2Influenza, a communicable disease, affects thousands of people worldwide. Young children, 3 elderly, immunocompromised individuals and pregnant women are at higher risk for being 4 infected by the influenza virus. Our study aims to highlight differentially expressed genes in 5 influenza disease compared to influenza vaccination. We also investigate genetic variation 6 due to the age and sex of samples. To accomplish our goals, we conducted a meta-analysis 7 using publicly available microarray expression data. Our inclusion criteria included subjects with 8 influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 9 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 10 influenza vaccination). We pre-processed the raw microarray expression data in R using packages 11 available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power 12 transformation of the data prior to our down-stream analysis to identify differentially expressed 13 genes. Statistical analyses were based on linear mixed effects model with all study factors and 14 successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT 15 results by disease (Bonferroni adjusted p-value < 0.05) and used a two-tailed 10% quantile cutoff 16 to identify biologically significant genes. Furthermore, we assessed age and sex effects on the 17 disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p-value < 18 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically 19 significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis 20 (gene ontology and pathways) included innate immune response, viral process, defense response 21 to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene 22 lists comprised of 978 genes each associated with influenza infection and vaccination. We also 23 identified 907 and 48 genes with statistically significant (Bonferroni adjusted p-value < 0.05) 24 disease-age and disease-sex interactions respectively. Our meta-analysis approach highlights 25 key gene signatures and their associated pathways for both influenza infection and vaccination. 26 1 Rogers LRK et al.
Variability in Influenza Infection and VaccinationWe also were able to identify genes with an age and sex effect. This gives potential for improving 27 current vaccines and exploring genes that are expressed equally across ages when considering 28 universal vaccinations for influenza. 29 30