As metabolomics can provide a biochemical snapshot of an organism's phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms.

As tomatoes are one of the most important vegetables in the world, improvements in the quality and yield of tomato are strongly required. For this purpose, omics approaches such as metabolomics and transcriptomics are used not only for basic research to understand relationships between important traits and metabolism but also for the development of next generation breeding strategies of tomato plants, because an increase in the knowledge improves the taste and quality, stress resistance and/or potentially health-beneficial metabolites and is connected to improvements in the biochemical composition of tomatoes. Such omics data can be applied to network analyses to potentially reveal unknown cellular regulatory networks in tomato plants. The high-quality tomato genome that was sequenced in 2012 will likely accelerate the application of omics strategies, including next generation sequencing for tomato breeding. In this review, we highlight the current studies of omics network analyses of tomatoes and other plant species, in particular, a gene coexpression network. Key applications of omics approaches are also presented as case examples to improve economically important traits for tomato breeding.

To identify candidate genes involved in Arabidopsis flavonoid biosynthesis, we applied transcriptome coexpression analysis and independent component analyses with 1388 microarray data from publicly available databases. Two glycosyltransferases, UGT79B1 and UGT84A2 were found to cluster with anthocyanin biosynthetic genes. Anthocyanin was drastically reduced in ugt79b1 knockout mutants. Recombinant UGT79B1 protein converted cyanidin 3-O-glucoside to cyanidin 3-O-xylosyl(1→2)glucoside. UGT79B1 recognized 3-O-glucosylated anthocyanidins/flavonols and uridine diphosphate (UDP)-xylose, but not 3,5-O-diglucosylated anthocyanidins, indicating that UGT79B1 encodes anthocyanin 3-O-glucoside: 2''-O-xylosyltransferase. UGT84A2 is known to encode sinapic acid: UDP-glucosyltransferase. In ugt84a2 knockout mutants, a major sinapoylated anthocyanin was drastically reduced. A comparison of anthocyanin profiles in ugt84a knockout mutants indicated that UGT84A2 plays a major role in sinapoylation of anthocyanin, and that other UGT84As contribute the production of 1-O-sinapoylglucose to a lesser extent. These data suggest major routes from cyanidin 3-O-glucoside to the most highly modified cyanidin in the potential intricate anthocyanin modification pathways in Arabidopsis.

Metabolites are not only the catalytic products of enzymatic reactions but also the active regulators or the ultimate phenotype of metabolic homeostasis in highly complex cellular processes. The modes of regulation at the metabolome level can be revealed by metabolic networks. We investigated the metabolic network between wild-type and 2 mutant (methionine-over accumulation 1 [mto1] and transparent testa4 [tt4]) plants regarding the alteration of metabolite accumulation in Arabidopsis thaliana.

Alternative promoter usage is a proteome-expanding mechanism that allows multiple pre-mRNAs to be transcribed from a single gene. The impact of this mechanism on the proteome and whether it is positively exploited in normal organismal responses remain unclear. We found that the plant photoreceptor phytochrome induces genome-wide changes in alternative promoter selection in Arabidopsis thaliana. Through this mechanism, protein isoforms with different N termini are produced that display light-dependent differences in localization. For instance, shade-grown plants accumulate a cytoplasmic isoform of glycerate kinase (GLYK), an essential photorespiration enzyme that was previously thought to localize exclusively to the chloroplast. Cytoplasmic GLYK constitutes a photorespiratory bypass that alleviates fluctuating light-induced photoinhibition. Therefore, phytochrome controls alternative promoter selection to modulate protein localization in response to changing light conditions. This study suggests that alternative promoter usage represents another ubiquitous layer of gene expression regulation in eukaryotes that contributes to diversification of the proteome.

Tomato (Solanum lycopersicum) is a model crop for studying development regulation and ripening in flesh fruits and vegetables. Supplementary light to maintain the optimal light environment can lead to the stable growth of tomatoes in greenhouses and areas without sufficient daily light integral. Technological advances in genome-wide molecular phenotyping have dramatically enhanced our understanding of metabolic shifts in the plant metabolism across tomato fruit development. However, comprehensive metabolic and transcriptional behaviors along the developmental process under supplementary light provided by light-emitting diodes (LEDs) remain to be fully elucidated. We present integrative omic approaches to identify the impact on the metabolism of a single tomato plant leaf exposed to monochromatic red LEDs of different intensities during the fruit development stage. Our special light delivery system, the "simplified source-sink model," involves the exposure of a single leaf below the second truss to red LED light of different intensities. We evaluated fruit-size- and fruit-shape variations elicited by different light intensities. Our findings suggest that more than high-light treatment (500 μmol m-2 s-1) with the red LED light is required to accelerate fruit growth for 2 weeks after anthesis. To investigate transcriptomic and metabolomic changes in leaf- and fruit samples we used microarray-, RNA sequencing-, and gas chromatography-mass spectrometry techniques. We found that metabolic shifts in the carbohydrate metabolism and in several key pathways contributed to fruit development, including ripening and cell-wall modification. Our findings suggest that the proposed workflow aids in the identification of key metabolites in the central metabolism that respond to monochromatic red-LED treatment and contribute to increase the fruit size of tomato plants. This study expands our understanding of systems-level responses mediated by low-, appropriate-, and high levels of red light irradiation in the fruit growth of tomato plants.

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.

No abstract available

Recently, earthworms have become a useful model for research into the immune system, and it is expected that results obtained using this model will shed light on the sophisticated vertebrate immune system and the evolution of the immune response, and additionally help identify new biomolecules with therapeutic applications. However, for earthworms to be used as a genetic model of the invertebrate immune system, basic molecular and genetic resources, such as an expressed sequence tag (EST) database, must be developed for this organism. Next-generation sequencing technologies have generated EST libraries by RNA-seq in many model species. In this study, we used Illumina RNA-sequence technology to perform a comprehensive transcriptome analysis using an RNA sample pooled from sterile-cultured Eisenia andrei. All clean reads were assembled de novo into 41,423 unigenes using the Trinity program. Using this transcriptome data, we performed BLAST analysis against the GenBank non-redundant (NR) database and obtained a total of 12,285 significant BLAST hits. Furthermore, gene ontology (GO) analysis assigned 78 unigenes to 24 immune class GO terms. In addition, we detected a unigene with high similarity to beta-1,3-glucuronyltransferase 1 (GlcAT-P), which mediates a glucuronyl transfer reaction during the biosynthesis of the carbohydrate epitope HNK-1 (human natural killer-1, also known as CD57), a marker of NK cells. The identified transcripts will be used to facilitate future research into the immune system using E. andrei.

Rice (Oryza sativa) is an excellent resource; it comprises 25% of the total caloric intake of the world's population, and rice plants yield many types of bioactive compounds. To determine the number of metabolites in rice and their chemical diversity, the metabolite composition of cultivated rice has been investigated with analytical techniques such as mass spectrometry (MS) and/or nuclear magnetic resonance spectroscopy and rice metabolite databases have been constructed. This review summarizes current knowledge on metabolites in rice including sugars, amino and organic acids, aromatic compounds, and phytohormones detected by gas chromatography-MS, liquid chromatography-MS, and capillary electrophoresis-MS. The biological properties and the activities of polar and nonpolar metabolites produced by rice plants are also presented. Challenges in the estimation of the structure(s) of unknown metabolites by metabolomic approaches are introduced and discussed. Lastly, examples are presented of the successful application of metabolite profiling of rice to characterize the gene(s) that are potentially critical for improving its quality by combining metabolite quantitative trait loci analysis and to identify potential metabolite biomarkers that play a critical role when rice is grown under abiotic stress conditions.

Insulin-induced phosphatidylinositol 3-kinase (PI3K)/Akt signaling and interleukin-6 (IL-6)-instigated JAK/STAT3-signaling pathways in the liver inhibit the expression of gluconeogenic genes to decrease hepatic glucose output. The insulin receptor (IR) and JAK1 tyrosine kinases and STAT3 can serve as direct substrates for the T-cell protein tyrosine phosphatase (TCPTP). Homozygous TCPTP-deficiency results in perinatal lethality prohibiting any informative assessment of TCPTP's role in glucose homeostasis. Here we have used Ptpn2+/- mice to investigate TCPTP's function in glucose homeostasis.

Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.

Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology.

The photoperiod is an important environmental signal for plants, and influences a wide range of physiological processes. For woody species in northern latitudes, cessation of growth is induced by short photoperiods. In many plant species, short photoperiods stop elongational growth after a few weeks. It is known that plant daylength detection is mediated by Phytochrome A (PHYA) in the woody hybrid aspen species. However, the mechanism of dormancy involving primary metabolism remains unclear. We studied changes in metabolite profiles in hybrid aspen leaves (young, middle, and mature leaves) during short-day-induced growth cessation, using a combination of gas chromatography-time-of-flight mass spectrometry, and multivariate projection methods. Our results indicate that the metabolite profiles in mature source leaves rapidly change when the photoperiod changes. In contrast, the differences in young sink leaves grown under long and short-day conditions are less distinct. We found short daylength induced growth cessation in aspen was associated with rapid changes in the distribution and levels of diverse primary metabolites. In addition, we conducted metabolite profiling of leaves of PHYA overexpressor (PHYAOX) and those of the control to find the discriminative metabolites between PHYAOX and the control under the short-day conditions. The metabolite changes observed in PHYAOX leaves, together with those in the source leaves, identified possible candidates for the metabolite signature (e.g., 2-oxo-glutarate, spermidine, putrescine, 4-amino-butyrate, and tryptophan) during short-day-induced growth cessation in aspen leaves.

Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways.

Secretory leukocyte protease inhibitor (SLPI) is a serine protease inhibitor that diminishes tissue destruction during inflammation. A recent report revealed high levels of SLPI expression in the oral carcinoma cell. In addition, overexpression of SLPI up-regulates metastasis in lung carcinoma cells. On the other hand, matrix metalloproteinases (MMPs) are proteinases that participate in extracellular matrix degradation. SLPI and MMPs are involved as accelerators of the tumor invasion process; however, their exact roles are not fully understood. Understanding the mechanism of tumor invasion requires models that take the effect of microenvironmental factors into account. In one such in vitro model, different carcinoma cells have been shown to invade myoma tissue in highly distinct patterns. We have used this myoma model, as it provides a more natural stroma-like environment, to investigate the role of SLPI in tumor invasion. Our results indicate that the model provides a relevant matrix for tumor invasion studies, and that SLPI is important for the invasion of oral carcinoma Ca9-22 cells in conjunction with MMPs. Furthermore, using bioinformatics analysis, we have identified candidates as key molecules involved in SLPI-mediated tumor invasion.

Gene co-expression analysis has been widely used in recent years for predicting unknown gene function and its regulatory mechanisms. The predictive accuracy depends on the quality and the diversity of data set used. In this report, we applied singular value decomposition (SVD) to array experiments in public databases to find that co-expression linkage could be estimated by a much smaller number of array data. Correlations of co-expressed gene were assessed using two regulatory mechanisms (feedback loop of the fundamental circadian clock and a global transcription factor Myb28), as well as metabolic pathways in the AraCyc database. Our conclusion is that a smaller number of informative arrays across tissues can suffice to reproduce comparable results with a state-of-the-art co-expression software tool. In our SVD analysis on Arabidopsis data set, array experiments that contributed most as the principal components included stamen development, germinating seed and stress responses on leaf.

Light-emitting diodes (LEDs) are an artificial light source used in closed-type plant factories and provide a promising solution for a year-round supply of green leafy vegetables, such as lettuce (Lactuca sativa L.). Obtaining high-quality seedlings using controlled irradiation from LEDs is critical, as the seedling health affects the growth and yield of leaf lettuce after transplantation. Because key molecular pathways underlying plant responses to a specific light quality and intensity remain poorly characterised, we used a multi-omics-based approach to evaluate the metabolic and transcriptional reprogramming of leaf lettuce seedlings grown under narrow-band LED lighting. Four types of monochromatic LEDs (one blue, two green and one red) and white fluorescent light (control) were used at low and high intensities (100 and 300 μmol·m-2·s-1, respectively). Multi-platform mass spectrometry-based metabolomics and RNA-Seq were used to determine changes in the metabolome and transcriptome of lettuce plants in response to different light qualities and intensities. Metabolic pathway analysis revealed distinct regulatory mechanisms involved in flavonoid and phenylpropanoid biosynthetic pathways under blue and green wavelengths. Taken together, these data suggest that the energy transmitted by green light is effective in creating a balance between biomass production and the production of secondary metabolites involved in plant defence.