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Background: Influenza challenge trials are important for vaccine efficacy testing. Currently, disease severity is determined by self-reported scores to a list of symptoms which can be highly subjective. A more objective measure would allow for improved data analysis. Methods: Twenty one volunteers participated in an influenza challenge trial. We calculated the Daily Sum of Scores (DSS) for a list of 16 influenza symptoms. Whole blood collected at baseline and 24, 48, 72 and 96 hours post challenge was profiled on Illumina HT12v4 microarrays. Changes in gene expression most strongly correlated with DSS were selected to train a Random Forest model and tested on two independent test sets consisting of 41 individuals profiled on a differentmicroarray platform and 33 volunteers assayed by qRT-PCR. Results: 1,456 probes are significantly associated with DSS at 1% false discovery rate. We selected 19 genes with the largest fold change to train a random forest model. We observed good concordance between predicted and actual scores in the first test set (r = 0.57; RMSE = -16.1%) with the greatest agreement achieved on samples collected approximately 72 hours post challenge. Therefore, we assayed samples collected at baseline and 72 hours post challenge in the second test set by qRT-PCR and observed good concordance (r=0.81; RMSE = -36.1%). Conclusion: We developed a 19-gene qRT-PCR panel to predict DSS, validated on two independent datasets. A transcriptomics based panel could provide a more objective measure of symptom scoring in future influenza challenge studies.

Original publication




Journal article


Journal of Translational Medicine


BioMed Central

Publication Date