Coexpression landscape in ATTED-II: usage of gene list and gene network for various types of pathways

Approaches in Gene Coexpression Analysis in Eukaryotes

Simple Summary

Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it.

Abstract

Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.

Transcriptome-based approaches for clarification of nutritional responses and improvement of crop production

Genome-wide transcriptome profiling is a powerful tool for identifying key genes and pathways involved in plant development and physiological processes. This review summarizes studies that have used transcriptome profiling mainly in rice to focus on responses to macronutrients such as nitrogen, phosphorus and potassium, and spatio-temporal root profiling in relation to the regulation of root system architecture as well as nutrient uptake and transport. We also discuss strategies based on meta- and co-expression analyses with different attributed transcriptome data, which can be used for investigating the regulatory mechanisms and dynamics of nutritional responses and adaptation, and speculate on further advances in transcriptome profiling that could have potential application to crop breeding and cultivation.

COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference

The advent of RNA-sequencing and microarray technologies has led to rapid growth of transcriptome data generated for a wide range of organisms, under various cellular, organ and individual conditions. Since the number of possible combinations of intercellular and extracellular conditions is almost unlimited, cataloging all transcriptome conditions would be an immeasurable challenge. Gene coexpression refers to the similarity of gene expression patterns under various conditions, such as disease states, tissue types, and developmental stages. Since the quality of gene coexpression data depends on the quality and quantity of transcriptome data, timely usage of the growing data is key to promoting individual research in molecular biology. COXPRESdb (http://coxpresdb.jp) is a database providing coexpression information for 11 animal species. One characteristic feature of COXPRESdb is its ability to compare multiple coexpression data derived from different transcriptomics technologies and different species, which strongly reduces false positive relationships in individual gene coexpression data. Here, we summarized the current version of this database, including 23 coexpression platforms with the highest-level quality till date. Using various functionalities in COXPRESdb, the new coexpression data would support a broader area of research from molecular biology to medical sciences.

Genetic Contribution of Paleopolyploidy to Adaptive Evolution in Angiosperms

Ancient whole-genome duplications (WGDs or polyploidy) are prevalent in plants, and some WGDs occurred during the timing of severe global environmental changes. It has been suggested that WGDs may have contributed to plant adaptation. However, this still lacks empirical evidence at the genetic level to support the hypothesis. Here, we investigated the survivors of gene duplicates from multiple ancient WGD events on the major branches of angiosperm phylogeny, and aimed to explore genetic evidence supporting the significance of polyploidy. Duplicated genes co-retained from three waves of independent WGDs (∼120 million years ago [Ma], ∼66, and <20 Ma) were investigated in 25 selected species. Gene families functioning in low temperature and darkness were commonly retained gene duplicates after the eight independently occurring WGDs in many lineages around the Cretaceous-Paleocene boundary, when the global cooling and darkness were the two main stresses. Moreover, the commonly retained duplicates could be key factors which may have contributed to the robustness of the critical stress-related pathways. In addition, genome-wide transcription factors (TFs) functioning in stresses tend to retain duplicates after waves of WGDs, and the coselected gene duplicates in many lineages may play critical roles during severe environmental stresses. Collectively, these results shed new light on the significant contribution of paleopolyploidy to plant adaptation during global environmental changes in the evolutionary history of angiosperms.

The Arabidopsis thaliana gene annotated by the locus tag At3g08860 encodes alanine aminotransferase

The aminotransferase gene family in the model plant Arabidopsis thaliana consists of 44 genes, eight of which are suggested to be alanine aminotransferases. One of the putative alanine aminotransferases genes, At3g08860, was attributed the function of alanine:glyoxylate aminotransferase/β-alanine:pyruvate aminotransferase based on the analysis of gene expression networks and homology to other β-alanine aminotransferases in plants. It was earlier demonstrated that At3g08860 is specifically upregulated in response to osmotic stress, but not other stresses (β-alanine is an osmoprotectant in plants). Furthermore, it was shown that the expression of At3g08860 is highly coordinated with the genes of the uracil degradation pathway leading to the non-proteinogenic amino acid β-alanine. These evidence were suggestive of the involvement of At3g08860 in β-alanine metabolism. However, direct experimental evidence for the function of At3g08860 was lacking, and therefore, the goal of this study was to elucidate the function of the uncharacterized aminotransferase annotated by the locus tag At3g08860. The cDNA of At3g08860 was demonstrated to functionally complement two E. coli mutants auxotrophic for the amino acids, L-alanine (proteinogenic) and β-alanine (non-proteinogenic). Enzyme activity using purified recombinant At3g08860 further demonstrated that the enzyme is endowed with L-alanine:glyoxylate aminotransferase activity.

Sulphur systems biology-making sense of omics data

Systems biology approaches have been applied over the last two decades to study plant sulphur metabolism. These 'sulphur-omics' approaches have been developed in parallel with the advancing field of systems biology, which is characterized by permanent improvements of high-throughput methods to obtain system-wide data. The aim is to obtain a holistic view of sulphur metabolism and to generate models that allow predictions of metabolic and physiological responses. Besides known sulphur-responsive genes derived from previous studies, numerous genes have been identified in transcriptomics studies. This has not only increased our knowledge of sulphur metabolism but has also revealed links between metabolic processes, thus indicating a previously unexpected complex interconnectivity. The identification of response and control networks has been supported through metabolomics and proteomics studies. Due to the complex interlacing nature of biological processes, experimental validation using targeted or systems approaches is ongoing. There is still room for improvement in integrating the findings from studies of metabolomes, proteomes, and metabolic fluxes into a single unifying concept and to generate consistent models. We therefore suggest a joint effort of the sulphur research community to standardize data acquisition. Furthermore, focusing on a few different model plant systems would help overcome the problem of fragmented data, and would allow us to provide a standard data set against which future experiments can be designed and compared.

Combinatorial Regulation of Stilbene Synthase Genes by WRKY and MYB Transcription Factors in Grapevine (Vitis vinifera L.)

Stilbene synthase (STS) is the key enzyme leading to the biosynthesis of resveratrol. Recently we reported two R2R3-MYB transcription factor (TF) genes that regulate the stilbene biosynthetic pathway in grapevine: VviMYB14 and VviMYB15. These genes are strongly co-expressed with STS genes under a range of stress and developmental conditions, in agreement with the specific activation of STS promoters by these TFs. Genome-wide gene co-expression analysis using two separate transcriptome compendia based on microarray and RNA sequencing data revealed that WRKY TFs were the top TF family correlated with STS genes. On the basis of correlation frequency, four WRKY genes, namely VviWRKY03, VviWRKY24, VviWRKY43 and VviWRKY53, were further shortlisted and functionally validated. Expression analyses under both unstressed and stressed conditions, together with promoter-luciferase reporter assays, suggested different hierarchies for these TFs in the regulation of the stilbene biosynthetic pathway. In particular, VviWRKY24 seems to act as a singular effector in the activation of the VviSTS29 promoter, while VviWRKY03 acts through a combinatorial effect with VviMYB14, suggesting that these two regulators may interact at the protein level as previously reported in other species.

ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index

ATTED-II (http://atted.jp) is a coexpression database for plant species to aid in the discovery of relationships of unknown genes within a species. As an advanced coexpression analysis method, multispecies comparisons have the potential to detect alterations in gene relationships within an evolutionary context. However, determining the validity of comparative coexpression studies is difficult without quantitative assessments of the quality of coexpression data. ATTED-II (version 9) provides 16 coexpression platforms for nine plant species, including seven species supported by both microarray- and RNA sequencing (RNAseq)-based coexpression data. Two independent sources of coexpression data enable the assessment of the reproducibility of coexpression. The latest coexpression data for Arabidopsis (Ath-m.c7-1 and Ath-r.c3-0) showed the highest reproducibility (Jaccard coefficient = 0.13) among previous coexpression data in ATTED-II. We also investigated the statistical basis of the mutual rank (MR) index as a coexpression measure by bootstrap sampling of experimental units. We found that the error distribution of the logit-transformed MR index showed normality with equal variances for each coexpression platform. Because the MR error was strongly correlated with the number of samples for the coexpression data, typical confidence intervals for the MR index can be estimated for any coexpression platform. These new, high-quality coexpression data can be analyzed with any tool in ATTED-II and combined with external resources to obtain insight into plant biology.

Altered lignification in mur1-1 a mutant deficient in GDP-L-fucose synthesis with reduced RG-II cross linking

In the plant cell wall, boron links two pectic domain rhamnogalacturonan II (RG-II) chains together to form a dimer and thus contributes to the reinforcement of cell adhesion. We studied the mur1-1 mutant of Arabidopsis thaliana which has lost the ability to form GDP-fucose in the shoots and show that the extent of RG-II cross-linking is reduced in the lignified stem of this mutant. Surprisingly, MUR1 mutation induced an enrichment of resistant interunit bonds in lignin and triggered the overexpression of many genes involved in lignified tissue formation and in jasmonic acid signaling. The defect in GDP-fucose synthesis induced a loss of cell adhesion at the interface between stele and cortex, as well as between interfascicular fibers. This led to the formation of regenerative xylem, where tissue detachment occurred, and underlined a loss of resistance to mechanical forces. Similar observations were also made on bor1-3 mutant stems which are altered in boron xylem loading, leading us to suggest that diminished RG-II dimerization is responsible for regenerative xylem formation.

GENIUS: web server to predict local gene networks and key genes for biological functions

Summary

GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods.

Availability and Implementation

GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of MAP kinase MPK4/MEKK1/MKK genes of Carica papaya L. comparative to other plant homologues

Mitogen-activated protein kinase 4 (MPK4) interacts with the (Mitogen-activated protein kinase kinase kinase 1) MEKK1/ Mitogenactivated protein kinase kinase 1 (MKK1)/ Mitogen-activated protein kinase kinase 2 (MKK2) complex to affect its function in plant development or against pathogen attacks. The KEGG (Kyoto Encyclopedia of Genes and Genomes) network analysis of Arabidopsis thaliana revealed close interactions between those four genes in the same plant-pathogen interaction pathway, which warrants further study of these genes due to their evolutionary conservation in different plant species. Through targeting the signature sequence in MPK4 of papaya using orthologs from Arabidopsis, the predicted sequence of MPK4 was studied using a comparative in silico approach between different plant species and the MAP cascade complex of MEKK1/MKK1/MKK2. This paper reported that MPK4 was highly conserved in papaya with 93% identical across more than 500 bases compared in each species predicted. Slight variations found in the MEKK1/MKK1/MKK2 complex nevertheless still illustrated sequence similarities between most of the species. Localization of each gene in the cascade network was also predicted, potentiating future functional verification of these genes interactions using knock out or/and gene silencing tactics.

ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression

ATTED-II (http://atted.jp) is a coexpression database for plant species with parallel views of multiple coexpression data sets and network analysis tools. The user can efficiently find functional gene relationships and design experiments to identify gene functions by reverse genetics and general molecular biology techniques. Here, we report updates to ATTED-II (version 8.0), including new and updated coexpression data and analysis tools. ATTED-II now includes eight microarray- and six RNA sequencing-based coexpression data sets for seven dicot species (Arabidopsis, field mustard, soybean, barrel medick, poplar, tomato and grape) and two monocot species (rice and maize). Stand-alone coexpression analyses tend to have low reliability. Therefore, examining evolutionarily conserved coexpression is a more effective approach from the viewpoints of reliability and evolutionary importance. In contrast, the reliability of species-specific coexpression data remains poor. Our assessment scores for individual coexpression data sets indicated that the quality of the new coexpression data sets in ATTED-II is higher than for any previous coexpression data set. In addition, five species (Arabidopsis, soybean, tomato, rice and maize) in ATTED-II are now supported by both microarray- and RNA sequencing-based coexpression data, which has increased the reliability. Consequently, ATTED-II can now provide lineage-specific coexpression information. As an example of the use of ATTED-II to explore lineage-specific coexpression, we demonstrate monocot- and dicot-specific coexpression of cell wall genes. With the expanded coexpression data for multilevel evaluation, ATTED-II provides new opportunities to investigate lineage-specific evolution in plants.

Coexpression Network Analysis of Macronutrient Deficiency Response Genes in Rice

BACKGROUND

Macronutrients are pivotal elements for proper plant growth and development. Although extensive gene expression profiling revealed a large number of genes differentially expressed under various nutrient deprivation, characterization of these genes has never been fully explored especially in rice. Coexpression network analysis is a useful tool to elucidate the functional relationships of genes based on common expression. Therefore, we performed microarray analysis of rice shoot under nitrogen (N), phosphorus (P), and potassium (K) deficiency conditions. Moreover, we conducted a large scale coexpression analysis by integrating the data with previously generated gene expression profiles of organs and tissues at different developmental stages to obtain a global view of gene networks associated with plant response to nutrient deficiency.

RESULTS

We statistically identified 5400 differentially expressed genes under the nutrient deficiency treatments. Subsequent coexpression analysis resulted in the extraction of 6 modules (groups of highly interconnected genes) with distinct gene expression signatures. Three of these modules comprise mostly of downregulated genes under N deficiency associated with distinct functions such as development of immature organs, protein biosynthesis and photosynthesis in chloroplast of green tissues, and fundamental cellular processes in all organs and tissues. Furthermore, we identified one module containing upregulated genes under N and K deficiency conditions, and a number of genes encoding protein kinase, kinase-like domain containing protein and nutrient transporters. This module might be particularly involved in adaptation to nutrient deficiency via phosphorylation-mediated signal transduction and/or post-transcriptional regulation.

CONCLUSIONS

Our study demonstrated that large scale coexpression analysis is an efficient approach in characterizing the nutrient response genes based on biological functions and could provide new insights in understanding plant response to nutrient deficiency.

Phylogeny and evolution of plant macrophage migration inhibitory factor/D-dopachrome tautomerase-like proteins

Background

The human (Homo sapiens) chemokine-like protein macrophage migration inhibitory factor (HsMIF) is a pivotal mediator of inflammatory, infectious and immune diseases including septic shock, colitis, malaria, rheumatoid arthritis, and atherosclerosis, as well as tumorigenesis. HsMIF has been found to exhibit several sequential and three-dimensional sequence motifs that in addition to its receptor binding sites include catalytic sites for oxidoreductase and tautomerase activity, which provide this 12.5 kDa protein with a remarkable functional complexity. A human MIF paralog, D-dopachrome tautomerase (HsDDT), has been identified, but its physiological relevance is incompletely understood. MIF/DDT-like proteins have been described in animals, protists and bacteria. Although based on sequence data banks the presence of MIF/DDT-like proteins has also been recognized in the model plant species Arabidopsis thaliana, details on these plant proteins have not been reported.

Results

To broaden the understanding of the biological role of these proteins across kingdoms we performed a comprehensive in silico analysis of plant MIF/DDT-like (MDL) genes/proteins. We found that the A. thaliana genome harbors three MDL genes, of which two are chiefly constitutively expressed in aerial plant organs, while the third gene shows stress-inducible transcript accumulation. The product of the latter gene likely localizes to peroxisomes. Structure prediction suggests that all three Arabidopsis proteins resemble the secondary and tertiary structure of human MIF. MIF-like proteins are found in all species across the plant kingdom, with an increasing family complexity towards evolutionarily advanced plant taxa. Plant MDL proteins are predicted to lack oxidoreductase activity, but possibly share tautomerase activity with human MIF/DDT.

Conclusions

Peroxisome localization seems to be a specific feature of a subset of MIF/DDT orthologs found in dicotyledonous plant species, which together with its stress-inducible gene expression might point to convergent evolution in higher plants and vertebrates towards neofunctionalization of MIF/MDL proteins in stress response pathways including innate immunity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-015-0337-x) contains supplementary material, which is available to authorized users.

Physiological characterization and genetic modifiers of aberrant root thigmomorphogenesis in mutants of Arabidopsis thaliana MILDEW LOCUS O genes

Root architecture and growth patterns are plant features that are still poorly understood. When grown under in vitro conditions, seedlings with mutations in Arabidopsis thaliana genes MLO4 or MLO11 exhibit aberrant root growth patterns upon contact with hard surfaces, exemplified as tight root spirals. We used a set of physiological assays and genetic tools to characterize this thigmomorphogenic defect in detail. We observed that the mlo4/mlo11-associated root curling phenotype is not recapitulated in a set of mutants with altered root growth patterns or architecture. We further found that mlo4/mlo11-conditioned root curling is not dependent upon light and endogenous flavonoids, but is pH-sensitive and affected by exogenous calcium levels. Based upon the latter two characteristics, mlo4-associated root coiling appears to be mechanistically different from the natural strong root curvature of the Arabidopsis ecotype Landsberg erecta. Gravistimulation reversibly overrides the aberrant thigmomorphogenesis of mlo4 seedlings. Mutants with dominant negative defects in α-tubulin modulate the extent and directionality of mlo4/mlo11-conditioned root coils, whereas mutants defective in polar auxin transport (axr4, aux1) or gravitropism (pgm1) completely suppress the mlo4 root curling phenotype. Our data implicate a joint contribution of calcium signalling, pH regulation, microtubular function, polar auxin transport and gravitropism in root thigmomorphogenesis.

Transcriptional responses of Arabidopsis thaliana to chewing and sucking insect herbivores

We tested the hypothesis that Arabidopsis can recognize and respond differentially to insect species at the transcriptional level using a genome wide microarray. Transcriptional reprogramming was characterized using co-expression analysis in damaged and undamaged leaves at two times in response to mechanical wounding and four insect species. In all, 2778 (10.6%) of annotated genes on the array were differentially expressed in at least one treatment. Responses differed mainly between aphid and caterpillar and sampling times. Responses to aphids and caterpillars shared only 10% of up-regulated and 8% of down-regulated genes. Responses to two caterpillars shared 21 and 12% of up- and down-regulated genes, whereas responses to the two aphids shared only 7 and 4% of up-regulated and down-regulated genes. Overlap in genes expressed between 6 and 24 h was 3-15%, and depended on the insect species. Responses in attacked and unattacked leaves differed at 6 h but converged by 24 h. Genes responding to the insects are also responsive to many stressors and included primary metabolism. Aphids down-regulated amino acid catabolism; caterpillars stimulated production of amino acids involved in glucosinolate synthesis. Co-expression analysis revealed 17 response networks. Transcription factors were a major portion of differentially expressed genes throughout and responsive genes shared most of the known or postulated binding sites. However, cis-element composition of genes down regulated by the aphid M. persicae was unique, as were those of genes down-regulated by caterpillars. As many as 20 cis-elements were over-represented in one or more treatments, including some from well-characterized classes and others as yet uncharacterized. We suggest that transcriptional changes elicited by wounding and insects are heavily influenced by transcription factors and involve both enrichment of a common set of cis-elements and a unique enrichment of a few cis-elements in responding genes.

COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems

The COXPRESdb (http://coxpresdb.jp) provides gene coexpression relationships for animal species. Here, we report the updates of the database, mainly focusing on the following two points. For the first point, we added RNAseq-based gene coexpression data for three species (human, mouse and fly), and largely increased the number of microarray experiments to nine species. The increase of the number of expression data with multiple platforms could enhance the reliability of coexpression data. For the second point, we refined the data assessment procedures, for each coexpressed gene list and for the total performance of a platform. The assessment of coexpressed gene list now uses more reasonable P-values derived from platform-specific null distribution. These developments greatly reduced pseudo-predictions for directly associated genes, thus expanding the reliability of coexpression data to design new experiments and to discuss experimental results.

Annotation of gene function in citrus using gene expression information and co-expression networks

BACKGROUND

The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world's most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a "guilt-by-association" principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed.

RESULTS

We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit.

CONCLUSIONS

Integration of citrus gene co-expression networks, functional enrichment analysis and gene expression information provide opportunities to infer gene function in citrus. We present a publicly accessible tool, Network Inference for Citrus Co-Expression (NICCE, http://citrus.adelaide.edu.au/nicce/home.aspx), for the gene co-expression analysis in citrus.

Identification of Novel Peptidyl Serine α-Galactosyltransferase Gene Family in Plants

In plants, serine residues in extensin, a cell wall protein, are glycosylated with O-linked galactose. However, the enzyme that is involved in the galactosylation of serine had not yet been identified. To identify the peptidyl serine O-α-galactosyltransferase (SGT), we chose Chlamydomonas reinhardtii as a model. We established an assay system for SGT activity using C. reinhardtii and Arabidopsis thaliana cell extracts. SGT protein was partially purified from cell extracts of C. reinhardtii and analyzed by tandem mass spectrometry to determine its amino acid sequence. The sequence matched the open reading frame XP_001696927 in the C. reinhardtii proteome database, and a corresponding DNA fragment encoding 748 amino acids (BAL63043) was cloned from a C. reinhardtii cDNA library. The 748-amino acid protein (CrSGT1) was produced using a yeast expression system, and the SGT activity was examined. Hydroxylation of proline residues adjacent to a serine in acceptor peptides was required for SGT activity. Genes for proteins containing conserved domains were found in various plant genomes, including A. thaliana and Nicotiana tabacum. The AtSGT1 and NtSGT1 proteins also showed SGT activity when expressed in yeast. In addition, knock-out lines of AtSGT1 and knockdown lines of NtSGT1 showed no or reduced SGT activity. The SGT1 sequence, which contains a conserved DXD motif and a C-terminal membrane spanning region, is the first example of a glycosyltransferase with type I membrane protein topology, and it showed no homology with known glycosyltransferases, indicating that SGT1 belongs to a novel glycosyltransferase gene family existing only in the plant kingdom.

Database construction for PromoterCAD: synthetic promoter design for mammals and plants

Synthetic promoters can control a gene's timing, location, and expression level. The PromoterCAD web server ( http://promotercad.org ) allows the design of synthetic promoters to control plant gene expression, by novel arrangement of cis-regulatory elements. Recently, we have expanded PromoterCAD's scope with additional plant and animal data: (1) PLACE (Plant Cis-acting Regulatory DNA Elements), including various sized sequence motifs; (2) PEDB (Mammalian Promoter/Enhancer Database), including gene expression data for mammalian tissues. The plant PromoterCAD data now contains 22 000 Arabidopsis thaliana genes, 2 200 000 microarray measurements in 20 growth conditions and 79 tissue organs and developmental stages, while the new mammalian PromoterCAD data contains 679 Mus musculus genes and 65 000 microarray measurements in 96 tissue organs and cell types ( http://promotercad.org/mammal/ ). This work presents step-by-step instructions for adding both regulatory motif and gene expression data to PromoterCAD, to illustrate how users can expand PromoterCAD functionality for their own applications and organisms.

ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants

ATTED-II (http://atted.jp) is a database of coexpressed genes that was originally developed to identify functionally related genes in Arabidopsis and rice. Herein, we describe an updated version of ATTED-II, which expands this resource to include additional agriculturally important plants. To improve the quality of the coexpression data for Arabidopsis and rice, we included more gene expression data from microarray and RNA sequencing studies. The RNA sequencing-based coexpression data now cover 94% of the Arabidopsis protein-encoding genes, representing a substantial increase from previously available microarray-based coexpression data (76% coverage). We also generated coexpression data for four dicots (soybean, poplar, grape and alfalfa) and one monocot (maize). As both the quantity and quality of expression data for the non-model species are generally poorer than for the model species, we verified coexpression data associated with these new species using multiple methods. First, the overall performance of the coexpression data was evaluated using gene ontology annotations and the coincidence of a genomic feature. Secondly, the reliability of each guide gene was determined by comparing coexpressed gene lists between platforms. With the expanded and newly evaluated coexpression data, ATTED-II represents an important resource for identifying functionally related genes in agriculturally important plants.

Roles of JnRAP2.6-like from the transition zone of black walnut in hormone signaling

An EST sequence, designated JnRAP2-like, was isolated from tissue at the heartwood/sapwood transition zone (TZ) in black walnut (Juglans nigra L). The deduced amino acid sequence of JnRAP2-like protein consists of a single AP2-containing domain with significant similarity to conserved AP2/ERF DNA-binding domains in other species. Based on multiple sequence alignment, JnRAP2-like appears to be an ortholog of RAP2.6L (At5g13330), which encodes an ethylene response element binding protein in Arabidopsis thaliana. Real-time PCR revealed that the JnRAP2-like was expressed most abundantly in TZ of trees harvested in fall when compared with other xylem tissues harvested in the fall or summer. Independent transgenic lines over-expressing JnRAP2-like in Arabidopsis developed dramatic ethylene-related phenotypes when treated with 50 µM methyl jasmonate (MeJA). Taken together, these results indicated that JnRAP2-like may participate in the integration of ethylene and jasmonate signals in the xylem and other tissues. Given the role of ethylene in heartwood formation, it is possible JnRAP2-like expression in the transition zone is part of the signal transduction pathway leading to heartwood formation in black walnut.

Incorporating motif analysis into gene co-expression networks reveals novel modular expression pattern and new signaling pathways

Understanding of gene regulatory networks requires discovery of expression modules within gene co-expression networks and identification of promoter motifs and corresponding transcription factors that regulate their expression. A commonly used method for this purpose is a top-down approach based on clustering the network into a range of densely connected segments, treating these segments as expression modules, and extracting promoter motifs from these modules. Here, we describe a novel bottom-up approach to identify gene expression modules driven by known cis-regulatory motifs in the gene promoters. For a specific motif, genes in the co-expression network are ranked according to their probability of belonging to an expression module regulated by that motif. The ranking is conducted via motif enrichment or motif position bias analysis. Our results indicate that motif position bias analysis is an effective tool for genome-wide motif analysis. Sub-networks containing the top ranked genes are extracted and analyzed for inherent gene expression modules. This approach identified novel expression modules for the G-box, W-box, site II, and MYB motifs from an Arabidopsis thaliana gene co-expression network based on the graphical Gaussian model. The novel expression modules include those involved in house-keeping functions, primary and secondary metabolism, and abiotic and biotic stress responses. In addition to confirmation of previously described modules, we identified modules that include new signaling pathways. To associate transcription factors that regulate genes in these co-expression modules, we developed a novel reporter system. Using this approach, we evaluated MYB transcription factor-promoter interactions within MYB motif modules.

Gene co-expression networks unite genes with similar expression patterns. From these networks, gene co-expression modules can be identified. A specific family of transcription factor(s) may regulate the genes within a co-expression module. Thus, module identification is important to decipher the gene regulatory network. Previously, module identification relied on clustering the gene network into gene clusters that were then treated as modules. This represents a top-down approach. Here, we introduce a reverse approach aiming at identifying gene co-expression modules regulated by known promoter motifs. For a given promoter motif, we calculated the probability of each gene within the network to belong to a module regulated by that motif via motif enrichment analysis or motif position bias analysis. A sub-network containing the genes with a high probability of belonging to a motif driven module was then extracted from the gene co-expression network. From this sub-network, the modular structure can be identified via visual inspection. Our bottom-up approach recovered many known and novel modules for the G-box, MYB, W-box and site II elements motif, whose expression may be regulated by the transcription factors that bind to these motifs. Additionally, we developed a rapid transcription factor-promoter interaction screening system to validate predicted interactions.

MULTIPASS, a rice R2R3-type MYB transcription factor, regulates adaptive growth by integrating multiple hormonal pathways

Growth regulation is an important aspect of plant adaptation during environmental perturbations. Here, the role of MULTIPASS (OsMPS), an R2R3-type MYB transcription factor of rice, was explored. OsMPS is induced by salt stress and expressed in vegetative and reproductive tissues. Over-expression of OsMPS reduces growth under non-stress conditions, while knockdown plants display increased biomass. OsMPS expression is induced by abscisic acid and cytokinin, but is repressed by auxin, gibberellin and brassinolide. Growth retardation caused by OsMPS over-expression is partially restored by auxin application. Expression profiling revealed that OsMPS negatively regulates the expression of EXPANSIN (EXP) and cell-wall biosynthesis as well as phytohormone signaling genes. Furthermore, the expression of OsMPS-dependent genes is regulated by auxin, cytokinin and abscisic acid. Moreover, we show that OsMPS is a direct upstream regulator of OsEXPA4, OsEXPA8, OsEXPB2, OsEXPB3, OsEXPB6 and the endoglucanase genes OsGLU5 and OsGLU14. The multiple responses of OsMPS and its target genes to various hormones suggest an integrative function of OsMPS in the cross-talk between phytohormones and the environment to regulate adaptive growth.

PromoterCAD: Data-driven design of plant regulatory DNA

Synthetic promoters can control the timing, location and amount of gene expression for any organism. PromoterCAD is a web application for designing synthetic promoters with altered transcriptional regulation. We use a data-first approach, using published high-throughput expression and motif data from for Arabidopsis thaliana to guide DNA design. We demonstrate data mining tools for finding motifs related to circadian oscillations and tissue-specific expression patterns. PromoterCAD is built on the LinkData open platform for data publication and rapid web application development, allowing new data to be easily added, and the source code modified to add new functionality. PromoterCAD URL: http://promotercad.org. LinkData URL: http://linkdata.org.

Kinetics and functional diversity among the five members of the NADP-malic enzyme family from Zea mays, a C4 species

NADP-malic enzyme (NADP-ME) is involved in different metabolic pathways in several organisms due to the relevant physiological functions of the substrates and products of its reaction. In plants, it is one of the most important proteins that were recruited to fulfil key roles in C4 photosynthesis. Recent advances in genomics allowed the characterization of the complete set of NADP-ME genes from some C3 species, as Arabidopsis thaliana and Oryza sativa; however, the characterization of the complete NADP-ME family from a C4 species has not been performed yet. In this study, while taking advantage of the complete Zea mays genome sequence recently released, the characterization of the whole NADP-ME family is presented. The maize NADP-ME family is composed of five genes, two encoding plastidic NADP-MEs (ZmC4- and ZmnonC4-NADP-ME), and three cytosolic enzymes (Zmcyt1-, Zmcyt2-, and Zmcyt3-NADP-ME). The results presented clearly show that each maize NADP-ME displays particular organ distribution, response to stress stimuli, and differential biochemical properties. Phylogenetic footprinting studies performed with the NADP-MEs from several grasses, indicate that four members of the maize NADP-ME family share conserved transcription factor binding motifs with their orthologs, indicating conserved physiological functions for these genes in monocots. Based on the results obtained in this study, and considering the biochemical plasticity shown by the NADP-ME, it is discussed the relevance of the presence of a multigene family, in which each member encodes an isoform with particular biochemical properties, in the evolution of the C4 NADP-ME, improved to fulfil the requirements for an efficient C4 mechanism.

Identification of putative rhamnogalacturonan-II specific glycosyltransferases in Arabidopsis using a combination of bioinformatics approaches

Rhamnogalacturonan-II (RG-II) is a complex plant cell wall polysaccharide that is composed of an α(1,4)-linked homogalacturonan backbone substituted with four side chains. It exists in the cell wall in the form of a dimer that is cross-linked by a borate di-ester. Despite its highly complex structure, RG-II is evolutionarily conserved in the plant kingdom suggesting that this polymer has fundamental functions in the primary wall organisation. In this study, we have set up a bioinformatics strategy aimed at identifying putative glycosyltransferases (GTs) involved in RG-II biosynthesis. This strategy is based on the selection of candidate genes encoding type II membrane proteins that are tightly coexpressed in both rice and Arabidopsis with previously characterised genes encoding enzymes involved in the synthesis of RG-II and exhibiting an up-regulation upon isoxaben treatment. This study results in the final selection of 26 putative Arabidopsis GTs, including 10 sequences already classified in the CAZy database. Among these CAZy sequences, the screening protocol allowed the selection of α-galacturonosyltransferases involved in the synthesis of α4-GalA oligogalacturonides present in both homogalacturonans and RG-II, and two sialyltransferase-like sequences previously proposed to be involved in the transfer of Kdo and/or Dha on the pectic backbone of RG-II. In addition, 16 non-CAZy GT sequences were retrieved in the present study. Four of them exhibited a GT-A fold. The remaining sequences harbored a GT-B like fold and a fucosyltransferase signature. Based on homologies with glycosyltransferases of known functions, putative roles in the RG-II biosynthesis are proposed for some GT candidates.

COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals

Coexpressed gene databases are valuable resources for identifying new gene functions or functional modules in metabolic pathways and signaling pathways. Although coexpressed gene databases are a fundamental platform in the field of plant biology, their use in animal studies is relatively limited. The COXPRESdb (http://coxpresdb.jp) provides coexpression relationships for multiple animal species, as comparisons of coexpressed gene lists can enhance the reliability of gene coexpression determinations. Here, we report the updates of the database, mainly focusing on the following two points. First, we updated our coexpression data by including recent microarray data for the previous seven species (human, mouse, rat, chicken, fly, zebrafish and nematode) and adding four new species (monkey, dog, budding yeast and fission yeast), along with a new human microarray platform. A reliability scoring function was also implemented, based on coexpression conservation to filter out coexpression with low reliability. Second, the network drawing function was updated, to implement automatic cluster analyses with enrichment analyses in Gene Ontology and in cis elements, along with interactive network analyses with Cytoscape Web. With these updates, COXPRESdb will become a more powerful tool for analyses of functional and regulatory networks of genes in a variety of animal species.

RiceFREND: a platform for retrieving coexpressed gene networks in rice

Similarity of gene expression across a wide range of biological conditions can be efficiently used in characterization of gene function. We have constructed a rice gene coexpression database, RiceFREND (http://ricefrend.dna.affrc.go.jp/), to identify gene modules with similar expression profiles and provide a platform for more accurate prediction of gene functions. Coexpression analysis of 27 201 genes was performed against 815 microarray data derived from expression profiling of various organs and tissues at different developmental stages, mature organs throughout the growth from transplanting until harvesting in the field and plant hormone treatment conditions, using a single microarray platform. The database is provided with two search options, namely, 'single guide gene search' and 'multiple guide gene search' to efficiently retrieve information on coexpressed genes. A user-friendly web interface facilitates visualization and interpretation of gene coexpression networks in HyperTree, Cytoscape Web and Graphviz formats. In addition, analysis tools for identification of enriched Gene Ontology terms and cis-elements provide clue for better prediction of biological functions associated with the coexpressed genes. These features allow users to clarify gene functions and gene regulatory networks that could lead to a more thorough understanding of many complex agronomic traits.

Benzoylation and sinapoylation of glucosinolate R-groups in Arabidopsis

Glucosinolates (GSLs) are nitrogen- and sulfur-containing metabolites that contribute to human health and plant defense. The biological activities of these molecules are largely dependent on modification of the GSL R-groups derived from their corresponding amino acid precursors. In Arabidopsis seeds, esterification of the R-group of hydroxylated GSLs (OH-GSLs) leads to the accumulation of benzoylated GSLs (BzGSLs) and sinapoylated GSLs (SnGSLs). BzGSLs were thought to be synthesized from OH-GSLs and benzoyl CoA by a BAHD acyltransferase, but no BAHD gene is strongly co-expressed with the two reference genes BZO1 and AOP3 that are required for BzGSL biosynthesis. In contrast, three genes encoding serine carboxypeptidase-like (SCPL) acyltransferases [SCPL5, SCPL17 and SCPL19 (SNG2)] do exhibit strong co-expression. Using a reverse genetic approach, we found that the GSL profile of the scpl5 mutant was identical to that of wild-type, but both BzGSLs and SnGSLs were barely detectable in scpl17 mutants and their amounts were decreased in the sng2 mutant. In addition, both scpl17 and sng2 mutants accumulate the putative BzGSL precursors OH-GSLs and benzoylglucose. The results of further GSL analyses in other phenylpropanoid mutants and benzoate feeding experiments suggested that SCPL17 mediates the acyltransferase reaction directly, while the mutation in sng2 causes a decrease in BzGSLs and SnGSLs via an unknown indirect mechanism. Finally, benzoate feeding experiments using bzo1 mutants and BZO1 biochemical characterization indicated that the in vivo role of BZO1 is to synthesize the benzoate precursor cinnamoyl CoA rather than to generate benzoyl CoA from benzoate and CoA as previously predicted.

Metabolic module mining based on Independent Component Analysis in Arabidopsis thaliana

Independent Component Analysis (ICA) has been introduced as one of the useful tools for gene-functional discovery in animals. However, this approach has been poorly utilized in the plant sciences. In the present study, we have exploited ICA combined with pathway enrichment analysis to address the statistical challenges associated with genome-wide analysis in plant system. To generate an Arabidopsis metabolic platform, we collected 4,373 Affy-metrix ATH1 microarray datasets. Out of the 3,232 metabolic genes and transcription factors, 99.47% of these genes were identified in at least one component, indicating the coverage of most of the metabolic pathways by the components. During the metabolic pathway enrichment analysis, we found components that indicate an independent regulation between the isoprenoid biosynthesis pathways. We also utilized this analysis tool to investigate some transcription factors involved in secondary cell wall biogenesis. This approach has identified remarkably more transcription factors compared to previously reported analysis tools. A website providing user-friendly searching and downloading of the entire dataset analyzed by ICA is available at http://kimjy.gnu.ac.kr/ICA.files/slide0002.htm . ICA combined with pathway enrichment analysis might provide a powerful approach for the extraction of the components responsible for a biological process of interest in plant systems.

Uncovering Arabidopsis membrane protein interactome enriched in transporters using mating-based split ubiquitin assays and classification models

High-throughput data are a double-edged sword; for the benefit of large amount of data, there is an associated cost of noise. To increase reliability and scalability of high-throughput protein interaction data generation, we tested the efficacy of classification to enrich potential protein-protein interactions. We applied this method to identify interactions among Arabidopsis membrane proteins enriched in transporters. We validated our method with multiple retests. Classification improved the quality of the ensuing interaction network and was effective in reducing the search space and increasing true positive rate. The final network of 541 interactions among 239 proteins (of which 179 are transporters) is the first protein interaction network enriched in membrane transporters reported for any organism. This network has similar topological attributes to other published protein interaction networks. It also extends and fills gaps in currently available biological networks in plants and allows building a number of hypotheses about processes and mechanisms involving signal-transduction and transport systems.

AtPAN: an integrated system for reconstructing transcriptional regulatory networks in Arabidopsis thaliana

Background

Construction of transcriptional regulatory networks (TRNs) is of priority concern in systems biology. Numerous high-throughput approaches, including microarray and next-generation sequencing, are extensively adopted to examine transcriptional expression patterns on the whole-genome scale; those data are helpful in reconstructing TRNs. Identifying transcription factor binding sites (TFBSs) in a gene promoter is the initial step in elucidating the transcriptional regulation mechanism. Since transcription factors usually co-regulate a common group of genes by forming regulatory modules with similar TFBSs. Therefore, the combinatorial interactions of transcription factors must be modeled to reconstruct the gene regulatory networks.

Description For systems biology applications, this work develops a novel database called Arabidopsis thaliana Promoter Analysis Net (AtPAN), capable of detecting TFBSs and their corresponding transcription factors (TFs) in a promoter or a set of promoters in Arabidopsis. For further analysis, according to the microarray expression data and literature, the co-expressed TFs and their target genes can be retrieved from AtPAN. Additionally, proteins interacting with the co-expressed TFs are also incorporated to reconstruct co-expressed TRNs. Moreover, combinatorial TFs can be detected by the frequency of TFBSs co-occurrence in a group of gene promoters. In addition, TFBSs in the conserved regions between the two input sequences or homologous genes in Arabidopsis and rice are also provided in AtPAN. The output results also suggest conducting wet experiments in the future.

Conclusions

The AtPAN, which has a user-friendly input/output interface and provide graphical view of the TRNs. This novel and creative resource is freely available online at http://AtPAN.itps.ncku.edu.tw/.

Frontiers in metabolic reconstruction and modeling of plant genomes

A major goal of post-genomic biology is to reconstruct and model in silico the metabolic networks of entire organisms. Work on bacteria is well advanced, and is now under way for plants and other eukaryotes. Genome-scale modelling in plants is much more challenging than in bacteria. The challenges come from features characteristic of higher organisms (subcellular compartmentation, tissue differentiation) and also from the particular severity in plants of a general problem: genome content whose functions remain undiscovered. This problem results in thousands of genes for which no function is known ('undiscovered genome content') and hundreds of enzymatic and transport functions for which no gene is yet identified. The severity of the undiscovered genome content problem in plants reflects their genome size and complexity. To bring the challenges of plant genome-scale modelling into focus, we first summarize the current status of plant genome-scale models. We then highlight the challenges - and ways to address them - in three areas: identifying genes for missing processes, modelling tissues as opposed to single cells, and finding metabolic functions encoded by undiscovered genome content. We also discuss the emerging view that a significant fraction of undiscovered genome content encodes functions that counter damage to metabolites inflicted by spontaneous chemical reactions or enzymatic mistakes.

Inferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana

Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein-protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain-domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.

Reverse engineering: a key component of systems biology to unravel global abiotic stress cross-talk

Understanding the global abiotic stress response is an important stepping stone for the development of universal stress tolerance in plants in the era of climate change. Although co-occurrence of several stress factors (abiotic and biotic) in nature is found to be frequent, current attempts are poor to understand the complex physiological processes impacting plant growth under combinatory factors. In this review article, we discuss the recent advances of reverse engineering approaches that led to seminal discoveries of key candidate regulatory genes involved in cross-talk of abiotic stress responses and summarized the available tools of reverse engineering and its relevant application. Among the universally induced regulators involved in various abiotic stress responses, we highlight the importance of (i) abscisic acid (ABA) and jasmonic acid (JA) hormonal cross-talks and (ii) the central role of WRKY transcription factors (TF), potentially mediating both abiotic and biotic stress responses. Such interactome networks help not only to derive hypotheses but also play a vital role in identifying key regulatory targets and interconnected hormonal responses. To explore the full potential of gene network inference in the area of abiotic stress tolerance, we need to validate hypotheses by implementing time-dependent gene expression data from genetically engineered plants with modulated expression of target genes. We further propose to combine information on gene-by-gene interactions with data from physical interaction platforms such as protein-protein or TF-gene networks.

Potential for modulation of the fas apoptotic pathway by epidermal growth factor in sarcomas

One important mechanism by which cancer cells parasitize their host is by escaping apoptosis. Thus, selectively facilitating apoptosis is a therapeutic mechanism by which oncotherapy may prove highly advantageous. One major apoptotic pathway is mediated by Fas ligand (FasL). The death-inducing signaling Ccmplex (DISC) and subsequent death-domain aggregations are created when FasL is bound by its receptor thereby enabling programmed cell death. Conceptually, if a better understanding of the Fas pathway can be garnered, an oncoselective prodeath therapeutic approach can be tailored. Herein, we propose that EGF and CTGF play essential roles in the regulation of the Fas apoptotic pathway in sarcomas. Tumor and in vitro data suggest viable cells counter the prodeath signal induced by FasL by activating EGF, which in turn induces prosurvival CTGF. The prosurvival attributes of CTGF ultimately predominate over the death-inducing FasL. Cells destined for elimination inhibit this prosurvival response via a presently undefined pathway. This scenario represents a novel role for EGF and CTGF as regulators of the Fas pathway in sarcomas.

Assessing the utility of gene co-expression stability in combination with correlation in the analysis of protein-protein interaction networks

Background

Gene co-expression, in the form of a correlation coefficient, has been valuable in the analysis, classification and prediction of protein-protein interactions. However, it is susceptible to bias from a few samples having a large effect on the correlation coefficient. Gene co-expression stability is a means of quantifying this bias, with high stability indicating robust, unbiased co-expression correlation coefficients. We assess the utility of gene co-expression stability as an additional measure to support the co-expression correlation in the analysis of protein-protein interaction networks.

Results

We studied the patterns of co-expression correlation and stability in interacting proteins with respect to their interaction promiscuity, levels of intrinsic disorder, and essentiality or disease-relatedness. Co-expression stability, along with co-expression correlation, acts as a better classifier of hub proteins in interaction networks, than co-expression correlation alone, enabling the identification of a class of hubs that are functionally distinct from the widely accepted transient (date) and obligate (party) hubs. Proteins with high levels of intrinsic disorder have low co-expression correlation and high stability with their interaction partners suggesting their involvement in transient interactions, except for a small group that have high co-expression correlation and are typically subunits of stable complexes. Similar behavior was seen for disease-related and essential genes. Interacting proteins that are both disordered have higher co-expression stability than ordered protein pairs. Using co-expression correlation and stability, we found that transient interactions are more likely to occur between an ordered and a disordered protein while obligate interactions primarily occur between proteins that are either both ordered, or disordered.

Conclusions

We observe that co-expression stability shows distinct patterns in structurally and functionally different groups of proteins and interactions. We conclude that it is a useful and important measure to be used in concert with gene co-expression correlation for further insights into the characteristics of proteins in the context of their interaction network.

Comprehensive network analysis of anther-expressed genes in rice by the combination of 33 laser microdissection and 143 spatiotemporal microarrays

Co-expression networks systematically constructed from large-scale transcriptome data reflect the interactions and functions of genes with similar expression patterns and are a powerful tool for the comprehensive understanding of biological events and mining of novel genes. In Arabidopsis (a model dicot plant), high-resolution co-expression networks have been constructed from very large microarray datasets and these are publicly available as online information resources. However, the available transcriptome data of rice (a model monocot plant) have been limited so far, making it difficult for rice researchers to achieve reliable co-expression analysis. In this study, we performed co-expression network analysis by using combined 44 K agilent microarray datasets of rice, which consisted of 33 laser microdissection (LM)-microarray datasets of anthers, and 143 spatiotemporal transcriptome datasets deposited in RicexPro. The entire data of the rice co-expression network, which was generated from the 176 microarray datasets by the Pearson correlation coefficient (PCC) method with the mutual rank (MR)-based cut-off, contained 24,258 genes and 60,441 genes pairs. Using these datasets, we constructed high-resolution co-expression subnetworks of two specific biological events in the anther, “meiosis” and “pollen wall synthesis”. The meiosis network contained many known or putative meiotic genes, including genes related to meiosis initiation and recombination. In the pollen wall synthesis network, several candidate genes involved in the sporopollenin biosynthesis pathway were efficiently identified. Hence, these two subnetworks are important demonstrations of the efficiency of co-expression network analysis in rice. Our co-expression analysis included the separated transcriptomes of pollen and tapetum cells in the anther, which are able to provide precise information on transcriptional regulation during male gametophyte development in rice. The co-expression network data presented here is a useful resource for rice researchers to elucidate important and complex biological events.

A 5-formyltetrahydrofolate cycloligase paralog from all domains of life: comparative genomic and experimental evidence for a cryptic role in thiamin metabolism

A paralog (here termed COG0212) of the ATP-dependent folate salvage enzyme 5-formyltetrahydrofolate cycloligase (5-FCL) occurs in all domains of life and, although typically annotated as 5-FCL in pro- and eukaryotic genomes, is of unknown function. COG0212 is similar in overall structure to 5-FCL, particularly in the substrate binding region, and has distant similarity to other kinases. The Arabidopsis thaliana COG0212 protein was shown to be targeted to chloroplasts and to be required for embryo viability. Comparative genomic analysis revealed that a high proportion (19%) of archaeal and bacterial COG0212 genes are clustered on the chromosome with various genes implicated in thiamin metabolism or transport but showed no such association between COG0212 and folate metabolism. Consistent with the bioinformatic evidence for a role in thiamin metabolism, ablating COG0212 in the archaeon Haloferax volcanii caused accumulation of thiamin monophosphate. Biochemical and functional complementation tests of several known and hypothetical thiamin-related activities (involving thiamin, its breakdown products, and their phosphates) were, however, negative. Also consistent with the bioinformatic evidence, the COG0212 proteins from A. thaliana and prokaryote sources lacked 5-FCL activity in vitro and did not complement the growth defect or the characteristic 5-formyltetrahydrofolate accumulation of a 5-FCL-deficient (ΔygfA) Escherichia coli strain. We therefore propose (a) that COG0212 has an unrecognized yet sometimes crucial role in thiamin metabolism, most probably in salvage or detoxification, and (b) that is not a 5-FCL and should no longer be so annotated.

Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in Arabidopsis and rice

Microarray experiments have yielded massive amounts of expression information measured under various conditions for the model species Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa). Expression compendia grouping multiple experiments make it possible to define correlated gene expression patterns within one species and to study how expression has evolved between species. We developed a robust framework to measure expression context conservation (ECC) and found, by analyzing 4,630 pairs of orthologous Arabidopsis and rice genes, that 77% showed conserved coexpression. Examples of nonconserved ECC categories suggested a link between regulatory evolution and environmental adaptations and included genes involved in signal transduction, response to different abiotic stresses, and hormone stimuli. To identify genomic features that influence expression evolution, we analyzed the relationship between ECC, tissue specificity, and protein evolution. Tissue-specific genes showed higher expression conservation compared with broadly expressed genes but were fast evolving at the protein level. No significant correlation was found between protein and expression evolution, implying that both modes of gene evolution are not strongly coupled in plants. By integration of cis-regulatory elements, many ECC conserved genes were significantly enriched for shared DNA motifs, hinting at the conservation of ancestral regulatory interactions in both model species. Surprisingly, for several tissue-specific genes, patterns of concerted network evolution were observed, unveiling conserved coexpression in the absence of conservation of tissue specificity. These findings demonstrate that orthologs inferred through sequence similarity in many cases do not share similar biological functions and highlight the importance of incorporating expression information when comparing genes across species.

iTRAQ analysis reveals mechanisms of growth defects due to excess zinc in Arabidopsis

The micronutrient zinc is essential for all living organisms, but it is toxic at high concentrations. Here, to understand the effects of excess zinc on plant cells, we performed an iTRAQ (for isobaric tags for relative and absolute quantification)-based quantitative proteomics approach to analyze microsomal proteins from Arabidopsis (Arabidopsis thaliana) roots. Our approach was sensitive enough to identify 521 proteins, including several membrane proteins. Among them, IRT1, an iron and zinc transporter, and FRO2, a ferric-chelate reductase, increased greatly in response to excess zinc. The expression of these two genes has been previously reported to increase under iron-deficient conditions. Indeed, the concentration of iron was significantly decreased in roots and shoots under excess zinc. Also, seven subunits of the vacuolar H(+)-ATPase (V-ATPase), a proton pump on the tonoplast and endosome, were identified, and three of them decreased significantly in response to excess zinc. In addition, excess zinc in the wild type decreased V-ATPase activity and length of roots and cells to levels comparable to those of the untreated de-etiolated3-1 mutant, which bears a mutation in V-ATPase subunit C. Interestingly, excess zinc led to the formation of branched and abnormally shaped root hairs, a phenotype that correlates with decreased levels of proteins of several root hair-defective mutants. Our results point out mechanisms of growth defects caused by excess zinc in which cross talk between iron and zinc homeostasis and V-ATPase activity might play a central role.

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.

Bioinformatic analysis of molecular network of glucosinolate biosynthesis

Glucosinolates constitute a major group of secondary metabolites in Arabidopsis, which play an important role in plant interaction with pathogens and insects. Advances in glucosinolate research have defined the biosynthetic pathways. However, cross-talk and interaction between glucosinolate pathway and other molecular pathways are largely unknown. Here three bioinformatics tools were used to explore novel components and pathway connections in glucosinolate network. Although none of the software tools were prefect to predict glucosinolate genes, combination of results generated by all the tools led to successful prediction of all known glucosinolate genes. This approach was used to predict new genes in glucosinolate network. A total of 330 genes were found with high potential to relate to glucosinolate biosynthesis. Among them 64 genes were selected to construct glucosinolate network because their individual connection to at least one known glucosinolate gene was predicted by all the software tools. Microarray data of candidate gene mutants were used for validation of the results. The mutants of nine genes predicted by glucosinolate seed genes all exhibited changes in the expression of glucosinolate genes. Four of the genes have been well-known to functionally interact with glucosinolate biosynthesis. These results indicate that the approach we took provides a powerful way to reveal new players in glucosinolate networks. Creation of an in silico network of glucosinolate biosynthesis will allow the generation of many testable hypotheses and ultimately enable predictive biology.

COXPRESdb: a database to compare gene coexpression in seven model animals

Publicly available databases of coexpressed gene sets are a valuable resource for a wide variety of experimental studies, including gene targeting for functional identification, and for investigations of regulatory mechanisms or protein-protein interaction networks. Although coexpressed gene databases are becoming more and more popular in the field of plant biology, those with animal data are rather limited, possibly due to the lower reliability of the coexpression data. The original COXPRESdb (coexpressed gene database) (http://coxpresdb.jp) represented the coexpression relationship for human and mouse. Here, we report updates of this database that especially focus on the enhancement of the reliability of gene coexpression data in animals. For this purpose, we implemented a new comparable coexpression measure, Mutual Rank, included five other animal species, rat, chicken, zebrafish, fly and nematoda, to assess the conservation of coexpression, and added different layers of omics data into the integrated network of genes. Comparison of coexpression is a key concept to enhance the reliability of gene coexpression, and the integration of different information can reduce the noise inherent in the information. With the functions for gene network representation, COXPRESdb can help researchers to clarify the functional and regulatory networks of genes in a broad array of animal species.

RiceXPro: a platform for monitoring gene expression in japonica rice grown under natural field conditions

Elucidating the function of all predicted genes in rice remains as the ultimate goal in cereal genomics in order to ensure the development of improved varieties that will sustain an expanding world population. We constructed a gene expression database (RiceXPro, URL: http://ricexpro.dna.affrc.go.jp/) to provide an overview of the transcriptional changes throughout the growth of the rice plant in the field. RiceXPro contains two data sets corresponding to spatiotemporal gene expression profiles of various organs and tissues, and continuous gene expression profiles of leaf from transplanting to harvesting. A user-friendly web interface enables the extraction of specific gene expression profiles by keyword and chromosome search, and basic data analysis, thereby providing useful information as to the organ/tissue and developmental stage specificity of expression of a particular gene. Analysis tools such as t-test, calculation of fold change and degree of correlation facilitate the comparison of expression profiles between two random samples and the prediction of function of uncharacterized genes. As a repository of expression data encompassing growth in the field, this database can provide baseline information of genes that underlie various agronomically important traits in rice.