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Human Molecular Genetics, 2002, Vol. 11, No. 17 1909-1910
© 2002 Oxford University Press
Microarrays and polyglutamine disorders: reports from the Hereditary Disease Array Group
Institute of Human Genetics, University of Minnesota, Minneapolis, MN 55455, USA
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Advances in molecular biology and genetics have allowed researchers to probe function and dysfunction at the level of the individual gene and/or protein. The integration of such information into an understanding of function has been a challenge facing all molecular geneticists and has contributed to the widening gap between molecular and systems scientists. In the neurosciences, this rift is especially dramatic. The ability to simultaneously monitor changes in the expression of thousands of genes using DNA microarrays has the potential to provide a global or ‘genomic’ view of brain function/dysfunction.
Two important barriers in the application of this technology to the study of neurological disease have been the availability of high-powered analytical approaches and appropriate genetic models. This issue of Human Molecular Genetics contains six contributions that approach these hurdles. These papers are centered on the application of DNA microarrays to survey alterations in gene expression associated with the expression of mutant polyglutamine proteins. A consortium headed by Dr Jim Olson of the Fred Hutchinson Cancer Research Center in Seattle and funded by the Hereditary Disease Foundation initiated this effort. Thus, this work is focused on the effects of expressing a mutant form of the Huntington disease (HD) gene product, huntingtin.
Three papers, two by Luthi-Carter and colleagues (1,2) and one from Chan and co-workers (3), examine polyglutamine-induced changes in gene expression using several mouse models of HD. In the Luthi-Carter et al. contributions, mice expressing a truncated form of mutant huntingtin are examined. The first paper provides data that, in R6/2 mice (the often designated Bates mouse), a considerable number of genes have an altered pattern of expression and this pattern shows little sign of regional specificity (1). These are consistent with the previous pathological data indicating that this mouse model of HD shows little sign of the regional-specificity of disease as seen in patients. The second paper from the Luthi-Carter group compares gene expression changes between mouse models of dentatorubral–pallidoluysian atrophy and HD (2). At least in the cerebella of these mice, there was a considerable overlap in the genes showing altered gene expression. Thus, polyglutamine-induced changes in gene expression can be identified that are independent of the protein context in which the glutamine tract is located. Perhaps such context-independent genes reflect pathways that are common among the different polyglutamine diseases. In contrast, however, the paper by Chan et al. reports that, even in different mouse models of HD, protein context does have an effect on polyglutamine-induced changes in gene expression (3). Mice expressing a truncated-version of mutant huntingtin had many more genes with altered expression than mice expressing a full-length mutant protein. The stage is now set to pursue a more targeted analysis of specific genes in a variety of mouse models expressing mutant forms of HD or other polyglutamine-containing proteins.
To focus on alterations at the primary cellular site of pathology in HD, Sipione et al. examined gene expression profiles in clonal striatal cells with an inducible expression of an N-terminal 548 amino fragment of huntingtin (4). At a timepoint preceding the formation of mutant protein aggregates and cell death, changes in genes encoding components of cell signaling, transcription, vesicle trafficking and lipid metabolism were detected. While most of these pathways have previously been implicated in HD, this is the first study to suggest that lipid metabolism has a role in HD pathology.
Shifting gears somewhat, the paper by Liebeman et al. used microarrays to study expression changes associated with another polyglutamine disorder, spinal and bulbar muscular dystrophy (SBMA) (5). These investigators found that in a tissue culture system, glutamine expansion in the androgen receptor promoted its degradation and decreased its ability to function as a transcription factor. This provides further evidence that a partial loss-of-function of mutant androgen receptor is a component of the disease process.
A critical challenge in designing and interpreting microarray gene expression datasets is the development of new analytical methods of data analysis. The final contribution from the consortium in this issue, by Xu et al. (6) introduces a new regression-based statistical approach for the detection of expression changes using a microarray time-course analysis. The hope is that this approach will prove to be a useful alternative means of analysis. Certainly, the initial application of this approach to the HD disease model microarray data indicates that this will be the case. This series of papers is complemented by two additional analytical papers from the Hereditary Disease Array Group, by Kooperberg et al. and Strand et al. (7,8), that address the vexing problem of estimating the statistical significance of gene expression differences in microarray experiments and prioritizing the ‘interesting’ genes worthy of potential follow-up. (These papers will be published in a subsequent issue.)
As a collection, these papers from the Hereditary Disease Array Group emphasize the equal and complementary importance of disease models and statistical approaches in the application of microarray technology to the study of HD (and, by extension, other diseases). Their microarray data indicate that there are substantial differences between the various models of HD. Thus, in comparing data between laboratories one will have to take into consideration which model was analyzed. In this sense they provide important insight to others attempting to apply this new technology to models of other human neurological diseases. At this time it is difficult to judge which of these models best replicates the human disease. Furthermore, this will likely be very hard to discern by micoarray data alone, given the enormous difficulty of getting similar time-course data from HD patients. A more likely outcome is that these data can be improved upon and distilled to allow investigators to identify a molecular pathway that proves to be central to the disease process. In that light, they mark just the next steps in a long journey to understand the molecular pathology and genetics of the polyglutamine disorders.
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* To whom correspondence should be addressed. Tel: 6126253647; Fax: 6126262600; Email: harry{at}lenti.med.umn.edu
Special Series Editor. |
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1 Luthi-Carter, R., Hanson, S.A., Strand, A.D., Bergstrom, D.A., Chun, W., Peters, N.L., Woods, A.M., Chan, E.Y., Kooperberg, C., Krainc, D. et al. (2002) Dysregulation of gene expression in the R6/2 model of polyglutamine disease: parallel changes in muscle and brain. Hum. Mol. Genet., 17, 1911–1926.
2 Luthi-Carter, R., Strand, A.D., Hanson, S.A., Kooperberg, C., Schilling, G., La Spada, A.R., Merry, D.E., Young, A.B., Ross, C.A., Borchelt, D.R. et al. (2002) Polyglutamine and transcription: gene expression changes shared by DRPLA and Huntington's disease mouse models reveal context-independent effects. Hum. Mol. Genet., 17, 1927–1937.
3 Chan, E.Y.W., Luthi-Carter, R., Strand, A.D., Solano, S.M., Hanson, S.A., DeJohn, M.M., Kooperberg, C., Chase, K.O., DiFiglia, M., Young, A.B. et al. (2002) Increased huntingtin protein length reduces the number of polyglutamine-induced gene expression changes in mouse models of Huntington disease. Hum. Mol. Genet., 17, 1939–1951.
4 Sipione, S., Rigamonti, D., Valenza, M., Zuccato, C., Conti, L., Pritchard, J., Kooperberg, C., Olson, J., Cattaneo, E. (2002) Early transcriptional profiles in huntingtin-inducible striatal cells by microarrary analyses. Hum. Mol. Genet., 17, 1953–1965.
5 Lieberman, A.P., Harmison, G., Strand, A.D., Olson, J.M., Fischbeck, K.H. (2002) Altered transcriptional regulation in cells expressing the expanded polyglutamine androgen receptor. Hum. Mol. Genet., 17, 1967–1976.
6 Xu, X.L., Olson, J.M., Zhao, L.P. (2002) A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model. Hum. Mol. Genet., 17, 1977–1985.
7 Strand, A.D., Olson, J.M., Kooperberg, C. (2002) Estimating the statistical significance of gene expression changes observed with oligonucleotide arrays. Hum. Mol. Genet., 17, in press.
8 Kooperberg, C., Sipione, S., LeBlanc, M., Strand, A.D., Cattaneo, E., Olson, J.M. (2002) Evaluating test-statistics to select interesting genes in microarrary experiments. Hum. Mol. Genet., 17, in press.
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