MarVis: a tool for clustering and visualization of metabolic biomarkers

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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

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

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, VipCVapB 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-VipCVapB methyltransferase pathway combines control of nuclear import and stability of transcription factors with histone modification to foster appropriate differentiation responses

Targeting novel chemical and constitutive primed metabolites against Plectosphaerella cucumerina

Priming 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

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

Divergent co-transcriptomes of different host cells infected with Toxoplasma gondii reveal cell type-specific host-parasite interactions.

The apicomplexan parasite Toxoplasma gondii infects various cell types in avian and mammalian hosts including humans. Infection of immunocompetent hosts is mostly asymptomatic or benign, but leads to development of largely dormant bradyzoites that persist predominantly within neurons and muscle cells. Here we have analyzed the impact of the host cell type on the co-transcriptomes of host and parasite using high-throughput RNA sequencing. Murine cortical neurons and astrocytes, skeletal muscle cells (SkMCs) and fibroblasts differed by more than 16,200 differentially expressed genes (DEGs) before and after infection with T. gondii. However, only a few hundred of them were regulated by infection and these largely diverged in neurons, SkMCs, astrocytes and fibroblasts indicating host cell type-specific transcriptional responses after infection. The heterogeneous transcriptomes of host cells before and during infection coincided with ~5,400 DEGs in T. gondii residing in different cell types. Finally, we identified gene clusters in both T. gondii and its host, which correlated with the predominant parasite persistence in neurons or SkMCs as compared to astrocytes or fibroblasts. Thus, heterogeneous expression profiles of different host cell types and the parasites' ability to adapting to them may govern the parasite-host cell interaction during toxoplasmosis

Big Data analysis to improve care for people living with serious illness: The potential to use new emerging technology in palliative care.

<p>The table contains the high-ranking pathways from enrichment analysis of data set M1 based on the Kolmogorov-Smirnov (KS) and rank-sum test. The pathways are sorted according to the restandardized p-values derived from the rank-sum test. The last two columns comprise the false discovery rates calculated from the restandardized p-values.</p

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

<div><p>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.</p></div