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

疾病相关遗传变异对病毒蛋白 DNA 结合的影响

基本信息

批准号：
9189640
负责人：
Matthew Tyson Weirauch
金额：
$ 15.6万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-12-15 至 2018-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9189640
关键词：
BZLF1 gene Belief Binding Binding Proteins Binding Sites Budgets Cells ChIP-seq Computing Methodologies DNA DNA Binding DNA Sequence DNA-Binding Proteins DNA-Protein Interaction Data Databases Disease Disease Progression Disease susceptibility Environment Environmental Risk Factor Epidemiology Epstein-Barr Virus Infections Etiology Event GTP-Binding Proteins Gene Expression Gene Expression Regulation Genes Genetic Genetic Predisposition to Disease Genetic Transcription Genome Genomics Genotype Grant Hereditary Disease Herpesvirus 1 Human Human Herpesvirus 4 Investigation Laboratories Link Location Lupus Methods MicroRNAs Mission Modeling Nucleic Acid Binding Nucleic Acid Regulatory Sequences Onset of illness Patients Platelet Factor 4 Play Positioning Attribute Prevalence Proteins Public Health RNA-Binding Proteins Research Research Infrastructure Resources Risk Role System TimeLine United States National Institutes of Health Untranslated RNA Variant Viral Viral Proteins Virus Virus Diseases Work base convict disorder risk functional genomics gene environment interaction genetic variant genome wide association study genomic data human disease innovation interest preference protein E public health relevance risk variant synergism tool transcription factor virus genetics virus host interaction

项目摘要

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.

描述（由申请人提供）：尽管许多病毒与人类疾病之间存在明确的联系，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得到探索。对于大多数疾病，遗传因素只能解释一小部分疾病易感性，这表明与病毒感染等环境因素的协同作用在其病因学中发挥着关键作用。病毒与其宿主相互作用的常见方式是通过病毒蛋白和宿主基因组之间的调节相互作用来操纵宿主基因表达。许多与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 的结合来发挥作用。我们提出了一个计算系统，该系统将使用可用的数据来识别这些变体，并就它们影响其结合的病毒蛋白产生可检验的假设：目标 1. 构建一个将病毒蛋白连接到宿主基因组结合位置的调控网络。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 2. 确定人类宿主病毒转录因子的 DNA 结合偏好。我们将使用蛋白质结合微阵列通过实验确定病毒转录因子的 DNA 结合偏好。我们将用已经建立的病毒 TF DNA 结合基序来补充这些数据。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与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改 TF 结合位点来改变病毒蛋白结合的变异。本研究开发的免费数据库和工具将促进新的基因组分析，研究疾病相关遗传变异对病毒与宿主相互作用的影响。拟议的研究具有创新性，因为它将是第一项将可用的相关数据结合起来在单一预测框架中研究这一机制的研究。它的贡献将是巨大的，因为它将能够研究与疾病相关的遗传变异对任何具有已知或可疑病毒成分的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此类相互作用。该资源对于分析当前可用的和新的 GWAS 数据至关重要，这些数据涉及任何具有已知或可疑病毒环境成分的疾病。根据我们的专业知识，我们将专注于识别与特定疾病（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，为探索疾病发病和进展的机制开辟新途径。