Kate Dreher

The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools

The Arabidopsis Information Resource (TAIR, http://arabidopsis.org) is a genome database for Arabidopsis thaliana, an important reference organism for many fundamental aspects of biology as well as basic and applied plant biology research. TAIR serves as a central access point for Arabidopsis data, annotates gene function and expression patterns using controlled vocabulary terms, and maintains and updates the A. thaliana genome assembly and annotation. TAIR also provides researchers with an extensive set of visualization and analysis tools. Recent developments include several new genome releases (TAIR8, TAIR9 and TAIR10) in which the A. thaliana assembly was updated, pseudogenes and transposon genes were re-annotated, and new data from proteomics and next generation transcriptome sequencing were incorporated into gene models and splice variants. Other highlights include progress on functional annotation of the genome and the release of several new tools including Textpresso for Arabidopsis which provides the capability to carry out full text searches on a large body of research literature.

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).

FQR1, a Novel Primary Auxin-Response Gene, Encodes a Flavin Mononucleotide-Binding Quinone Reductase

FQR1 is a novel primary auxin-response gene that codes for a flavin mononucleotide-binding flavodoxin-like quinone reductase. Accumulation of FQR1 mRNA begins within 10 min of indole-3-acetic acid application and reaches a maximum of approximately 10-fold induction 30 min after treatment. This increase in FQR1 mRNA abundance is not diminished by the protein synthesis inhibitor cycloheximide, demonstrating thatFQR1 is a primary auxin-response gene. Sequence analysis reveals that FQR1 belongs to a family of flavin mononucleotide-binding quinone reductases. Partially purified His-tagged FQR1 isolated fromEscherichia coli catalyzes the transfer of electrons from NADH and NADPH to several substrates and exhibits in vitro quinone reductase activity. Overexpression of FQR1 in plants leads to increased levels of FQR1 protein and quinone reductase activity, indicating that FQR1 functions as a quinone reductase in vivo. In mammalian systems, glutathione S-transferases and quinone reductases are classified as phase II detoxification enzymes. We hypothesize that the auxin-inducible glutathioneS-transferases and quinone reductases found in plants also act as detoxification enzymes, possibly to protect against auxin-induced oxidative stress.

PlantMetabolomics.org: A Web Portal for Plant Metabolomics Experiments

PlantMetabolomics.org (PM) is a web portal and database for exploring, visualizing, and downloading plant metabolomics data. Widespread public access to well-annotated metabolomics datasets is essential for establishing metabolomics as a functional genomics tool. PM integrates metabolomics data generated from different analytical platforms from multiple laboratories along with the key visualization tools such as ratio and error plots. Visualization tools can quickly show how one condition compares to another and which analytical platforms show the largest changes. The database tries to capture a complete annotation of the experiment metadata along with the metabolite abundance databased on the evolving Metabolomics Standards Initiative. PM can be used as a platform for deriving hypotheses by enabling metabolomic comparisons between genetically unique Arabidopsis (Arabidopsis thaliana) populations subjected to different environmental conditions. Each metabolite is linked to relevant experimental data and information from various annotation databases. The portal also provides detailed protocols and tutorials on conducting plant metabolomics experiments to promote metabolomics in the community. PM currently houses Arabidopsis metabolomics data generated by a consortium of laboratories utilizing metabolomics to help elucidate the functions of uncharacterized genes. PM is publicly available at http://www.plantmetabolomics.org.

Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes

The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, biochemical pathways, genome organization, images of mutant plants, protein sub‐cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web‐based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and describes several new tools such as a new TAIR genome browser (GBrowse), and the TAIR synteny viewer (GBrowse_syn). We also describe how to use AraCyc for mining plant metabolic pathways. Curr. Protoc. Bioinform. 30:1.11.1‐1.11.51.

Genome-wide prediction of metabolic enzymes, pathways and gene clusters in plants

A computational pipeline generates high-quality and genome-scale sets of metabolic enzymes, pathways, and gene clusters from plant genomes. Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.

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.

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Global patterns of metabolic responses upon single gene perturbations are specific to gene functions, but they are coordinated with characteristics of the perturbed genes. 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.

The challenge of constructing, classifying, and representing metabolic pathways

Scientists, educators, and students benefit from having free and centralized access to the wealth of metabolic information that has been gathered over the decades. Curators of the MetaCyc database work to present this information in an easily understandable pathway-based framework. MetaCyc is used not only as an encyclopedic resource for metabolic information but also as a template for the pathway prediction software that generates pathway/genome databases for thousands of organisms with sequenced genomes (available at www.biocyc.org). Curators need to define pathway boundaries and classify pathways within a broader pathway ontology to maximize the utility of the pathways to both users and the pathway prediction software. These seemingly simple tasks pose several challenges. This review describes these challenges as well as the criteria that need to be considered, and the rules that have been developed by MetaCyc curators as they make decisions regarding the representation and classification of metabolic pathway information in MetaCyc. The functional consequences of these decisions in regard to pathway prediction in new species are also discussed.

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.