Host genetic determinants of diversity in viral-induced neuropathology

项目摘要 给定病毒的感染可引起不同的神经学结果和疾病病理，其受神经系统疾病的影响。 宿主的遗传背景。在小鼠中，泰勒鼠脑脊髓炎病毒（TMEV）感染导致 异质性神经系统疾病，这取决于感染的小鼠品系。由于相关的 TMEV感染作为研究病毒影响的人类神经系统疾病的工具， 需要确定TMEV感染与疾病结果之间的遗传变异及其机制。的 长期目标是确定和表征环境和遗传相互作用，有助于 个体对病毒感染反应的差异。该应用程序的目的是确定遗传性如何 背景影响TMEV感染后的疾病多样性。核心假设是， 背景，如一个新的基于群体的小鼠模型所模拟的，将差异地改变对 TMEV诱导的疾病基于遗传多态性。拟议研究的理由是， 描述TMEV感染的不同结果背后的遗传效应可能有助于新的 深入了解病毒引起的人类神经系统疾病的异质性。假设将被检验 具有三个具体目的：1）评估协作杂交（CC）小鼠资源的品系的表型 2）评估宿主基因组区域与TMEV诱导的TMEV感染的相关性， 表型，和3）识别由共享机制支配的表型模块。目标1基于 已发表的和初步的数据表明，TMEV感染会导致不同的结果，这取决于 老鼠的遗传背景神经和行为表型测试，ELISA和组织学将 用于检验以下假设：不同CC小鼠中TMEV感染的疾病病理学表现出 层次结构，以便于识别具有相似特征的小鼠组， 基因多样的CC系。对于目标2，将使用RNA测序和QTL分析来检验假设 CC小鼠内的基因组多样性影响不同的疾病表型。在目标3中，细胞因子分析， 模块聚类和候选基因等位基因关联将被用来测试假设， CC菌株之间的表型多样性可以聚类成模块，其可以用于鉴定共享的 机制等我们在此的贡献有望是对遗传决定因素的理解 TMEV感染后，使用CC小鼠群体的表型多样性负责。拟议 这项研究是创新的，因为它使用了一种实验小鼠模型， 多样性通常存在于人群中，从而全面解决宿主对病毒的贡献 影响神经系统状况。这项研究意义重大，因为它有望构成一个早期的 这是一项连续研究的一步，将增加对病毒影响的复杂神经系统的了解。 研究人员在人类中的疾病，并最终导致新的预测模型和疗法的发展。

PROJECT SUMMARY Infection by a given virus can cause diverse neurological outcomes and disease pathologies, influenced by the genetic background of the host. In mice, Theiler's murine encephalomyelitis virus (TMEV) infection leads to heterogeneous neurological conditions, depending on mouse strain infected. Because of the relevance of TMEV infection as a tool for studying virally-influenced neurological conditions in humans, there is a critical need to determine genetic variants and their mechanisms that link TMEV infection to disease outcome. The long-term goal is to identify and characterize environmental and genetic interactions that contribute to individual variation in response to viral infection. The objective of this application is to determine how genetic background influences disease diversity following TMEV infection. The central hypothesis is that genetic background, as modeled by a new population-based mouse model, will differentially modify susceptibility to TMEV-induced diseases based upon genetic polymorphisms. The rationale for the proposed research is that a delineation of the genetic effects underlying the diverse outcomes of TMEV infection is likely to contribute new insights into the heterogeneity of virally induced human neurological conditions. The hypothesis will be tested with three specific aims: 1) Evaluate strains of the Collaborative Cross (CC) mouse resource for phenotypic variation in response to TMEV infection, 2) Evaluate host genome regions for associations with TMEV-induced phenotypes, and 3) Identify phenotypic modules governed by shared mechanisms. Aim 1 is based on published and preliminary data showing that TMEV infection causes diverse outcomes depending on the genetic background of the mouse. Neurological and behavioral phenotyping tests, ELISAs, and histology will be used to test the hypothesis that the disease pathologies of TMEV infection in different CC mice demonstrate a hierarchy, to facilitate the identification of groups of mice with similar characteristics that can be linked to genetically-diverse CC lines. For Aim 2, RNA sequencing and QTL analyses will be used to test the hypothesis that genomic diversity within CC mice influences variable disease phenotypes. In Aim 3, cytokine analyses, modularity clustering and candidate gene allele association will be used to test the hypothesis that the phenotypic diversity among CC strains can be clustered into modules which can be used to identify shared mechanisms. Our contribution here is expected to be an understanding of the genetic determinants responsible for phenotypic diversity following TMEV infection using the CC population of mice. The proposed research is innovative because it uses an experimental mouse model that captures the breadth of genetic diversity typically found in human populations, thus comprehensively addressing host contributions to virally influenced neurological conditions. This research is significant because it is expected to constitute an early step in a continuum of research that will increase knowledge about virally influenced complex neurological conditions in humans and ultimately lead to the development of novel predictive models and therapies.