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Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana . A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster . Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

Leaf growth is a complex developmental process that is continuously fine-tuned by the environment. Various abiotic stresses, including mild drought stress, have been shown to inhibit leaf growth in Arabidopsis (Arabidopsis thaliana), but the underlying mechanisms remain largely unknown. Here, we identify the redundant Arabidopsis transcription factors ETHYLENE RESPONSE FACTOR5 (ERF5) and ERF6 as master regulators that adapt leaf growth to environmental changes. ERF5 and ERF6 gene expression is induced very rapidly and specifically in actively growing leaves after sudden exposure to osmotic stress that mimics mild drought. Subsequently, enhanced ERF6 expression inhibits cell proliferation and leaf growth by a process involving gibberellin and DELLA signaling. Using an ERF6-inducible overexpression line, we demonstrate that the gibberellin-degrading enzyme GIBBERELLIN 2-OXIDASE6 is transcriptionally induced by ERF6 and that, consequently, DELLA proteins are stabilized. As a result, ERF6 gain-of-function lines are dwarfed and hypersensitive to osmotic stress, while the growth of erf5erf6 loss-of-function mutants is less affected by stress. Besides its role in plant growth under stress, ERF6 also activates the expression of a plethora of osmotic stress-responsive genes, including the well-known stress tolerance genes STZ, MYB51, and WRKY33. Interestingly, activation of the stress tolerance genes by ERF6 occurs independently from the ERF6-mediated growth inhibition. Together, these data fit into a leaf growth regulatory model in which ERF5 and ERF6 form a missing link between the previously observed stress-induced 1-aminocyclopropane-l-carboxylic acid accumulation and DELLA-media ted cell cycle exit and execute a dual role by regulating both stress tolerance and growth inhibition.

Recent analysis of complete eukaryotic genome sequences has revealed that gene duplication has been rampant. Moreover, next to a continuous mode of gene duplication, in many eukaryotic organisms the complete genome has been duplicated in their evolutionary past. Such large-scale gene duplication events have been associated with important evolutionary transitions or major leaps in development and adaptive radiations of species. Here, we present an evolutionary model that simulates the duplication dynamics of genes, considering genome-wide duplication events and a continuous mode of gene duplication. Modeling the evolution of the different functional categories of genes assesses the importance of different duplication events for gene families involved in specific functions or processes. By applying our model to the Arabidopsis genome, for which there is compelling evidence for three whole-genome duplications, we show that gene loss is strikingly different for large-scale and small-scale duplication events and highly biased toward certain functional classes. We provide evidence that some categories of genes were almost exclusively expanded through large-scale gene duplication events. In particular, we show that the three whole-genome duplications in Arabidopsis have been directly responsible for >90% of the increase in transcription factors, signal transducers, and developmental genes in the last 350 million years. Our evolutionary model is widely applicable and can be used to evaluate different assumptions regarding small- or large-scale gene duplication events in eukaryotic genomes.

The transcriptional coactivator ANGUSTIFOLIA3 (AN3) stimulates cell proliferation during Arabidopsis thaliana leaf development, but the molecular mechanism is largely unknown. Here, we show that inducible nuclear localization of AN3 during initial leaf growth results in differential expression of important transcriptional regulators, including GROWTH REGULATING FACTORS (GRFs). Chromatin purification further revealed the presence of AN3 at the loci of GRF5, GRF6, CYTOKININ RESPONSE FACTOR2, CONSTANS-UKE5 (COL5), HECATE1 (HEC1), and ARABIDOPSIS RESPONSE REGULATOR4 (ARR4). Tandem affinity purification of protein complexes using AN3 as bait identified plant SWITCH/SUCROSE NONFERMENTING (SWI/SNF) chromatin remodeling complexes formed around the ATPases BRAHMA (BRM) or SPLAYED. Moreover, SWI/SNF ASSOCIATED PROTEIN 73B (SWP73B) is recruited by AN3 to the promoters of GRF5, GRF3, COL5, and ARR4, and both SWP73B and BRM occupy the HEC1 promoter. Furthermore, we show that AN3 and BRM genetically interact. The data indicate that AN3 associates with chromatin remodelers to regulate transcription. In addition, modification of SWI3C expression levels increases leaf size, underlining the importance of chromatin dynamics for growth regulation. Our results place the SWI/SNF-AN3 module as a major player at the transition from cell proliferation to cell differentiation in a developing leaf.

To enable easy access and interpretation of heterogenous 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.

When subjected to stress, plants reprogram their growth by largely unknown mechanisms. To provide insights into this process, the growth of Arabidopsis (Arabidopsis thaliana) leaves that develop under mild osmotic stress was studied. Early during leaf development, cell number and size were reduced by stress, but growth was remarkably adaptable, as division and expansion rates were identical to controls within a few days of leaf initiation. To investigate the molecular basis of the observed adaptability, leaves with only proliferating, exclusively expanding, and mature cells were analyzed by transcriptomics and targeted metabolomics. The stress response measured in growing and mature leaves was largely distinct; several hundred transcripts and multiple metabolites responded exclusively in the proliferating and/or expanding leaves. Only a few genes were differentially expressed across the three stages. Data analysis showed that proliferation and expansion were regulated by common regulatory circuits, involving ethylene and gibberellins but not abscisic acid. The role of ethylene was supported by the analysis of ethylene-insensitive mutants. Exclusively in proliferating cells, stress induced genes of the so-called \"mitochondrial dysfunction regulon,\" comprising alternative oxidase. Up-regulation for eight of these genes was confirmed with promoter:β-glucuronidase reporter lines. Furthermore, mitochondria of stress-treated dividing cells were morphologically distinct from control ones, and growth of plants overexpressing the alternative oxidase gene was more tolerant to osmotic and drought stresses. Taken together, our data underline the value of analyzing stress responses in development and demonstrate the importance of mitochondrial respiration for sustaining cell proliferation under osmotic stress conditions.

The final size of plant organs, such as leaves, is tightly controlled by environmental and genetic factors that must spatially and temporally coordinate cell expansion and cell cycle activity. However, this regulation of organ growth is still poorly understood. The aim of this study is to gain more insight into the genetic control of leaf size in Arabidopsis (Arabidopsis thaliana) by performing a comparative analysis of transgenic lines that produce enlarged leaves under standardized environmental conditions. To this end, we selected five genes belonging to different functional classes that all positively affect leaf size when overexpressed: AVP1, GRF5, JAW, BRI1, and GA20OX1. We show that the increase in leaf area in these lines depended on leaf position and growth conditions and that all five lines affected leaf size differently; however, in all cases, an increase in cell number was, entirely or predominantly, responsible for the leaf size enlargement. By analyzing hormone levels, transcriptome, and metabolome, we provide deeper insight into the molecular basis of the growth phenotype for the individual lines. A comparative analysis between these data sets indicates that enhanced organ growth is governed by different, seemingly independent pathways. The analysis of transgenic lines simultaneously overexpressing two growth-enhancing genes further supports the concept that multiple pathways independently converge on organ size control in Arabidopsis.

Mature indeterminate Medicago truncatula nodules are zonated with an apical meristem, an infection zone, a fixation zone with nitrogen-fixing bacteroids, and a \"developmental\" senescence zone that follows nodule growth with a conical front originating in the center of the fixation zone. In nitrogen-fixing cells, senescence is initiated coincidently with the expression of a family of conserved cysteine proteases that might be involved in the degradation of symbiotic structures. Environmental stress, such as prolonged dark treatment, interferes with nodule functioning and triggers a fast and global nodule senescence. Developmental and dark stress-induced senescence have several different structural and expression features, suggesting at least partly divergent underlying molecular mechanisms.

As an overwhelming amount of functional genomics data have been generated, the retrieval, integration, and interpretation of these data need to be facilitated to enable the advance of (systems) biological research. For example, gathering and processing microarray data that are related to a particular biological process is not straightforward, nor is the compilation of protein-protein interactions from numerous partially overlapping databases identified through diverse approaches. However, these tasks are inevitable to address the following questions. Does a group of differentially expressed genes show similar expression in diverse microarray experiments? Was an identified protein-protein interaction previously detected by other approaches? Are the interacting proteins encoded by genes with similar expression profiles and localization? We developed CORNET (for CORrelation NETworks) as an access point to transcriptome, protein interactome, and localization data and functional information on Arabidopsis (Arabidopsis thaliana). It consists of two flexible and versatile tools, namely the coexpression tool and the protein-protein interaction tool. The ability to browse and search microarray experiments using ontology terms and the incorporation of personal microarray data are distinctive features of the microarray repository. The coexpression tool enables either the alternate or simultaneous use of diverse expression compendia, whereas the protein-protein interaction tool searches experimentally and computationally identified protein-protein interactions. Different search options are implemented to enable the construction of coexpression and/or protein-protein interaction networks centered around multiple input genes or proteins. Moreover, networks and associated evidence are visualized in Cytoscape. Localization is visualized in pie charts, thereby allowing multiple localizations per protein. CORNET is available at http://bioinformatics.psb.ugent.be/cornet.

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. This limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.