Wolfenbüttel, Germany
Wolfenbüttel, Germany

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Agency: Cordis | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2010.2.1.2-1 | Award Amount: 16.62M | Year: 2011

Colorectal cancer (CRC) is one of the most common cancers in both males and females, and it is perhaps the best understood of all epithelial tumors in terms of its molecular origin. Yet, despite large amount of work that has concentrated on understanding of colon tumorigenesis, we still do not know the full complement of molecular lesions that are individually necessary and together sufficient to cause colorectal cancer. Neither do we understand why some specific mutations that are relatively rare in other tumors (e.g. loss of the APC tumor suppressor) are extremely common in colorectal cancer. We propose here to use the tools of systems biology to develop a quantitative and comprehensive model of colorectal tumorigenesis. The model will include a wiring diagram that identifies cell-type specific and oncogenic pathways that contribute to colon tumorigenesis, and explains in molecular detail how a genotype of an individual CRC leads to activation of downstream genes that drive uncontrolled cell growth. This model will subsequently be used to find novel therapeutic targets, to guide genetic screening to identify individuals with elevated risk for developing CRC, and to classify patients into molecular subgroups to select the treatment combination which is optimal for each patient (personalized medicine). The specific objectives of the SYSCOL project are: 1. Identify genetic markers for individual risk using genotyping and sequencing of germline DNA from sporadic and familial CRC cases and controls 2. Identify genes and regulatory elements that contribute to colorectal cancer cell growth 3. Use data from Aims 1-2 to develop a quantitative model for colorectal tumorigenesis 4. Apply the model for identification of high-risk individuals, for molecular classification of the disease, and for identification of novel molecular treatment targets

Agency: Cordis | Branch: H2020 | Program: RIA | Phase: NMBP-09-2016 | Award Amount: 6.23M | Year: 2017

Optogenerapy proposes a new interferon- (IFN-) drug delivery system to revolutionize Multiple Sclerosis treatment. The aim is to develop and validate a new bio-electronic cell based implant device to be implanted subcutaneously providing controlled drug release during at least 6 months. The cell confinement within a chamber sealed by a porous membrane allows the device to be easily implanted or removed. At the same time, this membrane acts to prevent immune rejection and offers long-term safety in drug release while overcoming the adverse effects of current cellular therapies. Wireless powered optogenetics light controlling the cellular response of genetically engineered cells is used to control the production of IFN-. Replacing standard intravenous IFN- delivery by subcutaneous delivery prevents short and long term side effects and efficiency-losses related to drug peaks and discontinuation, while saving non-adherence costs. It is a low-cost system enabling large scale manufacturing and reduction of time to market up to 30% compared to other cell therapies, combining: - Polymeric biomaterials with strong optical, biocompatibility and barrier requirements, to build the cell chamber and to encapsulate the optoelectronics. - Optoelectronics miniaturization, autonomy and optical performance. - Optimal cellular engineering design, enhanced by computer modelling, for stability and performance of the synthetic optogenetic gene pathway over long-term implantation. - Micro moulding enabling optoelectronics and membrane embedding for safety and minimal invasiveness. The innovation potential is so huge that a proof-of-concept was listed by Scientist Magazine as one of the 2014s big advances in science. In our top-class consortium, industrial pull meets technological push, ensuring that the preclinically validated prototype obtained at the end responds to market demands. BOSTON SCIENTIFIC, worldwide leader in neuromodulation active implants, has clear exploitation plans and high market penetration potential. 4 research intensive SMEs: TWO, GENEXPLAIN, NEOS and ULTRASION bring specific competences while increasing their own competitiveness.

Through combining basic pre-clinical and clinical research, network analysis and computational modelling, RESOLVE aims at resolving the disturbed dynamics and mechanisms underlying the high triglyceride (HTG) and low high-density lipoprotein cholesterol (HDL-C) phenotype and insulin resistance in patients with metabolic syndrome (MetS) and its associated co-morbidities (cardiovascular disease, CVD; type 2 diabetes, T2DM; non-alcoholic fatty liver disease, NAFLD). RESOLVE will deliver on 6 specific objectives: i) build a computational model for analyzing the kinetics of plasma lipids, lipoproteins and their interactions with glucose metabolism. ii) apply the iterative systems biology cycle for calibrating, validating and improving the computational model in dedicated studies in mice iii) build, calibrate and validate the computational model for use in humans. iv) analyze based on model and experimental data which processes in the murine metabolic network regulate the physiological response to perturbations in lipid, lipoprotein and glucose metabolism and how these interact. v) analyze based on model and experimental data which processes in the human metabolic network regulate the physiological response to perturbations in lipid, lipoprotein and glucose metabolism and how these interact vi) use the human model to identify network-based drug targets aimed at restoring the metabolic dyslipidemia and glycemic control in patients with MetS and associated comorbidities. RESOLVE 60-months project will result into a novel strategy for development of therapeutic strategies for MetS patients with T2DM, NAFLD or CVD.

Agency: Cordis | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2012.2.1.2-2 | Award Amount: 15.73M | Year: 2012

Inflammatory bowel disease (IBD) is a major health problem with severe co-morbidities, requiring life-long treatment. Oscillating processes, like biological clocks are well studied and modeled in a number of systems. Circadian rhythms are extremely important for optimal treatments of patients. Recently, the NfkB pathway has been shown to be oscillating. In this project, we will model NfkB oscillation in chronic inflammatory bowel diseases in animal models and patient cohorts with immunosuppressive treatments and controls. The aim is to build an experimentally validated model the NfkB oscillation in 4D within the gut tissue. Dynamic, experimental validation will be done for various types of cells in the gut by a combination of methods, including single-cell based transcriptomics, multi-photon microscopy and time-dependent, multi-component profiling. The validated model framework will enable searching for critical components of the NfB oscillation and to assess their relevance for the disease in patients. Interfering with the oscillation of biological pathways may provide new possibilities to influence biological processes like inflammation. Hence, we will search (assisted by the models and databases developed) for small molecules interfering with the NfkB oscillation in chemical databases and validate selected candidates in experimental systems. To this end, we will use cell lines with the correct indicator constructs using high content microscopy. To better translate the findings in animal models to patients, we will use a mouse model with transplanted human tissue so that we can verify the mathematical model in human tissue and verify functionality of small molecules in vivo. Owing to its systems, highly focused approach, the project will generate substantial insights into key mechanisms underlying IBD and will provide ways to modulate the oscillatory behavior of the NfB in IBD and IBD-dependent co-morbidities.

Agency: Cordis | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2012.2.1.1-3 | Award Amount: 7.88M | Year: 2012

MIMOmics develops statistical methods for the integrated analysis of metabolomics, proteomics, glycomics and genomic datasets in large studies. Our project is based on our involvement in studies participating in EU funded projects, i.e. GEHA, IDEAL, Mark-Age, ENGAGE and EuroSpan. In these consortia the primary goal is to identify molecular profiles that monitor and explain complex traits with novel findings so far. Support for methodological development is missing. The state-of-the-art methodology does not match by far the complexity of the biological problem. Complex data are being analysed in a rather simple way which misses the opportunity to uncover combinations of predictive profiles among the omics data. The objectives of MIMOmics are: to develop a statistical framework of methods for all analysis steps needed for identifying and interpreting omics-based biomarkers; and to integrate data derived from multiple omics platforms across several study designs and populations. Specific steps include: experimental design; pipelines for data gathering; cleaning of noisy spectra; predictive modeling of biomarkers; meta analysis; and causality assessment. To enhance our understanding, systems approaches will be considered for pathways and structural modelling of biological networks. The major challenge in the joint analysis of omics datasets will be to develop methods that deal with the high dimensionality, noisy spectral data, heterogeneity, and structure of these datasets. To perform these tasks successfully we bring together established EU academic and industrial researchers in metabolomics, glycomics, biostatistics, bioinformatics, scientific computing and epidemiology, with complementary expertise. A key feature of our project is the validation of novel methodology by performing a proof of principle (Metabolic Health) . Special effort will be made for rapid uptake of methods by communication with associated consortia and development of user-friendly software

Gabdoulline R.,Heinrich Heine University Düsseldorf | Eckweiler D.,Helmholtz Center for Infection Research | Kel A.,GeneXplain GmbH | Kel A.,BIOBASE GmbH | And 2 more authors.
Nucleic Acids Research | Year: 2012

We present the webserver 3D transcription factor (3DTF) to compute position-specific weight matrices (PWMs) of transcription factors using a knowledge-based statistical potential derived from crystallographic data on protein-DNA complexes. Analysis of available structures that can be used to construct PWMs shows that there are hundreds of 3D structures from which PWMs could be derived, as well as thousands of proteins homologous to these. Therefore, we created 3DTF, which delivers binding matrices given the experimental or modeled protein-DNA complex. The webserver can be used by biologists to derive novel PWMs for transcription factors lacking known binding sites and is freely accessible at http://www.gene-regulation.com/pub/programs/ 3dtf/. © 2012 The Author(s).

Nikulenkov F.,Karolinska Institutet | Spinnler C.,Karolinska Institutet | Li H.,Karolinska Institutet | Tonelli C.,Karolinska Institutet | And 11 more authors.
Cell Death and Differentiation | Year: 2012

The tumor-suppressor p53 can induce various biological responses. Yet, it is not clear whether it is p53 in vivo promoter selectivity that triggers different transcription programs leading to different outcomes. Our analysis of genome-wide chromatin occupancy by p53 using chromatin immunoprecipitation (ChIP)-seq revealed p53 default program, that is, the pattern of major p53-bound sites that is similar upon p53 activation by nutlin3a, reactivation of p53 and induction of tumor cell apoptosis (RITA) or 5-fluorouracil in breast cancer cells, despite different biological outcomes. Parallel analysis of gene expression allowed identification of 280 novel p53 target genes, including p53-repressed AURKA. We identified Sp1 as one of the p53 modulators, which confer specificity to p53-mediated transcriptional response upon RITA. Further, we found that STAT3 antagonizes p53-mediated repression of a subset of genes, including AURKA. © 2012 Macmillan Publishers Limited All rights reserved.

Wingender E.,University of Gottingen | Wingender E.,GeneXplain GmbH | Schoeps T.,University of Gottingen | Haubrock M.,University of Gottingen | Donitz J.,University of Gottingen
Nucleic Acids Research | Year: 2015

TFClass aims at classifying eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs). For this, a classification schema comprising four generic levels (superclass, class, family and subfamily) was defined that could accommodate all known DNA-binding human TFs. They were assigned to their (sub-)families as instances at two different levels, the corresponding TF genes and individual gene products (protein isoforms). In the present version, all mouse and rat orthologs have been linked to the human TFs, and the mouse orthologs have been arranged in an independent ontology. Many TFs were assigned with typical DNA-binding patterns and positional weight matrices derived from high-throughput in-vitro binding studies. Predicted TF binding sites from human gene upstream sequences are now also attached to each human TF whenever a PWM was available for this factor or one of his paralogs. TFClass is freely available at http://tfclass.bioinf.med.uni-goettingen.de/ through a web interface and for download in OBO format. © The Author(s) 2014.

Wingender E.,University of Gottingen | Wingender E.,GeneXplain GmbH | Schoeps T.,University of Gottingen | Donitz J.,University of Gottingen
Nucleic Acids Research | Year: 2013

TFClass (http://tfclass.bioinf.med.uni-goettingen.de/) provides a comprehensive classification of human transcription factors based on their DNA-binding domains. Transcription factors constitute a large functional family of proteins directly regulating the activity of genes. Most of them are sequence-specific DNA-binding proteins, thus reading out the information encoded in cis-regulatory DNA elements of promoters, enhancers and other regulatory regions of a genome. TFClass is a database that classifies human transcription factors by a six-level classification schema, four of which are abstractions according to different criteria, while the fifth level represents TF genes and the sixth individual gene products. Altogether, nine superclasses have been identified, comprising 40 classes and 111 families. Counted by genes, 1558 human TFs have been classified so far or >2900 different TFs when including their isoforms generated by alternative splicing or protein processing events. With this classification, we hope to provide a basis for deciphering protein-DNA recognition codes; moreover, it can be used for constructing expanded transcriptional networks by inferring additional TF-target gene relations. © The Author(s) 2012.

Wingender E.,University of Gottingen | Wingender E.,GeneXplain GmbH
Journal of Bioinformatics and Computational Biology | Year: 2013

By binding to cis-regulatory elements in a sequence-specific manner, transcription factors regulate the activity of nearby genes. Here, we discuss the criteria for a comprehensive classification of human TFs based on their DNA-binding domains. In particular, classification of basic leucine zipper (bZIP) and zinc finger factors is exemplarily discussed. The resulting classification can be used as a template for TFs of other biological species. © 2013 The Author.

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