncorhynchus mykiss) and three-spined stickleback (Gasterosteus aculeatus). In total, 344 biological samples and 28 QC samples were analysed, revealing 646 and 215 m/z peaks from trout and stickleback, respectively. The measured exometabolomes were principally affected by the type of Empore™ (Hemel Hempstead, UK) disk and also by the sampling time. Many peaks were putatively annotated, including several bile acids (e.g., chenodeoxycholate, taurocholate, glycocholate, glycolithocholate, glycochenodeoxycholate, glycodeoxycholate). Collectively these observations show the ability of Chemcatcher^® passive samplers to capture endogenous metabolites excreted from fish.

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.

NMR-based metabolomics reveals compartmental metabolic heterogeneity in liver of Arctic char (Salvelinus alpinus)

Metabolomics involves systematic study of low-molecular-mass metabolites in cells, tissues, or biofluids and is nowadays widely applied to characterize the physiological status of aquatic organisms under a set of conditions, such as disease and toxin exposure. Liver, an important metabolic center in the fish body, is often used for metabolomics analysis. Compared with the whole fish liver, the proportion of liver sample needed for metabolomics analysis is relatively small. The homogeneity of metabolites in liver is thus an important issue, especially for comparative studies and biomarker discovery. This study examined the homogeneity of the metabolic profile in liver of Arctic char (Salvelinus alpinus (L., 1758)) using a NMR-based metabolomics approach. For the analysis, whole liver samples were cut into four parts along the direction of gall bladder and at right angles to this, and metabolites in each part of the liver were extracted and analyzed by multivariate and univariate data analyses. Although the multivariate model was not significant due to variation within the data, the metabolic differences in polar portion of liver extract between the parts were seen, indicating non-homogeneity of Arctic char liver. Therefore, when sampling fish liver for further metabolomics studies, this heterogeneity should be taken into consideration.

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.

Plant Ionomics: From Elemental Profiling to Environmental Adaptation

Ionomics is a high-throughput elemental profiling approach to study the molecular mechanistic basis underlying mineral nutrient and trace element composition (also known as the ionome) of living organisms. Since the concept of ionomics was first introduced more than 10 years ago, significant progress has been made in the identification of genes and gene networks that control the ionome. In this update, we summarize the progress made in using the ionomics approach over the last decade, including the identification of genes by forward genetics and the study of natural ionomic variation. We further discuss the potential application of ionomics to the investigation of the ecological functions of ionomic alleles in adaptation to the environment.

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.

Metabolomics reveals energetic impairments in Daphnia magna exposed to diazinon, malathion and bisphenol-A

(1)H nuclear magnetic resonance (NMR)-based metabolomics was used to study the response of Daphnia magna to increasing sub-lethal concentrations of either an organophosphate (diazinon or malathion) or bisphenol-A (BPA). Principal component analysis (PCA) of (1)H NMR spectra were used to screen metabolome changes after 48h of contaminant exposure. The PCA scores plots showed that diazinon exposures resulted in aberrant metabolomic profiles at all exposure concentrations tested (0.009-0.135 μg/L), while for malathion the second lowest (0.08μg/L) and two highest exposure concentrations (0.32μg/L and 0.47μg/L) caused significant shifts from the control. Individual metabolite changes for both organophosphates indicated that the response to increasing exposure was non-linear and described perturbations in the metabolome that were characteristic of the severity of exposure. For example, intermediate concentrations of diazinon (0.045μg/L and 0.09μg/L) and malathion (0.08μg/L) elicited a decrease in amino acids such as leucine, valine, arginine, glycine, lysine, glutamate, glutamine, phenylalanine and tyrosine, with concurrent increases in glucose and lactate, suggesting a mobilization of energy resources to combat stress. At the highest exposure concentrations for both organophosphates there was evidence of a cessation in metabolic activity, where the same amino acids increased and glucose and lactate decreased, suggesting a slowdown in protein synthesis and depletion of energy stocks. This demonstrated a similar response in the metabolome between two organophosphates but also that intermediate and severe stress levels could be differentiated by changes in the metabolome. For BPA exposures, the PCA scores plot showed a significant change in metabolome at 0.1mg/L, 1.4mg/L and 2.1mg/L of exposure. Individual metabolite changes from 0.7 to 2.1mg/L of BPA exposure showed increases in amino acids such as alanine, valine, isoleucine, leucine, arginine, phenylalanine and tyrosine. These metabolite changes were correlated with decreases in glucose and lactate. This pattern of response was also seen in the highest organophosphate exposures and suggested a generalized stress response that could be related to altered energy dynamics in D. magna. Through studying increasing exposure responses, we have demonstrated the ability of metabolomics to identify discrete differences between intermediate and severe stress, and also to characterize how systemic stress is manifested in the metabolome.

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.