MetaCyc and AraCyc. Metabolic pathway databases for plant research

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

Topoisomerase VI participates in an insulator-like function that prevents H3K9me2 spreading

The organization of the genome into transcriptionally active and inactive chromatin domains requires well-delineated chromatin boundaries and insulator functions in order to maintain the identity of adjacent genomic loci with antagonistic chromatin marks and functionality. In plants that lack known chromatin insulators, the mechanisms that prevent heterochromatin spreading into euchromatin remain to be identified. Here, we show that DNA Topoisomerase VI participates in a chromatin boundary function that safeguards the expression of genes in euchromatin islands within silenced heterochromatin regions. While some transposable elements are reactivated in mutants of the Topoisomerase VI complex, genes insulated in euchromatin islands within heterochromatic regions of the Arabidopsis thaliana genome are specifically down-regulated. H3K9me2 levels consistently increase at euchromatin island loci and decrease at some transposable element loci. We further show that Topoisomerase VI physically interacts with S-adenosylmethionine synthase methionine adenosyl transferase 3 (MAT3), which is required for H3K9me2. A Topoisomerase VI defect affects MAT3 occupancy on heterochromatic elements and its exclusion from euchromatic islands, thereby providing a possible mechanistic explanation to the essential role of Topoisomerase VI in the delimitation of chromatin domains.

Reprogramming of Plant Central Metabolism in Response to Abiotic Stresses: A Metabolomics View

Abiotic stresses rewire plant central metabolism to maintain metabolic and energy homeostasis. Metabolites involved in the plant central metabolic network serve as a hub for regulating carbon and energy metabolism under various stress conditions. In this review, we introduce recent metabolomics techniques used to investigate the dynamics of metabolic responses to abiotic stresses and analyze the trend of publications in this field. We provide an updated overview of the changing patterns in central metabolic pathways related to the metabolic responses to common stresses, including flooding, drought, cold, heat, and salinity. We extensively review the common and unique metabolic changes in central metabolism in response to major abiotic stresses. Finally, we discuss the challenges and some emerging insights in the future application of metabolomics to study plant responses to abiotic stresses.

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.

Reconstruction and Analysis of Cattle Metabolic Networks in Normal and Acidosis Rumen Tissue

Simple Summary

Economics of feedlot beef production dictate that beef cattle must gain weight at their maximum potential rate; this involves getting them quickly onto a full feed of high fermentable diet which can induce the ruminal acidosis disease. The molecular host mechanisms that occur as a response to the acidosis, are mostly unknown. For answering this question, the rumen epithelial transcriptome in acidosis and control fattening steers were obtained. By RNA sequencing we found the different expression profiles of genes in normal and acidosis induced steers. Then we constructed two metabolic networks for normal and acidosis tissue based on gene expression profile. Our results suggest that rapid shifts to diets rich in fermentable carbohydrates cause an increased concentration of ruminal volatile fatty acids (VFA) and toxins and significant changes in transcriptome profiles and metabolites of rumen epithelial tissue, with negative effects on economic consequences of poor performance and animal health.

Abstract

The objective of this study was to develop a system-level understanding of acidosis biology. Therefore, the genes expression differences between the normal and acidosis rumen epithelial tissues were first examined using the RNA-seq data in order to understand the molecular mechanisms involved in the disease and then their corresponding metabolic networks constructed. A number of 1074 genes, 978 isoforms, 1049 transcription start sites (TSS), 998 coding DNA sequence (CDS) and 2 promoters were identified being differentially expressed in the rumen tissue between the normal and acidosis samples (p < 0.05). The functional analysis of 627 up-regulated genes revealed their involvement in ion transmembrane transport, filament organization, regulation of cell adhesion, regulation of the actin cytoskeleton, ATP binding, glucose transmembrane transporter activity, carbohydrate binding, growth factor binding and cAMP metabolic process. Additionally, 111 differentially expressed enzymes were identified between the rumen epithelial tissue of the normal and acidosis steers with 46 up-regulated and 65 down-regulated ones in the acidosis group. The pathways and reactions analyses associated with the up-regulated enzymes indicate that most of these enzymes are involved in the fatty acid metabolism, biosynthesis of amino acids, pyruvate and carbon metabolism while most of the down-regulated ones are involved in purine and pyrimidine, vitamin B6 and antibiotics metabolisms. The degree distribution of both metabolic networks follows a power-law one, hence displaying a scale-free property. The top 15 hub metabolites were determined in the acidosis metabolic network with most of them involved in the fatty acid oxidation, VFA biosynthesis, amino acid biogenesis and glutathione metabolism which plays an important role in the stress condition. The limitations of this study were low number of animals and using only epithelial tissue (ventral sac) for RNA-seq.

A systems genetics analysis in Eucalyptus reveals coordination of metabolic pathways associated with xylan modification in wood-forming tissues

Acetyl- and methylglucuronic acid decorations of xylan, the dominant hemicellulose in secondary cell walls (SCWs) of woody dicots, affect its interaction with cellulose and lignin to determine SCW structure and extractability. Genes and pathways involved in these modifications may be targets for genetic engineering; however, little is known about the regulation of xylan modifications in woody plants. To address this, we assessed genetic and gene expression variation associated with xylan modification in developing xylem of Eucalyptus grandis × Eucalyptus urophylla interspecific hybrids. Expression quantitative trait locus (eQTL) mapping identified potential regulatory polymorphisms affecting gene expression modules associated with xylan modification. We identified 14 putative xylan modification genes that are members of five expression modules sharing seven trans-eQTL hotspots. The xylan modification genes are prevalent in two expression modules. The first comprises nucleotide sugar interconversion pathways supplying the essential precursors for cellulose and xylan biosynthesis. The second contains genes responsible for phenylalanine biosynthesis and S-adenosylmethionine biosynthesis required for glucuronic acid and monolignol methylation. Co-expression and co-regulation analyses also identified four metabolic sources of acetyl coenxyme A that appear to be transcriptionally coordinated with xylan modification. Our systems genetics analysis may provide new avenues for metabolic engineering to alter wood SCW biology for enhanced biomass processability.

The Structure and Function of Major Plant Metabolite Modifications

Plants produce a myriad of structurally and functionally diverse metabolites that play many different roles in plant growth and development and in plant response to continually changing environmental conditions as well as abiotic and biotic stresses. This metabolic diversity is, to a large extent, due to chemical modification of the basic skeletons of metabolites. Here, we review the major known plant metabolite modifications and summarize the progress that has been achieved and the challenges we are facing in the field. We focus on discussing both technical and functional aspects in studying the influences that various modifications have on biosynthesis, degradation, transport, and storage of metabolites, as well as their bioactivity and toxicity. Finally, we discuss some emerging insights into the evolution of metabolic pathways and metabolite functionality.

Metabolomics in Plant Stress Physiology

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

Using a Multi-compartmental Metabolic Model to Predict Carbon Allocation in Arabidopsis thaliana

The molecular mechanism of loading/unloading of sucrose into/from the phloem plays an important role in sucrose translocation among plant tissues. Perturbation of this mechanism results in growth phenotypes of a plant. In order to better understand the coupling of sucrose translocation with metabolic processes a multi-compartmental metabolic network of Arabidopsis thaliana was reconstructed and optimized with respect to biomass growth, both in light and in dark conditions. The model can be used to perform flux balance analysis of metabolic fluxes through the central carbon metabolism and catabolic and anabolic pathways. Balances and turnover of energy (ATP/ADP) and redox metabolites (NAD(P)H/NAD(P)) as well as proton concentrations in different compartments can be estimated. Importantly, the model can be used to quantify the translocation of sucrose from source to sink tissues through phloem in association with an integral balance of protons, which in turn is defined by the operational modes of the energy metabolism (light and dark conditions). This chapter describes how a multi-compartmental model to predict carbon allocation is constructed and used.

Topological assessment of metabolic networks reveals evolutionary information

Evolutionary information was inferred from the topology of metabolic networks corresponding to 17 plant species belonging to major plant lineages Chlorophytes, Bryophytes, Lycophytes and Angiosperms. The plant metabolic networks were built using the substrate-product network modeling based on the metabolic reactions available on the PlantCyc database (version 9.5), from which their local topological properties such as degree, in-degree, out-degree, clustering coefficient, hub-score, authority-score, local efficiency, betweenness and eigencentrality were measured. The topological measurements corresponding to each metabolite within the networks were considered as a set of metabolic characters to compound a feature vector representing each plant. Our results revealed that some local topological characters are able to discern among plant kinships, since similar phylogenies were found when comparing dendrograms obtained by topological metrics to the one obtained by DNA sequences of chloroplast genes. Furthermore, we also found that even a smaller number of metabolic characters is able to separate among major clades with high bootstrap support (BS > 95), while for some suborders a bigger content has been required.

PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency

Genome-scale metabolic reconstructions help us to understand and engineer metabolism. Next-generation sequencing technologies are delivering genomes and transcriptomes for an ever-widening range of plants. While such omic data can, in principle, be used to compare metabolic reconstructions in different species, organs and environmental conditions, these comparisons require a standardized framework for the reconstruction of metabolic networks from transcript data. We previously introduced PlantSEED as a framework covering primary metabolism for 10 species. We have now expanded PlantSEED to include 39 species and provide tools that enable automated annotation and metabolic reconstruction from transcriptome data. The algorithm for automated annotation in PlantSEED propagates annotations using a set of signature k-mers (short amino acid sequences characteristic of particular proteins) that identify metabolic enzymes with an accuracy of about 97%. PlantSEED reconstructions are built from a curated template that includes consistent compartmentalization for more than 100 primary metabolic subsystems. Together, the annotation and reconstruction algorithms produce reconstructions without gaps and with more accurate compartmentalization than existing resources. These tools are available via the PlantSEED web interface at http://modelseed.org, which enables users to upload, annotate and reconstruct from private transcript data and simulate metabolic activity under various conditions using flux balance analysis. We demonstrate the ability to compare these metabolic reconstructions with a case study involving growth on several nitrogen sources in roots of four species.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.

AraNet: A Network Biology Server for Arabidopsis thaliana and Other Non-Model Plant Species

Functional gene networks link genes based on their functional relatedness, which is inferred from various complementary biological datasets. Gene networks comprising vast amounts of data can be used to predict which genes are associated with complex traits. Decades of studies in plant biology using the model organism Arabidopsis thaliana have generated large amounts of information, enabling the development of a system-level molecular network. AraNet (currently version 2) is a genome-scale functional gene network for Arabidopsis thaliana, constructed by integrating 19 types of genomics data and can be explored through a web-server (http://www.inetbio.org/aranet) to identify candidate genes for traits of interest. AraNet provides two alternative search paths for users to identify candidate genes and functions. The web server also exploits ortholog relationships between plant species and projects the genes of 28 other plant species (as of April, 2016) into the network of Arabidopsis genes. This allows researchers to use AraNet to predict genes/functions of not only Arabidopsis but also other non-model plants by expanding the functional knowledge of Arabidopsis. Here, we present a detailed description of how to search the AraNet network and interpret the search results to study plant gene functions and their associations with complex phenotypes.

Transcriptome analysis during ripening of table grape berry cv. Thompson Seedless

Ripening is one of the key processes associated with the development of major organoleptic characteristics of the fruit. This process has been extensively characterized in climacteric fruit, in contrast with non-climacteric fruit such as grape, where the process is less understood. With the aim of studying changes in gene expression during ripening of non-climacteric fruit, an Illumina based RNA-Seq transcriptome analysis was performed on four developmental stages, between veraison and harvest, on table grapes berries cv Thompson Seedless. Functional analysis showed a transcriptional increase in genes related with degradation processes of chlorophyll, lipids, macromolecules recycling and nucleosomes organization; accompanied by a decrease in genes related with chloroplasts integrity and amino acid synthesis pathways. It was possible to identify several processes described during leaf senescence, particularly close to harvest. Before this point, the results suggest a high transcriptional activity associated with the regulation of gene expression, cytoskeletal organization and cell wall metabolism, which can be related to growth of berries and firmness loss characteristic to this stage of development. This high metabolic activity could be associated with an increase in the transcription of genes related with glycolysis and respiration, unexpected for a non-climacteric fruit ripening.

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/.

RNA-Seq analysis of resistant and susceptible sub-tropical maize lines reveals a role for kauralexins in resistance to grey leaf spot disease, caused by Cercospora zeina

BACKGROUND

Cercospora zeina is a foliar pathogen responsible for maize grey leaf spot in southern Africa that negatively impacts maize production. Plants use a variety of chemical and structural mechanisms to defend themselves against invading pathogens such as C. zeina, including the production of secondary metabolites with antimicrobial properties. In maize, a variety of biotic and abiotic stressors induce the accumulation of the terpenoid phytoalexins, zealexins and kauralexins.

RESULTS

C. zeina-susceptible line displayed pervasive rectangular grey leaf spot lesions, running parallel with the leaf veins in contrast to C. zeina-resistant line that had restricted disease symptoms. Analysis of the transcriptome of both lines indicated that genes involved in primary and secondary metabolism were up-regualted, and although different pathways were prioritized in each line, production of terpenoid compounds were common to both. Targeted phytoalexin analysis revealed that C. zeina-inoculated leaves accumulated zealexins and kauralexins. The resistant line shows a propensity toward accumulation of the kauralexin B series metabolites in response to infection, which contrasts with the susceptible line that preferentially accumulates the kauralexin A series. Kauralexin accumulation was correlated to expression of the kauralexin biosynthetic gene, ZmAn2 and a candidate biosynthetic gene, ZmKSL2. We report the expression of a putative copalyl diphosphate synthase gene that is induced by C. zeina in the resistant line exclusively.

DISCUSSION

This study shows that zealexins and kauralexins, and expression of their biosynthetic genes, are induced by C. zeina in both resistant and susceptible germplasm adapted to the southern African climate. The data presented here indicates that different forms of kauralexins accumulate in the resistant and susceptible maize lines in response to C. zeina, with the accumulation of kauralexin B compounds in a resistant maize line and kauralexin A compounds accumulating in the susceptible line.

Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets

Motivation:Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.

Enhancing gene regulatory network inference through data integration with markov random fields

A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

Metabolic model of central carbon and energy metabolisms of growing Arabidopsis thaliana in relation to sucrose translocation

BACKGROUND

Sucrose translocation between plant tissues is crucial for growth, development and reproduction of plants. Systemic analysis of these metabolic and underlying regulatory processes allow a detailed understanding of carbon distribution within the plant and the formation of associated phenotypic traits. Sucrose translocation from 'source' tissues (e.g. mesophyll) to 'sink' tissues (e.g. root) is tightly bound to the proton gradient across the membranes. The plant sucrose transporters are grouped into efflux exporters (SWEET family) and proton-symport importers (SUC, STP families). To better understand regulation of sucrose export from source tissues and sucrose import into sink tissues, there is a need for a metabolic model that takes in account the tissue organisation of Arabidopsis thaliana with corresponding metabolic specificities of respective tissues in terms of sucrose and proton production/utilization. An ability of the model to operate under different light modes ('light' and 'dark') and correspondingly in different energy producing modes is particularly important in understanding regulatory modules.

RESULTS

Here, we describe a multi-compartmental model consisting of a mesophyll cell with plastid and mitochondrion, a phloem cell, as well as a root cell with mitochondrion. In this model, the phloem was considered as a non-growing transport compartment, the mesophyll compartment was considered as both autotrophic (growing on CO₂ under light) and heterotrophic (growing on starch in darkness), and the root was always considered as heterotrophic tissue dependent on sucrose supply from the mesophyll compartment. In total, the model includes 413 balanced compounds interconnected by 400 transformers. The structured metabolic model accounts for central carbon metabolism, photosynthesis, photorespiration, carbohydrate metabolism, energy and redox metabolisms, proton metabolism, biomass growth, nutrients uptake, proton gradient generation and sucrose translocation between tissues. Biochemical processes in the model were associated with gene-products (742 ORFs). Flux Balance Analysis (FBA) of the model resulted in balanced carbon, nitrogen, proton, energy and redox states under both light and dark conditions. The main H⁺-fluxes were reconstructed and their directions matched with proton-dependent sucrose translocation from 'source' to 'sink' under any light condition.

CONCLUSIONS

The model quantified the translocation of sucrose between plant tissues in association with an integral balance of protons, which in turn is defined by operational modes of the energy metabolism.

Pathway Analysis and Omics Data Visualization Using Pathway Genome Databases: FragariaCyc, a Case Study

The species-specific plant Pathway Genome Databases (PGDBs) based on the BioCyc platform provide a conceptual model of the cellular metabolic network of an organism. Such frameworks allow analysis of the genome-scale expression data to understand changes in the overall metabolisms of an organism (or organs, tissues, and cells) in response to various extrinsic (e.g. developmental and differentiation) and/or extrinsic signals (e.g. pathogens and abiotic stresses) from the surrounding environment. Using FragariaCyc, a pathway database for the diploid strawberry Fragaria vesca, we show (1) the basic navigation across a PGDB; (2) a case study of pathway comparison across plant species; and (3) an example of RNA-Seq data analysis using Omics Viewer tool. The protocols described here generally apply to other Pathway Tools-based PGDBs.

The Arabidopsis phytohormone crosstalk network involves a consecutive metabolic route and circular control units of transcription factors that regulate enzyme-encoding genes

Background

Phytohormone synergies and signaling interdependency are important topics in plant developmental biology. Physiological and genetic experimental evidence for phytohormone crosstalk has been accumulating and a genome-scale enzyme correlation model representing the Arabidopsis metabolic pathway has been published. However, an integrated molecular characterization of phytohormone crosstalk is still not available.

Results

A novel modeling methodology and advanced computational approaches were used to construct an enzyme-based Arabidopsis phytohormone crosstalk network (EAPCN) at the biosynthesis level. The EAPCN provided the structural connectivity architecture of phytohormone biosynthesis pathways and revealed a surprising result; that enzymes localized at the highly connected nodes formed a consecutive metabolic route. Furthermore, our analysis revealed that the transcription factors (TFs) that regulate enzyme-encoding genes in the consecutive metabolic route formed structures, which we describe as circular control units operating at the transcriptional level. Furthermore, the downstream TFs in phytohormone signal transduction pathways were found to be involved in the circular control units that included the TFs regulating enzyme-encoding genes. In addition, multiple functional enzymes in the EAPCN were found to be involved in ion and pH homeostasis, environmental signal perception, cellular redox homeostasis, and circadian clocks. Last, publicly available transcriptional profiles and a protein expression map of the Arabidopsis root apical meristem were used as a case study to validate the proposed framework.

Conclusions

Our results revealed multiple scales of coupled mechanisms in that hormonal crosstalk networks that play a central role in coordinating internal developmental processes with environmental signals, and give a broader view of Arabidopsis phytohormone crosstalk. We also uncovered potential key regulators that can be further analyzed in future studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0333-9) contains supplementary material, which is available to authorized users.

RNA-Seq Links the Transcription Factors AINTEGUMENTA and AINTEGUMENTA-LIKE6 to Cell Wall Remodeling and Plant Defense Pathways

AINTEGUMENTA (ANT) and AINTEGUMENTA-LIKE6 (AIL6) are two related transcription factors in Arabidopsis (Arabidopsis thaliana) that have partially overlapping roles in several aspects of flower development, including floral organ initiation, identity specification, growth, and patterning. To better understand the biological processes regulated by these two transcription factors, we performed RNA sequencing (RNA-Seq) on ant ail6 double mutants. We identified thousands of genes that are differentially expressed in the double mutant compared with the wild type. Analyses of these genes suggest that ANT and AIL6 regulate floral organ initiation and growth through modifications to the cell wall polysaccharide pectin. We found reduced levels of demethylesterified homogalacturonan and altered patterns of auxin accumulation in early stages of ant ail6 flower development. The RNA-Seq experiment also revealed cross-regulation of AIL gene expression at the transcriptional level. The presence of a number of overrepresented Gene Ontology terms related to plant defense in the set of genes differentially expressed in ant ail6 suggest that ANT and AIL6 also regulate plant defense pathways. Furthermore, we found that ant ail6 plants have elevated levels of two defense hormones: salicylic acid and jasmonic acid, and show increased resistance to the bacterial pathogen Pseudomonas syringae These results suggest that ANT and AIL6 regulate biological pathways that are critical for both development and defense.

Flavonols Mediate Root Phototropism and Growth through Regulation of Proliferation-to-Differentiation Transition

Roots normally grow in darkness, but they may be exposed to light. After perceiving light, roots bend to escape from light (root light avoidance) and reduce their growth. How root light avoidance responses are regulated is not well understood. Here, we show that illumination induces the accumulation of flavonols in Arabidopsis thaliana roots. During root illumination, flavonols rapidly accumulate at the side closer to light in the transition zone. This accumulation promotes asymmetrical cell elongation and causes differential growth between the two sides, leading to root bending. Furthermore, roots illuminated for a long period of time accumulate high levels of flavonols. This high flavonol content decreases both auxin signaling and PLETHORA gradient as well as superoxide radical content, resulting in reduction of cell proliferation. In addition, cytokinin and hydrogen peroxide, which promote root differentiation, induce flavonol accumulation in the root transition zone. As an outcome of prolonged light exposure and flavonol accumulation, root growth is reduced and a different root developmental zonation is established. Finally, we observed that these differentiation-related pathways are required for root light avoidance. We propose that flavonols function as positional signals, integrating hormonal and reactive oxygen species pathways to regulate root growth direction and rate in response to light.

Heartwood-specific transcriptome and metabolite signatures of tropical sandalwood (Santalum album) reveal the final step of (Z)-santalol fragrance biosynthesis

Tropical sandalwood (Santalum album) produces one of the world's most highly prized fragrances, which is extracted from mature heartwood. However, in some places such as southern India, natural populations of this slow-growing tree are threatened by over-exploitation. Sandalwood oil contains four major and fragrance-defining sesquiterpenols: (Z)-α-santalol, (Z)-β-santalol, (Z)-epi-β-santalol and (Z)-α-exo-bergamotol. The first committed step in their biosynthesis is catalyzed by a multi-product santalene/bergamotene synthase. Sandalwood cytochromes P450 of the CYP76F sub-family were recently shown to hydroxylate santalenes and bergamotene; however, these enzymes produced mostly (E)-santalols and (E)-α-exo-bergamotol. We hypothesized that different santalene/bergamotene hydroxylases evolved in S. album to stereo-selectively produce (E)- or (Z)-sesquiterpenols, and that genes encoding (Z)-specific P450s contribute to sandalwood oil formation if co-expressed in the heartwood with upstream genes of sesquiterpene biosynthesis. This hypothesis was validated by the discovery of a heartwood-specific transcriptome signature for sesquiterpenoid biosynthesis, including highly expressed SaCYP736A167 transcripts. We characterized SaCYP736A167 as a multi-substrate P450, which stereo-selectively produces (Z)-α-santalol, (Z)-β-santalol, (Z)-epi-β-santalol and (Z)-α-exo-bergamotol, matching authentic sandalwood oil. This work completes the discovery of the biosynthetic enzymes of key components of sandalwood fragrance, and highlights the evolutionary diversification of stereo-selective P450s in sesquiterpenoid biosynthesis. Bioengineering of microbial systems using SaCYP736A167, combined with santalene/bergamotene synthase, has potential for development of alternative industrial production systems for sandalwood oil fragrances.

Metabolite Damage and Metabolite Damage Control in Plants

It is increasingly clear that (a) many metabolites undergo spontaneous or enzyme-catalyzed side reactions in vivo, (b) the damaged metabolites formed by these reactions can be harmful, and (c) organisms have biochemical systems that limit the buildup of damaged metabolites. These damage-control systems either return a damaged molecule to its pristine state (metabolite repair) or convert harmful molecules to harmless ones (damage preemption). Because all organisms share a core set of metabolites that suffer the same chemical and enzymatic damage reactions, certain damage-control systems are widely conserved across the kingdoms of life. Relatively few damage reactions and damage-control systems are well known. Uncovering new damage reactions and identifying the corresponding damaged metabolites, damage-control genes, and enzymes demands a coordinated mix of chemistry, metabolomics, cheminformatics, biochemistry, and comparative genomics. This review illustrates the above points using examples from plants, which are at least as prone to metabolite damage as other organisms.

Representation and inference of cellular architecture for metabolic reconstruction and modeling

MOTIVATION

Metabolic modeling depends on accurately representing the cellular locations of enzyme-catalyzed and transport reactions. We sought to develop a representation of cellular compartmentation that would accurately capture cellular location information. We further sought a representation that would support automated inference of the cellular compartments present in newly sequenced organisms to speed model development, and that would enable representing the cellular compartments present in multiple cell types within a multicellular organism.

RESULTS

We define the cellular architecture of a unicellular organism, or of a cell type from a multicellular organism, as the collection of cellular components it contains plus the topological relationships among those components. We developed a tool for inferring cellular architectures across many domains of life and extended our Cell Component Ontology to enable representation of the inferred architectures. We provide software for visualizing cellular architectures to verify their correctness and software for editing cellular architectures to modify or correct them. We also developed a representation that records the cellular compartment assignments of reactions with minimal duplication of information.

AVAILABILITY AND IMPLEMENTATION

The Cell Component Ontology is freely available. The Pathway Tools software is freely available for academic research and is available for a fee for commercial use.

CONTACT

pkarp@ai.sri.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Transcriptome analyses reveal genotype- and developmental stage-specific molecular responses to drought and salinity stresses in chickpea

Drought and salinity are the major factors that limit chickpea production worldwide. We performed whole transcriptome analyses of chickpea genotypes to investigate the molecular basis of drought and salinity stress response/adaptation. Phenotypic analyses confirmed the contrasting responses of the chickpea genotypes to drought or salinity stress. RNA-seq of the roots of drought and salinity related genotypes was carried out under control and stress conditions at vegetative and/or reproductive stages. Comparative analysis of the transcriptomes revealed divergent gene expression in the chickpea genotypes at different developmental stages. We identified a total of 4954 and 5545 genes exclusively regulated in drought-tolerant and salinity-tolerant genotypes, respectively. A significant fraction (~47%) of the transcription factor encoding genes showed differential expression under stress. The key enzymes involved in metabolic pathways, such as carbohydrate metabolism, photosynthesis, lipid metabolism, generation of precursor metabolites/energy, protein modification, redox homeostasis and cell wall component biogenesis, were affected by drought and/or salinity stresses. Interestingly, transcript isoforms showed expression specificity across the chickpea genotypes and/or developmental stages as illustrated by the AP2-EREBP family members. Our findings provide insights into the transcriptome dynamics and components of regulatory network associated with drought and salinity stress responses in chickpea.

Five omic technologies are concordant in differentiating the biochemical characteristics of the berries of five grapevine (Vitis vinifera L.) cultivars

Background

Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems.

Results

Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity (°Brix-to-titratable acidity ratio) from the same experimental vineyard. The cultivars were exposed to a mild, seasonal water-deficit treatment from fruit set until harvest in 2011. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNAseq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNAseq and microarray data revealed a strong Pearson’s correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson’s correlation of 0.27 and 0.24 for the RNAseq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. The mild water deficit treatment did not significantly alter the abundance of proteins or metabolites measured in the five cultivars, but did have a small effect on gene expression.

Conclusions

The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2115-y) contains supplementary material, which is available to authorized users.

Structural identification of putative USPs in Catharanthus roseus

Nucleotide sequences of the C. roseus SRA database were assembled and translated in order to detect putative universal stress proteins (USPs). Based on the known conserved USPA domain, 24 Pfam putative USPA proteins in C. roseus were detected and arranged in six architectures. The USPA-like domain was detected in all architectures, while the protein kinase-like (or PK-like), (tyr)PK-like and/or U-box domains are shown downstream it. Three other domains were also shown to coexist with the USPA domain in C. roseus putative USPA sequences. These domains are tetratricopeptide repeat (or TPR), apolipophorin III (or apoLp-III) and Hsp90 co-chaperone Cdc37. Subsequent analysis divided USPA-like domains based on the ability to bind ATP. The multiple sequence alignment indicated the occurrence of eight C. roseus residues of known features of the bacterial 1MJH secondary structure. The data of the phylogenetic tree indicated several distinct groups of USPA-like domains confirming the presence of high level of sequence conservation between the plant and bacterial USPA-like sequences.

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.

Network analysis of postharvest senescence process in citrus fruits revealed by transcriptomic and metabolomic profiling

Citrus (Citrus spp.), a nonclimacteric fruit, is one of the most important fruit crops in global fruit industry. However, the biological behavior of citrus fruit ripening and postharvest senescence remains unclear. To better understand the senescence process of citrus fruit, we analyzed data sets from commercial microarrays, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry and validated physiological quality detection of four main varieties in the genus Citrus. Network-based approaches of data mining and modeling were used to investigate complex molecular processes in citrus. The Citrus Metabolic Pathway Network and correlation networks were constructed to explore the modules and relationships of the functional genes/metabolites. We found that the different flesh-rind transport of nutrients and water due to the anatomic structural differences among citrus varieties might be an important factor that influences fruit senescence behavior. We then modeled and verified the citrus senescence process. As fruit rind is exposed directly to the environment, which results in energy expenditure in response to biotic and abiotic stresses, nutrients are exported from flesh to rind to maintain the activity of the whole fruit. The depletion of internal substances causes abiotic stresses, which further induces phytohormone reactions, transcription factor regulation, and a series of physiological and biochemical reactions.

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.

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.

Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

Background

Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms.

Results

We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families.

Conclusions

Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/~jinchen/NETSIM.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0474-7) contains supplementary material, which is available to authorized users.

Genetic variation in the nuclear and organellar genomes modulates stochastic variation in the metabolome, growth, and defense

Recent studies are starting to show that genetic control over stochastic variation is a key evolutionary solution of single celled organisms in the face of unpredictable environments. This has been expanded to show that genetic variation can alter stochastic variation in transcriptional processes within multi-cellular eukaryotes. However, little is known about how genetic diversity can control stochastic variation within more non-cell autonomous phenotypes. Using an Arabidopsis reciprocal RIL population, we showed that there is significant genetic diversity influencing stochastic variation in the plant metabolome, defense chemistry, and growth. This genetic diversity included loci specific for the stochastic variation of each phenotypic class that did not affect the other phenotypic classes or the average phenotype. This suggests that the organism's networks are established so that noise can exist in one phenotypic level like metabolism and not permeate up or down to different phenotypic levels. Further, the genomic variation within the plastid and mitochondria also had significant effects on the stochastic variation of all phenotypic classes. The genetic influence over stochastic variation within the metabolome was highly metabolite specific, with neighboring metabolites in the same metabolic pathway frequently showing different levels of noise. As expected from bet-hedging theory, there was more genetic diversity and a wider range of stochastic variation for defense chemistry than found for primary metabolism. Thus, it is possible to begin dissecting the stochastic variation of whole organismal phenotypes in multi-cellular organisms. Further, there are loci that modulate stochastic variation at different phenotypic levels. Finding the identity of these genes will be key to developing complete models linking genotype to phenotype.

TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.

Steady-state and instationary modeling of proteinogenic and free amino acid isotopomers for flux quantification

Metabolic flux analysis (MFA) is a powerful tool for exploring and quantifying carbon traffic in metabolic networks. Accurate flux quantification requires (1) high-quality isotopomer measurements, usually of biomass components including proteinogenic/free amino acids or central carbon metabolites, and (2) a mathematical model that relates the unknown fluxes to the measured isotopomers. Modeling requires a thorough knowledge of the structure of the underlying metabolic network, often available from many databases, as well as the ability to make reasonable assumptions that will enable simplification of the model. Here we describe a general methodology underlying computer-aided mathematical modeling of a flux-isotopomer relationship and some of the accompanying data-processing steps. One of two modeling strategies will need to be employed, depending on the type of isotope labeling experiment performed. These strategies-steady-state modeling and instationary modeling-have different experimental and computational demands. We discuss the concepts underlying these two types of modeling and demonstrate steady-state modeling in a step-by-step manner. Our methodology should be applicable to most isotope-assisted MFA applications and should serve as a general framework applicable to many realistic metabolic networks with little modification.

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

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

Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses

Constraint-based approaches have been used for integrating data in large-scale metabolic networks to obtain insights into metabolism of various organisms. Due to the underlying steady-state assumption, these approaches are usually not suited for making predictions about metabolite levels. Here, we ask whether we can make inferences about the variability of metabolite levels from a constraint-based analysis based on the integration of transcriptomics data. To this end, we analyze time-resolved transcriptomics and metabolomics data from Arabidopsis thaliana under a set of eight different light and temperature conditions. In a previous study, the gene expression data have already been integrated in a genome-scale metabolic network to predict pathways, termed modulators and sustainers, which are differentially regulated with respect to a biochemically meaningful data-driven null model. Here, we present a follow-up analysis which bridges the gap between flux- and metabolite-centric methods. One of our main findings demonstrates that under certain environmental conditions, the levels of metabolites acting as substrates in modulators or sustainers show significantly lower temporal variations with respect to the remaining measured metabolites. This observation is discussed within the context of a systems-view of plasticity and robustness of metabolite contents and pathway fluxes. Our study paves the way for investigating the existence of similar principles in other species for which both genome-scale networks and high-throughput metabolomics data of high quality are becoming increasingly available.

Arabidopsis database and stock resources

The volume of Arabidopsis information has increased enormously in recent years as a result of the sequencing of the reference genome and other large-scale functional genomics projects. Much of the data is stored in public databases, where data are organized, analyzed, and made freely accessible to the research community. These databases are resources that researchers can utilize for making predictions and developing testable hypotheses. The methods in this chapter describe ways to access and utilize Arabidopsis data and genomic resources found in databases and stock centers.

The common ground of genomics and systems biology

The rise of systems biology is intertwined with that of genomics, yet their primordial relationship to one another is ill-defined. We discuss how the growth of genomics provided a critical boost to the popularity of systems biology. We describe the parts of genomics that share common areas of interest with systems biology today in the areas of gene expression, network inference, chromatin state analysis, pathway analysis, personalized medicine, and upcoming areas of synergy as genomics continues to expand its scope across all biomedical fields.

Novel roles for the polyphenol oxidase enzyme in secondary metabolism and the regulation of cell death in walnut

The enzyme polyphenol oxidase (PPO) catalyzes the oxidation of phenolic compounds into highly reactive quinones. Polymerization of PPO-derived quinones causes the postharvest browning of cut or bruised fruit, but the native physiological functions of PPOs in undamaged, intact plant cells are not well understood. Walnut (Juglans regia) produces a rich array of phenolic compounds and possesses a single PPO enzyme, rendering it an ideal model to study PPO. We generated a series of PPO-silenced transgenic walnut lines that display less than 5% of wild-type PPO activity. Strikingly, the PPO-silenced plants developed spontaneous necrotic lesions on their leaves in the absence of pathogen challenge (i.e. a lesion mimic phenotype). To gain a clearer perspective on the potential functions of PPO and its possible connection to cell death, we compared the leaf transcriptomes and metabolomes of wild-type and PPO-silenced plants. Silencing of PPO caused major alterations in the metabolism of phenolic compounds and their derivatives (e.g. coumaric acid and catechin) and in the expression of phenylpropanoid pathway genes. Several observed metabolic changes point to a direct role for PPO in the metabolism of tyrosine and in the biosynthesis of the hydroxycoumarin esculetin in vivo. In addition, PPO-silenced plants displayed massive (9-fold) increases in the tyrosine-derived metabolite tyramine, whose exogenous application elicits cell death in walnut and several other plant species. Overall, these results suggest that PPO plays a novel and fundamental role in secondary metabolism and acts as an indirect regulator of cell death in walnut.

The Arabidopsis cytosolic proteome: the metabolic heart of the cell

The plant cytosol is the major intracellular fluid that acts as the medium for inter-organellar crosstalk and where a plethora of important biological reactions take place. These include its involvement in protein synthesis and degradation, stress response signaling, carbon metabolism, biosynthesis of secondary metabolites, and accumulation of enzymes for defense and detoxification. This central role is highlighted by estimates indicating that the majority of eukaryotic proteins are cytosolic. Arabidopsis thaliana has been the subject of numerous proteomic studies on its different subcellular compartments. However, a detailed study of enriched cytosolic fractions from Arabidopsis cell culture has been performed only recently, with over 1,000 proteins reproducibly identified by mass spectrometry. The number of proteins allocated to the cytosol nearly doubles to 1,802 if a series of targeted proteomic characterizations of complexes is included. Despite this, few groups are currently applying advanced proteomic approaches to this important metabolic space. This review will highlight the current state of the Arabidopsis cytosolic proteome since its initial characterization a few years ago.

Mathematical modeling of isotope labeling experiments for metabolic flux analysis

Isotope labeling experiments (ILEs) offer a powerful methodology to perform metabolic flux analysis. However, the task of interpreting data from these experiments to evaluate flux values requires significant mathematical modeling skills. Toward this, this chapter provides background information and examples to enable the reader to (1) model metabolic networks, (2) simulate ILEs, and (3) understand the optimization and statistical methods commonly used for flux evaluation. A compartmentalized model of plant glycolysis and pentose phosphate pathway illustrates the reconstruction of a typical metabolic network, whereas a simpler example network illustrates the underlying metabolite and isotopomer balancing techniques. We also discuss the salient features of commonly used flux estimation software 13CFLUX2, Metran, NMR2Flux+, FiatFlux, and OpenFLUX. Furthermore, we briefly discuss methods to improve flux estimates. A graphical checklist at the end of the chapter provides a reader a quick reference to the mathematical modeling concepts and resources.

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.

Cheminformatic analysis of high-throughput compound screens

This article gives an overview of basic computational methods that are commonly used for analyzing small molecule screening data in the chemical genomics field. First, we introduce cheminformatic concepts for analyzing drug-like small molecule structures and their properties. Second, we introduce compound selection approaches for assembling screening libraries using compound property and diversity analyses. Finally, we discuss methods for interpreting screening hits by analyzing compound structures and induced phenotypes using similarity search and clustering approaches. These are critical steps for optimizing screening hits, and relating structure to bioactivity and phenotype.