Competence Center for Systems Physiology and Metabolic Diseases

Zürich, Switzerland

Competence Center for Systems Physiology and Metabolic Diseases

Zürich, Switzerland
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Gerber P.A.,University of Zürich | Gerber P.A.,Competence Center for Systems Physiology and Metabolic Diseases | Gouni-Berthold I.,University of Cologne | Berneis K.,University of Zürich | Berneis K.,Center for Integrative Human Physiology
Current Pharmaceutical Design | Year: 2013

The inverse association of cardiovascular risk with intake of omega-3 polyunsaturated fatty acids was suspected early in populations that are known to have a high consumption of fish and fish oil. Subsequent cohort studies confirmed such associations in other populations. Further evidence of possible beneficial effects on metabolism and cardiovascular health was provided by many studies that were able to show specific mechanisms that may underlie these observations. These include improvement of the function of tissues involved in the alterations occurring during the development of obesity and the metabolic syndrome, as adipose tissue, the liver and skeletal muscle. Direct action on the cardiovascular system was not only shown regarding vascular function and the formation of atherosclerotic plaques, but also by providing antiarrhythmic effects on the heart. Data on these effects come from in vitro as well as in vivo studies that were conducted in animal models of disease, in healthy humans and in humans suffering from cardiovascular disease. To define prophylactic as well as treatment options in primary and secondary prevention, large clinical trial assessed the effect of omega-3 polyunsaturated fatty acids on end points as cardiovascular morbidity and mortality. However, so far these trials provided ambiguous data that do allow recommendations regarding the use of omega-3 polyunsaturated fatty acids in higher dosages and beyond the dietary advice of regular fish intake only in few clinical situations, such as severe hypertriglyceridemia. © 2013 Bentham Science Publishers.

Stekhoven D.J.,ETH Zurich | Stekhoven D.J.,Competence Center for Systems Physiology and Metabolic Diseases | Buhlmann P.,ETH Zurich | Buhlmann P.,Competence Center for Systems Physiology and Metabolic Diseases
Bioinformatics | Year: 2012

Motivation: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously.Results: We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. © The Author 2011. Published by Oxford University Press. All rights reserved.

Rost H.,ETH Zurich | Malmstrom L.,ETH Zurich | Aebersold R.,ETH Zurich | Aebersold R.,Competence Center for Systems Physiology and Metabolic Diseases | Aebersold R.,University of Zürich
Molecular and Cellular Proteomics | Year: 2012

Selected reaction monitoring (SRM), also called multiple reaction monitoring, has become an invaluable tool for targeted quantitative proteomic analyses, but its application can be compromised by nonoptimal selection of transitions. In particular, complex backgrounds may cause ambiguities in SRM measurement results because peptides with interfering transitions similar to those of the target peptide may be present in the sample. Here, we developed a computer program, the SRMCollider, that calculates nonredundant theoretical SRM assays, also known as unique ion signatures (UIS), for a given proteomic background. We show theoretically that UIS of three transitions suffice to conclusively identify 90% of all yeast peptides and 85% of all human peptides. Using predicted retention times, the SRMCollider also simulates time-scheduled SRM acquisition, which reduces the number of interferences to consider and leads to fewer transitions necessary to construct an assay. By integrating experimental fragment ion intensities from large scale proteome synthesis efforts (SRMAtlas) with the information content-based UIS, we combine two orthogonal approaches to create high quality SRM assays ready to be deployed. We provide a user friendly, open source implementation of an algorithm to calculate UIS of any order that can be accessed online at http://www. to find interfering transitions. Finally, our tool can also simulate the specificity of novel data-independent MS acquisition methods in Q1-Q3 space. This allows us to predict parameters for these methods that deliver a specificity comparable with that of SRM. Using SRM interference information in addition to other sources of information can increase the confidence in an SRM measurement. We expect that the consideration of information content will become a standard step in SRM assay design and analysis, facilitated by the SRMCollider. © 2012 by The American Society for Biochemistry and Molecular Biology, Inc.

Picotti P.,ETH Zurich | Aebersold R.,ETH Zurich | Aebersold R.,Competence Center for Systems Physiology and Metabolic Diseases | Aebersold R.,University of Zürich
Nature Methods | Year: 2012

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that is emerging in the field of proteomics as a complement to untargeted shotgun methods. SRM is particularly useful when predetermined sets of proteins, such as those constituting cellular networks or sets of candidate biomarkers, need to be measured across multiple samples in a consistent, reproducible and quantitatively precise manner. Here we describe how SRM is applied in proteomics, review recent advances, present selected applications and provide a perspective on the future of this powerful technology. © 2012 Nature America, Inc. All rights reserved.

Fendt S.-M.,ETH Zurich | Fendt S.-M.,Competence Center for Systems Physiology and Metabolic Diseases | Sauer U.,ETH Zurich | Sauer U.,Competence Center for Systems Physiology and Metabolic Diseases
BMC Systems Biology | Year: 2010

Background: Depending on the carbon source, Saccharomyces cerevisiae displays various degrees of respiration. These range from complete respiration as in the case of ethanol, to almost complete fermentation, and thus very low degrees of respiration on glucose. While many key regulators are known for these extreme cases, we focus here on regulators that are relevant at intermediate levels of respiration.Results: We address this question by linking the functional degree of respiration to transcriptional regulation via enzyme abundances. Specifically, we investigated aerobic batch cultures with the differently repressive carbon sources glucose, mannose, galactose and pyruvate. Based on 13C flux analysis, we found that the respiratory contribution to cellular energy production was largely absent on glucose and mannose, intermediate on galactose and highest on pyruvate. In vivo abundances of 40 respiratory enzymes were quantified by GFP-fusions under each condition. During growth on the partly and fully respired substrates galactose and pyruvate, several TCA cycle and respiratory chain enzymes were significantly up-regulated. From these enzyme levels and the known regulatory network structure, we determined the probability for a given transcription factor to cause the coordinated expression changes. The most probable transcription factors to regulate the different degrees of respiration were Gcr1p, Cat8p, the Rtg-proteins and the Hap-complex. For the latter three ones we confirmed their importance for respiration by quantifying the degree of respiration and biomass yields in the corresponding deletion strains.Conclusions: Cat8p is required for wild-type like respiration, independent of its known activation of gluconeogenic genes. The Rtg-proteins and the Hap-complex are essential for wild-type like respiration under partially respiratory conditions. Under fully respiratory conditions, the Hap-complex, but not the Rtg-proteins are essential for respiration. © 2010 Fendt and Sauer; licensee BioMed Central Ltd.

Collins B.C.,ETH Zurich | Gillet L.C.,ETH Zurich | Rosenberger G.,ETH Zurich | Rost H.L.,ETH Zurich | And 5 more authors.
Nature Methods | Year: 2013

Protein complexes and protein interaction networks are essential mediators of most biological functions. Complexes supporting transient functions such as signal transduction processes are frequently subject to dynamic remodeling. Currently, the majority of studies on the composition of protein complexes are carried out by affinity purification and mass spectrometry (AP-MS) and present a static view of the system. For a better understanding of inherently dynamic biological processes, methods to reliably quantify temporal changes of protein interaction networks are essential. Here we used affinity purification combined with sequential window acquisition of all theoretical spectra (AP-SWATH) mass spectrometry to study the dynamics of the 14-3-3β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway. The consistent and reproducible quantification of 1,967 proteins across all stimulation time points provided insights into the 14-3-3β interactome and its dynamic changes following IGF1 stimulation. We therefore establish AP-SWATH as a tool to quantify dynamic changes in protein-complex interaction networks. © 2013 Nature America, Inc.

Dechant R.,ETH Zurich | Dechant R.,Competence Center for Systems Physiology and Metabolic Diseases | Saad S.,ETH Zurich | Ibanez A.J.,ETH Zurich | And 2 more authors.
Molecular Cell | Year: 2014

Regulation of cell growth by nutrients is governed by highly conserved signaling pathways, yet mechanisms of nutrient sensing are still poorly understood. In yeast, glucose activates both the Ras/PKA pathway and TORC1, which coordinately regulate growth through enhancing translation and ribosome biogenesis and suppressing autophagy. Here, we show that cytosolic pH acts as a cellular signal to activate Ras and TORC1 in response to glucose availability. We demonstrate that cytosolic pH is sensitive to the quality and quantity of the available carbon source (C-source). Interestingly, Ras/PKA and TORC1 are both activated through the vacuolar ATPase (V-ATPase), which was previously identified as a sensor for cytosolic pH in vivo. V-ATPase interacts with two distinct GTPases, Arf1 and Gtr1, which are required for Ras and TORC1 activation, respectively. Together, these data provide a molecular mechanism for how cytosolic pH links C-source availability to the activity of signaling networks promoting cell growth. © 2014 Elsevier Inc.

Picotti P.,ETH Zurich | Rinner O.,ETH Zurich | Rinner O.,Biognosys | Stallmach R.,ETH Zurich | And 8 more authors.
Nature Methods | Year: 2010

Selected reaction monitoring (SRM) uses sensitive and specific mass spectrometric assays to measure target analytes across multiple samples, but it has not been broadly applied in proteomics owing to the tedious assay development process for each protein. We describe a method based on crude synthetic peptide libraries for the high-throughput development of SRM assays. We illustrate the power of the approach by generating and applying validated SRM assays for all Saccharomyces cerevisiae kinases and phosphatases. © 2010 Nature America, Inc. All rights reserved.

Gerster S.,ETH Zurich | Qeli E.,University of Zürich | Ahrens C.H.,University of Zürich | Buhlmann P.,ETH Zurich | Buhlmann P.,Competence Center for Systems Physiology and Metabolic Diseases
Proceedings of the National Academy of Sciences of the United States of America | Year: 2010

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference.We present Markovian Inference of Proteins and Gene Models (MIPGEM), a statistical model based on clearly stated assumptions to address the problem of protein and gene model inference for shotgun proteomics data. In particular, we are dealing with dependencies among peptides and proteins using a Markovian assumption on k-partite graphs. We are also addressing the problems of shared peptides and ambiguous proteins by scoring the encoding gene models. Empirical results on two control datasets with synthetic mixtures of proteins and on complex protein samples of Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana suggest that the results with MIPGEM are competitive with existing tools for protein inference.

Blank P.R.,University of Zürich | Blank P.R.,Competence Center for Systems Physiology and Metabolic Diseases | Moch H.,University of Zürich | Moch H.,Competence Center for Systems Physiology and Metabolic Diseases | And 3 more authors.
Clinical Cancer Research | Year: 2011

Purpose: Monoclonal antibodies against the epidermal growth factor receptor (EGFR), such as cetuximab, have led to significant clinical benefits for metastatic colorectal cancer (mCRC) patients but have also increased treatment costs considerably. Recent evidence associates KRAS and BRAF mutations with resistance to EGFR antibodies. We assessed the cost-effectiveness of predictive testing for KRAS and BRAF mutations, prior to cetuximab treatment of chemorefractory mCRC patients. Experimental Design: A life-long Markov simulation model was used to estimate direct medical costs (€) and clinical effectiveness [quality-adjusted life-years (QALY)] of the following strategies: KRAS testing, KRAS testing with subsequent BRAF testing of KRAS wild-types (KRAS/BRAF), cetuximab treatment without testing. Comparison was against no cetuximab treatment (reference strategy). In the testing strategies, cetuximab treatment was initiated if no mutations were detected. Best supportive care was given to all patients. Survival times/utilities were derived from published randomized clinical trials. Costs were assessed from the perspective of the Swiss health system. Results: Average remaining lifetime costs ranged from €3,983 (no cetuximab) to €38,662 (no testing). Cetuximab treatment guided by KRAS/BRAF achieved gains of 0.491 QALYs compared with the reference strategy. The KRAS testing strategy achieved an additional gain of 0.002 QALYs compared with KRAS/BRAF. KRAS/BRAF testing was the most cost-effective approach when compared with the reference strategy (incremental cost-effectiveness ratio: €62,653/QALY). Conclusion: New predictive tests for KRAS and BRAF status are currently being introduced in pathology. Despite substantial costs of predictive testing, it is economically favorable to identify patients with KRAS and BRAF wild-type status. ©2011 AACR.

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