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

项目概要/摘要 据估计，1918年的流感大流行在全世界每20人中就有1人死亡。甲型流感病毒（IAV） 对于美国每年约3000万的感染者来说，感染通常不会引起如此严重的疾病 （2014-2015年）。然而，IAV的易感性和严重性存在广泛差异， 无症状感染（~16%）至死亡（2014-2015年为0.2%）。这些差异源于复杂的 暴露、环境、IAV遗传学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。儿童和老年人 就个人而言，男性更有可能患上严重的疾病，而育龄女性则更容易患上严重的疾病。 严重性。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）人类遗传变异一般影响传染病， 特别是IAV感染，这三个区域的综合可能提供关键的机制见解。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力。阐明这些 差异，该项目将整合尖端的方法，以确定转录本中的性别特异性差异 丰度和剪接调节IAV负荷和宿主反应在人类细胞中，IAV挑战志愿者， 自然人口。此外，我们将定义基因型x性别的相互作用，形成机制的基础， 遗传多样性如何导致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，人IAV攻毒受试者的鼻刮片和外周血中，以及 自然感染IAV的生物库样本，结果从轻度感染到死亡。计算 这些数据集的分析将定义1）与IAV负荷相关的基因表达的性别差异， 症状严重程度和2）调节性别偏向基因表达和流感严重程度的人类SNP。的 这些数据集的转录谱将用于产生IAV感染的性别特异性生物标志物 严重性使用机器学习方法。最后，我们将通过实验来确定所识别的 在细胞感染模型中，性别偏向基因和SNPs调节IAV负荷和宿主反应。所有结果 将通过一个易于使用的网络数据库，探索这个丰富的数据集作为一个发射台， 机制和临床研究。 这个项目将开发和应用计算方法，以产生一个高分辨率的分析， 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 感染可能导致新的诊断方法，以确定风险个体和新的治疗方法， 战略布局

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.