Project Summary/Abstract
It is estimated that 50-100 million people (~5% of the global population) died from the 1918 influenza pandemic.
While influenza infections usually do not cause such severe disease, ~30 million are infected every year in the
United States alone (2014-2015). However, there are broad differences in influenza 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, influenza genetics, and human genetics.
The overall goal of my lab is to understand how human genetic diversity regulates susceptibility and severity of
infections. Famous examples of genetic differences that profoundly impact susceptibility include sickle cell allele
protection against malaria and CCR5 deletion protection against HIV. Such genetic differences can lead to
insights on pathogenesis, drug targets (e.g. CCR5 inhibitors), and more personalized care. For influenza,
common genetic variation has been most convincingly shown to influence flu severity at a single locus (IFITM3)
that regulates a single step (cytosolic entry) in the complex influenza life cycle. We hypothesize that other human
genetic differences affect influenza infection and can be identified through measuring inter-individual variation in
cellular infection phenotypes. To facilitate identification of SNPs that affect cellular infection phenotypes, we
developed and validated a cell-based GWAS approach called Hi-HOST. SNPs identified as important for
influenza infection by Hi-HOST can then be examined for relevance in human infection using already completed
human flu challenge studies and population-based studies. We propose that the intersection of human subject
and cell line data facilitates discovery of novel pathways and genetic determinants of susceptibility.
This project will generate a high resolution analysis of how human genetic variants impact transcription,
cellular phenotypes, and human disease following influenza exposure. We will accomplish this through 1)
identifying human SNPs that confer resistance/susceptibility to cellular and molecular phenotypes of flu infection,
including entry, replication, cell death, cytokine levels, and host transcriptional responses, 2) determining the
impact of SNPs on host transcription during influenza challenge of healthy volunteers, and 3) integrating the
generated cellular and human challenge datasets to generate and test hypotheses linking transcriptional
response and cellular susceptibility. Understanding these differences could lead to new diagnostic approaches
in identifying at-risk individuals and novel therapeutic strategies for treatment.
