AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species

BiomeNet: a database for construction and analysis of functional interaction networks for any species with a sequenced genome

Motivation

Owing to advanced DNA sequencing and genome assembly technology, the number of species with sequenced genomes is rapidly increasing. The aim of the recently launched Earth BioGenome Project is to sequence genomes of all eukaryotic species on Earth over the next 10 years, making it feasible to obtain genomic blueprints of the majority of animal and plant species by this time. Genetic models of the sequenced species will later be subject to functional annotation, and a comprehensive molecular network should facilitate functional analysis of individual genes and pathways. However, network databases are lagging behind genome sequencing projects as even the largest network database provides gene networks for less than 10% of sequenced eukaryotic genomes, and the knowledge gap between genomes and interactomes continues to widen.

Results

We present BiomeNet, a database of 95 scored networks comprising over 8 million co-functional links, which can build and analyze gene networks for any species with the sequenced genome. BiomeNet transfers functional interactions between orthologous proteins from source networks to the target species within minutes and automatically constructs gene networks with the quality comparable to that of existing networks. BiomeNet enables assembly of the first-in-species gene networks not available through other databases, which are highly predictive of diverse biological processes and can also provide network analysis by extracting subnetworks for individual biological processes and network-based gene prioritizations. These data indicate that BiomeNet could enhance the benefits of decoding the genomes of various species, thus improving our understanding of the Earth’ biodiversity.

Availability and implementation

The BiomeNet is freely available at http://kobic.re.kr/biomenet/.

Supplementary information

Supplementary data are available at Bioinformatics online.

BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L

Cultivated barley (Hordeum vulgare L.) is one of the most produced cereal crops worldwide after maize, bread wheat, and rice. Barley is an important crop species not only as a food source, but also in plant genetics because it harbors numerous stress response alleles in its genome that can be exploited for crop engineering. However, the functional annotation of its genome is relatively poor compared with other major crops. Moreover, bioinformatics tools for system-wide analyses of omics data from barley are not yet available. We have thus developed BarleyNet, a co-functional network of 26,145 barley genes, along with a web server for network-based predictions (http://www.inetbio.org/barleynet). We demonstrated that BarleyNet's prediction of biological processes is more accurate than that of an existing barley gene network. We implemented three complementary network-based algorithms for prioritizing genes or functional concepts to study genetic components of complex traits such as environmental stress responses: (i) a pathway-centric search for candidate genes of pathways or complex traits; (ii) a gene-centric search to infer novel functional concepts for genes; and (iii) a context-centric search for novel genes associated with stress response. We demonstrated the usefulness of these network analysis tools in the study of stress response using proteomics and transcriptomics data from barley leaves and roots upon drought or heat stresses. These results suggest that BarleyNet will facilitate our understanding of the underlying genetic components of complex traits in barley.

netGO: R-Shiny package for network-integrated pathway enrichment analysis

Summary

We present an R-Shiny package, netGO, for novel network-integrated pathway enrichment analysis. The conventional Fisher’s exact test (FET) considers the extent of overlap between target genes and pathway gene-sets, while recent network-based analysis tools consider only network interactions between the two. netGO implements an intuitive framework to integrate both the overlap and networks into a single score, and adaptively resamples genes based on network degrees to assess the pathway enrichment. In benchmark tests for gene expression and genome-wide association study (GWAS) data, netGO captured the relevant gene-sets better than existing tools, especially when analyzing a small number of genes. Specifically, netGO provides user-interactive visualization of the target genes, enriched gene-set and their network interactions for both netGO and FET results for further analysis. For this visualization, we also developed a standalone R-Shiny package shinyCyJS to connect R-shiny and the JavaScript version of cytoscape.

Availability and implementation

netGO R-Shiny package is freely available from github, https://github.com/unistbig/netGO.

Supplementary information

Supplementary data are available at Bioinformatics online.

Diurnal.plant.tools: Comparative Transcriptomic and Co-expression Analyses of Diurnal Gene Expression of the Archaeplastida Kingdom

Almost all organisms coordinate some aspects of their biology through the diurnal cycle. Photosynthetic organisms, and plants especially, have established complex programs that coordinate physiological, metabolic and developmental processes with the changing light. The diurnal regulation of the underlying transcriptional processes is observed when groups of functionally related genes (gene modules) are expressed at a specific time of the day. However, studying the diurnal regulation of these gene modules in the plant kingdom was hampered by the large amount of data required for the analyses. To meet this need, we used gene expression data from 17 diurnal studies spanning the whole Archaeplastida kingdom (Plantae kingdom in the broad sense) to make an online diurnal database. We have equipped the database with tools that allow user-friendly cross-species comparisons of gene expression profiles, entire co-expression networks, co-expressed clusters (involved in specific biological processes), time-specific gene expression and others. We exemplify how these tools can be used by studying three important biological questions: (i) the evolution of cell division, (ii) the diurnal control of gene modules in algae and (iii) the conservation of diurnally controlled modules across species. The database is freely available at http://diurnal.plant.tools.

Bioinformatics-assisted, integrated omics studies on medicinal plants

The immense therapeutic and economic values of medicinal plants have attracted increasing attention from the worldwide researchers. It has been recognized that production of the authentic and high-quality herbal drugs became the prerequisite for maintaining the healthy development of the traditional medicine industry. To this end, intensive research efforts have been devoted to the basic studies, in order to pave a way for standardized authentication of the plant materials, and bioengineering of the metabolic pathways in the medicinal plants. In this paper, the recent advances of omics studies on the medicinal plants were summarized from several aspects, including phenomics and taxonomics, genomics, transcriptomics, proteomics and metabolomics. We proposed a multi-omics data-based workflow for medicinal plant research. It was emphasized that integration of the omics data was important for plant authentication and mechanistic studies on plant metabolism. Additionally, the computational tools for proper storage, efficient processing and high-throughput analyses of the omics data have been introduced into the workflow. According to the workflow, authentication of the medicinal plant materials should not only be performed at the phenomics level but also be implemented by genomic and metabolomic marker-based examination. On the other hand, functional genomics studies, transcriptional regulatory networks and protein-protein interactions will contribute greatly for deciphering the secondary metabolic pathways. Finally, we hope that our work could inspire further efforts on the bioinformatics-assisted, integrated omics studies on the medicinal plants.

Systems genomics approaches provide new insights into Arabidopsis thaliana root growth regulation under combinatorial mineral nutrient limitation

The molecular mechanisms by which plants modulate their root growth rate (RGR) in response to nutrient deficiency are largely unknown. Using Arabidopsis thaliana accessions, we analyzed RGR variation under combinatorial mineral nutrient deficiencies involving phosphorus (P), iron (Fe), and zinc (Zn). While -P stimulated early RGR of most accessions, -Fe or -Zn reduced it. The combination of either -P-Fe or -P-Zn led to suppression of the growth inhibition exerted by -Fe or -Zn alone. Surprisingly, root growth responses of the reference accession Columbia (Col-0) were not representative of the species under -P nor -Zn. Using a systems approach that combines GWAS, network-based candidate identification, and reverse genetic screen, we identified new genes that regulate root growth in -P-Fe: VIM1, FH6, and VDAC3. Our findings provide a framework to systematically identifying favorable allelic variations to improve root growth, and to better understand how plants sense and respond to multiple environmental cues.

Plants thrive in highly heterogenous soils. How they compute a multitude of contrasting stimuli and mount an adaptive response without a centralized information processing unit is an intriguing question. For instance, below ground, roots can sense and respond to the single or multiple nutrient stresses, and adjust its growth rate accordingly. Nevertheless, the genetic architecture of root growth responses under single and combined stress remains poorly understood. To fill this gap in our understanding about such crucial phenomenon for plant survival, we explored the natural variation of root growth rate (RGR) in Arabidopsis grown under single and combined nutritional stress, including deficiencies of iron (-Fe), zinc (-Zn), phosphate and iron (-P-Fe) and phosphate and zinc (-P-Zn). Our GWAS revealed distinct genetic architectures underlying root growth responses to single or combined nutrient stresses. By integrating GWAS and coexpression networks, we identified and validated genes controlling the variation of root growth to combined nutrient-deficiency, namely VARIANT IN METHYLATION 1, FORMIN-LIKE-PROTEIN-6 and VOLTAGE-DEPENDENT ANION-SELECTIVE CHANNEL PROTEIN 3. Our findings provide a framework to accelerate future research aiming at better understanding how plants sense and respond to multiple environmental inputs, and promise to help designing new agronomical and biotechnological strategies to improve root growth.

Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea

Background:

Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance.

Objective:

The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners.

Methods:

For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions.

Results:

A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium.

Conclusion:

This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

Co-expression networks for plant biology: why and how

Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.

Common and specific responses to iron and phosphorus deficiencies in roots of apple tree (Malus × domestica)

Iron and phosphorus are abundant elements in soils but poorly available for plant nutrition. The availability of these two nutrients represents a major constraint for fruit tree cultivation such as apple (Malus × domestica) leading very often to a decrease of fruit productivity and quality worsening. Aim of this study was to characterize common and specific features of plant response to Fe and P deficiencies by ionomic, transcriptomic and exudation profiling of apple roots. Under P deficiency, the root release of oxalate and flavonoids increased. Genes encoding for transcription factors and transporters involved in the synthesis and release of root exudates were upregulated by P-deficient roots, as well as those directly related to P acquisition. In Fe-deficiency, plants showed an over-accumulation of P, Zn, Cu and Mn and induced the transcription of those genes involved in the mechanisms for the release of Fe-chelating compounds and Fe mobilization inside the plants. The intriguing modulation in roots of some transcription factors, might indicate that, in this condition, Fe homeostasis is regulated by a FIT-independent pathway. In the present work common and specific features of apple response to Fe and P deficiency has been reported. In particular, data indicate similar modulation of a. 230 genes, suggesting the occurrence of a crosstalk between the two nutritional responses involving the transcriptional regulation, shikimate pathway, and the root release of exudates.

PhytoNet: comparative co-expression network analyses across phytoplankton and land plants

Phytoplankton consists of autotrophic, photosynthesizing microorganisms that are a crucial component of freshwater and ocean ecosystems. However, despite being the major primary producers of organic compounds, accounting for half of the photosynthetic activity worldwide and serving as the entry point to the food chain, functions of most of the genes of the model phytoplankton organisms remain unknown. To remedy this, we have gathered publicly available expression data for one chlorophyte, one rhodophyte, one haptophyte, two heterokonts and four cyanobacteria and integrated it into our PlaNet (Plant Networks) database, which now allows mining gene expression profiles and identification of co-expressed genes of 19 species. We exemplify how the co-expressed gene networks can be used to reveal functionally related genes and how the comparative features of PhytoNet allow detection of conserved transcriptional programs between cyanobacteria, green algae, and land plants. Additionally, we illustrate how the database allows detection of duplicated transcriptional programs within an organism, as exemplified by two putative DNA repair programs within Chlamydomonas reinhardtii. PhytoNet is available from www.gene2function.de.

MaizeNet: a co-functional network for network-assisted systems genetics in Zea mays

Maize (Zea mays) has multiple uses in human food, animal fodder, starch and sweetener production and as a biofuel, and is accordingly the most extensively cultivated cereal worldwide. To enhance maize production, genetic factors underlying important agricultural traits, including stress tolerance and flowering, have been explored through forward and reverse genetics approaches. Co-functional gene networks are systems biology resources useful in identifying trait-associated genes in plants by prioritizing candidate genes. Here, we present MaizeNet (http://www.inetbio.org/maizenet/), a genome-scale co-functional network of Z. mays genes, and a companion web server for network-assisted systems genetics. We describe the validation of MaizeNet network quality and its ability to functionally predict molecular pathways and complex traits in maize. Furthermore, we demonstrate that MaizeNet-based prioritization of candidate genes can facilitate the identification of cell wall biosynthesis genes and detect network communities associated with flowering-time candidate genes derived from genome-wide association studies. The demonstrated gene prioritization and subnetwork analysis can be conducted by simply submitting maize gene models based on the commonly used B73 RefGen_v3 and the latest B73 RefGen_v4 reference genomes on the MaizeNet web server. MaizeNet-based network-assisted systems genetics will substantially accelerate the discovery of trait-associated genes for crop improvement.

SuCComBase: a manually curated repository of plant sulfur-containing compounds

Plants produce a wide range of secondary metabolites that play important roles in plant defense and immunity, their interaction with the environment and symbiotic associations. Sulfur-containing compounds (SCCs) are a group of important secondary metabolites produced in members of the Brassicales order. SCCs constitute various groups of phytochemicals, but not much is known about them. Findings from previous studies on SCCs were scattered in published literatures, hence SuCComBase was developed to store all molecular information related to the biosynthesis of SCCs. Information that includes genes, proteins and compounds that are involved in the SCC biosynthetic pathway was manually identified from databases and published scientific literatures. Sets of co-expression data was analyzed to search for other possible (previously unknown) genes that might be involved in the biosynthesis of SCC. These genes were named as potential SCC-related encoding genes. A total of 147 known and 92 putative Arabidopsis thaliana SCC-related genes from literatures were used to identify other potential SCC-related encoding genes. We identified 778 potential SCC-related encoding genes, 4026 homologs to the SCC-related encoding genes and 116 SCCs as shown on SuCComBase homepage. Data entries are searchable from the Main page, Search, Browse and Datasets tabs. Users can easily download all data stored in SuCComBase. All publications related to SCCs are also indexed in SuCComBase, which is currently the first and only database dedicated to plant SCCs. SuCComBase aims to become a manually curated and au fait knowledge-based repository for plant SCCs.

PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation

Transcription factor (TF) has a significant influence on the state of a cell by regulating multiple down-stream genes. Thus, experimental and computational biologists have made great efforts to construct TF gene networks for regulatory interactions between TFs and their target genes. Now, an important research question is how to utilize TF networks to investigate the response of a plant to stress at the transcription control level using time-series transcriptome data. In this article, we present a new computational network, PropaNet, to investigate dynamics of TF networks from time-series transcriptome data using two state-of-the-art network analysis techniques, influence maximization and network propagation. PropaNet uses the influence maximization technique to produce a ranked list of TFs, in the order of TF that explains differentially expressed genes (DEGs) better at each time point. Then, a network propagation technique is used to select a group of TFs that explains DEGs best as a whole. For the analysis of Arabidopsis time series datasets from AtGenExpress, we used PlantRegMap as a template TF network and performed PropaNet analysis to investigate transcriptional dynamics of Arabidopsis under cold and heat stress. The time varying TF networks showed that Arabidopsis responded to cold and heat stress quite differently. For cold stress, bHLH and bZIP type TFs were the first responding TFs and the cold signal influenced histone variants, various genes involved in cell architecture, osmosis and restructuring of cells. However, the consequences of plants under heat stress were up-regulation of genes related to accelerating differentiation and starting re-differentiation. In terms of energy metabolism, plants under heat stress show elevated metabolic process and resulting in an exhausted status. We believe that PropaNet will be useful for the construction of condition-specific time-varying TF network for time-series data analysis in response to stress. PropaNet is available at http://biohealth.snu.ac.kr/software/PropaNet.

Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach

BACKGROUND

Drought is a severe environmental stress. It is estimated that about 50% of the world rice production is affected mainly by drought. Apart from conventional breeding strategies to develop drought-tolerant crops, innovative computational approaches may provide insights into the underlying molecular mechanisms of stress response and identify drought-responsive markers. Here we propose a network-based computational approach involving a meta-analytic study of seven drought-tolerant rice genotypes under drought stress.

RESULTS

Co-expression networks enable large-scale analysis of gene-pair associations and tightly coupled clusters that may represent coordinated biological processes. Considering differentially expressed genes in the co-expressed modules and supplementing external information such as resistance/tolerance QTLs, transcription factors, network-based topological measures, we identify and prioritize drought-adaptive co-expressed gene modules and potential candidate genes. Using the candidate genes that are well-represented across the datasets as 'seed' genes, two drought-specific protein-protein interaction networks (PPINs) are constructed with up- and down-regulated genes. Cluster analysis of the up-regulated PPIN revealed ABA signalling pathway as a central process in drought response with a probable crosstalk with energy metabolic processes. Tightly coupled gene clusters representing up-regulation of core cellular respiratory processes and enhanced degradation of branched chain amino acids and cell wall metabolism are identified. Cluster analysis of down-regulated PPIN provides a snapshot of major processes associated with photosynthesis, growth, development and protein synthesis, most of which are shut down during drought. Differential regulation of phytohormones, e.g., jasmonic acid, cell wall metabolism, signalling and posttranslational modifications associated with biotic stress are elucidated. Functional characterization of topologically important, drought-responsive uncharacterized genes that may play a role in important processes such as ABA signalling, calcium signalling, photosynthesis and cell wall metabolism is discussed. Further transgenic studies on these genes may help in elucidating their biological role under stress conditions.

CONCLUSION

Currently, a large number of resources for rice functional genomics exist which are mostly underutilized by the scientific community. In this study, a computational approach integrating information from various resources such as gene co-expression networks, protein-protein interactions and pathway-level information is proposed to provide a systems-level view of complex drought-responsive processes across the drought-tolerant genotypes.

Gene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana

A comprehensive network of the Arabidopsis transcriptome was analyzed and may serve as a valuable resource for candidate gene function investigations. A web tool to explore module information was also provided. Arabidopsis thaliana is a widely studied model plant whose transcriptome has been substantially profiled in various tissues, development stages and other conditions. These data can be reused for research on gene function through a systematic analysis of gene co-expression relationships. We collected microarray data from National Center for Biotechnology Information Gene Expression Omnibus, identified modules of co-expressed genes and annotated module functions. These modules were associated with experiments/traits, which provided potential signature modules for phenotypes. Novel heat shock proteins were implicated according to guilt by association. A higher-order module networks analysis suggested that the Arabidopsis network can be further organized into 15 meta-modules and that a chloroplast meta-module has a distinct gene expression pattern from the other 14 meta-modules. A comparison with the rice transcriptome revealed preserved modules and KEGG pathways. All the module gene information was available from an online tool at http://bioinformatics.fafu.edu.cn/arabi/ . Our findings provide a new source for future gene discovery in Arabidopsis.

croFGD: Catharanthus roseus Functional Genomics Database

Catharanthus roseus is a medicinal plant, which can produce monoterpene indole alkaloid (MIA) metabolites with biological activity and is rich in vinblastine and vincristine. With release of the scaffolded genome sequence of C. roseus, it is necessary to annotate gene functions on the whole-genome level. Recently, 53 RNA-seq datasets are available in public with different tissues (flower, root, leaf, seedling, and shoot) and different treatments (MeJA, PnWB infection and yeast elicitor). We used in-house data process pipeline with the combination of PCC and MR algorithms to construct a co-expression network exploring multi-dimensional gene expression (global, tissue preferential, and treat response) through multi-layered approaches. In the meanwhile, we added miRNA-target pairs, predicted PPI pairs into the network and provided several tools such as gene set enrichment analysis, functional module enrichment analysis, and motif analysis for functional prediction of the co-expression genes. Finally, we have constructed an online croFGD database (http://bioinformatics.cau.edu.cn/croFGD/). We hope croFGD can help the communities to study the C. roseus functional genomics and make novel discoveries about key genes involved in some important biological processes.

Evolution and Identification of the WRKY Gene Family in Quinoa (Chenopodium quinoa)

The WRKY gene family plays a unique role in plant stress tolerance. Quinoa is a cultivated crop worldwide that is known for its high stress tolerance. The WRKY gene family in quinoa has not yet been studied. Using a genome-wide search method, we identified 1226 WRKY genes in 15 plant species, seven animal species, and seven fungi species. WRKY proteins were not found in animal species and five fungi species, but were, however, widespread in land plants. A total of 92 CqWRKY genes were identified in quinoa. Based on the phylogenetic analysis, these CqWRKY genes were classified into three groups. The CqWRKY proteins have a highly conserved heptapeptide WRKYGQK with 15 conserved elements. Furthermore, a total of 25 CqWRKY genes were involved in the co-expression pathway of organ development and osmotic stress. The expression level of more than half of these CqWRKY genes showed significant variation under salt or drought stress. This study reports, for the first time, the findings of the CqWRKY gene family in quinoa at the genome-wide level. This information will be beneficial for our understanding of the molecular mechanisms of stress tolerance in crops, such as quinoa.

Elucidation and analyses of the regulatory networks of upland and lowland ecotypes of switchgrass in response to drought and salt stresses

Switchgrass is an important bioenergy crop typically grown in marginal lands, where the plants must often deal with abiotic stresses such as drought and salt. Alamo is known to be more tolerant to both stress types than Dacotah, two ecotypes of switchgrass. Understanding of their stress response and adaptation programs can have important implications to engineering more stress tolerant plants. We present here a computational study by analyzing time-course transcriptomic data of the two ecotypes to elucidate and compare their regulatory systems in response to drought and salt stresses. A total of 1,693 genes (target genes or TGs) are found to be differentially expressed and possibly regulated by 143 transcription factors (TFs) in response to drought stress together in the two ecotypes. Similarly, 1,535 TGs regulated by 110 TFs are identified to be involved in response to salt stress. Two regulatory networks are constructed to predict their regulatory relationships. In addition, a time-dependent hidden Markov model is derived for each ecotype responding to each stress type, to provide a dynamic view of how each regulatory network changes its behavior over time. A few new insights about the response mechanisms are predicted from the regulatory networks and the time-dependent models. Comparative analyses between the network models of the two ecotypes reveal key commonalities and main differences between the two regulatory systems. Overall, our results provide new information about the complex regulatory mechanisms of switchgrass responding to drought and salt stresses.

PlaD: A Transcriptomics Database for Plant Defense Responses to Pathogens, Providing New Insights into Plant Immune System

High-throughput transcriptomics technologies have been widely used to study plant transcriptional reprogramming during the process of plant defense responses, and a large quantity of gene expression data have been accumulated in public repositories. However, utilization of these data is often hampered by the lack of standard metadata annotation. In this study, we curated 2444 public pathogenesis-related gene expression samples from the model plant Arabidopsis and three major crops (maize, rice, and wheat). We organized the data into a user-friendly database termed as PlaD. Currently, PlaD contains three key features. First, it provides large-scale curated data related to plant defense responses, including gene expression and gene functional annotation data. Second, it provides the visualization of condition-specific expression profiles. Third, it allows users to search co-regulated genes under the infections of various pathogens. Using PlaD, we conducted a large-scale transcriptome analysis to explore the global landscape of gene expression in the curated data. We found that only a small fraction of genes were differentially expressed under multiple conditions, which might be explained by their tendency of having more network connections and shorter network distances in gene networks. Collectively, we hope that PlaD can serve as an important and comprehensive knowledgebase to the community of plant sciences, providing insightful clues to better understand the molecular mechanisms underlying plant immune responses. PlaD is freely available at http://systbio.cau.edu.cn/plad/index.php or http://zzdlab.com/plad/index.php.

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using 'Omics'-Based Network Approaches: A Future Perspective

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, 'omics'-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight 'omics'-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in 'omics' analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with 'omics'-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.

AraNet: A Network Biology Server for Arabidopsis thaliana and Other Non-Model Plant Species

Functional gene networks link genes based on their functional relatedness, which is inferred from various complementary biological datasets. Gene networks comprising vast amounts of data can be used to predict which genes are associated with complex traits. Decades of studies in plant biology using the model organism Arabidopsis thaliana have generated large amounts of information, enabling the development of a system-level molecular network. AraNet (currently version 2) is a genome-scale functional gene network for Arabidopsis thaliana, constructed by integrating 19 types of genomics data and can be explored through a web-server (http://www.inetbio.org/aranet) to identify candidate genes for traits of interest. AraNet provides two alternative search paths for users to identify candidate genes and functions. The web server also exploits ortholog relationships between plant species and projects the genes of 28 other plant species (as of April, 2016) into the network of Arabidopsis genes. This allows researchers to use AraNet to predict genes/functions of not only Arabidopsis but also other non-model plants by expanding the functional knowledge of Arabidopsis. Here, we present a detailed description of how to search the AraNet network and interpret the search results to study plant gene functions and their associations with complex phenotypes.

Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana

Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists.

The role of Arabidopsis thaliana RASD1 gene in ABA-dependent abiotic stress response

Abiotic stress is one of the key parameters affecting plant productivity. Drought and soil salinity, in particular, challenge plants to activate various response mechanisms to withstand these adverse growth conditions. While the molecular events that take place are complex and to a large extent unclear, the plant hormone abscisic acid (ABA) is considered a major player in mediating the adaptation of plants to stress. Here we report the identification of an ABA-insensitive mutant from Arabidopsis thaliana. A combination of molecular, genetic and physiology approaches were implemented, to characterise the AtRASD1 locus (RESPONSIVENESS TO ABA SALT AND DROUGHT 1) and to investigate its role in plant development. RASD1 is expressed predominantly in the vascular system of A. thaliana and encodes a peptide of unknown function with no similarity to any known sequence to date. The protein is localised in the nucleus and the cytoplasm, and RASD1-impaired plants are drought-intolerant and insensitive to exogenous ABA and NaCl during germination and root growth. Our data indicate that RASD1 is involved in ABA-dependent signal transduction pathways and therefore in enabling plants to activate response mechanisms related to seed germination and abiotic stress.

araGWAB: Network-based boosting of genome-wide association studies in Arabidopsis thaliana

Genome-wide association studies (GWAS) have been applied for the genetic dissection of complex phenotypes in Arabidopsis thaliana. However, the significantly associated single-nucleotide polymorphisms (SNPs) could not explain all the phenotypic variations. A major reason for missing true phenotype-associated loci is the strict P-value threshold after adjustment for multiple hypothesis tests to reduce false positives. This statistical limitation can be partly overcome by increasing the sample size, but at a much higher cost. Alternatively, weak phenotype-association signals can be boosted by integrating other types of data. Here, we present a web application for network-based Arabidopsis genome-wide association boosting-araGWAB-which augments the likelihood of association with the given phenotype by integrating GWAS summary statistics (SNP P-values) and co-functional gene network information. The integration utilized the inherent values of SNPs with subthreshold significance, thus substantially increasing the information usage of GWAS data. We found that araGWAB could more effectively retrieve genes known to be associated with various phenotypes relevant to defense against bacterial pathogens, flowering time regulation, and organ development in A. thaliana. We also found that many of the network-boosted candidate genes for the phenotypes were supported by previous publications. The araGWAB is freely available at http://www.inetbio.org/aragwab/ .

Genome-Wide Identification and Expression Analysis of the HD-Zip Gene Family in Wheat (Triticum aestivum L.)

The homeodomain-leucine zipper (HD-Zip) gene family, as plant-specific transcription factors, plays an important role in plant development and growth as well as in the response to diverse stresses. Although HD-Zip genes have been extensively studied in many plants, they had not yet been studied in wheat, especially those involved in response to abiotic stresses. In this study, 46 wheat HD-Zip genes were identified using a genome-wide search method. Phylogenetic analysis classified these genes into four groups, numbered 4, 5, 17 and 20 respectively. In total, only three genes with A, B and D homoeologous copies were identified. Furthermore, the gene interaction networks found that the TaHDZ genes played a critical role in the regulatory pathway of organ development and osmotic stress. Finally, the expression profiles of the wheat HD-Zips in different tissues and under various abiotic stresses were investigated using the available RNA sequencing (RNA-Seq) data and then validated by quantitative real-time polymerase chain reaction (qRT-PCR) to obtain the tissue-specific and stress-responsive candidates. This study systematically identifies the HD-Zip gene family in wheat at the genome-wide level, providing important candidates for further functional analysis and contributing to the better understanding of the molecular basis of development and stress tolerance in wheat.

MorphDB: Prioritizing Genes for Specialized Metabolism Pathways and Gene Ontology Categories in Plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest.

LSTrAP: efficiently combining RNA sequencing data into co-expression networks

BACKGROUND

Since experimental elucidation of gene function is often laborious, various in silico methods have been developed to predict gene function of uncharacterized genes. Since functionally related genes are often expressed in the same tissues, conditions and developmental stages (co-expressed), functional annotation of characterized genes can be transferred to co-expressed genes lacking annotation. With genome-wide expression data available, the construction of co-expression networks, where genes are nodes and edges connect significantly co-expressed genes, provides unprecedented opportunities to predict gene function. However, the construction of such networks requires large volumes of high-quality data, multiple processing steps and a considerable amount of computation power. While efficient tools exist to process RNA-Seq data, pipelines which combine them to construct co-expression networks efficiently are currently lacking.

RESULTS

LSTrAP (Large-Scale Transcriptome Analysis Pipeline), presented here, combines all essential tools to construct co-expression networks based on RNA-Seq data into a single, efficient workflow. By supporting parallel computing on computer cluster infrastructure, processing hundreds of samples becomes feasible as shown here for Arabidopsis thaliana and Sorghum bicolor, which comprised 876 and 215 samples respectively. The former was used here to show how the quality control, included in LSTrAP, can detect spurious or low-quality samples. The latter was used to show how co-expression networks are able to group known photosynthesis genes and imply a role in this process of several, currently uncharacterized, genes.

CONCLUSIONS

LSTrAP combines the most popular and performant methods to construct co-expression networks from RNA-Seq data into a single workflow. This allows large amounts of expression data, required to construct co-expression networks, to be processed efficiently and consistently across hundreds of samples. LSTrAP is implemented in Python 3.4 (or higher) and available under MIT license from https://github.molgen.mpg.de/proost/LSTrAP.

The G-Box Transcriptional Regulatory Code in Arabidopsis

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes; these expansions are linked to adaptation to environmental stressors. Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determined that the flanking sequences near G-boxes help determine in vitro specificity but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we constructed a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a Web site that provides interactive visualizations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

Genome-wide analysis of basic helix-loop-helix (bHLH) transcription factors in Brachypodium distachyon

BACKGROUND

As a superfamily of transcription factors (TFs), the basic helix-loop-helix (bHLH) proteins have been characterized functionally in many plants with a vital role in the regulation of diverse biological processes including growth, development, response to various stresses, and so on. However, no systemic analysis of the bHLH TFs has been reported in Brachypodium distachyon, an emerging model plant in Poaceae.

RESULTS

A total of 146 bHLH TFs were identified in the Brachypodium distachyon genome and classified into 24 subfamilies. BdbHLHs in the same subfamily share similar protein motifs and gene structures. Gene duplication events showed a close relationship to rice, maize and sorghum, and segment duplications might play a key role in the expansion of this gene family. The amino acid sequence of the bHLH domains were quite conservative, especially Leu-27 and Leu-54. Based on the predicted binding activities, the BdbHLHs were divided into DNA binding and non-DNA binding types. According to the gene ontology (GO) analysis, BdbHLHs were speculated to function in homodimer or heterodimer manner. By integrating the available high throughput data in public database and results of quantitative RT-PCR, we found the expression profiles of BdbHLHs were different, implying their differentiated functions.

CONCLUSION

One hundred fourty-six BdbHLHs were identified and their conserved domains, sequence features, phylogenetic relationship, chromosomal distribution, GO annotations, gene structures, gene duplication and expression profiles were investigated. Our findings lay a foundation for further evolutionary and functional elucidation of BdbHLH genes.

CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping

Broad-scale protein-protein interaction mapping is a major challenge given the cost, time, and sensitivity constraints of existing technologies. Here, we present a massively multiplexed yeast two-hybrid method, CrY2H-seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next-generation DNA sequencing to identify these interactions en masse. We applied CrY2H-seq to investigate sparsely annotated Arabidopsis thaliana transcription factors interactions. By performing ten independent screens testing a total of 36 million binary interaction combinations, and uncovering a network of 8,577 interactions among 1,453 transcription factors, we demonstrate CrY2H-seq's improved screening capacity, efficiency, and sensitivity over those of existing technologies. The deep-coverage network resource we call AtTFIN-1 recapitulates one-third of previously reported interactions derived from diverse methods, expands the number of known plant transcription factor interactions by three-fold, and reveals previously unknown family-specific interaction module associations with plant reproductive development, root architecture, and circadian coordination.

Genome-wide characterization of the aldehyde dehydrogenase gene superfamily in soybean and its potential role in drought stress response

BACKGROUND

Aldehyde dehydrogenases (ALDHs) represent a group of enzymes that detoxify aldehydes by facilitating their oxidation to carboxylic acids, and have been shown to play roles in plant response to abiotic stresses. However, the comprehensive analysis of ALDH superfamily in soybean (Glycine max) has been limited.

RESULTS

In present study, a total of 53 GmALDHs were identified in soybean, and grouped into 10 ALDH families according to the ALDH Gene Nomenclature Committee and phylogenetic analysis. These groupings were supported by their gene structures and conserved motifs. Soybean ALDH superfamily expanded mainly by whole genome duplication/segmental duplications. Gene network analysis identified 1146 putative co-functional genes of 51 GmALDHs. Gene Ontology (GO) enrichment analysis suggested the co-functional genes of these 51 GmALDHs were enriched (FDR < 1e-3) in the process of lipid metabolism, photosynthesis, proline catabolism, and small molecule catabolism. In addition, 22 co-functional genes of GmALDHs are related to plant response to water deprivation/water transport. GmALDHs exhibited various expression patterns in different soybean tissues. The expression levels of 13 GmALDHs were significantly up-regulated and 14 down-regulated in response to water deficit. The occurrence frequencies of three drought-responsive cis-elements (ABRE, CRT/DRE, and GTGCnTGC/G) were compared in GmALDH genes that were up-, down-, or non-regulated by water deficit. Higher frequency of these three cis-elements was observed for the group of up-regulated GmALDH genes as compared to the group of down- or non- regulated GmALDHs by drought stress, implying their potential roles in the regulation of soybean response to drought stress.

CONCLUSIONS

A total of 53 ALDH genes were identified in soybean genome and their phylogenetic relationships and duplication patterns were analyzed. The potential functions of GmALDHs were predicted by analyses of their co-functional gene networks, gene expression profiles, and cis-regulatory elements. Three GmALDH genes, including GmALDH3H2, GmALDH12A2 and GmALDH18B3, were highly induced by drought stress in soybean leaves. Our study provides a foundation for future investigations of GmALDH gene function in soybean.

Co-expressed Pathways DataBase for Tomato: a database to predict pathways relevant to a query gene

BACKGROUND

Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes.

RESULTS

In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA.

CONCLUSIONS

We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato . The database allows users to predict pathways that are relevant to a query gene, which would help to infer gene functions.

Phylogenomic analysis of gene co-expression networks reveals the evolution of functional modules

Molecular evolutionary studies correlate genomic and phylogenetic information with the emergence of new traits of organisms. These traits are, however, the consequence of dynamic gene networks composed of functional modules, which might not be captured by genomic analyses. Here, we established a method that combines large-scale genomic and phylogenetic data with gene co-expression networks to extensively study the evolutionary make-up of modules in the moss Physcomitrella patens, and in the angiosperms Arabidopsis thaliana and Oryza sativa (rice). We first show that younger genes are less annotated than older genes. By mapping genomic data onto the co-expression networks, we found that genes from the same evolutionary period tend to be connected, whereas old and young genes tend to be disconnected. Consequently, the analysis revealed modules that emerged at a specific time in plant evolution. To uncover the evolutionary relationships of the modules that are conserved across the plant kingdom, we added phylogenetic information that revealed duplication and speciation events on the module level. This combined analysis revealed an independent duplication of cell wall modules in bryophytes and angiosperms, suggesting a parallel evolution of cell wall pathways in land plants. We provide an online tool allowing plant researchers to perform these analyses at http://www.gene2function.de.

Beyond Genomics: Studying Evolution with Gene Coexpression Networks

Understanding how genomes change as organisms become more complex is a central question in evolution. Molecular evolutionary studies typically correlate the appearance of genes and gene families with the emergence of biological pathways and morphological features. While such approaches are of great importance to understand how organisms evolve, they are also limited, as functionally related genes work together in contexts of dynamic gene networks. Since functionally related genes are often transcriptionally coregulated, gene coexpression networks present a resource to study the evolution of biological pathways. In this opinion article, we discuss recent developments in this field and how coexpression analyses can be merged with existing genomic approaches to transfer functional knowledge between species to study the appearance or extension of pathways.

Enhancing gene regulatory network inference through data integration with markov random fields

A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

SoyNet: a database of co-functional networks for soybean Glycine max

Soybean (Glycine max) is a legume crop with substantial economic value, providing a source of oil and protein for humans and livestock. More than 50% of edible oils consumed globally are derived from this crop. Soybean plants are also important for soil fertility, as they fix atmospheric nitrogen by symbiosis with microorganisms. The latest soybean genome annotation (version 2.0) lists 56 044 coding genes, yet their functional contributions to crop traits remain mostly unknown. Co-functional networks have proven useful for identifying genes that are involved in a particular pathway or phenotype with various network algorithms. Here, we present SoyNet (available at www.inetbio.org/soynet), a database of co-functional networks for G. max and a companion web server for network-based functional predictions. SoyNet maps 1 940 284 co-functional links between 40 812 soybean genes (72.8% of the coding genome), which were inferred from 21 distinct types of genomics data including 734 microarrays and 290 RNA-seq samples from soybean. SoyNet provides a new route to functional investigation of the soybean genome, elucidating genes and pathways of agricultural importance.

PlaNet: Comparative Co-Expression Network Analyses for Plants

Functional relations between genes can be represented as networks. These networks have been successfully used to infer gene function and to mediate transfer of functional knowledge between species. Transcriptionally coordinated or co-expressed genes tend to be functionally related, which combined with availability of transcriptomic data for multiple plant species make the co-expression networks a useful resource for the plant community. In this chapter, we describe PlaNet ( www.gene2function.de ), a database that includes comparative analyses for co-expression networks of 11 plant species. We exemplify how the tools included in PlaNet can be used to predict gene function, transfer knowledge, and discover conserved and multiplied gene modules.

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.

There remain genes with no known function even in the most well studied, model species. One common way to hypothesize gene function is based on the assumption that genes with similar expression profiles tend to have similar functions. However, using datasets and biological pathway information from the model plant Arabidopsis thaliana as an example, we discovered that, although genes in the same pathways are functionally related, genes in only a subset of the pathways have highly similar expression patterns. In addition, our ability to hypothesize gene functions based on expression is significantly impacted by how the dataset is processed and combined as well as the methodology used to identify genes with similar expression. Therefore, multiple datasets and methods should be tested to maximize the functional information that we can get based on similarity in gene expression.

Central Metabolic Responses to Ozone and Herbivory Affect Photosynthesis and Stomatal Closure

Plants have evolved adaptive mechanisms that allow them to tolerate a continuous range of abiotic and biotic stressors. Tropospheric ozone (O3), a global anthropogenic pollutant, directly affects living organisms and ecosystems, including plant-herbivore interactions. In this study, we investigate the stress responses of Brassica nigra (wild black mustard) exposed consecutively to O3 and the specialist herbivore Pieris brassicae Transcriptomics and metabolomics data were evaluated using multivariate, correlation, and network analyses for the O3 and herbivory responses. O3 stress symptoms resembled those of senescence and phosphate starvation, while a sequential shift from O3 to herbivory induced characteristic plant defense responses, including a decrease in central metabolism, induction of the jasmonic acid/ethylene pathways, and emission of volatiles. Omics network and pathway analyses predicted a link between glycerol and central energy metabolism that influences the osmotic stress response and stomatal closure. Further physiological measurements confirmed that while O3 stress inhibited photosynthesis and carbon assimilation, sequential herbivory counteracted the initial responses induced by O3, resulting in a phenotype similar to that observed after herbivory alone. This study clarifies the consequences of multiple stress interactions on a plant metabolic system and also illustrates how omics data can be integrated to generate new hypotheses in ecology and plant physiology.

ccNET: Database of co-expression networks with functional modules for diploid and polyploid Gossypium

Plant genera with both diploid and polyploid species are a common evolutionary occurrence. Polyploids, especially allopolyploids such as cotton and wheat, are a great model system for heterosis research. Here, we have integrated genome sequences and transcriptome data of Gossypium species to construct co-expression networks and identified functional modules from different cotton species, including 1155 and 1884 modules in G. arboreum and G. hirsutum, respectively. We overlayed the gene expression results onto the co-expression network. We further provided network comparison analysis for orthologous genes across the diploid and allotetraploid Gossypium. We also constructed miRNA-target networks and predicted PPI networks for both cotton species. Furthermore, we integrated in-house ChIP-seq data of histone modification (H3K4me3) together with cis-element analysis and gene sets enrichment analysis tools for studying possible gene regulatory mechanism in Gossypium species. Finally, we have constructed an online ccNET database (http://structuralbiology.cau.edu.cn/gossypium) for comparative gene functional analyses at a multi-dimensional network and epigenomic level across diploid and polyploid Gossypium species. The ccNET database will be beneficial for community to yield novel insights into gene/module functions during cotton development and stress response, and might be useful for studying conservation and diversity in other polyploid plants, such as T. aestivum and Brassica napus.

Towards integration of population and comparative genomics in forest trees

Contents 338 I. 338 II. 339 III. 340 IV. 342 343 References 343 SUMMARY: The past decade saw the initiation of an ongoing revolution in sequencing technologies that is transforming all fields of biology. This has been driven by the advent and widespread availability of high-throughput, massively parallel short-read sequencing (MPS) platforms. These technologies have enabled previously unimaginable studies, including draft assemblies of the massive genomes of coniferous species and population-scale resequencing. Transcriptomics studies have likewise been transformed, with RNA-sequencing enabling studies in nonmodel organisms, the discovery of previously unannotated genes (novel transcripts), entirely new classes of RNAs and previously unknown regulatory mechanisms. Here we touch upon current developments in the areas of genome assembly, comparative regulomics and population genetics as they relate to studies of forest tree species.

Genome-wide identification, phylogeny and expressional profiles of mitogen activated protein kinase kinase kinase (MAPKKK) gene family in bread wheat (Triticum aestivum L.)

BACKGROUND

Mitogen-activated protein kinase kinase kinases (MAPKKKs) are the important components of MAPK cascades, which play the crucial role in plant growth and development as well as in response to diverse stresses. Although this family has been systematically studied in many plant species, little is known about MAPKKK genes in wheat (Triticum aestivum L.), especially those involved in the regulatory network of stress processes.

RESULTS

In this study, we identified 155 wheat MAPKKK genes through a genome-wide search method based on the latest available wheat genome information, of which 29 belonged to MEKK, 11 to ZIK and 115 to Raf subfamily, respectively. Then, chromosome localization, gene structure and conserved protein motifs and phylogenetic relationship as well as regulatory network of these TaMAPKKKs were systematically investigated and results supported the prediction. Furthermore, a total of 11 homologous groups between A, B and D sub-genome and 24 duplication pairs among them were detected, which contributed to the expansion of wheat MAPKKK gene family. Finally, the expression profiles of these MAPKKKs during development and under different abiotic stresses were investigated using the RNA-seq data. Additionally, 10 tissue-specific and 4 salt-responsive TaMAPKKK genes were selected to validate their expression level through qRT-PCR analysis.

CONCLUSIONS

This study for the first time reported the genome organization, evolutionary features and expression profiles of the wheat MAPKKK gene family, which laid the foundation for further functional analysis of wheat MAPKKK genes, and contributed to better understanding the roles and regulatory mechanism of MAPKKKs in wheat.

Genome-wide identification, phylogeny and expression analysis of AP2/ERF transcription factors family in Brachypodium distachyon

Background

The AP2/ERF transcription factor is one of the most important gene families in plants, which plays the vital role in regulating plant growth and development as well as in response to diverse stresses. Although AP2/ERFs have been thoroughly characterized in many plant species, little is known about this family in the model plant Brachypodium distachyon, especially those involved in the regulatory network of stress processes.

Results

In this study, a comprehensive genome-wide search was performed to identify AP2/ERF gene family in Brachypodium and a total of 141 BdAP2/ERFs were obtained. Phylogenetic analysis classified them into four subfamilies, of which 112 belonged to ERF, four to RAV and 24 to AP2 as well as one to soloist subfamily respectively, which was in accordance with the number of AP2 domains and gene structure analysis. Chromosomal localization, gene structure, conserved protein motif and cis-regulatory elements as well as gene duplication events analysis were further performed to systematically investigate the evolutionary features of these BdAP2/ERF genes. Furthermore, the regulatory network between BdAP2/ERF and other genes were constructed using the orthology-based method, and 39 BdAP2/ERFs were found to be involved in the regulatory network and 517 network branches were identified. The expression profiles of BdAP2/ERF during development and under diverse stresses were investigated using the available RNA-seq and microarray data and ten tissue-specific and several stress-responsive BdAP2/ERF genes were identified. Finally, 11 AP2/ERF genes were selected to validate their expressions in different tissues and under different stress treatments using RT-PCR method and results verified that these AP2/ERFs were involved in various developmental and physiological processes.

Conclusions

This study for the first time reported the characteristics of the BdAP2/ERF family, which will provide the invaluable information for further evolutionary and functional studies of AP2/ERF in Brachypodium, and also contribute to better understanding the molecular basis for development and stresses tolerance in this model species and beyond.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2968-8) contains supplementary material, which is available to authorized users.

PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants

Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants.

Techniques for the Analysis of Protein-Protein Interactions in Vivo

Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique.

Tools of the trade: studying molecular networks in plants

Driven by recent technological improvements, genes can be now studied in a larger biological context. Genes and their protein products rarely operate as a single entity and large-scale mapping by protein-protein interactions can unveil the molecular complexes that form in the cell to carry out various functions. Expression analysis under multiple conditions, supplemented with protein-DNA binding data can highlight when genes are active and how they are regulated. Representing these data in networks and finding strongly connected sub-graphs has proven to be a powerful tool to predict the function of unknown genes. As such networks are gradually becoming available for various plant species, it becomes possible to study how networks evolve. This review summarizes currently available network data and related tools for plants. Furthermore we aim to provide an outlook of future analyses that can be done in plants based on work done in other fields.