metaP-Server: A Web-Based Metabolomics Data Analysis Tool

Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes. We developed metaP-server to facilitate data interpretation. metaP-server provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross-references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided. metaP-server is freely accessible at http://metabolomics.helmholtz-muenchen.de/metap2/

Insulin Sensitivity Is Reflected by Characteristic Metabolic Fingerprints - A Fourier Transform Mass Spectrometric Non-Targeted Metabolomics Approach

BACKGROUND: A decline in body insulin sensitivity in apparently healthy individuals indicates a high risk to develop type 2 diabetes. Investigating the metabolic fingerprints of individuals with different whole body insulin sensitivity according to the formula of Matsuda, et al. (ISI(Matsuda)) by a non-targeted metabolomics approach we aimed a) to figure out an unsuspicious and altered metabolic pattern, b) to estimate a threshold related to these changes based on the ISI, and c) to identify the metabolic pathways responsible for the discrimination of the two patterns. METHODOLOGY AND PRINCIPAL FINDINGS: By applying infusion ion cyclotron resonance Fourier transform mass spectrometry, we analyzed plasma of 46 non-diabetic subjects exhibiting high to low insulin sensitivities. The orthogonal partial least square model revealed a cluster of 28 individuals with alterations in their metabolic fingerprints associated with a decline in insulin sensitivity. This group could be separated from 18 subjects with an unsuspicious metabolite pattern. The orthogonal signal correction score scatter plot suggests a threshold of an ISI(Matsuda) of 15 for the discrimination of these two groups. Of note, a potential subgroup represented by eight individuals (ISI(Matsuda) value between 8.5 and 15) was identified in different models. This subgroup may indicate a metabolic transition state, since it is already located within the cluster of individuals with declined insulin sensitivity but the metabolic fingerprints still show some similarities with unaffected individuals (ISI >15). Moreover, the highest number of metabolite intensity differences between unsuspicious and altered metabolic fingerprints was detected in lipid metabolic pathways (arachidonic acid metabolism, metabolism of essential fatty acids and biosynthesis of unsaturated fatty acids), steroid hormone biosyntheses and bile acid metabolism, based on data evaluation using the metabolic annotation interface MassTRIX. CONCLUSIONS: Our results suggest that altered metabolite patterns that reflect changes in insulin sensitivity respectively the ISI(Matsuda) are dominated by lipid-related pathways. Furthermore, a metabolic transition state reflected by heterogeneous metabolite fingerprints may precede severe alterations of metabolism. Our findings offer future prospects for novel insights in the pathogenesis of the pre-diabetic phase

Mapping of transcript- and protein sequences on genomic reference data

Neue Entwicklungen in der biotechnologischen Apparatetechnik ermöglichen die umfassende Analyse von Organismen in den Bereichen der Genomik, Transkriptomik und Proteomik. Um die heterogenen Datentypen in ihrem genomischen Kontext graphisch darzustellen, wurden in dieser Arbeit generische Lösungen für die Abbildung von Transkript- und Proteinsequenzen auf genomische Referenzdatensätze entwickelt. Ergänzend wurde eine Applikation zur automatischen Funktionsannotation von EST-Datensätzen geschaffen, die statistische Analysen zur Über- bzw. Unterrepräsentation funktioneller Module durchführt. Um die Datenintegration für systembiologische Analysen zu unterstützen, wurde ein Werkzeug entwickelt, das die automatische Cross-Referenzierung von Einträgen aus verschiedenen Datenbanken erlaubt. Dazu wurden Listen mehrdeutiger biologischer Terme erstellt, die das Text Mining in den biomedizinischen Wissenschaften verbessern werden.Recent developments in bioanalytical techniques allow for comprehensive analyses of organisms with respect to genomics, transcriptomics and proteomics. To visualize the heterogeneous data types in their genomic context, generic methods for mapping transcript and protein sequences to genomic reference data sets have been developed. Additionally, an application for automated functional annotation of sets of ESTs, that statistically analyses the enrichment and depletion of functional modules has been established. To provide data integration for systems biology approaches, a tool for automated cross-referencing of entries from different databases has been designed. For that purpose lists of ambiguous biological terms were created, that will allow for enhanced text mining in biomedical science

Abundance and characteristics of Polychaeta taxa and associated sediment during POLARSTERN campaign ANT-XXII/3 (ANDEEP-IIII)

During the austral summer of 2005, the Weddell deep sea and adjacent basins were sampled in the course of the ANDEEP III project. In this study, 19 epibenthic-sledge stations are analyzed, with a focus on species diversity and distribution patterns of polychaetes. The polychaete fauna of the deep Southern Ocean has been found to be similarly speciose and diverse compared with deep-sea basins worldwide. Also, in depths below 2,000 m many polychaete species do not seem to be endemic for certain areas but are rather far spread within the Southern Ocean and beyond. Therefore, ongoing faunal exchanges between adjacent basins, even beyond the Antarctic convergence, are strongly suggested, ruling out a general isolation of the Southern Ocean deep-sea benthos. Driving forces behind species distribution patterns were investigated. The findings indicate that polychaete species' distribution in the Southern Ocean deep sea is rather dependent on local environment than depths

MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data.

Systems Biology is a field in biological science that focuses on the combination of several or all "omics"-approaches in order to find out how genes, transcripts, proteins and metabolites act together in the network of life. Metabolomics as analog to genomics, transcriptomics and proteomics is more and more integrated into biological studies and often transcriptomic and metabolomic experiments are combined in one setup. At a first glance both data types seem to be completely different, but both produce information on biological entities, either transcripts or metabolites. Both types can be overlaid on metabolic pathways to obtain biological information on the studied system. For the joint analysis of both data types the MassTRIX webserver was updated. MassTRIX is freely available at www.masstrix.org

Representation of the systematic workflow of MassTRIX for annotation of MS and transcriptomic data.

<p>Both data types are combined and submitted to KEGG via the KEGG API to obtain colored pathway maps. The coloring for transcriptomic data can be defined by the user.</p

Possible comparisons between jobs in MassTRIX.

<p>(A) Screen shot from the “Compare jobs” functionality and the obtained result page comparing jobs on pathway level. (B) Result from comparing 3 different jobs on pathway level represented in a barplot. X-Axis represents the different pathway maps and Y-Axis the number of annotated compounds on this pathway. (C) Result from comparing 3 different jobs on compound level. The file can directlybe downloaded and opened in MS Excel.</p