Effect of disease-associated genetic variants on viral protein DNA binding

描述（由申请人提供）：尽管许多病毒与人类疾病之间存在明显联系，但疾病遗传易感性与病毒感染之间的关系在很大程度上仍未探索。对于大多数疾病，遗传因素只能解释疾病易感性的一小部分，这表明与环境因素（如病毒感染）的协同作用必须在其病因学中发挥关键作用。病毒与其宿主相互作用的一种常见方式是通过病毒蛋白与宿主基因组之间的调控相互作用来操纵宿主基因表达。许多疾病相关的遗传变异是非编码的，因此可能影响基因调控。我们的假设是，与人类疾病相关的特定遗传变异通过影响病毒转录因子（TF）与宿主DNA的结合而起作用。我们提出了一个计算系统，将使用现有的数据来识别这些变体，并产生可检验的假设，病毒蛋白的结合，他们的影响：目的1。构建连接病毒蛋白与宿主基因组结合位点的调控网络。我们将创建一个基因组结合位置网络，以及病毒和人类蛋白质之间的物理相互作用。 目标二。确定人类宿主病毒转录因子的DNA结合偏好。我们将使用蛋白结合微阵列实验性地确定病毒TF的DNA结合偏好。我们将用已经建立的病毒TF DNA结合基序补充这些数据。 目标3：创建一个系统来识别影响病毒蛋白结合的疾病相关遗传变异。我们将创建一个计算系统来识别疾病相关的遗传变异与目标1中开发的调控网络以及目标2中确定的基序的结合。该系统将系统地搜索可能通过修饰TF结合位点来改变病毒蛋白结合的变体。 这项研究中开发的免费数据库和工具将促进新的基因组分析，调查疾病相关遗传变异对病毒-宿主相互作用的影响。拟议的研究是创新的，因为它将是第一个研究联合收割机可用的相关数据，在一个单一的预测框架研究这一机制。它的贡献将是显著的，因为它将使得能够调查疾病相关的遗传变异对具有已知或疑似病毒组分的任何疾病的病毒蛋白结合的影响。由于全基因组关联研究（GWAS）和功能基因组学数据的大量增加，现在可以使用现有数据系统地识别这种相互作用。这一资源对于分析涉及任何已知或疑似病毒环境成分疾病的现有和新的GWAS数据至关重要。根据我们的专业知识，我们将专注于确定与特定疾病（狼疮）和特定病毒（EB病毒和单纯疱疹病毒1）相关的预测。我们期望该系统确定的机制将有助于揭示许多人类疾病的环境-基因型联系，为探索疾病发作和进展的机制开辟新的途径。

DESCRIPTION (provided by applicant): Despite clear ties between many viruses and human diseases, the relationship between disease genetic susceptibility and viral infection remains largely unexplored. For most diseases, genetic factors only explain a small fraction of disease susceptibility, suggesting that synergism with environmental factors such as viral infection must play a key role in their etiology. A common way that viruses interact with their host is through manipulation of host gene expression via regulatory interactions between viral proteins and the host genome. Many disease-associated genetic variants are non-coding, and thus likely to impact gene regulation. Our hypothesis is that specific genetic variants associated with human diseases act by influencing the binding of viral transcription factors (TFs) to host DNA. We propose a computational system that will use available data to identify these variants, and produce testable hypotheses about the viral proteins whose binding they influence: Aim 1. Construct a regulatory network linking viral proteins to host genomic binding locations. We will create a network of genomic binding locations, and physical interactions between viral and human proteins. Aim 2. Determine the DNA binding preferences of human-hosted viral transcription factors. We will experimentally determine the DNA binding preferences of viral TFs using Protein Binding Microarrays. We will supplement these data with already established viral TF DNA binding motifs. Aim 3. Create a system to identify disease-associated genetic variants influencing viral protein binding. We will create a computational system to identify the union of the disease-associated genetic variants with the regulatory network developed in Aim 1, and the motifs determined in Aim 2. This system will systematically search for variants that are likely to alter the binding of viral proteins by modifying TF binding sites. The freely available database and tools developed in this study will facilitate new genomic analyses investigating the impact of disease-associated genetic variants on virus-host interactions. The proposed research is innovative because it will be the first study to combine available relevant data for studying this mechanism in a single predictive framework. Its contribution will be significant because it will enable the investigation of the effect of disease-associated genetic variants on viral protein binding for any disease with a known or suspected viral component. It is now possible to systematically identify such interactions using existing data, thanks to massive increases in genome-wide association study (GWAS) and functional genomics data. This resource will be essential for analysis of both currently available and new GWAS data involving any disease with a known or suspected viral environmental component. Based on our expertise, we will focus on identifying predictions relevant to a specific disease (lupus) and specific viruses (Epstein-Barr virus and Herpes Simplex Virus 1). We expect that the mechanisms identified by this system will help reveal environment-genotype links for many human diseases, opening new avenues of exploration into the mechanisms of disease onset and progression.