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

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.