Summary-Analysis of a synthetic ABA agonist uncovers a new family of ABA binding proteins that control signal transduction by directly regulating the activity of type 2C protein phosphatases.-PP2Cs are vital phosphatases that play important roles in abscisic acid (ABA) signaling. Using chemical genetics, we previously identified a synthetic growth inhibitor called pyrabactin. Here we show that pyrabactin is a selective ABA agonist that acts through 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 therefore suggest that PYR/PYLs are ABA-receptors that function at the apex of a negative regulatory pathway that controls ABA signaling by inhibiting PP2Cs. Our results

BackgroundThe 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.Methodology/Principal FindingsIn 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).Conclusions/SignificanceThe 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.

The coordinated expression of highly related homoeologous genes in polyploid species underlies the phenotypes of many of the world's major crops. Here we combine extensive gene expression datasets to produce a comprehensive, genome-wide analysis of homoeolog expression patterns in hexaploid bread wheat. Bias in homoeolog expression varies between tissues, with ~30% of wheat homoeologs showing nonbalanced expression. We found expression asymmetries along wheat chromosomes, with homoeologs showing the largest inter-tissue, inter-cultivar, and coding sequence variation, most often located in high-recombination distal ends of chromosomes. These transcriptionally dynamic genes potentially represent the first steps toward neo- or subfunctionalization of wheat homoeologs. Coexpression networks reveal extensive coordination of homoeologs throughout development and, alongside a detailed expression atlas, provide a framework to target candidate genes underpinning agronomic traits in wheat.

We have analyzed the maize leaf transcriptome using Illumina sequencing. We mapped more than 120 million reads to define gene structure and alternative splicing events and to quantify transcript abundance along a leaf developmental gradient and in mature bundle sheath and mesophyll cells. We detected differential mRNA processing events for most maize genes. We found that 64% and 21% of genes were differentially expressed along the developmental gradient and between bundle sheath and mesophyll cells, respectively. We implemented Gbrowse, an electronic fluorescent pictograph browser, and created a two-cell biochemical pathway viewer to visualize datasets. Cluster analysis of the data revealed a dynamic transcriptome, with transcripts for primary cell wall and basic cellular metabolism at the leaf base transitioning to transcripts for secondary cell wall biosynthesis and C(4) photosynthetic development toward the tip. This dataset will serve as the foundation for a systems biology approach to the understanding of photosynthetic development.

SummaryThe Botany Array Resource provides the means for obtaining and archiving microarray data for Arabidopsis thaliana as well as biologist-friendly tools for viewing and mining both our own and other's data, for example, from the AtGenExpress Consortium. All the data produced are publicly available through the web interface of the database at http://bbc.botany.utoronto.ca. The database has been designed in accordance with the Minimum Information About a Microarray Experiment [Brazma, A., Hingamp, P., Quackenbush, J. et al. (2001) Minimum information about a microarray experiment (MIAME) -toward standards for microarray data. Nat. Genet. 29, 365] convention -all expression data are associated with the corresponding experimental details. The database is searchable and it also provides a set of useful and easy-to-use web-based data-mining tools for researchers with sophisticated yet understandable output graphics. These include Expression Browser for performing 'electronic Northerns', Expression Angler for identifying genes that are co-regulated with a gene of interest, and Promomer for identifying potential cis-elements in the promoters of individual or co-regulated genes.