Extensive signal integration by the phytohormone protein network.

Plant hormones coordinate responses to environmental cues with developmental programs1, and are fundamental for stress resilience and agronomic yield2. The core signalling pathways underlying the effects of phytohormones have been elucidated by genetic screens and hypothesis-driven approaches, and extended by interactome studies of select pathways3. However, fundamental questions remain about how information from different pathways is integrated. Genetically, most phenotypes seem to be regulated by several hormones, but transcriptional profiling suggests that hormones trigger largely exclusive transcriptional programs4. We hypothesized that protein-protein interactions have an important role in phytohormone signal integration. Here, we experimentally generated a systems-level map of the Arabidopsis phytohormone signalling network, consisting of more than 2,000 binary protein-protein interactions. In the highly interconnected network, we identify pathway communities and hundreds of previously unknown pathway contacts that represent potential points of crosstalk. Functional validation of candidates in seven hormone pathways reveals new functions for 74% of tested proteins in 84% of candidate interactions, and indicates that a large majority of signalling proteins function pleiotropically in several pathways. Moreover, we identify several hundred largely small-molecule-dependent interactions of hormone receptors. Comparison with previous reports suggests that noncanonical and nontranscription-mediated receptor signalling is more common than hitherto appreciated.

Publisher Correction: Extensive signal integration by the phytohormone protein network.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Brassinosteroids participate in the control of basal and acquired freezing tolerance of plants.

Brassinosteroids (BRs) are growth-promoting plant hormones that play a role in abiotic stress responses, but molecular modes that enable this activity remain largely unknown. Here we show that BRs participate in the regulation of freezing tolerance. BR signaling-defective mutants of Arabidopsis thaliana were hypersensitive to freezing before and after cold acclimation. The constitutive activation of BR signaling, in contrast, enhanced freezing resistance. Evidence is provided that the BR-controlled basic helix-loop-helix transcription factor CESTA (CES) can contribute to the constitutive expression of the C-REPEAT/DEHYDRATION-RESPONSIVE ELEMENT BINDING FACTOR (CBF) transcriptional regulators that control cold responsive (COR) gene expression. In addition, CBF-independent classes of BR-regulated COR genes are identified that are regulated in a BR- and CES-dependent manner during cold acclimation. A model is presented in which BRs govern different cold-responsive transcriptional cascades through the posttranslational modification of CES and redundantly acting factors. This contributes to the basal resistance against freezing stress, but also to the further improvement of this resistance through cold acclimation.

Brassinosteroids Are Master Regulators of Gibberellin Biosynthesis in Arabidopsis.

Plant growth and development are highly regulated processes that are coordinated by hormones including the brassinosteroids (BRs), a group of steroids with structural similarity to steroid hormones of mammals. Although it is well understood how BRs are produced and how their signals are transduced, BR targets, which directly confer the hormone's growth-promoting effects, have remained largely elusive. Here, we show that BRs regulate the biosynthesis of gibberellins (GAs), another class of growth-promoting hormones, in Arabidopsis thaliana. We reveal that Arabidopsis mutants deficient in BR signaling are severely impaired in the production of bioactive GA, which is correlated with defective GA biosynthetic gene expression. Expression of the key GA biosynthesis gene GA20ox1 in the BR signaling mutant bri1-301 rescues many of its developmental defects. We provide evidence that supports a model in which the BR-regulated transcription factor BES1 binds to a regulatory element in promoters of GA biosynthesis genes in a BR-induced manner to control their expression. In summary, our study underscores a role of BRs as master regulators of GA biosynthesis and shows that this function is of major relevance for the growth and development of vascular plants.

PGSB PlantsDB: updates to the database framework for comparative plant genome research.

PGSB (Plant Genome and Systems Biology: formerly MIPS) PlantsDB (http://pgsb.helmholtz-muenchen.de/plant/index.jsp) is a database framework for the comparative analysis and visualization of plant genome data. The resource has been updated with new data sets and types as well as specialized tools and interfaces to address user demands for intuitive access to complex plant genome data. In its latest incarnation, we have re-worked both the layout and navigation structure and implemented new keyword search options and a new BLAST sequence search functionality. Actively involved in corresponding sequencing consortia, PlantsDB has dedicated special efforts to the integration and visualization of complex triticeae genome data, especially for barley, wheat and rye. We enhanced CrowsNest, a tool to visualize syntenic relationships between genomes, with data from the wheat sub-genome progenitor Aegilops tauschii and added functionality to the PGSB RNASeqExpressionBrowser. GenomeZipper results were integrated for the genomes of barley, rye, wheat and perennial ryegrass and interactive access is granted through PlantsDB interfaces. Data exchange and cross-linking between PlantsDB and other plant genome databases is stimulated by the transPLANT project (http://transplantdb.eu/).

Ribosome quality control is a central protection mechanism for yeast exposed to deoxynivalenol and trichothecin.

BACKGROUND: The trichothecene mycotoxins deoxynivalenol (DON) and trichothecin (TTC) are inhibitors of eukaryotic protein synthesis. Their effect on cellular homeostasis is poorly understood. We report a systematic functional investigation of the effect of DON and TTC on the yeast Saccharomyces cerevisiae using genetic array, network and microarray analysis. To focus the genetic analysis on intracellular consequences of toxin action we eliminated the PDR5 gene coding for a potent pleiotropic drug efflux protein potentially confounding results. We therefore used a knockout library with a pdr5Δ strain background. RESULTS: DON or TTC treatment creates a fitness bottleneck connected to ribosome efficiency. Genes isolated by systematic genetic array analysis as contributing to toxin resistance function in ribosome quality control, translation fidelity, and in transcription. Mutants in the E3 ligase Hel2, involved in ribosome quality control, and several members of the Rpd3 histone deacetylase complex were highly sensitive to DON. DON and TTC have similar genetic profiles despite their different toxic potency. Network analysis shows a coherent and tight network of genetic interactions among the DON and TTC resistance conferring gene products. The networks exhibited topological properties commonly associated with efficient processing of information. Many sensitive mutants have a "slow growth" gene expression signature. DON-exposed yeast cells increase transcripts of ribosomal protein and histone genes indicating an internal signal for growth enhancement. CONCLUSIONS: The combination of gene expression profiling and analysis of mutants reveals cellular pathways which become bottlenecks under DON and TTC stress. These are generally directly or indirectly connected to ribosome biosynthesis such as the general secretory pathway, cytoskeleton, cell cycle delay, ribosome synthesis and translation quality control. Gene expression profiling points to an increased demand of ribosomal components and does not reveal activation of stress pathways. Our analysis highlights ribosome quality control and a contribution of a histone deacetylase complex as main sources of resistance against DON and TTC.

Reticulate evolution of the rye genome.

Rye (Secale cereale) is closely related to wheat (Triticum aestivum) and barley (Hordeum vulgare). Due to its large genome (~8 Gb) and its regional importance, genome analysis of rye has lagged behind other cereals. Here, we established a virtual linear gene order model (genome zipper) comprising 22,426 or 72% of the detected set of 31,008 rye genes. This was achieved by high-throughput transcript mapping, chromosome survey sequencing, and integration of conserved synteny information of three sequenced model grass genomes (Brachypodium distachyon, rice [Oryza sativa], and sorghum [Sorghum bicolor]). This enabled a genome-wide high-density comparative analysis of rye/barley/model grass genome synteny. Seventeen conserved syntenic linkage blocks making up the rye and barley genomes were defined in comparison to model grass genomes. Six major translocations shaped the modern rye genome in comparison to a putative Triticeae ancestral genome. Strikingly dissimilar conserved syntenic gene content, gene sequence diversity signatures, and phylogenetic networks were found for individual rye syntenic blocks. This indicates that introgressive hybridizations (diploid or polyploidy hybrid speciation) and/or a series of whole-genome or chromosome duplications played a role in rye speciation and genome evolution.

TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists.

High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format - rankings - for the same set of items. Here we present the R package TopKLists, which allows for statistical inference on the lengths of informative (top-k) partial lists, for stochastic aggregation of full or partial lists, and for graphical exploration of the input and consolidated output. A graphical user interface has also been implemented for providing access to the underlying algorithms. To illustrate the applicability and usefulness of the package, we integrated microRNA data of non-small cell lung cancer across different measurement techniques and draw conclusions. The package can be obtained from CRAN under a LGPL-3 license.

RNASeqExpressionBrowser--a web interface to browse and visualize high-throughput expression data.

MOTIVATION: RNA-seq techniques generate massive amounts of expression data. Several pipelines (e.g. Tophat and Cufflinks) are broadly applied to analyse these datasets. However, accessing and handling the analytical output remain challenging for non-experts. RESULTS: We present the RNASeqExpressionBrowser, an open-source web interface that can be used to access the output from RNA-seq expression analysis packages in different ways, as it allows browsing for genes by identifiers, annotations or sequence similarity. Gene expression information can be loaded as long as it is represented in a matrix-like format. Additionally, data can be made available by setting up the tool on a public server. For demonstration purposes, we have set up a version providing expression information from the barley genome. AVAILABILITY AND IMPLEMENTATION: The source code and a show case are accessible at http://mips.helmholtz-muenchen.de/plant/RNASeqExpressionBrowser/.

Bacteria-triggered systemic immunity in barley is associated with WRKY and ETHYLENE RESPONSIVE FACTORs but not with salicylic acid.

Leaf-to-leaf systemic immune signaling known as systemic acquired resistance is poorly understood in monocotyledonous plants. Here, we characterize systemic immunity in barley (Hordeum vulgare) triggered after primary leaf infection with either Pseudomonas syringae pathovar japonica (Psj) or Xanthomonas translucens pathovar cerealis (Xtc). Both pathogens induced resistance in systemic, uninfected leaves against a subsequent challenge infection with Xtc. In contrast to systemic acquired resistance in Arabidopsis (Arabidopsis thaliana), systemic immunity in barley was not associated with NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1 or the local or systemic accumulation of salicylic acid. Instead, we documented a moderate local but not systemic induction of abscisic acid after infection of leaves with Psj. In contrast to salicylic acid or its functional analog benzothiadiazole, local applications of the jasmonic acid methyl ester or abscisic acid triggered systemic immunity to Xtc. RNA sequencing analysis of local and systemic transcript accumulation revealed unique gene expression changes in response to both Psj and Xtc and a clear separation of local from systemic responses. The systemic response appeared relatively modest, and quantitative reverse transcription-polymerase chain reaction associated systemic immunity with the local and systemic induction of two WRKY and two ETHYLENE RESPONSIVE FACTOR (ERF)-like transcription factors. Systemic immunity against Xtc was further associated with transcriptional changes after a secondary/systemic Xtc challenge infection; these changes were dependent on the primary treatment. Taken together, bacteria-induced systemic immunity in barley may be mediated in part by WRKY and ERF-like transcription factors, possibly facilitating transcriptional reprogramming to potentiate immunity.

Genetic analysis of DEFECTIVE KERNEL1 loop function in three-dimensional body patterning in Physcomitrella patens.

DEFECTIVE KERNEL1 (DEK1) of higher plants plays an essential role in position-dependent signaling and consists of a large transmembrane domain (MEM) linked to a protease catalytic domain and a regulatory domain. Here, we show that the postulated sensory Loop of the MEM domain plays an important role in the developmental regulation of DEK1 activity in the moss Physcomitrella patens. Compared with P. patens lacking DEK1 (∆dek1), the dek1∆loop mutant correctly positions the division plane in the bud apical cell. In contrast with an early developmental arrest of ∆dek1 buds, dek1∆loop develops aberrant gametophores lacking expanded phyllids resulting from misregulation of mitotic activity. In contrast with the highly conserved sequence of the protease catalytic domain, the Loop is highly variable in land plants. Functionally, the sequence from Marchantia polymorpha fully complements the dek1∆loop phenotype, whereas sequences from maize (Zea mays) and Arabidopsis (Arabidopsis thaliana) give phenotypes with retarded growth and affected phyllid development. Bioinformatic analysis identifies MEM as a member of the Major Facilitator Superfamily, membrane transporters reacting to stimuli from the external environment. Transcriptome analysis comparing wild-type and ∆dek1 tissues identifies an effect on two groups of transcripts connected to dek1 mutant phenotypes: transcripts related to cell wall remodeling and regulation of the AINTEGUMENTA, PLETHORA, and BABY BOOM2 (APB2) and APB3 transcription factors known to regulate bud initiation. Finally, sequence data support the hypothesis that the advanced charophyte algae that evolved into ancestral land plants lost cytosolic calpains, retaining DEK1 as the sole calpain in the evolving land plant lineage.

chromoWIZ: a web tool to query and visualize chromosome-anchored genes from cereal and model genomes.

BACKGROUND: Over the last years reference genome sequences of several economically and scientifically important cereals and model plants became available. Despite the agricultural significance of these crops only a small number of tools exist that allow users to inspect and visualize the genomic position of genes of interest in an interactive manner. DESCRIPTION: We present chromoWIZ, a web tool that allows visualizing the genomic positions of relevant genes and comparing these data between different plant genomes. Genes can be queried using gene identifiers, functional annotations, or sequence homology in four grass species (Triticum aestivum, Hordeum vulgare, Brachypodium distachyon, Oryza sativa). The distribution of the anchored genes is visualized along the chromosomes by using heat maps. Custom gene expression measurements, differential expression information, and gene-to-group mappings can be uploaded and can be used for further filtering. CONCLUSIONS: This tool is mainly designed for breeders and plant researchers, who are interested in the location and the distribution of candidate genes as well as in the syntenic relationships between different grass species. chromoWIZ is freely available and online accessible at http://mips.helmholtz-muenchen.de/plant/chromoWIZ/index.jsp.

Quantitative trait loci-dependent analysis of a gene co-expression network associated with Fusarium head blight resistance in bread wheat (Triticum aestivum L.).

BACKGROUND: Fusarium head blight (FHB) caused by Fusarium graminearum Schwabe is one of the most prevalent diseases of wheat (Triticum aestivum L.) and other small grain cereals. Resistance against the fungus is quantitative and more than 100 quantitative trait loci (QTL) have been described. Two well-validated and highly reproducible QTL, Fhb1 and Qfhs.ifa-5A have been widely investigated, but to date the underlying genes have not been identified. RESULTS: We have investigated a gene co-expression network activated in response to F. graminearum using RNA-seq data from near-isogenic lines, harboring either the resistant or the susceptible allele for Fhb1 and Qfhs.ifa-5A. The network identified pathogen-responsive modules, which were enriched for differentially expressed genes between genotypes or different time points after inoculation with the pathogen. Central gene analysis identified transcripts associated with either QTL within the network. Moreover, we present a detailed gene expression analysis of four gene families (glucanases, NBS-LRR, WRKY transcription factors and UDP-glycosyltransferases), which take prominent roles in the pathogen response. CONCLUSIONS: A combination of a network-driven approach and differential gene expression analysis identified genes and pathways associated with Fhb1 and Qfhs.ifa-5A. We find G-protein coupled receptor kinases and biosynthesis genes for jasmonate and ethylene earlier induced for Fhb1. Similarly, we find genes involved in the biosynthesis and metabolism of riboflavin more abundant for Qfhs.ifa-5A.

Serum-autoantibodies for discovery of prostate cancer specific biomarkers.

BACKGROUND: The currently used prostate cancer serum marker has a low cancer specificity and improved diagnostics are needed. Here we evaluated whether autoantibodies are present in sera of prostate cancer patients and whether they are useful diagnostic markers for prostate cancer. METHODS: Sera from 20 prostate cancer patients and 20 healthy controls were incubated on expression clone arrays containing more than 37,000 recombinant human proteins. Functional annotation clustering of the identified autoantigens was performed using the DAVID database. Autoantigens identified in the prostate cancer group were validated on microarrays using sera of 40 prostate cancer patients, 40 patients with elevated PSA levels but prostate cancer negative biopsies (benign disease), and 40 healthy controls. RESULTS: We detected autoantibodies against 408 different antigens in sera of prostate cancer patients. One hundred seventy-four of these were exclusively detected in the cancer group compared to the healthy control group. Functional annotation clustering revealed an enrichment of RNA-associated, cytoskeleton, and nuclear proteins. The autoantibody panel was validated in serum samples of independent prostate cancer patients. Autoantibody profiles discriminated between prostate cancer patients and benign disease patients with an ROC curve AUC of 0.71. TTLL12, a protein recently described to be over-expressed in prostate cancer, was the highest ranked discrimination autoantigen. CONCLUSION: A variety of autoantibodies were identified in sera of prostate cancer patients and provide a first step towards autoantibody diagnostics. Serum autoantibodies reflect the disease and represent valuable tools not only for prostate cancer, but also for other diseases affecting the immune response.

QuACN: an R package for analyzing complex biological networks quantitatively.

MOTIVATION: Network-based representations of biological data have become an important way to analyze high-throughput data. To interpret the large amount of data that is produced by different high-throughput technologies, networks offer multifaceted aspects to analyze the data. As networks represent biological relationships within their structure, it turned out to be fruitful to analyze their topology. Therefore, we developed a freely available, open source R-package called Quantitative Analysis of Complex Networks (QuACN) to meet this challenge. QuACN contains different, information-theoretic and non-information-theoretic, topological network descriptors to analyze, classify and compare biological networks. AVAILABILITY: QuACN is freely available under LGPL via CRAN (http://cran.r-project.org/web/packages/QuACN/).

A network-based feature selection approach to identify metabolic signatures in disease.

The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.

Structural measures for network biology using QuACN.

BACKGROUND: Structural measures for networks have been extensively developed, but many of them have not yet demonstrated their sustainably. That means, it remains often unclear whether a particular measure is useful and feasible to solve a particular problem in network biology. Exemplarily, the classification of complex biological networks can be named, for which structural measures are used leading to a minimal classification error. Hence, there is a strong need to provide freely available software packages to calculate and demonstrate the appropriate usage of structural graph measures in network biology. RESULTS: Here, we discuss topological network descriptors that are implemented in the R-package QuACN and demonstrate their behavior and characteristics by applying them to a set of example graphs. Moreover, we show a representative application to illustrate their capabilities for classifying biological networks. In particular, we infer gene regulatory networks from microarray data and classify them by methods provided by QuACN. Note that QuACN is the first freely available software written in R containing a large number of structural graph measures. CONCLUSION: The R package QuACN is under ongoing development and we add promising groups of topological network descriptors continuously. The package can be used to answer intriguing research questions in network biology, e.g., classifying biological data or identifying meaningful biological features, by analyzing the topology of biological networks.

Examining the Transcriptional Response in Wheat Near-Isogenic Lines to Infection and Deoxynivalenol Treatment.

head blight (FHB) is a disease caused predominantly by the fungal pathogen that affects wheat and other small-grain cereals and can lead to severe yield loss and reduction in grain quality. Trichothecene mycotoxins, such as deoxynivalenol (DON), accumulate during infection and increase pathogen virulence and decrease grain quality. The locus on wheat chromosome 3BS confers Type II resistance to FHB and resistance to the spread of infection on the spike and has been associated with resistance to DON accumulation. To gain a better genetic understanding of the functional role of and resistance or susceptibility to FHB, we examined DON and ergosterol accumulation, FHB resistance, and the whole-genome transcriptomic response using RNA-seq in a near-isogenic line (NIL) pair carrying the resistant and susceptible alleles for during infection and DON treatment. Our results provide a gene expression atlas for the resistant and susceptible wheat- interaction. The DON concentration and transcriptomic results show that the rachis is a key location for conferring Type II resistance. In addition, the wheat transcriptome analysis revealed a set of -responsive genes that may play a role in resistance and a set of DON-responsive genes that may play a role in trichothecene resistance. Transcriptomic results from the pathogen show that the genome responds differently to the host level of resistance. The results of this study extend our understanding of host and pathogen responses in the wheat- interaction.

A protocol for the systematic and quantitative measurement of protein-lipid interactions using the liposome-microarray-based assay.

Lipids organize the activity of the cell's proteome through a complex network of interactions. The assembly of comprehensive atlases embracing all protein-lipid interactions is an important challenge that requires innovative methods. We recently developed a liposome-microarray-based assay (LiMA) that integrates liposomes, microfluidics and fluorescence microscopy and which is capable of measuring protein recruitment to membranes in a quantitative and high-throughput manner. Compared with previous assays that are labor-intensive and difficult to scale up, LiMA improves the protein-lipid interaction assay throughput by at least three orders of magnitude. Here we provide a step-by-step LiMA protocol that includes the following: (i) the serial and generic production of the liposome microarray; (ii) its integration into a microfluidic format; (iii) the measurement of fluorescently labeled protein (either purified proteins or from cell lysate) recruitment to liposomal membranes using high-throughput microscopy; (iv) automated image analysis pipelines to quantify protein-lipid interactions; and (v) data quality analysis. In addition, we discuss the experimental design, including the relevant quality controls. Overall, the protocol-including device preparation, assay and data analysis-takes 6-8 d. This protocol paves the way for protein-lipid interaction screens to be performed on the proteome and lipidome scales.

The deca-GX3 proteins Yae1-Lto1 function as adaptors recruiting the ABC protein Rli1 for iron-sulfur cluster insertion.

Cytosolic and nuclear iron-sulfur (Fe-S) proteins are involved in many essential pathways including translation and DNA maintenance. Their maturation requires the cytosolic Fe-S protein assembly (CIA) machinery. To identify new CIA proteins we employed systematic protein interaction approaches and discovered the essential proteins Yae1 and Lto1 as binding partners of the CIA targeting complex. Depletion of Yae1 or Lto1 results in defective Fe-S maturation of the ribosome-associated ABC protein Rli1, but surprisingly no other tested targets. Yae1 and Lto1 facilitate Fe-S cluster assembly on Rli1 in a chain of binding events. Lto1 uses its conserved C-terminal tryptophan for binding the CIA targeting complex, the deca-GX3 motifs in both Yae1 and Lto1 facilitate their complex formation, and Yae1 recruits Rli1. Human YAE1D1 and the cancer-related ORAOV1 can replace their yeast counterparts demonstrating evolutionary conservation. Collectively, the Yae1-Lto1 complex functions as a target-specific adaptor that recruits apo-Rli1 to the generic CIA machinery.