Entwicklung und Durchführung von Metabolomanalysen an Pseudomonas aeruginosa mit Hilfe der Gaschromatographie/Massenspektrometrie

Eine Infektion mit Pseudomonas aeruginosa bei Patienten mit Zystischer Fibrose führt aufgrund fehlender Therapiemöglichkeiten zu einer Verschlechterung des Krankheitsbildes und in vielen Fällen zu einem frühen Tod. Um eine Behandlungsmöglichkeit gegen das Bakterium zu finden, ist es wichtig, Einblicke in dessen Funktionsweise zu erhalten. Die Aufklärung des Genoms bildet die Grundlage für solch eine Untersuchung, während die Durchführung von Metabolomanalysen einen weiteren Schritt darstellt. In dieser Arbeit wurde eine auf Gaschromatographie/Massenspektrometrie basierende Methode zur Analyse des Metaboloms von Bakterienextrakten von P. aeruginosa entwickelt. Durch Messung von Standardsubstanzen und vergleichender Untersuchung von metabolischen Profilen wurde eine Bibliothek aus Massenspektren von Metaboliten und etlicher zugehörender Informationen wie Retentionsindices, Strukturen, Massen und chemischer Identifikationsnummern erstellt. Für die Verwaltung der Daten wurde ein Programm mit graphischer Benutzeroberfläche und vielfältigen Funktionen für die Eingabe und Bearbeitung der Spektren und zugehöriger Kenngrößen entwickelt. Es erlaubt den Import und Export in verschiedene Dateiformate und lässt sich mit Hilfe von Skripten dynamisch erweitern. Ein bereits bekanntes Aufarbeitungsprotokoll wurde an P. aeruginosa angepasst, was zum Nachweis von 195 Substanzen und 117 unidentifizierten Komponenten in den Bakterienextrakten führte und damit eine Steigerung um bis zu 30% zu vergleichbaren Arbeiten darstellt. Die Quantifizierung der Metabolite lieferte vielfältige Einsichten in das Wachstum und ermöglichte es, mit der Lysin-Decarboxylase ein Enzym zu identifizieren, das für das Biofilmwachstum der Bakterien bedeutsam erscheint und einen zukünftigen Ansatzpunkt für Humantherapien gegen eine Infektion von P. aeruginosa darstellen könnte

KID - an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes

<p>Abstract</p> <p>Background</p> <p>The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed.</p> <p>Description</p> <p>Here we present a text mining algorithm for the extraction of kinetic information such as K_M, K_i, k_catetc. as well as associated information such as enzyme names, EC numbers, ligands, organisms, localisations, pH and temperatures. Using this rule- and dictionary-based approach, it was possible to extract 514,394 kinetic parameters of 13 categories (K_M, K_i, k_cat, k_cat/K_M, V_max, IC₅₀, S_0.5, K_d, K_a, t_1/2, pI, n_H, specific activity, V_max/K_M) from about 17 million PubMed abstracts and combine them with other data in the abstract.</p> <p>A manual verification of approx. 1,000 randomly chosen results yielded a recall between 51% and 84% and a precision ranging from 55% to 96%, depending of the category searched.</p> <p>The results were stored in a database and are available as "KID the KInetic Database" via the internet.</p> <p>Conclusions</p> <p>The presented algorithm delivers a considerable amount of information and therefore may aid to accelerate the research and the automated analysis required for today's systems biology approaches. The database obtained by analysing PubMed abstracts may be a valuable help in the field of chemical and biological kinetics. It is completely based upon text mining and therefore complements manually curated databases.</p> <p>The database is available at <url>http://kid.tu-bs.de</url>. The source code of the algorithm is provided under the GNU General Public Licence and available on request from the author.</p

mSpecs: a software tool for the administration and editing of mass spectral libraries in the field of metabolomics

<p>Abstract</p> <p>Background</p> <p>Metabolome analysis with GC/MS has meanwhile been established as one of the "omics" techniques. Compound identification is done by comparison of the MS data with compound libraries. Mass spectral libraries in the field of metabolomics ought to connect the relevant mass traces of the metabolites to other relevant data, e.g. formulas, chemical structures, identification numbers to other databases etc. Since existing solutions are either commercial and therefore only available for certain instruments or not capable of storing such information, there is need to provide a software tool for the management of such data.</p> <p>Results</p> <p>Here we present mSpecs, an open source software tool to manage mass spectral data in the field of metabolomics. It provides editing of mass spectra and virtually any associated information, automatic calculation of formulas and masses and is extensible by scripts. The graphical user interface is capable of common techniques such as copy/paste, undo/redo and drag and drop. It owns import and export filters for the major public file formats in order to provide compatibility to commercial instruments.</p> <p>Conclusion</p> <p>mSpecs is a versatile tool for the management and editing of mass spectral libraries in the field of metabolomics. Beyond that it provides capabilities for the automatic management of libraries though its scripting functionality. mSpecs can be used on all major platforms and is licensed under the GNU General Public License and available at <url>http://mspecs.tu-bs.de</url>.</p

SYSTOMONAS — an integrated database for systems biology analysis of Pseudomonas

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at

Genotypic tropism testing by massively parallel sequencing: qualitative and quantitative analysis

<p>Abstract</p> <p>Background</p> <p>Inferring viral tropism from genotype is a fast and inexpensive alternative to phenotypic testing. While being highly predictive when performed on clonal samples, sensitivity of predicting CXCR4-using (X4) variants drops substantially in clinical isolates. This is mainly attributed to minor variants not detected by standard bulk-sequencing. Massively parallel sequencing (MPS) detects single clones thereby being much more sensitive. Using this technology we wanted to improve genotypic prediction of coreceptor usage.</p> <p>Methods</p> <p>Plasma samples from 55 antiretroviral-treated patients tested for coreceptor usage with the Monogram Trofile Assay were sequenced with standard population-based approaches. Fourteen of these samples were selected for further analysis with MPS. Tropism was predicted from each sequence with geno2pheno_[coreceptor].</p> <p>Results</p> <p>Prediction based on bulk-sequencing yielded 59.1% sensitivity and 90.9% specificity compared to the trofile assay. With MPS, 7600 reads were generated on average per isolate. Minorities of sequences with high confidence in CXCR4-usage were found in all samples, irrespective of phenotype. When using the default false-positive-rate of geno2pheno_[coreceptor](10%), and defining a minority cutoff of 5%, the results were concordant in all but one isolate.</p> <p>Conclusions</p> <p>The combination of MPS and coreceptor usage prediction results in a fast and accurate alternative to phenotypic assays. The detection of X4-viruses in all isolates suggests that coreceptor usage as well as fitness of minorities is important for therapy outcome. The high sensitivity of this technology in combination with a quantitative description of the viral population may allow implementing meaningful cutoffs for predicting response to CCR5-antagonists in the presence of X4-minorities.</p

Determination of local optical response functions of nanostructures with increasing complexity by using single and coupled Lorentzian oscillator models

Aeschlimann M, Brixner T, Fischer A, et al. Determination of local optical response functions of nanostructures with increasing complexity by using single and coupled Lorentzian oscillator models. APPLIED PHYSICS B-LASERS AND OPTICS. 2016;122(7): 199.We reconstruct the optical response of nanostructures of increasing complexity by fitting interferometric time-resolved photoemission electron microscopy (PEEM) data from an ultrashort (21 fs) laser excitation source with different harmonic oscillator-based models. Due to its high spatial resolution of similar to 40 nm, PEEM is a true near-field imaging system and enables in normal incidence mode a mapping of plasmon polaritons and an intuitive interpretation of the plasmonic behaviour. Using an actively stabilized Mach-Zehnder interferometer, we record two-pulse correlation signals with 50 as time resolution that contain information about the temporal plasmon polariton evolution. Spectral amplitude and phase of excited plasmon polaritons are extracted from the recorded phase-resolved interferometric two-pulse correlation traces. We show that the optical response of a plasmon polariton generated at a gold nanoparticle can be reconstructed from the interferometric two-pulse correlation signal using a single harmonic oscillator model. In contrast, for a corrugated silver surface, a system with increased plasmonic complexity, in general an unambiguous reconstruction of the local optical response based on coupled and uncoupled harmonic oscillators, fails. Whereas for certain local responses different models can be discriminated, this is impossible for other positions. Multidimensional spectroscopy offers a possibility to overcome this limitation