MedicCyc: a biochemical pathway database for Medicago truncatula

Localized osmotic stress activates systemic responses to N limitation in Medicago truncatula-Sinorhizobium symbiotic plants

In mature symbiotic root nodules, differentiated rhizobia fix atmospheric dinitrogen and provide ammonium to fulfill the plant nitrogen (N) demand. The plant enables this process by providing photosynthates to the nodules. The symbiosis is adjusted to the whole plant N demand thanks to systemic N signaling controlling nodule development. Symbiotic plants under N deficit stimulate nodule expansion and activate nodule senescence under N satiety. Besides, nodules are highly sensitive to drought. Here, we used split-root systems to characterize the systemic responses of symbiotic plants to a localized osmotic stress. We showed that polyéthylène glycol (PEG) application rapidly inhibited the symbiotic dinitrogen fixation activity of nodules locally exposed to the treatment, resulting to the N limitation of the plant supplied exclusively by symbiotic dinitrogen fixation. The localized PEG treatment triggered systemic signaling stimulating nodule development in the distant untreated roots. This response was associated with an enhancement of the sucrose allocation. Our analyses showed that transcriptomic reprogramming associated with PEG and N deficit systemic signaling(s) shared many targets transcripts. Altogether, our study suggests that systemic N signaling is a component of the adaptation of the symbiotic plant to the local variations of its edaphic environment.

Medicago truncatula resources to study legume biology and symbiotic nitrogen fixation

Medicago truncatula is a chosen model for legumes towards deciphering fundamental legume biology, especially symbiotic nitrogen fixation. Current genomic resources for M. truncatula include a completed whole genome sequence information for R108 and Jemalong A17 accessions along with the sparse draft genome sequences for other 226 M. truncatula accessions. These genomic resources are complemented by the availability of mutant resources such as retrotransposon (Tnt1) insertion mutants in R108 and fast neutron bombardment (FNB) mutants in A17. In addition, several M. truncatula databases such as small secreted peptides (SSPs) database, transporter protein database, gene expression atlas, proteomic atlas, and metabolite atlas are available to the research community. This review describes these resources and provide information regarding how to access these resources.

Systemic control of nodule formation by plant nitrogen demand requires autoregulation-dependent and independent mechanisms

In legumes interacting with rhizobia, the formation of symbiotic organs involved in the acquisition of atmospheric nitrogen gas (N2) is dependent on the plant nitrogen (N) demand. We used Medicago truncatula plants cultivated in split-root systems to discriminate between responses to local and systemic N signaling. We evidenced a strong control of nodule formation by systemic N signaling but obtained no clear evidence of a local control by mineral nitrogen. Systemic signaling of the plant N demand controls numerous transcripts involved in root transcriptome reprogramming associated with early rhizobia interaction and nodule formation. SUPER NUMERIC NODULES (SUNN) has an important role in this control, but we found that major systemic N signaling responses remained active in the sunn mutant. Genes involved in the activation of nitrogen fixation are regulated by systemic N signaling in the mutant, explaining why its hypernodulation phenotype is not associated with higher nitrogen fixation of the whole plant. We show that the control of transcriptome reprogramming of nodule formation by systemic N signaling requires other pathway(s) that parallel the SUNN/CLE (CLAVATA3/EMBRYO SURROUNDING REGION-LIKE PEPTIDES) pathway.

Responses of mature symbiotic nodules to the whole-plant systemic nitrogen signaling

In symbiotic root nodules of legumes, terminally differentiated rhizobia fix atmospheric N2 producing an NH4+ influx that is assimilated by the plant. The plant, in return, provides photosynthates that fuel the symbiotic nitrogen acquisition. Mechanisms responsible for the adjustment of the symbiotic capacity to the plant N demand remain poorly understood. We have investigated the role of systemic signaling of whole-plant N demand on the mature N2-fixing nodules of the model symbiotic association Medicago truncatula/Sinorhizobium using split-root systems. The whole-plant N-satiety signaling rapidly triggers reductions of both N2 fixation and allocation of sugars to the nodule. These responses are associated with the induction of nodule senescence and the activation of plant defenses against microbes, as well as variations in sugars transport and nodule metabolism. The whole-plant N-deficit responses mirror these changes: a rapid increase of sucrose allocation in response to N-deficit is associated with a stimulation of nodule functioning and development resulting in nodule expansion in the long term. Physiological, transcriptomic, and metabolomic data together provide evidence for strong integration of symbiotic nodules into whole-plant nitrogen demand by systemic signaling and suggest roles for sugar allocation and hormones in the signaling mechanisms.

Transcriptional and Chemical Changes in Soybean Leaves in Response to Long-Term Aphid Colonization

Soybean aphids (Aphis glycines Matsumura) are specialized insects that feed on soybean (Glycine max) phloem sap. Transcriptome analyses have shown that resistant soybean plants mount a fast response that limits aphid feeding and population growth. Conversely, defense responses in susceptible plants are slower and it is hypothesized that aphids block effective defenses in the compatible interaction. Unlike other pests, aphids can colonize plants for long periods of time; yet the effect on the plant transcriptome after long-term aphid feeding has not been analyzed for any plant-aphid interaction. We analyzed the susceptible and resistant (Rag1) transcriptome response to aphid feeding in soybean plants colonized by aphids (biotype 1) for 21 days. We found a reduced resistant response and a low level of aphid growth on Rag1 plants, while susceptible plants showed a strong response consistent with pattern-triggered immunity. GO-term analyses identified chitin regulation as one of the most overrepresented classes of genes, suggesting that chitin could be one of the hemipteran-associated molecular pattern that triggers this defense response. Transcriptome analyses also indicated the phenylpropanoid pathway, specifically isoflavonoid biosynthesis, was induced in susceptible plants in response to long-term aphid feeding. Metabolite analyses corroborated this finding. Aphid-treated susceptible plants accumulated daidzein, formononetin, and genistein, although glyceollins were present at low levels in these plants. Choice experiments indicated that daidzein may have a deterrent effect on aphid feeding. Mass spectrometry imaging showed these isoflavones accumulate likely in the mesophyll cells or epidermis and are absent from the vasculature, suggesting that isoflavones are part of a non-phloem defense response that can reduce aphid feeding. While it is likely that aphid can initially block defense responses in compatible interactions, it appears that susceptible soybean plants can eventually mount an effective defense in response to long-term soybean aphid colonization.

Multi-tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome

Common bean (Phaseolus vulgaris L.) is an important legume species with a rich natural diversity of landraces that originated from the wild forms following multiple independent domestication events. After the publication of its genome, several resources for this relevant crop have been made available. A comprehensive characterization of specialized metabolism in P. vulgaris, however, is still lacking. In this study, we used a metabolomics approach based on liquid chromatography-mass spectrometry to dissect the chemical composition at a tissue-specific level in several accessions of common bean belonging to different gene pools. Using a combination of literature search, mass spectral interpretation, 13 C-labeling, and correlation analyses, we were able to assign chemical classes and/or putative structures for approximately 39% of all measured metabolites. Additionally, we integrated this information with transcriptomics data and phylogenetic inference from multiple legume species to reconstruct the possible metabolic pathways and identify sets of candidate genes involved in the biosynthesis of specialized metabolites. A particular focus was given to flavonoids, triterpenoid saponins and hydroxycinnamates, as they represent metabolites involved in important ecological interactions and they are also associated with several health-promoting benefits when integrated into the human diet. The data are presented here in the form of an accessible resource that we hope will set grounds for further studies on specialized metabolism in legumes.

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

MOTIVATION

Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance.

RESULTS

We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling

Genome-scale metabolic network models can be used for various analyses including the prediction of metabolic responses to changes in the environment. Legumes are well known for their rhizobial symbiosis that introduces nitrogen into the global nutrient cycle. Here, we describe a fully compartmentalised, mass and charge-balanced, genome-scale model of the clover Medicago truncatula, which has been adopted as a model organism for legumes. We employed flux balance analysis to demonstrate that the network is capable of producing biomass components in experimentally observed proportions, during day and night. By connecting the plant model to a model of its rhizobial symbiont, Sinorhizobium meliloti, we were able to investigate the effects of the symbiosis on metabolic fluxes and plant growth and could demonstrate how oxygen availability influences metabolic exchanges between plant and symbiont, thus elucidating potential benefits of inter organism amino acid cycling. We thus provide a modelling framework, in which the interlinked metabolism of plants and nodules can be studied from a theoretical perspective.

The Medicago truncatula Genome: Genomic Data Availability

Medicago truncatula emerged in 1990 as a model for legumes, comprising the third largest land plant family. Most legumes form symbiotic nitrogen-fixing root nodules with compatible soil bacteria and thus are important contributors to the global nitrogen cycle and sustainable agriculture. Legumes and legume products are important sources for human and animal protein as well as for edible and industrial oils. In the years since M. truncatula was chosen as a legume model, many genetic, genomic, and molecular resources have become available, including reference quality genome sequences for two widely used genotypes. Accessibility of genomic data is important for many different types of studies with M. truncatula as well as for research involving crop and forage legumes. In this chapter, we discuss strategies to obtain archived M. truncatula genomic data originally deposited into custom databases that are no longer maintained but are now accessible in general databases. We also review key current genomic databases that are specific to M. truncatula as well as those that contain M. truncatula data in addition to data from other plants.

SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases

Database URL

https://solgenomics.net/tools/solcyc/.

Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, we suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data, and we demonstrate that our method predicts fluxes that achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems.

Mass Spectrometry in Plant-omics

Plant-omics is rapidly becoming an important field of study in the scientific community due to the urgent need to address many of the most important questions facing humanity today with regard to agriculture, medicine, biofuels, environmental decontamination, ecological sustainability, etc. High-performance mass spectrometry is a dominant tool for interrogating the metabolomes, peptidomes, and proteomes of a diversity of plant species under various conditions, revealing key insights into the functions and mechanisms of plant biochemistry.

FragariaCyc: A Metabolic Pathway Database for Woodland Strawberry Fragaria vesca

FragariaCyc is a strawberry-specific cellular metabolic network based on the annotated genome sequence of Fragaria vesca L. ssp. vesca, accession Hawaii 4. It was built on the Pathway-Tools platform using MetaCyc as the reference. The experimental evidences from published literature were used for supporting/editing existing entities and for the addition of new pathways, enzymes, reactions, compounds, and small molecules in the database. To date, FragariaCyc comprises 66 super-pathways, 488 unique pathways, 2348 metabolic reactions, 3507 enzymes, and 2134 compounds. In addition to searching and browsing FragariaCyc, researchers can compare pathways across various plant metabolic networks and analyze their data using Omics Viewer tool. We view FragariaCyc as a resource for the community of researchers working with strawberry and related fruit crops. It can help understanding the regulation of overall metabolism of strawberry plant during development and in response to diseases and abiotic stresses. FragariaCyc is available online at http://pathways.cgrb.oregonstate.edu.

Gramene: A Resource for Comparative Analysis of Plants Genomes and Pathways

Gramene is an integrated informatics resource for accessing, visualizing, and comparing plant genomes and biological pathways. Originally targeting grasses, Gramene has grown to host annotations for economically important and research model crops, including wheat, potato, tomato, banana, grape, poplar, and Chlamydomonas. Its strength derives from the application of a phylogenetic framework for genome comparison and the use of ontologies to integrate structural and functional annotation data. This chapter outlines system requirements for end users and database hosting, data types and basic navigation within Gramene, and provides examples of how to (1) view a phylogenetic tree for a family of transcription factors, (2) explore genetic variation in the orthologues of a gene with a known trait association, and (3) upload, visualize, and privately share end user data into a new genome browser track.Moreover, this is the first publication describing Gramene's new web interface-intended to provide a simplified portal to the most complete and up-to-date set of plant genome and pathway annotations.

Plant Pathway Databases

Pathway databases provide information about the role of chemicals, genes, and gene products in the form of protein or RNA, their interactions leading to the formulation of metabolic, transport, regulatory, and signaling reactions. The reactions can then be tethered by the principle of inputs and outputs of one or more reaction to create pathways. This chapter provides a list of various online databases that carry information about plant pathways and provides a brief overview of how to use the pathway databases such as WikiPathways Plants Portal, MapMan and the cereal crop pathway databases like RiceCyc and MaizeCyc, that were developed using the Pathway Tools software.

Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement

The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several studies on proteomics and metabolomics in model and crop legumes have been discussed. Additionally, applications of advanced proteomics and metabolomics approaches have also been included in this review for future applications in legume research. The integration of these "omics" approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs.

kpath: integration of metabolic pathway linked data

In the last few years, the Life Sciences domain has experienced a rapid growth in the amount of available biological databases. The heterogeneity of these databases makes data integration a challenging issue. Some integration challenges are locating resources, relationships, data formats, synonyms or ambiguity. The Linked Data approach partially solves the heterogeneity problems by introducing a uniform data representation model. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. This article introduces kpath, a database that integrates information related to metabolic pathways. kpath also provides a navigational interface that enables not only the browsing, but also the deep use of the integrated data to build metabolic networks based on existing disperse knowledge. This user interface has been used to showcase relationships that can be inferred from the information available in several public databases.

Database URL: The public Linked Data repository can be queried at http://sparql.kpath.khaos.uma.es using the graph URI “www.khaos.uma.es/metabolic-pathways-app”. The GUI providing navigational access to kpath database is available at http://browser.kpath.khaos.uma.es.

Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis using a genome-scale metabolic network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.

Integrated metabolomics and transcriptomics reveal enhanced specialized metabolism in Medicago truncatula root border cells

Integrated metabolomics and transcriptomics of Medicago truncatula seedling border cells and root tips revealed substantial metabolic differences between these distinct and spatially segregated root regions. Large differential increases in oxylipin-pathway lipoxygenases and auxin-responsive transcript levels in border cells corresponded to differences in phytohormone and volatile levels compared with adjacent root tips. Morphological examinations of border cells revealed the presence of significant starch deposits that serve as critical energy and carbon reserves, as documented through increased β-amylase transcript levels and associated starch hydrolysis metabolites. A substantial proportion of primary metabolism transcripts were decreased in border cells, while many flavonoid- and triterpenoid-related metabolite and transcript levels were increased dramatically. The cumulative data provide compounding evidence that primary and secondary metabolism are differentially programmed in border cells relative to root tips. Metabolic resources normally destined for growth and development are redirected toward elevated accumulation of specialized metabolites in border cells, resulting in constitutively elevated defense and signaling compounds needed to protect the delicate root cap and signal motile rhizobia required for symbiotic nitrogen fixation. Elevated levels of 7,4'-dihydroxyflavone were further increased in border cells of roots exposed to cotton root rot (Phymatotrichopsis omnivora), and the value of 7,4'-dihydroxyflavone as an antimicrobial compound was demonstrated using in vitro growth inhibition assays. The cumulative and pathway-specific data provide key insights into the metabolic programming of border cells that strongly implicate a more prominent mechanistic role for border cells in plant-microbe signaling, defense, and interactions than envisioned previously.

Genetic basis and detection of unintended effects in genetically modified crop plants

In January 2014, an international meeting sponsored by the International Life Sciences Institute/Health and Environmental Sciences Institute and the Canadian Food Inspection Agency titled “Genetic Basis of Unintended Effects in Modified Plants” was held in Ottawa, Canada, bringing together over 75 scientists from academia, government, and the agro-biotech industry. The objectives of the meeting were to explore current knowledge and identify areas requiring further study on unintended effects in plants and to discuss how this information can inform and improve genetically modified (GM) crop risk assessments. The meeting featured presentations on the molecular basis of plant genome variability in general, unintended changes at the molecular and phenotypic levels, and the development and use of hypothesis-driven evaluations of unintended effects in assessing conventional and GM crops. The development and role of emerging “omics” technologies in the assessment of unintended effects was also discussed. Several themes recurred in a number of talks; for example, a common observation was that no system for genetic modification, including conventional methods of plant breeding, is without unintended effects. Another common observation was that “unintended” does not necessarily mean “harmful”. This paper summarizes key points from the information presented at the meeting to provide readers with current viewpoints on these topics.

An ontology approach to comparative phenomics in plants

BACKGROUND

Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework.

RESULTS

We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes.

CONCLUSIONS

The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.

Natural product biosynthesis in Medicago species

The genus Medicago, a member of the legume (Fabaceae) family, comprises 87 species of flowering plants, including the forage crop M. sativa (alfalfa) and the model legume M. truncatula (barrel medic). Medicago species synthesize a variety of bioactive natural products that are used to engage into symbiotic interactions but also serve to deter pathogens and herbivores. For humans, these bioactive natural products often possess promising pharmaceutical properties. In this review, we focus on the two most interesting and well characterized secondary metabolite classes found in Medicago species, the triterpene saponins and the flavonoids, with a detailed overview of their biosynthesis, regulation, and profiling methods. Furthermore, their biological role within the plant as well as their potential utility for human health or other applications is discussed. Finally, we give an overview of the advances made in metabolic engineering in Medicago species and how the development of novel molecular and omics toolkits can influence a better understanding of this genus in terms of specialized metabolism and chemistry. Throughout, we critically analyze the current bottlenecks and speculate on future directions and opportunities for research and exploitation of Medicago metabolism.

Putting The Plant Metabolic Network pathway databases to work: going offline to gain new capabilities

Metabolic databases such as The Plant Metabolic Network/MetaCyc and KEGG PATHWAY are publicly accessible resources providing organism-specific information on reactions and metabolites. KEGG PATHWAY depicts metabolic networks as wired, electronic circuit-like maps, whereas the MetaCyc family of databases uses a canonical textbook-like representation. The first MetaCyc-based database for a plant species was AraCyc, which describes metabolism in the model plant Arabidopsis. This database was created over 10 years ago and has since then undergone extensive manual curation to reflect updated information on enzymes and pathways in Arabidopsis. This chapter describes accessing and using AraCyc and its underlying Pathway Tools software. Specifically, methods for (1) navigating Pathway Tools, (2) visualizing omics data and superimposing the data on a metabolic pathway map, and (3) creating pathways and pathway components are discussed.

VitisCyc: a metabolic pathway knowledgebase for grapevine (Vitis vinifera)

We have developed VitisCyc, a grapevine-specific metabolic pathway database that allows researchers to (i) search and browse the database for its various components such as metabolic pathways, reactions, compounds, genes and proteins, (ii) compare grapevine metabolic networks with other publicly available plant metabolic networks, and (iii) upload, visualize and analyze high-throughput data such as transcriptomes, proteomes, metabolomes etc. using OMICs-Viewer tool. VitisCyc is based on the genome sequence of the nearly homozygous genotype PN40024 of Vitis vinifera "Pinot Noir" cultivar with 12X v1 annotations and was built on BioCyc platform using Pathway Tools software and MetaCyc reference database. Furthermore, VitisCyc was enriched for plant-specific pathways and grape-specific metabolites, reactions and pathways. Currently VitisCyc harbors 68 super pathways, 362 biosynthesis pathways, 118 catabolic pathways, 5 detoxification pathways, 36 energy related pathways and 6 transport pathways, 10,908 enzymes, 2912 enzymatic reactions, 31 transport reactions and 2024 compounds. VitisCyc, as a community resource, can aid in the discovery of candidate genes and pathways that are regulated during plant growth and development, and in response to biotic and abiotic stress signals generated from a plant's immediate environment. VitisCyc version 3.18 is available online at http://pathways.cgrb.oregonstate.edu.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

A genome scale metabolic network for rice and accompanying analysis of tryptophan, auxin and serotonin biosynthesis regulation under biotic stress

BACKGROUND

Functional annotations of large plant genome projects mostly provide information on gene function and gene families based on the presence of protein domains and gene homology, but not necessarily in association with gene expression or metabolic and regulatory networks. These additional annotations are necessary to understand the physiology, development and adaptation of a plant and its interaction with the environment.

RESULTS

RiceCyc is a metabolic pathway networks database for rice. It is a snapshot of the substrates, metabolites, enzymes, reactions and pathways of primary and intermediary metabolism in rice. RiceCyc version 3.3 features 316 pathways and 6,643 peptide-coding genes mapped to 2,103 enzyme-catalyzed and 87 protein-mediated transport reactions. The initial functional annotations of rice genes with InterPro, Gene Ontology, MetaCyc, and Enzyme Commission (EC) numbers were enriched with annotations provided by KEGG and Gramene databases. The pathway inferences and the network diagrams were first predicted based on MetaCyc reference networks and plant pathways from the Plant Metabolic Network, using the Pathologic module of Pathway Tools. This was enriched by manually adding metabolic pathways and gene functions specifically reported for rice. The RiceCyc database is hierarchically browsable from pathway diagrams to the associated genes, metabolites and chemical structures. Through the integrated tool OMICs Viewer, users can upload transcriptomic, proteomic and metabolomic data to visualize expression patterns in a virtual cell. RiceCyc, along with additional species-specific pathway databases hosted in the Gramene project, facilitates comparative pathway analysis.

CONCLUSIONS

Here we describe the RiceCyc network development and discuss its contribution to rice genome annotations. As a case study to demonstrate the use of RiceCyc network as a discovery environment we carried out an integrated bioinformatic analysis of rice metabolic genes that are differentially regulated under diurnal photoperiod and biotic stress treatments. The analysis of publicly available rice transcriptome datasets led to the hypothesis that the complete tryptophan biosynthesis and its dependent metabolic pathways including serotonin biosynthesis are induced by taxonomically diverse pathogens while also being under diurnal regulation. The RiceCyc database is available online for free access at http://www.gramene.org/pathway/.

CathaCyc, a metabolic pathway database built from Catharanthus roseus RNA-Seq data

The medicinal plant Madagascar periwinkle (Catharanthus roseus) synthesizes numerous terpenoid indole alkaloids (TIAs), such as the anticancer drugs vinblastine and vincristine. The TIA pathway operates in a complex metabolic network that steers plant growth and survival. Pathway databases and metabolic networks reconstructed from 'omics' sequence data can help to discover missing enzymes, study metabolic pathway evolution and, ultimately, engineer metabolic pathways. To date, such databases have mainly been built for model plant species with sequenced genomes. Although genome sequence data are not available for most medicinal plant species, next-generation sequencing is now extensively employed to create comprehensive medicinal plant transcriptome sequence resources. Here we report on the construction of CathaCyc, a detailed metabolic pathway database, from C. roseus RNA-Seq data sets. CathaCyc (version 1.0) contains 390 pathways with 1,347 assigned enzymes and spans primary and secondary metabolism. Curation of the pathways linked with the synthesis of TIAs and triterpenoids, their primary metabolic precursors, and their elicitors, the jasmonate hormones, demonstrated that RNA-Seq resources are suitable for the construction of pathway databases. CathaCyc is accessible online (http://www.cathacyc.org) and offers a range of tools for the visualization and analysis of metabolic networks and 'omics' data. Overlay with expression data from publicly available RNA-Seq resources demonstrated that two well-characterized C. roseus terpenoid pathways, those of TIAs and triterpenoids, are subject to distinct regulation by both developmental and environmental cues. We anticipate that databases such as CathaCyc will become key to the study and exploitation of the metabolism of medicinal plants.

Metabolomics in agriculture

Metabolome refers to the complete set of metabolites synthesized through a series of multiple enzymatic steps from various biochemical pathways processing the information encrypted in the plant genome. Knowledge about synthesis and regulation of various plant metabolic substances has improved substantially with availability of Omics data originating from sequencing of plant genomes. Metabolic profiling of crops is increasingly becoming popular in assessing plant phenotypes and genetic diversity. Metabolic compositional changes vividly reflect the changes occurring during plant growth, development, and in response to stress. Hence, study of plant metabolic pathways, the interconnections between them in context of systems biology is increasingly becoming popular in identification of candidate genes. The present article reviews recent developments in analysis of plant metabolomics, available bioinformatics techniques and databases employed for comparative pathway analysis, metabolic QTLs, and their application in plants.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30,000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups.

Toward systems biology in brown algae to explore acclimation and adaptation to the shore environment

Brown algae belong to a phylogenetic lineage distantly related to land plants and animals. They are almost exclusively found in the intertidal zone, a harsh and frequently changing environment where organisms are submitted to marine and terrestrial constraints. In relation with their unique evolutionary history and their habitat, they feature several peculiarities, including at the level of their primary and secondary metabolism. The establishment of Ectocarpus siliculosus as a model organism for brown algae has represented a framework in which several omics techniques have been developed, in particular, to study the response of these organisms to abiotic stresses. With the recent publication of medium to high throughput profiling data, it is now possible to envision integrating observations at the cellular scale to apply systems biology approaches. As a first step, we propose a protocol focusing on integrating heterogeneous knowledge gained on brown algal metabolism. The resulting abstraction of the system will then help understanding how brown algae cope with changes in abiotic parameters within their unique habitat, and to decipher some of the mechanisms underlying their (1) acclimation and (2) adaptation, respectively consequences of (1) the behavior or (2) the topology of the system resulting from the integrative approach.

Databases on food phytochemicals and their health-promoting effects

Considerable information on the chemistry and biological properties of dietary phytochemicals has accumulated over the past three decades. The scattering of the data in tens of thousands publications and the diversity of experimental approaches and reporting formats all make the exploitation of this information very difficult. Some of the data have been collected and stored in electronic databases so that they can be automatically updated and retrieved. These databases will be particularly important in the evaluation of the effects on health of phytochemicals and in facilitating the exploitation of nutrigenomic data. The content of over 50 databases on chemical structures, spectra, metabolic pathways in plants, occurrence and concentrations in foods, metabolism in humans and animals, biological properties, and effects on health or surrogate markers of health is reviewed. Limits of these databases are emphasized, and needs and recommendations for future developments are underscored. More investments in the construction of databases on phytochemicals and their effects on health are clearly needed. They should greatly contribute to the success of future research in this field.

A survey of metabolic databases emphasizing the MetaCyc family

Thanks to the confluence of genome sequencing and bioinformatics, the number of metabolic databases has expanded from a handful in the mid-1990s to several thousand today. These databases lie within distinct families that have common ancestry and common attributes. The main families are the MetaCyc, KEGG, Reactome, Model SEED, and BiGG families. We survey these database families, as well as important individual metabolic databases, including multiple human metabolic databases. The MetaCyc family is described in particular detail. It contains well over 1,000 databases, including highly curated databases for Escherichia coli, Saccharomyces cerevisiae, Mus musculus, and Arabidopsis thaliana. These databases are available through a number of web sites that offer a range of software tools for querying and visualizing metabolic networks. These web sites also provide multiple tools for analysis of gene expression and metabolomics data, including visualization of those datasets on metabolic network diagrams and over-representation analysis of gene sets and metabolite sets.

Gramene database in 2010: updates and extensions

Now in its 10th year, the Gramene database (http://www.gramene.org) has grown from its primary focus on rice, the first fully-sequenced grass genome, to become a resource for major model and crop plants including Arabidopsis, Brachypodium, maize, sorghum, poplar and grape in addition to several species of rice. Gramene began with the addition of an Ensembl genome browser and has expanded in the last decade to become a robust resource for plant genomics hosting a wide array of data sets including quantitative trait loci (QTL), metabolic pathways, genetic diversity, genes, proteins, germplasm, literature, ontologies and a fully-structured markers and sequences database integrated with genome browsers and maps from various published studies (genetic, physical, bin, etc.). In addition, Gramene now hosts a variety of web services including a Distributed Annotation Server (DAS), BLAST and a public MySQL database. Twice a year, Gramene releases a major build of the database and makes interim releases to correct errors or to make important updates to software and/or data.

miSolRNA: A tomato micro RNA relational database

BACKGROUND

The economic importance of Solanaceae plant species is well documented and tomato has become a model for functional genomics studies. In plants, important processes are regulated by microRNAs (miRNA).

DESCRIPTION

We describe here a data base integrating genetic map positions of miRNA-targeted genes, their expression profiles and their relations with quantitative fruit metabolic loci and yield associated traits. miSolRNA provides a metadata source to facilitate the construction of hypothesis aimed at defining physiological modes of action of regulatory process underlying the metabolism of the tomato fruit.

CONCLUSIONS

The MiSolRNA database allows the simple extraction of metadata for the proposal of new hypothesis concerning possible roles of miRNAs in the regulation of tomato fruit metabolism. It permits i) to map miRNAs and their predicted target sites both on expressed (SGN-UNIGENES) and newly annotated sequences (BAC sequences released), ii) to co-locate any predicted miRNA-target interaction with metabolic QTL found in tomato fruits, iii) to retrieve expression data of target genes in tomato fruit along their developmental period and iv) to design further experiments for unresolved questions in complex trait biology based on the use of genetic materials that have been proven to be a useful tools for map-based cloning experiments in Solanaceae plant species.

Root hair systems biology

Plant functional genomic studies have largely measured the response of whole plants, organs and tissues, resulting in the dilution of the signal from individual cells. Methods are needed where the full repertoire of functional genomic tools can be applied to a single plant cell. Root hair cells are an attractive model to study the biology of a single, differentiated cell type because of their ease of isolation, polar growth, and role in water and nutrient uptake, as well as being the site of infection by nitrogen-fixing bacteria. This review highlights the recent advances in our understanding of plant root hair biology and examines whether the root hair has potential as a model for plant cell systems biology.

Creation of a genome-wide metabolic pathway database for Populus trichocarpa using a new approach for reconstruction and curation of metabolic pathways for plants

Metabolic networks reconstructed from sequenced genomes or transcriptomes can help visualize and analyze large-scale experimental data, predict metabolic phenotypes, discover enzymes, engineer metabolic pathways, and study metabolic pathway evolution. We developed a general approach for reconstructing metabolic pathway complements of plant genomes. Two new reference databases were created and added to the core of the infrastructure: a comprehensive, all-plant reference pathway database, PlantCyc, and a reference enzyme sequence database, RESD, for annotating metabolic functions of protein sequences. PlantCyc (version 3.0) includes 714 metabolic pathways and 2,619 reactions from over 300 species. RESD (version 1.0) contains 14,187 literature-supported enzyme sequences from across all kingdoms. We used RESD, PlantCyc, and MetaCyc (an all-species reference metabolic pathway database), in conjunction with the pathway prediction software Pathway Tools, to reconstruct a metabolic pathway database, PoplarCyc, from the recently sequenced genome of Populus trichocarpa. PoplarCyc (version 1.0) contains 321 pathways with 1,807 assigned enzymes. Comparing PoplarCyc (version 1.0) with AraCyc (version 6.0, Arabidopsis [Arabidopsis thaliana]) showed comparable numbers of pathways distributed across all domains of metabolism in both databases, except for a higher number of AraCyc pathways in secondary metabolism and a 1.5-fold increase in carbohydrate metabolic enzymes in PoplarCyc. Here, we introduce these new resources and demonstrate the feasibility of using them to identify candidate enzymes for specific pathways and to analyze metabolite profiling data through concrete examples. These resources can be searched by text or BLAST, browsed, and downloaded from our project Web site (http://plantcyc.org).

Analysis of expressed sequence tags from Prunus mume flower and fruit and development of simple sequence repeat markers

Background

Expressed Sequence Tag (EST) has been a cost-effective tool in molecular biology and represents an abundant valuable resource for genome annotation, gene expression, and comparative genomics in plants.

Results

In this study, we constructed a cDNA library of Prunus mume flower and fruit, sequenced 10,123 clones of the library, and obtained 8,656 expressed sequence tag (EST) sequences with high quality. The ESTs were assembled into 4,473 unigenes composed of 1,492 contigs and 2,981 singletons and that have been deposited in NCBI (accession IDs: GW868575 - GW873047), among which 1,294 unique ESTs were with known or putative functions. Furthermore, we found 1,233 putative simple sequence repeats (SSRs) in the P. mume unigene dataset. We randomly tested 42 pairs of PCR primers flanking potential SSRs, and 14 pairs were identified as true-to-type SSR loci and could amplify polymorphic bands from 20 individual plants of P. mume. We further used the 14 EST-SSR primer pairs to test the transferability on peach and plum. The result showed that nearly 89% of the primer pairs produced target PCR bands in the two species. A high level of marker polymorphism was observed in the plum species (65%) and low in the peach (46%), and the clustering analysis of the three species indicated that these SSR markers were useful in the evaluation of genetic relationships and diversity between and within the Prunus species.

Conclusions

We have constructed the first cDNA library of P. mume flower and fruit, and our data provide sets of molecular biology resources for P. mume and other Prunus species. These resources will be useful for further study such as genome annotation, new gene discovery, gene functional analysis, molecular breeding, evolution and comparative genomics between Prunus species.

Genomic and coexpression analyses predict multiple genes involved in triterpene saponin biosynthesis in Medicago truncatula

Saponins, an important group of bioactive plant natural products, are glycosides of triterpenoid or steroidal aglycones (sapogenins). Saponins possess many biological activities, including conferring potential health benefits for humans. However, most of the steps specific for the biosynthesis of triterpene saponins remain uncharacterized at the molecular level. Here, we use comprehensive gene expression clustering analysis to identify candidate genes involved in the elaboration, hydroxylation, and glycosylation of the triterpene skeleton in the model legume Medicago truncatula. Four candidate uridine diphosphate glycosyltransferases were expressed in Escherichia coli, one of which (UGT73F3) showed specificity for multiple sapogenins and was confirmed to glucosylate hederagenin at the C28 position. Genetic loss-of-function studies in M. truncatula confirmed the in vivo function of UGT73F3 in saponin biosynthesis. This report provides a basis for future studies to define genetically the roles of multiple cytochromes P450 and glycosyltransferases in triterpene saponin biosynthesis in Medicago.

Quantification of variation in expression networks

Gene expression microarrays allow rapid and easy quantification of transcript accumulation for almost transcripts present in a genome. This technology has been utilized for diverse investigations from studying gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Typical analysis techniques focus on responses of individual genes in isolation of other genes. However, emerging evidence indicates that genes are organized into regulons, i.e., they respond as groups due to individual transcription factors binding multiple promoters, creating what is commonly called a network. We have developed a set of statistical approaches that allow researchers to test specific network hypothesis using a priori-defined gene networks. When applied to Arabidopsis thaliana this approach has been able to identify natural genetic variation that controls networks. In this chapter we describe approaches to develop and test specific network hypothesis utilizing natural genetic variation. This approach can be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. Finally, the use of a priori network definitions can be applied to any microarray experiment to directly conduct hypothesis testing at a genomics level.

TrichOME: a comparative omics database for plant trichomes

Plant secretory trichomes have a unique capacity for chemical synthesis and secretion and have been described as biofactories for the production of natural products. However, until recently, most trichome-specific metabolic pathways and genes involved in various trichome developmental stages have remained unknown. Furthermore, only a very limited amount of plant trichome genomics information is available in scattered databases. We present an integrated "omics" database, TrichOME, to facilitate the study of plant trichomes. The database hosts a large volume of functional omics data, including expressed sequence tag/unigene sequences, microarray hybridizations from both trichome and control tissues, mass spectrometry-based trichome metabolite profiles, and trichome-related genes curated from published literature. The expressed sequence tag/unigene sequences have been annotated based upon sequence similarity with popular databases (e.g. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Transporter Classification Database). The unigenes, metabolites, curated genes, and probe sets have been mapped against each other to enable comparative analysis. The database also integrates bioinformatics tools with a focus on the mining of trichome-specific genes in unigenes and microarray-based gene expression profiles. TrichOME is a valuable and unique resource for plant trichome research, since the genes and metabolites expressed in trichomes are often underrepresented in regular non-tissue-targeted cDNA libraries. TrichOME is freely available at http://www.planttrichome.org/.

The Medicago truncatula gene expression atlas web server

BACKGROUND

Legumes (Leguminosae or Fabaceae) play a major role in agriculture. Transcriptomics studies in the model legume species, Medicago truncatula, are instrumental in helping to formulate hypotheses about the role of legume genes. With the rapid growth of publically available Affymetrix GeneChip Medicago Genome Array GeneChip data from a great range of tissues, cell types, growth conditions, and stress treatments, the legume research community desires an effective bioinformatics system to aid efforts to interpret the Medicago genome through functional genomics. We developed the Medicago truncatula Gene Expression Atlas (MtGEA) web server for this purpose.

DESCRIPTION

The Medicago truncatula Gene Expression Atlas (MtGEA) web server is a centralized platform for analyzing the Medicago transcriptome. Currently, the web server hosts gene expression data from 156 Affymetrix GeneChip(R) Medicago genome arrays in 64 different experiments, covering a broad range of developmental and environmental conditions. The server enables flexible, multifaceted analyses of transcript data and provides a range of additional information about genes, including different types of annotation and links to the genome sequence, which help users formulate hypotheses about gene function. Transcript data can be accessed using Affymetrix probe identification number, DNA sequence, gene name, functional description in natural language, GO and KEGG annotation terms, and InterPro domain number. Transcripts can also be discovered through co-expression or differential expression analysis. Flexible tools to select a subset of experiments and to visualize and compare expression profiles of multiple genes have been implemented. Data can be downloaded, in part or full, in a tabular form compatible with common analytical and visualization software. The web server will be updated on a regular basis to incorporate new gene expression data and genome annotation, and is accessible at: http://bioinfo.noble.org/gene-atlas/.

CONCLUSIONS

The MtGEA web server has a well managed rich data set, and offers data retrieval and analysis tools provided in the web platform. It's proven to be a powerful resource for plant biologists to effectively and efficiently identify Medicago transcripts of interest from a multitude of aspects, formulate hypothesis about gene function, and overall interpret the Medicago genome from a systematic point of view.

The Medicago truncatula gene expression atlas web server

Background

Legumes (Leguminosae or Fabaceae) play a major role in agriculture. Transcriptomics studies in the model legume species, Medicago truncatula, are instrumental in helping to formulate hypotheses about the role of legume genes. With the rapid growth of publically available Affymetrix GeneChip Medicago Genome Array GeneChip data from a great range of tissues, cell types, growth conditions, and stress treatments, the legume research community desires an effective bioinformatics system to aid efforts to interpret the Medicago genome through functional genomics. We developed the Medicago truncatula Gene Expression Atlas (MtGEA) web server for this purpose.

Description

The Medicago truncatula Gene Expression Atlas (MtGEA) web server is a centralized platform for analyzing the Medicago transcriptome. Currently, the web server hosts gene expression data from 156 Affymetrix GeneChip^® Medicago genome arrays in 64 different experiments, covering a broad range of developmental and environmental conditions. The server enables flexible, multifaceted analyses of transcript data and provides a range of additional information about genes, including different types of annotation and links to the genome sequence, which help users formulate hypotheses about gene function. Transcript data can be accessed using Affymetrix probe identification number, DNA sequence, gene name, functional description in natural language, GO and KEGG annotation terms, and InterPro domain number. Transcripts can also be discovered through co-expression or differential expression analysis. Flexible tools to select a subset of experiments and to visualize and compare expression profiles of multiple genes have been implemented. Data can be downloaded, in part or full, in a tabular form compatible with common analytical and visualization software. The web server will be updated on a regular basis to incorporate new gene expression data and genome annotation, and is accessible at: http://bioinfo.noble.org/gene-atlas/.

Conclusions

The MtGEA web server has a well managed rich data set, and offers data retrieval and analysis tools provided in the web platform. It's proven to be a powerful resource for plant biologists to effectively and efficiently identify Medicago transcripts of interest from a multitude of aspects, formulate hypothesis about gene function, and overall interpret the Medicago genome from a systematic point of view.

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism.

LeishCyc: a biochemical pathways database for Leishmania major

Background

Leishmania spp. are sandfly transmitted protozoan parasites that cause a spectrum of diseases in more than 12 million people worldwide. Much research is now focusing on how these parasites adapt to the distinct nutrient environments they encounter in the digestive tract of the sandfly vector and the phagolysosome compartment of mammalian macrophages. While data mining and annotation of the genomes of three Leishmania species has provided an initial inventory of predicted metabolic components and associated pathways, resources for integrating this information into metabolic networks and incorporating data from transcript, protein, and metabolite profiling studies is currently lacking. The development of a reliable, expertly curated, and widely available model of Leishmania metabolic networks is required to facilitate systems analysis, as well as discovery and prioritization of new drug targets for this important human pathogen.

Description

The LeishCyc database was initially built from the genome sequence of Leishmania major (v5.2), based on the annotation published by the Wellcome Trust Sanger Institute. LeishCyc was manually curated to remove errors, correct automated predictions, and add information from the literature. The ongoing curation is based on public sources, literature searches, and our own experimental and bioinformatics studies. In a number of instances we have improved on the original genome annotation, and, in some ambiguous cases, collected relevant information from the literature in order to help clarify gene or protein annotation in the future. All genes in LeishCyc are linked to the corresponding entry in GeneDB (Wellcome Trust Sanger Institute).

Conclusion

The LeishCyc database describes Leishmania major genes, gene products, metabolites, their relationships and biochemical organization into metabolic pathways. LeishCyc provides a systematic approach to organizing the evolving information about Leishmania biochemical networks and is a tool for analysis, interpretation, and visualization of Leishmania Omics data (transcriptomics, proteomics, metabolomics) in the context of metabolic pathways. LeishCyc is the first such database for the Trypanosomatidae family, which includes a number of other important human parasites. Flexible query/visualization capabilities are provided by the Pathway Tools software and its Web interface. The LeishCyc database is made freely available over the Internet .

ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii

Background

The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern high-throughput technologies there is an imperative need to integrate large-scale data sets from high-throughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism.

Results

In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification.

Conclusion

ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under .

Reconstruction of metabolic pathways for the cattle genome

Background

Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement.

Results

An amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly.

Conclusion

CattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology.

Metabolic and genetic perturbations accompany the modification of galactomannan in seeds of Medicago truncatula expressing mannan synthase from guar (Cyamopsis tetragonoloba L.)

Galactomannan gums are widely used in the food and oil industries, and there is considerable interest in applying biotechnological approaches to improve their physical properties. A mannan synthase from guar (Cyamopsis tetragonoloba) was expressed under the control of a bean beta-phaseolin promoter in transgenic Medicago truncatula. Although the expression of exogenous mannan synthase caused a slight decrease in galactomannan levels in Medicago, the molecular weight and viscosity of the polymer were significantly increased, although the mannose to galactose ratio and degree of polydispersity remained unchanged. At the same time, expression of about 2.8% of the genes was altered significantly in the seeds of transgenic Medicago lines analysed by Affymetrix genome chip, with a particularly striking induction of putative trehalose phosphate synthase genes. Mannan synthase expression also caused large alterations in the levels of a number of sugars and sugar alcohols, suggesting that over-expression of a processive glycosyltransferase perturbs the mechanisms of sugar sensing and/or homeostasis, possibly involving signalling via trehalose-6-phosphate.