Microarray Gene Expression Dataset Re-Analysis Reveals Variability in Influenza Infection and Vaccination

Abstract

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

Metabolomic and transcriptomic signatures of influenza vaccine response in healthy young and older adults

“…We used a systems vaccinology approach to study the response to influenza vaccination and found that both young and older subjects develop an antibody response to immunization using different immunometabolic paths. Our study suggests that the main variable in response to influenza vaccination is age (Figure 1c), a finding that is in agreement with previous studies (Furman & Jojic, 2013; Haschemi & Kosma, 2012; Kennedy & Ovsyannikova, 2016; Nakaya et al, 2011; Rogers et al, 2019; Thakar et al, 2015; Tsang et al, 2014; Voigt et al, 2019). The differences we observed reflect a differential metabolic baseline of young versus older adults.…”

“…Longitudinal studies showed consistent transcriptomic and microRNA signatures of influenza vaccination across seasons in the same cohort (Nakaya et al, 2015). One of the most consistent findings is that age is an important contributor to influenza vaccine response (Furman & Jojic, 2013; Haschemi & Kosma, 2012; Nakaya et al, 2011; Rogers et al, 2019; Tsang et al, 2014). We previously investigated the transcriptomic signature of immune response to influenza vaccination and found that a mitochondrial biogenesis signature was associated with vaccine antibody response (Thakar et al, 2015).…”

Aging induces B cell defects and decreased antibody responses to influenza infection and vaccination

“…Results have identified around 1000 differentially expressed genes when results were filtered by disease factor, and gene enrichment analysis, with statistically significant disease-age interactions. Genes included innate immune response, viral process, defense response to virus, hematopoietic cell lineage and NF-kB signaling pathway [ 49 ]. A mitochondrial signature of young and old influenza vaccine responders has identified genes and proteins controlling mitochondrial biogenesis and pathways of oxidative phosphorylation [ 50 ].…”

Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer’s Disease

“…Expression profiling through microarray analysis is a valuable high-throughput tool that allows researchers to determine quantitative gene expression for a large number of mRNA transcripts 1 . Recent meta-analyses of publicly available microarray data have proven to be beneficial for identifying novel contributing genes for several diseases including influenza 2 , atherosclerosis 3 , chronic pain 4 , cancer 5, 6 , and Parkinson’s disease 7 . To date, however, few studies have used this approach to identify novel gene targets in the Alzheimer’s disease (AD) brain.…”