Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium.

BACKGROUND: For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. RESULTS: The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. CONCLUSIONS: In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances.

Transcriptomic responses generated by hepatocarcinogens in a battery of liver-based in vitro models.

As the conventional approach to assess the potential of a chemical to cause cancer in humans still includes the 2-year rodent carcinogenicity bioassay, development of alternative methodologies is needed. In the present study, the transcriptomics responses following exposure to genotoxic (GTX) and non-genotoxic (NGTX) hepatocarcinogens and non-carcinogens (NC) in five liver-based in vitro models, namely conventional and epigenetically stabilized cultures of primary rat hepatocytes, the human hepatoma-derived cell lines HepaRG and HepG2 and human embryonic stem cell-derived hepatocyte-like cells, are examined. For full characterization of the systems, several bioinformatics approaches are employed including gene-based, ConsensusPathDB-based and classification analysis. They provide convincingly similar outcomes, namely that upon exposure to carcinogens, the HepaRG generates a gene classifier (a gene classifier is defined as a selected set of characteristic gene signatures capable of distinguishing GTX, NGTX carcinogens and NC) able to discriminate the GTX carcinogens from the NGTX carcinogens and NC. The other in vitro models also yield cancer-relevant characteristic gene groups for the GTX exposure, but some genes are also deregulated by the NGTX carcinogens and NC. Irrespective of the tested in vitro model, the most uniformly expressed pathways following GTX exposure are the p53 and those that are subsequently induced. The NGTX carcinogens triggered no characteristic cancer-relevant gene profiles in all liver-based in vitro systems. In conclusion, liver-based in vitro models coupled with transcriptomics techniques, especially in the case when the HepaRG cell line is used, represent valuable tools for obtaining insight into the mechanism of action and identification of GTX carcinogens.

Development and application of a DNA microarray-based yeast two-hybrid system.

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein-protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms.

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.

Human embryonic stem cell derived hepatocyte-like cells as a tool for in vitro hazard assessment of chemical carcinogenicity.

Hepatocyte-like cells derived from the differentiation of human embryonic stem cells (hES-Hep) have potential to provide a human relevant in vitro test system in which to evaluate the carcinogenic hazard of chemicals. In this study, we have investigated this potential using a panel of 15 chemicals classified as noncarcinogens, genotoxic carcinogens, and nongenotoxic carcinogens and measured whole-genome transcriptome responses with gene expression microarrays. We applied an ANOVA model that identified 592 genes highly discriminative for the panel of chemicals. Supervised classification with these genes achieved a cross-validation accuracy of > 95%. Moreover, the expression of the response genes in hES-Hep was strongly correlated with that in human primary hepatocytes cultured in vitro. In order to infer mechanistic information on the consequences of chemical exposure in hES-Hep, we developed a computational method that measures the responses of biochemical pathways to the panel of treatments and showed that these responses were discriminative for the three toxicity classes and linked to carcinogenesis through p53, mitogen-activated protein kinases, and apoptosis pathway modules. It could further be shown that the discrimination of toxicity classes was improved when analyzing the microarray data at the pathway level. In summary, our results demonstrate, for the first time, the potential of human embryonic stem cell--derived hepatic cells as an in vitro model for hazard assessment of chemical carcinogenesis, although it should be noted that more compounds are needed to test the robustness of the assay.

A gene expression map of human chromosome 21 orthologues in the mouse.

The DNA sequence of human chromosome 21 (HSA21) has opened the route for a systematic molecular characterization of all of its genes. Trisomy 21 is associated with Down's syndrome, the most common genetic cause of mental retardation in humans. The phenotype includes various organ dysmorphies, stereotypic craniofacial anomalies and brain malformations. Molecular analysis of congenital aneuploidies poses a particular challenge because the aneuploid region contains many protein-coding genes whose function is unknown. One essential step towards understanding their function is to analyse mRNA expression patterns at key stages of organism development. Seminal works in flies, frogs and mice showed that genes whose expression is restricted spatially and/or temporally are often linked with specific ontogenic processes. Here we describe expression profiles of mouse orthologues to HSA21 genes by a combination of large-scale mRNA in situ hybridization at critical stages of embryonic and brain development and in silico (computed) mining of expressed sequence tags. This chromosome-scale expression annotation associates many of the genes tested with a potential biological role and suggests candidates for the pathogenesis of Down's syndrome.