Web-queryable large-scale data sets for hypothesis generation in plant biology

Detection and validation of cis-regulatory motifs in osmotic stress-inducible synthetic gene switches via computational and experimental approaches

Synthetic cis -regulatory modules can improve our understanding of gene regulatory networks. We applied an ensemble approach for de novo cis motif discovery among the promoters of 181 drought inducible differentially expressed soybean (Glycine max L.) genes. A total of 43 cis motifs were identified in promoter regions of all gene sets using the binding site estimation suite of tools (BEST). Comparative analysis of these motifs revealed similarities with known cis -elements found in PLACE database and led to the discovery of cis -regulatory motifs that were not yet implicated in drought response. Compiled with the proposed synthetic promoter design rationale, three synthetic assemblies were constructed by concatenating multiple copies of drought-inducible cis motifs in a specific order with inter-motif spacing using random bases and placed upstream of 35s minimal core promoter. Each synthetic module substituted 35S promoter in pBI121 and pCAMBIA3301 to drive glucuronidase expression in soybean hairy roots and Arabidopsis thaliana L. Chimeric soybean seedlings and 3-week-old transgenic Arabidopsis plants were treated with simulated with different levels of osmotic stress. Histochemical staining of transgenic soybean hairy roots and Arabidopsis displayed drought-inducible GUS activity of synthetic promoters. Fluorometric assay and expression analysis revealed that SP2 is the better manual combination of cis -elements for stress-inducible expression. qRT-PCR results further demonstrated that designed synthetic promoters are not tissue-specific and thus active in different parts upon treatment with osmotic stress in Arabidopsis plants. This study provides tools for transcriptional upgradation of valuable crops against drought stress and adds to the current knowledge of synthetic biology.

Transcriptome Meta-Analysis Associated Targeting Hub Genes and Pathways of Drought and Salt Stress Responses in Cotton (Gossypium hirsutum): A Network Biology Approach

Abiotic stress tolerance is an intricate feature controlled through several genes and networks in the plant system. In abiotic stress, salt, and drought are well known to limit cotton productivity. Transcriptomics meta-analysis has arisen as a robust method to unravel the stress-responsive molecular network in crops. In order to understand drought and salt stress tolerance mechanisms, a meta-analysis of transcriptome studies is crucial. To confront these issues, here, we have given details of genes and networks associated with significant differential expression in response to salt and drought stress. The key regulatory hub genes of drought and salt stress conditions have notable associations with functional drought and salt stress-responsive (DSSR) genes. In the network study, nodulation signaling pathways 2 (NSP2), Dehydration-responsive element1 D (DRE1D), ethylene response factor (ERF61), cycling DOF factor 1 (CDF1), and tubby like protein 3 (TLP3) genes in drought and tubby like protein 1 (TLP1), thaumatin-like proteins (TLP), ethylene-responsive transcription factor ERF109 (EF109), ETS-Related transcription Factor (ELF4), and Arabidopsis thaliana homeodomain leucine-zipper gene (ATHB7) genes in salt showed the significant putative functions and pathways related to providing tolerance against drought and salt stress conditions along with the significant expression values. These outcomes provide potential candidate genes for further in-depth functional studies in cotton, which could be useful for the selection of an improved genotype of Gossypium hirsutum against drought and salt stress conditions.

Arabidopsis bioinformatics: tools and strategies

The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other '-omic' data. In this review, we cover some more recent tools (and highlight the 'classics') for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co-expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein-protein and protein-DNA interactions. We will touch on tools that integrate different data sets at the end of the article.

An Arabidopsis lipid map reveals differences between tissues and dynamic changes throughout development

Mass spectrometry is the predominant analytical tool used in the field of plant lipidomics. However, there are many challenges associated with the mass spectrometric detection and identification of lipids because of the highly complex nature of plant lipids. Studies into lipid biosynthetic pathways, gene functions in lipid metabolism, lipid changes during plant growth and development, and the holistic examination of the role of plant lipids in environmental stress responses are often hindered. Here, we leveraged a robust pipeline that we previously established to extract and analyze lipid profiles of different tissues and developmental stages from the model plant Arabidopsis thaliana. We analyzed seven tissues at several different developmental stages and identified more than 200 lipids from each tissue analyzed. The data were used to create a web-accessible in silico lipid map that has been integrated into an electronic Fluorescent Pictograph (eFP) browser. This in silico library of Arabidopsis lipids allows the visualization and exploration of the distribution and changes of lipid levels across selected developmental stages. Furthermore, it provides information on the characteristic fragments of lipids and adducts observed in the mass spectrometer and their retention times, which can be used for lipid identification. The Arabidopsis tissue lipid map can be accessed at http://bar.utoronto.ca/efp_arabidopsis_lipid/cgi-bin/efpWeb.cgi.

A method to analyze time expression profiles demonstrated in a database of chili pepper fruit development

RNA-Seq experiments allow genome-wide estimation of relative gene expression. Estimation of gene expression at different time points generates time expression profiles of phenomena of interest, as for example fruit development. However, such profiles can be complex to analyze and interpret. We developed a methodology that transforms original RNA-Seq data from time course experiments into standardized expression profiles, which can be easily interpreted and analyzed. To exemplify this methodology we used RNA-Seq data obtained from 12 accessions of chili pepper (Capsicum annuum L.) during fruit development. All relevant data, as well as functions to perform analyses and interpretations from this experiment, were gathered into a publicly available R package: “Salsa”. Here we explain the rational of the methodology and exemplify the use of the package to obtain valuable insights into the multidimensional time expression changes that occur during chili pepper fruit development. We hope that this tool will be of interest for researchers studying fruit development in chili pepper as well as in other angiosperms.

Anno genominis XX: 20 years of Arabidopsis genomics

Twenty years ago, the Arabidopsis thaliana genome sequence was published. This was an important moment as it was the first sequenced plant genome and explicitly brought plant science into the genomics era. At the time, this was not only an outstanding technological achievement, but it was characterized by a superb global collaboration. The Arabidopsis genome was the seed for plant genomic research. Here, we review the development of numerous resources based on the genome that have enabled discoveries across plant species, which has enhanced our understanding of how plants function and interact with their environments.

Bioinformatic Tools in Arabidopsis Research

Bioinformatic tools are now an everyday part of a plant researcher's collection of protocols. They allow almost instantaneous access to large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other "-omes," which are now being generated with increasing speed and decreasing cost. With the appropriate queries, such tools can generate quality hypotheses, sometimes without the need for new experimental data. In this chapter, we will investigate some of the tools used for examining gene expression and coexpression patterns, performing promoter analyses and functional classification enrichment for sets of genes, and exploring protein-protein and protein-DNA interactions in Arabidopsis. We will also cover additional tools that allow integration of data from several sources for improved hypothesis generation.

Identification and characterization of unconventional membrane protein trafficking regulators in Arabidopsis: A genetic approach

Proper trafficking and subcellular localization of membrane proteins are essential for plant growth and development. The plant endomembrane system contains several membrane-bound organelles with distinct functions including the endoplasmic reticulum (ER), Golgi apparatus, trans-Golgi network (TGN) or early endosome, prevacuolar compartment (PVC) or multivesicular body (MVB) and vacuole. Multiple approaches have been successfully used to identify and study the regulators and components important for signal transduction, growth and development, as well as membrane trafficking in the endomembrane system in plants. These include the homologous characterization of the counterparts in mammals or yeast employing both reverse genetic as well as the forward genetic screen approaches. However, the deletion or mutation of membrane trafficking related proteins usually leads to seedling lethality due to their essential roles in plant development and organelle biogenesis. To overcome the limitation of lethal phenotype of the target proteins, we used DEX-inducible RNAi knock-down lines to study their function in plants. More recently, we developed and used both RNAi knock-down and T-DNA insertional lines as starting materials to screen for mutations that could suppress and rescue the lethal phenotype, or a suppressor screening. Further characterization of the newly identified suppressor mutants has resulted in the identification of novel negative regulators in mediating membrane trafficking and organelle biogenesis in plants. In this review, we summarize the current approaches in studying protein trafficking in the endomembrane system. We then describe three examples of suppressor screening with distinct starting materials (i.e. FREE1, MON1, and SH3P2 that are regulators of MVB, vacuole, and autophagosomes, respectively) to discuss the rationale, procedures, advantages and disadvantages, and possible outcomes of such a suppressor screening. We finally propose that these novel screening approaches will lead to the identification of new unconventional players in regulating protein trafficking and organelle biogenesis in plants and discuss their impact on plant cell biology research.

A Portion of the Apomixis Locus of Paspalum Simplex is Microsyntenic with an Unstable Chromosome Segment Highly Conserved Among Poaceae

The introgression of apomixis in major seed crops, would guarantee self-seeding of superior heterotic seeds over generations. In the grass species Paspalum simplex, apomixis is controlled by a single locus in which recombination is blocked. In the perspective of isolating the genetic determinants of apomixis, we report data on sequencing, in silico mapping and expression analysis of some of the genes contained in two cloned genomic regions of the apomixis locus of P. simplex. In silico mapping allowed us to identify a conserved synteny group homoeologous to the apomixis locus, located on a telomeric position of chromosomes 12, 8, 3 and 4 of rice, Sorghum bicolor, Setaria italica and Brachypodium distachyum, respectively, and on a more centromeric position of maize chromosome 1. Selected genes of the apomixis locus expressed sense and antisense transcripts in reproductively committed cells of sexual and apomictic ovules. Some of the genes considered here expressed apomixis-specific allelic variants which showed partial non-overlapping expression patterns with alleles shared by sexual and apomictic reproductive phenotypes. Our findings open new routes for the isolation of the genetic determinants of apomixis and, in perspective, for its introgression in crop grasses.

Comparative analysis of the male inflorescence transcriptome profiles of an ms22 mutant of maize

In modern agricultural production, maize is the most successful crop utilizing heterosis. 712C-ms22 is an important male sterile material in maize. In this study, we performed transcriptome sequencing analysis of the V10 stage of male inflorescence. Through this analysis, 27.63 million raw reads were obtained, and trimming of the raw data revealed 26.63 million clean reads, with an average match rate of 94.64%. Using Tophat software, we matched these clean reads to the maize reference genome. The abundance of 39,622 genes was measured, and 35,399 genes remained after filtering out the non-expressed genes across all the samples. These genes were classified into 19 categories by clusters of orthologous groups of protein annotation. Transcriptome sequencing analysis of the male sterile and fertile 712C-ms22 maize revealed some key DEGs that may be related to metabolic pathways. qRT-PCR analysis validated the gene expression patterns identified by RNA-seq. This analysis revealed some of the essential genes responsible for pollen development and for pollen tube elongation. Our findings provide useful markers of male sterility and new insights into the global mechanisms mediating male sterility in maize.

The Bio-Analytic Resource for Plant Biology

Bioinformatic tools have become part of the way plant researchers undertake investigations. Large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other "-omes" that have been generated in the past decade may be easily accessed with such tools, such that hypotheses may be generated at the click of a mouse. In this chapter, we'll cover the use of bioinformatic tools available at the Bio-Analytic Resource for Plant Biology at http://bar.utoronto.ca for exploring gene expression and coexpression patterns, undertaking promoter analyses, performing functional classification enrichment analyses for sets of genes, and examining protein-protein interactions. We also touch on some newer bioinformatic tools that allow integration of data from several sources for improved hypothesis generation, both for Arabidopsis and translationally. Most of the data sets come from Arabidopsis, but useful BAR tools for other species will be mentioned where appropriate.

Root Cell-Specific Regulators of Phosphate-Dependent Growth

Cellular specialization in abiotic stress responses is an important regulatory feature driving plant acclimation. Our in silico approach of iterative coexpression, interaction, and enrichment analyses predicted root cell-specific regulators of phosphate starvation response networks in Arabidopsis (Arabidopsis thaliana). This included three uncharacterized genes termed Phosphate starvation-induced gene interacting Root Cell Enriched (PRCE1, PRCE2, and PRCE3). Root cell-specific enrichment of 12 candidates was confirmed in promoter-GFP lines. T-DNA insertion lines of 11 genes showed changes in phosphate status and growth responses to phosphate availability compared with the wild type. Some mutants (cbl1, cipk2, prce3, and wdd1) displayed strong biomass gain irrespective of phosphate supply, while others (cipk14, mfs1, prce1, prce2, and s6k2) were able to sustain growth under low phosphate supply better than the wild type. Notably, root or shoot phosphate accumulation did not strictly correlate with organ growth. Mutant response patterns markedly differed from those of master regulators of phosphate homeostasis, PHOSPHATE STARVATION RESPONSE1 (PHR1) and PHOSPHATE2 (PHO2), demonstrating that negative growth responses in the latter can be overcome when cell-specific regulators are targeted. RNA sequencing analysis highlighted the transcriptomic plasticity in these mutants and revealed PHR1-dependent and -independent regulatory circuits with gene coexpression profiles that were highly correlated to the quantified physiological traits. The results demonstrate how in silico prediction of cell-specific, stress-responsive genes uncovers key regulators and how their manipulation can have positive impacts on plant growth under abiotic stress.

50 years of Arabidopsis research: highlights and future directions

922 I. 922 II. 922 III. 925 IV. 925 V. 926 VI. 927 VII. 928 VIII. 929 IX. 930 X. 931 XI. 932 XII. 933 XIII. Natural variation and genome-wide association studies 934 XIV. 934 XV. 935 XVI. 936 XVII. 937 937 References 937 SUMMARY: The year 2014 marked the 25(th) International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. We present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlighting some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome-wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology.

Bioinformatics Tools for Exploring the SUMO Gene Network

Plant sumoylation research has seen significant advances in recent years, particularly since high-throughput proteomics strategies have enabled the discovery of hundreds of potential SUMO targets and interactors of SUMO pathway components. In the present chapter, we introduce the SUMO Gene Network (SGN), a curated assembly of Arabidopsis thaliana genes that have been functionally associated with sumoylation, from SUMO pathway components to targets and interactors. The enclosed tutorial helps interpret and manage these datasets, and details bioinformatics tools that can be used for in silico-based hypothesis generation. The latter include tools for sumoylation site prediction, comparative genomics, and gene network analysis.

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.

CressInt: a user-friendly web resource for genome-scale exploration of gene regulation in Arabidopsis thaliana

The thale cress Arabidopsis thaliana is a powerful model organism for studying a wide variety of biological processes. Recent advances in sequencing technology have resulted in a wealth of information describing numerous aspects of A. thaliana genome function. However, there is a relative paucity of computational systems for efficiently and effectively using these data to create testable hypotheses. We present CressInt, a user-friendly web resource for exploring gene regulatory mechanisms in A. thaliana on a genomic scale. The CressInt system incorporates a variety of genome-wide data types relevant to gene regulation, including transcription factor (TF) binding site models, ChIP-seq, DNase-seq, eQTLs, and GWAS. We demonstrate the utility of CressInt by showing how the system can be used to (1) Identify TFs binding to the promoter of a gene of interest; (2) identify genetic variants that are likely to impact TF binding based on a ChIP-seq dataset; and (3) identify specific TFs whose binding might be impacted by phenotype-associated variants. CressInt is freely available at http://cressint.cchmc.org.

Araport: the Arabidopsis information portal

The Arabidopsis Information Portal (https://www.araport.org) is a new online resource for plant biology research. It houses the Arabidopsis thaliana genome sequence and associated annotation. It was conceived as a framework that allows the research community to develop and release ‘modules’ that integrate, analyze and visualize Arabidopsis data that may reside at remote sites. The current implementation provides an indexed database of core genomic information. These data are made available through feature-rich web applications that provide search, data mining, and genome browser functionality, and also by bulk download and web services. Araport uses software from the InterMine and JBrowse projects to expose curated data from TAIR, GO, BAR, EBI, UniProt, PubMed and EPIC CoGe. The site also hosts ‘science apps,’ developed as prototypes for community modules that use dynamic web pages to present data obtained on-demand from third-party servers via RESTful web services. Designed for sustainability, the Arabidopsis Information Portal strategy exploits existing scientific computing infrastructure, adopts a practical mixture of data integration technologies and encourages collaborative enhancement of the resource by its user community.

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

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

Comparative transcriptome atlases reveal altered gene expression modules between two Cleomaceae C3 and C4 plant species

C(4) photosynthesis outperforms the ancestral C(3) state in a wide range of natural and agro-ecosystems by affording higher water-use and nitrogen-use efficiencies. It therefore represents a prime target for engineering novel, high-yielding crops by introducing the trait into C(3) backgrounds. However, the genetic architecture of C(4) photosynthesis remains largely unknown. To define the divergence in gene expression modules between C(3) and C(4) photosynthesis during leaf ontogeny, we generated comprehensive transcriptome atlases of two Cleomaceae species, Gynandropsis gynandra (C(4)) and Tarenaya hassleriana (C(3)), by RNA sequencing. Overall, the gene expression profiles appear remarkably similar between the C(3) and C(4) species. We found that known C(4) genes were recruited to photosynthesis from different expression domains in C(3), including typical housekeeping gene expression patterns in various tissues as well as individual heterotrophic tissues. Furthermore, we identified a structure-related module recruited from the C(3) root. Comparison of gene expression patterns with anatomy during leaf ontogeny provided insight into genetic features of Kranz anatomy. Altered expression of developmental factors and cell cycle genes is associated with a higher degree of endoreduplication in enlarged C(4) bundle sheath cells. A delay in mesophyll differentiation apparent both in the leaf anatomy and the transcriptome allows for extended vein formation in the C(4) leaf.

Integrating bioinformatic resources to predict transcription factors interacting with cis-sequences conserved in co-regulated genes

Background

Using motif detection programs it is fairly straightforward to identify conserved cis-sequences in promoters of co-regulated genes. In contrast, the identification of the transcription factors (TFs) interacting with these cis-sequences is much more elaborate. To facilitate this, we explore the possibility of using several bioinformatic and experimental approaches for TF identification. This starts with the selection of co-regulated gene sets and leads first to the prediction and then to the experimental validation of TFs interacting with cis-sequences conserved in the promoters of these co-regulated genes.

Results

Using the PathoPlant database, 32 up-regulated gene groups were identified with microarray data for drought-responsive gene expression from Arabidopsis thaliana. Application of the binding site estimation suite of tools (BEST) discovered 179 conserved sequence motifs within the corresponding promoters. Using the STAMP web-server, 49 sequence motifs were classified into 7 motif families for which similarities with known cis-regulatory sequences were identified. All motifs were subjected to a footprintDB analysis to predict interacting DNA binding domains from plant TF families. Predictions were confirmed by using a yeast-one-hybrid approach to select interacting TFs belonging to the predicted TF families. TF-DNA interactions were further experimentally validated in yeast and with a Physcomitrella patens transient expression system, leading to the discovery of several novel TF-DNA interactions.

Conclusions

The present work demonstrates the successful integration of several bioinformatic resources with experimental approaches to predict and validate TFs interacting with conserved sequence motifs in co-regulated genes.

Identification of a novel type of WRKY transcription factor binding site in elicitor-responsive cis-sequences from Arabidopsis thaliana

Using a combination of bioinformatics and synthetic promoters, novel elicitor-responsive cis-sequences were discovered in promoters of pathogen-upregulated genes from Arabidopsis thaliana. One group of functional sequences contains the conserved core sequence GACTTTT. This core sequence and adjacent nucleotides are essential for elicitor-responsive gene expression in a parsley protoplast system. By yeast one-hybrid screening, WRKY70 was selected with a cis-sequence harbouring the core sequence GACTTTT but no known WRKY binding site (W-box). Transactivation experiments, mutation analyses, and electrophoretic mobility shift assays demonstrate that the sequence CGACTTTT is the binding site for WRKY70 in the investigated cis-sequence and is required for WRKY70-activated gene expression. Using several cis-sequences in transactivation experiments and binding studies, the CGACTTTT sequence can be extended to propose YGACTTTT as WRKY70 binding site. This binding site, designated WT-box, is enriched in promoters of genes upregulated in a WRKY70 overexpressing line. Interestingly, functional WRKY70 binding sites are present in the promoter of WRKY30, supporting recent evidence that both factors play a role in the same regulatory network.

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology

BACKGROUND

Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology.

METHODOLOGY AND PRINCIPAL FINDINGS

In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1).

CONCLUSIONS

Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

AthaMap web tools for the analysis of transcriptional and posttranscriptional regulation of gene expression in Arabidopsis thaliana

The AthaMap database provides a map of verified and predicted transcription factor (TF) and small RNA-binding sites for the A. thaliana genome. The database can be used for bioinformatic predictions of putative regulatory sites. Several online web tools are available that address specific questions. Starting with the identification of transcription factor-binding sites (TFBS) in any gene of interest, colocalizing TFBS can be identified as well as common TFBS in a set of user-provided genes. Furthermore, genes can be identified that are potentially targeted by specific transcription factors or small inhibitory RNAs. This chapter provides detailed information on how each AthaMap web tool can be used online. Examples on how this database is used to address questions in circadian and diurnal regulation are given. Furthermore, complementary databases and databases that go beyond questions addressed with AthaMap are discussed.

Bioinformatic tools in Arabidopsis research

Bioinformatic tools are an increasingly important resource for Arabidopsis researchers. With them, it is possible to rapidly query the large data sets covering genomes, transcriptomes, proteomes, epigenomes, and other "omes" that have been generated in the past decade. Often these tools can be used to generate quality hypotheses at the click of a mouse. In this chapter, we cover the use of bioinformatic tools for examining gene expression and coexpression patterns, performing promoter analyses, looking for functional classification enrichment for sets of genes, and investigating protein-protein interactions. We also introduce bioinformatic tools that allow integration of data from several sources for improved hypothesis generation.

Galanthamine, an anticholinesterase drug, effects plant growth and development in Artemisia tridentate Nutt. via modulation of auxin and neutrotransmitter signaling

Galanthamine is a naturally occurring acetylcholinesterase (AchE) inhibitor that has been well established as a drug for treatment of mild to moderate Alzheimer disease, but the role of the compound in plant metabolism is not known. The current study was designed to investigate whether galanthamine could redirect morphogenesis of Artemisia tridentata Nutt. cultures by altering concentration of endogenous neurosignaling molecules acetylcholine (Ach), auxin (IAA), melatonin (Mel), and serotonin (5HT). Exposure of axenic A. tridentata cultures to 10 µM galanthamine decreased the concentration of endogenous Ach, IAA, MEL, and AchE, and altered plant growth in a manner reminiscent of 2-4D toxicity. Galanthamine itself demonstrated IAA activity in an oat coleotile elongation bioassay, 20 µM galanthamine showed no significant difference compared with 5 μM IAA or 5 μM 1-Naphthaleneacetic acid (NAA). Metabolomic analysis detected between 20,921 to 27,891 compounds in A. tridentata plantlets and showed greater commonality between control and 5 µM treatments. Furthermore, metabolomic analysis putatively identified coumarins scopoletin/isoscopoletin, and scopolin in A. tridentata leaf extracts and these metabolites linearly increased in response to galanthamine treatments. Overall, these data indicate that galanthamine is an allelopathic phytochemical and support the hypothesis that neurologically active compounds in plants help ensure plant survival and adaptation to environmental challenges.

Network analysis for gene discovery in plant-specialized metabolism

Recent omics technologies provide information on multiple components of biological networks. Web-based data mining tools are continuously being developed. Because genes involved in specialized (secondary) metabolism are often co-ordinately regulated at the transcriptional level, a number of gene discovery studies have been successfully conducted using network analysis, especially by integrating gene co-expression network analysis and metabolomic investigation. In addition, next-generation sequencing technologies are currently utilized in functional genomics investigations of Arabidopsis and non-model plant species including medicinal plants. Systems-based approaches are expected to gain importance in medicinal plant research. This review discussed network analysis in Arabidopsis and gene discovery in plant-specialized metabolism in non-model plants.

RNA-seq of Arabidopsis pollen uncovers novel transcription and alternative splicing

Pollen grains of Arabidopsis (Arabidopsis thaliana) contain two haploid sperm cells enclosed in a haploid vegetative cell. Upon germination, the vegetative cell extrudes a pollen tube that carries the sperm to an ovule for fertilization. Knowing the identity, relative abundance, and splicing patterns of pollen transcripts will improve our understanding of pollen and allow investigation of tissue-specific splicing in plants. Most Arabidopsis pollen transcriptome studies have used the ATH1 microarray, which does not assay splice variants and lacks specific probe sets for many genes. To investigate the pollen transcriptome, we performed high-throughput sequencing (RNA-Seq) of Arabidopsis pollen and seedlings for comparison. Gene expression was more diverse in seedling, and genes involved in cell wall biogenesis were highly expressed in pollen. RNA-Seq detected at least 4,172 protein-coding genes expressed in pollen, including 289 assayed only by nonspecific probe sets. Additional exons and previously unannotated 5' and 3' untranslated regions for pollen-expressed genes were revealed. We detected regions in the genome not previously annotated as expressed; 14 were tested and 12 were confirmed by polymerase chain reaction. Gapped read alignments revealed 1,908 high-confidence new splicing events supported by 10 or more spliced read alignments. Alternative splicing patterns in pollen and seedling were highly correlated. For most alternatively spliced genes, the ratio of variants in pollen and seedling was similar, except for some encoding proteins involved in RNA splicing. This study highlights the robustness of splicing patterns in plants and the importance of ongoing annotation and visualization of RNA-Seq data using interactive tools such as Integrated Genome Browser.

The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.

Efficiency of neural network-based combinatorial model predicting optimal culture conditions for maximum biomass yields in hairy root cultures

KEY MESSAGE : ANN-based combinatorial model is proposed and its efficiency is assessed for the prediction of optimal culture conditions to achieve maximum productivity in a bioprocess in terms of high biomass. A neural network approach is utilized in combination with Hidden Markov concept to assess the optimal values of different environmental factors that result in maximum biomass productivity of cultured tissues after definite culture duration. Five hidden Markov models (HMMs) were derived for five test culture conditions, i.e. pH of liquid growth medium, volume of medium per culture vessel, sucrose concentration (%w/v) in growth medium, nitrate concentration (g/l) in the medium and finally the density of initial inoculum (g fresh weight) per culture vessel and their corresponding fresh weight biomass. The artificial neural network (ANN) model was represented as the function of these five Markov models, and the overall simulation of fresh weight biomass was done with this combinatorial ANN-HMM. The empirical results of Rauwolfia serpentina hairy roots were taken as model and compared with simulated results obtained from pure ANN and ANN-HMMs. The stochastic testing and Cronbach's α-value of pure and combinatorial model revealed more internal consistency and skewed character (0.4635) in histogram of ANN-HMM compared to pure ANN (0.3804). The simulated results for optimal conditions of maximum fresh weight production obtained from ANN-HMM and ANN model closely resemble the experimentally optimized culture conditions based on which highest fresh weight was obtained. However, only 2.99 % deviation from the experimental values could be observed in the values obtained from combinatorial model when compared to the pure ANN model (5.44 %). This comparison showed 45 % better potential of combinatorial model for the prediction of optimal culture conditions for the best growth of hairy root cultures.

Correlation of aquaporins and transmembrane solute transporters revealed by genome-wide analysis in developing maize leaf

Aquaporins are multifunctional membrane channels that facilitate the transmembrane transport of water and solutes. When transmembrane mineral nutrient transporters exhibit the same expression patterns as aquaporins under diverse temporal and physiological conditions, there is a greater probability that they interact. In this study, genome-wide temporal profiling of transcripts analysis and coexpression network-based approaches are used to examine the significant specificity correlation of aquaporins and transmembrane solute transporters in developing maize leaf. The results indicate that specific maize aquaporins are related to specific transmembrane solute transporters. The analysis demonstrates a systems-level correlation between aquaporins, nutrient transporters, and the homeostasis of mineral nutrients in developing maize leaf. Our results provide a resource for further studies into the physiological function of these aquaporins.

Integration of bioinformatics and synthetic promoters leads to the discovery of novel elicitor-responsive cis-regulatory sequences in Arabidopsis

A combination of bioinformatic tools, high-throughput gene expression profiles, and the use of synthetic promoters is a powerful approach to discover and evaluate novel cis-sequences in response to specific stimuli. With Arabidopsis (Arabidopsis thaliana) microarray data annotated to the PathoPlant database, 732 different queries with a focus on fungal and oomycete pathogens were performed, leading to 510 up-regulated gene groups. Using the binding site estimation suite of tools, BEST, 407 conserved sequence motifs were identified in promoter regions of these coregulated gene sets. Motif similarities were determined with STAMP, classifying the 407 sequence motifs into 37 families. A comparative analysis of these 37 families with the AthaMap, PLACE, and AGRIS databases revealed similarities to known cis-elements but also led to the discovery of cis-sequences not yet implicated in pathogen response. Using a parsley (Petroselinum crispum) protoplast system and a modified reporter gene vector with an internal transformation control, 25 elicitor-responsive cis-sequences from 10 different motif families were identified. Many of the elicitor-responsive cis-sequences also drive reporter gene expression in an Agrobacterium tumefaciens infection assay in Nicotiana benthamiana. This work significantly increases the number of known elicitor-responsive cis-sequences and demonstrates the successful integration of a diverse set of bioinformatic resources combined with synthetic promoter analysis for data mining and functional screening in plant-pathogen interaction.

CORNET 2.0: integrating plant coexpression, protein-protein interactions, regulatory interactions, gene associations and functional annotations

To enable easy access and interpretation of heterogeneous and scattered data, we have developed a user-friendly tool for data mining and integration in Arabidopsis, named CORNET. This tool allows the browsing of microarray data, the construction of coexpression and protein-protein interaction (PPI) networks and the exploration of diverse functional annotations. Here, we present the new functionalities of CORNET 2.0 for data integration in plants. First of all, CORNET allows the integration of regulatory interaction datasets accessible through the new transcription factor (TF) tool that can be used in combination with the coexpression tool or the PPI tool. In addition, we have extended the PPI tool to enable the analysis of gene-gene associations from AraNet as well as newly identified PPIs. Different search options are implemented to enable the construction of networks centered around multiple input genes or proteins. New functional annotation resources are included to retrieve relevant literature, phenotypes, plant ontology and biological pathways. We have also extended CORNET to attain the construction of coexpression and PPI networks in the crop species maize. Networks and associated evidence of the majority of currently available data types are visualized in Cytoscape. CORNET is available at https://bioinformatics.psb.ugent.be/cornet.

Assessment of community-submitted ontology annotations from a novel database-journal partnership

As the scientific literature grows, leading to an increasing volume of published experimental data, so does the need to access and analyze this data using computational tools. The most commonly used method to convert published experimental data on gene function into controlled vocabulary annotations relies on a professional curator, employed by a model organism database or a more general resource such as UniProt, to read published articles and compose annotation statements based on the articles' contents. A more cost-effective and scalable approach capable of capturing gene function data across the whole range of biological research organisms in computable form is urgently needed. We have analyzed a set of ontology annotations generated through collaborations between the Arabidopsis Information Resource and several plant science journals. Analysis of the submissions entered using the online submission tool shows that most community annotations were well supported and the ontology terms chosen were at an appropriate level of specificity. Of the 503 individual annotations that were submitted, 97% were approved and community submissions captured 72% of all possible annotations. This new method for capturing experimental results in a computable form provides a cost-effective way to greatly increase the available body of annotations without sacrificing annotation quality.

Database URL:www.arabidopsis.org

Ontologies as integrative tools for plant science

PREMISE OF THE STUDY

Bio-ontologies are essential tools for accessing and analyzing the rapidly growing pool of plant genomic and phenomic data. Ontologies provide structured vocabularies to support consistent aggregation of data and a semantic framework for automated analyses and reasoning. They are a key component of the semantic web.

METHODS

This paper provides background on what bio-ontologies are, why they are relevant to botany, and the principles of ontology development. It includes an overview of ontologies and related resources that are relevant to plant science, with a detailed description of the Plant Ontology (PO). We discuss the challenges of building an ontology that covers all green plants (Viridiplantae).

KEY RESULTS

Ontologies can advance plant science in four keys areas: (1) comparative genetics, genomics, phenomics, and development; (2) taxonomy and systematics; (3) semantic applications; and (4) education.

CONCLUSIONS

Bio-ontologies offer a flexible framework for comparative plant biology, based on common botanical understanding. As genomic and phenomic data become available for more species, we anticipate that the annotation of data with ontology terms will become less centralized, while at the same time, the need for cross-species queries will become more common, causing more researchers in plant science to turn to ontologies.

Systematic identification of functional plant modules through the integration of complementary data sources

A major challenge is to unravel how genes interact and are regulated to exert specific biological functions. The integration of genome-wide functional genomics data, followed by the construction of gene networks, provides a powerful approach to identify functional gene modules. Large-scale expression data, functional gene annotations, experimental protein-protein interactions, and transcription factor-target interactions were integrated to delineate modules in Arabidopsis (Arabidopsis thaliana). The different experimental input data sets showed little overlap, demonstrating the advantage of combining multiple data types to study gene function and regulation. In the set of 1,563 modules covering 13,142 genes, most modules displayed strong coexpression, but functional and cis-regulatory coherence was less prevalent. Highly connected hub genes showed a significant enrichment toward embryo lethality and evidence for cross talk between different biological processes. Comparative analysis revealed that 58% of the modules showed conserved coexpression across multiple plants. Using module-based functional predictions, 5,562 genes were annotated, and an evaluation experiment disclosed that, based on 197 recently experimentally characterized genes, 38.1% of these functions could be inferred through the module context. Examples of confirmed genes of unknown function related to cell wall biogenesis, xylem and phloem pattern formation, cell cycle, hormone stimulus, and circadian rhythm highlight the potential to identify new gene functions. The module-based predictions offer new biological hypotheses for functionally unknown genes in Arabidopsis (1,701 genes) and six other plant species (43,621 genes). Furthermore, the inferred modules provide new insights into the conservation of coexpression and coregulation as well as a starting point for comparative functional annotation.

Integrative systems biology: an attempt to describe a simple weed

Genome-scale studies hold great promise for revealing novel plant biology. Because of the complexity of these techniques, numerous considerations need to be made before embarking on a study. Here we focus on the Arabidopsis model system because of the wealth of available genome-scale data. Many approaches are available that provide genome-scale information regarding the state of a given organism (e.g. genomics, epigenomics, transcriptomics, proteomics, metabolomics interactomics, ionomics, phenomics, etc.). Integration of all of these types of data will be necessary for a comprehensive description of Arabidopsis. In this review we propose that 'triangulation' among transcriptomics, proteomics and metabolomics is a meaningful approach for beginning this integrative analysis and uncovering a systems level perspective of Arabidopsis biology.

Correlation networks visualization

New, in silico ways of generating hypotheses based on large data sets have emerged in the past decade. These data sets have been used to investigate different aspects of plant biology, especially at the level of transcriptome, from tissue-specific expression patterns to patterns in as little as a few cells. Such publicly available data are a boon to researchers for hypothesis generation by providing a guide for experimental work such as phenotyping or genetic analysis. More advanced computational methods can leverage these data via gene coexpression analysis, the results of which can be visualized and refined using network analysis. Other kinds of networks of, e.g., protein-protein interactions, can also be used to inform biology. These networks can be visualized and analyzed with additional information on gene expression levels, subcellular localization, etc., or with other emerging kinds information. Finally, cross-level correlation is an area that will become increasingly important. Visualizing these cross-level correlations will require new data visualization tools.

A strategy for the identification of new abiotic stress determinants in Arabidopsis using web-based data mining and reverse genetics

Since the sequencing of the Arabidopsis thaliana genome in 2000, plant researchers have faced the complex challenge of assigning function to thousands of genes. Functional discovery by in silico prediction or homology search resolved a significant number of genes, but only a minor part has been experimentally validated. Arabidopsis entry into the post-genomic era signified a massive increase in high-throughput approaches to functional discovery, which have since become available through publicly-available web-based resources. The present work focuses on an easy and straightforward strategy that couples data-mining to reverse genetics principles, to allow for the identification of new abiotic stress determinant genes. The strategy explores systematic microarray-based transcriptomics experiments, involving Arabidopsis abiotic stress responses. An overview of the most significant resources and databases for functional discovery in Arabidopsis is presented. The successful application of the outlined strategy is illustrated by the identification of a new abiotic stress determinant gene, HRR, which displays a heat-stress-related phenotype after a loss-of-function reverse genetics approach.

Toward the storage metabolome: profiling the barley vacuole

While recent years have witnessed dramatic advances in our capacity to identify and quantify an ever-increasing number of plant metabolites, our understanding of how metabolism is spatially regulated is still far from complete. In an attempt to partially address this question, we studied the storage metabolome of the barley (Hordeum vulgare) vacuole. For this purpose, we used highly purified vacuoles isolated by silicon oil centrifugation and compared their metabolome with that found in the mesophyll protoplast from which they were derived. Using a combination of gas chromatography-mass spectrometry and Fourier transform-mass spectrometry, we were able to detect 59 (primary) metabolites for which we know the exact chemical structure and a further 200 (secondary) metabolites for which we have strong predicted chemical formulae. Taken together, these metabolites comprise amino acids, organic acids, sugars, sugar alcohols, shikimate pathway intermediates, vitamins, phenylpropanoids, and flavonoids. Of the 259 putative metabolites, some 12 were found exclusively in the vacuole and 34 were found exclusively in the protoplast, while 213 were common in both samples. When analyzed on a quantitative basis, however, there is even more variance, with more than 60 of these compounds being present above the detection limit of our protocols. The combined data were also analyzed with respect to the tonoplast proteome in an attempt to infer specificities of the transporter proteins embedded in this membrane. Following comparison with recent observations made using nonaqueous fractionation of Arabidopsis (Arabidopsis thaliana), we discuss these data in the context of current models of metabolic compartmentation in plants.

Functional network construction in Arabidopsis using rule-based machine learning on large-scale data sets

The meta-analysis of large-scale postgenomics data sets within public databases promises to provide important novel biological knowledge. Statistical approaches including correlation analyses in coexpression studies of gene expression have emerged as tools to elucidate gene function using these data sets. Here, we present a powerful and novel alternative methodology to computationally identify functional relationships between genes from microarray data sets using rule-based machine learning. This approach, termed "coprediction," is based on the collective ability of groups of genes co-occurring within rules to accurately predict the developmental outcome of a biological system. We demonstrate the utility of coprediction as a powerful analytical tool using publicly available microarray data generated exclusively from Arabidopsis thaliana seeds to compute a functional gene interaction network, termed Seed Co-Prediction Network (SCoPNet). SCoPNet predicts functional associations between genes acting in the same developmental and signal transduction pathways irrespective of the similarity in their respective gene expression patterns. Using SCoPNet, we identified four novel regulators of seed germination (ALTERED SEED GERMINATION5, 6, 7, and 8), and predicted interactions at the level of transcript abundance between these novel and previously described factors influencing Arabidopsis seed germination. An online Web tool to query SCoPNet has been developed as a community resource to dissect seed biology and is available at http://www.vseed.nottingham.ac.uk/.

Systems biology approaches to abscisic acid signaling

Recent advances in our understanding of abscisic acid (ABA) signaling have identified a core pathway consisting of receptors (PYR/PYL/RCAR), protein phosphatases (PP2C), protein kinases (SnRK2), and several downstream factors that will lead to the next stage of ABA research. Systems biology will be an important concept for further understanding ABA responses in plants. In this review, two practical approaches of systems biology to ABA signaling are presented: the one is 'transcriptome analysis', which covers coding genes as well as unannotated transcripts, and the other is 'phosphoproteomics'. The latter technology will offer an unprecedented overview of the regulatory networks involved in ABA signaling because protein phosphorylation/dephosphorylation is a major center of such regulation. Systematic studies will contribute to our understanding of the network structure and dynamics of ABA signaling; moreover, systems biology will facilitate ABA signaling studies as well as future biotechnological applications in crops or trees.

Global landscape of a co-expressed gene network in barley and its application to gene discovery in Triticeae crops

Accumulated transcriptome data can be used to investigate regulatory networks of genes involved in various biological systems. Co-expression analysis data sets generated from comprehensively collected transcriptome data sets now represent efficient resources that are capable of facilitating the discovery of genes with closely correlated expression patterns. In order to construct a co-expression network for barley, we analyzed 45 publicly available experimental series, which are composed of 1,347 sets of GeneChip data for barley. On the basis of a gene-to-gene weighted correlation coefficient, we constructed a global barley co-expression network and classified it into clusters of subnetwork modules. The resulting clusters are candidates for functional regulatory modules in the barley transcriptome. To annotate each of the modules, we performed comparative annotation using genes in Arabidopsis and Brachypodium distachyon. On the basis of a comparative analysis between barley and two model species, we investigated functional properties from the representative distributions of the gene ontology (GO) terms. Modules putatively involved in drought stress response and cellulose biogenesis have been identified. These modules are discussed to demonstrate the effectiveness of the co-expression analysis. Furthermore, we applied the data set of co-expressed genes coupled with comparative analysis in attempts to discover potentially Triticeae-specific network modules. These results demonstrate that analysis of the co-expression network of the barley transcriptome together with comparative analysis should promote the process of gene discovery in barley. Furthermore, the insights obtained should be transferable to investigations of Triticeae plants. The associated data set generated in this analysis is publicly accessible at http://coexpression.psc.riken.jp/barley/.

More than the sum of its parts--how to achieve a specific transcriptional response to abiotic stress

A rapid and appropriate response to stress is key to survival. A major part of plant adaptation to abiotic stresses is regulated at the level of gene expression. The regulatory steps involved in accurate expression of stress related genes need to be tailored to the specific stress for optimal plant performance. Accumulating evidence suggests that there are several processes contributing to signalling specificity: post-translational activation and selective nuclear import of transcription factors, regulation of DNA accessibility by chromatin modifying and remodelling enzymes, and cooperation between two or more response elements in a stress-responsive promoter. These mechanisms should not be viewed as independent events, instead the nuclear DNA is in a complex landscape where many proteins interact, compete, and regulate each other. Hence future studies should consider an integrated view of gene regulation composed of numerous chromatin associated proteins in addition to transcription factors. Although most studies have focused on a single regulatory mechanism, it is more likely the combined actions of several mechanisms that provide a stress specific output. In this review recent progress in abiotic stress signalling is discussed with emphasis on possible mechanisms for generating specific responses.

New perspectives on glutamine synthetase in grasses

Members of the glutamine synthetase (GS) gene family have now been characterized in many crop species such as wheat, rice, and maize. Studies have shown that cytosolic GS isoforms are involved in nitrogen remobilization during leaf senescence and emphasized a role in seed production particularly in small grain crop species. Data from the sequencing of genomes for model crops and expressed sequence tag (EST) libraries from non-model species have strengthened the idea that the cytosolic GS genes are organized in three functionally and phylogenetically conserved subfamilies. Using a bioinformatic approach, the considerable publicly available information on high throughput gene expression was mined to search for genes having patterns of expression similar to GS. Interesting new hypotheses have emerged from searching for co-expressed genes across multiple unfiltered experimental data sets in rice. This approach should inform new experimental designs and studies to explore the regulation of the GS gene family further. It is expected that understanding the regulation of GS under varied climatic conditions will emerge as an important new area considering the results from recent studies that have shown nitrogen assimilation to be critical to plant acclimation to high CO(2) concentrations.