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To elucidate the time-resolved molecular events underlying the LV remodeling (LVR) process, we developed a large-scale network model that integrates the 24 molecular variables (plasma proteins and non-coding RNAs) collected in the REVE-2 study at four time points (baseline, 1month, 3months and 1year) after MI. The REVE-2 network model was built by extending the set of REVE-2 variables with their mechanistic context based on known molecular interactions (1310 nodes and 8639 edges). Changes in the molecular variables between the group of patients with high LVR (>20%) and low LVR (

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

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease's omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

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 Illumina whole-genome expression bead arrays are a widely used platform for transcriptomics. Most of the tools available for the analysis of the resulting data are not easily applicable by less experienced users. ArrayAnalysis.org provides researchers with an easy-to-use and comprehensive interface to the functionality of R and Bioconductor packages for microarray data analysis. As a modular open source project, it allows developers to contribute modules that provide support for additional types of data or extend workflows. Results To enable data analysis of Illumina bead arrays for a broad user community, we have developed a module for ArrayAnalysis.org that provides a free and user-friendly web interface for quality control and pre-processing for these arrays. This module can be used together with existing modules for statistical and pathway analysis to provide a full workflow for Illumina gene expression data analysis. The module accepts data exported from Illumina’s GenomeStudio, and provides the user with quality control plots and normalized data. The outputs are directly linked to the existing statistics module of ArrayAnalysis.org, but can also be downloaded for further downstream analysis in third-party tools. Conclusions The Illumina bead arrays analysis module is available at http://www.arrayanalysis.org. A user guide, a tutorial demonstrating the analysis of an example dataset, and R scripts are available. The module can be used as a starting point for statistical evaluation and pathway analysis provided on the website or to generate processed input data for a broad range of applications in life sciences research. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1689-8) 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.

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.

Scope: Olive products are rich in phenolic compounds, which are natural antioxidants in vitro. We tested the in vivo effects of alperujo, an olive production by-product, as well as hydroxytyrosol and 3,4-dihydroxyphenylglycol (DHPG) isolated from alperujo, on indices and pathways of oxidative and metabolic stress in a vitamin E-deficient rat model. Methods and results: Rats were fed a vitamin E-deficient diet for 10 weeks, followed by this diet supplemented with either 100 mg/kg diet dα-tocopherol, alperujo extract, hydroxytyrosol, or 10 mg/kg diet DHPG, for a further 2 weeks. We detected alperujo phenolics in tissues and blood, indicating they are bioavailable. Alperujo extract partially ameliorated elevated plasma levels of thiobarbituric acid reactive substances and also lowered plasma cholesterol levels, whereas hydroxytyrosol increased plasma triglyceride levels. Proteomics and subsequent network analysis revealed that hepatic mitochondrial aldehyde dehydrogenase (ALDH2), of which protein and activity levels were regulated by dα-tocopherol and olive phenolics, represents a novel central regulatory protein hub affected by the dietary interventions. Conclusion: The in vivo free radical scavenging properties of olive phenolics appear relatively modest in our model. But alternative mechanisms, including regulation of ALDH2, may represent relevant antioxidant mechanisms by which dietary olive phenolics could have beneficial impact on cardiovascular health. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Background Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset. Results We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar. Conclusions Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well.

Current high-throughput technology in genomics creates a large amount of biological data. Bioinformatics approaches are directed towards understanding such data on a systems biology level. Advanced mathematical methods like principal component analysis, clustering, neural networks, support vector machine (SVM) approaches and neural networks can help to find patterns in the data. However, to really understand the data the patterns must be combined with existing knowledge. One of the approaches to do so is to associate these data to functional classifications such as can be found in the Gene Ontology. Other methods focus on using biological pathways coming from both public and private pathway databases like KEGG, WikiPathways, Reactome, and MetaCore. Some of these pathway databases contain rather aspecific information about which genes are involved in reactions. A single identifier, like an Enzyme Code, may correspond to a full family of enzymes, whereas only one family member is responsible for the reaction in the given biological context. Another problem arises in the availability of relevant biological pathways for both rodents and humans. This is especially true in the field of toxicology, where many pathways are still lacking. The focus of this project was to create phase I and phase II detoxification pathways using PathVisio, the pathway editor of WikiPathways. New pathway content was generated using information obtained from several trusted resources: a) handbooks, b) manual/automated PubMed literature searches; c) online pathway resources; d) UniProt Knowledgebase; e) EnsEMBL; f) GeneCards. In addition, the content of some existing toxicology pathways was improved. To illustrate the usefulness of the new pathways data from experiments with carcinogenic compounds were retrieved from the main online microarray data repositories Gene Expression Omnibus (GEO) and ArrayExpress. The resulting data were visualized on the new pathways using PathVisio. All these pathways are made available to the community at WikiPathways, where they can be further used for statistical pathway analysis and visualization in the (toxico)genomics field.

Depot-dependent differences in adipose tissue physiology may reflect specialized functions and local interactions between adipocytes and surrounding tissues. We combined time-resolved microarray analyses of mesenteric- (MWAT), subcutaneous- (SWAT) and epididymal adipose tissue (EWAT) during high-fat feeding of male transgenic ApoE3Leiden mice with histology, targeted lipidomics and biochemical analyses of metabolic pathways to identify differentially regulated processes and site-specific functions. EWAT was found to exhibit physiological zonation. De novo lipogenesis in fat proximal to epididymis was stably low, whereas de novo lipogenesis distal to epididymis and at other locations was down-regulated in response to high-fat diet. The contents of linoleic acid and a-linolenic acid in EWAT were increased compared to other depots. Expression of the androgen receptor (Ar) was higher in EWAT than in MWAT and SWAT. We suggest that Ar may mediate depot-dependent differences in de novo lipogenesis rate and propose that accumulation of linoleic acid and alinolenic acid in EWAT is favored by testosterone-mediated inhibition of de novo lipogenesis and may promote further elongation and desaturation of these polyunsaturated fatty acids during spermatogenesis. © 2010 Caesar et al.

WikiPathways is a platform for creating, updating, and sharing biological pathways [1]. Pathways can be edited and downloaded using the wiki-style website. Here we present a SOAP web service that provides programmatic access to WikiPathways that is complementary to the website. We describe the functionality that this web service offers and discuss several use cases in detail. Exposing WikiPathways through a web service opens up new ways of utilizing pathway information and assisting the community curation process. © 2009 Kelder et al.

The exponential growth of diverse types of biological data presents the research community with an unprecedented challenge and opportunity. The challenge is to stay afloat in the flood of biological data, keeping it as accessible, up-to-date, and integrated as possible. The opportunity is to cultivate new models of data curation and exchange that take advantage of direct participation by a greater portion of the community. This combination of challenge and opportunity is especially relevant to the task of collecting biological pathway information. Pathways are critical to understanding the functions of individual genes and proteins in terms of systems and processes that contribute to normal physiology and to disease. Each biological pathway must be hewn from a mass of biological information distributed across multiple publications and databases. The particular challenge of pathway curation is amplified, because pathways are often presented as static images that are not amenable to computation, integration, or data exchange. Furthermore, pathway experts are distributed throughout the world, and most have limited time to learn about complex databases that need their expertise. This challenge can be met by taking the opportunity to develop a new community-based model for pathway curation. One way to engage the community is with a wiki model, as exemplified by Wikipedia [1]. We see the potential for a wiki-based pathway curation resource, coupled with an embedded graphical pathway editing tool, to meet the growing challenge presented by the influx of biological data and to provide an innovative example of content curation by the biology community (Figure 1). Figure 1 Two Models for Managing Biological Data

Material and Methods: C57BL/6 mice, fed a high-fat diet (HFD) for 24 weeks, were confirmed to result in NASH, and devided into four groups for additional 24 weeks treatment. HFD group was fed HFD for 48 weeks, UDCA group was fed with HFD for 24 weeks, and then HFD+UDCA at a dose of 20mg/(kg.d) via gavages for additional 24 weeks, PUFA group was fed with HFD for 24 weeks, and then HFD+PUFA at a dose of 70mg/(kg.d) via gavages for following 24 weeks, and UDCA+PUFA group was fed with HFD for 24 wk, followed by HFD+UDCA+PUFA for 24 weeks. At the end of the experiment, body weight, serum biochemical index, and hepatopathologic changes by CRN scoring system were examined. Results: Liver showed hypertrophic and yellowish color change and all mice developed steatohepatitis after 24-week-HFD. The mean CRN scores by their liver histology as follows: 2.9±0.3 in steatosis, 2.65±0.7 in lobular inflammation, 1.8±0.4 in ballooning and 1.9±0.3 in fibrosis. After additional 24 weeks of HFD, only fibrosis score was increased significantly to 2.4±0.3. Treatment with UDCA and/or PUFA reduced liver/body weight ratio. Compared with the HFD group, significant improvement of steatosis was noted only in the UDCA group. Lobular inflammation was improved significantly both in UDCA and UDCA+PUFA groups compared with the HFD group. However, significant improvement of fibrosis was seen only in the UDCA+PUFA group. Conclusion: UDCA might play a role in the improvement of steatosis and lobular inflammation. Co-treatment of UDCA and PUFA reduces inflammation and finally fibrosis. For clinical application for NASH, UDCA and PUFA co-administration can be beneficial to prevent progression into cirrhosis.

Background Biological pathways are a useful abstraction of biological concepts, and software tools to deal with pathway diagrams can help biological research. PathVisio is a new visualization tool for biological pathways that mimics the popular GenMAPP tool with a completely new Java implementation that allows better integration with other open source projects. The GenMAPP MAPP file format is replaced by GPML, a new XML file format that provides seamless exchange of graphical pathway information among multiple programs. Results PathVisio can be combined with other bioinformatics tools to open up three possible uses: visual compilation of biological knowledge, interpretation of high-throughput expression datasets, and computational augmentation of pathways with interaction information. PathVisio is open source software and available at http://www.pathvisio.org. Conclusion PathVisio is a graphical editor for biological pathways, with flexibility and ease of use as primary goals.

Diagrams and models of biological pathways are useful tools in biology. Pathway diagrams are mainly used for illustrative purposes for instance in textbooks and in presentations. Pathway models are used in the analysis of genomic data. Bridging the gap between diagrams and models allows not only the analysis of genomics data and interactions but also the visualisation of the results in a variety of different ways. The knowledge needed for pathway creation and curation is available from three distinct sources: databases, literature and experts. We describe the role of bioinformatics in facilitating the creation and curation of pathway.

Optimal health is maintained by interaction of multiple intrinsic and environmental factors at different levels of complexity-from molecular, to physiological, to social. Understanding and quantification of these interactions will aid design of successful health interventions. We introduce the reference network concept as a platform for multi-level exploration of biological relations relevant for metabolic health, by integration and mining of biological interactions derived from public resources and context-specific experimental data. A White Adipose Tissue Health Reference Network (WATRefNet) was constructed as a resource for discovery and prioritization of mechanism-based biomarkers for white adipose tissue (WAT) health status and the effect of food and drug compounds on WAT health status. The WATRefNet (6,797 nodes and 32,171 edges) is based on (1) experimental data obtained from 10 studies addressing different adiposity states, (2) seven public knowledge bases of molecular interactions, (3) expert's definitions of five physiologically relevant processes key to WAT health, namely WAT expandability, Oxidative capacity, Metabolic state, Oxidative stress and Tissue inflammation, and (4) a collection of relevant biomarkers of these processes identified by BIOCLAIMS (http://bioclaims.uib.es). The WATRefNet comprehends multiple layers of biological complexity as it contains various types of nodes and edges that represent different biological levels and interactions. We have validated the reference network by showing overrepresentation with anti-obesity drug targets, pathology-associated genes and differentially expressed genes from an external disease model dataset. The resulting network has been used to extract subnetworks specific to the above-mentioned expert-defined physiological processes. Each of these process-specific signatures represents a mechanistically supported composite biomarker for assessing and quantifying the effect of interventions on a physiological aspect that determines WAT health status. Following this principle, five anti-diabetic drug interventions and one diet intervention were scored for the match of their expression signature to the five biomarker signatures derived from the WATRefNet. This confirmed previous observations of successful intervention by dietary lifestyle and revealed WAT-specific effects of drug interventions. The WATRefNet represents a sustainable knowledge resource for extraction of relevant relationships such as mechanisms of action, nutrient intervention targets and biomarkers and for assessment of health effects for support of health claims made on food products., We thank Francesc Puiggros for his contribution to definition of relevant physiological processes determining WAT health status, critical assessment of the manuscript and support of collaboration logistics from CTNS partner institute. The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7 2007-2013 under grant agreement no 244995 (BIOCLAIMS Project).