The genopolis microarray database

Both Treg cells and Tconv cells are defective in the Myasthenia gravis thymus: roles of IL-17 and TNF-α

Myasthenia gravis (MG) is an autoimmune disease in which the thymus frequently presents follicular hyperplasia and signs of inflammation and T cells display a defect in suppressive regulation. Defects in a suppressive assay can indicate either the defective function of Treg cells or the resistance of Tconv cells to suppression by Treg cells. The aim of this study was to determine which cells were responsible for this defect and to address the mechanisms involved. We first performed cross-experiment studies using purified thymic Treg cells and Tconv cells from controls (CTRL) and MG patients. We confirmed that MG Treg cells were defective in suppressing CTRL Tconv proliferation, and we demonstrated for the first time that MG Tconv cells were resistant to Treg cell suppression. The activation of MG Tconv cells triggered a lower upregulation of FoxP3 and a higher upregulation of CD4 and CD25 than CTRL cells. To investigate the factors that could explain these differences, we analyzed the transcriptomes of purified thymic Treg and Tconv cells from MG patients in comparison to CTRL cells. Many of the pathways revealed by this analysis are involved in other autoimmune diseases, and T cells from MG patients exhibit a Th1/Th17/Tfh signature. An increase in IL-17-related genes was only observed in Treg cells, while increases in IFN-γ, IL-21, and TNF-α were observed in both Treg and Tconv cells. These results were confirmed by PCR studies. In addition, the role of TNF-α in the defect in Tconv cells from MG patients was further confirmed by functional studies. Altogether, our results indicate that the immunoregulatory defects observed in MG patients are caused by both Treg cell and Tconv cell impairment and involve several pro-inflammatory cytokines, with TNF-α playing a key role in this process. The chronic inflammation present in the thymus of MG patients could provide an explanation for the escape of thymic T cells from regulation in the MG thymus.

ProfileDB: a resource for proteomics and cross-omics biomarker discovery

The increasing size and complexity of high-throughput datasets pose a growing challenge for researchers. Often very different (cross-omics) techniques with individual data analysis pipelines are employed making a unified biomarker discovery strategy and a direct comparison of different experiments difficult and time consuming. Here we present the comprehensive web-based application ProfileDB. The application is designed to integrate data from different high-throughput 'omics' data types (Transcriptomics, Proteomics, Metabolomics) with clinical parameters and prior knowledge on pathways and ontologies. Beyond data storage, ProfileDB provides a set of dedicated tools for study inspection and data visualization. The user can gain insights into a complex experiment with just a few mouse clicks. We will demonstrate the application by presenting typical use cases for the identification of proteomics biomarkers. All presented analyses can be reproduced using the public ProfileDB web server. The ProfileDB application is available by standard browser (Firefox 18+, Internet Explorer Version 9+) technology via http://profileDB.-microdiscovery.de/ (login and pass-word: profileDB). The installation contains several public datasets including different cross-'omics' experiments. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

eXframe: reusable framework for storage, analysis and visualization of genomics experiments

Background

Genome-wide experiments are routinely conducted to measure gene expression, DNA-protein interactions and epigenetic status. Structured metadata for these experiments is imperative for a complete understanding of experimental conditions, to enable consistent data processing and to allow retrieval, comparison, and integration of experimental results. Even though several repositories have been developed for genomics data, only a few provide annotation of samples and assays using controlled vocabularies. Moreover, many of them are tailored for a single type of technology or measurement and do not support the integration of multiple data types.

Results

We have developed eXframe - a reusable web-based framework for genomics experiments that provides 1) the ability to publish structured data compliant with accepted standards 2) support for multiple data types including microarrays and next generation sequencing 3) query, analysis and visualization integration tools (enabled by consistent processing of the raw data and annotation of samples) and is available as open-source software. We present two case studies where this software is currently being used to build repositories of genomics experiments - one contains data from hematopoietic stem cells and another from Parkinson's disease patients.

Conclusion

The web-based framework eXframe offers structured annotation of experiments as well as uniform processing and storage of molecular data from microarray and next generation sequencing platforms. The framework allows users to query and integrate information across species, technologies, measurement types and experimental conditions. Our framework is reusable and freely modifiable - other groups or institutions can deploy their own custom web-based repositories based on this software. It is interoperable with the most important data formats in this domain. We hope that other groups will not only use eXframe, but also contribute their own useful modifications.

Knowledge sharing and collaboration in translational research, and the DC-THERA Directory

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of ‘big data’ research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European ‘Network of Excellence’ (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science.

Towards large-scale sample annotation in gene expression repositories

Background

Large repositories of biomedical research data are most useful to translational researchers if their data can be aggregated for efficient queries and analyses. However, inconsistent or non-existent annotations describing important sample details such as name of tissue or cell line, histopathological type, and subject characteristics like demographics, treatment, and survival are seldom present in data repositories, making it difficult to aggregate data.

Results

We created a flexible software tool that allows efficient annotation of samples using a controlled vocabulary, and report on its use for the annotation of over 12,500 samples.

Conclusion

While the amount of data is very large and seemingly poorly annotated, a lot of information is still within reach. Consistent tool-based re-annotation enables many new possibilities for large scale interpretation and analyses that would otherwise be impossible.

From transcriptome analysis to immunogenomics: current status and future direction

In 1994, we pioneered a complementary DNA (cDNA) sequencing project that aimed to predict the primary structures of unknown human proteins. Although our cDNA project was focused on the sequencing of large cDNAs, the following cDNA sequencing projects conducted by other groups have more extensively characterized mammalian transcriptome. In parallel, many groups have made a tremendous amount of effort to develop various resources for functional human genomics. In this context, to demonstrate the power of functional genomic approaches in practice, we have applied them for a comprehensive understanding of the immune system, which we term 'immunogenomics'. This mini-review first describes the historical background of our cDNA project and then provides perspectives on the present and future of immunogenomics based on our experiences.

The Immunological Genome Project: networks of gene expression in immune cells

The Immunological Genome Project combines immunology and computational biology laboratories in an effort to establish a complete 'road map' of gene-expression and regulatory networks in all immune cells.

MiMiR--an integrated platform for microarray data sharing, mining and analysis

Background

Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data.

Results

A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package.

Conclusion

The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies.