Endogenous retrovirus analyses in myalgic encephalomyelitis

项目总结/摘要 肌痛性脑脊髓炎（ME）是一种致残和复杂的疾病，在美国有1- 250万患者 States. ME患者的生活质量很低，四分之一的患者卧床不起。ME是研究不足的， 资金不足，并且没有现有的诊断测试，FDA批准的治疗或治愈ME。 我是未知的。ME患者表现出升高的免疫应答和增强的炎症。 内源性逆转录病毒（ERV）是由古老的逆转录病毒感染的固定和整合到逆转录病毒中产生的。 人类基因组ERV（n = 400，000）通常保持沉默;然而，一些ERV可以在转录水平上被抑制。 重新激活。活化的ERV类似于病毒RNA，因此可以引发免疫应答和慢性炎症。 炎症，可能导致ME。与我们的合作者一起，我们已经收集了最大的深 对来自ME患者的具有深度表型的数据集进行测序。我们最近开发了一套生物信息学 这些工具使我们能够对全基因组的个体ERV进行基因分型，并定量高表达的个体ERV表达。 精度在这个R21应用程序中，我们建议使用这些最先进的工具来分析现有的和新的 深度测序数据，以解决两个具体目标。目的1：识别表达与ERV相关的不同ERV。 使用RNA测序数据与ME相关。这些分析将首次量化转录组- 单独广泛表达的ERV;并允许鉴定活化的不同ERV和ERV基因 目的2：使用全基因组测序鉴定其基因型与ME相关的ERV变体。 基因组测序数据。这些分析将产生全基因组不同的ERV基因型， 时间，允许鉴定ME相关的个体ERV和相关基因。在完成这些 两个目的，我们将知道ERVs和ME是否在转录组和/或基因组水平上连锁。 阐明ERV作为ME的危险因素可能会导致ME治疗的突破，例如再利用 现有的FDA批准的抗逆转录病毒药物可能逆转ERV的影响。

Project Summary/Abstract Myalgic encephalomyelitis (ME) is a disabling and complex disease with 1-2.5 million patients in the United States. ME patients have low quality of life and one in four are bed- or house-bound. ME is under-studied and under-funded, and there are no existing diagnostic tests, FDA-approved treatments or cure for ME. The causes of ME are unknown. ME patients exhibit elevated immune responses and enhanced inflammation. Endogenous retroviruses (ERVs) result from the fixation of ancient retroviral infections and integrations into the human genome. ERVs (n≈400,000) typically remain silenced; however, some ERVs can be transcriptionally reactivated. Activated ERVs resemble viral RNA and can therefore trigger immune responses and chronic inflammation, potentially leading to ME. Together with our collaborators, we have collected the largest deep sequencing data sets with deep phenotypes from ME patients. We recently developed a set of bioinformatics tools that allow us to genotype genome-wide individual ERVs and quantify individual ERV expression with high accuracy. With this R21 application, we propose to use these state-of-the-art tools to analyze existing and new deep sequencing data to address two Specific Aims. Aim 1: Identify distinct ERVs whose expression is associated with ME using RNA-Sequencing data. These analyses will, for the first time, quantify transcriptome- wide expressed ERVs individually; and allow for identification of activated distinct ERVs and ERV genes associated with ME. Aim 2: Identify ERV variants whose genotypes are associated with ME using whole- genome sequencing data. These analyses will produce genome-wide distinct ERV genotypes, and, for the first time, allow for identification of ME-associated individual ERVs and related genes. Upon completion of these two Aims, we will know whether ERVs and ME are linked at the transcriptomic and/or genomic level. Elucidating ERVs as risk factors in ME may lead to breakthroughs in ME treatment, such as repurposing existing FDA-approved anti-retroviral drugs which may reverse ERV effects.