67 research outputs found
DEEP - A tool for differential expression effector prediction
No full textHigh-throughput methods for measuring transcript abundance, like SAGE or microarrays, are widely used for determining differences in gene expression between different tissue types, dignities (normal/malignant) or time points. Further analysis of such data frequently aims at the identification of gene interaction networks that form the causal basis for the observed properties of the systems under examination. To this end, it is usually not sufficient to rely on the measured gene expression levels alone; rather, additional biological knowledge has to be taken into account in order to generate useful hypotheses about the molecular mechanism leading to the realization of a certain phenotype. We present a method that combines gene expression data with biological expert knowledge on molecular interaction networks, as described by the TRANSPATH(1) database on signal transduction, to predict additional - and not necessarily differentially expressed - genes or gene products which might participate in processes specific for either of the examined tissues or conditions. In a first step, significance values for over-expression in tissue/condition A or B are assigned to all genes in the expression data set. Genes with a significance value exceeding a certain threshold are used as starting points for the reconstruction of a graph with signaling components as nodes and signaling events as edges. In a subsequent graph traversal process, again starting from the previously identified differentially expressed genes, all encountered nodes 'inherit' all their starting nodes' significance values. In a final step, the graph is visualized, the nodes being colored according to a weighted average of their inherited significance values. Each node's, or sub-network's, predominant color, ranging from green (significant for tissue/condition A) over yellow (not significant for either tissue/condition) to red (significant for tissue/condition B), thus gives an immediate visual clue on which molecules - differentially expressed or not - may play pivotal roles in the tissues or conditions under examination. The described method has been implemented in Java as a client/server application and a web interface called DEEP (Differential Expression Effector Prediction). The client, which features an easy-to-use graphical interface, can freely be downloaded from the following URL: http://deep.bioinf.med.uni-goettingen.de
Microbial extracellular polymeric substances in the environment, technology and medicine
No full textMicrobial biofilms exhibit a self-produced matrix of extracellular polymeric substances (EPS), including polysaccharides, proteins, extracellular DNA and lipids. EPS promote interactions of the biofilm with other cells and sorption of organics, metals and chemical pollutants, and they facilitate cell adhesion at interfaces and ensure matrix cohesion. EPS have roles in various natural environments, such as soils, sediments and marine habitats. In addition, EPS are relevant in technical environments, such as wastewater and drinking water treatment facilities, and water distribution systems, and they contribute to biofouling and microbially influenced corrosion. In medicine, EPS protect pathogens within the biofilm against the host immune system and antimicrobials, and emerging evidence suggests that EPS can represent potential virulence factors. By contrast, EPS yield a wide range of valuable products that include their role in self-repairing concrete. In this Review, we aim to explore EPS as a functional unit of biofilms in the environment, in technology and in medicine.</p
Who put the film in biofilm? The migration of a term from wastewater engineering to medicine and beyond
No full textSessile microorganisms were described as early as the seventeenth century. However, the term biofilm arose only in the 1960s in wastewater treatment research and was adopted later in marine fouling and in medical and dental microbiology. The sessile mode of microbial life was gradually recognized to be predominant on Earth, and the term biofilm became established for the growth of microorganisms in aggregates, frequently associated with interfaces, although many, if not the majority, of them not being continuous “films” in the strict sense. In this sessile form of life, microorganisms live in close proximity in a matrix of extracellular polymeric substances (EPS). They share emerging properties, clearly distinct from solitary free floating planktonic microbial cells. Common characteristics include the formation of synergistic microconsortia, using the EPS matrix as an external digestion system, the formation of gradients and high biodiversity over microscopically small distances, resource capture and retention, facilitated gene exchange as well as intercellular communication, and enhanced tolerance to antimicrobials. Thus, biofilms belong to the class of collective systems in biology, like forests, beehives, or coral reefs, although the term film addresses only one form of the various manifestations of microbial aggregates. The uncertainty of this term is discussed, and it is acknowledged that it will not likely be replaced soon, but it is recommended to understand these communities in the broader sense of microbial aggregates.</p
Analysis of Biological Signal Transduction Networks based on Gene Expression Data
No full textEine Methode, die Genexpressionsdaten mit biologischem Expertenwissen über Signaltransduktionsnetzwerke kombiniert, um zusätzliche, nicht differentiell exprimierte, Gene oder Genprodukte zu identifizieren, die eine spezifische Rolle für die untersuchten Gewebe bzw. Zustände spielen könnten.A method that combines gene expression data with biological expert knowledge on molecular interaction networks, as described by the TRANSPATH database on signal transduction, to predict additional and not necessarily differentially expressed genes or gene products which might participate in processes specific for either of the examined tissues or conditions
Mikrobiologische Trinkwasserqualität in der Wasserverteilung bei veränderten Temperaturen aufgrund des Klimawandels
No full textAufgrund des Klimawandels kann es zu einer Erwärmung der oberen Bodenzonen und indirekt dadurch auch zu einer Beeinflussung der Trinkwassertemperatur im Verteilernetz kommen. Es ist bekannt, dass dies unter Umständen zu mikrobiologischen Veränderungen in Form einer Aufkeimung (Erhöhung der Koloniezahlen) führen kann. Ob dies jedoch auf ein erhöhtes Risiko der Einistung, des Verbleibs oder sogar der Vermehrung hygenisch relevanter Bakterien zutrifft, wird im Rahmen des vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Projekts dynaklim am IWW in Kooperation mit der Universität Duisburg-Essen untersucht
- …
