CressExpress: a tool for large-scale mining of expression data from Arabidopsis

CressExpress is a user-friendly, online, coexpression analysis tool for Arabidopsis (Arabidopsis thaliana) microarray expression data that computes patterns of correlated expression between user-entered query genes and the rest of the genes in the genome. Unlike other coexpression tools, CressExpress allows characterization of tissue-specific coexpression networks through user-driven filtering of input data based on sample tissue type. CressExpress also performs pathway-level coexpression analysis on each set of query genes, identifying and ranking genes based on their common connections with two or more query genes. This allows identification of novel candidates for involvement in common processes and functions represented by the query group. Users launch experiments using an easy-to-use Web-based interface and then receive the full complement of results, along with a record of tool settings and parameters, via an e-mail link to the CressExpress Web site. Data sets featured in CressExpress are strictly versioned and include expression data from MAS5, GCRMA, and RMA array processing algorithms. To demonstrate applications for CressExpress, we present coexpression analyses of cellulose synthase genes, indolic glucosinolate biosynthesis, and flowering. We show that subselecting sample types produces a richer network for genes involved in flowering in Arabidopsis. CressExpress provides direct access to expression values via an easy-to-use URL-based Web service, allowing users to determine quickly if their query genes are coexpressed with each other and likely to yield informative pathway-level coexpression results. The tool is available at http://www.cressexpress.org.     

Microarray expression profiling and functional characterization of AtTPS genes: duplicated Arabidopsis thaliana sesquiterpene synthase genes At4g13280 and At4g13300 encode root-specific and wound-inducible (Z)-gamma-bisabolene synthases

The Arabidopsis thaliana genome contains at least 32 terpenoid synthase (AtTPS) genes [Aubourg et al., Mol. Genet. Genom. 267 (2002) 730] a few of which have recently been characterized. Based on hierarchical cluster analysis of AtTPS gene expression, measured by microarray profiling and validated with published expression data, we identified two groups of predominantly root expressed AtTPS genes containing five members with previously unknown biochemical functions (At4g13280, At4g13300, At5g48110, At1g33750, and At3g29410). Among the root expressed AtTPS genes, a pair of tandem-organized genes, At4g13280 (AtTPS12) and At4g13300 (AtTPS13), shares 91% predicted amino acid identity indicating recent gene duplication. Bacterial expression of cDNAs and enzyme assays showed that both At4g13280 and At4g13300 encode sesquiterpene synthases catalyzing the conversion of farnesyl diphosphate to (Z)-gamma-bisabolene and the additional minor products E-nerolidol and alpha-bisabolol. Expression of beta-glucuronidase (GUS) reporter gene fused to upstream genomic regions of At4g13280 or At4g13300 showed constitutive promoter activities in the cortex and sub-epidermal layers of Arabidopsis roots. In addition, highly localized promoter activities were found in leaf hydathodes and flower stigmata. Mechanical wounding of Arabidopsis leaves induced local expression of At4g13280 and At4g13300. The functional characterization of At4g13280 gene product AtTPS12 and At4g13230 gene product AtTPS13 as (Z)-gamma-bisabolene synthases, together with the recent characterization of two flower-specific AtTPS [At5g23960 and At5g44630; Tholl et al., Plant J. 42 (2005) 757], concludes the biochemical functional annotation of all four predicted Arabidopsis sesquiterpene synthase genes. Our data suggest biological functions for At4g13280 and At4g13300 in the rhizosphere with additional roles in aerial plant tissues.     

ATTED-II updates: condition-specific gene coexpression to extend coexpression analyses and applications to a broad range of flowering plants

ATTED-II (http://atted.jp) is a gene coexpression database for a wide variety of experimental designs, such as prioritizations of genes for functional identification and analyses of the regulatory relationships among genes. Here, we report updates of ATTED-II focusing on two new features: condition-specific coexpression and homologous coexpression with rice. To analyze a broad range of biological phenomena, it is important to collect data under many diverse experimental conditions, but the meaning of coexpression can become ambiguous under these conditions. One approach to overcome this difficulty is to calculate the coexpression for each set of conditions with a clear biological meaning. With this viewpoint, we prepared five sets of experimental conditions (tissue, abiotic stress, biotic stress, hormones and light conditions), and users can evaluate the coexpression by employing comparative gene lists and switchable gene networks. We also developed an interactive visualization system, using the Cytoscape web system, to improve the network representation. As the second update, rice coexpression is now available. The previous version of ATTED-II was specifically developed for Arabidopsis, and thus coexpression analyses for other useful plants have been difficult. To solve this problem, we extended ATTED-II by including comparison tables between Arabidopsis and rice. This representation will make it possible to analyze the conservation of coexpression among flowering plants. With the ability to investigate condition-specific coexpression and species conservation, ATTED-II can help researchers to clarify the functional and regulatory networks of genes in a broad array of plant species.     

Microarray expression profiling resources for plant genomics

Large volumes of genomic data have been generated for several plant species over the past decade, including structural sequence data and functional annotation at the genome level. Various technologies such as expressed sequence tags (ESTs), massively parallel signature sequencing (MPSS) and microarrays have been used to study gene expression and to provide functional data for many genes simultaneously. This review focuses on recent advances in the application of microarrays in plant genomic research and in gene expression databases available for plants. Large sets of Arabidopsis microarray data are publicly available. Recently developed array platforms are currently being used to generate genome-wide expression profiles for several crop species. Coupled to these platforms are public databases that provide access to these large-scale expression data, which can be used to aid the functional discovery of gene function.     

Annotating genes of known and unknown function by large-scale coexpression analysis

About 40% of the proteins encoded in eukaryotic genomes are proteins of unknown function (PUFs). Their functional characterization remains one of the main challenges in modern biology. In this study we identified the PUF encoding genes from Arabidopsis (Arabidopsis thaliana) using a combination of sequence similarity, domain-based, and empirical approaches. Large-scale gene expression analyses of 1,310 publicly available Affymetrix chips were performed to associate the identified PUF genes with regulatory networks and biological processes of known function. To generate quality results, the study was restricted to expression sets with replicated samples. First, genome-wide clustering and gene function enrichment analysis of clusters allowed us to associate 1,541 PUF genes with tightly coexpressed genes for proteins of known function (PKFs). Over 70% of them could be assigned to more specific biological process annotations than the ones available in the current Gene Ontology release. The most highly overrepresented functional categories in the obtained clusters were ribosome assembly, photosynthesis, and cell wall pathways. Interestingly, the majority of the PUF genes appeared to be controlled by the same regulatory networks as most PKF genes, because clusters enriched in PUF genes were extremely rare. Second, large-scale analysis of differentially expressed genes was applied to identify a comprehensive set of abiotic stress-response genes. This analysis resulted in the identification of 269 PKF and 104 PUF genes that responded to a wide variety of abiotic stresses, whereas 608 PKF and 206 PUF genes responded predominantly to specific stress treatments. The provided coexpression and differentially expressed gene data represent an important resource for guiding future functional characterization experiments of PUF and PKF genes. Finally, the public Plant Gene Expression Database (http://bioweb.ucr.edu/PED) was developed as part of this project to provide efficient access and mining tools for the vast gene expression data of this study.     

Coordinated Expression Domains in Mammalian Genomes

Background

Gene order in eukaryotic genomes is not random. Genes showing similar expression (coexpression) patterns are often clustered along the genome. The goal of this study is to characterize coexpression clustering in mammalian genomes and to investigate the underlying mechanisms.

Methodology/Principal Findings

We detect clustering of coexpressed genes across multiple scales, from neighboring genes to chromosomal domains that span tens of megabases and, in some cases, entire chromosomes. Coexpression domains may be positively or negatively correlated with other domains, within and between chromosomes. We find that long-range expression domains are associated with gene density, which in turn is related to physical organization of the chromosomes within the nucleus. We show that gene expression changes between healthy and diseased tissue samples occur in a gene density-dependent manner.

Conclusions/Significance

We demonstrate that coexpression domains exist across multiple scales. We identify potential mechanisms for short-range as well as long-range coexpression domains. We provide evidence that the three-dimensional architecture of the chromosomes may underlie long-range coexpression domains. Chromosome territory reorganization may play a role in common human diseases such as Alzheimer''s disease and psoriasis.     

Transcriptional divergence of the duplicated oxidative stress-responsive genes in the Arabidopsis genome

Previous studies have indicated that Arabidopsis thaliana experienced a genome-wide duplication event shortly before its divergence from Brassica followed by extensive chromosomal rearrangements and deletions. While a large number of the duplicated genes have significantly diverged or lost their sister genes, we found 4222 pairs that are still highly conserved, and as a result had similar functional assignments during the annotation of the genome sequence. Using whole-genome DNA microarrays, we identified 906 duplicated gene pairs in which at least one member exhibited a significant response to oxidative stress. Among these, only 117 pairs were up- or down-regulated in both pairs and many of these exhibited dissimilar patterns of expression. Examination of the expression patterns of PAL1 and PAL2, ACD1 and ACD2, genes coding for two Hsp20s, various P450s, and electron transfer flavoproteins suggests Arabidopsis evolved a number of distinct oxidative stress response mechanisms using similar gene sets following the duplication of its genome.     

Islands of co-expressed neighbouring genes in Arabidopsis thaliana suggest higher-order chromosome domains

Biochemical and cytogenetic experiments have led to the hypothesis that eukaryotic chromatin is organized into a series of distinct domains that are functionally independent. Two expectations of this hypothesis are: (i) adjacent genes are more frequently co-expressed than is expected by chance; and (ii) co-expressed neighbouring genes are often functionally related. Here we report that over 10% of Arabidopsis thaliana genes are within large, co-expressed chromosomal regions. Two per cent (497/22,520) of genes are highly co-expressed (r > 0.7), about five times the number expected by chance. These genes fall into 226 groups distributed across the genome, and each group typically contains two to three genes. Among the highly co-expressed groups, 40% (91/226) have genes with high amino acid sequence similarity. Nonetheless, duplicate genes alone do not explain the observed levels of co-expression. Co-expressed, non-homologous genes are transcribed in parallel, share functions, and lie close together more frequently than expected. Our results show that the A. thaliana genome contains domains of gene expression. Small domains have highly co-expressed genes that often share functional and sequence similarity and are probably co-regulated by nearby regulatory sequences. Genes within large, significantly correlated groups are typically co-regulated at a low level, suggesting the presence of large chromosomal domains.     

Computing gene expression data with a knowledge-based gene clustering approach

Computational analysis methods for gene expression data gathered in microarray experiments can be used to identify the functions of previously unstudied genes. While obtaining the expression data is not a difficult task, interpreting and extracting the information from the datasets is challenging. In this study, a knowledge-based approach which identifies and saves important functional genes before filtering based on variability and fold change differences was utilized to study light regulation. Two clustering methods were used to cluster the filtered datasets, and clusters containing a key light regulatory gene were located. The common genes to both of these clusters were identified, and the genes in the common cluster were ranked based on their coexpression to the key gene. This process was repeated for 11 key genes in 3 treatment combinations. The initial filtering method reduced the dataset size from 22,814 probes to an average of 1134 genes, and the resulting common cluster lists contained an average of only 14 genes. These common cluster lists scored higher gene enrichment scores than two individual clustering methods. In addition, the filtering method increased the proportion of light responsive genes in the dataset from 1.8% to 15.2%, and the cluster lists increased this proportion to 18.4%. The relatively short length of these common cluster lists compared to gene groups generated through typical clustering methods or coexpression networks narrows the search for novel functional genes while increasing the likelihood that they are biologically relevant.