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Open Access Research article

A novel approach to investigate tissue-specific trinucleotide repeat instability

Jong-Min Lee1*, Jie Zhang2, Andrew I Su2, John R Walker2, Tim Wiltshire2, Kihwa Kang36, Ella Dragileva1, Tammy Gillis1, Edith T Lopez1, Marie-Josee Boily4, Michel Cyr4, Isaac Kohane5, James F Gusella1, Marcy E MacDonald1 and Vanessa C Wheeler1*

Author affiliations

1 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA

2 The Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA

3 Department of Genetics and Complex Diseases, Harvard School of Public Health, Boston, MA, USA

4 Neuroscience Research Group, University of Quebec at Trois-Rivieres, Trois-Rivieres, Quebec, Canada

5 Children's Hospital Informatics program, Children's Hospital, Boston, MA, USA

6 Current address: Obesity and Metabolic Diseases, Regeneron Pharmaceutical, Inc., Tarrytown, NY, USA

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Citation and License

BMC Systems Biology 2010, 4:29  doi:10.1186/1752-0509-4-29

Published: 19 March 2010

Abstract

Background

In Huntington's disease (HD), an expanded CAG repeat produces characteristic striatal neurodegeneration. Interestingly, the HD CAG repeat, whose length determines age at onset, undergoes tissue-specific somatic instability, predominant in the striatum, suggesting that tissue-specific CAG length changes could modify the disease process. Therefore, understanding the mechanisms underlying the tissue specificity of somatic instability may provide novel routes to therapies. However progress in this area has been hampered by the lack of sensitive high-throughput instability quantification methods and global approaches to identify the underlying factors.

Results

Here we describe a novel approach to gain insight into the factors responsible for the tissue specificity of somatic instability. Using accurate genetic knock-in mouse models of HD, we developed a reliable, high-throughput method to quantify tissue HD CAG repeat instability and integrated this with genome-wide bioinformatic approaches. Using tissue instability quantified in 16 tissues as a phenotype and tissue microarray gene expression as a predictor, we built a mathematical model and identified a gene expression signature that accurately predicted tissue instability. Using the predictive ability of this signature we found that somatic instability was not a consequence of pathogenesis. In support of this, genetic crosses with models of accelerated neuropathology failed to induce somatic instability. In addition, we searched for genes and pathways that correlated with tissue instability. We found that expression levels of DNA repair genes did not explain the tissue specificity of somatic instability. Instead, our data implicate other pathways, particularly cell cycle, metabolism and neurotransmitter pathways, acting in combination to generate tissue-specific patterns of instability.

Conclusion

Our study clearly demonstrates that multiple tissue factors reflect the level of somatic instability in different tissues. In addition, our quantitative, genome-wide approach is readily applicable to high-throughput assays and opens the door to widespread applications with the potential to accelerate the discovery of drugs that alter tissue instability.