Alexander Kaever

The COP9 signalosome mediates transcriptional and metabolic response to hormones, oxidative stress protection and cell wall rearrangement during fungal development.

The COP9 signalosome complex (CSN) is a crucial regulator of ubiquitin ligases. Defects in CSN result in embryonic impairment and death in higher eukaryotes, whereas the filamentous fungus Aspergillus nidulans survives without CSN, but is unable to complete sexual development. We investigated overall impact of CSN activity on A. nidulans cells by combined transcriptome, proteome and metabolome analysis. Absence of csn5/csnE affects transcription of at least 15% of genes during development, including numerous oxidoreductases. csnE deletion leads to changes in the fungal proteome indicating impaired redox regulation and hypersensitivity to oxidative stress. CSN promotes the formation of asexual spores by regulating developmental hormones produced by PpoA and PpoC dioxygenases. We identify more than 100 metabolites, including orsellinic acid derivatives, accumulating preferentially in the csnE mutant. We also show that CSN is required to activate glucanases and other cell wall recycling enzymes during development. These findings suggest a dual role for CSN during development: it is required early for protection against oxidative stress and hormone regulation and is later essential for control of the secondary metabolism and cell wall rearrangement.

Verticillium transcription activator of adhesion Vta2 suppresses microsclerotia formation and is required for systemic infection of plant roots

Six transcription regulatory genes of the Verticillium plant pathogen, which reprogrammed nonadherent budding yeasts for adhesion, were isolated by a genetic screen to identify control elements for early plant infection. Verticillium transcription activator of adhesion Vta2 is highly conserved in filamentous fungi but not present in yeasts. The Magnaporthe grisea ortholog conidiation regulator Con7 controls the formation of appressoria which are absent in Verticillium species. Vta2 was analyzed by using genetics, cell biology, transcriptomics, secretome proteomics and plant pathogenicity assays. Nuclear Vta2 activates the expression of the adhesin-encoding yeast flocculin genes FLO1 and FLO11. Vta2 is required for fungal growth of Verticillium where it is a positive regulator of conidiation. Vta2 is mandatory for accurate timing and suppression of microsclerotia as resting structures. Vta2 controls expression of 270 transcripts, including 10 putative genes for adhesins and 57 for secreted proteins. Vta2 controls the level of 125 secreted proteins, including putative adhesins or effector molecules and a secreted catalase-peroxidase. Vta2 is a major regulator of fungal pathogenesis, and controls host-plant root infection and H2 O2 detoxification. Verticillium impaired in Vta2 is unable to colonize plants and induce disease symptoms. Vta2 represents an interesting target for controlling the growth and development of these vascular pathogens.

MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data.

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

Arabidopsis mutants of sphingolipid fatty acid α‐hydroxylases accumulate ceramides and salicylates

In Arabidopsis, the fatty acid moiety of sphingolipids is mainly α-hydroxylated. The consequences of a reduction in this modification were analysed. Mutants of both Fatty Acid Hydroxylase genes (AtFAH1 and AtFAH2) were analysed for sphingolipid profiles. To elucidate further consequences of the mutations, metabolic analyses were performed and the influence on pathogen defence was determined. Ceramide and glucosylceramide profiles of double-mutant plants showed a reduction in sphingolipids with α-hydroxylated fatty acid moieties, and an accumulation of sphingolipids without these moieties. In addition, the free trihydroxylated long-chain bases and ceramides were increased by five- and ten-fold, respectively, whereas the amount of glucosylceramides was decreased by 25%. Metabolite analysis of the double mutant revealed salicylates as enriched metabolites. Infection experiments supported the metabolic changes, as the double mutant showed an enhanced disease-resistant phenotype for infection with the obligate biotrophic pathogen Golovinomyces cichoracearum. In summary, these results suggest that fatty acid hydroxylation of ceramides is important for the biosynthesis of complex sphingolipids. Its absence leads to the accumulation of long-chain bases and ceramides as their precursors. This increases salicylate levels and resistance towards obligate biotrophic fungal pathogens, confirming a role of sphingolipids in salicylic acid-dependent defence reactions.

Membrane-bound methyltransferase complex VapA-VipC-VapB guides epigenetic control of fungal development.

Epigenetic and transcriptional control of gene expression must be coordinated in response to external signals to promote alternative multicellular developmental programs. The membrane-associated trimeric complex VapA-VipC-VapB controls a signal transduction pathway for fungal differentiation. The VipC-VapB methyltransferases are tethered to the membrane by the FYVE-like zinc finger protein VapA, allowing the nuclear VelB-VeA-LaeA complex to activate transcription for sexual development. Once the release from VapA is triggered, VipC-VapB is transported into the nucleus. VipC-VapB physically interacts with VeA and reduces its nuclear import and protein stability, thereby reducing the nuclear VelB-VeA-LaeA complex. Nuclear VapB methyltransferase diminishes the establishment of facultative heterochromatin by decreasing histone 3 lysine 9 trimethylation (H3K9me3). This favors activation of the regulatory genes brlA and abaA, which promote the asexual program. The VapA-VipC-VapB methyltransferase pathway combines control of nuclear import and stability of transcription factors with histone modification to foster appropriate differentiation responses.

Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets

A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.

MarVis: a tool for clustering and visualization of metabolic biomarkers

BackgroundA central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.ResultsWe present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.ConclusionMarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps.

BackgroundOne of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters.ResultsWe present an approach for data mining on metabolite intensity profiles as obtained from mass spectrometry measurements. We propose one-dimensional self-organizing maps for metabolite-based clustering and visualization of marker candidates. In a case study on the wound response of Arabidopsis thaliana, based on metabolite profile intensities from eight different experimental conditions, we show how the clustering and visualization capabilities can be used to identify relevant groups of markers.ConclusionOur specialized realization of self-organizing maps is well-suitable to gain insight into complex pattern variation in a large set of metabolite profiles. In comparison to other methods our visualization approach facilitates the identification of interesting groups of metabolites by means of a convenient overview on relevant intensity patterns. In particular, the visualization effectively supports researchers in analyzing many putative clusters when the true number of biologically meaningful groups is unknown.

MarVis-Filter: Ranking, Filtering, Adduct and Isotope Correction of Mass Spectrometry Data

Statistical ranking, filtering, adduct detection, isotope correction, and molecular formula calculation are essential tasks in processing mass spectrometry data in metabolomics studies. In order to obtain high-quality data sets, a framework which incorporates all these methods is required. We present the MarVis-Filter software, which provides well-established and specialized methods for processing mass spectrometry data. For the task of ranking and filtering multivariate intensity profiles, MarVis-Filter provides the ANOVA and Kruskal-Wallis tests with adjustment for multiple hypothesis testing. Adduct and isotope correction are based on a novel algorithm which takes the similarity of intensity profiles into account and allows user-defined ionization rules. The molecular formula calculation utilizes the results of the adduct and isotope correction. For a comprehensive analysis, MarVis-Filter provides an interactive interface to combine data sets deriving from positive and negative ionization mode. The software is exemplarily applied in a metabolic case study, where octadecanoids could be identified as markers for wounding in plants.

Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data with the interactive MarVis-Graph software

State of the art high-throughput technologies allow comprehensive experimental studies of organism metabolism and induce the need for a convenient presentation of large heterogeneous datasets. Especially, the combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics. We present here the MarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and sub-networks, according to connected high-scoring reactions, are identified. Finally, MarVis-Graph scores the detected sub-networks, evaluates them by means of a random permutation test and presents them as a ranked list. Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results. The key advantage of MarVis-Graph is the analysis of reactions detached from their pathways so that it is possible to identify new pathways or to connect known pathways by previously unrelated reactions. The MarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.