A systems-level and epigenetic gene control approach to Huntington's Disease

Christiaan Henkel, Xiao-Xian Yang, Timo M. Breit & Pernette J. Verschure

Integrative Bioinformatics Unit & Nuclear organization group
Swammerdam Institute for Life Sciences & Informatics Institute
Universiteit van Amsterdam

Huntington's disease (HD) is a well known model for autosomal dominant neurogenetic disorders. However, the molecular mechanisms underlying the multifaceted character of the disease are still poorly understood. We have initiated two related lines of HD research following a combined systems level approach with two perspectives:

• an experiment-based focus on epigenetic gene control to understand cellular and molecular mechanisms underlying HD;
• a dry lab focus on integration of experimental data from disparate sources into a comprehensive HD knowledge base.

There is an iterative interaction between these efforts: experimental data will be fed into the growing knowledge model, which will in turn supply novel hypotheses for laboratory validation. We hope understanding of the complex mechanisms underlying HD will benefit from this multi-disciplinary approach.

Disregulation of gene expression in Huntington's disease by epigenetic changes in chromatin structure

Gene expression control is crucial to maintain differentiated cell types in a multi-cellular organism, whereas aberrant gene regulation can lead to pathological situations. Packaging of genomic DNA into higher order chromatin structures is a basic epigenetic mechanism to achieve proper control of critical cellular processes. The linear genome is partitioned into distinct functional chromatin domains that help to assure independent regulation of such domains. Posttranslational modifications of histones (i.e. acetylation, methylation, phosphorylation and ubiquitination) are critical determinants of functional changes in chromatin structure over large distances. Our research focuses on the functional organization of chromosomes and chromatin in the mammalian interphase cell nucleus.

We investigate whether triplet repeat expansions as observed in HD are involved in altering gene expression via epigenetic gene control mechanisms, i.e. changes in chromatin structure. We will start using cells of a rat model, carrying varying glutamine repeat sizes that are well characterized and correlate with disease progression. In addition, we will use lymphoblastoid cell lines derived from HD patients. We will study chromatin structure in these cells using chromatin immunoprecipitation analysis, nuclease accessibility analysis and Western (protein) blotting. Moreover, we will interfere with the chromatin organization by using several drugs that specifically inhibit histone deacetylation or DNA methylation. In parallel, we analyze the overall nuclear structure of these cell lines using immunofluorescent labelingand state-of-the-art microscopy techniques.

Towards a knowledge base for Huntington's disease

The amounts of experimental data, information, and knowledge on HD and related phenomena are vast. However, these resources differ greatly in scope, format, availability and detail (literature, omics data, and so on), which prevents straightforward interpretation of the relationships in such data. To overcome this problem, we will employ a novel systems-based approach in which we will focus on creating an HD knowledge base.

This knowledge base will consist of two parts, a resource identification (RI) model and a knowledge model. The first component, the HD-RI model, encompasses potentially all resources of HD related data, information, and knowledge. It will consist of a generic resource identification model for life sciences, instantiated for HD with the help of domain experts. The second part is a knowledge model in which the HD associated data, information and knowledge themselves are formalized, together with their relationships. The HD knowledge model can be used for computational experimentation, such as mining for as yet undiscovered relationships, and machine-reasoning to generate novel hypotheses.

All parts will be implemented in a problem-solving environment of the Virtual Lab for e-Science. This environment is enabled by emerging ICT technologies, including grid computing and semantic web technology. Domain interaction will take place in a physical e-BioLab, which is currently under construction. As such, the HD model will serve as a case study in the development of generic methodology for integrative bioinformatics and e-bioscience.

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