Exploring the role of epigenetic mechanisms in the manifestation of Huntington's disease

亨廷顿病(HD)是一种神经退行性疾病，通常由亨廷顿(HTT)基因中DNA代码“CAG”的40个或更多重复扩大引起。这种疾病的特点是运动障碍、认知障碍和精神症状，目前还没有改善疾病的治疗方法。CAG重复序列的大小与症状出现的年龄密切相关，重复序列数量高的人在年轻时就会患上这种疾病。然而，CAG重复长度相同的个体之间出现症状的年龄是不同的。众所周知，基因的表达不仅依赖于一个人特定的DNA代码(他们的基因组)，而且还可以被称为“表观基因组”的额外水平的信息所改变。表观遗传过程是添加到DNA或组蛋白蛋白上的化学标签，可以打开和关闭基因，并可以受到外部因素的影响。我们最近在阿尔茨海默病(AD)的两个表观遗传标记(DNA甲基化(DNaM)和H3K27ac)中显示出强烈的变化。我们还在HD大脑的一项初步研究中看到了dNaM的差异。我们假设表观遗传机制有助于HD的表现，并计划使用最先进的基因组技术和计算方法来进行迄今为止最全面的HD脑表观遗传机制研究。我们有以下免费的工作包：WP1：(EPI)使用长读测序确定HTT的遗传特征我们将使用尖端的长读测序技术来测量HTT基因该区域的CAG重复长度和dNaM的程度。这将使我们能够准确地确定dNaM在HD大脑样本的CAG重复中的位置。我们计划研究两个大脑区域：纹状体和前额叶皮质，这两个区域在疾病的不同阶段受到影响。据报道，某些细胞的CAG重复长度会随着年龄的增长而增加，这被称为体细胞嵌合体。使用长读测序的一个优点是我们可以测量同一分子上的CAG重复长度和dNaM，这将使我们能够在我们的样本中探索这一现象。WP2：通过EWAS破译HD的表观遗传格局我们将对HD大脑样本的表观遗传变异进行第一次基因组规模的评估，分析dNaM、H3K27ac、染色质可及性和纹状体和前额皮质的遗传变异。我们将调查是否在HD的特定基因区域看到dNaM、H3K27ac或开放染色质区域。由于我们已经在其他神经退行性疾病中生成了类似的数据集，因此我们可以检查我们在HD中识别的表观遗传学变化是否与例如AD有任何重叠。使用综合计算方法，我们将探索不同层次的表观遗传信息之间的关系。通过整合遗传数据，我们可以确定数量性状基因座(QTL)，其中遗传差异改变了表观遗传标记，然后调查这些QTL是否富含我们已知的HD和其他相关疾病的基因。WP3：使用FANS确定基因座的细胞特异性我们将使用荧光激活核分类(FANS)从前额叶皮质不同类型的细胞中分离出细胞核，包括已知在疾病中受到影响的抑制性(GABA)和兴奋性(谷氨酸)神经元，以及小胶质细胞、少突胶质细胞和星形胶质细胞等神经胶质细胞。我们将确定哪些细胞类型是导致我们在早期WP中观察到的表观遗传学变化的原因，这些细胞类型包括重度病理(N=20)、中度病理(N=20)或无病理(N=20)WP4：HDT转录本异构体多样性的特征。我们将使用长读测序来测量HTT基因和我们已识别的其他基因的表达。这项技术的优势是我们可以识别完全新的亚型，这是我们以前在AD中所做的。

Huntington's disease (HD) is a neurodegenerative disease caused by an expansion of typically 40 or more repeats of the DNA code "CAG" in the Huntingtin (HTT) gene. The disease is characterised by movement disturbances, cognitive impairments, and psychiatric symptoms and there is currently no disease-modifying treatment. The size of the CAG repeat is closely associated with the age of symptom onset, with individuals with high numbers of repeats developing the disease at a young age. However, there is variation in the age of symptom onset between individuals with the same CAG repeat length. It is known that the expression of genes relies not only on a person's specific DNA code (their genome) but can also be altered by an extra level of information called the "epigenome". Epigenetic processes are chemical tags added to the DNA or histone proteins that turn genes on and off and can be influenced by external factors. We have recently shown robust alterations in two epigenetic marks (DNA methylation (DNAm) and H3K27ac) in Alzheimer's disease (AD). We have also seen DNAm differences in a pilot study of HD brain. We hypothesise that epigenetic mechanisms contribute to the manifestation of HD and plan to use state-of-the art genomic technology and computational approaches to undertake the most comprehensive study of epigenetic mechanisms in HD brain to date. We have the following complimentary work-packages:WP1: (EPI)GENETIC CHARACTERISATION OF HTT USING LONG-READ SEQUENCINGWe will use cutting-edge long-read sequencing technology to measure the length of the CAG repeat and extent of DNAm across that region of the HTT gene. This will allow us to determine exactly where DNAm is seen in the CAG repeat in HD brain samples. We plan to study two brain regions: the striatum and prefrontal cortex, which are affected at different stages of the disease. It is reported that the CAG repeat length can increase in some cells with age, which is termed somatic mosaicism. One advantage of using long-read sequencing is that we can measure both the CAG repeat length and DNAm on the same molecule, which will allow us to explore this phenomenon in our samples.WP2: DECIPHERING THE EPIGENETIC LANDSCAPE IN HD VIA EWASWe will perform the first genome-scale assessment of epigenetic variation in HD brain samples, profiling DNAm, H3K27ac, chromatin accessibility and genetic variation in the striatum and prefrontal cortex. We will investigate whether DNAm, H3K27ac or areas of open chromatin are seen at specific gene regions in HD. As we have generated similar datasets in other neurodegenerative diseases, we can then examine whether there is any overlap in the epigenetic changes we identify in HD to, for example, AD. Using integrative computational approaches, we will explore the relationship between different layers of epigenetic information. By integrating genetic data, we can identify quantitative trait loci (QTLs), where genetic differences alter the epigenetic marks, and then investigate if these QTLs are enriched in genes we know are affected in HD, and other related disorders. WP3: DETERMINING THE CELLULAR SPECIFICITY OF LOCI USING FANSWe will isolate nuclei from different cell types in the prefrontal cortex using fluorescence-activated nuclei sorting (FANS), including inhibitory (GABA) and excitatory (glutamatergic) neurons that are known to be affected in disease, as well as glial cells such as microglia, oligodendrocytes and astrocytes. We will determine which cell types are responsible for the epigenetic changes we observed in the earlier WP in a subset of the cohort with severe pathology (N=20), moderate pathology (N=20) or no pathology (N=20)WP4: CHARACTERISATION OF HTT TRANSCRIPT ISOFORM DIVERSITY IN HDWe will use long-read sequencing to measure expression of the HTT gene, and other genes we have identified. The advantage of this technology is that we can identify completely novel isoforms, which we have previously done in AD.