Host genetic determinants of diversity in viral-induced neuropathology

项目总结 感染一种特定的病毒可导致不同的神经系统结果和疾病病理，受 寄主的遗传背景。在小鼠中，泰勒氏小鼠脑脊髓炎病毒(TMEV)感染导致 不同的神经状况，取决于感染的小鼠品系。因为这与 TMEV感染作为研究病毒影响人类神经系统疾病的工具，有一个关键的 需要确定将TMEV感染与疾病结局联系起来的遗传变异及其机制。这个 长期目标是识别和表征环境和遗传相互作用，这些相互作用有助于 对病毒感染反应的个体差异。这个应用程序的目标是确定基因 背景影响TMEV感染后的疾病多样性。中心假说是基因 背景，正如一种新的基于种群的小鼠模型所模拟的那样，将不同地改变对 基于基因多态的TMEV诱导的疾病。这项拟议研究的理由是 TMEV感染的不同结果背后的遗传效应的描绘可能有助于新的 对病毒引起的人类神经疾病的异质性的洞察。这一假设将得到检验。 有三个具体目标：1)评估合作杂交(CC)小鼠资源品系的表型 TMEV感染反应的变异，2)评估宿主基因组区域与TMEV诱导的相关性 表型，以及3)识别由共享机制管理的表型模块。AIM 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TMEV感染在不同CC小鼠中的疾病病理表现的假设 层级，以便于识别具有相似特征的小鼠组，这些组可以链接到 遗传多样性的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， 模块化聚类和候选基因等位基因关联将被用来检验以下假设： CC菌株之间的表型多样性可聚为模块，可用于识别共享的 机械装置。我们在这里的贡献是对遗传决定因素的理解 利用小鼠的CC种群负责TMEV感染后的表型多样性。建议数 这项研究具有创新性，因为它使用了一种实验小鼠模型，捕捉到了基因的广度 通常在人类种群中发现的多样性，从而全面解决宿主对病毒的贡献 影响了神经状况。这项研究具有重要意义，因为它有望构成一个早期的 在一系列研究中迈出一步，这将增加对病毒影响的复杂神经系统的了解 并最终导致新的预测模型和治疗方法的发展。

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.