Predicting gene function from uncontrolled expression variation among individual wild-type Arabidopsis plants.

Gene expression profiling studies are usually performed on pooled samples grown under tightly controlled experimental conditions to suppress variability among individuals and increase experimental reproducibility. In addition, to mask unwanted residual effects, the samples are often subjected to relatively harsh treatments that are unrealistic in a natural context. Here, we show that expression variations among individual wild-type Arabidopsis thaliana plants grown under the same macroscopic growth conditions contain as much information on the underlying gene network structure as expression profiles of pooled plant samples under controlled experimental perturbations. We advocate the use of subtle uncontrolled variations in gene expression between individuals to uncover functional links between genes and unravel regulatory influences. As a case study, we use this approach to identify ILL6 as a new regulatory component of the jasmonate response pathway.

Comparative transcriptomics as a tool for the identification of root branching genes in maize.

The root system is fundamental for plant development, is crucial for overall plant growth and is recently being recognized as the key for future crop productivity improvement. A major determinant of root system architecture is the initiation of lateral roots. While knowledge of the genetic and molecular mechanisms regulating lateral root initiation has mainly been achieved in the dicotyledonous plant Arabidopsis thaliana, only scarce data are available for major crop species, generally monocotyledonous plants. The existence of both similarities and differences at the morphological and anatomical level between plant species from both clades raises the question whether regulation of lateral root initiation may or may not be conserved through evolution. Here, we performed a targeted genome-wide transcriptome analysis during lateral root initiation both in primary and in adventitious roots of Zea mays and found evidence for the existence of common transcriptional regulation. Further, based on a comparative analysis with Arabidopsis transcriptome data, a core of genes putatively conserved across angiosperms could be identified. Therefore, it is plausible that common regulatory mechanisms for lateral root initiation are at play in maize and Arabidopsis, a finding that might encourage the extrapolation of knowledge obtained in Arabidopsis to crop species at the level of root system architecture.

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.

The progeny of Arabidopsis thaliana plants exposed to salt exhibit changes in DNA methylation, histone modifications and gene expression.

Plants are able to acclimate to new growth conditions on a relatively short time-scale. Recently, we showed that the progeny of plants exposed to various abiotic stresses exhibited changes in genome stability, methylation patterns and stress tolerance. Here, we performed a more detailed analysis of methylation patterns in the progeny of Arabidopsis thaliana (Arabidopsis) plants exposed to 25 and 75 mM sodium chloride. We found that the majority of gene promoters exhibiting changes in methylation were hypermethylated, and this group was overrepresented by regulators of the chromatin structure. The analysis of DNA methylation at gene bodies showed that hypermethylation in the progeny of stressed plants was primarily due to changes in the 5' and 3' ends as well as in exons rather than introns. All but one hypermethylated gene tested had lower gene expression. The analysis of histone modifications in the promoters and coding sequences showed that hypermethylation and lower gene expression correlated with the enrichment of H3K9me2 and depletion of H3K9ac histones. Thus, our work demonstrated a high degree of correlation between changes in DNA methylation, histone modifications and gene expression in the progeny of salt-stressed plants.

Specific impact of tobamovirus infection on the Arabidopsis small RNA profile.

Tobamoviruses encode a silencing suppressor that binds small RNA (sRNA) duplexes in vitro and supposedly in vivo to counteract antiviral silencing. Here, we used sRNA deep-sequencing combined with transcriptome profiling to determine the global impact of tobamovirus infection on Arabidopsis sRNAs and their mRNA targets. We found that infection of Arabidopsis plants with Oilseed rape mosaic tobamovirus causes a global size-specific enrichment of miRNAs, ta-siRNAs, and other phased siRNAs. The observed patterns of sRNA enrichment suggest that in addition to a role of the viral silencing suppressor, the stabilization of sRNAs might also occur through association with unknown host effector complexes induced upon infection. Indeed, sRNA enrichment concerns primarily 21-nucleotide RNAs with a 5'-terminal guanine. Interestingly, ORMV infection also leads to accumulation of novel miRNA-like sRNAs from miRNA precursors. Thus, in addition to canonical miRNAs and miRNA*s, miRNA precursors can encode additional sRNAs that may be functional under specific conditions like pathogen infection. Virus-induced sRNA enrichment does not correlate with defects in miRNA-dependent ta-siRNA biogenesis nor with global changes in the levels of mRNA and ta-siRNA targets suggesting that the enriched sRNAs may not be able to significantly contribute to the normal activity of pre-loaded RISC complexes. We conclude that tobamovirus infection induces the stabilization of a specific sRNA pool by yet unknown effector complexes. These complexes may sequester viral and host sRNAs to engage them in yet unknown mechanisms involved in plant:virus interactions.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana.

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)-cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK-cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.

Flexible network reconstruction from relational databases with Cytoscape and CytoSQL.

BACKGROUND: Molecular interaction networks can be efficiently studied using network visualization software such as Cytoscape. The relevant nodes, edges and their attributes can be imported in Cytoscape in various file formats, or directly from external databases through specialized third party plugins. However, molecular data are often stored in relational databases with their own specific structure, for which dedicated plugins do not exist. Therefore, a more generic solution is presented. RESULTS: A new Cytoscape plugin 'CytoSQL' is developed to connect Cytoscape to any relational database. It allows to launch SQL ('Structured Query Language') queries from within Cytoscape, with the option to inject node or edge features of an existing network as SQL arguments, and to convert the retrieved data to Cytoscape network components. Supported by a set of case studies we demonstrate the flexibility and the power of the CytoSQL plugin in converting specific data subsets into meaningful network representations. CONCLUSIONS: CytoSQL offers a unified approach to let Cytoscape interact with relational databases. Thanks to the power of the SQL syntax, this tool can rapidly generate and enrich networks according to very complex criteria. The plugin is available at http://www.ptools.ua.ac.be/CytoSQL.

Functional modules in the Arabidopsis core cell cycle binary protein-protein interaction network.

As in other eukaryotes, cell division in plants is highly conserved and regulated by cyclin-dependent kinases (CDKs) that are themselves predominantly regulated at the posttranscriptional level by their association with proteins such as cyclins. Although over the last years the knowledge of the plant cell cycle has considerably increased, little is known on the assembly and regulation of the different CDK complexes. To map protein-protein interactions between core cell cycle proteins of Arabidopsis thaliana, a binary protein-protein interactome network was generated using two complementary high-throughput interaction assays, yeast two-hybrid and bimolecular fluorescence complementation. Pairwise interactions among 58 core cell cycle proteins were tested, resulting in 357 interactions, of which 293 have not been reported before. Integration of the binary interaction results with cell cycle phase-dependent expression information and localization data allowed the construction of a dynamic interaction network. The obtained interaction map constitutes a framework for further in-depth analysis of the cell cycle machinery.

Transgenerational adaptation of Arabidopsis to stress requires DNA methylation and the function of Dicer-like proteins.

Epigenetic states and certain environmental responses in mammals and seed plants can persist in the next sexual generation. These transgenerational effects have potential adaptative significance as well as medical and agronomic ramifications. Recent evidence suggests that some abiotic and biotic stress responses of plants are transgenerational. For example, viral infection of tobacco plants and exposure of Arabidopsis thaliana plants to UVC and flagellin can induce transgenerational increases in homologous recombination frequency (HRF). Here we show that exposure of Arabidopsis plants to stresses, including salt, UVC, cold, heat and flood, resulted in a higher HRF, increased global genome methylation, and higher tolerance to stress in the untreated progeny. This transgenerational effect did not, however, persist in successive generations. Treatment of the progeny of stressed plants with 5-azacytidine was shown to decrease global genomic methylation and enhance stress tolerance. Dicer-like (DCL) 2 and DCL3 encode Dicer activities important for small RNA-dependent gene silencing. Stress-induced HRF and DNA methylation were impaired in dcl2 and dcl3 deficiency mutants, while in dcl2 mutants, only stress-induced stress tolerance was impaired. Our results are consistent with the hypothesis that stress-induced transgenerational responses in Arabidopsis depend on altered DNA methylation and smRNA silencing pathways.

DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data.

SUMMARY: Many large 'omics' datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape. AVAILABILITY: Source code, pre-compiled binaries for different systems, a comprehensive tutorial, case studies and many additional datasets are freely available at http://www.ifr.ac.uk/dass/gui/. DASS-GUI is implemented in Qt.

CORNET: a user-friendly tool for data mining and integration.

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.

Protein subcomplexes--molecular machines with highly specialized functions.

Complex cellular processes are accomplished by the concerted action of hierarchically organized functional modules. Protein complexes are major components which act as highly specialized molecular machines. Here we present a statistical procedure to find insightful substructures in protein complexes based on large-scale protein complex data: we identify statistically significant common protein subcomplexes (SCs) contained in different protein complexes. We analyze recently published data of the two model organisms Saccharomyces cerevisiae (four different data sets) and Escherichia coli, as well as human protein complex data. Our method identifies well-characterized protein assemblies with known functions which act as own functional entities in the cell. In addition, we also identified hitherto unknown functional entities that should be studied experimentally in future. We discuss two typical properties of protein subcomplexes: 1) subcomplexes are enriched with essential proteins (which implies that the whole SCs may be strongly conserved) and 2) SCs are functionally and spatially more homogeneous than the experimentally found protein assemblies. The latter property is exploited to propose functions for so far unknown proteins of S. cerevisiae.

DASS: efficient discovery and p-value calculation of substructures in unordered data.

MOTIVATION: Pattern identification in biological sequence data is one of the main objectives of bioinformatics research. However, few methods are available for detecting patterns (substructures) in unordered datasets. Data mining algorithms mainly developed outside the realm of bioinformatics have been adapted for that purpose, but typically do not determine the statistical significance of the identified patterns. Moreover, these algorithms do not exploit the often modular structure of biological data. RESULTS: We present the algorithm DASS (Discovery of All Significant Substructures) that first identifies all substructures in unordered data (DASS(Sub)) in a manner that is especially efficient for modular data. In addition, DASS calculates the statistical significance of the identified substructures, for sets with at most one element of each type (DASS(P(set))), or for sets with multiple occurrence of elements (DASS(P(mset))). The power and versatility of DASS is demonstrated by four examples: combinations of protein domains in multi-domain proteins, combinations of proteins in protein complexes (protein subcomplexes), combinations of transcription factor target sites in promoter regions and evolutionarily conserved protein interaction subnetworks. AVAILABILITY: The program code and additional data are available at http://www.fli-leibniz.de/tsb/DASS

Integrated assessment and prediction of transcription factor binding.

Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms and humans. However, measurement of chromatin binding does not necessarily imply regulation, and binding may be difficult to detect if it is condition or cofactor dependent. To address these challenges, we present an approach for reliably assigning transcription factors (TFs) to target genes that integrates many lines of direct and indirect evidence into a single probabilistic model. Using this approach, we analyze publicly available chIP-chip binding profiles measured for yeast TFs in standard conditions, showing that our model interprets these data with significantly higher accuracy than previous methods. Pooling the high-confidence interactions reveals a large network containing 363 significant sets of factors (TF modules) that cooperate to regulate common target genes. In addition, the method predicts 980 novel binding interactions with high confidence that are likely to occur in so-far untested conditions. Indeed, using new chIP-chip experiments we show that predicted interactions for the factors Rpn4p and Pdr1p are observed only after treatment of cells with methyl-methanesulfonate, a DNA-damaging agent. We outline the first approach for consistently integrating all available evidences for TF-target interactions and we comprehensively identify the resulting TF module hierarchy. Prioritizing experimental conditions for each factor will be especially important as increasing numbers of chIP-chip assays are performed in complex organisms such as humans, for which "standard conditions" are ill defined.

Common patterns in type II restriction enzyme binding sites.

Restriction enzymes are among the best studied examples of DNA binding proteins. In order to find general patterns in DNA recognition sites, which may reflect important properties of protein-DNA interaction, we analyse the binding sites of all known type II restriction endonucleases. We find a significantly enhanced GC content and discuss three explanations for this phenomenon. Moreover, we study patterns of nucleotide order in recognition sites. Our analysis reveals a striking accumulation of adjacent purines (R) or pyrimidines (Y). We discuss three possible reasons: RR/YY dinucleotides are characterized by (i) stronger H-bond donor and acceptor clusters, (ii) specific geometrical properties and (iii) a low stacking energy. These features make RR/YY steps particularly accessible for specific protein-DNA interactions. Finally, we show that the recognition sites of type II restriction enzymes are underrepresented in host genomes and in phage genomes.

Identification and characterization of protein subcomplexes in yeast.

Protein complexes are major components of cellular organization. Based on large-scale protein complex data, we present the first statistical procedure to find insightful substructures in protein complexes: we identify protein subcomplexes (SCs), i.e., multiprotein assemblies residing in different protein complexes. Four protein complex datasets with different origins and variable reliability are separately analyzed. Our method identifies well-characterized protein assemblies with known functions, thereby confirming the utility of the procedure. In addition, we also identify hitherto unknown functional entities consisting of either functionally unknown proteins or proteins with different functional annotation. We show that SCs represent more reliable protein assemblies than the original complexes. Finally, we demonstrate unique properties of subcomplex proteins that underline the distinct roles of SCs: (i) SCs are functionally and spatially more homogeneous than complete protein complexes (this fact is utilized to predict functional roles and subcellular localizations for so far unannotated proteins); (ii) the abundance of subcomplex proteins is less variable than the abundance of other proteins; (iii) SCs are enriched with essential and synthetic lethal proteins; and (iv) mutations in SC-proteins have higher fitness effects than mutations in other proteins.

Post-transcriptional expression regulation in the yeast Saccharomyces cerevisiae on a genomic scale.

Based on large-scale data for the yeast Saccharomyces cerevisiae (protein and mRNA abundance, translational status, transcript length), we investigate the relation of transcription, translation, and protein turnover on a genome-wide scale. We elucidate variations between different spatial cell compartments and functional modules by comparing protein-to-mRNA ratios, translational activity, and a novel descriptor for protein-specific degradation (protein half-life descriptor). This analysis helps to understand the cell's strategy to use transcriptional and post-transcriptional regulation mechanisms for managing protein levels. For instance, it is possible to identify modules that are subject to suppressed translation under normal conditions ("translation on demand"). In order to reduce inconsistencies between the datasets, we compiled a new reference mRNA abundance dataset and we present a novel approach to correct large microarray signals for a saturation bias. Accounting for ribosome density based on transcript length rather than ORF length improves the correlation of observed protein levels to translational activity. We discuss potential causes for the deviations of these correlations. Finally, we introduce a quantitative descriptor for protein degradation (protein half-life descriptor) and compare it to measured half-lives. The study demonstrates significant post-transcriptional control of protein levels for a number of different compartments and functional modules, which is missed when exclusively focusing on transcript levels.