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Hruz T.,ETH Zurich | Wyss M.,ETH Zurich | Docquier M.,University of Geneva | Pfaffl M.W.,Physiology Weihenstephan | And 9 more authors.
BMC Genomics | Year: 2011

Background: RT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts.Results: Using the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions.Conclusion: We therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at © 2011 Hruz et al; licensee BioMed Central Ltd.

Agency: European Commission | Branch: FP7 | Program: CP-TP | Phase: KBBE.2011.3.1-01 | Award Amount: 8.94M | Year: 2012

Most plants use the C3 pathway of photosynthesis that is compromised by gross inefficiencies in CO2 fixation. However, some plants use a super-charged photosynthetic mechanism called C4 photosynthesis. The C4 pathway is used by the most productive vegetation and crops on Earth. In addition to faster photosynthesis, C4 plants demand less water and less nitrogen. Overall, our aim is to introduce the characteristics of C4 into C3 crops. This would increase yield, reduce land area needed for cultivation, decrease irrigation, and limit fertiliser applications. If current C3 crops could be converted to use C4 photosynthesis, large economic and environmental benefits would ensue from both their increased productivity and the reduced inputs associated with the C4 pathway. It is important to note that the huge advances in agricultural production associated with the Green Revolution were not associated with increases in photosynthesis, and so its manipulation remains an unexplored target for crop improvement both for food and biomass. Even partial long-term success would have significant economic and environmental benefits. Efficient C4 photosynthesis would be achieved by alterations to leaf development, cell biology and biochemistry. Although initially we will be using model species such as rice and Arabidopsis we envisage rapid transfer of technological advances into mainstream EU crops, such as wheat and rape, that are used both for food and fuel. We will build capacity for C4 research in Europe in this area by the training of future generations of researchers. To achieve this aim we need to increase our understanding of the basic biology underlying the C4 pathway. Our specific objectives will therefore address fundamental aspects of C4 biology that are needed for a full understanding the pathway. Specifically we aim: 1. To understand the roles and development of the two cell types (mesophyll and bundle sheath) in C4 plants. 2. To identify mechanisms controlling the ex

Hiss M.,University of Marburg | Hiss M.,Albert Ludwigs University of Freiburg | Laule O.,NEBION AG | Meskauskiene R.M.,NEBION AG | And 24 more authors.
Plant Journal | Year: 2014

The moss Physcomitrella patens is an important model organism for studying plant evolution, development, physiology and biotechnology. Here we have generated microarray gene expression data covering the principal developmental stages, culture forms and some environmental/stress conditions. Example analyses of developmental stages and growth conditions as well as abiotic stress treatments demonstrate that (i) growth stage is dominant over culture conditions, (ii) liquid culture is not stressful for the plant, (iii) low pH might aid protoplastation by reduced expression of cell wall structure genes, (iv) largely the same gene pool mediates response to dehydration and rehydration, and (v) AP2/EREBP transcription factors play important roles in stress response reactions. With regard to the AP2 gene family, phylogenetic analysis and comparison with Arabidopsis thaliana shows commonalities as well as uniquely expressed family members under drought, light perturbations and protoplastation. Gene expression profiles for P. patens are available for the scientific community via the easy-to-use tool at By providing large-scale expression profiles, the usability of this model organism is further enhanced, for example by enabling selection of control genes for quantitative real-time PCR. Now, gene expression levels across a broad range of conditions can be accessed online for P. patens. © 2014 The Authors The Plant Journal © 2014 John Wiley & Sons Ltd.

Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2013.2.4.2-1 | Award Amount: 7.63M | Year: 2013

Cardiovascular disease (CVD) is the leading cause of death in Europe and the world. Drugs that lower cholesterol in blood are the most efficient drugs used to date but we are currently still facing a 50-70% residual risk of suffering from CVD. This clearly illustrates an urgent need for the identification, characterization and validation of novel therapeutically relevant targets. Over the last decade, numerous targets have emerged but not led to successful treatment of patients. In addition to missing opportunities of health improvement beyond statins, the 92% failure rate of novel drugs also puts a large burden on the EU economy. While the causal relation between levels of lipids in plasma and CVD forms the basis of this proposal, the challenge taken up by TransCard is Translating disease into Cardiovascular health: It means that insight into the molecular origin of disturbed lipid metabolism in patients as well as the identification of lipoprotein phenotypes with causal relationships to atherosclerosis can provide immediate targets for pharmaceutical intervention leading to successful treatment of this disease. TransCard will prioritize existing and emerging targets and embark on novel unbiased approaches to identify and prioritize novel key regulators of lipid and lipoprotein metabolism. Achieving this aim will be ensured by a multidisciplinary research consortium of two SMEs and four academic partners who use innovative biotechnological and bioinformatics tools and have direct access to unique biobanks and large-scale population cohorts. This team will also help to translate the basic insight into biology into clinical use by subjecting promising targets from top level basic research to validation of their CVD association in large prospective and patient cohorts. Targets passing these tests will be studied in mice to shed further light on the feasibility of pharmaceutical intervention thereby providing lead targets for further development by other parties.

Hruz T.,ETH Zurich | Wyss M.,NEBION AG | Lucas C.,ETH Zurich | Laule O.,NEBION AG | And 3 more authors.
Advances in Bioinformatics | Year: 2013

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. © 2013 Tomas Hruz et al.

Prasad A.,ETH Zurich | Kumar S.S.,ETH Zurich | Dessimoz C.,Swiss Institute of Bioinformatics | Dessimoz C.,ETH Zurich | And 8 more authors.
BMC Genomics | Year: 2013

Background: Predicting molecular responses in human by extrapolating results from model organisms requires a precise understanding of the architecture and regulation of biological mechanisms across species.Results: Here, we present a large-scale comparative analysis of organ and tissue transcriptomes involving the three mammalian species human, mouse and rat. To this end, we created a unique, highly standardized compendium of tissue expression. Representative tissue specific datasets were aggregated from more than 33,900 Affymetrix expression microarrays. For each organism, we created two expression datasets covering over 55 distinct tissue types with curated data from two independent microarray platforms. Principal component analysis (PCA) revealed that the tissue-specific architecture of transcriptomes is highly conserved between human, mouse and rat. Moreover, tissues with related biological function clustered tightly together, even if the underlying data originated from different labs and experimental settings. Overall, the expression variance caused by tissue type was approximately 10 times higher than the variance caused by perturbations or diseases, except for a subset of cancers and chemicals. Pairs of gene orthologs exhibited higher expression correlation between mouse and rat than with human. Finally, we show evidence that tissue expression profiles, if combined with sequence similarity, can improve the correct assignment of functionally related homologs across species.Conclusion: The results demonstrate that tissue-specific regulation is the main determinant of transcriptome composition and is highly conserved across mammalian species. © 2013 Prasad et al.; licensee BioMed Central Ltd.

Meskauskiene R.,NEBION AG | Laule O.,ETH Zurich | Laule O.,NEBION AG | Ivanov N.V.,Philip Morris International R and D | And 5 more authors.
Plant Methods | Year: 2013

Background: It is generally accepted that controlled vocabularies are necessary to systematically integrate data from various sources. During the last decade, several plant ontologies have been developed, some of which are community specific or were developed for a particular purpose. In most cases, the practical application of these ontologies has been limited to systematically storing experimental data. Due to technical constraints, complex data structures and term redundancies, it has been difficult to apply them directly into analysis tools.Results: Here, we describe a simplified and cross-species compatible set of controlled vocabularies for plant anatomy, focussing mainly on monocotypledonous and dicotyledonous crop and model plants. Their content was designed primarily for their direct use in graphical visualization tools. Specifically, we created annotation vocabularies that can be understood by non-specialists, are minimally redundant, simply structured, have low tree depth, and we tested them practically in the frame of Genevestigator.Conclusions: The application of the proposed ontologies enabled the aggregation of data from hundreds of experiments to visualize gene expression across tissue types. It also facilitated the comparison of expression across species. The described controlled vocabularies are maintained by a dedicated curation team and are available upon request. © 2013 Meskauskiene et al.; licensee BioMed Central Ltd.

Zimmermann P.,NEBION AG | Bleuler S.,NEBION AG | Laule O.,NEBION AG | Martin F.,Philip Morris International R and D | And 7 more authors.
BioData Mining | Year: 2014

Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, several reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions.Here, we describe a new type of standardized datasets representative for the spatial and temporal dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments. Expression data is aggregated by anatomical part or stage of development to yield a representative transcriptome for each category. For example, we created a genome-wide expression dataset representing the FDA tissue panel across 35 tissue types. The proposed datasets were created for human and several model organisms and are publicly available at. © 2014Zimmermann et al.; licensee BioMed Central Ltd.

Svoboda M.,Medical University of Vienna | Meshcheryakova A.,Medical University of Vienna | Heinze G.,Medical University of Vienna | Jaritz M.,Research Institute of Molecular Pathology | And 14 more authors.
BMC Genomics | Year: 2016

Background: Building up of pathway-/disease-relevant signatures provides a persuasive tool for understanding the functional relevance of gene alterations and gene network associations in multifactorial human diseases. Ovarian cancer is a highly complex heterogeneous malignancy in respect of tumor anatomy, tumor microenvironment including pro-/antitumor immunity and inflammation; still, it is generally treated as single disease. Thus, further approaches to investigate novel aspects of ovarian cancer pathogenesis aiming to provide a personalized strategy to clinical decision making are of high priority. Herein we assessed the contribution of the AID/APOBEC family and their associated genes given the remarkable ability of AID and APOBECs to edit DNA/RNA, and as such, providing tools for genetic and epigenetic alterations potentially leading to reprogramming of tumor cells, stroma and immune cells. Results: We structured the study by three consecutive analytical modules, which include the multigene-based expression profiling in a cohort of patients with primary serous ovarian cancer using a self-created AID/APOBEC-associated gene signature, building up of multivariable survival models with high predictive accuracy and nomination of top-ranked candidate/target genes according to their prognostic impact, and systems biology-based reconstruction of the AID/APOBEC-driven disease-relevant mechanisms using transcriptomics data from ovarian cancer samples. We demonstrated that inclusion of the AID/APOBEC signature-based variables significantly improves the clinicopathological variables-based survival prognostication allowing significant patient stratification. Furthermore, several of the profiling-derived variables such as ID3, PTPRC/CD45, AID, APOBEC3G, and ID2 exceed the prognostic impact of some clinicopathological variables. We next extended the signature-/modeling-based knowledge by extracting top genes co-regulated with target molecules in ovarian cancer tissues and dissected potential networks/pathways/regulators contributing to pathomechanisms. We thereby revealed that the AID/APOBEC-related network in ovarian cancer is particularly associated with remodeling/fibrotic pathways, altered immune response, and autoimmune disorders with inflammatory background. Conclusions: The herein study is, to our knowledge, the first one linking expression of entire AID/APOBECs and interacting genes with clinical outcome with respect to survival of cancer patients. Overall, data propose a novel AID/APOBEC-derived survival model for patient risk assessment and reconstitute mapping to molecular pathways. The established study algorithm can be applied further for any biologically relevant signature and any type of diseased tissue. © 2016 The Author(s).

Meshcheryakova A.,Medical University of Vienna | Svoboda M.,Medical University of Vienna | Tahir A.,University of Vienna | Kofeler H.C.,Medical University of Graz | And 7 more authors.
Oncotarget | Year: 2016

The epithelial to mesenchymal transition (EMT) program is activated in epithelial cancer cells and facilitates their ability to metastasize based on enhanced migratory, proliferative, anti-apoptotic, and pluripotent capacities. Given the fundamental impact of sphingolipid machinery to each individual process, the sphingolipid-related mechanisms might be considered among the most prominent drivers/players of EMT; yet, there is still limited knowledge. Given the complexity of the interconnected sphingolipid system, which includes distinct sphingolipid mediators, their synthesizing enzymes, receptors and transporters, we herein apply an integrative approach for assessment of the sphingolipid-associated mechanisms underlying EMT program. We created the sphingolipid-/EMT-relevant 41-gene/23-gene signatures which were applied to denote transcriptional events in a lung cancer cell-based EMT model. Based on defined 35-gene sphingolipid/EMT-attributed signature of regulated genes, we show close associations between EMT markers, genes comprising the sphingolipid network at multiple levels and encoding sphingosine 1-phosphate (S1P)-/ceramide-metabolizing enzymes, S1P and lysophosphatidic acid (LPA) receptors and S1P transporters, pluripotency genes and inflammation-related molecules, and demonstrate the underlying biological pathways and regulators. Mass spectrometry-based sphingolipid analysis revealed an EMT-attributed shift towards increased S1P and LPA accompanied by reduced ceramide levels. Notably, using transcriptomics data across various cell-based perturbations and neoplastic tissues (24193 arrays), we identified the sphingolipid/EMT signature primarily in lung adenocarcinoma tissues; besides, bladder, colorectal and prostate cancers were among the top-ranked. The findings also highlight novel regulatory associations between influenza virus and the sphingolipid/EMT-associated mechanisms. In sum, data propose the multidimensional contribution of sphingolipid machinery to pathological EMT and may yield new biomarkers and therapeutic targets.

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