A novel approach to investigate tissue-specific trinucleotide repeat instability

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.亨廷顿病（Huntington's disease, HD）中，扩增的CAG重复序列会引发特征性的纹状体神经退行性变。值得注意的是，决定HD发病年龄的CAG重复序列会发生组织特异性体细胞不稳定性，且以纹状体最为显著，这提示组织特异性的CAG长度改变可能会调控疾病进程。因此，阐明体细胞不稳定性组织特异性的潜在机制，有望为治疗提供全新方向。然而，该领域的研究进展长期受制于缺乏灵敏的高通量不稳定性定量方法，以及难以通过全局手段识别其潜在调控因子。本研究介绍了一种全新的研究策略，以解析调控体细胞不稳定性组织特异性的相关因子。本研究利用精准的HD基因敲入小鼠模型，建立了一种可靠的高通量组织HD CAG不稳定性定量方法，并将其与全基因组生物信息学分析手段相结合。研究以16种组织中定量得到的体细胞不稳定性作为表型，以组织微阵列基因表达数据作为预测因子，构建了数学模型，并鉴定出可精准预测组织不稳定性的基因表达特征。借助该特征的预测能力，本研究发现体细胞不稳定性并非疾病病理过程的产物。为验证这一结论，研究通过与加速神经病理进程的模型进行遗传杂交，未观察到体细胞不稳定性的诱导现象。此外，本研究还筛选了与组织不稳定性相关的基因及通路。研究发现，DNA修复基因的表达水平无法解释体细胞不稳定性的组织特异性。与之相反，本研究数据表明，细胞周期、代谢及神经递质通路等多条通路的协同作用，共同塑造了组织特异性的不稳定性模式。本研究明确证实，多种组织源性因子可反映不同组织中的体细胞不稳定性水平。此外，本研究建立的定量全基因组分析方法可轻松适配高通量实验，为其广泛应用奠定了基础，有望加速筛选可调控组织不稳定性的药物。实验所用样本为10周龄小鼠的纹状体与小脑，检测采用Affymetrix MG430 2.0基因芯片，数据分析使用gcRMA算法。