Despite increases in vaccination coverage, reductions in influenza-related mortality have not been observed. Better vaccines are therefore required and influenza challenge studies can be used to test the efficacy of new vaccines. However, this requires the accurate post-challenge classification of subjects by outcome, which is limited in current methods that use artificial thresholds to assign “symptomatic” and “asymptomatic” phenotypes. We present data from an influenza challenge study in which 22 healthy adults (11 vaccinated) were inoculated with H3N2 influenza (A/Wisconsin/67/2005). We generated genome-wide gene expression data from peripheral blood taken immediately before the challenge and at 12, 24, and 48 hours post-challenge. Variation in symptomatic scoring was found among those with laboratory confirmed influenza. By combining the dynamic transcriptomic data with the clinical parameters this variability can be reduced. We identified four subjects with severe laboratory confirmed flu that show differential gene expression in 1,103 probes 48 hours post-challenge compared to the remaining subjects. We have further reduced this profile to 6 genes that can be used to define these subjects. We have used this gene set to predict symptomatic infection from an independent study. This analysis gives further insight into host-pathogen interactions during influenza infection. However, the major potential value is in the clinical trial setting by providing a more quantitative method to better classify symptomatic individuals post influenza challenge.

Twenty two healthy volunteers were enrolled for an influenza challenge study. Eleven were vaccinated thirty days before challenge with H3N2 influenza. Whole blood was collected in PAXgene tubes prior to influenza challenge and then at three further timepoints (12, 24 and 48 hours post-challenge).