53 research outputs found
Non-targeted metabolomics by high resolution mass spectrometry in HPRT knockout mice
No full textAims: Lesch-Nyhan disease (LND) is characterized by hyperuricemia as well as neurological and neuropsychiatric symptoms including repetitive self-injurious behavior. Symptoms are caused by a deficiency of the enzyme hypoxanthine-guanine phosphoribosyltransferase (HPRT) as a result of a mutation on the X chromosome. To elucidate the pathophysiology of LND, we performed a metabolite screening for brain and serum extracts from HPRT knockout mice as an animal model for LND. Main methods: Analyses were performed by high performance liquid chromatography (HPLC)-coupled quadrupole time-of-flight mass spectrometry (QTOF-MS). Key findings: In brain extracts, we found six metabolites with significantly different contents in wild-type and HPRT-deficient mice. Two compounds we could identify as 5-aminoimidazole-4-carboxamide ribotide (AICAR) and 1-methylimidazole-4-acetic acid (1-MI4AA). Whereas AICAR was accumulated in brains of HPRT knockout mice, 1-MI4AA was decreased in these mice. Significance: Both metabolites play a role in histidine metabolism and, as a consequence, histamine metabolism. AICAR, in addition, is part of the purine metabolism. Our findings may help to better understand the mechanisms leading to the behavioral phenotype of LND. (C) 2016 Elsevier Inc. All rights reserved
Targeting novel chemical and constitutive primed metabolites against Plectosphaerella cucumerina
No full textPriming is a physiological state for protection of plants against a broad range of pathogens, and is achievedthrough stimulation of the plant immune system. Various stimuli, such as beneficial microbes and chemicalinduction, activate defense priming. In the present study, we demonstrate that impairment of the high-affinity nitrate transporter 2.1 (encoded by NRT2.1) enables Arabidopsis to respond more quickly andstrongly to Plectosphaerella cucumerina attack, leading to enhanced resistance. The Arabidopsis thalianamutant lin1 (affected in NRT2.1) is a priming mutant that displays constitutive resistance to this necrotroph,with no associated developmental or growth costs. Chemically induced priming by b–aminobutyric acidtreatment, the constitutive priming mutant ocp3 and the constitutive priming present in the lin1 mutantresult in a common metabolic profile within the same plant–pathogen interactions. The defense priming sig-nificantly affects sugar metabolism, cell-wall remodeling and shikimic acid derivatives levels, and results inspecific changes in the amino acid profile and three specific branches of Trp metabolism, particularly accu-mulation of indole acetic acid, indole-3–carboxaldehyde and camalexin, but not the indolic glucosinolates.Metabolomic analysis facilitated identification of three metabolites in the priming fingerprint: galacturonicacid, indole-3–carboxylic acid and hypoxanthine. Treatment of plants with the latter two metabolites by soildrenching induced resistance against P. cucumerina, demonstrating that these compounds are key compo-nents of defense priming against this necrotrophic fungus. Here we demonstrate that indole-3–carboxylicacid induces resistance by promoting papillae deposition and H2O2production, and that this is independentof PR1, VSP2 and PDF1.2 primin
MarVis: a tool for clustering and visualization of metabolic biomarkers
No full textAbstract Background A 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. Results We 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. Conclusion MarVis 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.</p
Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data with the interactive MarVis-Graph software
No full textState 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
Identification of metabolic changes after wounding in Arabidopsis thaliana by an unbiased UPLC-MS approach
No full textMetabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps
No full textBackground: One 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. Results: We 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. Conclusion: Our 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
No full textStatistical 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.Open-Access-Publikationsfonds 201
MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data
No full textA 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
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