Genetic Contributors to the Impact of Sex on Heterogeneity in Flu Infection

项目摘要/摘要 据估计，1918年的流感大流行导致全球每20人中就有1人死亡。甲型流感病毒(IAV) 对于美国每年约3000万感染者来说，感染通常不会导致如此严重的疾病 单单(2014-2015)。然而，IAV的易感性和严重性有很大的差异，其结果是 无症状感染(约16%)致死(2014-2015年为0.2%)。这些差异源于情结 暴露、环境、IAV遗传和宿主因素的相互作用。 导致IAV感染异质性的一个关键宿主因素是性别。适合儿童及以上人士 就个人而言，男性更容易患严重疾病，而育龄女性更容易患上严重疾病 严肃性。由于有强有力的证据表明1)性别在IAV感染中的重要性，2)基因表达差异 男性和女性之间的差异，以及3)影响一般传染病的人类基因变异和 尤其是IAV感染，这三个区域的综合可能提供关键的机制洞察力。我们假设 基因表达的性别差异是IAV感染异质性的主要驱动因素。为了阐明这些 差异，该项目将整合尖端方法，以确定成绩单中特定性别的差异 调节人体细胞中IAV负荷和宿主反应的丰度和剪接，IAV挑战志愿者， 和自然人口。此外，我们将定义形成机理基础的基因x性别交互作用 基因多样性如何导致IAV感染的性别差异。 为了实现这些目标，我们拥有来自数十个细胞的IAV感染异质性的独特数据集 男性和女性供体，鼻刮术和人类IAV挑战受试者的外周血，以及 自然IAV感染的生物库样本，结果从轻微感染到死亡不等。计算型 对这些数据集的分析将定义1)与IAV负担相关的基因表达的性别差异 症状严重程度和2)调节性别偏见基因表达和流感严重程度的人类SNP。这个 来自这些数据集的转录图谱将被用于生成IAV感染的性别特异性生物标记物 使用机器学习方法的严重性。最后，我们将通过实验确定识别出的 在感染的细胞模型中，性别偏见基因和SNPs调节IAV负荷和宿主反应。所有结果 将通过易于使用的Web数据库提供，用于探索这一丰富的数据集，作为进一步开发的平台 机制和临床研究。 该项目将开发和应用计算方法来生成高分辨率分析 性别和基因相互作用影响IAV感染。了解IAV性别差异的遗传基础 感染可能导致识别高危个体的新诊断方法和新的治疗方法 战略。

Project Summary/Abstract The 1918 influenza pandemic is estimated to have killed 1 in 20 people worldwide. Influenza A virus (IAV) infections usually do not cause such severe disease for the ~30 million infected every year in the United States alone (2014-2015). However, there are broad differences in IAV susceptibility and severity, with outcomes from asymptomatic infections (~16%) to death (0.2% in 2014-2015). These differences arise from the complex interplay of exposure, environment, IAV genetics, and host factors. A crucial host factor that contributes to heterogeneity of IAV infection is sex. For children and older individuals, males are more likely to experience severe disease, while females of child-bearing age have greater severity. As there is strong evidence for 1) the importance of sex in IAV infection, 2) gene expression differences between males and females, and 3) human genetic variation impacting infectious disease in general and specifically IAV infection, synthesis of these three areas may provide crucial mechanistic insight. We hypothesize that sex differences in gene expression are a major driver of heterogeneity in IAV infection. To elucidate these differences, this project will integrate cutting-edge approaches to identify sex-specific differences in transcript abundance and splicing that regulate IAV burden and host response in human cells, IAV challenge volunteers, and natural populations. Further, we will define the genotype x sex interactions that form the mechanistic basis for how genetic diversity contributes to sex differences in IAV infection. To achieve these goals, we have unique datasets of IAV infection heterogeneity in cells from dozens of male and female donors, in nasal curettage and peripheral blood from human IAV challenge subjects, and biobanked samples of natural IAV infection with outcomes ranging from mild infection to death. Computational analyses of these datasets will define 1) sex differences in gene expression that correlate with IAV burden and symptom severity and 2) human SNPs that regulate sex-biased gene expression and flu severity. The transcriptional profiles from these datasets will be used to generate sex-specific biomarkers of IAV infection severity using machine learning approaches. Finally, we will experimentally determine whether the identified sex-biased genes and SNPs regulate IAV burden and host response in cellular models of infection. All results will be available through an easy-to-use web database for exploring this rich dataset as a launchpad for further mechanistic and clinical studies. This project will develop and apply computational methods to generate a high-resolution analysis of how sex and genes interact to impact IAV infection. Understanding the genetic basis for sex differences in IAV infection could lead to new diagnostic approaches in identifying at-risk individuals and novel therapeutic strategies.