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Bile acid sequestrants (BASs) are cholesterollowering drugs that also affect hyperglycemia. The mechanism by which BASs exert these and other metabolic effects beyond cholesterol lowering remains poorly understood. The present study aimed to investigate the effects of a BAS, colestilan, on body weight, energy expenditure, and glucose and lipid metabolism and its mechanisms of action in high-fat-fed hyperlipidemic APOE*3 Leiden (E3L) transgenic mice. Mildly insulin-resistant E3L mice were fed a high-fat diet with or without 1.5% colestilan for 8 weeks. Colestilan treatment decreased body weight, visceral and subcutaneous fat, and plasma cholesterol and triglyceride levels but increased food intake. Blood glucose and plasma insulin levels were decreased, and hyperinsulinemic-euglycemic clamp analysis demonstrated improved insulin sensitivity, particularly in peripheral tissues. In addition, colestilan decreased energy expenditure and physical activity, whereas it increased the respiratory exchange ratio, indicating that colestilan induced carbohydrate catabolism. Moreover, kinetic analysis revealed that colestilan increased [ 3 H]NEFA incorporation in biliary cholesterol and phospholipids and increased fecal lipid excretion. Gene expression analysis in liver, fat, and muscle supported the above findings. In summary, colestilan decreases weight gain and improves peripheral insulin sensitivity in high-fat-fed E3L mice by enhanced NEFA incorporation in biliary lipids and increased fecal lipid excretion. Copyright © 2013 by the American Society for Biochemistry and Molecular Biology, Inc.

Motivation Much of the biological knowledge accumulated over the last decades is stored in different databases governed by various organizations and institutes. Integrating and connecting these vast knowledge repositories is an extremely useful method to support life sciences research and help formulate novel hypotheses. Results We developed the Network Library (NL), a framework and toolset to rapidly integrate different knowledge sources to build a network biology resource that matches a specific research question. As a use-case we explore the interactions of genes related to heart failure with miRNAs and diseases through the integration of 6 databases. Availability and Implementation The NL is open-source, developed in Java and available on Github (https://github.com/gsummer). Contact georg.summer@gmail.com

Summary: We developed cyNeo4j, a Cytoscape App to link Cytoscape and Neo4j databases to utilize the performance and storage capacities Neo4j offers. We implemented a Neo4j NetworkAnalyzer, ForceAtlas2 layout and Cypher component to demonstrate the possibilities a distributed setup of Cytoscape and Neo4j have. Availability and implementation: The app is available from the Cytoscape App Store at http://apps.cytoscape.org/apps/cyneo4j, the Neo4j plugins at www.github.com/gsummer/cyneo4j-parent and the community and commercial editions of Neo4j can be found at http://www.neo4j.com. Contact: georg.summer@gmail.com

Health is influenced by interplay of molecular, physiological and environmental factors. To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity. Network-based methods provide a powerful platform for integration and mining of data and knowledge characterizing different aspects of health. Previously, we have reported physiological and gene expression changes associated with adaptation of murine epididymal white adipose tissue (eWAT) to 5 days and 12 weeks of high-fat diet (HFD) and low-fat diet feeding (Voigt et al. in Mol Nutr Food Res 57:1423–1434, 2013. doi:10.1002/mnfr.201200671). In the current study, we apply network analysis on this dataset to comprehensively characterize mechanisms driving the short- and long-term adaptation of eWAT to HFD across multiple levels of complexity. We built a three-layered interaction network comprising enriched biological processes, their transcriptional regulators and associated changes in physiological parameters. The multi-layered network model reveals that early eWAT adaptation to HFD feeding involves major changes at a molecular level, including activation of TGF-β signalling pathway, immune and stress response and downregulation of mitochondrial functioning. Upon prolonged HFD intake, initial transcriptional response tails off, mitochondrial functioning is even further diminished, and in turn the relation between eWAT gene expression and physiological changes becomes more prominent. In particular, eWAT weight and total energy intake negatively correlate with cellular respiration process, revealing mitochondrial dysfunction as a hallmark of late eWAT adaptation to HFD. Apart from global understanding of the time-resolved adaptation to HFD, the multi-layered network model allows several novel mechanistic hypotheses to emerge: (1) early activation of TGF-β signalling as a trigger for structural and morphological changes in mitochondrial organization in eWAT, (2) modulation of cellular respiration as an intervention strategy to effectively deal with excess dietary fat and (3) discovery of putative intervention targets, such those in pathways related to appetite control. In conclusion, the generated network model comprehensively characterizes eWAT adaptation to high-fat diet, spanning from global aspects to mechanistic details. Being open to further exploration by the research community, it provides a resource of health-relevant interactions ready to be used in a broad range of research applications. Electronic supplementary material The online version of this article (doi:10.1007/s12263-015-0470-6) contains supplementary material, which is available to authorized users.

Background : Cardiac pathological hypertrophy is associated with a significantly increased risk of coronary heart disease and has been observed in diabetic patients treated with rosiglitazone whereas most published studies do not suggest a similar increase in risk of cardiovascular events in pioglitazone-treated diabetic subjects. This study sought to understand the pathophysiological and molecular mechanisms underlying the disparate cardiovascular effects of rosiglitazone and pioglitazone and yield knowledge as to the causative nature of rosiglitazone-associated cardiac hypertrophy. Methods : We used a high-fat diet-induced pre-diabetic mouse model to allow bioinformatics analysis of the transcriptome of the heart of mice treated with rosiglitazone or pioglitazone. Results : Our data show that rosiglitazone and pioglitazone both markedly improved systemic markers for glucose homeostasis, fasting plasma glucose and insulin, and the urinary excretion of albumin. Only rosiglitazone, but not pioglitazone, tended to increase atherosclerosis and induced pathological cardiac hypertrophy, based on a significant increase in heart weight and increased expression of the validated markers, ANP and BNP. Functional enrichment analysis of the rosiglitazone-specific cardiac gene expression suggests that a shift in cardiac energy metabolism, in particular decreased fatty acid oxidation toward increased glucose utilization as indicated by down regulation of relevant PPARα and PGC1α target genes. This underlies the rosiglitazone-associated pathological hypertrophic cardiac phenotype in the current study. Conclusion : Application of a systems biology approach uncovered a shift in energy metabolism by rosiglitazone that may impact cardiac pathological hypertrophy.

Background: Recent evidence suggests that the gut microbiota plays an important role in human metabolism and energy homeostasis and is therefore a relevant factor in the assessment of metabolic health and flexibility. Understanding of these host–microbiome interactions aids the design of nutritional strategies that act via modulation of the microbiota. Nevertheless, relating gut microbiota composition to host health states remains challenging because of the sheer complexity of these ecosystems and the large degrees of interindividual variation in human microbiota composition. Methods: We assessed fecal microbiota composition and host response patterns of metabolic and inflammatory markers in 10 apparently healthy men subjected to a high-fat high-caloric diet (HFHC, 1300 kcal/day extra) for 4 weeks. DNA was isolated from stool and barcoded 16S rRNA gene amplicons were sequenced. Metabolic health parameters, including anthropomorphic and blood parameters, where determined at t=0 and t=4 weeks. Results: A correlation network approach revealed diet-induced changes in Bacteroides levels related to changes in carbohydrate oxidation rates, whereas the change in Firmicutes correlates with changes in fat oxidation. These results were confirmed by multivariate models. We identified correlations between microbial diversity indices and several inflammation-related host parameters that suggest a relation between diet-induced changes in gut microbiota diversity and inflammatory processes. Conclusions: This approach allowed us to identify significant correlations between abundances of microbial taxa and diet-induced shifts in several metabolic health parameters. Constructed correlation networks provide an overview of these relations, revealing groups of correlations that are of particular interest for explaining host health aspects through changes in the gut microbiota.

Introduction: The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach integrating our understanding on different regulators is essential in interpreting the regulation of molecular biological processes. Implementation: We developed CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker) a Cytoscape app, for integrating regulatory interactions in network analysis. Recently we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides a user-friendly and flexible interface to extend biological networks with regulatory interactions, such as microRNA-target, transcription factor-target and/or drug-target. Importantly, CyTargetLinker employs identifier mapping to combine various interaction data resources that use different types of identifiers. Results: Three case studies demonstrate the strength and broad applicability of CyTargetLinker, (i) extending a mouse molecular interaction network, containing genes linked to diabetes mellitus, with validated and predicted microRNAs, (ii) enriching a molecular interaction network, containing DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory meta-network in which a biological process is extended with information on transcription factor, microRNA and drug regulation. Conclusions: CyTargetLinker provides a simple and extensible framework for biologists and bioinformaticians to integrate different regulatory interactions into their network analysis approaches. Visualization options enable biological interpretation of complex regulatory networks in a graphical way. Importantly the incorporation of our tool into the Cytoscape framework allows the application of CyTargetLinker in combination with a wide variety of other apps for state-ofthe- art network analysis. Copyright: © 2013 Kutmon et al.

Background:Chronic metabolic overload results in lipid accumulation and subsequent inflammation in white adipose tissue (WAT), often accompanied by non-alcoholic fatty liver disease (NAFLD). In response to metabolic overload, the expression of genes involved in lipid metabolism and inflammatory processes is adapted. However, it still remains unknown how these adaptations in gene expression in expanding WAT and liver are orchestrated and whether they are interrelated.Methodology/Principal Findings:ApoE*3Leiden mice were fed HFD or chow for different periods up to 12 weeks. Gene expression in WAT and liver over time was evaluated by micro-array analysis. WAT hypertrophy and inflammation were analyzed histologically. Bayesian hierarchical cluster analysis of dynamic WAT gene expression identified groups of genes ('clusters') with comparable expression patterns over time. HFD evoked an immediate response of five clusters of 'lipid metabolism' genes in WAT, which did not further change thereafter. At a later time point (>6 weeks), inflammatory clusters were induced. Promoter analysis of clustered genes resulted in specific key regulators which may orchestrate the metabolic and inflammatory responses in WAT. Some master regulators played a dual role in control of metabolism and inflammation. When WAT inflammation developed (>6 weeks), genes of lipid metabolism and inflammation were also affected in corresponding livers. These hepatic gene expression changes and the underlying transcriptional responses in particular, were remarkably similar to those detected in WAT.Conclusion:In WAT, metabolic overload induced an immediate, stable response on clusters of lipid metabolism genes and induced inflammatory genes later in time. Both processes may be controlled and interlinked by specific transcriptional regulators. When WAT inflammation began, the hepatic response to HFD resembled that in WAT. In all, WAT and liver respond to metabolic overload by adaptations in expression of gene clusters that control lipid metabolism and inflammatory processes in an orchestrated and interrelated manner. © 2013 Liang et al.

Excess caloric intake leads to metabolic overload and is associated with development of type 2 diabetes (T2DM). Current disease management concentrates on risk factors of the disease such as blood glucose, however with limited success. We hypothesize that normalizing blood glucose levels by itself is insufficient to reduce the development of T2DM and complications, and that removal of the metabolic overload with dietary interventions may be more efficacious. We explored the efficacy and systems effects of pharmaceutical interventions versus dietary lifestyle intervention (DLI) in developing T2DM and complications. To mimic the situation in humans, high fat diet (HFD)-fed LDLr-/- mice with already established disease phenotype were treated with ten different drugs mixed into HFD or subjected to DLI (switch to low-fat chow), for 7 weeks. Interventions were compared to untreated reference mice kept on HFD or chow only. Although most of the drugs improved HFD-induced hyperglycemia, drugs only partially affected other risk factors and also had limited effect on disease progression towards microalbuminuria, hepatosteatosis and atherosclerosis. By contrast, DLI normalized T2DM risk factors, fully reversed hepatosteatosis and microalbuminuria, and tended to attenuate atherogenesis. The comprehensive beneficial effect of DLI was reflected by normalized metabolite profiles in plasma and liver. Analysis of disease pathways in liver confirmed reversion of the metabolic distortions with DLI. This study demonstrates that the pathogenesis of T2DM towards complications is reversible with DLI and highlights the differential effects of current pharmacotherapies and their limitation to resolve the disease. © 2013 Radonjic et al.

AIMS: Combination-drug therapy takes advantage of the complementary action of their individual components, thereby potentiating its therapeutic effect. Potential disadvantages include side effects that are not foreseen on basis of the data available from drug monotherapy. Here, we used a systems biology approach to understand both the efficacy and the side effects of a cholesterol-lowering drug-combination therapy on the basis of the biological pathways and molecular processes affected by each drug alone or in combination. METHODS AND RESULTS: ApoE*3Leiden transgenic mice, a mouse model with human-like cholesterol-lowering drug responses, were treated with rosuvastatin and ezetimibe, alone and in combination. Analyses included functional responses, viz. effects on cardiovascular risk factors, inflammation, and atherosclerosis, and measurement of global gene expression, and identification of enriched biological pathways and molecular processes. Combination therapy reduced plasma cholesterol, plasma inflammation markers, and atherosclerosis stronger than the single drugs did. Systems biology analysis at the level of biological processes shows that the therapeutic benefit of combined therapy is largely the result of additivity of the complementary mechanisms of action of the two single drugs. Importantly, combination therapy also exerted a significant effect on 16 additional and mostly NF-κB-linked signaling processes, 11 of which tended to be regulated in a similar direction with monotherapy. CONCLUSION: This study shows that gene expression analysis together with bioinformatics pathway analysis has the potential to help predict and identify drug combination-specific complementary and side effects. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.

Scope: The dietary fatty acid cis9,trans11 conjugated linoleic acid (cis9,trans11 CLA) has been shown to modify the function of endothelial cells, monocytes, and platelets, all of which are involved in the development of atherosclerosis. Potential mechanisms for the platelet effects have not been assessed previously. In this study, we assessed how supplementation of the diet with an 80:20 cis9,trans11 CLA blend affects the platelet proteome. Methods and results: In a double-blind, randomized, placebo-controlled, parallel-group trial, 40 overweight but apparently healthy adults received either 4 g per day of cis9,trans11 CLA-enriched oil or placebo oil, consisting of palm oil and soybean oil, for 3 months. Total platelet proteins were extracted from washed platelets, separated using two-dimensional gel electrophoresis and differentially regulated protein spots were identified by LC-ESI-MS/MS. Supplementation with the CLA blend, compared with placebo, resulted in significant alterations in levels of 46 spots (p < 0.05), of which 40 were identified. Network analysis revealed that the majority of these proteins participate in regulation of the cytoskeleton and platelet structure, as well as receptor action, signaling, and focal adhesion. Conclusion: The platelet proteomics approach revealed novel insights into regulation of cellular biomarkers of atherogenic and thrombotic pathways by an 80:20 cis9,trans11 CLA blend. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

BACKGROUND: The sequence of events leading to the development of insulin resistance (IR) as well as the underlying pathophysiological mechanisms are incompletely understood. As reductionist approaches have been largely unsuccessful in providing an understanding of the pathogenesis of IR, there is a need for an integrative, time-resolved approach to elucidate the development of the disease. METHODOLOGY/PRINCIPAL FINDINGS: Male ApoE3Leiden transgenic mice exhibiting a humanized lipid metabolism were fed a high-fat diet (HFD) for 0, 1, 6, 9, or 12 weeks. Development of IR was monitored in individual mice over time by performing glucose tolerance tests and measuring specific biomarkers in plasma, and hyperinsulinemic-euglycemic clamp analysis to assess IR in a tissue-specific manner. To elucidate the dynamics and tissue-specificity of metabolic and inflammatory processes key to IR development, a time-resolved systems analysis of gene expression and metabolite levels in liver, white adipose tissue (WAT), and muscle was performed. During HFD feeding, the mice became increasingly obese and showed a gradual increase in glucose intolerance. IR became first manifest in liver (week 6) and then in WAT (week 12), while skeletal muscle remained insulin-sensitive. Microarray analysis showed rapid upregulation of carbohydrate (only liver) and lipid metabolism genes (liver, WAT). Metabolomics revealed significant changes in the ratio of saturated to polyunsaturated fatty acids (liver, WAT, plasma) and in the concentrations of glucose, gluconeogenesis and Krebs cycle metabolites, and branched amino acids (liver). HFD evoked an early hepatic inflammatory response which then gradually declined to near baseline. By contrast, inflammation in WAT increased over time, reaching highest values in week 12. In skeletal muscle, carbohydrate metabolism, lipid metabolism, and inflammation was gradually suppressed with HFD. CONCLUSIONS/SIGNIFICANCE: HFD-induced IR is a time- and tissue-dependent process that starts in liver and proceeds in WAT. IR development is paralleled by tissue-specific gene expression changes, metabolic adjustments, changes in lipid composition, and inflammatory responses in liver and WAT involving p65-NFkB and SOCS3. The alterations in skeletal muscle are largely opposite to those in liver and WAT.

Background: Multifactorial diseases such as type 2 diabetes mellitus (T2DM), are driven by a complex network of interconnected mechanisms that translate to a diverse range of complications at the physiological level. To optimally treat T2DM, pharmacological interventions should, ideally, target key nodes in this network that act as determinants of disease progression.Results: We set out to discover key nodes in molecular networks based on the hepatic transcriptome dataset from a preclinical study in obese LDLR-/- mice recently published by Radonjic et al. Here, we focus on comparing efficacy of anti-diabetic dietary (DLI) and two drug treatments, namely PPARA agonist fenofibrate and LXR agonist T0901317. By combining knowledge-based and data-driven networks with a random walks based algorithm, we extracted network signatures that link the DLI and two drug interventions to dyslipidemia-related disease parameters.Conclusions: This study identified specific and prioritized sets of key nodes in hepatic molecular networks underlying T2DM, uncovering pathways that are to be modulated by targeted T2DM drug interventions in order to modulate the complex disease phenotype.