Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

A Walk Through the Maze of Secondary Metabolism in Orchids: A Transcriptomic Approach

Orchids have a huge reservoir of secondary metabolites making these plants of immense therapeutic importance. Their potential as curatives has been realized since times immemorial and are extensively studied for their medicinal properties. Secondary metabolism is under stringent genetic control in plants and several molecular factors are involved in regulating the production of the metabolites. However, due to the complex molecular networks, a complete understanding of the specific molecular cues is lacking. High-throughput omics technologies have the potential to fill up this lacuna. The present study deals with comparative analysis of high-throughput transcript data involving gene identification, functional annotation, and differential expression in more than 30 orchid transcriptome data sets, with a focus to elucidate the role of various factors in alkaloid and flavonoid biosynthesis. Comprehensive analysis of the mevalonate (MVA) pathway, methyl-d-erythritol 4-phosphate (MEP) pathway, and phenylpropanoid pathway provide specific insights to the potential gene targets for drug discovery. It is envisaged that a positive stimulation of these pathways through regulation of pivotal genes and alteration of specific gene expression, could facilitate the production of secondary metabolites and enable efficient tapping of the therapeutic potential of orchids. This further would lay the foundation for developing strategies for genetic and epigenetic improvement of these plants for development of therapeutic products.

Identification of MicroRNAs and Their Targets That Respond to Powdery Mildew Infection in Cucumber by Small RNA and Degradome Sequencing

Powdery mildew (PM) is a prevalent disease known to limit cucumber production worldwide. MicroRNAs (miRNAs) are single-stranded molecules that regulate host defense responses through posttranscriptional gene regulation. However, which specific miRNAs are involved and how they regulate cucumber PM resistance remain elusive. A PM-resistant single-segment substitution line, SSSL508-28, was developed previously using marker-assisted backcrossing of the PM-susceptible cucumber inbred D8 line. In this study, we applied small RNA and degradome sequencing to identify PM-responsive miRNAs and their target genes in the D8 and SSSL508-28 lines. The deep sequencing resulted in the identification of 156 known and 147 novel miRNAs. Among them, 32 and six differentially expressed miRNAs (DEMs) were detected in D8 and SSSL508-28, respectively. The positive correlation between DEMs measured by small RNA sequencing and stem-loop quantitative real-time reverse transcription-polymerase chain reaction confirmed the accuracy of the observed miRNA abundances. The 32 DEMs identified in the PM-susceptible D8 were all upregulated, whereas four of the six DEMs identified in the PM-resistant SSSL508-28 were downregulated. Using in silico and degradome sequencing approaches, 517 and 20 target genes were predicted for the D8 and SSSL508-28 DEMs, respectively. Comparison of the DEM expression profiles with the corresponding mRNA expression profiles obtained in a previous study with the same experimental design identified 60 and three target genes in D8 and SSSL508-28, respectively, which exhibited inverse expression patterns with their respective miRNAs. In particular, five DEMs were located in the substituted segment that contained two upregulated DEMs, Csa-miR172c-3p and Csa-miR395a-3p, in D8 and two downregulated DEMs, Csa-miR395d-3p and Csa-miR398b-3p, in SSSL508-28. One gene encoding L-aspartate oxidase, which was targeted by Csa-miR162a, was also located on the same segment and was specifically downregulated in PM-inoculated D8 leaves. Our results will facilitate the future use of miRNAs in breeding cucumber varieties with enhanced resistance to PM.

Centralization Within Sub-Experiments Enhances the Biological Relevance of Gene Co-expression Networks: A Plant Mitochondrial Case Study

Gene co-expression networks (GCNs) can be prepared using a variety of mathematical approaches based on data sampled across diverse developmental processes, tissue types, pathologies, mutant backgrounds, and stress conditions. These networks are used to identify genes with similar expression dynamics but are prone to introducing false-positive and false-negative relationships, especially in the instance of large and heterogenous datasets. With the aim of optimizing the relevance of edges in GCNs and enhancing global biological insight, we propose a novel approach that involves a data-centering step performed simultaneously per gene and per sub-experiment, called centralization within sub-experiments (CSE). Using a gene set encoding the plant mitochondrial proteome as a case study, our results show that all CSE-based GCNs assessed had significantly more edges within the majority of the considered functional sub-networks, such as the mitochondrial electron transport chain and its complexes, than GCNs not using CSE; thus demonstrating that CSE-based GCNs are efficient at predicting canonical functions and associated pathways, here referred to as the core gene network. Furthermore, we show that correlation analyses using CSE-processed data can be used to fine-tune prediction of the function of uncharacterized genes; while its use in combination with analyses based on non-CSE data can augment conventional stress analyses with the innate connections underpinning the dynamic system being examined. Therefore, CSE is an effective alternative method to conventional batch correction approaches, particularly when dealing with large and heterogenous datasets. The method is easy to implement into a pre-existing GCN analysis pipeline and can provide enhanced biological relevance to conventional GCNs by allowing users to delineate a core gene network.

AUTHOR SUMMARY

Gene co-expression networks (GCNs) are the product of a variety of mathematical approaches that identify causal relationships in gene expression dynamics but are prone to the misdiagnoses of false-positives and false-negatives, especially in the instance of large and heterogenous datasets. In light of the burgeoning output of next-generation sequencing projects performed on a variety of species, and developmental or clinical conditions; the statistical power and complexity of these networks will undoubtedly increase, while their biological relevance will be fiercely challenged. Here, we propose a novel approach to generate a "core" GCN with enhanced biological relevance. Our method involves a data-centering step that effectively removes all primary treatment/tissue effects, which is simple to employ and can be easily implemented into pre-existing GCN analysis pipelines. The gain in biological relevance resulting from the adoption of this approach was assessed using a plant mitochondrial case study.

The effector GpRbp-1 of Globodera pallida targets a nuclear HECT E3 ubiquitin ligase to modulate gene expression in the host

Plant-parasitic nematodes secrete effectors that manipulate plant cell morphology and physiology to achieve host invasion and establish permanent feeding sites. Effectors from the highly expanded SPRYSEC (SPRY domain with a signal peptide for secretion) family in potato cyst nematodes have been implicated in activation and suppression of plant immunity, but the mechanisms underlying these activities remain largely unexplored. To study the host mechanisms used by SPRYSEC effectors, we identified plant targets of GpRbp-1 from the potato cyst nematode Globodera pallida. Here, we show that GpRbp-1 interacts in yeast and in planta with a functional potato homologue of the Homology to E6-AP C-Terminus (HECT)-type ubiquitin E3 ligase UPL3, which is located in the nucleus. Potato lines lacking StUPL3 are not available, but the Arabidopsis mutant upl3-5 displaying a reduced UPL3 expression showed a consistently small but not significant decrease in susceptibility to cyst nematodes. We observed a major impact on the root transcriptome by the lower levels of AtUPL3 in the upl3-5 mutant, but surprisingly only in association with infections by cyst nematodes. To our knowledge, this is the first example that a HECT-type ubiquitin E3 ligase is targeted by a pathogen effector and that a member of this class of proteins specifically regulates gene expression under biotic stress conditions. Together, our data suggest that GpRbp-1 targets a specific component of the plant ubiquitination machinery to manipulate the stress response in host cells.

Identifying Early Warning Signals for the Sudden Transition from Mild to Severe Tobacco Etch Disease by Dynamical Network Biomarkers

Complex systems exhibit critical thresholds at which they transition among alternative phases. Complex systems theory has been applied to analyze disease progression, distinguishing three stages along progression: (i) a normal noninfected state; (ii) a predisease state, in which the host is infected and responds and therapeutic interventions could still be effective; and (iii) an irreversible state, where the system is seriously threatened. The dynamical network biomarker (DNB) theory sought for early warnings of the transition from health to disease. Such DNBs might range from individual genes to complex structures in transcriptional regulatory or protein-protein interaction networks. Here, we revisit transcriptomic data obtained during infection of tobacco plants with tobacco etch potyvirus to identify DNBs signaling the transition from mild/reversible to severe/irreversible disease. We identified genes showing a sudden transition in expression along disease categories. Some of these genes cluster in modules that show the properties of DNBs. These modules contain both genes known to be involved in response to pathogens (e.g., ADH2, CYP19, ERF1, KAB1, LAP1, MBF1C, MYB58, PR1, or TPS5) and other genes not previously related to biotic stress responses (e.g., ABCI6, BBX21, NAP1, OSM34, or ZPN1).

Dynamic architecture and regulatory implications of the miRNA network underlying the response to stress in melon

miRNAs are small RNAs that regulate mRNAs at both transcriptional and posttranscriptional level. In plants, miRNAs are involved in the regulation of different processes including development and stress-response. Elucidating how stress-responsive miRNAs are regulated is key to understand the global response to stress but also to develop efficient biotechnological tools that could help to cope with stress. Here, we describe a computational approach based on sRNA sequencing, transcript quantification and degradome data to analyse the accumulation, function and structural organization of melon miRNAs reactivated under seven biotic and abiotic stress conditions at two and four days post-treatment. Our pipeline allowed us to identify fourteen stress-responsive miRNAs (including evolutionary conserved such as miR156, miR166, miR172, miR319, miR398, miR399, miR894 and miR408) at both analysed times. According to our analysis miRNAs were categorized in three groups showing a broad-, intermediate- or narrow- response range. miRNAs reactive to a broad range of environmental cues appear as central components in the stress-response network. The strictly coordinated response of miR398 and miR408 (broad response-range) to the seven stress treatments during the period analysed here reinforces this notion. Although both, the amplitude and diversity of the miRNA-related response to stress changes during the exposition time, the architecture of the miRNA-network is conserved. This organization of miRNA response to stress is also conserved in rice and soybean supporting the conservation of miRNA-network organization in other crops. Overall, our work sheds light into how miRNA networks in plants organize and function during stress.

Identification of key genes involved in the biosynthesis of triterpenic acids in the mint family

Triterpenic acids (TAs), a large group of natural compounds with diverse biological activity, are produced by several plant taxa. Betulinic, oleanolic, and ursolic acids are the most medicinally important TAs and are mainly found in plants of the mint family. Metabolic engineering is strongly dependent on identifying the key genes in biosynthetic pathways toward the products of interest. In this study, gene expression tracking was performed by transcriptome mining, co-expression network analysis, and tissue-specific metabolite-expression analysis in order to identify possible key genes involved in TAs biosynthetic pathways. To this end, taxa-specific degenerate primers of six important genes were designed using an effective method based on the MEME algorithm in a phylogenetically related group of sequences and successfully applied in three members of the Lamiaceae (Rosmarinus officinalis, Salvia officinalis, and Thymus persicus). Based on the results of in-depth data analysis, genes encoding squalene epoxidase and oxido squalene cyclases are proposed as targets for boosting triterpene production. The results emphasize the importance of identifying key genes in triterpene biosynthesis, which may facilitate genetic manipulation or overexpression of target genes.

Inferring the regulatory network of the miRNA-mediated response to biotic and abiotic stress in melon

BACKGROUND

MiRNAs have emerged as key regulators of stress response in plants, suggesting their potential as candidates for knock-in/out to improve stress tolerance in agricultural crops. Although diverse assays have been performed, systematic and detailed studies of miRNA expression and function during exposure to multiple environments in crops are limited.

RESULTS

Here, we present such pioneering analysis in melon plants in response to seven biotic and abiotic stress conditions. Deep-sequencing and computational approaches have identified twenty-four known miRNAs whose expression was significantly altered under at least one stress condition, observing that down-regulation was preponderant. Additionally, miRNA function was characterized by high scale degradome assays and quantitative RNA measurements over the intended target mRNAs, providing mechanistic insight. Clustering analysis provided evidence that eight miRNAs showed a broad response range under the stress conditions analyzed, whereas another eight miRNAs displayed a narrow response range. Transcription factors were predominantly targeted by stress-responsive miRNAs in melon. Furthermore, our results show that the miRNAs that are down-regulated upon stress predominantly have as targets genes that are known to participate in the stress response by the plant, whereas the miRNAs that are up-regulated control genes linked to development.

CONCLUSION

Altogether, this high-resolution analysis of miRNA-target interactions, combining experimental and computational work, Illustrates the close interplay between miRNAs and the response to diverse environmental conditions, in melon.

Identification of cis-regulatory elements associated with salinity and drought stress tolerance in rice from co-expressed gene interaction networks

Rice, a staple food crop, is often subjected to drought and salinity stresses thereby limiting its yield potential. Since there is a cross talk between these abiotic stresses, identification of common and/or overlapping regulatory elements is pivotal for generating rice cultivars that showed tolerance towards them. Analysis of the gene interaction network (GIN) facilitates identifying the role of individual genes and their interactions with others that constitute important molecular determinants in sensing and signaling cascade governing drought and/or salinity stresses. Identification of the various cis-regulatory elements of the genes constituting GIN is equally important. Here, in this study graphical Gaussian model (GGM) was used for generating GIN for an array of genes that were differentially regulated during salinity and/or drought stresses to contrasting rice cultivars (salt-tolerant [CSR11], salt-sensitive [VSR156], drought-tolerant [Vandana], drought-sensitive [IR64]). Whole genome transcriptom profiling by using microarray were employed in this study. Markov Chain completed co-expression analyses of differentially expressed genes using Dynamic Bayesian Network, Probabilistic Boolean Network and Steady State Analysis. A compact GIN was identified for commonly co-expressed genes during salinity and drought stresses with three major hubs constituted by Myb2 transcription factor (TF), phosphoglycerate kinase and heat shock protein (Hsp). The analysis suggested a pivotal role of these genes in salinity and/or drought stress responses. Further, analysis of cis-regulatory elements (CREs) of commonly differentially expressed genes during salinity and drought stresses revealed the presence of 20 different motifs.

Systems biology approach in plant abiotic stresses

Plant abiotic stresses are the major constraint on plant growth and development, causing enormous crop losses across the world. Plants have unique features to defend themselves against these challenging adverse stress conditions. They modulate their phenotypes upon changes in physiological, biochemical, molecular and genetic information, thus making them tolerant against abiotic stresses. It is of paramount importance to determine the stress-tolerant traits of a diverse range of genotypes of plant species and integrate those traits for crop improvement. Stress-tolerant traits can be identified by conducting genome-wide analysis of stress-tolerant genotypes through the highly advanced structural and functional genomics approach. Specifically, whole-genome sequencing, development of molecular markers, genome-wide association studies and comparative analysis of interaction networks between tolerant and susceptible crop varieties grown under stress conditions can greatly facilitate discovery of novel agronomic traits that protect plants against abiotic stresses.

Multilevel Regulation of Abiotic Stress Responses in Plants

The sessile lifestyle of plants requires them to cope with stresses in situ. Plants overcome abiotic stresses by altering structure/morphology, and in some extreme conditions, by compressing the life cycle to survive the stresses in the form of seeds. Genetic and molecular studies have uncovered complex regulatory processes that coordinate stress adaptation and tolerance in plants, which are integrated at various levels. Investigating natural variation in stress responses has provided important insights into the evolutionary processes that shape the integrated regulation of adaptation and tolerance. This review primarily focuses on the current understanding of how transcriptional, post-transcriptional, post-translational, and epigenetic processes along with genetic variation orchestrate stress responses in plants. We also discuss the current and future development of computational tools to identify biologically meaningful factors from high dimensional, genome-scale data and construct the signaling networks consisting of these components.

A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways

A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus.

Defining the robust behaviour of the plant clock gene circuit with absolute RNA timeseries and open infrastructure

Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in Arabidopsis thaliana seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of PRR9 and ELF4, and regulation of PRR5 by GI. Absolute RNA quantification revealed low levels of PRR9 transcripts (peak approx. 50 copies cell⁻¹) compared with other clock genes, and threefold higher levels of LHY RNA (more than 1500 copies cell⁻¹) than of its close relative CCA1. The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.

Transcriptome Analysis of Sunflower Genotypes with Contrasting Oxidative Stress Tolerance Reveals Individual- and Combined- Biotic and Abiotic Stress Tolerance Mechanisms

In nature plants are often simultaneously challenged by different biotic and abiotic stresses. Although the mechanisms underlying plant responses against single stress have been studied considerably, plant tolerance mechanisms under combined stress is not understood. Also, the mechanism used to combat independently and sequentially occurring many number of biotic and abiotic stresses has also not systematically studied. From this context, in this study, we attempted to explore the shared response of sunflower plants to many independent stresses by using meta-analysis of publically available transcriptome data and transcript profiling by quantitative PCR. Further, we have also analyzed the possible role of the genes so identified in contributing to combined stress tolerance. Meta-analysis of transcriptomic data from many abiotic and biotic stresses indicated the common representation of oxidative stress responsive genes. Further, menadione-mediated oxidative stress in sunflower seedlings showed similar pattern of changes in the oxidative stress related genes. Based on this a large scale screening of 55 sunflower genotypes was performed under menadione stress and those contrasting in oxidative stress tolerance were identified. Further to confirm the role of genes identified in individual and combined stress tolerance the contrasting genotypes were individually and simultaneously challenged with few abiotic and biotic stresses. The tolerant hybrid showed reduced levels of stress damage both under combined stress and few independent stresses. Transcript profiling of the genes identified from meta-analysis in the tolerant hybrid also indicated that the selected genes were up-regulated under individual and combined stresses. Our results indicate that menadione-based screening can identify genotypes not only tolerant to multiple number of individual biotic and abiotic stresses, but also the combined stresses.

A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions

BACKGROUND

We address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This comparative evaluation is performed on both synthetic and real data, the latter consisting of two manually curated microarray compendia comprising several E. coli and Yeast studies, respectively. Furthermore, the reconstruction of the regulatory network of the transcription factor Ikaros in human Peripheral Blood Mononuclear Cells (PBMCs) is presented as a case-study.

RESULTS

The meta-analysis and data-merging methods included in our experimentations provided comparable performances on both synthetic and real data. Furthermore, both approaches outperformed (a) the naïve solution of merging data together ignoring possible biases, and (b) the results that are expected when only one dataset out of the available ones is analyzed in isolation. Using correlation statistics proved to be more effective than using p-values for correctly ranking candidate interactions. The results from the PBMC case-study indicate that the findings of the present study generalize to different types of network reconstruction algorithms.

CONCLUSIONS

Ignoring the systematic variations that differentiate heterogeneous studies can produce results that are statistically indistinguishable from random guessing. Meta-analysis and data merging methods have proved equally effective in addressing this issue, and thus researchers may safely select the approach that best suit their specific application.

Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

A coherent transcriptional feed-forward motif model for mediating auxin-sensitive PIN3 expression during lateral root development

Multiple plant developmental processes, such as lateral root development, depend on auxin distribution patterns that are in part generated by the PIN-formed family of auxin-efflux transporters. Here we propose that AUXIN RESPONSE FACTOR7 (ARF7) and the ARF7-regulated FOUR LIPS/MYB124 (FLP) transcription factors jointly form a coherent feed-forward motif that mediates the auxin-responsive PIN3 transcription in planta to steer the early steps of lateral root formation. This regulatory mechanism might endow the PIN3 circuitry with a temporal ‘memory' of auxin stimuli, potentially maintaining and enhancing the robustness of the auxin flux directionality during lateral root development. The cooperative action between canonical auxin signalling and other transcription factors might constitute a general mechanism by which transcriptional auxin-sensitivity can be regulated at a tissue-specific level.

Lateral root development is dependent on precise control of the distribution of the plant hormone auxin. Here Chen et al. propose the transcription factors ARF7 and FLP participate in a feed forward motif to mediate expression of the auxin transporter PIN3 and consequently regulate lateral root development.

Modelling signaling networks underlying plant defence

Transcriptional reprogramming plays a significant role in governing plant responses to pathogens. The underlying regulatory networks are complex and dynamic, responding to numerous input signals. Most network modelling studies to date have used large-scale expression data sets from public repositories but defence network models with predictive ability have also been inferred from single time series data sets, and sophisticated biological insights generated from focused experiments containing multiple network perturbations. Using multiple network inference methods, or combining network inference with additional data, such as promoter motifs, can enhance the ability of the model to predict gene function or regulatory relationships. Network topology can highlight key signaling components and provides a systems level understanding of plant defence.

Expression-based network biology identifies immune-related functional modules involved in plant defense

Background

Plants respond to diverse environmental cues including microbial perturbations by coordinated regulation of thousands of genes. These intricate transcriptional regulatory interactions depend on the recognition of specific promoter sequences by regulatory transcription factors. The combinatorial and cooperative action of multiple transcription factors defines a regulatory network that enables plant cells to respond to distinct biological signals. The identification of immune-related modules in large-scale transcriptional regulatory networks can reveal the mechanisms by which exposure to a pathogen elicits a precise phenotypic immune response.

Results

We have generated a large-scale immune co-expression network using a comprehensive set of Arabidopsis thaliana (hereafter Arabidopsis) transcriptomic data, which consists of a wide spectrum of immune responses to pathogens or pathogen-mimicking stimuli treatments. We employed both linear and non-linear models to generate Arabidopsis immune co-expression regulatory (AICR) network. We computed network topological properties and ascertained that this newly constructed immune network is densely connected, possesses hubs, exhibits high modularity, and displays hallmarks of a “real” biological network. We partitioned the network and identified 156 novel modules related to immune functions. Gene Ontology (GO) enrichment analyses provided insight into the key biological processes involved in determining finely tuned immune responses. We also developed novel software called OCCEAN (One Click Cis-regulatory Elements ANalysis) to discover statistically enriched promoter elements in the upstream regulatory regions of Arabidopsis at a whole genome level. We demonstrated that OCCEAN exhibits higher precision than the existing promoter element discovery tools. In light of known and newly discovered cis-regulatory elements, we evaluated biological significance of two key immune-related functional modules and proposed mechanism(s) to explain how large sets of diverse GO genes coherently function to mount effective immune responses.

Conclusions

We used a network-based, top-down approach to discover immune-related modules from transcriptomic data in Arabidopsis. Detailed analyses of these functional modules reveal new insight into the topological properties of immune co-expression networks and a comprehensive understanding of multifaceted plant defense responses. We present evidence that our newly developed software, OCCEAN, could become a popular tool for the Arabidopsis research community as well as potentially expand to analyze other eukaryotic genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-421) contains supplementary material, which is available to authorized users.

Arguments in the evo-devo debate: say it with flowers!

A key question in evolutionary developmental biology is how DNA sequence changes have directed the evolution of morphological diversity. The widely accepted view was that morphological changes resulted from differences in number and/or type of transcription factors, or even from small changes in the amino acid sequence of similar proteins. Research over the last two decades indicated that most of the developmental and genetic mechanisms that produce new structures involve proteins that are deeply conserved. These proteins are encoded by a type of genes known as 'toolkit' genes that control a plethora of processes essential for the correct development of the organism. Mutations in these toolkit genes produce deleterious pleiotropic effects. In contrast, alterations in regulatory regions affect their expression only at specific sites in the organism, facilitating morphological change at the tissue and organ levels. However, some examples from the animal and plant fields indicate that coding mutations also contributed to phenotypic evolution. Therefore, the main question at this point is to what extent these mechanisms have contributed to the evolution of morphological diversity. Today, an increasing amount of data, especially from the plant field, implies that changes in cis-regulatory sequences in fact played a major role in evolution.

ComPlEx: conservation and divergence of co-expression networks in A. thaliana, Populus and O. sativa

Background

Divergence in gene regulation has emerged as a key mechanism underlying species differentiation. Comparative analysis of co-expression networks across species can reveal conservation and divergence in the regulation of genes.

Results

We inferred co-expression networks of A. thaliana, Populus spp. and O. sativa using state-of-the-art methods based on mutual information and context likelihood of relatedness, and conducted a comprehensive comparison of these networks across a range of co-expression thresholds. In addition to quantifying gene-gene link and network neighbourhood conservation, we also applied recent advancements in network analysis to do cross-species comparisons of network properties such as scale free characteristics and gene centrality as well as network motifs. We found that in all species the networks emerged as scale free only above a certain co-expression threshold, and that the high-centrality genes upholding this organization tended to be conserved. Network motifs, in particular the feed-forward loop, were found to be significantly enriched in specific functional subnetworks but where much less conserved across species than gene centrality. Although individual gene-gene co-expression had massively diverged, up to ~80% of the genes still had a significantly conserved network neighbourhood. For genes with multiple predicted orthologs, about half had one ortholog with conserved regulation and another ortholog with diverged or non-conserved regulation. Furthermore, the most sequence similar ortholog was not the one with the most conserved gene regulation in over half of the cases.

Conclusions

We have provided a comprehensive analysis of gene regulation evolution in plants and built a web tool for Comparative analysis of Plant co-Expression networks (ComPlEx, http://complex.plantgenie.org/). The tool can be particularly useful for identifying the ortholog with the most conserved regulation among several sequence-similar alternatives and can thus be of practical importance in e.g. finding candidate genes for perturbation experiments.

Integrated network analysis and effective tools in plant systems biology

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

Network modeling to understand plant immunity

Deciphering the networks that underpin complex biological processes using experimental data remains a significant, but promising, challenge, a task made all the harder by the added complexity of host-pathogen interactions. The aim of this article is to review the progress in understanding plant immunity made so far by applying network modeling algorithms and to show how this computational/mathematical strategy is facilitating a systems view of plant defense. We review the different types of network modeling that have been used, the data required, and the type of insight that such modeling can provide. We discuss the current challenges in modeling the regulatory networks that underlie plant defense and the future developments that may help address these challenges.

Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

Background

Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking.

Results

In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p < 0.01) in 10 ecotypes, including 498 transcription factors and 315 transposable elements. The majority of the transcripts (75%) showed ecotype specific expression pattern. By using sequence data available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about regulatory interactions between transcription factors and their target genes in the model plant A. thaliana, we have adopted a powerful systems genetics approach- Network Component Analysis (NCA) to construct an in-silico transcriptional regulatory network model during response to cold stress. The resulting regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes.

Conclusions

A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted regulatory network model was able to identify new ecotype specific transcription factors and their regulatory interactions, which might be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating biological process in any biological systems.

Conserved versatile master regulators in signalling pathways in response to stress in plants

From the first land plants to the complex gymnosperms and angiosperms of today, environmental conditions have forced plants to develop molecular strategies to surpass natural obstacles to growth and proliferation, and these genetic gains have been transmitted to the following generations. In this long natural process, novel and elaborate mechanisms have evolved to enable plants to cope with environmental limitations. Elements in many signalling cascades enable plants to sense different, multiple and simultaneous ambient cues. A group of versatile master regulators of gene expression control plant responses to stressing conditions. For crop breeding purposes, the task is to determine how to activate these key regulators to enable accurate and optimal reactions to common stresses. In this review, we discuss how plants sense biotic and abiotic stresses, how and which master regulators are implied in the responses to these stresses, their evolution in the life kingdoms, and the domains in these proteins that interact with other factors to lead to a proper and efficient plant response.

Copy number variation of chromosome 5A and its association with Q gene expression, morphological aberrations, and agronomic performance of winter wheat cultivars

KEY MESSAGE

Our investigations combine chromosome 5A copy number variation associated with relative 5A Q gene expression and morphological and agronomic data to characterize the occurrence of speltoid plants in winter wheat cultivars. The occurrence of speltoid aberrants in wheat breeding is a serious problem that may result in rejection of a candidate cultivar during licensing. The spear-shaped, hard threshing spike is caused by copy number reduction of the domestication gene Q, located on the long arm of wheat chromosome 5A. As a member of the APETALA2-like transcription factor family, the 5AQ gene is involved in flower development and pleiotropically controls other agronomic traits. In this report, a characterization of instability of chromosome 5A is given and effects due to the loss of the Q gene and other genes are discussed. Based on pyrosequencing, we correctly predicted the 5AQ copy number for 392 of 402 tested offspring plants (97.5 %) originating from single speltoid plants of eleven wheat cultivars. The findings indicate that the resulting speltoid plants were either reduced in chromosome 5A copy number or possessed a partial deletion of the distal end of chromosome arm 5AL. 5AQ specific real-time PCR analysis revealed varying transcription levels among cultivars. During early spike development, the relative transcription of the 5AQ gene was always lower in speltoids than in normal square headed wheat plants, most likely leading to the occurrence of the characteristic speltoid spike phenotype. The parallel analysis of 18 agronomic traits revealed pleiotropic effects governed by genes located on 5A. Our results demonstrate that through pyrosequencing one can identify aneuploidy or deletions within chromosome 5A to select against the occurrence of speltoid plants in wheat seedlings.

Transcriptome responses to combinations of stresses in Arabidopsis

Biotic and abiotic stresses limit agricultural yields, and plants are often simultaneously exposed to multiple stresses. Combinations of stresses such as heat and drought or cold and high light intensity have profound effects on crop performance and yields. Thus, delineation of the regulatory networks and metabolic pathways responding to single and multiple concurrent stresses is required for breeding and engineering crop stress tolerance. Many studies have described transcriptome changes in response to single stresses. However, exposure of plants to a combination of stress factors may require agonistic or antagonistic responses or responses potentially unrelated to responses to the corresponding single stresses. To analyze such responses, we initially compared transcriptome changes in 10 Arabidopsis (Arabidopsis thaliana) ecotypes using cold, heat, high-light, salt, and flagellin treatments as single stress factors as well as their double combinations. This revealed that some 61% of the transcriptome changes in response to double stresses were not predic from the responses to single stress treatments. It also showed that plants prioritized between potentially antagonistic responses for only 5% to 10% of the responding transcripts. This indicates that plants have evolved to cope with combinations of stresses and, therefore, may be bred to endure them. In addition, using a subset of this data from the Columbia and Landsberg erecta ecotypes, we have delineated coexpression network modules responding to single and combined stresses.

Genome scale transcriptional response diversity among ten ecotypes of Arabidopsis thaliana during heat stress

In the scenario of global warming and climate change, heat stress is a serious threat to crop production worldwide. Being sessile, plants cannot escape from heat. Plants have developed various adaptive mechanisms to survive heat stress. Several studies have focused on diversity of heat tolerance levels in divergent Arabidopsis thaliana (A. thaliana) ecotypes, but comprehensive genome scale understanding of heat stress response in plants is still lacking. Here we report the genome scale transcript responses to heat stress of 10 A. thaliana ecotypes (Col, Ler, C24, Cvi, Kas1, An1, Sha, Kyo2, Eri, and Kond) originated from different geographical locations. During the experiment, A. thaliana plants were subjected to heat stress (38°C) and transcript responses were monitored using Arabidopsis NimbleGen ATH6 microarrays. The responses of A. thaliana ecotypes exhibited considerable variation in the transcript abundance levels. In total, 3644 transcripts were significantly heat regulated (p < 0.01) in the 10 ecotypes, including 244 transcription factors and 203 transposable elements. By employing a systems genetics approach- Network Component Analysis (NCA), we have constructed an in silico transcript regulatory network model for 35 heat responsive transcription factors during cellular responses to heat stress in A. thaliana. The computed activities of the 35 transcription factors showed ecotype specific responses to the heat treatment.

Computational design of host transcription-factors sets whose misregulation mimics the transcriptomic effect of viral infections

The molecular mechanisms underlying viral pathogenesis are yet poorly understood owed to the large number of factors involved and the complexity of their interactions. Could we identify a minimal set of host transcription factors (TF) whose misregulation would result in the transcriptional profile characteristic of infected cells in absence of the virus? How many of such sets exist? Are all orthogonal or share critical TFs involved in specific biological functions? We have developed a computational methodology that uses a quantitative model of the transcriptional regulatory network (TRN) of Arabidopsis thaliana to explore the landscape of all possible re-engineered TRNs whose transcriptomic profiles mimic those observed in infected plants. We found core sets containing between six and 34 TFs, depending on the virus, whose in silico knockout or overexpression in the TRN resulted in transcriptional profiles that minimally deviate from those observed in infected plants.

Towards multiscale plant models: integrating cellular networks

One of the ambitions of 'crop systems biology' is to combine information from molecular biology with a broader view of plant development and growth. In the context of modeling, this calls for a multiscale perspective that focuses on the interplay between cellular and macroscopic studies. With this in mind, in this review we aim to draw attention to a panel of approaches that were developed in the context of systems biology and are used for analyzing and describing the behavior of cellular networks. Ultimately, insights obtained from these methods can be exploited to refine the description of plant processes, leading to integrated plant-cellular models.

Towards multiscale plant models: integrating cellular networks

One of the ambitions of 'crop systems biology' is to combine information from molecular biology with a broader view of plant development and growth. In the context of modeling, this calls for a multiscale perspective that focuses on the interplay between cellular and macroscopic studies. With this in mind, in this review we aim to draw attention to a panel of approaches that were developed in the context of systems biology and are used for analyzing and describing the behavior of cellular networks. Ultimately, insights obtained from these methods can be exploited to refine the description of plant processes, leading to integrated plant-cellular models.

Reverse engineering and analysis of large genome-scale gene networks

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.

Towards an integrated molecular model of plant-virus interactions

The application in recent years of network theory methods to the study of host-virus interactions is providing a new perspective to the way viruses manipulate the host to promote their own replication. An integrated molecular model of such pathosystems require three detailed maps describing, firstly, the interactions between viral elements, secondly, the interactions between host elements, and thirdly, the cross-interactions between viral and host elements. Here, we compile available information for Potyvirus infecting Arabidopsis thaliana. With an integrated model, it is possible to analyze the mode of virus action and how the perturbation of the virus targets propagates along the network. These studies suggest that viral pathogenicity results not only from the alteration of individual elements but it is a systemic property.

Computational design of genomic transcriptional networks with adaptation to varying environments

Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

BACKGROUND

Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).

RESULTS

Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines.

CONCLUSIONS

In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplasts.

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules.

Fine-tuning tomato agronomic properties by computational genome redesign

Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites.

Genome-wide mapping of transcription factor binding reveals developmental process integration and a fresh look at evolutionary dynamics

How does evolution forge adaptive responses? Are many changes required or few? Just how complex are the transcriptional networks that control development? Diverse questions like these are being newly addressed by next-generation sequencing-based techniques. Facilitating a mechanistic understanding, these approaches reveal the direct in vivo interactions between transcription factors and their physical targets, combined with genome-scale readouts to comprehensively map adaptive gene regulatory networks (GRNs). Here I focus on pioneering work from the last 3 years that has leveraged these data to investigate diverse aspects of GRN circuitry controlling the reproductive transition in plants. These approaches have revealed surprising new functions for long-investigated key players in developmental programs and laid bare the basis for pleiotropy in many others, suggesting widespread process integration at the transcriptional level. Evolutionary questions begged by the recent deluge of GRN mapping data are being assessed anew, both by emerging work outside Arabidopsis thaliana and novel analyses within. These studies have swiftly exposed the distinctive power and adaptability of genome-wide GRN mapping and illustrate that this unique data type holds tremendous promise for plant biology.

Comprehensive literature review and statistical considerations for microarray meta-analysis

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

Effects of abiotic stress on plants: a systems biology perspective

The natural environment for plants is composed of a complex set of abiotic stresses and biotic stresses. Plant responses to these stresses are equally complex. Systems biology approaches facilitate a multi-targeted approach by allowing one to identify regulatory hubs in complex networks. Systems biology takes the molecular parts (transcripts, proteins and metabolites) of an organism and attempts to fit them into functional networks or models designed to describe and predict the dynamic activities of that organism in different environments. In this review, research progress in plant responses to abiotic stresses is summarized from the physiological level to the molecular level. New insights obtained from the integration of omics datasets are highlighted. Gaps in our knowledge are identified, providing additional focus areas for crop improvement research in the future.

A systems biology approach to the evolution of plant-virus interactions

Omic approaches to the analysis of plant-virus interactions are becoming increasingly popular. These types of data, in combination with models of interaction networks, will aid in revealing not only host components that are important for the virus life cycle, but also general patterns about the way in which different viruses manipulate host regulation of gene expression for their own benefit and possible mechanisms by which viruses evade host defenses. Here, we review studies identifying host genes regulated by viruses and discuss how these genes integrate in host regulatory and interaction networks, with a particular focus on the physical properties of these networks.

Many needles in a haystack: cell-type specific abiotic stress responses

Plants react to abiotic stress with a number of physiological, biochemical, and developmental alterations. These responses include changes in signaling components, gene transcription, non-coding RNAs, proteins, and metabolites that occur in a cell-type and tissue-specific manner. Recent advances in cell-type specifically isolating protoplasts and nuclei from plants, extracting mRNA from targeted cells, and whole-genome transcriptional profiling have enabled scientists to gain insight into how cells and tissues respond transcriptionally to abiotic stress. Continued technological advances in profiling the proteomes, metabolomes, and other biological components of specific cells will continue to broaden our understanding of plant stress responses.

Plant systems biology: network matters

Systems biology is all about networks. A recent trend has been to associate systems biology exclusively with the study of gene regulatory or protein-interaction networks. However, systems biology approaches can be applied at many other scales, from the subatomic to the ecosystem scales. In this review, we describe studies at the sub-cellular, tissue, whole plant and crop scales and highlight how these studies can be related to systems biology. We discuss the properties of system approaches at each scale as well as their current limits, and pinpoint in each case advances unique to the considered scale but representing potential for the other scales. We conclude by examining plant models bridging different scales and considering the future prospects of plant systems biology.

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

BACKGROUND

Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species.

RESULTS

We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis.

CONCLUSIONS

We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis.

Plant molecular stress responses face climate change

Environmental stress factors such as drought, elevated temperature, salinity and rising CO₂ affect plant growth and pose a growing threat to sustainable agriculture. This has become a hot issue due to concerns about the effects of climate change on plant resources, biodiversity and global food security. Plant adaptation to stress involves key changes in the '-omic' architecture. Here, we present an overview of the physiological and molecular programs in stress adaptation focusing on how genes, proteins and metabolites change after individual and multiple environmental stresses. We address the role which '-omics' research, coupled to systems biology approaches, can play in future research on plants seemingly unable to adapt as well as those which can tolerate climatic change.

Orchestration of the floral transition and floral development in Arabidopsis by the bifunctional transcription factor APETALA2

The Arabidopsis thaliana transcription factor APETALA2 (AP2) has numerous functions, including roles in seed development, stem cell maintenance, and specification of floral organ identity. To understand the relationship between these different roles, we mapped direct targets of AP2 on a genome-wide scale in two tissue types. We find that AP2 binds to thousands of loci in the developing flower, many of which exhibit AP2-dependent transcription. Opposing, logical effects are evident in AP2 binding to two microRNA genes that influence AP2 expression, with AP2 positively regulating miR156 and negatively regulating miR172, forming a complex direct feedback loop, which also included all but one of the AP2-like miR172 target clade members. We compare the genome-wide direct target repertoire of AP2 with that of SCHLAFMUTZE, a closely related transcription factor that also represses the transition to flowering. We detect clear similarities and important differences in the direct target repertoires that are also tissue specific. Finally, using an inducible expression system, we demonstrate that AP2 has dual molecular roles. It functions as both a transcriptional activator and repressor, directly inducing the expression of the floral repressor AGAMOUS-LIKE15 and directly repressing the transcription of floral activators like SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1.

Transcriptional regulation of the CRK/DUF26 group of receptor-like protein kinases by ozone and plant hormones in Arabidopsis

BACKGROUND

Plant Receptor-like/Pelle kinases (RLK) are a group of conserved signalling components that regulate developmental programs and responses to biotic and abiotic stresses. One of the largest RLK groups is formed by the Domain of Unknown Function 26 (DUF26) RLKs, also called Cysteine-rich Receptor-like Kinases (CRKs), which have been suggested to play important roles in the regulation of pathogen defence and programmed cell death. Despite the vast number of RLKs present in plants, however, only a few of them have been functionally characterized.

RESULTS

We examined the transcriptional regulation of all Arabidopsis CRKs by ozone (O3), high light and pathogen/elicitor treatment - conditions known to induce the production of reactive oxygen species (ROS) in various subcellular compartments. Several CRKs were transcriptionally induced by exposure to O3 but not by light stress. O3 induces an extracellular oxidative burst, whilst light stress leads to ROS production in chloroplasts. Analysis of publicly available microarray data revealed that the transcriptional responses of the CRKs to O3 were very similar to responses to microbes or pathogen-associated molecular patterns (PAMPs). Several mutants altered in hormone biosynthesis or signalling showed changes in basal and O3-induced transcriptional responses.

CONCLUSIONS

Combining expression analysis from multiple treatments with mutants altered in hormone biosynthesis or signalling suggest a model in which O3 and salicylic acid (SA) activate separate signaling pathways that exhibit negative crosstalk. Although O3 is classified as an abiotic stress to plants, transcriptional profiling of CRKs showed strong similarities between the O3 and biotic stress responses.