Virus-driven human gene misregulation in disease

摘要 数以千计的遗传变异已被确定为数百种人类疾病。然而， 这些疾病大多数仍然是特发性的。遗传和环境之间的相互作用可能起着作用 在许多疾病中。特别是，病毒暴露和疾病风险之间的数百种联系已经被发现。 确立了习但除了极少数例外，疾病风险增加的潜在机制尚不清楚。我们有 最近发现，EB病毒EBNA2转录辅因子与多达一半的风险位点结合， 与7种自身免疫性疾病（共142个位点）相关，其中许多等位基因依赖性EBNA2 与自身免疫风险变体结合。我们假设病毒转座子的风险等位基因依赖性结合解释了 为什么其他病毒会导致或影响特定的疾病。然而，发现这些所需的数据 机制目前还不完善。疱疹病毒和人乳头瘤病毒在人类免疫系统中发挥着既定的作用。 几种人类疾病，它们的基因组编码许多TF。我们将产生功能基因组学 这些数据集需要发现这些病毒TF（vTF）在人类疾病过程中的作用。我们预计 发现多种致病性人类疾病变体，其机制作用于病毒TF和等位基因， 依赖的方式，从而了解疾病机制和新的治疗机会。我们 方法是一个概括性的蓝图，对宿主基因调控的致病性影响的全球表征。 目标1。创建病毒TF驱动的人类基因调控的全球地图。对于八种病毒，我们将 将病毒TF转染到生理和病理相关的人原代细胞和细胞系中。我们将 通过在具有和不具有vTF的细胞中进行RNA-seq来测量vTF对人基因表达的影响 转染我们将使用染色质免疫沉淀法监测vTF与人类基因组的结合 并使用我们的RELI算法计算每个vTF在所有人类疾病的已建立的风险基因座处的富集。 目标二。揭示病毒TF-人类基因组的机制和下游功能影响 交互.我们将描述vTF改变人类监管格局的机制。我们 将测量vTF对人类染色质可及性（ATAC-seq）和DNA成环（HiChIP-seq）的影响。 我们将使用这些数据集来构建评估疾病相关机制的计算模型。我们将 通过监测细胞增殖、细胞因子和细胞因子水平，研究vTF活性对人细胞表型的下游影响。 释放，以及vTF转染后的生长因子释放。 目标3：测试病毒TF引起的人类疾病机制的等位基因依赖性。我们将 鉴定涉及人类疾病风险等位基因依赖性机制的vTF相互作用。我们将在功能上 使用大规模平行试验筛选vTF和疾病风险等位基因依赖性对基因调控活性的影响 报告者测定。我们将使用我们的MARIO来询问病毒宿主基因组数据集的等位基因行为 计算流水线我们将使用基于CRISPR的基因组验证风险等位基因依赖的vTF机制。 编辑.

ABSTRACT Thousands of genetic variants have been established for hundreds of human diseases. Yet, the vast majority of these diseases remain idiopathic. Interplay between genetics and the environment likely plays a role in many diseases. In particular, hundreds of associations between viral exposure and disease risk have been established. But with rare exceptions, the mechanisms underlying increased disease risk are unknown. We have recently discovered that the Epstein-Barr virus EBNA2 transcriptional co-factor binds up to half of the risk loci associated with seven autoimmune diseases (142 loci in total), with many examples of allele-dependent EBNA2 binding to autoimmune risk variants. We hypothesize that risk allele-dependent binding of viral TFs explains why other viruses cause or influence specific diseases. However, the data required to discover these mechanisms are currently incomplete. Herpesviruses and human papilloma virus play established roles in several human diseases, and their genomes encode many TFs. We will generate the functional genomics datasets needed to discover the roles of these viral TFs (vTFs) in human disease processes. We anticipate discovering multiple causal human disease variants whose mechanisms act in a viral TF and allele- dependent manner, leading to understanding of disease mechanisms and new therapeutic opportunities. Our approach is a generalizable blueprint for global characterization of pathogenic effects on host gene regulation. Aim 1. Create global maps of viral TF-driven human gene regulation. For eight viruses, we will transfect a viral TF into physiologically and pathologically relevant human primary cells and cell lines. We will measure the effect of vTFs on human gene expression by performing RNA-seq in cells with and without vTF transfection. We will monitor the binding of vTFs to the human genome using chromatin immunoprecipitation and calculate the enrichment of each vTF at established risk loci for all human diseases using our RELI algorithm. Aim 2. Uncover the mechanisms and downstream functional impact of viral TF-human genome interactions. We will characterize the mechanisms by which vTFs alter the human regulatory landscape. We will measure the effect of vTFs on human chromatin accessibility (ATAC-seq) and DNA looping (HiChIP-seq). We will use these datasets to construct computational models evaluating disease-relevant mechanisms. We will examine downstream effects of vTF activity on human cell phenotypes by monitoring cell proliferation, cytokine release, and growth factor release subsequent to vTF transfection. Aim 3. Test the allele-dependency of viral TF-provoked human disease mechanisms. We will identify vTF interactions involving human disease risk allele-dependent mechanisms. We will functionally screen for vTF- and disease risk allele-dependent effects on gene regulatory activity using Massively Parallel Reporter Assays. We will interrogate virus-host genomic datasets for allelic behavior using our MARIO computational pipeline. We will validate risk allele-dependent vTF mechanisms using CRISPR-based genome editing.