Project description: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. 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 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. 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. Mouse striatum and cerebellum, 10 weeks old, Affymetrix MG430 2.0 arrays, gcRMA.

Project description: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. 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 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. 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. Mouse striatum and cerebellum, 5 months old, Affymetrix MG430 2.0 arrays, gcRMA.

Project description:In Huntington’s disease (HD), expanded HTT CAG repeat length correlates strongly with age at motor onset, indicating that it determines the rate of the disease process leading to diagnostic clinical manifestations. Similarly, in normal individuals, HTT CAG repeat length is correlated with biochemical differences that reveal it as a functional polymorphism. Here, we tested the hypothesis that gene expression signatures can capture continuous, length-dependent effects of the HTT CAG repeat. Using gene expression datasets for 107 HD and control lymphoblastoid cell lines, we constructed mathematical models in an iterative manner, based upon CAG correlated gene expression patterns in randomly chosen training samples, and tested their predictive power in test samples. Predicted CAG repeat lengths were significantly correlated with experimentally determined CAG repeat lengths, whereas models based upon randomly permuted CAGs were not at all predictive. Predictions from different batches of mRNA for the same cell lines were significantly correlated, implying that CAG length-correlated gene expression is reproducible. Notably, HTT expression was not itself correlated with HTT CAG repeat length. Taken together, these findings confirm the concept of a gene expression signature representing the continuous effect of HTT CAG length and not primarily dependent on the level of huntingtin expression. Such global and unbiased approaches, applied to additional cell types and tissues, may facilitate the discovery of therapies for HD by providing a comprehensive view of molecular changes triggered by HTT CAG repeat length for use in screening for and testing compounds that reverse effects of the HTT CAG expansion. To evaluate the continuous analytical approach as a strategy to discover the molecular consequences of the HTT CAG repeat, genome-wide gene expression datasets were generated from a panel of 107 human lymphoblastoid cell lines with HTT CAGs spanning the entire spectrum of allele sizes.

Dataset Information

Dense time series gene expression data from striatum of Huntington’s disease CAG knock-in mice across multiple genetic backgrounds

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure