Constructing A Transcriptomic Atlas of Retrotransposon in Alzheimer's Disease

项目综述AD患者的数量每年都在逐渐增加，而经济负担 AD患者的医疗保健在2021年估计为3350亿美元，预计到2050年将增加两倍。为了公众的利益 健康和经济，了解AD遗传学和找到有效的AD预防和治疗 很重要。大量研究表明，阿尔茨海默病是一种复杂的遗传性疾病，通常与基因组有关 结构变化和监管。因此，不仅需要研究正常的基因、蛋白质， 以及他们的规定，还有AD中的其他遗传成分。反转录转座子(RTES)是DNA 自我复制并将其拷贝插入基因组的序列。已经有一些人对 反转录转座子在AD研究中的应用。例如，已知Tau介导的染色质松弛 蛋白质的积累可能会过度激活反转录转座子。这种巨大的激活可能会引发一种与生俱来的 免疫反应和破坏基因组，这可能导致神经退化。此外，一项研究表明， 抗病毒药物可以通过抑制其逆转录酶来抑制AD患者RTES的激活，而 抑制会导致神经退化的预防。这些研究表明，调查 反转录转座子在AD中的特征和作用将为我们提供另一种重要的方式来理解 RTES在AD发病机制中的调控。然而，阿尔茨海默病中RTE的分子特征，如细胞 类型/性别特异性，目前仍不清楚。表征RTE需要生成大规模的RTE表达式 数据集。这样的数据还没有公开，尽管AD研究社区已经做出了巨大的贡献 努力生成大规模死后AD转录组数据，包括约2,000名受试者的批量RNA序列 以及约260,000个细胞的单细胞核RNA序列。因此，我们提出了两个具体目标来实现第一个目标 通过构建AD研究的RTE图集资源对RTE进行系统研究：目的1.生成大规模的 通过挖掘和处理公共AD转录组数据集来获取RTE表达数据集。我们将延长我们的 用SalmonTE算法从AD转录组数据集中挖掘组织水平和单个组织水平的RTE表达 单元格分辨率。目的2.通过使用RTES和AD患者的特征来生成AD RTE图谱资源 统计和机器学习方法。我们将扩展我们的内部计算方法，以计算 人类AD大脑中每个RTE的上下文特异性(例如，大脑区域、细胞类型和性别)。我们还将发展 使用RTE表达和多组学数据的无监督图形神经网络来表征AD患者。 最后，我们将创建一个地图集网站，与AD研究社区分享我们的发现。成功 该项目的完成将提供1)新的计算方法来严格表征AD中的RTE，2) 识别AD中特定于上下文的RTE，并使用RTE表达来表征AD患者，以及3) 一份注释良好的AD RTE图集，加深我们对AD分子基础的了解。

Project Summary The number of AD patients is gradually increasing every year, and the economic burden of health care of AD patients, estimated at $335 billion in 2021, is predicted to triple by 2050. In the interest of public health and the economy, understanding AD genetics and finding effective AD prevention and treatment are important. Numerous studies have suggested that AD is a complicated genetic disorder, often involving genomic structural changes and regulation. Thus, there is a strong need to investigate not only regular genes, proteins, and their regulations but also the other genetic components in AD. Retrotransposons (RTEs) are DNA sequences that copy themselves and insert their copies into the genome. There has been some interest in studying retrotransposons in AD research. For example, it is known that chromatin relaxation mediated by Tau protein accumulation may overly activate the retrotransposons. This massive activation may provoke an innate immune response and damage the genome, which can result in neurodegeneration. Moreover, a study showed that antiviral drugs could suppress activation of RTEs in AD by inhibiting their reverse transcriptase, and the suppression results in the prevention of neurodegeneration. These studies suggested that investigating the features and roles of retrotransposons in AD will provide an additional and important way to understand the regulations of RTEs in AD pathogenesis. However, molecular characteristics of the RTEs in AD, such as cell type-/sex-specificity, are still unknown. Characterizing RTEs requires generating large-scale RTE expression datasets. Such data has not been available publicly, although the AD research community has made tremendous efforts to generate large-scale postmortem AD transcriptome data, including bulk RNA-seq of ~2,000 subjects and single-cell nuclei RNA-seq of ~260,000 cells. Therefore, we propose two specific aims to perform the first systematic study of RTE by constructing an RTE atlas resource for AD study: Aim 1. To generate large-scale RTE expression datasets by mining and processing public AD transcriptome datasets. We will extend our SalmonTE algorithm to mine RTE expressions from AD transcriptome datasets at both tissue-level and single- cell resolution. Aim 2. To generate AD RTE atlas resources by characterizing RTEs and AD patients using statistical and machine learning methods. We will expand our in-house computational methods to calculate context-specificity (e.g., brain region, cell type, and sex) of each RTE in human AD brains. We will also develop an unsupervised graph neural network using RTE expression and multi-omics data to characterize AD patients. In the end, we will create an atlas website to share our findings with the AD research community. Successful completion of this project will provide 1) novel computational methods to rigorously characterize RTEs in AD, 2) identification of context-specific RTEs in AD and characterization of AD patients using RTE expression, and 3) a well-annotated AD RTE atlas to deepen our knowledge in the molecular basis of AD.