OryzaExpress: an integrated database of gene expression networks and omics annotations in rice

Over-expression of a plant-type phosphoenolpyruvate carboxylase derails Arabidopsis stamen formation

We examined whether plant-type phosphoenolpyruvate carboxylase (PEPC) is involved in flower organ formation or not by over-expression in Arabidopsis. A wheat PEPC isogene Tappc3A, belonging to the ppc3 group, was targeted due to its preferential expression pattern in pistils and stamens. Transgenic Arabidopsis over-expressing Tappc3A exhibited irregular stamen formation, i.e., a lesser number of stamens per flower and shorter filaments in T2 and T3 generations. Irregular stamens were frequently observed in homozygous T4 lines, but no morphological change was observed in other floral organs. High-degree gene co-expression of Tappc3 isogenes with wheat SEEDSTICKs but not with other homeotic transcription factor genes for flower formation implicates that Tappc3 is under control by the class D genes of the ABCDE model to flower development. In addition, the conservation of CArG box sequences on the Tappc3 promoters supported the developmentally programmed gene expression of ppc3 in wheat flowering organs. Thus, this study provides the first experimental evidence for the critical regulation of plant-type PEPC for flower formation.

Identification of Salt-Sensitive and Salt-Tolerant Genes through Weighted Gene Co-Expression Networks across Multiple Datasets: A Centralization and Differential Correlation Analysis

Salt stress is a significant challenge that severely hampers rice growth, resulting in decreased yield and productivity. Over the years, researchers have identified biomarkers associated with salt stress to enhance rice tolerance. However, the understanding of the mechanism underlying salt tolerance in rice remains incomplete due to the involvement of multiple genes. Given the vast amount of genomics and transcriptomics data available today, it is crucial to integrate diverse datasets to identify key genes that play essential roles during salt stress in rice. In this study, we propose an integration of multiple datasets to identify potential key transcription factors. This involves utilizing network analysis based on weighted co-expression networks, focusing on gene-centric measurement and differential co-expression relationships among genes. Consequently, our analysis reveals 86 genes located in markers from previous meta-QTL analysis. Moreover, six transcription factors, namely LOC_Os03g45410 (OsTBP2), LOC_Os07g42400 (OsGATA23), LOC_Os01g13030 (OsIAA3), LOC_Os05g34050 (OsbZIP39), LOC_Os09g29930 (OsBIM1), and LOC_Os10g10990 (transcription initiation factor IIF), exhibited significantly altered co-expression relationships between salt-sensitive and salt-tolerant rice networks. These identified genes hold potential as crucial references for further investigation into the functions of salt stress response in rice plants and could be utilized in the development of salt-resistant rice cultivars. Overall, our findings shed light on the complex genetic regulation underlying salt tolerance in rice and contribute to the broader understanding of rice's response to salt stress.

A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1

IMPORTANCE

We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.

Genomics and transcriptomics to protect rice (Oryza sativa. L.) from abiotic stressors: -pathways to achieving zero hunger

More over half of the world's population depends on rice as a major food crop. Rice (Oryza sativa L.) is vulnerable to abiotic challenges including drought, cold, and salinity since it grown in semi-aquatic, tropical, or subtropical settings. Abiotic stress resistance has bred into rice plants since the earliest rice cultivation techniques. Prior to the discovery of the genome, abiotic stress-related genes were identified using forward genetic methods, and abiotic stress-tolerant lines have developed using traditional breeding methods. Dynamic transcriptome expression represents the degree of gene expression in a specific cell, tissue, or organ of an individual organism at a specific point in its growth and development. Transcriptomics can reveal the expression at the entire genome level during stressful conditions from the entire transcriptional level, which can be helpful in understanding the intricate regulatory network relating to the stress tolerance and adaptability of plants. Rice (Oryza sativa L.) gene families found comparatively using the reference genome sequences of other plant species, allowing for genome-wide identification. Transcriptomics via gene expression profiling which have recently dominated by RNA-seq complements genomic techniques. The identification of numerous important qtl,s genes, promoter elements, transcription factors and miRNAs involved in rice response to abiotic stress was made possible by all of these genomic and transcriptomic techniques. The use of several genomes and transcriptome methodologies to comprehend rice (Oryza sativa, L.) ability to withstand abiotic stress have been discussed in this review.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

QTL Analysis for Bread Wheat Seed Size, Shape and Color Characteristics Estimated by Digital Image Processing

The size, shape, and color of wheat seeds are important traits that are associated with yield and flour quality (size, shape), nutritional value, and pre-harvest sprouting (coat color). These traits are under multigenic control, and to dissect their molecular and genetic basis, quantitative trait loci (QTL) analysis is used. We evaluated 114 recombinant inbred lines (RILs) in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative, ITMI/MP) grown in 2014 season. We used digital image analysis for seed phenotyping and obtained data for seven traits describing seed size and shape and 48 traits of seed coat color. We identified 212 additive and 34 pairs of epistatic QTLs on all the chromosomes of wheat genome except chromosomes 1A and 5D. Many QTLs were overlapping. We demonstrated that the overlap between QTL regions was low for seed size/shape traits and high for coat color traits. Using the literature and KEGG data, we identified sets of genes in Arabidopsis and rice from the networks controlling seed size and color. Further, we identified 29 and 14 candidate genes for seed size-related loci and for loci associated with seed coat color, respectively.

The Original Form of C4-Photosynthetic Phosphoenolpyruvate Carboxylase Is Retained in Pooids but Lost in Rice

Poaceae is the most prominent monocot family that contains the primary cereal crops wheat, rice, and maize. These cereal species exhibit physiological diversity, such as different photosynthetic systems and environmental stress tolerance. Phosphoenolpyruvate carboxylase (PEPC) in Poaceae is encoded by a small multigene family and plays a central role in C4-photosynthesis and dicarboxylic acid metabolism. Here, to better understand the molecular basis of the cereal species diversity, we analyzed the PEPC gene family in wheat together with other grass species. We could designate seven plant-type and one bacterial-type grass PEPC groups, ppc1a, ppc1b, ppc2a, ppc2b, ppc3, ppc4, ppcC4, and ppc-b, respectively, among which ppc1b is an uncharacterized type of PEPC. Evolutionary inference revealed that these PEPCs were derived from five types of ancient PEPCs (ppc1, ppc2, ppc3, ppc4, and ppc-b) in three chromosomal blocks of the ancestral Poaceae genome. C4-photosynthetic PEPC (ppcC4 ) had evolved from ppc1b, which seemed to be arisen by a chromosomal duplication event. We observed that ppc1b was lost in many Oryza species but preserved in Pooideae after natural selection. In silico analysis of cereal RNA-Seq data highlighted the preferential expression of ppc1b in upper ground organs, selective up-regulation of ppc1b under osmotic stress conditions, and nitrogen response of ppc1b. Characterization of wheat ppc1b showed high levels of gene expression in young leaves, transcriptional responses under nitrogen and abiotic stress, and the presence of a Dof1 binding site, similar to ppcC4 in maize. Our results indicate the evolving status of Poaceae PEPCs and suggest the functional association of ppc1-derivatives with adaptation to environmental changes.

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.

Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants

Environmental factors, including different stresses, can have an impact on the expression of genes and subsequently the phenotype and development of plants. Since a large number of genes are involved in response to the perturbation of the environment, identifying groups of co-expressed genes is meaningful. The gene co-expression network models can be used for the exploration, interpretation, and identification of genes responding to environmental changes. Once a gene co-expression network is constructed, one can determine gene modules and the association of gene modules to the phenotypic response. To link modules to phenotype, one approach is to find the correlated eigengenes of given modules or to integrate all eigengenes in regularized linear model. This manuscript describes the method from construction of co-expression network, module discovery, association between modules and phenotypic data, and finally to annotation/visualization.

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management

Rice (Oryza sativa) is an imperative staple crop for nearly half of the world's population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, "omics" techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.

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.

Rapidly evolving phosphoenolpyruvate carboxylase Gmppc1 and Gmppc7 are highly expressed in the external seed coat of immature soybean seeds

Phosphoenolpyruvate carboxylase (PEPC) is a carbon fixation enzyme which probably plays crucial roles in seed development. A greater number of PEPC isoforms are encoded in the soybean genome, while most of the PEPC isoforms are functionally unknown. In this study, we investigated on soybean PEPC expressed in the external layer of seed coat (ELSC) during seed formation. PEPC activity in ELSC ranged from 0.24 to 1.0 U/g F.W., which could be comparable to those in whole seeds at U per dry matter. Public RNA-Seq data in separated soybean seed tissues revealed that six plant-type PEPC isogenes were substantially expressed in ELSC, and Gmppc1 and Gmppc7 were highly expressed in hourglass cells of ELSC. Gene Ontology enrichment of co-expressed genes with Gmppc1 and Gmppc7 implicated a role of these isogenes in assisting energy production and cellulose biosynthesis. Comparison of PEPC sequences from 16 leguminous species hypothesized adaptive evolution of the Gmppc1 and Gmppc7 lineage after divergence from the other plant-type PEPC lineages. Molecular diversification of these plant-type PEPC was possibly accomplished by adaptation to the functions of the soybean seed tissues. This study indicates that energy demand in immature seeds may be a driving force for the molecular evolution of PEPC.

Cell wall Glycine-rich Protein2 is involved in tapetal differentiation and pollen maturation

The tapetum plays important roles in anther development by providing materials for pollen-wall formation and nutrients for pollen development. Here, we report the characterization of a male-sterile mutant of glycine-rich protein 2 (OsGRP2), which exhibits irregular cell division and dysfunction of the tapetum. GRP is a cellwall structural protein present in the cell walls of diverse plant species, but its function is unclear in pollen development. We found that few GRP genes are expressed in rice and thus focused on one highly expressed gene, OsGRP2. The tapetal cell walls of an OsGRP2 mutant did not thicken at the pollen mothercell stage, as a result, pollen maturation and fertility rate decreased. High OsGRP2 expression was detected in male-floral organs, and OsGRP2 was distributed in the tapetum. OsGRP2 participated in establishment of the cellwall network during early tapetum development. In conclusion, our results indicate that OsGRP2 plays important roles in the differentiation and function of the tapetum.

Proteomics Analysis of Plasma Membrane Fractions of the Root, Leaf, and Flower of Rice

The plasma membrane regulates biological processes such as ion transport, signal transduction, endocytosis, and cell differentiation/proliferation. To understand the functional characteristics and organ specificity of plasma membranes, plasma membrane protein fractions from rice root, etiolated leaf, green leaf, developing leaf sheath, and flower were analyzed by proteomics. Among the proteins identified, 511 were commonly accumulated in the five organs, whereas 270, 132, 359, 146, and 149 proteins were specifically accumulated in the root, etiolated leaf, green leaf, developing leaf sheath, and developing flower, respectively. The principle component analysis revealed that the functions of the plasma membrane in the root was different from those of green and etiolated leaves and that the plasma membrane protein composition of the leaf sheath was similar to that of the flower, but not that of the green leaf. Functional classification revealed that the root plasma membrane has more transport-related proteins than the leaf plasma membrane. Furthermore, the leaf sheath and flower plasma membranes were found to be richer in proteins involved in signaling and cell function than the green leaf plasma membrane. To validate the proteomics data, immunoblot analysis was carried out, focusing on four heterotrimeric G protein subunits, Gα, Gβ, Gγ1, and Gγ2. All subunits could be detected by both methods and, in particular, Gγ1 and Gγ2 required concentration by immunoprecipitation for mass spectrometry detection.

Cytosolic GLUTAMINE SYNTHETASE1;1 Modulates Metabolism and Chloroplast Development in Roots

Nitrogen (N) is an essential macronutrient, and the final form of endogenous inorganic N is ammonium, which is assimilated by Gln synthetase (GS) into Gln. However, how the multiple isoforms of cytosolic GSs contribute to metabolic systems via the regulation of ammonium assimilation remains unclear. In this study, we compared the effects of two rice (Oryza sativa) cytosolic GSs, namely OsGS1;1 and OsGS1;2, on central metabolism in roots using reverse genetics, metabolomic and transcriptomic profiling, and network analyses. We observed (1) abnormal sugar and organic N accumulation and (2) significant up-regulation of genes associated with photosynthesis and chlorophyll biosynthesis in the roots of Osgs1;1 but not Osgs1;2 knockout mutants. Network analysis of the Osgs1;1 mutant suggested that metabolism of Gln was coordinated with the metabolic modules of sugar metabolism, tricarboxylic acid cycle, and carbon fixation. Transcript profiling of Osgs1;1 mutant roots revealed that expression of the rice sigma-factor (OsSIG) genes in the mutants were transiently upregulated. GOLDEN2-LIKE transcription factor-encoding genes, which are involved in chloroplast biogenesis in rice, could not compensate for the lack of OsSIGs in the Osgs1;1 mutant. Microscopic analysis revealed mature chloroplast development in Osgs1;1 roots but not in the roots of Osgs1;2, Osgs1;2-complemented lines, or the wild type. Thus, organic N assimilated by OsGS1;1 affects a broad range of metabolites and transcripts involved in maintaining metabolic homeostasis and plastid development in rice roots, whereas OsGS1;2 has a more specific role, affecting mainly amino acid homeostasis but not carbon metabolism.

The plant-type phosphoenolpyruvate carboxylase Gmppc2 is developmentally induced in immature soy seeds at the late maturation stage: a potential protein biomarker for seed chemical composition

Phosphoenolpyruvate carboxylase (PEPC) is a carbon-fixing enzyme with critical roles in seed development. Previously we observed a positive correlation between PEPC activity and protein content in mature seeds among soybean cultivars and varietal differences of PEPC activity in immature seeds, which is concordant with seed protein accumulation. Here, we report a PEPC isoform (Gmppc2) which is preferentially expressed in immature soybean seeds at the late maturation stage. Gmppc2 was co-expressed with enzyme genes involved in starch degradation: α-amylase, hexokinase, and α-glucan phosphorylase. Gmppc2 was developmentally induced in the external seed coats, internal seed coats, hypocotyls, and cotyledons at the late maturation stage. The expression of Gmppc2 protein was negatively regulated by the application of a nitrogen fertilizer, which suppressed nodule formation. These results imply that Gmppc2 is involved in the metabolism of nitrogen originated from nodules into seeds, and Gmppc2 might be applicable as a biomarker of seed protein content.Abbreviations: PEP: phosphoenolpyruvate; PEPC: phosphoenolpyruvate carboxylase; RNA-Seq: RNA sequencing; PCA: principal component analysis; SE: standard error.

HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis

Background

Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples.

Results

HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify “response order preserving DEGs” that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools.

Conclusions

HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.

Infrastructures of systems biology that facilitate functional genomic study in rice

Rice (Oryza sativa L.) is both a major staple food for the worldwide population and a model crop plant for studying the mode of action of agronomically valuable traits, providing information that can be applied to other crop plants. Due to the development of high-throughput technologies such as next generation sequencing and mass spectrometry, a huge mass of multi-omics data in rice has been accumulated. Through the integration of those data, systems biology in rice is becoming more advanced.To facilitate such systemic approaches, we have summarized current resources, such as databases and tools, for systems biology in rice. In this review, we categorize the resources using six omics levels: genomics, transcriptomics, proteomics, metabolomics, integrated omics, and functional genomics. We provide the names, websites, references, working states, and number of citations for each individual database or tool and discuss future prospects for the integrated understanding of rice gene functions.

Revealing shared differential co-expression profiles in rice infected by virus from reoviridae and sequiviridae group

Differential co-expression is a cutting-edge approach to analyze gene expression data and identify both shared and divergent expression patterns. The availability of high-throughput gene expression datasets and efficient computational approaches have unfolded the opportunity to a systems level understanding of functional genomics of different stresses with respect to plants. We performed the meta-analysis of the available microarray data for reoviridae and sequiviridae infection in rice with the aim to identify the shared gene co-expression profile. The microarray data were downloaded from ArrayExpress and analyzed through a modified Weighted Gene Co-expression Network Analysis (WGCNA) protocol. WGCNA clustered the genes based on the expression intensities across the samples followed by identification of modules, eigengenes, principal components, topology overlap, module membership and module preservation. The module preservation analysis identified 4 modules; salmon (638 genes), midnightblue (584 genes), lightcyan (686 genes) and red (562 genes), which are highly preserved in both the cases. The networks in case of reoviridae infection showed neatly packed clusters whereas, in sequiviridae, the clusters were loosely connected which is due to the differences in the correlation values. We also identified 83 common transcription factors targeting the hub genes from all the identified modules. This study provides a coherent view of the comparative aspect of the expression of common genes involved in different virus infections which may aid in the identification of novel targets and development of new intervention strategy against the virus.

Pathway Editing Targets for Thiamine Biofortification in Rice Grains

Thiamine deficiency is common in populations consuming polished rice as a major source of carbohydrates. Thiamine is required to synthesize thiamine pyrophosphate (TPP), an essential cofactor of enzymes of central metabolism. Its biosynthesis pathway has been partially elucidated and the effect of overexpression of a few genes such as thi1 and thiC, on thiamine accumulation in rice has been reported. Based on current knowledge, this review focuses on the potential of gene editing in metabolic engineering of thiamine biosynthesis pathway to improve thiamine in rice grains. Candidate genes, suitable for modification of the structural part to evolve more efficient versions of enzymes in the pathway, are discussed. For example, adjacent cysteine residues may be introduced in the catalytic domain of thi4 to improve the turn over activity of thiamine thiazole synthase 2. Motif specific editing to modify promoter regulatory regions of genes is discussed to modulate gene expression. Editing cis acting regulatory elements in promoter region can shift the expression of transporters and thiamine binding proteins to endosperm. This can enhance dietary availability of thiamine from rice grains. Differential transcriptomics on rice varieties with contrasting grain thiamine and functional genomic studies will identify more strategic targets for editing in future. Developing functionally enhanced foods by biofortification is a sustainable approach to make diets wholesome.

Genome-wide identification of grain filling genes regulated by the OsSMF1 transcription factor in rice

BACKGROUND

Spatial- and temporal-specific expression patterns are primarily regulated at the transcriptional level by gene promoters. Therefore, it is important to identify the binding motifs of transcription factors to better understand the networks associated with embryogenesis.

RESULTS

Here, we used a protein-binding microarray (PBM) to identify the binding motifs of OsSMF1, which is a basic leucine zipper transcription factor involved in the regulation of rice seed maturation. OsSMF1 (previously called RISBZ1 or OsbZIP58) is known to interact with GCN4 motifs (TGA(G/C)TCA) to regulate seed storage protein synthesis, and it functions as a key regulator of starch synthesis. Quadruple 9-mer-based PBM analysis and electrophoretic mobility shift assay revealed that OsSMF1 bound to the GCN4 (TGA(G/C)TCA), ACGT (CCACGT(C/G)), and ATGA (GGATGAC) motifs with three different affinities. We predicted 44 putative OsSMF1 target genes using data obtained from both the PBM and RiceArrayNet. Among these putative target genes, 18, 21, and 13 genes contained GCN4, ACGT, and ATGA motifs within their 1-kb promoter regions, respectively. Among them, six genes encoding major grain filling proteins and transcription factors were chosen to confirm the activation of their expression in vivo. OsSMF1 was shown to bind directly to the promoters of Os03g0168500 (GCN4 motif), patatin-like gene (GCN4 motif), α-globulin (ACGT motif), rice prolamin box-binding factor (RPBF) (ATGA motif), and ONAC024 (GCN4 and ACGT motifs) and to regulate their expression.

CONCLUSIONS

The results of this study suggest that OsSMF1 is one of the key transcription factors that functions in a wide range of seed developmental processes with different specific binding affinities for the three DNA-binding motifs.

RiceAntherNet: a gene co-expression network for identifying anther and pollen development genes

In plants, normal anther and pollen development involves many important biological events and complex molecular regulatory coordination. Understanding gene regulatory relationships during male reproductive development is essential for fundamental biology and crop breeding. In this work, we developed a rice gene co-expression network for anther development (RiceAntherNet) that allows prediction of gene regulatory relationships during pollen development. RiceAntherNet was generated from 57 rice anther tissue microarrays across all developmental stages. The microarray datasets from nine rice male sterile mutants, including msp1-4, ostdl1a, gamyb-2, tip2, udt1-1, tdr, eat1-1, ptc1 and mads3-4, were used to explore and test the network. Among the changed genes, three clades showing differential expression patterns were constructed to identify genes associated with pollen formation. Many of these have known roles in pollen development, for example, seven genes in Clade 1 (OsABCG15, OsLAP5, OsLAP6, DPW, CYP703A3, OsNP1 and OsCP1) are involved in rice pollen wall formation. Furthermore, Clade 1 contained 12 genes whose predicted orthologs in Arabidopsis have been reported as key during pollen development and may play similar roles in rice. Genes in Clade 2 are expressed earlier than Clade 1 (anther stages 2-9), while genes in Clade 3 are expressed later (stages 10-12). RiceAntherNet serves as a valuable tool for identifying novel genes during plant anther and pollen development. A website is provided (https://www.cpib.ac.uk/anther/riceindex.html) to present the expression profiles for gene characterization. This will assist in determining the key relationships between genes, thus enabling characterization of critical genes associated with anther and pollen regulatory networks.

RiceMetaSys for salt and drought stress responsive genes in rice: a web interface for crop improvement

Background

Genome-wide microarray has enabled development of robust databases for functional genomics studies in rice. However, such databases do not directly cater to the needs of breeders. Here, we have attempted to develop a web interface which combines the information from functional genomic studies across different genetic backgrounds with DNA markers so that they can be readily deployed in crop improvement. In the current version of the database, we have included drought and salinity stress studies since these two are the major abiotic stresses in rice.

Results

RiceMetaSys, a user-friendly and freely available web interface provides comprehensive information on salt responsive genes (SRGs) and drought responsive genes (DRGs) across genotypes, crop development stages and tissues, identified from multiple microarray datasets. ‘Physical position search’ is an attractive tool for those using QTL based approach for dissecting tolerance to salt and drought stress since it can provide the list of SRGs and DRGs in any physical interval. To identify robust candidate genes for use in crop improvement, the ‘common genes across varieties’ search tool is useful. Graphical visualization of expression profiles across genes and rice genotypes has been enabled to facilitate the user and to make the comparisons more impactful. Simple Sequence Repeat (SSR) search in the SRGs and DRGs is a valuable tool for fine mapping and marker assisted selection since it provides primers for survey of polymorphism. An external link to intron specific markers is also provided for this purpose. Bulk retrieval of data without any limit has been enabled in case of locus and SSR search.

Conclusions

The aim of this database is to facilitate users with a simple and straight-forward search options for identification of robust candidate genes from among thousands of SRGs and DRGs so as to facilitate linking variation in expression profiles to variation in phenotype.

Database URL: http://14.139.229.201

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1846-y) contains supplementary material, which is available to authorized users.

RapaNet: A Web Tool for the Co-Expression Analysis of Brassica rapa Genes

Accumulated microarray data are used for assessing gene function by providing statistical values for co-expressed genes; however, only a limited number of Web tools are available for analyzing the co-expression of genes of Brassica rapa. We have developed a Web tool called RapaNet (http://bioinfo.mju.ac.kr/arraynet/brassica300k/query/), which is based on a data set of 143 B rapa microarrays compiled from various organs and at different developmental stages during exposure to biotic or abiotic stress. RapaNet visualizes correlated gene expression information via correlational networks and phylogenetic trees using Pearson correlation coefficient (r). In addition, RapaNet provides hierarchical clustering diagrams, scatterplots of log ratio intensities, related pathway maps, and cis-element lists of promoter regions. To ascertain the functionality of RapaNet, the correlated genes encoding ribosomal protein (L7Ae), photosystem II protein D1 (psbA), and cytochrome P450 monooxygenase in glucosinolate biosynthesis (CYP79F1) were retrieved from RapaNet and compared with their Arabidopsis homologues. An analysis of the co-expressed genes revealed their shared and unique features.

Rice Expression Database (RED): An integrated RNA-Seq-derived gene expression database for rice

Rice is one of the most important stable food as well as a monocotyledonous model organism for the plant research community. Here, we present RED (Rice Expression Database; http://expression.ic4r.org), an integrated database of rice gene expression profiles derived entirely from RNA-Seq data. RED features a comprehensive collection of 284 high-quality RNA-Seq experiments, integrates a large number of gene expression profiles and covers a wide range of rice growth stages as well as various treatments. Based on massive expression profiles, RED provides a list of housekeeping and tissue-specific genes and dynamically constructs co-expression networks for gene(s) of interest. Besides, it provides user-friendly web interfaces for querying, browsing and visualizing expression profiles of concerned genes. Together, as a core resource in BIG Data Center, RED bears great utility for characterizing the function of rice genes and better understanding important biological processes and mechanisms underlying complex agronomic traits in rice.

Integrated omics analysis of specialized metabolism in medicinal plants

Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.

Gene coexpression network for trans-1,4-polyisoprene biosynthesis involving mevalonate and methylerythritol phosphate pathways in Eucommia ulmoides Oliver

Eucommia ulmoides, a deciduous dioecious plant species, accumulates trans-1,4-polyisoprene (TPI) in its tissues such as pericarp and leaf. Probable TPI synthase (trans-isoprenyl diphosphate synthase (TIDS)) genes were identified by expressed sequence tags of this species; however, the metabolic pathway of TPI biosynthesis, including the role of TIDSs, is unknown. To understand the mechanism of TPI biosynthesis at the transcriptional level, comprehensive gene expression data from various organs were generated and TPI biosynthesis related genes were extracted by principal component analysis (PCA). The metabolic pathway was assessed by comparing the coexpression network of TPI genes with the isoprenoid gene coexpression network of model plants. By PCA, we dissected 27 genes assumed to be involved in polyisoprene biosynthesis, including TIDS genes, genes encoding enzymes of the mevalonate (MVA) pathway and the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, and genes related to rubber synthesis. The coexpression network revealed that 22 of the 27 TPI biosynthesis genes are coordinately expressed. The network was clustered into two modules, and this was also observed in model plants. The first module was mainly comprised of MEP pathway genes and TIDS1 gene, and the second module, of MVA pathway genes and TIDS5 gene. These results indicate that TPI is likely biosynthesized by both the MEP and MVA pathways and that TIDS gene expression is differentially controlled by these pathways.

Practical Utilization of OryzaExpress and Plant Omics Data Center Databases to Explore Gene Expression Networks in Oryza Sativa and Other Plant Species

Analysis of a gene expression network (GEN), which is constructed based on similarity of gene expression profiles, is a widely used approach to gain clues for new biological insights. The recent abundant availability of transcriptome data in public databases is enabling GEN analysis under various experimental conditions, and even comparative GEN analysis across species. To provide a platform to gain biological insights from public transcriptome data, valuable databases have been created and maintained. This chapter introduces the web database OryzaExpress, providing omics information on Oryza sativa (rice). The integrated database Plant Omics Data Center, supporting a wide variety of plant species, is also described to compare omics information among multiple plant species.

PlantExpress: A Database Integrating OryzaExpress and ArthaExpress for Single-species and Cross-species Gene Expression Network Analyses with Microarray-Based Transcriptome Data

Publicly available microarray-based transcriptome data on plants are remarkably valuable in terms of abundance and variation of samples, particularly for Oryza sativa (rice) and Arabidopsis thaliana (Arabidopsis). Here, we introduce the web database PlantExpress (http://plantomics.mind.meiji.ac.jp/PlantExpress/) as a platform for gene expression network (GEN) analysis with the public microarray data of rice and Arabidopsis. PlantExpress has two functional modes. The single-species mode is specialized for GEN analysis within one of the species, while the cross-species mode is optimized for comparative GEN analysis between the species. The single-species mode for rice is the new version of OryzaExpress, which we have maintained since 2006. The single-species mode for Arabidopsis, named ArthaExpress, was newly developed. PlantExpress stores data obtained from three microarrays, the Affymetrix Rice Genome Array, the Agilent Rice Gene Expression 4x44K Microarray, and the Affymetrix Arabidopsis ATH1 Genome Array, with respective totals of 2,678, 1,206, and 10,940 samples. This database employs a ‘MyList’ function with which users may save lists of arbitrary genes and samples (experimental conditions) to use in analyses. In cross-species mode, the MyList function allows performing comparative GEN analysis between rice and Arabidopsis. In addition, the gene lists saved in MyList can be directly exported to the PODC database, which provides information and a platform for comparative GEN analysis based on RNA-seq data and knowledge-based functional annotation of plant genes. PlantExpress will facilitate understanding the biological functions of plant genes.

Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants

In quantitative gene expression analysis, normalization using a reference gene as an internal control is frequently performed for appropriate interpretation of the results. Efforts have been devoted to exploring superior novel reference genes using microarray transcriptomic data and to evaluating commonly used reference genes by targeting analysis. However, because the number of specifically detectable genes is totally dependent on probe design in the microarray analysis, exploration using microarray data may miss some of the best choices for the reference genes. Recently emerging RNA sequencing (RNA-seq) provides an ideal resource for comprehensive exploration of reference genes since this method is capable of detecting all expressed genes, in principle including even unknown genes. We report the results of a comprehensive exploration of reference genes using public RNA-seq data from plants such as Arabidopsis thaliana (Arabidopsis), Glycine max (soybean), Solanum lycopersicum (tomato) and Oryza sativa (rice). To select reference genes suitable for the broadest experimental conditions possible, candidates were surveyed by the following four steps: (1) evaluation of the basal expression level of each gene in each experiment; (2) evaluation of the expression stability of each gene in each experiment; (3) evaluation of the expression stability of each gene across the experiments; and (4) selection of top-ranked genes, after ranking according to the number of experiments in which the gene was expressed stably. Employing this procedure, 13, 10, 12 and 21 top candidates for reference genes were proposed in Arabidopsis, soybean, tomato and rice, respectively. Microarray expression data confirmed that the expression of the proposed reference genes under broad experimental conditions was more stable than that of commonly used reference genes. These novel reference genes will be useful for analyzing gene expression profiles across experiments carried out under various experimental conditions.

OryzaGenome: Genome Diversity Database of Wild Oryza Species

The species in the genus Oryza, encompassing nine genome types and 23 species, are a rich genetic resource and may have applications in deeper genomic analyses aiming to understand the evolution of plant genomes. With the advancement of next-generation sequencing (NGS) technology, a flood of Oryza species reference genomes and genomic variation information has become available in recent years. This genomic information, combined with the comprehensive phenotypic information that we are accumulating in our Oryzabase, can serve as an excellent genotype-phenotype association resource for analyzing rice functional and structural evolution, and the associated diversity of the Oryza genus. Here we integrate our previous and future phenotypic/habitat information and newly determined genotype information into a united repository, named OryzaGenome, providing the variant information with hyperlinks to Oryzabase. The current version of OryzaGenome includes genotype information of 446 O. rufipogon accessions derived by imputation and of 17 accessions derived by imputation-free deep sequencing. Two variant viewers are implemented: SNP Viewer as a conventional genome browser interface and Variant Table as a text-based browser for precise inspection of each variant one by one. Portable VCF (variant call format) file or tab-delimited file download is also available. Following these SNP (single nucleotide polymorphism) data, reference pseudomolecules/scaffolds/contigs and genome-wide variation information for almost all of the closely and distantly related wild Oryza species from the NIG Wild Rice Collection will be available in future releases. All of the resources can be accessed through http://viewer.shigen.info/oryzagenome/.

TENOR: Database for Comprehensive mRNA-Seq Experiments in Rice

Here we present TENOR (Transcriptome ENcyclopedia Of Rice, http://tenor.dna.affrc.go.jp), a database that encompasses large-scale mRNA sequencing (mRNA-Seq) data obtained from rice under a wide variety of conditions. Since the elucidation of the ability of plants to adapt to various growing conditions is a key issue in plant sciences, it is of great interest to understand the regulatory networks of genes responsible for environmental changes. We used mRNA-Seq and performed a time-course transcriptome analysis of rice, Oryza sativa L. (cv. Nipponbare), under 10 abiotic stress conditions (high salinity; high and low phosphate; high, low and extremely low cadmium; drought; osmotic; cold; and flood) and two plant hormone treatment conditions (ABA and jasmonic acid). A large number of genes that were responsive to abiotic stresses and plant hormones were detected by differential expression analysis. Furthermore, several responsive genes were found to encode transcription factors that could control the transcriptional network of stress responses, but the timing of the induction of these genes was not uniform across conditions. A significant number of cis-regulatory elements were enriched in the promoter regions of the responsive genes and were shared among conditions. These data suggest that some key components of gene regulation networks are shared between different stress signaling pathways. All the resources (novel genes identified from mRNA-Seq data, expression profiles, co-expressed genes and cis-regulatory elements) can be searched for and are available in TENOR.

Learning from Co-expression Networks: Possibilities and Challenges

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

Even though vast amounts of genome-wide gene expression data have become available in plants, it remains a challenge to effectively mine this information for the discovery of genes and gene networks, for instance those that control agronomically important traits. These networks reflect potential interactions among genes and, therefore, can lead to a systematic understanding of the molecular mechanisms underlying targeted biological processes. We discuss methods to analyze gene networks using gene expression data, specifically focusing on four common statistical approaches used to reconstruct networks: correlation, feature selection in supervised learning, probabilistic graphical model, and meta-prediction. In addition, we discuss the effective use of these methods for acquiring an in-depth understanding of biological systems in plants.

Network-assisted crop systems genetics: network inference and integrative analysis

Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years.

Upstream regulatory architecture of rice genes: summarizing the baseline towards genus-wide comparative analysis of regulatory networks and allele mining

Dissecting the upstream regulatory architecture of rice genes and their cognate regulator proteins is at the core of network biology and its applications to comparative functional genomics. With the rapidly advancing comparative genomics resources in the genus Oryza, a reference genome annotation that defines the various cis-elements and trans-acting factors that interface each gene locus with various intrinsic and extrinsic signals for growth, development, reproduction and adaptation must be established to facilitate the understanding of phenotypic variation in the context of regulatory networks. Such information is also important to establish the foundation for mining non-coding sequence variation that defines novel alleles and epialleles across the enormous phenotypic diversity represented in rice germplasm. This review presents a synthesis of the state of knowledge and consensus trends regarding the various cis-acting and trans-acting components that define spatio-temporal regulation of rice genes based on representative examples from both foundational studies in other model and non-model plants, and more recent studies in rice. The goal is to summarize the baseline for systematic upstream sequence annotation of the rapidly advancing genome sequence resources in Oryza in preparation for genus-wide functional genomics. Perspectives on the potential applications of such information for gene discovery, network engineering and genomics-enabled rice breeding are also discussed.

Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation

Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources.

Identification and molecular characterization of tissue-preferred rice genes and their upstream regularly sequences on a genome-wide level

BACKGROUND

Gene upstream regularly sequences (URSs) can be used as one of the tools to annotate the biological functions of corresponding genes. In addition, tissue-preferred URSs are frequently used to drive the transgene expression exclusively in targeted tissues during plant transgenesis. Although many rice URSs have been molecularly characterized, it is still necessary and valuable to identify URSs that will benefit plant transformation and aid in analyzing gene function.

RESULTS

In this study, we identified and characterized root-, seed-, leaf-, and panicle-preferred genes on a genome-wide level in rice. Subsequently, their expression patterns were confirmed through quantitative real-time RT-PCR (qRT-PCR) by randomly selecting 9candidate tissue-preferred genes. In addition, 5 tissue-preferred URSs were characterized by investigating the URS::GUS transgenic plants. Of these URS::GUS analyses, the transgenic plants harboring LOC_Os03g11350 URS::GUS construct showed the GUS activity only in young pollen. In contrast, when LOC_Os10g22450 URS was used to drive the reporter GUS gene, the GUS activity was detected only in mature pollen. Interestingly, the LOC_Os10g34360 URS was found to be vascular bundle preferred and its activities were restricted only to vascular bundles of leaves, roots and florets. In addition, we have also identified two URSs from genes LOC_Os02G15090 and LOC_Os06g31070 expressed in a seed-preferred manner showing the highest expression levels of GUS activities in mature seeds.

CONCLUSION

By genome-wide analysis, we have identified tissue-preferred URSs, five of which were further characterized using transgenic plants harboring URS::GUS constructs. These data might provide some evidence for possible functions of the genes and be a valuable resource for tissue-preferred candidate URSs for plant transgenesis.

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.

HapRice, an SNP haplotype database and a web tool for rice

Genome-wide single nucleotide polymorphism (SNP) analysis is a promising tool to examine the genetic diversity of rice populations and genetic traits of scientific and economic importance. Next-generation sequencing technology has accelerated the re-sequencing of diverse rice varieties and the discovery of genome-wide SNPs. Notably, validation of these SNPs by a high-throughput genotyping system, such as an SNP array, could provide a manageable and highly accurate SNP set. To enhance the potential utility of genome-wide SNPs for geneticists and breeders, analysis tools need to be developed. Here, we constructed an SNP haplotype database, which allows visualization of the allele frequency of all SNPs in the genome browser. We calculated the allele frequencies of 3,334 SNPs in 76 accessions from the world rice collection and 3,252 SNPs in 177 Japanese rice accessions; all these SNPs have been validated in our previous studies. The SNP haplotypes were defined by the allele frequency in each cultivar group (aus, indica, tropical japonica and temperate japonica) for the world rice accessions, and in non-irrigated and three irrigated groups (three variety registration periods) for Japanese rice accessions. We also developed web tools for finding polymorphic SNPs between any two rice accessions and for the primer design to develop cleaved amplified polymorphic sequence markers at any SNP. The 'HapRice' database and the web tools can be accessed at http://qtaro.abr.affrc.go.jp/index.html. In addition, we established a core SNP set consisting of 768 SNPs uniformly distributed in the rice genome; this set is of a practically appropriate size for use in rice genetic analysis.

Unlocking Triticeae genomics to sustainably feed the future

The tribe Triticeae includes the major crops wheat and barley. Within the last few years, the whole genomes of four Triticeae species-barley, wheat, Tausch's goatgrass (Aegilops tauschii) and wild einkorn wheat (Triticum urartu)-have been sequenced. The availability of these genomic resources for Triticeae plants and innovative analytical applications using next-generation sequencing technologies are helping to revitalize our approaches in genetic work and to accelerate improvement of the Triticeae crops. Comparative genomics and integration of genomic resources from Triticeae plants and the model grass Brachypodium distachyon are aiding the discovery of new genes and functional analyses of genes in Triticeae crops. Innovative approaches and tools such as analysis of next-generation populations, evolutionary genomics and systems approaches with mathematical modeling are new strategies that will help us discover alleles for adaptive traits to future agronomic environments. In this review, we provide an update on genomic tools for use with Triticeae plants and Brachypodium and describe emerging approaches toward crop improvements in Triticeae.

Rice DB: an Oryza Information Portal linking annotation, subcellular location, function, expression, regulation, and evolutionary information for rice and Arabidopsis

Omics research in Oryza sativa (rice) relies on the use of multiple databases to obtain different types of information to define gene function. We present Rice DB, an Oryza information portal that is a functional genomics database, linking gene loci to comprehensive annotations, expression data and the subcellular location of encoded proteins. Rice DB has been designed to integrate the direct comparison of rice with Arabidopsis (Arabidopsis thaliana), based on orthology or 'expressology', thus using and combining available information from two pre-eminent plant models. To establish Rice DB, gene identifiers (more than 40 types) and annotations from a variety of sources were compiled, functional information based on large-scale and individual studies was manually collated, hundreds of microarrays were analysed to generate expression annotations, and the occurrences of potential functional regulatory motifs in promoter regions were calculated. A range of computational subcellular localization predictions were also run for all putative proteins encoded in the rice genome, and experimentally confirmed protein localizations have been collated, curated and linked to functional studies in rice. A single search box allows anything from gene identifiers (for rice and/or Arabidopsis), motif sequences, subcellular location, to keyword searches to be entered, with the capability of Boolean searches (such as AND/OR). To demonstrate the utility of Rice DB, several examples are presented including a rice mitochondrial proteome, which draws on a variety of sources for subcellular location data within Rice DB. Comparisons of subcellular location, functional annotations, as well as transcript expression in parallel with Arabidopsis reveals examples of conservation between rice and Arabidopsis, using Rice DB (http://ricedb.plantenergy.uwa.edu.au).

Genes and co-expression modules common to drought and bacterial stress responses in Arabidopsis and rice

Plants are simultaneously exposed to multiple stresses resulting in enormous changes in the molecular landscape within the cell. Identification and characterization of the synergistic and antagonistic components of stress response mechanisms contributing to the cross talk between stresses is of high priority to explore and enhance multiple stress responses. To this end, we performed meta-analysis of drought (abiotic), bacterial (biotic) stress response in rice and Arabidopsis by analyzing a total of 386 microarray samples belonging to 20 microarray studies and identified approximately 3100 and 900 DEGs in rice and Arabidopsis, respectively. About 38.5% (1214) and 28.7% (272) DEGs were common to drought and bacterial stresses in rice and Arabidopsis, respectively. A majority of these common DEGs showed conserved expression status in both stresses. Gene ontology enrichment analysis clearly demarcated the response and regulation of various plant hormones and related biological processes. Fatty acid metabolism and biosynthesis of alkaloids were upregulated and, nitrogen metabolism and photosynthesis was downregulated in both stress conditions. WRKY transcription family genes were highly enriched in all upregulated gene sets while ‘CO-like’ TF family showed inverse relationship of expression between drought and bacterial stresses. Weighted gene co-expression network analysis divided DEG sets into multiple modules that show high co-expression and identified stress specific hub genes with high connectivity. Detection of consensus modules based on DEGs common to drought and bacterial stress revealed 9 and 4 modules in rice and Arabidopsis, respectively, with conserved and reversed co-expression patterns.

A new omics data resource of Pleurocybella porrigens for gene discovery

Background

Pleurocybellaporrigens is a mushroom-forming fungus, which has been consumed as a traditional food in Japan. In 2004, 55 people were poisoned by eating the mushroom and 17 people among them died of acute encephalopathy. Since then, the Japanese government has been alerting Japanese people to take precautions against eating the P. porrigens mushroom. Unfortunately, despite efforts, the molecular mechanism of the encephalopathy remains elusive. The genome and transcriptome sequence data of P. porrigens and the related species, however, are not stored in the public database. To gain the omics data in P. porrigens, we sequenced genome and transcriptome of its fruiting bodies and mycelia by next generation sequencing.

Methodology/Principal Findings

Short read sequences of genomic DNAs and mRNAs in P. porrigens were generated by Illumina Genome Analyzer. Genome short reads were de novo assembled into scaffolds using Velvet. Comparisons of genome signatures among Agaricales showed that P. porrigens has a unique genome signature. Transcriptome sequences were assembled into contigs (unigenes). Biological functions of unigenes were predicted by Gene Ontology and KEGG pathway analyses. The majority of unigenes would be novel genes without significant counterparts in the public omics databases.

Conclusions

Functional analyses of unigenes present the existence of numerous novel genes in the basidiomycetes division. The results mean that the omics information such as genome, transcriptome and metabolome in basidiomycetes is short in the current databases. The large-scale omics information on P. porrigens, provided from this research, will give a new data resource for gene discovery in basidiomycetes.