ATTED-II provides coexpressed gene networks for Arabidopsis

ATTED-II (http://atted.jp) is a database of gene coexpression in Arabidopsis that can be used to design a wide variety of experiments, including the prioritization of genes for functional identification or for studies of regulatory relationships. Here, we report updates of ATTED-II that focus especially on functionalities for constructing gene networks with regard to the following points: (i) introducing a new measure of gene coexpression to retrieve functionally related genes more accurately, (ii) implementing clickable maps for all gene networks for step-by-step navigation, (iii) applying Google Maps API to create a single map for a large network, (iv) including information about protein–protein interactions, (v) identifying conserved patterns of coexpression and (vi) showing and connecting KEGG pathway information to identify functional modules. With these enhanced functions for gene network representation, ATTED-II can help researchers to clarify the functional and regulatory networks of genes in Arabidopsis

COXPRESdb: a database of coexpressed gene networks in mammals

A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein–protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species

Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

Information regarding gene coexpression is useful to predict gene function. Several databases have been constructed for gene coexpression in model organisms based on a large amount of publicly available gene expression data measured by GeneChip platforms. In these databases, Pearson's correlation coefficients (PCCs) of gene expression patterns are widely used as a measure of gene coexpression. Although the coexpression measure or GeneChip summarization method affects the performance of the gene coexpression database, previous studies for these calculation procedures were tested with only a small number of samples and a particular species. To evaluate the effectiveness of coexpression measures, assessments with large-scale microarray data are required. We first examined characteristics of PCC and found that the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function. In addition, we found that this problem could be overcome when we used correlation ranks instead of correlation values. This observation was evaluated by large-scale gene expression data for four species: Arabidopsis, human, mouse and rat

Coexpression Analysis of Tomato Genes and Experimental Verification of Coordinated Expression of Genes Found in a Functionally Enriched Coexpression Module

Gene-to-gene coexpression analysis is a powerful approach to infer the function of uncharacterized genes. Here, we report comprehensive identification of coexpression gene modules of tomato (Solanum lycopersicum) and experimental verification of coordinated expression of module member genes. On the basis of the gene-to-gene correlation coefficient calculated from 67 microarray hybridization data points, we performed a network-based analysis. This facilitated the identification of 199 coexpression modules. A gene ontology annotation search revealed that 75 out of the 199 modules are enriched with genes associated with common functional categories. To verify the coexpression relationships between module member genes, we focused on one module enriched with genes associated with the flavonoid biosynthetic pathway. A non-enzyme, non-transcription factor gene encoding a zinc finger protein in this module was overexpressed in S. lycopersicum cultivar Micro-Tom, and expression levels of flavonoid pathway genes were investigated. Flavonoid pathway genes included in the module were up-regulated in the plant overexpressing the zinc finger gene. This result demonstrates that coexpression modules, at least the ones identified in this study, represent actual transcriptional coordination between genes, and can facilitate the inference of tomato gene function

An “Electronic Fluorescent Pictograph” Browser for Exploring and Analyzing Large-Scale Biological Data Sets

Background. 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. Methodology/Principal Findings. 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 a

The Re-Establishment of Desiccation Tolerance in Germinated Arabidopsis thaliana Seeds and Its Associated Transcriptome

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

Altered Germination and Subcellular Localization Patterns for PUB44/SAUL1 in Response to Stress and Phytohormone Treatments

BACKGROUND: In plants, the ubiquitin-proteasome system is emerging as a significant regulatory system throughout the plant lifecycle. The ubiquitination of a target protein requires the sequential actions of the E1, E2 and E3 enzymes, with the latter E3 enzyme conferring target selection in this process. There are a large number of predicted E3 enzymes in plant genomes, and very little is known about the functions of many of these predicted genes. Here we report here an analysis of two closely-related members of the Arabidopsis Plant U-box (PUB) family of E3 ubiquitin ligases, PUB43 and PUB44. PRINCIPAL FINDINGS: Homozygous pub44/pub44 mutant seedlings were found displayed a seedling lethal phenotype and this corresponded with widespread cell death lesions throughout the cotyledons and roots. Interestingly, heterozygous PUB44/pub44 seedlings were wild-type in appearance yet displayed intermediate levels of cell death lesions in comparison to pub44/pub44 seedlings. In contrast, homozygous pub43/pub43 mutants were viable and did not show any signs of cell death despite the PUB43 gene being more highly expressed than PUB44. The PUB44 mutants are not classical lesion mimic mutants as they did not have increased resistance to plant pathogens. We also observed increased germination rates in mutant seeds for both PUB44 and PUB43 under inhibitory concentrations of abscisic acid. Finally, the subcellular localization of PUB44 was investigated with transient expression assays in BY-2 cells. Under varying conditions, PUB44 was observed to be localized to the cytoplasm, plasma membrane, or nucleus. CONCLUSIONS: Based on mutant plant analyses, the Arabidopsis PUB43 and PUB44 genes are proposed to function during seed germination and early seedling growth. Given PUB44's ability to shuttle from the nucleus to the plasma membrane, PUB44 may be active in different subcellular compartments as part of these biological functions

Direct targets of the transcription factors ABA-Insensitive(ABI)4 and ABI5 reveal synergistic action by ABI4 and several bZIP ABA response factors

The plant hormone abscisic acid (ABA) is a key regulator of seed development. In addition to promoting seed maturation, ABA inhibits seed germination and seedling growth. Many components involved in ABA response have been identified, including the transcription factors ABA insensitive (ABI)4 and ABI5. The genes encoding these factors are expressed predominantly in developing and mature seeds, and are positive regulators of ABA mediated inhibition of seed germination and growth. The direct effects of ABI4 and ABI5 in ABA response remain largely undefined. To address this question, plants over-expressing ABI4 or ABI5 were used to allow identification of direct transcriptional targets. Ectopically expressed ABI4 and ABI5 conferred ABA-dependent induction of slightly over 100 genes in 11 day old plants. In addition to effector genes involved in seed maturation and reserve storage, several signaling proteins and transcription factors were identified as targets of ABI4 and/or ABI5. Although only 12% of the ABA- and ABI-dependent transcriptional targets were induced by both ABI factors in 11 day old plants, 40% of those normally expressed in seeds had reduced transcript levels in both abi4 and abi5 mutants. Surprisingly, many of the ABI4 transcriptional targets do not contain the previously characterized ABI4 binding motifs, the CE1 or S box, in their promoters, but some of these interact with ABI4 in electrophoretic mobility shift assays, suggesting that sequence recognition by ABI4 may be more flexible than known canonical sequences. Yeast one-hybrid assays demonstrated synergistic action of ABI4 with ABI5 or related bZIP factors in regulating these promoters, and mutant analyses showed that ABI4 and these bZIPs share some functions in plants

ePlant and the 3D Data Display Initiative: Integrative Systems Biology on the World Wide Web

Visualization tools for biological data are often limited in their ability to interactively integrate data at multiple scales. These computational tools are also typically limited by two-dimensional displays and programmatic implementations that require separate configurations for each of the user's computing devices and recompilation for functional expansion. Towards overcoming these limitations we have developed “ePlant” (http://bar.utoronto.ca/eplant) – a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. These tools display data spanning multiple biological scales on interactive three-dimensional models. Currently, ePlant consists of the following modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. The ePlant's protein structure explorer module represents experimentally determined and theoretical structures covering >70% of the Arabidopsis proteome. The ePlant framework is accessed entirely through a web browser, and is therefore platform-independent. It can be applied to any model organism. To facilitate the development of three-dimensional displays of biological data on the world wide web we have established the “3D Data Display Initiative” (http://3ddi.org)

Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes

With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa) gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional annotation of those modules. Additionally, the expression patterns of genes across the treatments/conditions of an expression experiment comprise a second form of useful annotation