In the project EpiGeniusHD we investigate the role of chromatin and epigenetics markers in Huntington’s Disease, particularly the deregulated gene expression patterns of Huntington’s Disease.

The project was made possible by the Netherlands Bioinformatics Centre and the Human Genetics Department of the Leiden University Medical Center.


Epigenetic phenomena such as DNA methylation, histone modifications, and chromatin structure influence phenotype and gene expression. Epigenetic changes can cause long term effects on health and may have a pivotal role in disease etiology. We are only beginning to understand the molecular basis of these effects, for which we need to reconcile specific hypotheses about biomolecular mechanisms and large amounts of data and knowledge that is available through a highly heterogeneous set of resources. A representative example is Huntington’s Disease (HD). HD is principally caused by a straightforward genetic aberration (a CAG repeat expansion in the HD gene), but the downstream mechanisms are still poorly understood and no cure is available yet. There is preliminary evidence, generated in our laboratories and others, that epigenetic mechanisms play an important role. New hypotheses need to be formulated that integrate knowledge beyond a single domain of expertise. We can support this process computationally by combining several technologies following an e-Science approach that helps exploiting multi-disciplinary expertise. In a cycle that emphasizes continuous communication between biology and technology experts, we employ state-of-the-art text mining and data integration in the form of repeatable workflows whilst incrementally building a knowledge base of structured, machine-readable knowledge that links to other knowledge resources across the web. Semantic Web tools allow us to search for novel relations across experiments, our own and those of others. By composing the epiGenius ‘e-Laboratory’ we leverage our technological advancements for biological experts and stimulate collaboration between scientists with different backgrounds (computational scientists, bench biologists, medical doctors). We anticipate that this approach will result in breakthroughs in the elucidation of epigenetic mechanisms in the context of HD and beyond, which in turn can lead to recommendations for medical research.


Epigenetic phenomena are a key factor in orchestrating the gene expression patterns that determine cellular identity. Chemical modifications of nucleotides and DNA-binding proteins, especially histones, form a yet poorly understood epigenetic ‘code’ that is heritable through mitosis and via germ cells. Aberrations in this code can have a substantial influence on health [1, 2]. External factors may cause these aberrations. For example, some nutrients have an effect on epigenetic gene control that appear to affect aging, brain development, obesity, forms of cancer [3-5], and even susceptibility to adult disease in utero [6, 7]. Epigenetic changes possibly underlie the aberrant patterns of transcriptional activity that are observed in many diseases. A good example is Huntington’s Disease (HD). The genetic cause of this disease, an expansion of CAG repeats in the gene for Huntingtin, is clearly identified, but the downstream molecular mechanisms leading to the HD phenotype are still poorly understood. Changes in mRNA levels for various proteins and receptors are visible in early grade HD brains before any recognizable neuropathology [8-10] and are associated with neuronal dysfunction prior to neuronal death [11], while the pattern of transcriptional pathology in the different brain regions agrees with the pattern of neurodegeneration [12]. The relation between gene expression and HD pathology was confirmed in animal models [13, 14]. New hypotheses that take epigenetic mechanisms into account may explain these observations more comprehensibly. For instance, mutant huntingtin may cause pathologic changes by interacting with transcription factors [15-17] and histones H3 and H4 [18] or by directly interfering with DNA; there is evidence that huntingtin affects DNA conformation and transcription factor binding by occupying gene promoters in vivo in a polyglutamine-dependent manner [19]. At the same time, ensuring that all potentially relevant facts in literature and databases are considered when conceiving these hypotheses has become extremely difficult. Looking at literature (PubMed) alone, we find that the number of publications that mention ‘epigenetics’ has grown from its first mention in 1964 to over 30000 [20], a conservative estimate considering that many key epigenetic components were previously studied in different contexts (e.g. ‘Histone deacetylase’ without ‘epigenetics’ adds another 6000 publications). It is clear that new technologies for systematic data analysis plus mechanisms that support multidisciplinary collaboration are desired to bridge between hypothesis and data. A number of developments in e-science (see Box 1 for a glossary) aim to provide such support and are ready to be tested for challenging applications in life science. (i) Biological assertions in text can be extracted automatically from the large volume of biomedical literature by matching terms to predefined terminologies [21, 22], or by machine learning techniques [20, 23, 24]. Additional relations can be predicted by statistically comparing ‘concept profiles’ (Box 1 & [25-27]). We recently extended the predictive power of this method by statistically incorporating data sources other than literature (Haagen et al., manuscript in preparation). (ii) The Linked Data movement (http://linkeddata.org; [28]) and the Concept Web Alliance help to create a machine readable semantic web of data, following the same principles that created the human-readable world wide web [29, 30]. This enhances our potential to investigate hypotheses [31-33]. (iii) Semantic models or Ontologies stored using the Resource Description Framework (RDF; [34]) and the Web Ontology Language (OWL; [35]) represent knowledge in a form that can be used for the digital conservation of bioinformatics methods and results, and machine inference across the web [36, 37]. RDF is the model of choice for Linked Data. (iv) Workflow systems offer a platform for the design of computational experiments that run services created by diverse experts across the Internet [38] or a grid [39, 40]. A workflow can be seen as a digital analogue of a wet laboratory protocol. This approach has been used for data integration [41, 42], systematic analysis of micro-array data [43], and text mining supported by Semantic Web tools [24]. The workflow tool Taverna is being extended to produce Semantically Linked Data directly, among others for epiGeniusHD (matching activity). (v) Community web sites such as myExperiment.org [44], and BioCatalogue.org [45] support the social aspect of scientific collaboration [46-48]. This is extended by ‘e-laboratories’ that leverage e-Science technologies for specific communities of domain users [46]. Apart from developing technology that supports collaboration, we have also gained experience in social mechanisms that support multidisciplinary collaboration. Inspired by the success of an open and agile approach, including the early involvement of ‘power-users’, epiGeniusHD also emphasizes communication and short feedback cycles between selected epigenetics/HD experts, computer scientists, and software engineers. We expect that the application of this approach and the aforementioned technologies will enhance the way we conceive hypotheses and lead to breakthroughs in our understanding of the role of epigenetics in HD.


The main aim of our research is to enhance the process of conceiving and testing hypotheses about the role of epigenetic mechanisms in HD pathology by an e-science approach.

Key objectives:

  • to explore e-Science tools and a collaborative e-Science research cycle for a real-life application in life science that will benefit from bridging between biologists and technologists, and hypothesis and data.
  • to extend our understanding of epigenetics mechanisms in HD by applying a combination of workflow and semantic technologies for (i) text mining and data integration experiments, (ii) ‘post-hoc’ analysis of semantically linked data across experiments, (iii) leveraging workflows and semantically linked data in an e-laboratory for the epigenetics/HD community.
  • to develop an approach for bridging between specific mechanistic hypotheses and the results from data driven analysis by placing hypothesis and mining results in one semantic framework.
  • to learn how to extract new information from linked data to find for instance
    • novel epigenetic factors that explain HD pathology
    • new evidence for hypotheses that suggest a role for epigenetics in HD
    • more specific drug targets than for instance HDAC inhibitors


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