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

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.

X-ray computed tomography for 3D plant imaging

X-ray computed tomography (CT) is a valuable tool for 3D imaging of plant tissues and organs. Applications include the study of plant development and organ morphogenesis, as well as modeling of transport processes in plants. Some challenges remain, however, including attaining higher contrast for easier quantification, increasing the resolution for imaging subcellular features, and decreasing image acquisition and processing time for high-throughput phenotyping. In addition, phase contrast, multispectral, dark-field, soft X-ray, and time-resolved imaging are emerging. At the same time, a large amount of 3D image data are becoming available, posing challenges for data management. We review recent advances in the area of X-ray CT for plant imaging, and describe opportunities for using such images for studying transport processes in plants.

Regulation of the Plant Cell Cycle in Response to Hormones and the Environment

Developmental and environmental signals converge on cell cycle machinery to achieve proper and flexible organogenesis under changing environments. Studies on the plant cell cycle began 30 years ago, and accumulated research has revealed many links between internal and external factors and the cell cycle. In this review, we focus on how phytohormones and environmental signals regulate the cell cycle to enable plants to cope with a fluctuating environment. After introducing key cell cycle regulators, we first discuss how phytohormones and their synergy are important for regulating cell cycle progression and how environmental factors positively and negatively affect cell division. We then focus on the well-studied example of stress-induced G2 arrest and view the current model from an evolutionary perspective. Finally, we discuss the mechanisms controlling the transition from the mitotic cycle to the endocycle, which greatly contributes to cell enlargement and resultant organ growth in plants.

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.

BrassicaEDB: A Gene Expression Database for Brassica Crops

The genus Brassica contains several economically important crops, including rapeseed (Brassica napus, 2n = 38, AACC), the second largest source of seed oil and protein meal worldwide. However, research in rapeseed is hampered because it is complicated and time-consuming for researchers to access different types of expression data. We therefore developed the Brassica Expression Database (BrassicaEDB) for the research community. In the current BrassicaEDB, we only focused on the transcriptome level in rapeseed. We conducted RNA sequencing (RNA-Seq) of 103 tissues from rapeseed cultivar ZhongShuang11 (ZS11) at seven developmental stages (seed germination, seedling, bolting, initial flowering, full-bloom, podding, and maturation). We determined the expression patterns of 101,040 genes via FPKM analysis and displayed the results using the eFP browser. We also analyzed transcriptome data for rapeseed from 70 BioProjects in the SRA database and obtained three types of expression level data (FPKM, TPM, and read counts). We used this information to develop the BrassicaEDB, including “eFP”, “Treatment”, “Coexpression”, and “SRA Project” modules based on gene expression profiles and “Gene Feature”, “qPCR Primer”, and “BLAST” modules based on gene sequences. The BrassicaEDB provides comprehensive gene expression profile information and a user-friendly visualization interface for rapeseed researchers. Using this database, researchers can quickly retrieve the expression level data for target genes in different tissues and in response to different treatments to elucidate gene functions and explore the biology of rapeseed at the transcriptome level.

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.

LeGOO: An Expertized Knowledge Database for the Model Legume Medicago truncatula

Medicago truncatula was proposed, about three decades ago, as a model legume to study the Rhizobium-legume symbiosis. It has now been adopted to study a wide range of biological questions, including various developmental processes (in particular root, symbiotic nodule and seed development), symbiotic (nitrogen-fixing and arbuscular mycorrhizal endosymbioses) and pathogenic interactions, as well as responses to abiotic stress. With a number of tools and resources set up in M. truncatula for omics, genetics and reverse genetics approaches, massive amounts of data have been produced, as well as four genome sequence releases. Many of these data were generated with heterogeneous tools, notably for transcriptomics studies, and are consequently difficult to integrate. This issue is addressed by the LeGOO (for Legume Graph-Oriented Organizer) knowledge base (https://www.legoo.org), which finds the correspondence between the multiple identifiers of the same gene. Furthermore, an important goal of LeGOO is to collect and represent biological information from peer-reviewed publications, whatever the technical approaches used to obtain this information. The information is modeled in a graph-oriented database, which enables flexible representation, with currently over 200,000 relations retrieved from 298 publications. LeGOO also provides the user with mining tools, including links to the Mt5.0 genome browser and associated information (on gene functional annotation, expression, methylome, natural diversity and available insertion mutants), as well as tools to navigate through different model species. LeGOO is, therefore, an innovative database that will be useful to the Medicago and legume community to better exploit the wealth of data produced on this model species.

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.

Genome sequence analysis of the fairy ring-forming fungus Lepista sordida and gene candidates for interaction with plants

Circular patterns called “fairy rings” in fields are a natural phenomenon that arises through the interaction between basidiomycete fungi and plants. Acceleration or inhibition of plant vegetative growth and the formation of mushroom fruiting bodies are both commonly observed when fairy rings form. The gene of an enzyme involved in the biosynthesis of these regulators was recently isolated in the fairy ring-forming fungus, Lepista sordida. To identify other genes involved in L. sordida fairy ring formation, we used previously generated sequence data to produce a more complete draft genome sequence for this species. Finally, we predicted the metabolic pathways of the plant growth regulators and 29 candidate enzyme-coding genes involved in fairy-ring formation based on gene annotations. Comparisons of protein coding genes among basidiomycete fungi revealed two nitric oxide synthase gene candidates that were uniquely encoded in genomes of fairy ring-forming fungi. These results provide a basis for the discovery of genes involved in fairy ring formation and for understanding the mechanisms involved in the interaction between fungi and plants. We also constructed a new web database F-RINGS (http://bioinf.mind.meiji.ac.jp/f-rings/) to provide the comprehensive genomic information for L. sordida.

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.

The parthenocarpic gene Pat-k is generated by a natural mutation of SlAGL6 affecting fruit development in tomato (Solanum lycopersicum L.)

BACKGROUND

Parthenocarpy is a desired trait in tomato because it can overcome problems with fruit setting under unfavorable environmental conditions. A parthenocarpic tomato cultivar, 'MPK-1', with a parthenocarpic gene, Pat-k, exhibits stable parthenocarpy that produces few seeds. Because 'MPK-1' produces few seeds, seedlings are propagated inefficiently via cuttings. It was reported that Pat-k is located on chromosome 1. However, the gene had not been isolated and the relationship between the parthenocarpy and low seed set in 'MPK-1' remained unclear. In this study, we isolated Pat-k to clarify the relationship between parthenocarpy and low seed set in 'MPK-1'.

RESULTS

Using quantitative trait locus (QTL) analysis for parthenocarpy and seed production, we detected a major QTL for each trait on nearly the same region of the Pat-k locus on chromosome 1. To isolate Pat-k, we performed fine mapping using an F₄ population following the cross between a non-parthenocarpic cultivar, 'Micro-Tom' and 'MPK-1'. The results showed that Pat-k was located in the 529 kb interval between two markers, where 60 genes exist. By using data from a whole genome re-sequencing and genome sequence analysis of 'MPK-1', we could identify that the SlAGAMOUS-LIKE 6 (SlAGL6) gene of 'MPK-1' was mutated by a retrotransposon insertion. The transcript level of SlAGL6 was significantly lower in ovaries of 'MPK-1' than a non-parthenocarpic cultivar. From these results, we could conclude that Pat-k is SlAGL6, and its down-regulation in 'MPK-1' causes parthenocarpy and low seed set. In addition, we observed abnormal micropyles only in plants homozygous for the 'MPK-1' allele at the Pat-k/SlAGL6 locus. This result suggests that Pat-k/SlAGL6 is also related to ovule formation and that the low seed set in 'MPK-1' is likely caused by abnormal ovule formation through down-regulation of Pat-k/SlAGL6.

CONCLUSIONS

Pat-k is identical to SlAGL6, and its down-regulation causes parthenocarpy and low seed set in 'MPK-1'. Moreover, down-regulation of Pat-k/SlAGL6 could cause abnormal ovule formation, leading to a reduction in the number of seeds.