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The exploration of microarray data and data from other high-throughput projects for hypothesis generation has become a vital aspect of post-genomic research. For the non-bioinformatics specialist, however, many of the currently available tools provide overwhelming amounts of data that are presented in a non-intuitive way. In order to facilitate the interpretation and analysis of microarray data and data from other large-scale data sets, we have developed a tool, which we have dubbed the electronic Fluorescent Pictograph - or eFP - Browser, available at http://www.bar.utoronto.ca/, for exploring microarray and other data for hypothesis generation. This eFP Browser engine paints data from large-scale data sets onto pictographic representations of the experimental samples used to generate the data sets. We give examples of using the tool to present Arabidopsis gene expression data from the AtGenExpress Consortium (Arabidopsis eFP Browser), data for subcellular localization of Arabidopsis proteins (Cell eFP Browser), and mouse tissue atlas microarray data (Mouse eFP Browser). The eFP Browser software is easily adaptable to microarray or other large-scale data sets from any organism and thus should prove useful to a wide community for visualizing and interpreting these data sets for hypothesis generation.

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an “app” on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research.

A high-throughput assay is used to analyse 40,000 potential extracellular domain interactions of a large family of plant cell surface receptors (LRR-RKs) and provide a cell surface interaction network for these receptors. A network of cell surface interactions Cell surface receptors mediate communication between the interior of a cell and its external environment. Specifically, the extracellular domains (ECDs) of such receptors interact with external molecules. It is less clear how interactions between ECDs of different receptors help to form receptor complexes for signal transduction. Youssef Belkhadir and colleagues investigate systems-level organization of leucine-rich repeat receptor kinases (LRR-RKs)—a large family of plant cell surface receptors with roles in processes including plant defence and development. The authors use a high-throughput assay to study 40,000 potential ECD interactions. They develop a cell surface interaction network for these receptors and study its dynamics. The team demonstrate the power of this network for detecting biologically relevant interactions by predicting and validating the function of previously uncharacterized LRR-RKs in plant growth and immunity. The cells of multicellular organisms receive extracellular signals using surface receptors. The extracellular domains (ECDs) of cell surface receptors function as interaction platforms, and as regulatory modules of receptor activation 1 , 2 . Understanding how interactions between ECDs produce signal-competent receptor complexes is challenging because of their low biochemical tractability 3 , 4 . In plants, the discovery of ECD interactions is complicated by the massive expansion of receptor families, which creates tremendous potential for changeover in receptor interactions 5 . The largest of these families in Arabidopsis thaliana consists of 225 evolutionarily related leucine-rich repeat receptor kinases (LRR-RKs) 5 , which function in the sensing of microorganisms, cell expansion, stomata development and stem-cell maintenance 6 , 7 , 8 , 9 . Although the principles that govern LRR-RK signalling activation are emerging 1 , 10 , the systems-level organization of this family of proteins is unknown. Here, to address this, we investigated 40,000 potential ECD interactions using a sensitized high-throughput interaction assay 3 , and produced an LRR-based cell surface interaction network (CSI LRR ) that consists of 567 interactions. To demonstrate the power of CSI LRR for detecting biologically relevant interactions, we predicted and validated the functions of uncharacterized LRR-RKs in plant growth and immunity. In addition, we show that CSI LRR operates as a unified regulatory network in which the LRR-RKs most crucial for its overall structure are required to prevent the aberrant signalling of receptors that are several network-steps away. Thus, plants have evolved LRR-RK networks to process extracellular signals into carefully balanced responses.

922 I. 922 II. 922 III. 925 IV. 925 V. 926 VI. 927 VII. 928 VIII. 929 IX. 930 X. 931 XI. 932 XII. 933 XIII. Natural variation and genome‐wide association studies 934 XIV. 934 XV. 935 XVI. 936 XVII. 937 937 References 937 SUMMARY: The year 2014 marked the 25ᵗʰ International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. We present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlighting some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome‐wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology.

Type 2C protein phosphatases (PP2Cs) are vitally involved in abscisic acid (ABA) signaling. Here, we show that a synthetic growth inhibitor called pyrabactin functions as a selective ABA agonist. Pyrabactin acts through PYRABACTIN RESISTANCE 1 (PYR1), the founding member of a family of START proteins called PYR/PYLs, which are necessary for both pyrabactin and ABA signaling in vivo. We show that ABA binds to PYR1, which in turn binds to and inhibits PP2Cs. We conclude that PYR/PYLs are ABA receptors functioning at the apex of a negative regulatory pathway that controls ABA signaling by inhibiting PP2Cs. Our results illustrate the power of the chemical genetic approach for sidestepping genetic redundancy.

Seed germination is a complex trait of key ecological and agronomic significance. Few genetic factors regulating germination have been identified, and the means by which their concerted action controls this developmental process remains largely unknown. Using publicly available gene expression data from Arabidopsis thaliana, we generated a condition-dependent network model of global transcriptional interactions (SeedNet) that shows evidence of evolutionary conservation in flowering plants. The topology of the SeedNet graph reflects the biological process, including two state-dependent sets of interactions associated with dormancy or germination. SeedNet highlights interactions between known regulators of this process and predicts the germination-associated function of uncharacterized hub nodes connected to them with 50% accuracy. An intermediate transition region between the dormancy and germination subdomains is enriched with genes involved in cellular phase transitions. The phase transition regulators SERRATE and EARLY FLOWERING IN SHORT DAYS from this region affect seed germination, indicating that conserved mechanisms control transitions in cell identity in plants. The SeedNet dormancy region is strongly associated with vegetative abiotic stress response genes. These data suggest that seed dormancy, an adaptive trait that arose evolutionarily late, evolved by coopting existing genetic pathways regulating cellular phase transition and abiotic stress. SeedNet is available as a community resource (http://vseed.nottingham.ac.uk) to aid dissection of this complex trait and gene function in diverse processes.

The complex cellular functions of an organism frequently rely on physical interactions between proteins. A map of all protein-protein interactions, an interactome, is thus an invaluable tool. We present an interactome for Arabidopsis (Arabidopsis thaliana) predicted from interacting orthologs in yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), fruitfly (Drosophila melanogaster), and human (Homo sapiens). As an internal quality control, a confidence value was generated based on the amount of supporting evidence for each interaction. A total of 1,159 high confidence, 5,913 medium confidence, and 12,907 low confidence interactions were identified for 3,617 conserved Arabidopsis proteins. There was significant coexpression of genes whose proteins were predicted to interact, even among low confidence interactions. Interacting proteins were also significantly more likely to be found within the same subcellular location, and significantly less likely to be found in conflicting localizations than randomly paired proteins. A notable exception was that proteins located in the Golgi were more likely to interact with Golgi, vacuolar, or endoplasmic reticulum sorted proteins, indicating possible docking or trafficking interactions. These predictions can aid researchers by extending known complexes and pathways with candidate proteins. In addition we have predicted interactions for many previously unknown proteins in known pathways and complexes. We present this interactome, and an online Web interface the Arabidopsis Interactions Viewer, as a first step toward understanding global signaling in Arabidopsis, and to whet the appetite for those who are awaiting results from high-throughput experimental approaches.

Background Transcriptomic studies combined with a well annotated genome have laid the foundations for new understanding of molecular processes. Tools which visualise gene expression patterns have further added to these resources. The manual annotation of the Actinidia chinensis (kiwifruit) genome has resulted in a high quality set of 33,044 genes. Here we investigate gene expression patterns in diverse tissues, visualised in an Electronic Fluorescent Pictograph (eFP) browser, to study the relationship of transcription factor (TF) expression using network analysis. Results Sixty-one samples covering diverse tissues at different developmental time points were selected for RNA-seq analysis and an eFP browser was generated to visualise this dataset. 2839 TFs representing 57 different classes were identified and named. Network analysis of the TF expression patterns separated TFs into 14 different modules. Two modules consisting of 237 TFs were correlated with floral bud and flower development, a further two modules containing 160 TFs were associated with fruit development and maturation. A single module of 480 TFs was associated with ethylene-induced fruit ripening. Three “hub” genes correlated with flower and fruit development consisted of a HAF-like gene central to gynoecium development, an ERF and a DOF gene. Maturing and ripening hub genes included a KNOX gene that was associated with seed maturation, and a GRAS-like TF. Conclusions This study provides an insight into the complexity of the transcriptional control of flower and fruit development, as well as providing a new resource to the plant community. The Actinidia eFP browser is provided in an accessible format that allows researchers to download and work internally.

The combination of robust physiological models with \"omics\" studies holds promise for the discovery of genes and pathways linked to how organisms deal with drying. Here we used a transcriptomics approach in combination with an in vivo physiological model of re-establishment of desiccation tolerance (DT) in Arabidopsis thaliana seeds. We show that the incubation of desiccation sensitive (DS) germinated Arabidopsis seeds in a polyethylene glycol (PEG) solution re-induces the mechanisms necessary for expression of DT. Based on a SNP-tile array gene expression profile, our data indicates that the re-establishment of DT, in this system, is related to a programmed reversion from a metabolic active to a quiescent state similar to prior to germination. Our findings show that transcripts of germinated seeds after the PEG-treatment are dominated by those encoding LEA, seed storage and dormancy related proteins. On the other hand, a massive repression of genes belonging to many other classes such as photosynthesis, cell wall modification and energy metabolism occurs in parallel. Furthermore, comparison with a similar system for Medicago truncatula reveals a significant overlap between the two transcriptomes. Such overlap may highlight core mechanisms and key regulators of the trait DT. Taking into account the availability of the many genetic and molecular resources for Arabidopsis, the described system may prove useful for unraveling DT in higher plants.

Plants possess many metabolic genes for the production of a wide variety of phytochemicals in a tissue-specific manner. However, the metabolic systems behind the diversity and tissue-dependent regulation still remain unknown due to incomplete characterization of phytochemicals produced in a single plant species. Thus, having a metabolome dataset in addition to the genome and transcriptome information resources would enrich our knowledge of plant secondary metabolism. Here we analyzed phytochemical accumulation during development of the model plant Arabidopsis (Arabidopsis thaliana) using liquid chromatography-mass spectrometry in samples covering many growth stages and organs. We also obtained tandem mass spectrometry spectral tags of many metabolites as a resource for elucidation of metabolite structure. These are part of the AtMetExpress metabolite accumulation atlas. Based on the dataset, we detected 1,589 metabolite signals from which the structures of 167 metabolites were elucidated. The integrated analyses with transcriptome data demonstrated that Arabidopsis produces various phytochemicals in a highly tissue-specific manner, which often accompanies the expression of key biosynthesis-related genes. We also found that a set of biosynthesis-related genes is coordinately expressed among the tissues. These data suggested that the simple mode of regulation, transcript to metabolite, is an origin of the dynamics and diversity of plant secondary metabolism.