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

性别对流感感染异质性影响的遗传因素

基本信息

批准号：
10663342
负责人：
Dennis Chun-Yone Ko
金额：
$ 51.72万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-11 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10663342
关键词：
1918 influenza pandemic African Alleles Area Asian Bacterial Infections Biological Markers Cell Line Cell model Cells Cessation of life Child Clinical Research Clustered Regularly Interspaced Short Palindromic Repeats Communicable Diseases Communities Complex Computer Analysis Computing Methodologies Coupled Curettage procedure Data Set Databases Development Disease Environment European Exhibits Female Female of child bearing age Gene Expression Gene Expression Profile Genes Genetic Genetic Transcription Genetic Variation Genotype Goals Heterogeneity Hospitalization Human Human Genetics Hypoxia Immune response In Vitro Individual Infection Influenza Influenza A virus Information Dissemination Integration Host Factors Internet Link Machine Learning Measures Modeling Nose Outcome Persons Phenotype Population Predisposition Publishing Quantitative Trait Loci RNA Interference RNA Splicing RNA interference screen Research Resolution Resources Respiratory Tract Infections Risk SARS coronavirus Sampling Severities Sex Bias Sex Differences Symptoms Testing Time Transcript Tube United States Validation Variant Viral Virus Diseases Virus Replication biobank candidate identification computational pipelines computing resources constitutive expression data dissemination diagnostic strategy differential expression experience flu gene interaction genome wide association study influenza infection insight lymphoblastoid cell line male multidisciplinary novel diagnostics novel therapeutic intervention overexpression pandemic disease peripheral blood response sex small molecule specific biomarkers transcriptomics virus genetics volunteer web-based tool

项目摘要

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.

项目概要/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 万人来说，感染通常不会导致如此严重的疾病独自一人（2014-2015）。然而，IAV 的易感性和严重程度存在广泛差异，结果如下：无症状感染者（~16%）导致死亡（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。对于儿童和老年人就个体而言，男性患严重疾病的可能性更大，而育龄女性则更容易患上严重疾病。严重程度。因为有强有力的证据证明 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) 人类遗传变异对传染病的影响特别是 IAV 感染，这三个领域的综合可能提供重要的机制见解。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因素。为了阐明这些差异，该项目将整合尖端方法来识别转录本中的性别特异性差异调节人类细胞中 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，我们将定义构成机制基础的基因型 x 性别相互作用了解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，来自人类 IAV 攻击受试者的鼻刮除术和外周血，以及自然 IAV 感染的生物库样本，其结果从轻度感染到死亡不等。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。这这些数据集的转录谱将用于生成 IAV 感染的性别特异性生物标志物使用机器学习方法的严重程度。最后，我们将通过实验来确定所识别的性别偏见基因和 SNP 调节感染细胞模型中的 IAV 负荷和宿主反应。所有结果将通过易于使用的网络数据库提供，以探索这个丰富的数据集作为进一步研究的启动板机制和临床研究。该项目将开发并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会导致识别高危个体的新诊断方法和新的治疗方法策略。