Characterization of protein-interaction networks in tumors

<p>Abstract</p> <p>Background</p> <p>Analyzing differential-gene-expression data in the context of protein-interaction networks (PINs) yields information on the functional cellular status. PINs can be formally represented as graphs, and approximating PINs as undirected graphs allows the network properties to be characterized using well-established graph measures.</p> <p>This paper outlines features of PINs derived from 29 studies on differential gene expression in cancer. For each study the number of differentially regulated genes was determined and used as a basis for PIN construction utilizing the Online Predicted Human Interaction Database.</p> <p>Results</p> <p>Graph measures calculated for the largest subgraph of a PIN for a given differential-gene-expression data set comprised properties reflecting the size, distribution, biological relevance, density, modularity, and cycles. The values of a distinct set of graph measures, namely <it>Closeness Centrality</it>, <it>Graph Diameter</it>, <it>Index of Aggregation</it>, <it>Assortative Mixing Coefficient</it>, <it>Connectivity</it>, <it>Sum of the Wiener Number</it>, <it>modified Vertex Distance Number</it>, and <it>Eigenvalues </it>differed clearly between PINs derived on the basis of differential gene expression data sets characterizing malignant tissue and PINs derived on the basis of randomly selected protein lists.</p> <p>Conclusion</p> <p>Cancer PINs representing differentially regulated genes are larger than those of randomly selected protein lists, indicating functional dependencies among protein lists that can be identified on the basis of transcriptomics experiments. However, the prevalence of hub proteins was not increased in the presence of cancer. Interpretation of such graphs in the context of robustness may yield novel therapies based on synthetic lethality that are more effective than focusing on single-action drugs for cancer treatment.</p

EffectiveDB--updates and novel features for a better annotation of bacterial secreted proteins and Type III, IV, VI secretion systems

Protein secretion systems play a key role in the interaction of bacteria and hosts. EffectiveDB (http://effectivedb.org) contains pre-calculated predictions of bacterial secreted proteins and of intact secretion systems. Here we describe a major update of the database, which was previously featured in the NAR Database Issue. EffectiveDB bundles various tools to recognize Type III secretion signals, conserved binding sites of Type III chaperones, Type IV secretion peptides, eukaryotic-like domains and subcellular targeting signals in the host. Beyond the analysis of arbitrary protein sequence collections, the new release of EffectiveDB also provides a ‘genome-mode’, in which protein sequences from nearly complete genomes or metagenomic bins can be screened for the presence of three important secretion systems (Type III, IV, VI). EffectiveDB contains pre-calculated predictions for currently 1677 bacterial genomes from the EggNOG 4.0 database and for additional bacterial genomes from NCBI RefSeq. The new, user-friendly and informative web portal offers a submission tool for running the EffectiveDB prediction tools on user-provided data

Plasmapheresis in a Patient With "Refractory" Urticarial Vasculitis

Immune complexes are found in the circulation of 30%-75% of patients with urticarial vasculitis and much evidence supports the role of these immune complexes in the pathogenesis of urticarial vasculitis. Plasmapheresis is effective for removing these immune complexes; however, there are few reports on the use of plasmapheresis in the treatment of urticarial vasculitis. We describe a case of "refractory" urticarial vasculitis in which the symptoms improved after plasmapheresis treatment. We suggest that plasmapheresis be considered as an option in patients with severe or treatment-resistant urticarial vasculitis

Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites

Wärme und Effizienz für die Industrie

Im Rahmen der Energiewende haben sich erneuerbare Energien zur Stromerzeugung in Deutschland bereits etabliert. Um jedoch das volle Potenzial der Reduktion von fossilen Energien und Treibhausgasen (THG) auszuschöpfen, muss aus der Energiewende auch eine Wärmewende werden. Der Energieeinsatz für die Wärmebereitstellung der Industrie betrug im Jahr 2012 etwa 535 TWh (22 % des Endenergiebedarfs Deutschlands), hauptsächlich bereitgestellt durch Erdgas (48 %) und Steinkohle (17 %) 1. Damit wurden für die Wärmebereitstellung im Industriesektor rund 159 Mio. t CO2-äq emittiert, was 17 % der THG-Emissionen Deutschlands entspricht. Aufgrund der Vielseitigkeit der einzelnen Branchen und Wärmeanwendungen im Industriesektor kann dieser Beitrag nur beispielhaft einzelne Komponenten für eine Wärmewende aufzeigen, die auch wiederum die Aktivitäten der einzelnen Autoren widerspiegeln. Ausgehend von einer nationalen Betrachtung und expliziten Modellierungsergebnissen für die energieintensive Industrie in NRW, werden einzelne Potenziale und Aktivitäten im Bereich der Wärmebereitstellung, -speicherung und -integration behandelt

Interleukin-6 Gene Expression Changes after a 4-Week Intake of a Multispecies Probiotic in Major Depressive Disorder-Preliminary Results of the PROVIT Study

Major depressive disorder (MDD) is a prevalent disease, in which one third of sufferers do not respond to antidepressants. Probiotics have the potential to be well-tolerated and cost-effcient treatment options. However, the molecular pathways of their effects are not fully elucidated yet. Based on previous literature, we assume that probiotics can positively influence inflammatory mechanisms. We aimed at analyzing the effects of probiotics on gene expression of inflammation genes as part of the randomized, placebo-controlled, multispecies probiotics PROVIT study in Graz, Austria. Fasting blood of 61 inpatients with MDD was collected before and after four weeks of probiotic intake or placebo. We analyzed the effects on gene expression of tumor necrosis factor (TNF), nuclear factor kappa B subunit 1 (NFKB1) and interleukin-6 (IL-6). In IL-6 we found no significant main effects for group (F(1,44) = 1.33, p = ns) nor time (F(1,44) = 0.00, p = ns), but interaction was significant (F(1,44) = 5.67, p < 0.05). The intervention group showed decreasing IL-6 gene expression levels while the placebo group showed increasing gene expression levels of IL-6. Probiotics could be a useful additional treatment in MDD, due to their anti-inflammatory effects. Results of the current study are promising, but further studies are required to investigate the beneficial effects of probiotic interventions in depressed individuals

Toward personalization of asthma treatment according to trigger factors

Asthma is a severe and chronic disabling disease affecting more than 300 million people worldwide. Although in the past few drugs for the treatment of asthma were available, new treatment options are currently emerging, which appear to be highly effective in certain subgroups of patients. Accordingly, there is a need for biomarkers that allow selection of patients for refined and personalized treatment strategies. Recently, serological chip tests based on microarrayed allergen molecules and peptides derived from the most common rhinovirus strains have been developed, which may discriminate 2 of the most common forms of asthma, that is, allergen- and virus-triggered asthma. In this perspective, we argue that classification of patients with asthma according to these common trigger factors may open new possibilities for personalized management of asthma.Fil: Niespodziana, Katarzyna. Vienna University of Technology; AustriaFil: Borochova, Kristina. Vienna University of Technology; AustriaFil: Pazderova, Petra. Vienna University of Technology; AustriaFil: Schlederer, Thomas. Vienna University of Technology; AustriaFil: Astafyeva, Natalia. Saratov State Medical University; RusiaFil: Baranovskaya, Tatiana. Belarusian Medical Academy of Post Diploma Studies; BielorrusiaFil: Barbouche, Mohamed Ridha. Institut Pasteur de Tunis; TúnezFil: Beltyukov, Evgeny. Ural State Medical University; RusiaFil: Berger, Angelika. Vienna University of Technology; AustriaFil: Borzova, Elena. Russian Medical Academy of Continuous Professional Education; RusiaFil: Bousquet, Jean. MACVIA; Francia. Humboldt-Universität zu Berlin; AlemaniaFil: Bumbacea, Roxana S.. University of Medicine and Pharmacy "Carol Davila"; RumaniaFil: Bychkovskaya, Snezhana. Krasnoyarsk Medical University; RusiaFil: Caraballo, Luis. Universidad de Cartagena; ColombiaFil: Chung, Kian Fan. Imperial College London; Reino Unido. MRC and Asthma UK Centre in Allergic Mechanisms of Asthma; Reino UnidoFil: Custovic, Adnan. Imperial College London; Reino Unido. MRC and Asthma UK Centre in Allergic Mechanisms of Asthma; Reino UnidoFil: Docena, Guillermo H.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Estudios Inmunológicos y Fisiopatológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Estudios Inmunológicos y Fisiopatológicos; ArgentinaFil: Eiwegger, Thomas. University Of Toronto. Hospital For Sick Children; CanadáFil: Evsegneeva, Irina. Sechenov First Moscow State Medical University; RusiaFil: Emelyanov, Alexander. North-Western Medical University; RusiaFil: Errhalt, Peter. University Hospital Krems and Karl Landsteiner University of Health Sciences; AustriaFil: Fassakhov, Rustem. Kazan Federal University; RusiaFil: Fayzullina, Rezeda. Bashkir State Medical University; RusiaFil: Fedenko, Elena. NRC Institute of Immunology FMBA of Russia; RusiaFil: Fomina, Daria. Sechenov First Moscow State Medical University; RusiaFil: Gao, Zhongshan. Zhejiang University; ChinaFil: Giavina Bianchi, Pedro. Universidade de Sao Paulo; BrasilFil: Gotua, Maia. David Tvildiani Medical University; GeorgiaFil: Greber Platzer, Susanne. Vienna University of Technology; AustriaFil: Hedlin, Gunilla. Karolinska Huddinge Hospital. Karolinska Institutet; Sueci

Missing effects of zinc in a porcine model of recurrent endotoxemia

BACKGROUND: Chronic human sepsis often is characterised by the compensatory anti-inflammatory response syndrome (CARS). During CARS, anti-inflammatory cytokines depress the inflammatory response leading to secondary and opportunistic infections. Proved in vitro as well as in vivo, zinc's pro-inflammatory effect might overcome this depression. METHODS: We used the model of porcine LPS-induced endotoxemia established by Klosterhalfen et al. 10 pigs were divided into two groups (n = 5). Endotoxemia was induced by recurrent intravenous LPS-application (1.0 μg/kg E. coli WO 111:B4) at hours 0, 5, and 12. At hour 10, each group received an intravenous treatment (group I = saline, group II = 5.0 mg/kg elementary zinc). Monitoring included hemodynamics, blood gas analysis, and the thermal dilution technique for the measurement of extravascular lung water and intrapulmonary shunt. Plasma concentrations of IL-6 and TNF-alpha were measured by ELISA. Morphology included weight of the lungs, width of the alveolar septae, and rate of paracentral liver necrosis. RESULTS: Zinc's application only trended to partly improve the pulmonary function. Compared to saline, significant differences were very rare. IL-6 and TNF-alpha were predominately measured higher in the zinc group. Again, significance was only reached sporadically. Hemodynamics and morphology revealed no significant differences at all. CONCLUSION: The application of zinc in this model of recurrent endotoxemia is feasible and without harmful effects. However, a protection or restoration of clinical relevance is not evident in our setting. The pulmonary function just trends to improve, cytokine liberation is only partly activated, hemodynamics and morphology were not influenced. Further pre-clinical studies have to define zinc's role as a therapeutic tool during CARS