DIPSBC--data integration platform for systems biology collaborations

BACKGROUND: Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis. RESULTS: We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de. CONCLUSIONS: DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use

A systems biology approach to deciphering the etiology of steatosis employing patient-derived dermal fibroblasts and iPS cells.

Nonalcoholic fatty liver disease (NAFLD) comprises a broad spectrum of disease states ranging from simple steatosis to nonalcoholic steatohepatitis (NASH). As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network. Increase in sugar or refined carbohydrate consumption results in an increase of insulin and insulin resistance that can lead to the accumulation of fat in the liver. Here we demonstrate how a multidisciplinary approach encompassing cellular reprogramming, transcriptomics, proteomics, metabolomics, modeling, network reconstruction and data management can be employed to unveil the mechanisms underlying the progression of steatosis. Proteomics revealed reduced AKT/mTOR signaling in fibroblasts derived from steatosis patients and further establishes that the insulin-resistant phenotype is present not only in insulin-metabolizing central organs, e.g. the liver, but is also manifested in skin fibroblasts. Transcriptome data enabled the generation of a regulatory network based on the transcription factor SREBF1, linked to a metabolic network of glycerolipid and fatty acid biosynthesis including the downstream transcriptional targets of SREBF1 which include LIPIN1 (LPIN) and low density lipoprotein receptor (LDLR). Glutathione metabolism was among the pathways enriched in steatosis patients in comparison to healthy controls. By using a model of the glutathione pathway we predict a significant increase in the flux through glutathione synthesis as both gamma-glutamylcysteine synthetase and glutathione synthetase have an increased flux. We anticipate that a larger sample of patients and matching controls will confirm our preliminary findings presented here

Nutritional Systems Biology of Fat : integration and modeling of transcriptomics datasets related to lipid homeostasis

Fatty acids, in the form of triglycerides, are the main constituent of the class of dietary lipids. They not only serve as a source of energy but can also act as potent regulators of gene transcription. It is well accepted that an energy rich diet characterized by high intakes of dietary fat is linked to the dramatic increase in the prevalence of obesity in both developed and developing countries in the last several decades. Obese individuals are at increased risk of developing the metabolic syndrome, a cluster of metabolic abnormalities that ultimately increase the risk of developing vascular diseases and type 2 diabetes. Many studies have been performed to uncover the role of fatty acids on gene expression in different organs, but integrative studies in different organs over time driven by high throughput data are lacking. Therefore, we first aimed to develop integrative approaches on the level of individual genes but also pathways using genome-wide transcriptomics datasets of mouse liver and small intestine that are related to fatty acid sensing transcription factor peroxisome proliferator activated receptor alpha (PPARα). We also aimed to uncover the behavior of PPARαtarget genes and their corresponding biological functions in a short time series experiment, and integrated and modeled the influence of different levels of dietary fat and the time dependency on transcriptomics datasets obtained from several organs by developing system level approaches. We developed an integrative statistical approach that properly adjusted for multiple testing while integrating data from two experiments, and was driven by biological inference. By quantifying pathway activities in different mouse tissues over time and subsequent integration by partial least squares path model, we found that the induced pathways at early time points are the main drivers for the induced pathways at late time points. In addition, using a time course microarray study of rat hepatocytes, we found that most of the PPARα target genes at early stage are involved in lipid metabolism-related processes and their expression level could be modeled using a quadratic regression function. In this study, we also found that the transcription factorsNR2F, CREB, EREF and RXR might work together with PPARα in the regulation of genes involved in lipid metabolism. By integrating time and dose dependent gene expression data of mouse liver and white adipose tissue (WAT), we found a set of time-dose dependent genes in liver and WAT including potential signaling proteinssecreted from WAT that may induce metabolic changes in liver, thereby contributing to the pathogenesis of obesity. Taken together, in this thesis integrative statistical approaches are presented that were applied to a variety of datasets related to metabolism of fatty acids. Results that were obtained provide a better understanding of the function of the fatty acid-sensor PPARa, and identified a set of secreted proteins that may be important for organ cross talk during the development of diet induced obesity. </p

DIPSBC - data integration platform for systems biology collaborations

Abstract Background Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis. Results We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de. Conclusions DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.</p