Recent progress in the development of metabolome databases for plant systems biology

Data-Driven Characterization of Metabolome Reprogramming during Early Development of Sorghum Seedlings

Specialized metabolites are produced via discrete metabolic pathways. These small molecules play significant roles in plant growth and development, as well as defense against environmental stresses. These include damping off or seedling blight at a post-emergence stage. Targeted metabolomics was followed to gain insights into metabolome changes characteristic of different developmental stages of sorghum seedlings. Metabolites were extracted from leaves at seven time points post-germination and analyzed using ultra-high performance liquid chromatography coupled to mass spectrometry. Multivariate statistical analysis combined with chemometric tools, such as principal component analysis, hierarchical clustering analysis, and orthogonal partial least squares-discriminant analysis, were applied for data exploration and to reduce data dimensionality as well as for the selection of potential discriminant biomarkers. Changes in metabolome patterns of the seedlings were analyzed in the early, middle, and late stages of growth (7, 14, and 29 days post-germination). The metabolite classes were amino acids, organic acids, lipids, cyanogenic glycosides, hormones, hydroxycinnamic acid derivatives, and flavonoids, with the latter representing the largest class of metabolites. In general, the metabolite content showed an increase with the progression of the plant growth stages. Most of the differential metabolites were derived from tryptophan and phenylalanine, which contribute to innate immune defenses as well as growth. Quantitative analysis identified a correlation of apigenin flavone derivatives with growth stage. Data-driven investigations of these metabolomes provided new insights into the developmental dynamics that occur in seedlings to limit post-germination mortality.

Information theory and machine learning illuminate large-scale metabolomic responses of Brachypodium distachyon to environmental change

Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC-MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC-MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ-condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)-based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML-based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co-accumulation analysis further identified condition-specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio-Analytic Resource for Plant Biology website (https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi-bin/efpWeb.cgi), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation.

Nutritional, functional, and bioactive properties of african underutilized legumes

Globally, legumes are vital constituents of diet and perform critical roles in maintaining well-being owing to the dense nutritional contents and functional properties of their seeds. While much emphasis has been placed on the major grain legumes over the years, the neglected and underutilized legumes (NULs) are gaining significant recognition as probable crops to alleviate malnutrition and give a boost to food security in Africa. Consumption of these underutilized legumes has been associated with several health-promoting benefits and can be utilized as functional foods due to their rich dietary fibers, vitamins, polyunsaturated fatty acids (PUFAs), proteins/essential amino acids, micro-nutrients, and bioactive compounds. Despite the plethora of nutritional benefits, the underutilized legumes have not received much research attention compared to common mainstream grain legumes, thus hindering their adoption and utilization. Consequently, research efforts geared toward improvement, utilization, and incorporation into mainstream agriculture in Africa are more convincing than ever. This work reviews some selected NULs of Africa (Adzuki beans (Vigna angularis), African yam bean (Sphenostylis stenocarpa), Bambara groundnut (Vigna subterranea), Jack bean (Canavalia ensiformis), Kidney bean (Phaseolus vulgaris), Lima bean (Phaseolus lunatus), Marama bean (Tylosema esculentum), Mung bean, (Vigna radiata), Rice bean (Vigna Umbellata), and Winged bean (Psophocarpus tetragonolobus)), and their nutritional, and functional properties. Furthermore, we highlight the prospects and current challenges associated with the utilization of the NULs and discusses the strategies to facilitate their exploitation as not only sources of vital nutrients, but also their integration for the development of cheap and accessible functional foods.

Untargeted Metabolomics Exploration of the Growth Stage-Dependent Chemical Space of the Sclareol-Converting Biocatalyst Hyphozyma roseonigra

Hyphozyma roseonigra is a dimorphic yeast used as a biocatalyst to convert sclareol, a plant diterpenoid to ambradiol. The latter is an intermediate in the synthesis of ambrafuran, a high-value chemical in the fragrance industry. Unfortunately, little is known about the underlying biochemistry of this microorganism. In this study, the integration of multi-platform-based metabolomics was used to better comprehend H. roseonigra from a biochemical perspective. The focus on metabolomic changes during growth and development was accomplished using untargeted LC–MS and NMR analyses. Cell suspensions were grown in batch culture over a 14-day period, and cells from the early-, log-, and stationary phases were harvested every second day using platform-compatible extraction procedures. Following chemometric analysis of LC–MS and NMR data acquired from both intra- and extracellular extracts, the identified discriminatory ions annotated from the endo- and exometabolomes (metabo-fingerprinting and metabo-footprinting) were found to fall predominantly in the primary metabolism class. Pathway mapping and feature-based network correlation analysis assisted in gaining insights into the active metabolic pathways during growth and development and did not flag terpene synthesis. This study provides novel insights into the basic metabolic capabilities of H. roseonigra and suggests that sclareol is metabolized as the detoxification of a hydrophobic xenobiotic compound.

Metabolomics as a Prospective Tool for Soybean (Glycine max) Crop Improvement

Global demand for soybean and its products has stimulated research into the production of novel genotypes with higher yields, greater drought and disease tolerance, and shorter growth times. Genetic research may be the most effective way to continue developing high-performing cultivars with desirable agronomic features and improved nutritional content and seed performance. Metabolomics, which predicts the metabolic marker for plant performance under stressful conditions, is rapidly gaining interest in plant breeding and has emerged as a powerful tool for driving crop improvement. The development of increasingly sensitive, automated, and high-throughput analytical technologies, paired with improved bioinformatics and other omics techniques, has paved the way for wide characterization of genetic characteristics for crop improvement. The combination of chromatography (liquid and gas-based) with mass spectrometry has also proven to be an indisputable efficient platform for metabolomic studies, notably plant metabolic fingerprinting investigations. Nevertheless, there has been significant progress in the use of nuclear magnetic resonance (NMR), capillary electrophoresis, and Fourier-transform infrared spectroscopy (FTIR), each with its own set of benefits and drawbacks. Furthermore, utilizing multivariate analysis, principal components analysis (PCA), discriminant analysis, and projection to latent structures (PLS), it is possible to identify and differentiate various groups. The researched soybean varieties may be correctly classified by using the PCA and PLS multivariate analyses. As metabolomics is an effective method for evaluating and selecting wild specimens with desirable features for the breeding of improved new cultivars, plant breeders can benefit from the identification of metabolite biomarkers and key metabolic pathways to develop new genotypes with value-added features.

Metabolome Profiling: A Breeding Prediction Tool for Legume Performance under Biotic Stress Conditions

Legume crops such as common bean, pea, alfalfa, cowpea, peanut, soybean and others contribute significantly to the diet of both humans and animals. They are also important in the improvement of cropping systems that employ rotation and fix atmospheric nitrogen. Biotic stresses hinder the production of leguminous crops, significantly limiting their yield potential. There is a need to understand the molecular and biochemical mechanisms involved in the response of these crops to biotic stressors. Simultaneous expressions of a number of genes responsible for specific traits of interest in legumes under biotic stress conditions have been reported, often with the functions of the identified genes unknown. Metabolomics can, therefore, be a complementary tool to understand the pathways involved in biotic stress response in legumes. Reports on legume metabolomic studies in response to biotic stress have paved the way in understanding stress-signalling pathways. This review provides a progress update on metabolomic studies of legumes in response to different biotic stresses. Metabolome annotation and data analysis platforms are discussed together with future prospects. The integration of metabolomics with other “omics” tools in breeding programmes can aid greatly in ensuring food security through the production of stress tolerant cultivars.

Development of RIKEN Plant Metabolome MetaDatabase

The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.

Salinity Stress Alters the Secondary Metabolic Profile of M. sativa, M. arborea and Their Hybrid (Alborea)

Increased soil salinity, and therefore accumulation of ions, is one of the major abiotic stresses of cultivated plants that negatively affect their growth and yield. Among Medicago species, only Medicago truncatula, which is a model plant, has been extensively studied, while research regarding salinity responses of two important forage legumes of Medicago sativa (M. sativa) and Medicago arborea (M. arborea) has been limited. In the present work, differences between M. arborea, M. sativa and their hybrid Alborea were studied regarding growth parameters and metabolomic responses. The entries were subjected to three different treatments: (1) no NaCl application (control plants), (2) continuous application of 100 mM NaCl (acute stress) and (3) gradual application of NaCl at concentrations of 50-75-150 mM by increasing NaCl concentration every 10 days. According to the results, M. arborea maintained steady growth in all three treatments and appeared to be more resistant to salinity. Furthermore, results clearly demonstrated that M. arborea presented a different metabolic profile from that of M. sativa and their hybrid. In general, it was found that under acute and gradual stress, M. sativa overexpressed saponins in the shoots while M. arborea overexpressed saponins in the roots, which is the part of the plant where most of the saponins are produced and overexpressed. Alborea did not perform well, as more metabolites were downregulated than upregulated when subjected to salinity stress. Finally, saponins and hydroxycinnamic acids were key players of increased salinity tolerance.

TOMATOMET: A metabolome database consists of 7118 accurate mass values detected in mature fruits of 25 tomato cultivars

The total number of low-molecular-weight compounds in the plant kingdom, most of which are secondary metabolites, is hypothesized to be over one million, although only a limited number of plant compounds have been characterized. Untargeted analysis, especially using mass spectrometry (MS), has been useful for understanding the plant metabolome; however, due to the limited availability of authentic compounds for MS-based identification, the identities of most of the ion peaks detected by MS remain unknown. Accurate mass values of peaks obtained by high accuracy mass measurement and, if available, MS/MS fragmentation patterns provide abundant annotation for each peak. Here, we carried out an untargeted analysis of compounds in the mature fruit of 25 tomato cultivars using liquid chromatography-Orbitrap MS for accurate mass measurement, followed by manual curation to construct the metabolome database TOMATOMET (http://metabolites.in/tomato-fruits/). The database contains 7,118 peaks with accurate mass values, in which 1,577 ion peaks are annotated as members of a chemical group. Remarkably, 71% of the mass values are not found in the accurate masses detected previously in Arabidopsis thaliana, Medicago truncatula or Jatropha curcas, indicating significant chemical diversity among plant species that remains to be solved. Interestingly, substantial chemical diversity exists also among tomato cultivars, indicating that chemical profiling from distinct cultivars contributes towards understanding the metabolome, even in a single organ of a species, and can prioritize some desirable metabolic targets for further applications such as breeding.

Metabolomics of Withania somnifera (L.) Dunal: Advances and applications

ETHNOPHARMACOLOGICAL RELEVANCE

Withania somnifera L. (Solanaceae), commonly known as Ashwagandha or Indian ginseng, is used in Ayurveda (Indian system of traditional medicine) for vitality, cardio-protection and treating other ailments, such as neurological disorders, gout, and skin diseases.

AIM OF THE REVIEW

We present a critical overview of the information on the metabolomics of W. somnifera and highlight the significance of the technique for use in quality control of medicinal products. We have also pointed out the use of metabolomics to distinguish varieties and to identify best methods of cultivation, collection, as well as extraction.

MATERIAL AND METHODS

The relevant information on medicinal value, phytochemical studies, metabolomics of W. somnifera, and their applications were collected from a rigorous electronic search through scientific databases, including Scopus, PubMed, Web of Science and Google Scholar. Structures of selected metabolites were from the PubChem.

RESULTS

The pharmacological activities of W. somnifera were well documented. Roots are the most important parts of the plant used in Ayurvedic preparations. Stem and leaves also have a rich content of bioactive phytochemicals like steroidal lactones, alkaloids, and phenolic acids. Metabolomic studies revealed that metabolite profiles of W. somnifera depended on plant parts collected and the developmental stage of the plant, besides the season of sample collection and geographical location. The levels of withanolides were variable, depending on the morpho/chemotypes within the species of W. somnifera. Although studies on W. somnifera were initiated several years ago, the complexity of secondary metabolites was not realized due to the lack of adequate and fool-proof technology for phytochemical fingerprinting. Sophistications in chromatography coupled to mass spectrometry facilitated the discovery of several new metabolites. Mutually complementary techniques like LC-MS, GC-MS, HPTLC, and NMR were employed to obtain a comprehensive metabolomic profile. Subsequent data analyses and searches against spectral databases enabled the annotation of signals and dereplication of metabolites in several numbers without isolating them individually.

CONCLUSIONS

The present review provides a critical update of metabolomic data and the diverse application of the technique. The identification of parameters for standardization and quality control of herbal products is essential to facilitate mandatory checks for the purity of formulation. Such studies would enable us to identify the best geographical location of plants and the time of collection. We recommend the use of metabolomic analysis of herbal products based on W. somnifera for quality control as well as the discovery of novel bioactive compounds.

Dataset on the Effects of Different Pre-Harvest Factors on the Metabolomics Profile of Lettuce (Lactuca sativa L.) Leaves

The study of the relationship between cultivated plants and environmental factors can provide information ranging from a deeper understanding of the plant biological system to the development of more effective management strategies for improving yield, quality, and sustainability of the produce. In this article, we present a comprehensive metabolomics dataset of two phytochemically divergent lettuce (Lactuca sativa L.) butterhead varieties under different growing conditions. Plants were cultivated in hydroponics in a growth chamber with ambient control. The pre-harvest factors that were independently investigated were light intensity (two levels), the ionic strength of the nutrient solutions (three levels), and the molar ratio of three macroelements (K, Mg, and Ca) in the nutrient solution (three levels). We used an untargeted, mass-spectrometry-based approach to characterize the metabolomics profiles of leaves harvested 19 days after transplant. The data revealed the ample impact on both primary and secondary metabolism and its range of variation. Moreover, our dataset is useful for uncovering the complex effects of the genotype, the environmental factor(s), and their interaction, which may deserve further investigation.

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Metabolomics Analysis of Mesenchymal Stem Cells

Various mesenchymal stem cells as easily accessible and multipotent cells can share different essential signaling pathways related to their stemness ability. Understanding the mechanism of stemness ability can be useful for controlling the stem cells for regenerative medicine targets. In this context, OMICs studies can analyze the mechanism of different stem cell properties or stemness ability via a broad range of current high-throughput techniques. This field is fundamentally directed toward the analysis of whole genome (genomics), mRNAs (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in biological samples. According to several studies, metabolomics is more effective than other OMICs ّfor various system biology concerns. Metabolomics can elucidate the biological mechanisms of various mesenchymal stem cell function by measuring their metabolites such as their secretome components. Analyzing the metabolic alteration of mesenchymal stem cells can be useful to promote their regenerative medicine application.

Systems biology of seeds: decoding the secret of biochemical seed factories for nutritional security

Seeds serve as biochemical factories of nutrition, processing, bio-energy and storage related important bio-molecules and act as a delivery system to transmit the genetic information to the next generation. The research pertaining towards delineating the complex system of regulation of genes and pathways related to seed biology and nutrient partitioning is still under infancy. To understand these, it is important to know the genes and pathway(s) involved in the homeostasis of bio-molecules. In recent past with the advent and advancement of modern tools of genomics and genetic engineering, multi-layered 'omics' approaches and high-throughput platforms are being used to discern the genes and proteins involved in various metabolic, and signaling pathways and their regulations for understanding the molecular genetics of biosynthesis and homeostasis of bio-molecules. This can be possible by exploring systems biology approaches via the integration of omics data for understanding the intricacy of seed development and nutrient partitioning. These information can be exploited for the improvement of biologically important chemicals for large-scale production of nutrients and nutraceuticals through pathway engineering and biotechnology. This review article thus describes different omics tools and other branches that are merged to build the most attractive area of research towards establishing the seeds as biochemical factories for human health and nutrition.

An integrated multi-layered analysis of the metabolic networks of different tissues uncovers key genetic components of primary metabolism in maize

Primary metabolism plays a pivotal role in normal plant growth, development and reproduction. As maize is a major crop worldwide, the primary metabolites produced by maize plants are of immense importance from both calorific and nutritional perspectives. Here a genome-wide association study (GWAS) of 61 primary metabolites using a maize association panel containing 513 inbred lines identified 153 significant loci associated with the level of these metabolites in four independent tissues. The genome-wide expression level of 760 genes was also linked with metabolite levels within the same tissue. On average, the genetic variants at each locus or transcriptional variance of each gene identified here were estimated to have a minor effect (4.4-7.8%) on primary metabolic variation. Thirty-six loci or genes were prioritized as being worthy of future investigation, either with regard to functional characterization or for their utility for genetic improvement. This target list includes the well-known opaque 2 (O2) and lkr/sdh genes as well as many less well-characterized genes. During our investigation of these 36 loci, we analyzed the genetic components and variations underlying the trehalose, aspartate and aromatic amino acid pathways, thereby functionally characterizing four genes involved in primary metabolism in maize.

The Chemistry of Plant-Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance

Plant roots communicate with microbes in a sophisticated manner through chemical communication within the rhizosphere, thereby leading to biofilm formation of beneficial microbes and, in the case of plant growth-promoting rhizomicrobes/-bacteria (PGPR), resulting in priming of defense, or induced resistance in the plant host. The knowledge of plant-plant and plant-microbe interactions have been greatly extended over recent years; however, the chemical communication leading to priming is far from being well understood. Furthermore, linkage between below- and above-ground plant physiological processes adds to the complexity. In metabolomics studies, the main aim is to profile and annotate all exo- and endo-metabolites in a biological system that drive and participate in physiological processes. Recent advances in this field has enabled researchers to analyze 100s of compounds in one sample over a short time period. Here, from a metabolomics viewpoint, we review the interactions within the rhizosphere and subsequent above-ground 'signalomics', and emphasize the contributions that mass spectrometric-based metabolomic approaches can bring to the study of plant-beneficial - and priming events.

How to boost marine fungal research: A first step towards a multidisciplinary approach by combining molecular fungal ecology and natural products chemistry

Marine fungi have attracted attention in recent years due to increased appreciation of their functional role in ecosystems and as important sources of new natural products. The concomitant development of various "omic" technologies has boosted fungal research in the fields of biodiversity, physiological ecology and natural product biosynthesis. Each of these research areas has its own research agenda, scientific language and quality standards, which have so far hindered an interdisciplinary exchange. Inter- and transdisciplinary interactions are, however, vital for: (i) a detailed understanding of the ecological role of marine fungi, (ii) unlocking their hidden potential for natural product discovery, and (iii) designing access routes for biotechnological production. In this review and opinion paper, we describe the two different "worlds" of marine fungal natural product chemists and marine fungal molecular ecologists. The individual scientific approaches and tools employed are summarised and explained, and enriched with a first common glossary. We propose a strategy to find a multidisciplinary approach towards a comprehensive view on marine fungi and their chemical potential.

Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.

Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield

A combined metabolomic, biochemical, fluxomic, and metabolic modeling approach was developed using 19 genetically distant maize (Zea mays) lines from Europe and America. Considerable differences were detected between the lines when leaf metabolic profiles and activities of the main enzymes involved in primary metabolism were compared. During grain filling, the leaf metabolic composition appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lines. During the same period, there was a significant correlation between the genetic distance of the lines and the activities of enzymes involved in carbon metabolism, notably glycolysis. Although large differences were observed in terms of leaf metabolic fluxes, these variations were not tightly linked to the genome structure of the lines. Both correlation studies and metabolic network analyses allowed the description of a maize ideotype with a high grain yield potential. Such an ideotype is characterized by low accumulation of soluble amino acids and carbohydrates in the leaves and high activity of enzymes involved in the C4 photosynthetic pathway and in the biosynthesis of amino acids derived from glutamate. Chlorogenates appear to be important markers that can be used to select for maize lines that produce larger kernels.

Understanding Plant Nitrogen Metabolism through Metabolomics and Computational Approaches

A comprehensive understanding of plant metabolism could provide a direct mechanism for improving nitrogen use efficiency (NUE) in crops. One of the major barriers to achieving this outcome is our poor understanding of the complex metabolic networks, physiological factors, and signaling mechanisms that affect NUE in agricultural settings. However, an exciting collection of computational and experimental approaches has begun to elucidate whole-plant nitrogen usage and provides an avenue for connecting nitrogen-related phenotypes to genes. Herein, we describe how metabolomics, computational models of metabolism, and flux balance analysis have been harnessed to advance our understanding of plant nitrogen metabolism. We introduce a model describing the complex flow of nitrogen through crops in a real-world agricultural setting and describe how experimental metabolomics data, such as isotope labeling rates and analyses of nutrient uptake, can be used to refine these models. In summary, the metabolomics/computational approach offers an exciting mechanism for understanding NUE that may ultimately lead to more effective crop management and engineered plants with higher yields.

The WEIZMASS spectral library for high-confidence metabolite identification

Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

Unambiguous metabolite annotation is a critical, yet problematic step, in mass spectrometry based metabolomics. Here, Shahaf et al. present WEIZMASS, a platform consisting of a diverse spectral library of more than 3500 plant metabolites and software to aid their identification in biological samples.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Liquid chromatography-quadrupole time of flight tandem mass spectrometry-based targeted metabolomic study for varietal discrimination of grapes according to plant sterols content

Grapevine and derived products are rich in a wide range of compounds and its quality mainly depends on its metabolites, as a result of viticulture practices. Plant sterols, also called phytosterols (PS), are secondary metabolites regarded as bioactive substance present in grape berries and other plant-based food. The present study deals with a metabolomic approach focusing on phytosterols family in six varieties of Rioja grapes (Cabernet Sauvignon, Tempranillo, Graciano, Garnacha, White Garnacha and Viura), in order to find significant differences among them. Liquid chromatography- mass spectrometry with a quadrupole-time of flight mass analyzer (LC-QTOF) was used to find as many metabolites as possible in the different grape berry fractions, and using statistics to help finding significant clustering of the metabolic profile of pulp, peel and seeds in relation to the variety. The best chromatographic and detection conditions were achieved by gas phase ionization via atmospheric pressure chemical ionization (APCI) in positive mode. Furthermore, analysis with electrospray (ESI) is also needed for phytosterol derivatives confirmation. Putative compounds of interest in the analyzed samples were found by an automated compound extraction algorithm (Molecular Feature Extraction, MFE) and an initial differential expression from the data was created with the aid of commercial software. Once the data were collected, the results were filtered, aligned and normalized, and evaluating applying one-way analysis of variance (ANOVA) with a 95% significance level. For sample class prediction, partial least square-discriminant analysis (PLS-DA) is used as a supervised pattern recognition method and excellent separation among the grape varieties is shown. An overall accuracy of 93.3% (pulp samples), 100.0% (peel) or 96.7% (seeds) in discriminating between grape varieties was achieved when comparing the different fractions. In general, 7 PS derivatives were identified with ID scores higher than 84%.

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

Mild Potassium Chloride Stress Alters the Mineral Composition, Hormone Network, and Phenolic Profile in Artichoke Leaves

There is a growing interest among consumers and researchers in the globe artichoke [Cynara cardunculus L. subsp. scolymus (L.) Hegi] leaf extract due to its nutraceutical and therapeutic properties. The application of an abiotic stress such as salinity can activate the stress-signaling pathways, thus enhancing the content of valuable phytochemicals. The aim of this study was to assess the metabolic changes in artichokes by probing the leaf metabolome of artichoke plants grown in a floating system and exposed to a relatively mild (30 mM) potassium chloride (KCl) salt stress. Potassium chloride treatment decreased the leaf dry biomass of artichoke, macro- and microelements in leaves (e.g., Ca, Mg, Mn, Zn, and B) but increased the concentrations of K and Cl. Metabolomics highlighted that the hormonal network of artichokes was strongly imbalanced by KCl. The indole-3-acetic acid conjugates, the brassinosteroids hormone 6-deoxocastasterone, and even more the cytokinin precursor N(6)-(Delta-2-isopentenyl)-adenosine-5'-triphosphate, strongly increased in leaves of KCl-treated plants. Moreover, KCl saline treatment induced accumulation of GA4, a bioactive form additional to the already known GA3. Another specific response to salinity was changes in the phenolic compounds profile, with flavones and isoflavones being decreased by KCl treatment, whereas flavonoid glycosides increased. The osmotic/oxidative stress that salinity generates also induced some expected changes at the biochemical level (e.g., ascorbate degradation, membrane lipid peroxidation, and accumulation of mannitol phosphate). These latter results help explain the molecular/physiological mechanisms that the plant uses to cope with potassium chloride stress exposure.

Metabolic Regulation of Carotenoid-Enriched Golden Rice Line

Vitamin A deficiency (VAD) is the leading cause of blindness among children and is associated with high risk of maternal mortality. In order to enhance the bioavailability of vitamin A, high carotenoid transgenic golden rice has been developed by manipulating enzymes, such as phytoene synthase (psy) and phytoene desaturase (crtI). In this study, proteome and metabolite analyses were carried out to comprehend metabolic regulation and adaptation of transgenic golden rice after the manipulation of endosperm specific carotenoid pathways. The main alteration was observed in carbohydrate metabolism pathways of the transgenic seeds. The 2D based proteomic studies demonstrated that carbohydrate metabolism-related enzymes, such as pullulanase, UDP-glucose pyrophosphorylase, and glucose-1-phosphate adenylyltransferase, were primarily up-regulated in transgenic rice seeds. In addition, the enzyme PPDK was also elevated in transgenic seeds thus enhancing pyruvate biosynthesis, which is the precursor in the carotenoids biosynthetic pathway. GC-MS based metabolite profiling demonstrated an increase in the levels of glyceric acid, fructo-furanose, and galactose, while decrease in galactonic acid and gentiobiose in the transgenic rice compared to WT. It is noteworthy to mention that the carotenoid content, especially β-carotene level in transgenic rice (4.3 μg/g) was significantly enhanced. The present study highlights the metabolic adaptation process of a transgenic golden rice line (homozygous T4 progeny of SKBR-244) after enhancing carotenoid biosynthesis. The presented information would be helpful in the development of crops enriched in carotenoids by expressing metabolic flux of pyruvate biosynthesis.

NMR spectroscopic search module for Spektraris, an online resource for plant natural product identification--Taxane diterpenoids from Taxus × media cell suspension cultures as a case study

Development and testing of Spektraris-NMR, an online spectral resource, is reported for the NMR-based structural identification of plant natural products (PNPs). Spektraris-NMR allows users to search with multiple spectra at once and returns a table with a list of hits arranged according to the goodness of fit between query data and database entries. For each hit, a link to a tabulated alignment of (1)H NMR and (13)C NMR spectroscopic peaks (query versus database entry) is provided. Furthermore, full spectroscopic records and experimental meta information about each database entry can be accessed online. To test the utility of Spektraris-NMR for PNP identification, the database was populated with NMR data (total of 466 spectra) for ∼ 250 taxanes, which are structurally complex diterpenoids (including the anticancer drug taxol) commonly found in the genus Taxus. NMR data generated with metabolites purified from Taxus cell suspension cultures were then used to search Spektraris-NMR, and enabled the identification of eight taxanes with high confidence. A ninth isolated metabolite could be assigned, based on spectral searches, to a taxane skeletal class, but no high confidence hit was produced. Using various spectroscopic methods, this metabolite was characterized as 2-deacetylbaccatin IV, a novel taxane. These results indicate that Spektraris-NMR is a valuable resource for rapid and reliable identification of known metabolites and has the potential to contribute to de-replication efforts in novel PNP discovery.

Metabolomics for phytochemical discovery: development of statistical approaches using a cranberry model system

Metabolomics is the qualitative and quantitative analysis of all of the small molecules in a biological sample at a specific time and influence. Technologies for metabolomics analysis have developed rapidly as new analytical tools for chemical separations, mass spectrometry, and NMR spectroscopy have emerged. Plants have one of the largest metabolomes, and it is estimated that the average plant leaf can contain upward of 30 000 phytochemicals. In the past decade, over 1200 papers on plant metabolomics have been published. A standard metabolomics data set contains vast amounts of information and can either investigate or generate hypotheses. The key factors in using plant metabolomics data most effectively are the experimental design, authentic standard availability, extract standardization, and statistical analysis. Using cranberry (Vaccinium macrocarpon) as a model system, this review will discuss and demonstrate strategies and tools for analysis and interpretation of metabolomics data sets including eliminating false discoveries and determining significance, metabolite clustering, and logical algorithms for discovery of new metabolites and pathways. Together these metabolomics tools represent an entirely new pipeline for phytochemical discovery.

Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses

Metabolomics – technology for comprehensive detection of small molecules in an organism – lags behind the other “omics” in terms of publication and dissemination of experimental data. Among the reasons for this are difficulty precisely recording information about complicated analytical experiments (metadata), existence of various databases with their own metadata descriptions, and low reusability of the published data, resulting in submitters (the researchers who generate the data) being insufficiently motivated. To tackle these issues, we developed Metabolonote, a Semantic MediaWiki-based database designed specifically for managing metabolomic metadata. We also defined a metadata and data description format, called “Togo Metabolome Data” (TogoMD), with an ID system that is required for unique access to each level of the tree-structured metadata such as study purpose, sample, analytical method, and data analysis. Separation of the management of metadata from that of data and permission to attach related information to the metadata provide advantages for submitters, readers, and database developers. The metadata are enriched with information such as links to comparable data, thereby functioning as a hub of related data resources. They also enhance not only readers’ understanding and use of data but also submitters’ motivation to publish the data. The metadata are computationally shared among other systems via APIs, which facilitate the construction of novel databases by database developers. A permission system that allows publication of immature metadata and feedback from readers also helps submitters to improve their metadata. Hence, this aspect of Metabolonote, as a metadata preparation tool, is complementary to high-quality and persistent data repositories such as MetaboLights. A total of 808 metadata for analyzed data obtained from 35 biological species are published currently. Metabolonote and related tools are available free of cost at http://metabolonote.kazusa.or.jp/.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Crop improvement using life cycle datasets acquired under field conditions

Crops are exposed to various environmental stresses in the field throughout their life cycle. Modern plant science has provided remarkable insights into the molecular networks of plant stress responses in laboratory conditions, but the responses of different crops to environmental stresses in the field need to be elucidated. Recent advances in omics analytical techniques and information technology have enabled us to integrate data from a spectrum of physiological metrics of field crops. The interdisciplinary efforts of plant science and data science enable us to explore factors that affect crop productivity and identify stress tolerance-related genes and alleles. Here, we describe recent advances in technologies that are key components for data driven crop design, such as population genomics, chronological omics analyses, and computer-aided molecular network prediction. Integration of the outcomes from these technologies will accelerate our understanding of crop phenology under practical field situations and identify key characteristics to represent crop stress status. These elements would help us to genetically engineer "designed crops" to prevent yield shortfalls because of environmental fluctuations due to future climate change.

Moving toward a comprehensive map of central plant metabolism

Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.

MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments

Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework.

Plant systems biology: insights, advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/.

Tools and databases of the KOMICS web portal for preprocessing, mining, and dissemination of metabolomics data

A metabolome—the collection of comprehensive quantitative data on metabolites in an organism—has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data.

Potential input from metabolomics for exploring and understanding the links between environment and health

Humans may be exposed via their environment to multiple chemicals as a consequence of human activities and use of synthetic products. Little knowledge is routinely generated on the hazards of these chemical mixtures. The metabolomic approach is widely used to identify metabolic pathways modified by diseases, drugs, or exposures to toxicants. This review, based on the state of the art of the current applications of metabolomics in environmental health, attempts to determine whether metabolomics might constitute an original approach to the study of associations between multiple, low-dose environmental exposures in humans. Studying the biochemical consequences of complex environmental exposures is a challenge demanding the development of careful experimental and epidemiological designs, in order to take into account possible confounders associated with the high level of interindividual variability induced by different lifestyles. The choices of populations studied, sampling and storage procedures, statistical tools used, and system biology need to be considered. Suggestions for improved experimental and epidemiological designs are described. Evidence indicates that metabolomics may be a powerful tool in environmental health in the identification of both complex exposure biomarkers directly in human populations and modified metabolic pathways, in an attempt to improve understanding the underlying environmental causes of diseases. Nevertheless, the validity of biomarkers and relevancy of animal-to-human extrapolation remain key challenges that need to be properly explored.

Unlocking Triticeae genomics to sustainably feed the future

The tribe Triticeae includes the major crops wheat and barley. Within the last few years, the whole genomes of four Triticeae species-barley, wheat, Tausch's goatgrass (Aegilops tauschii) and wild einkorn wheat (Triticum urartu)-have been sequenced. The availability of these genomic resources for Triticeae plants and innovative analytical applications using next-generation sequencing technologies are helping to revitalize our approaches in genetic work and to accelerate improvement of the Triticeae crops. Comparative genomics and integration of genomic resources from Triticeae plants and the model grass Brachypodium distachyon are aiding the discovery of new genes and functional analyses of genes in Triticeae crops. Innovative approaches and tools such as analysis of next-generation populations, evolutionary genomics and systems approaches with mathematical modeling are new strategies that will help us discover alleles for adaptive traits to future agronomic environments. In this review, we provide an update on genomic tools for use with Triticeae plants and Brachypodium and describe emerging approaches toward crop improvements in Triticeae.

Integrated -omics: a powerful approach to understanding the heterogeneous lignification of fibre crops

Lignin and cellulose represent the two main components of plant secondary walls and the most abundant polymers on Earth. Quantitatively one of the principal products of the phenylpropanoid pathway, lignin confers high mechanical strength and hydrophobicity to plant walls, thus enabling erect growth and high-pressure water transport in the vessels. Lignin is characterized by a high natural heterogeneity in its composition and abundance in plant secondary cell walls, even in the different tissues of the same plant. A typical example is the stem of fibre crops, which shows a lignified core enveloped by a cellulosic, lignin-poor cortex. Despite the great value of fibre crops for humanity, however, still little is known on the mechanisms controlling their cell wall biogenesis, and particularly, what regulates their spatially-defined lignification pattern. Given the chemical complexity and the heterogeneous composition of fibre crops' secondary walls, only the use of multidisciplinary approaches can convey an integrated picture and provide exhaustive information covering different levels of biological complexity. The present review highlights the importance of combining high throughput -omics approaches to get a complete understanding of the factors regulating the lignification heterogeneity typical of fibre crops.