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

Abstract

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

The role of cell-mediated immunity against influenza and its implications for vaccine evaluation

“…Several studies demonstrate that gene expression is altered after natural influenza infection or vaccination. The inter-individual variability observed after natural infection or vaccination can be explained by inter-individual differences in gene expression in airway epithelial cells and immune cells ( 121 ). Alcorn et al showed that these gene expression changes are transient in time with different differentially expressed genes at three and seven days after vaccination compared to pre-vaccination expression profiles ( 122 ).…”

Direct Measurement of B Lymphocyte Gene Expression Biomarkers in Peripheral Blood Transcriptomics Enables Early Prediction of Vaccine Seroconversion

“…Systemic biology approaches were applied to study the complexity problem of molecular signatures after vaccination, which was largely confounded by the presence of various leukocyte subpopulations in clinical samples, as reviewed by Pezeshki et al [ 9 ]. Previous studies of TA after vaccination were limited to either focusing on the genetic determinants of responders or the difference between vaccination and a full-blown infection [ 10 ].…”

“…So, the results are convoluted and confounded by different proportional cell counts of each of the cell subpopulations in these cell mixture samples. The research community has called for new biomarkers to enhance vaccine development, particularly in view of the current COVID-19 pandemic [ 9 , 10 , 11 , 12 ]. Here, we are interested to know if B lymphocyte TA could be a reliable early predictor for subsequent seroconversion of B lymphocyte as the precursor of plasma cells.…”

The Ratiometric Transcript Signature MX2/GPR183 Is Consistently Associated With RTS,S-Mediated Protection Against Controlled Human Malaria Infection

“…Previous analyses of transcriptional responses after RTS,S vaccination revealed strong AS01-driven peripheral innate immune activation and candidate protection-associated signatures for individual studies (17)(18)(19)(20)(21). While biomarker development and understanding of pathogenesis for other infectious diseases have been accelerated by multi-cohort transcriptional analyses (22)(23)(24)(25), a multi-study transcriptional analysis of RTS,S-mediated protection in CHMI has not been undertaken. Identification of simple, yet robustly predictive, signatures of RTS.S clinical activity would facilitate evaluation of additional alternative malaria vaccine regimens, providing preclinically testable hypotheses about the RTS,S mode of action and refining candidate correlative variables for testing in field studies.…”